ABSTRACT
Title of dissertation: The Antiviral Roles of Atg1
Against Drosophila X Virus
in Drosophila melanogaster
Qian Wang, Doctor of Philosophy, 2014
Dissertation directed by: Professor Louisa Wu
Department of Cell Biology and Molecular Genetics
In mammals, autophagy is important for the immune response against select viruses
and is responsible for delivering virus to the lysosome for degradation. In Drosophila
melanogaster, the roles of autophagy genes in an antiviral immune response are not fully
understood. Here we identify a novel antiviral role for Atg1 in Drosophila melanogaster
upon infection with Drosophila X virus (DXV). Flies with a decreased level of Atg1
expression in the fat body developed an increased susceptibility to DXV and have a higher
viral load compared to wildtype. However, silencing of other autophagy components
(Atg7, Atg8) does not have the same effect. Moreover, we could find no evidence that
classical autophagy is directly associated with DXV upon viral infection. This suggests
that the antiviral function of Atg1 may be independent of classical autophagy. To address
this, we examined the effect of Atg1 knockdown on the fly transcriptome in both DXV
infected and uninfected flies. Interestingly, lipid droplet lipolysis and β-oxidation, two
major processes responsible for energy production, are induced upon DXV infection.
Facilitating lipolysis by knocking down lsd2, a positive regulator of lipase bmm, results in
an increased host susceptibility to DXV, together with an increased viral load. In contrast,
blocking lipolysis in the negative regulator lsd1 null mutant renders the host more resistant
to the virus. This indicates that the increased energy production favors the virus for active
replication and does not favor the elimination of virus. Surprisingly, silencing of Atg1,
even in the absence of infection, also increases the rates of lipolysis and β-oxidation,
shown by an increased expression of genes that are involved in lipid metabolism and an
decreased lipid droplet size in the Atg1-silenced flies. The differences in gene expression
and lipid droplet size between Atg1 RNAi flies and WT flies become more apparent as the
infection progresses. In summary, we identify a novel role for Atg1 in restricting energy
production and limiting DXV replication. This finding may shed light on antiviral studies
against other dsRNA viruses that manipulate host energy homeostasis. Finally, our data
reveal an important and unexpected role for Atg1 in innate immune antiviral responses
independent of autophagy.
THE ANTIVIRAL ROLES OF ATG1 IN DROSOPHILA
MELANOGASTER: IMMUNE RESPONSES AGAINST DROSOPHILA
X VIRUS
by
Qian Wang
Dissertation submitted to the Faculty of the Graduate School of the
University of Maryland, College Park in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
2014
Advisory Committee:
Professor Louisa Wu, Chair/Advisor
Professor Najib M. El-Sayed
Professor Michael Ma
Professor David Mosser
Professor Steve Mount
c© Copyright by
Qian Wang
2014
Dedication
To my Mom and Dad.
ii
Acknowledgments
I owe my gratitude to all the people who have made this thesis possible and because
of whom my graduate experience has been one that I will cherish forever.
I would like to first thank my thesis adviser Dr. Louisa Wu, who has been very
supportive through the years. Your “Try it” attitude encourages me to explore various
research ideas and experiments, which lead me to become an independent researcher.
Your spirit for innovation guides me towards investigating novel and interesting research
topics. And I specifically appreciate your tremendous patience and devoted efforts in
honing my writing skills through modifying various writing scripts over and over.
Secondly, I would love to thank my labmates Dr. Javier Robalino, Dr. Jahda Hill,
Dr. Elizabeth Anne Gonzalez, Dr. Jessica Tang, Dr. Aprajita Garg, Ashley Nazario and
our past lab technician Mr. Junlin Wu. Dr. Javier Robalino had genuinely offered great
advice and coached me on various experimental techniques that I had no prior experience
in. I also could not forget the many times when you patiently discussed research problems
with me. I cannot imagine finishing the project without your help. I also thank Dr. Jahda
Hill for being such a wonderful labmate and a friend. As the only autophagy expert in the
lab, you’ve offered great suggestions and also provide me with numerous useful research
materials. Without you, my project to study autophagy wouldn’t be possible. Dr. Jahda
Hill is also a cheerful and energetic person, whose curiosity and adventurous spirit have
lead us to great moments from cultural discussion, Mandarin lessons, American cuisine
cooking events and even a band performance. I also want to thank Dr. Jessica Tang, who
has been so sweet and diligent in helping to develop our lab into a better place. Also in
iii
the same class, Jessica has been very supportive from the very first day since I was on
campus. To Dr. Elizabeth Anne Gonzalez, thank you for being so sweet and I enjoyed
the company of you not only as a labmate, an officemate but also a friend. To Ashley
Nazario, thank you for being so organized and volunteering for a lot of the lab duties after
Junlin’s leave.
Thirdly, I would love to give my special thanks to all my committee members and
collaborators: Dr. Najib M. El-Sayed, Dr. Michael Ma, Dr. David Mosser, Dr. Steve
Mount, Dr. He´ctor Corrada Bravo and his graduate student Kwame Okrah. Without your
constructive suggestions on the research projects in and out of the committee meetings,
I can not be where I am now. Specifically, I’d love to thank Dr. Michael Ma, Dr. Steve
Mount and Dr. Najib M. El-Sayed for your help on the bioinformatics project. Without
those detailed instructions, I would not be able to finish the RNA-seq analysis in a timely
manner. To Dr. David Mosser, thank you for taking me as a rotation student and inspiring
me to explore the host-pathogen interactions. To Dr. He´ctor Corrada Bravo and Kwame
Okrah, thank you for your advise on statistical analyses and contribution on the genera-
tion of statistical figures such as the PCA plots, scatter plots and heatmaps (Figure 3.3,
Figure 3.4, Figure 3.5, Figure 3.6A-C, Figure 3.7A-C, Figure 3.8A-C) on the RNA-seq
data.
Finally, I would love to thank my family and friends for your unconditional love
and support. To my mom and dad, thank you for being so supportive to allow me to
explore an academic life in the United States. Life in graduate school is not smooth and
I apologize for causing you stresses and concerns. But I appreciate your belief in me
through all of my ups and downs. To my boyfriend Dr. Qi Hu, thank you for being there
iv
for me providing not only emotional support but also critical advice. To my best friends
at University of Maryland, Dr. Vivian Guo, Dr. Qiang Li, Tao Liu, Jia Wen, Zhe Chen,
Lvyuan Chen, Yinghui Zheng and Zongzhong Peng and Nan Zhou, thank you for your
years of company. To my old friends, Dr. Chun Chen, Dr. Ran An, Naji Liu, Minmin
Pan, Lei Zhao, and Shi Yan, thank you for your continuous support no matter where I am.
v
Table of Contents
List of Tables ix
List of Figures x
List of Abbreviations xii
1 Introduction 1
1.1 The innate immune system in Drosophila melanogaster . . . . . . . . . 1
1.1.1 Microbial Recognition . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Drosophila viruses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Drosophila X virus . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1.1 Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1.2 Viral genome and proteins . . . . . . . . . . . . . . . . 5
1.2.1.3 Virus cycle . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3 Antiviral responses in Drosophila melanogaster . . . . . . . . . . . . . 9
1.3.1 The Toll Signaling Pathway . . . . . . . . . . . . . . . . . . . . 11
1.3.2 The Imd Signaling Pathway . . . . . . . . . . . . . . . . . . . . 13
1.3.3 JAK-STAT Signaling Pathway . . . . . . . . . . . . . . . . . . . 17
1.3.4 RNA interference pathway . . . . . . . . . . . . . . . . . . . . . 19
1.3.5 JNK Signaling Pathway . . . . . . . . . . . . . . . . . . . . . . 21
1.3.6 Autophagy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.3.6.1 Autophagy signaling pathway . . . . . . . . . . . . . . 24
1.3.6.2 Signaling regulation of autophagy . . . . . . . . . . . . 29
1.3.6.3 The immune function of autophagy . . . . . . . . . . . 30
1.3.6.4 Crosstalk between autophagy and other immune signaling 33
1.4 Lipid Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.4.1 Lipid droplets as energy storage compartments . . . . . . . . . . 37
1.4.2 The Perilipin family proteins in the regulation of LDs . . . . . . . 40
1.4.3 β-Oxidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
vi
2 Atg1 plays an antiviral role against Drosophila X Virus, but this effect appears to
be independent of classical autophagy 51
2.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.1.1 Atg1 plays an antiviral role against DXV infection in the fat body 51
2.1.2 Autophagy does not appear to be specifically activated by DXV
in both larval hemocytes and adult fat body . . . . . . . . . . . . 59
2.1.3 Autophagy is not induced in ex vivo hemocytes upon DXV infection 62
2.1.4 Autophagy is not induced in Drosophila S2 cells . . . . . . . . . 65
2.1.5 Autophagy is not induced in GFP-Atg8 Drosophila S2R+ cells . . 72
2.1.6 The antiviral function of Atg1 is not through regulating RNA in-
terference pathway upon DXV infection . . . . . . . . . . . . . . 74
2.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
2.2.1 Fly stocks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
2.2.2 Cell lines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
2.2.3 Virus infections. . . . . . . . . . . . . . . . . . . . . . . . . . . 77
2.2.4 Survival analyses. . . . . . . . . . . . . . . . . . . . . . . . . . . 78
2.2.5 RT-PCR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
2.2.6 Protein extraction and Western blot. . . . . . . . . . . . . . . . . 78
2.2.7 Staining and confocal imaging. . . . . . . . . . . . . . . . . . . . 79
2.2.8 Electron Microscopy . . . . . . . . . . . . . . . . . . . . . . . . 80
3 Transcriptome Profiling of WT and Atg1 RNAi flies upon DXV infection 81
3.1 Introduction to RNA-seq . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.1.1 Pipeline for RNA seq analysis . . . . . . . . . . . . . . . . . . . 82
3.1.1.1 Generation of RNA seq data set . . . . . . . . . . . . . 84
3.1.1.2 RNA seq data analysis . . . . . . . . . . . . . . . . . . 85
3.2 Differential expression analysis . . . . . . . . . . . . . . . . . . . . . . . 87
3.2.1 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.2.2 Statistical modeling of gene expression . . . . . . . . . . . . . . 89
3.2.3 Test for differential expression . . . . . . . . . . . . . . . . . . . 89
3.3 Exploratory studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.3.1 Scatter plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.3.2 Clustering and visualization . . . . . . . . . . . . . . . . . . . . 91
3.3.3 Principle component analysis and its visualization . . . . . . . . 92
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.4.1 Statistics diagnostics . . . . . . . . . . . . . . . . . . . . . . . . 94
3.4.2 Differential expression analysis in wildtype flies upon DXV in-
fection at day 3 and day 5 . . . . . . . . . . . . . . . . . . . . . 100
3.4.3 Differential expression analysis in wildtype flies upon DXV in-
fection at day 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
3.4.4 Differential expression analysis between wildtype and Atg1 RNAi
flies without infection . . . . . . . . . . . . . . . . . . . . . . . 107
3.5 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
vii
4 Atg1 is playing an antiviral role against DXV by regulating lipid metabolism 111
4.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.1.1 DXV infection induces lipolysis in the lipid droplet . . . . . . . . 111
4.1.2 Atg1 regulates lipid metabolism even in the absence of DXV in-
fection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
4.1.3 Lipid metabolism is important for DXV infection . . . . . . . . . 116
4.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
4.2.1 Fly stocks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
4.2.2 Lipid staining and confocal imaging. . . . . . . . . . . . . . . . . 120
5 Conclusions and discussions 121
A Appendix A 126
A.1 Differentially expressed genes between uninfected WT flies and DXV-
infected flies at day 7 post infection . . . . . . . . . . . . . . . . . . . . 126
A.2 Differentially expressed genes between wildtype flies and IR-Atg1 flies
without DXV infection . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
viii
List of Tables
3.1 Differentially expressed genes between uninfected WT flies and DXV-
infected flies at day 3 post infection. Genes presented in the table are
selected by log2 fold Change > 0.5 and p value < 0.05. Gene list is
sorted by log2 fold Change from the largest to the smallest. . . . . . . . . 102
3.2 Differentially expressed genes between uninfected WT flies and DXV-
infected flies at day 5 post infection. Genes presented in the table are
selected by log2 fold Change > 0.5 and p value < 0.05. Gene list is
sorted by log2 fold Change from the largest to the smallest. . . . . . . . . 102
3.3 Immune genes that are responsive to DXV infection in WT flies. . . . . . 106
A.1 Differentially expressed genes between uninfected WT flies and DXV-
infected flies at day7 post infection. Genes presented in the table are
selected by log2 fold Change > 0.5 and p value < 0.05. Gene list is
sorted by log2 fold Change from the largest to the smallest. . . . . . . . . 160
A.2 Differentially expressed genes between wildtype flies and IR-Atg1 flies
without DXV infection. Genes presented in the table are selected by log2
fold Change > 0.5 and p value < 0.05. Gene list is sorted by log2 fold
Change from the largest to the smallest. . . . . . . . . . . . . . . . . . . 179
ix
List of Figures
1.1 DXV viron structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 DXV Genome Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Immune signaling against virus. . . . . . . . . . . . . . . . . . . . . . . 11
1.4 Schematic representation of the Imd signaling pathway and TNF-α pathway. 14
1.5 Diagram of the RNA interference pathway in fighting against invading
virus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.6 Diagram of the Autophagy process. . . . . . . . . . . . . . . . . . . . . 23
1.7 The signaling components of Autophagy. . . . . . . . . . . . . . . . . . 25
1.8 Schematic representation of the lipid storage tissues in the Drosophila. . . 38
1.9 Schematic representation of the core enzymes in the Drosophila lipid
metabolism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
1.10 Diagram of Lipid droplet metabolism. . . . . . . . . . . . . . . . . . . . 47
1.11 Lipid metabolism and β-oxidation. . . . . . . . . . . . . . . . . . . . . . 49
2.1 Silencing of Atg1 renders flies more susceptible to DXV. . . . . . . . . . 53
2.2 Fat body is the tissue important for the Atg1-dependent immune response. 55
2.3 Western blots of DXV viral proteins in the dissected tissues of adult flies. 57
2.4 Atg genes are not important for the host response against CrPV and DCV. 58
2.5 Autophagy does not appear to be activated directly by DXV. . . . . . . . 61
2.6 Autophagy is not induced in ex vivo hemocytes upon TS-DXV infection. . 64
2.7 A representative picture showing DXV in multi-membraned vesicles by
electron microscopy-I. . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.8 A representative picture howing DXV in multi-membraned vesicles by
electron microscopy-II. . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2.9 DXV replicates in S2 cells and forms array-like structures. . . . . . . . . 68
2.10 Autophagy is induced to eliminate mitochondria. . . . . . . . . . . . . . 69
2.11 Electron Microscopy of an S2 cell upon DXV infection: Virus particles
within ER-like structures. . . . . . . . . . . . . . . . . . . . . . . . . . . 70
2.12 Magnified picture from Figure 2.11. . . . . . . . . . . . . . . . . . . . . 71
2.13 DXV does not induce autophagosome formation in S2R+ cells. . . . . . . 73
2.14 Ago2 is important for antiviral response against DXV. . . . . . . . . . . . 75
x
2.15 Loss of autophagy does not render the RNAi pathway ineffective against
DXV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.1 Diagram of RNA-seq analysis workflow . . . . . . . . . . . . . . . . . . 83
3.2 Experimental design of the RNA seq experiment. . . . . . . . . . . . . . 93
3.3 Normalization of RNA-seq data sets. . . . . . . . . . . . . . . . . . . . . 95
3.4 Voom. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.5 Principle component analysis. . . . . . . . . . . . . . . . . . . . . . . . 99
3.6 Differential expression analysis in wildtype flies upon DXV infection at
day 3 and day 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.7 DXV infection induces genes important for lipid droplet lipolysis and β-
oxidation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
3.8 Silencing of Atg1 in the fat body induces lipid droplet lipolysis and β-
oxidation genes in the absence of infection. . . . . . . . . . . . . . . . . 108
4.1 DXV infection decreases lipid droplet size. . . . . . . . . . . . . . . . . 113
4.2 Loss of Atg1 decreases lipid droplet size. . . . . . . . . . . . . . . . . . . 115
4.3 plin138 null mutants have bigger lipid droplet size and are not susceptible
to Drosophila C virus. . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.4 Lipid metabolism genes are important regulators for fly survival upon
DXV infection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
xi
List of Abbreviations
α alpha
β beta
γ gamma
∆ delta
Atgs Autophagy related genes
LD Lipid Droplet
RNA-Seq RNA Sequencing
MEF Mouse embryonic fibroblasts
NLR NOD-like receptors: The nucleotide-binding oligomerization domain receptors
NLRP3 NLR family, pyrin domain containing 3
RLR RIG-I-like receptors
IκK Inhibitor of κB kinase
TCT Tracheal Cytotoxin
PGN Peptidoglycan
PLIN Perilipins
PGRP PGN-recognition Proteins
PRR Pattern Recogonition Receptor
TOR Target of Rapamycin
TLR Toll-like Receptor
VSV Vesicular stomatitis virus
HCV Hepatitis C virus
DENV Dengue virus
JEV Japanese encephalitis virus
MAVS Mitochondrial antiviral-signaling protein
xii
Chapter 1: Introduction
1.1 The innate immune system in Drosophila melanogaster
Drosophila is a good model for the study of innate immunity because the cellular
and signaling events are conserved with mammalian systems (Anderson, 2000). In ad-
dition, Drosophila does not have an adaptive immune system, making it a good model
to study innate immunity specifically. With the advantage of powerful genetics toolkits
in Drosophila, novel genes can be identified and further characterized in vivo, which can
lead to functional discoveries of the mammalian homologs. For example, Drosophila Toll
was originally found as a protein required for the establishment of the dorsal-ventral axis
in the embryo (Morisato and Anderson, 1995). It was later demonstrated as a critical re-
ceptor controlling antimicrobial peptide gene expression in response to fungal infection
(Lemaitre et al., 1995a, 1996). The sequence homology study of Drosophila Toll led to
the discovery of the mammalian Toll-Like Receptors (TLRs), which turn out to be one of
the major pattern recognition receptors (PRRs) in the mammalian innate immune system
(Medzhitov et al., 1997; Rock et al., 1998; Takeuchi et al., 1999).
Upon infection, the epithelial barrier serves as the first physical defense against the
invading pathogens (Ferrandon et al., 1998). When pathogens are internalized, both cel-
lular and humoral responses are activated to fight the pathogens. In Drosophila, cellular
1
immunity is functionally executed by three types of hemocytes: plasmatocytes, crystal
cells and lamellocytes. These cells function by phagocytosis, melanization and encapsu-
lation, respectively (Williams, 2007). The humoral response is induced in the fat body, an
organ that is equivalent to the liver in mammals. Upon infection, large amounts of antimi-
crobial peptides (AMPs) are produced by the fat body and secreted into the hemolymph.
Some studies also show that AMPs can break the bacteria cell wall to kill them (Brogden,
2005).
1.1.1 Microbial Recognition
Following infection, the host initiates immune responses first by detecting specific
microbial pattern molecules. The host encoded pattern recognition receptors (e.g. PGN-
recognition proteins or PGRPs) can specifically bind to conserved structures (e.g. Pepti-
doglycan) that are found in the pathogens but absent in the host (Janeway, 2013). Pepti-
doglycan (PGN) is present on most bacteria and is composed of conserved polymers of
β -1,4-linked N-acetylglucosamine and N-acetylmuramic acid cross-linked by short stem
peptides, which vary between different types of bacteria. Mammals have a family of four
PGRPs, whereas insects have more (with 13 genes coding for 19 proteins in Drosophila)
(Werner et al., 2003, 2000). The mammalian PGRPs are secreted proteins that bind bac-
terial muramyl peptides. Some mammalian PGRPs have an amidase activity, probably
to eliminate the proinflammatory PGN, whereas others are more diverged from the insect
genes and function directly as bactericidal proteins (Dziarski and Gupta, 2006; Liu et al.,
2000; Swaminathan et al., 2006). Mammalian PGRPs do not appear to possess signaling
2
activity.
Insect PGRPs can act as amidases to degrade PGN and also activate of signal-
transduction pathways and proteolytic cascades. Insect PGRPs are classified as short (S)
or long (L), according to their transcript size: short PGRPs have signal peptides and can
be extracellular proteins, whereas long PGRPs can be intracellular, extracellular, or trans-
membrane proteins. In Drosophila, PGRP-SA and PGRP-SD are required for the Toll
signaling pathway (Bischoff et al., 2004; Michel et al., 2001; Valanne et al., 2011). No
immunological in vivo phenotype has been observed for the amidases PGRP-SB1 and
PGRP-SB2, even though PGRP-SB1 is strongly induced post-infection, and its bacteri-
cidal activity has been shown in vitro (Mellroth and Steiner, 2006; Paredes et al., 2011;
Zaidman-Re´my et al., 2011). The function of PGRP-LD is unclear. The other PGRPs
either positively or negatively regulate the Imd pathway.
1.2 Drosophila viruses
1.2.1 Drosophila X virus
Drosophila X virus was initially observed during the studies of Sigma Virus. Flies
infected with rhabdovirus Sigma virus developed anoxia sensitivity such that infected flies
would die after 15 mins of pure CO2 exposure (L’Heritier, 1958). However, in the course
of a control uninfected passage, Teninges et al. found that uninfected flies also develop
anoxia sensitivity occasionally. This made the scientists suspect that the anoxia sensitivity
does not come from Sigma virus but from other sources. By negative contrast electron
microscopy, it was found that a group of icosahedral viral particles were present that
3
are morphologically distinct from Sigma virus. Without information of the relationship
between this newly-discovered virus and other viruses, it was named Drosophila X virus
(Teninges et al., 1978).
1.2.1.1 Taxonomy
Drosophila X virus is a non-enveloped virus with a linear double stranded RNA
genome. It belongs to the Family Birnaviridae. This family is composed of three gen-
era (i) Genus Aquabirna-virus, represented by the type species infectious pancre- atic
necrosis virus (IPNV); (ii) Genus Avibirnavirus, type species infectious bursal disease
virus (IBDV); and (iii) Genus Entomobirnavirus, type species Drosophila X virus (Dobos,
1995). Birnaviruses are medium sized (60 nm), unenveloped viruses with an icosahedral
nucleocapsid that contains a bisegmented dsRNA genome.
DXV has a single-shelled T=13 icosahedral symmetry capsid of about 60 nm in di-
ameter, that is composed of 260 trimers of VP2 that form spikes projecting radially from
the capsid (Figure 1.1). The peptides derived from pre-VP2 C-terminal cleavages re-
main associated within virion. VP3 forms a ribonucleoprotein complex with the genomic
RNA. Minor amounts of VP1 are also incorporated in the virion. Based on morphological
properties, DXV is similar to several ungrouped vertebrate and invertebrate viruses like
Infectious pancreatic necrosis virus (IPNV) , infectious bursal disease virus (IBDV) and
Tellina tenuis virus.
4
Figure 1.1: DXV viron structure.
DXV has a single-shelled T=13 icosahedral symmetry capsid, which is composed of VP1,
VP2 and VP3. The dsRNA genome has two segments A and B.
1.2.1.2 Viral genome and proteins
Drosophila X virus has a two-segment dsRNA genome. These two segment dsRNA
are translated into two separate polypeptides (A and B) before mature viral proteins are
processed from the polypeptides. A representation of the viral protein polypeptides are
shown in Figure 1.2.
The Segment B includes only VP1, a 112kDa protein that encodes for the RNA-
dependent RNA polymerase (RdRp). VP1 is present not only in a free form but can be
found covalently attached to the 5’ genomic RNA end of both segments (VPg) (Kibenge
and Dhama, 1997; Nagy and Dobos, 1984a,b; Revet and Delain, 1982). As an internal
protein within the viron, VP1 has a low copy number and represents only 4% of total
protein present in the virion.
5
Figure 1.2: DXV Genome Structure.
DXV genome has two segments: A and B. Segment B encodes the RNA-dependent RNA
polymerase (RdRP). Segment A encodes the capsid proteins VP2 and VP3 and the pro-
tease VP4. It also encodes an alternative ORF translated possibly by leaky scanning (VP5)
(Hulo et al., 2011).
The structural properties of VP1 from DXV were predicted based on studies of
other Birnavirus including infectious bursal disease virus (IBDV) and infectious pancre-
atic necrosis virus (IPNV) since a close homology is shared between the viruses within
this family (Dobos, 1995; Mu¨ller et al., 2003). The IBDV RdRp contains several structural
motifs (I, II, III and IV) that are common to RNA-dependent RNA polymerases (RdRp)
of positive-strand RNA (ssRNA+) viruses (Bruenn, 1991; Gorbalenya and Koonin, 1988;
von Einem et al., 2004). VP1 can catalyze RNA dependent RNA polymerization in vitro.
During in vitro RNA transcription, VP1 serves as a primer and remains attached to the
5’ end of the RNA. Viral RNA transcribes in a semi-conservative, strand-displacement
mechanism (Dobos, 1995). Different from HCV RdRp, IBDV VP1 requires specific tem-
plate features to function. Experiments with various truncated RNA templates indicate
6
that the 3’ noncoding region (NCR) of segments A and B was critical for VP1-directed
RNA polymerization (von Einem et al., 2004).
.
Segment A is 3360bp-nucleotide in length and contains two overlapping open read-
ing frames (ORFs). The large ORF is composed of 3096 nucleotides, which is flanked by
a 107-bp 5’ and a 157-bp 3’-untranslated region. The small ORF is 711-nucleotide-long
and located within the carboxy half of the large ORF but in a different reading frame.
The large ORF encodes a 114kDa polyprotein (NH2-preVP2-VP4-VP3-COOH), which
is cotranslationally cleaved by viral-encoded protease VP4 to generate the major outsider
capsid protein VP2 (45kDa) and internal structual protein VP3 (34kDa) (Birghan et al.,
2000; Nagy and Dobos, 1984a,b). The small ORF starts at the junction of VP4 and VP3,
and is capable of encoding a basic, arginine-rich 27kDa polypeptide which so far has not
been detected in infected cells (Chung et al., 1996).
VP2 is the major outer capsid protein of DXV and represents approximately 60% of
a virions total protein (Dobos, 1995). Its mature form is processed through two protease
cleavages, from preVP2 to pVP2 (49kDa) and finally to VP2 (Chung et al., 1996). As a
result of these two cleavages and another one that hasn’t been fully characterized, four
peptides are generated. Associated with the virion, these peptides were proposed to fa-
cilitate cell membrane destabilization during viral entry (Coulibaly et al., 2005; Da Costa
et al., 2002).
VP3 is the inner capsid structural protein of birnaviruses and composes 34% of the
total virion (Dobos, 1995). Crystal structure data suggests that VP3 is important for the
viral coat assembly (Coulibaly et al., 2005). VP3 has been shown to bind to dsRNA and
7
form a threadlike ribonucleoprotein complex. In addition, the positively charged C termi-
nus of VP3 has been shown to interact with VP1 that is critical for the encapsidation into
virus-like particles (VLPs) (Bo¨ttcher et al., 1997; Hjalmarsson et al., 1999; Hudson et al.,
1986; Lombardo et al., 1999). The interactions between VP3 and other viral proteins have
been detected in multiple birnaviruses. This might indicate that VP3 is a crucial for the
birnavirus structure, assembly, and replication.
VP4 encodes for the viral protease that is responsible for the proteolytic maturation
of the polyprotein A (Fahey et al., 1985; Hudson et al., 1986). It has been shown that
VP4 forms a non-canonical RNA viral Lon protease, even though it does not contain an
ATPase domain (Birghan et al., 2000). In the birnavirus protease, two residues, conserved
across the Lon/VP4 protease family, form a Ser-Lys catalytic dyad and are critical for the
cleavage activity (Birghan et al., 2000). VP4 is responsible for the cleavage at the pVP2-
VP4 and VP4-VP3 junctions.
In addition to the 128kDa polyprotein ORF, birnavirus genome segment A also
encodes other smaller reading frames. In IPNV, a 17kDa nonstructural protein (VP5)
is encoded and is present in IPNV-infected cells (Magyar and Dobos, 1994). However,
IPNV VP5 is not required for viral replication in cell culture and that VP5-deficient mu-
tant viruses have replication kinetics similar to that of wild type virus (Mundt et al., 1997;
Weber et al., 2001; Yao et al., 1998). The function of this VP5 is still unknown. Although
no effect is observed in vitro, it could possibly have a role in vivo. In IBDV-infected cells,
VP5 has also been detected but it is not present in the virion (Mundt et al., 1995).
8
1.2.1.3 Virus cycle
The complete virus cycle for DXV is not fully elucidated yet. In Drosophila S2
cells, DXV can replicate and cause pathology such as a cytopathic effect (CPE). Prelim-
inary siRNA experiments in S2 cell lines indicate that endocytosis is important for viral
infection. Silencing the core components of the endocytoic pathway, including Rab5, and
clathrin inhibit DXV replication (Javier Robalino, unpublished).
In Drosophila, virus particles were found in larval hemocytes 30 mins after DXV
infection. However, the replication of DXV takes time to reach other tissues. Within a
week, virus can be found in multiple tissues including the digestive tracts, fat body, brain
muscle and ovaries (Teninges et al., 1978).
1.3 Antiviral responses in Drosophila melanogaster
Viruses are among the most threatening pathogens that cause human diseases. For
example, dengue virus, hepatitis C virus (HCV) and human immunodeficiency virus
(HIV) put millions of people at risk every year. However, the mechanisms of patho-
genesis and replication strategies often vary for different viruses, and the host utilizes a
variety of antiviral responses to combat different viral pathogens. Thus, it is critical to
establish infection models to understand the underlying mechanisms in vivo. Drosophila
melanogaster can be infected with a variety of insect viruses. Some are natural pathogens
found in wild Drosophila population including sigma virus, Drosophila C Virus (DCV),
and flock house virus (FHV) (Carpenter et al., 2007; Jovel and Schneemann, 2011; Kapun
et al., 2010). Some are viruses that can infect Drosophila and cause pathogenesis in lab-
9
oratories, for example, Drosophila X virus (DXV) and vesicular stomatitis virus (VSV)
(Pe´rie`s et al., 1966; Zambon et al., 2005). To combat invading viral pathogens, Drosophia
also initiate different immune responses against the pathogens. This makes Drosophila
a good host-pathogen model to study the antiviral response. So far, in addition to the
Toll and Imd pathways, two of the best characterized immune pathways in Drosophila,
RNAi interference, JAK-STAT, autophagy pathways have also been suggested to play an
antiviral role in Drosophila (Figure 1.3). In our lab, we show that energy homeostasis is
closely related with viral replication that modulation of lipid metabolism and β-oxidation
could be crucial for antiviral responses. We will present and discuss about this discovery
in detail in this thesis.
The Toll and Imd signaling pathways are the best characterized immune signaling
pathways in the Drosophila immune responses (De Gregorio et al., 2002). Although
both pathways were first identified to fight against fungi or bacteria, they also play roles
against virus infections (Costa et al., 2009; Lloyd and Taylor, 2010; Zambon et al., 2005).
Recently, other pathways such as autophagy, RNA interference, JAK-STAT and JNK,
have also been found to participate in antiviral responses (Dostert et al., 2005; Lee et al.,
2005; Sabin et al., 2009; Shelly et al., 2009). Here we will briefly review the immune
functions of these pathways and specifically the roles they play when the host is infected
with virus.
10
Figure 1.3: Immune signaling against virus.
The Toll pathway is important in the immune defense against fugi, Gram-positive bac-
teria, Drosophila X virus and dengue virus. The Imd pathway is important for defense
against Gram-negative bacteria, sindbis virus, cricket paralysis virus (CrPV). The JAK-
STAT pathway is critical for the response against Drosophila C virus, flock house virus
and dengue virus. Autophagy has been shown to play a role against vesicular stomatitis
virus (Sabin et al., 2010).
1.3.1 The Toll Signaling Pathway
Toll was first identified for its role in establishing the dorsal-ventral pattern in the
Drosophila embryo (Morisato and Anderson, 1995). It was later shown to be responsive to
Gram-positive bacteria and fungi (Lemaitre et al., 1996, 1997; Rutschmann et al., 2002).
The Toll pathway is highly conserved with the Toll-like receptor pathways in mammals.
The transmembrane protein Toll is activated by a proteolytically processed form of the
Spa¨tzle protein, a cystine-knot-cytokine-growth-factor-like polypeptide (Lemaitre et al.,
1996; Mizuguchi et al., 1998). The extracellular domain of Toll is composed largely of
11
leucine-rich repeats, and the cytoplasmic domain of Toll is similar to the cytoplasmic do-
main of the mammalian IL-1 receptor (Schneider et al., 1994). Toll activation results in
the phosphorylation of the downstream proteins Tube and Pelle, which regulate degra-
dation of the Cactus protein. Cactus, the Iκ-B homolog, holds the transcription factors
Dif and Dorsal in the cytoplasm by forming a Cactus-Dif / Cactus-Dorsal complex. The
degradation of Cactus results in the nucleus translocation of Dif and Dorsal, which lead
to expression of antimicrobial peptide genes (Ip et al., 1993; Lemaitre et al., 1995b). The
antimicrobial peptides are produced in the fat body and secreted into the hemolymph
for immune functions. Currently, there are seven characterized AMPs in Drosophila:
Drosomycin, Diptericin, Attacin, Drosocin, Cecropin, Defensin, Metchnikowin. Toll ac-
tivation usually leads to high upregulation of Defensin, Metchnikowin and Drosomycin.
The Toll pathway was discovered to be important for the antiviral response against
Drosophila X virus (DXV) (Zambon et al., 2005). The Toll10b mutant, which carries a
constitutively activated form of Toll, was susceptible to DXV infection. The Dif mutant
is also susceptible to DXV infection. This evidence suggests that Toll is important in
the antiviral response in insects. However, the downstream targeted AMPs genes do not
appear to affect the viral response. Overexpression of single AMPs does not cause resis-
tance to DXV (Zambon et al., 2005). Additional studies have shown the importance of
Toll during the infection of dengue virus in mosquito. Silencing of the positive regulator
dMyD88 results in an increase of viral load, whereas silencing of the negative regulator
cactus results in an decreased load of virus (Xi et al., 2008).
12
1.3.2 The Imd Signaling Pathway
The Imd pathway was found to be responsive to Gram-negative bacteria (Lemaitre
et al., 1995a). Unlike the Toll Pathway, the Imd pathway is activated through the trans-
membrane receptor PGRP-LC, which recognizes diaminopimelic acid-type peptidogly-
can on Gram-negative bacteria and certain Gram-positive bateria such as Bacillus spp
(Choe et al., 2005, 2002; Gottar et al., 2002; Lemaitre et al., 1997; Leulier et al., 2003;
Ra¨met et al., 2002b; Zaidman-Re´my et al., 2006). PGRP-LC is spliced into several iso-
forms, three of which have been characterized. For example, PGRP-LCx recognizes
polymeric PGN; PGRP-LCa does not directly bind PGN, but it acts as a coreceptor with
PGRP-LCx to bind monomeric PGN fragments called tracheal cytotoxin (TCT) (Chang
et al., 2006; Kaneko et al., 2004; Lim et al., 2006). A diagram of the Imd pathway is
shown in Figure 1.4.
13
Figure 1.4: Schematic representation of the Imd signaling pathway and TNF-α pathway.
The Drosophila Imd pathway is equivalent to the mammalian TNF-α pathway. Activa-
tion of both pathways results in the translocations of NF-κB transcription factors, Relish
in Drosophila and P65/P50 in mammals. Conserved components are represented by sim-
ilar shapes and colors. bend, bendless; eff, effete; Key, Kenny; K63 Ub, K63 polyubiq-
uitination; NEMO, NF-κB essential modulator; TAB, TAK1-binding protein; TRADD,
TNFR1-associated death domain (Myllyma¨ki et al., 2014).
14
Recognition of PGN by PGRP-LC results in the recruitment of the 25kDa death
domain protein Imd, whose death domain is homologous to that of mammalian RIP1, a
TNF-receptor interacting protein. Imd further associates with the mammalian homolog
of FADD (dFADD) and the caspase-8 homolog Dredd (Leulier et al., 2000, 2002). Dredd
becomes activated through ubiquitination by the E3-ligase Inhibitor of apoptosis 2 (Iap2) ,
which associates with the E2-ubiquitin-conjugating enzymes UEV1a, Bendless (Ubc13),
and Effete (Ubc5) (Meinander et al., 2012; Zhou et al., 2005).
When activated, Dredd cleaves Imd, removing a 30-aa N-terminal fragment, and
creates a novel binding site for Iap2, which can then K63-ubiquitinate Imd (Meinander
et al., 2012; Paquette et al., 2010). This leads to activation of the TAB2/TAK1 complex,
composed of a homolog of mammalian transforming growth factor-activated kinase 1
(TAK-1) and TAB2. Downstream of dTAK1, a signalosome equivalent, composed of Ird5
(the IκBKβ homolog) and Kenny (the IκBKγ homolog), phosphorylates Relish, lead-
ing to the proteolytic cleavage of the inhibitory carboxyl-terminal fragment (Rutschmann
et al., 2000; Sto¨ven et al., 2000; Wu and Anderson, 1998). Activated Relish translocates
into the nucleus and induces a large number of genes including antimicrobial peptide
genes, such as diptericin, cecropin, drosocin and attacin. It is proposed that Dredd, after
its initial interaction with the death domain protein dFADD, is responsible for cleavage
of Relish (Stoven et al., 2003).
In addition to PGRP-LC, PGRP-LE can also regulate the Imd pathway. PGRP-LE
exists in two forms (Kaneko et al., 2006; Neyen et al., 2012). The short form is secreted
into the hemolymph and can stimulate the Imd signaling by binding and presenting PGN
to PGRP-LC. However, the secretion mechanism of PGRP-LE is not yet well understood
15
(Takehana et al., 2002). The full-length PGRP-LE remains in the cytoplasm, where it
is thought to recognize TCT fragments that reach the inside of a cell. Binding of TCT
leads to the oligomerization of cytoplasmic PGRP-LE in a head-to-tail fashion (Lim et al.,
2006). Ectopic expression of PGRP-LE in the fat body is sufficient to activate AMP ex-
pression, in a cell-autonomous manner, even in the absence of infection. Cytoplasmic
PGRP-LE can activate the Imd pathway, independently of PGRP-LC, by interacting with
Imd (Kaneko et al., 2006; Neyen et al., 2012; Takehana et al., 2002; Yano et al., 2008).
PGRP-LE is the only intracellular microbial receptor identified in Drosophila (Choe et al.,
2005; Takehana et al., 2002). The intracellular form is able to activate autophagy, whereas
the transmembrane form can, together with PGRP-LC, activate a prophenoloxidase cas-
cade (Kurata, 2014; Schmidt et al., 2008; Takehana et al., 2002). However, the activation
of autophagy through PGRP-LE does not appear to involve the Imd pathway (Yano et al.,
2008).
PGRP-LF is a transmembrane protein that resembles PGRP-LC but lacks the intra-
cellular signaling domain and does not bind PGN. PGRP-LF acts as an inhibitor of Imd
signaling by binding PGRP-LC and preventing its dimerization (Basbous et al., 2011;
Maillet et al., 2008; Persson et al., 2007). PGRP-LA is also predicted not to bind PGN and
recently was shown to be dispensable for systemic infections. However, consistent with
its expression profile, PGRP-LA appears to positively regulate the Imd pathway in barrier
epithelia, such as the trachea and the gut (Gendrin et al., 2013). PGRP-LB, PGRP-SC1A,
PGRP-SC1B, and PGRP-SC2 have amidase activity and are shown to play somewhat re-
dundant roles in downregulating the Imd pathway during a systemic response. PGRP-LB
is the major regulator in the gut. The amidase PGRPs digest PGN into short, nonimmuno-
16
genic or less immunogenic fragments and, therefore, prevent or reduce the activation of
defense mechanisms (Paredes et al., 2011; Zaidman-Re´my et al., 2011).
Recent studies have suggested that the Imd pathway might have other roles in addi-
tion to the immune response. For example, antimicrobial peptides are upregulated during
metamorphosis in the absence of infection (Lee et al., 2003). In addition, overexpres-
sion of imd can promote apoptosis and induce expression of the pro-apoptotic Drosophila
reaper gene due to the death domain of Imd. Both the apoptosis and the antimicrobial
peptide gene expression induced by Imd activation can be blocked by the caspase inhibitor
p35 (Georgel et al., 2001).
Recently, the Imd pathway was shown to be important in the antiviral response
against Cricket Paralysis Virus and Sindbis Virus (Avadhanula et al., 2009; Costa et al.,
2009). Loss-of-function mutations in several Imd pathway genes (PGRP-LC, Tak1, ird5,
kenny, relish, imd, dFADD) displayed increased sensitivity to CrPV infection and higher
CrPV loads, while mutations in the Toll pathway fail to affect replication (Costa et al.,
2009). Similar results were obtained from Sindbis virus (SINV) infection both in Drosophila
(Rel, Imd, Fadd, Dredd, Tab2, Ird5, Key) and in the cultured mosquito cells (Avadhanula
et al., 2009).
1.3.3 JAK-STAT Signaling Pathway
The JAK/STAT (JAK: Janus Kinase, STAT: signal transducers and activators of
transcription) signal transduction pathway is conserved throughout evolution such that
structural and functional homologs of components originally identified in vertebrates are
17
also present in the model organism Drosophila melanogaster. The JAK-STAT pathway is
a key regulator of proliferation and differentiation of larval hematopoietic cells (Harrison
et al., 1995; Hou et al., 1996; Luo et al., 1997; Yan et al., 1996). In addition to its role
during larval hematopoiesis, the JAK/STAT pathway in Drosophila is also involved in
other developmental processes such as sexual identity, the segmentation of the embryo
and the establishment of polarity within the adult compound eye (Agaisse and Perrimon,
2004; Harrison et al., 1998; Jinks et al., 2000).
Recent studies have revealed novel immune functions of JAK-STAT pathway in
both the cellular and humoral responses. Upon infection, a cytokine-like protein Upd3
produced by hemocytes activates JAK-STAT signaling in the fat body, which results in
transcriptional expression of tot genes and tep1 (Agaisse et al., 2003). Specifically, totA
is a stress induced gene of unknown function, and tep1 is a thiolester-containing protein
that possibly acts as an opsonin (Agaisse et al., 2003; Boutros et al., 2002; Lagueux et al.,
2000). Additionally, the JAK-STAT target genes have shown a delayed and transient
expression pattern compared to the Toll- and Imd-dependent genes, although the reason
why still remains to be identified (Boutros et al., 2002).
JAK-STAT signaling has also been implicated in the activation of blood cells and
possibly important as an antiviral response in Drosophila against Drosophila C Virus.
Loss-of-function hopM38/msvl flies are more susceptible to Drosophila C Virus compared
to wildtype and there is an increased viral RNAs in the mutants compared to wildtype as
well (Dostert et al., 2005).
18
1.3.4 RNA interference pathway
RNA interference (RNAi) is a targeted gene silencing pathway that controls gene
expression by sequence-specific small RNAs. RNA silencing regulates the expression
of endogenous genes, and can also modulate exogenous gene expression. For exam-
ple, small RNAs generated by the RNAi pathway can specifically target viral genome
sequence to degrade viral RNAs, which is an effective antiviral strategy widely used in
plants and invertebrates (Aliyari and Ding, 2009; Bronkhorst and van Rij, 2014; van Rij
et al., 2006). Accumulating evidence suggests that most of virus-derived siRNAs are pro-
cessed by Dicer proteins, members of the RNase III family, which generate a 21-23nt
RNA duplex from a larger dsRNA precursor molecule (Aliyari and Ding, 2009; Bern-
stein et al., 2001; Jaskiewicz and Filipowicz, 2008). Upon infection, the virus-derived
dsRNA molecules (e.g., the dsRNA genome or dsRNA intermediates generated through
single strand RNA replication) are processed into viral siRNAs. The small interfering
RNA duplex (siRNA) is incorporated into the effector complex, in a sequence specific
manner, and followed by the recruitment of the RNA-induced silencing complex (RISC)
to cleave the complementary sequence on the mRNA target (Figure 1.5). Mutants of
the core siRNA machinery (dcr2, r2d2, ago2) showed increased sensitivity to infection
by several RNA viruses, such as Flock House virus (FHV), Drosophila C virus (DCV),
Cricket Paralysis virus (CrPV), Sindbis virus (SINV), Vesicular Stomatitis virus (VSV),
Drosophila X virus (DXV), West Nile virus (WNV), and Rift Valley Fever virus (RVFV)
(Chotkowski et al., 2008; Galiana-Arnoux et al., 2006; Li et al., 2002; Sabin et al., 2009;
van Rij et al., 2006; Wang et al., 2006; Zambon et al., 2006). To combat the RNA in-
19
terference, some viruses encode viral suppressors of RNA silencing (VSRs) to deactivate
the RNAi pathway (Li and Ding, 2006). For example, FHV infection requires an FHV-
encoded protein B2 to suppress RNA silencing (Aliyari et al., 2008; Li et al., 2002). B2
also inhibits RNA silencing in transgenic plants and Caenorhabditis elegans, providing
evidence for a conserved RNA silencing pathway in the plant and animal kingdoms (Guo
and Ding, 2002; Guo and Lu, 2013; Li et al., 2002).
Figure 1.5: Diagram of the RNA interference pathway in fighting against invading virus.
A systemic RNAi response against virus comprises two steps: biogenesis of vsiRNAs
and a vsiRNA-dependent effector response. The biogenesis of vsiRNA is initiated by
the enzyme Dicer, which detects dsRNA genome or dsRNA replication intermediates
and cleave them into short double stranded fragments of ∼21 nucleotide vsiRNAs. Each
vsiRNA is unwound into two single-stranded (ss) ssRNAs, the passenger strand and the
guide strand. The passenger strand is degraded and the guide strand is incorporated into
the RNA-induced silencing complex (RISC). When the guide strand pairs with a viral
RNA molecule, argonaute (Ago2), the catalytic component of the RISC complex can
facilitate the cleavage of the viral RNAs (Sabin et al., 2010).
20
1.3.5 JNK Signaling Pathway
The c-Jun NH2-terminal kinases (JNK) are central components of signal transduc-
tion pathways in the regulation of cell proliferation and differentiation, cytokine produc-
tion, apoptosis, and cell survival in mammals (Arthur and Ley, 2013; Garrington and
Johnson, 1999; Lamb et al., 2003). c-Jun N-terminal kinases (JNKs) bind and phospho-
rylate c-Jun on Ser-63 and Ser-73 within its transcriptional activation domain. Activated
JNK can bind and phosphorylate a variety of downstream substrates such as the transcrip-
tion factors c-Jun and ATF-2 (De´rijard et al., 1994; Kyriakis et al., 1994).
It is well established that JNK signaling is important in the Drosophila innate im-
mune response. Particularly it is involved in cellular processes such as phagocytosis,
wound healing, melanization, and defense against extracellular pathogens (Bidla et al.,
2007; Kim et al., 2005; Park et al., 2004; Ra¨met et al., 2002a; Schneider et al., 2007;
Silverman et al., 2003). However, there is discrepancy on whether JNK is positively
or negatively regulating AMP gene expression. One proposal is that JNK is a negative
regulator of the transcription factor Relish. This is supported by evidence that the JNK-
dependent transcription factors Drosophila activator protein 1 (dAP-1) and Stat92E form
a repressosome complex in response to continuous immune signaling (Kim et al., 2007).
The alternative proposal suggests that JNK signaling is essential for AMP gene induction
since the expression of a JNK inhibitor and an induction of JNK loss-of-function clones
suppress AMP gene expression (Delaney et al., 2006). Additional studies are required in
order to differentiate between these contradictory hypotheses.
Studies have also shown that many viruses can manipulate the JNK signaling path-
21
way to regulate viral replication and gene expression. These viruses include human im-
munodeficiency virus type 1 (HIV-1), echovirus 1, herpes simplex virus type 1 (HSV-1),
Kaposi’s sarcoma-associated herpes virus, coxsackievirus B3, varicella-zoster virus and
infectious bursal disease virus (IBDV) (Huttunen et al., 1998; Kumar et al., 1998; Pan
et al., 2006; Si et al., 2005; Wei et al., 2011; Xie et al., 2005; Zachos et al., 1999; Zapata
et al., 2007). In addition, the JNK pathway is involved in cell apoptotic death induced by
some viruses, including HSV-1, coxsackievirus B3, reovirus, swine influenza virus and
poliovirus (Autret et al., 2007; Choi et al., 2006; Clarke et al., 2004; Kim et al., 2004;
Perkins et al., 2003). For example, the activated JNK1/2, induced by IBDV, phospho-
rylates the downstream target c-Jun. Inhibition of JNK1/2 activation leads to reduced
viral progeny release, which is associated with decreased viral transcription and lower
virus protein expression. A decrease in apoptotic cell death is also observed since Bax
activation, cytochrome c release, and caspase activation are all blocked. These data sug-
gest that the JNK pathway plays an important role in viral replication and contributes to
virus-mediated changes in host cells (Wei et al., 2011).
1.3.6 Autophagy
Autophagy is a cell intrinsic mechanism for the degradation of cytoplasmic contents
(Figure 1.6). It was originally discovered as a starvation-induced response, that delivers
long-lived proteins and entire organelles for lysosomal degradation, so that cytoplasmic
contents can be recycled for new synthesis (Klionsky and Emr, 2000).
22
Figure 1.6: Diagram of the Autophagy process.
(A-C)The induction of autophagy starts with isolation membrane formation, followed by
membrane expansion, vesicle completion, autophagosome formation, fusion and degrada-
tion of cargo proteins within autolysosomes. (D-E) Electron microscopy pictures showing
autophagosome and autolysosome, respectively (Mele´ndez and Neufeld, 2008).
23
Three types of autophagy have been defined: microautophagy, chaperone-mediated
autophagy (CMA) and macroautophagy (Mizushima and Klionsky, 2007). Microau-
tophagy is characterized by budding into the lysosome so that cytoplasmic contents are
incorporated for degradation. The CMA pathway initiates protein degradation with the
recognition of a signaling motif, KFERQ, by the chaperone protein Hsc70. Hsc70 then in-
teracts with the lysosomal membrane protein (LAMP) and directs the targeted proteins for
degradation (Cuervo and Dice, 2000; Massey et al., 2006). Macroautophagy, also termed
autophagy, is the main pathway for degradation of cytoplasmic contents. When it is acti-
vated, the initiation sites form preautophagosomal assembly sites (PAS). By membranous
expansion, the phagophore will form double membrane vesicles called autophagosomes.
The autophagosome then fuses with the lysosome to become an autolysosome that even-
tually results in degradation of the incorporated contents. More than twenty autophagy
related genes (Atg) have been identified from the studies in yeast (Mizushima et al., 1998).
Most Atg homologs are also found in higher organisms, such as Drosophila melanogaster
and mammals.
1.3.6.1 Autophagy signaling pathway
Although autophagy was initially identified in mammals, the understanding of the
Atg genes mostly comes from the yeast screens (Tsukada and Ohsumi, 1993). The Atg
proteins can be divided into four groups: 1) the Atg1-Atg13-Atg17 kinase complex; 2) the
class III phosphatidylinositol 3-kinase (PtdIns3K) complex, consisting of Vps34, Vps15,
Atg6 and Atg14; 3) two ubiquitin-like protein conjugations systems (Atg8 and Atg12);
24
4) Atg9 and its cycling system (Yang and Klionsky, 2010). The signaling components of
autophagy are shown in Figure 1.7.
Figure 1.7: The signaling components of Autophagy.
The autophagy pathway is conserved from yeast to metazoans. The core regulators in-
clude the Atg1-Atg13-Atg17 kinase complex, the class III phosphatidylinositol 3-kinase
(PtdIns3K) complex (Vps34, Vps15, Atg6 and Atg14), two ubiquitin-like protein conju-
gations systems (Atg8 and Atg12) and the membrane cycling system (Atg9) (Mele´ndez
and Neufeld, 2008).
25
The Atg1-Atg13-Atg17 kinase complex is involved in the induction of autophagy.
The target of rapamycin kinase (TOR), negatively regulates autophagy, andnphosphory-
lates Atg13 (Funakoshi et al., 1997). In nutrient-rich conditions, Atg13 is highly phos-
phorylated and has a lower affinity towards Atg1. When there is a lack of nutrients, Atg13
is rapidly dephosphorylated and has a higher affinity with Atg1 that results in the activa-
tion of autophagy (Kamada et al., 2000). Among all the Atg proteins, Atg1 is the sole
serine/threonine protein kinase (Matsuura et al., 1997). The downstream targets of Atg1
are not known.
The class III phosphatidylinositol 3-kinase (PtdIns3K) complex is important in nu-
merous membrane trafficking events, and is involved in autophagic vesicle nucleation.
PtdIns3k is a lipid kinase and its activity is essential for autophagy. One possible role of
PtdIns3k is to generate PtdIn(3)P at the Pre-autophagosomal Structure (PAS) to recruit
PtdIn(3)P binding proteins such as Atg18 and Atg21 (Guan et al., 2001; Strømhaug et al.,
2004). In yeast, VPS34 is the only PtdIns3k, and it forms two distinct Atg6/Vps34 com-
plexes: complex I is composed of Vps34, Vps15, Vps30/Atg6 and Atg14, and complex
II contains the same proteins except that Atg14 is replaced by Vps38. The first com-
plex is thought to localize other Atg proteins to the pre-autophagosomal structure or PAS
and thus has a stimulating role in autophagy; the second complex is involved in vacuo-
lar protein sorting of carboxypeptidase Y (CPY), which is normally transported from the
late Golgi to the vacuole (Kihara et al., 2001). However, in metazoans, two kinds of Pt-
dIns3Ks are involved: Class I and Class III PtdIns3Ks. Similar to yeast VPS34, VPS34
in the metazoan is a Class III PtdIns3K and plays a stimulating role in autophagy. But
Class I PtdIns3K, downstream of the insulin signaling pathway, functions at the plasma
26
membrane and activate TOR; hence it inhibits autophagy (Jacinto and Hall, 2003).
There are two conjugation systems led by Atg5-Atg12 and Atg8, respectively. In
the Atg5-Atg12 system, Atg12 interacts with Atg5 through an irreversible isopeptide
bond that is formed between a C-terminal glycine residue of Atg12 and a central ly-
sine residue of Atg5 (Mizushima et al., 1998). Two other proteins are required for this
process: Atg7, a homolog of the E1 ubiquitin-activating enzyme, and Atg10, a homolog
of the E2 ubiquitin-activating enzyme (Kim et al., 1999; Mizushima et al., 1999; Shin-
tani et al., 1999). Atg7 transiently binds to the C-terminal glycine of Atg12 through its
active site cysteine through a thioester bond. After ATP hydrolysis, Atg12 is activated
and then temporarily interacts with Atg10. Finally, a covalent bond is formed between
Atg5 and Atg12. Atg 16 is also involved in the Atg5-Atg12 complex. Atg16 can form
a homo-oligomer to mediate the formation of a higher multimeric structure with Atg12,
Atg5, and Atg16 (Kuma et al., 2002).
The Atg8 conjugation system modifies a lipid called phosphatidylethanolamine
(PE) (Ichimura et al., 2000; Kirisako et al., 2000). Initially, the cysteine protease Atg4
proteolytically removes a C-terminal arginine residue from Atg8, exposing a glycine that
can be accessed by the E1-like Atg7. Atg8 is activated by Atg7 and then is transferred to
another E2-like enzyme, Atg3, and eventually conjugates to PE through an amide bond.
Atg8 conjugated to PE behaves like a membrane protein and can form protein aggregates
on the surface of the autophagosome. Under stress conditions, the ratio of Atg8-PE/Atg8
is increased and this has been widely applied as an indication of autophagy induction.
Unlike the Atg12-Atg5 conjugation, modification of PE with Atg8 is reversible, in that
Atg4 can cleave Atg8 after the glycine residue to remove it from the lipid (Kirisako et al.,
27
2000).
Atg9 is a transmembrane protein that is important for the autophagosomal retrieval
(Noda et al., 2000). Different from most Atg proteins that mainly localize to the PAS,
Atg9 displays a distribution at multiple punctate structures (Reggiori et al., 2005; Tucker
et al., 2003). The cycling event between PAS and non-PAS is essential for autophagy. The
recruitment of Atg9 to the PAS requires several Atg proteins, such as Atg11, Atg23 and
Atg23 (He et al., 2006; Legakis et al., 2007; Shintani and Klionsky, 2004). The retrieval
of Atg9 from PAS to other membrane structures requires the Atg1-Atg13 complex, Atg2,
Atg18 and the PtdIns3K complex I. Loss of any of these proteins will result in accumula-
tion of Atg9 at the PAS and thus block the autophagosomal activity (Reggiori et al., 2004).
Both Atg2 and Atg18 are peripheral membrane proteins that bind to Atg9, and the inter-
action of Atg18 with Atg9 requires Atg2 and Atg1 (Reggiori et al., 2004; Suzuki et al.,
2007; Wang et al., 2001). To localize to the PAS, Atg2 and Atg18 depend on each other,
Atg1, Atg13, Atg9 and the PtdIns3K complex I. Atg18 can bind to PtdIns(3)P, which
is generated by VPS34, and the binding is essential for autophagy function (Strømhaug
et al., 2004). One model is that Atg9 could shuttle between the peripheral structures and
the PAS, which is regulated by the Atg1-Atg13 kinase complex. This complex mediates
the interaction of Atg9 with Atg2 and Atg18 and the formation of this ternary complex
allows Atg9 to be retrieved from the PAS back to the peripheral sites (Reggiori et al.,
2004).
28
1.3.6.2 Signaling regulation of autophagy
The understanding of autophagy signaling pathways was expanded after the iden-
tification of the target of rapamycin kinase (TOR), which regulates cell growth, cell pro-
liferation, cytoskeletal rearrangement and protein synthesis (Brown et al., 1994; Kunz
et al., 1993). Since TOR negatively regulates autophagy, treatment with rapamycin, the
inhibitor of TOR, can induce autophagy (Noda and Ohsumi, 1998). This result was shown
both in rat hepatocytes (Blommaart et al., 1995) and in yeast (Noda and Ohsumi, 1998).
Downstream of TOR, the ribosomal protein S6 is phosphorylated when nutrition is suf-
ficient (Blommaart et al., 1995). Phosphorylation of S6 blocks autophagy and this sup-
pression can be reversed by rapamycin (Blommaart et al., 1995).
Both Class I and Class III PtdIns3K function as regulating complexes in autophagy
(Klionsky, 2005). Class I PtdIns3K activates TOR and thus has an inhibitory role. Con-
sistent with this result, overexpression of PTEN, which inactivates Class I PtdIns3K can
induce autophagy (Arico et al., 2001). In contrast, Class III PtdIns3K positively regulates
the VPS34/Atg6 complex and hence stimulates autophagy (Petiot et al., 2000). PtdIns3K
inhibitors such as wortmannin, LY294002 and 3-methyladenine have been used to mod-
ulate autophagy (Blommaart et al., 1997; Seglen and Gordon, 1982) However, recent
studies suggest that 3-MA could increase autophagosomal influx yet eventually still sup-
press autophagy because it interacts with both PI3Ks, but with different efficacy (Seglen
and Gordon, 1982; Wu et al., 2010). This indicates that caution should be applied when
interpreting results with 3-MA in the study of autophagy.
The insulin pathway has been shown to inhibit autophagy through the activation of
29
Class I PtdIns3K products. Activation of the insulin pathway by overexpressing protein
kinase B (Akt) or a constitutively active form of 3-phosphoinositide-dependent protein ki-
nase 1 (PDK1) leads to suppression of autophagy (Arico et al., 2001; Meijer and Codogno,
2004).
1.3.6.3 The immune function of autophagy
Genetic studies in Drosophila have deepened our understanding of autophagy and
its role in development (Berry and Baehrecke, 2007; Scott et al., 2004). Cumulative
evidence also connects autophagy to neurodegenerative diseases, cancer and immune dis-
eases (Liang et al., 1999; Lipinski et al., 2010; Orvedahl et al., 2010). In fly and mouse
Huntington disease models, induction of autophagy can protect neurons from accumula-
tion of toxic polyglutamine protein aggregates, while suppression of autophagy has the
converse effect (Ravikumar et al., 2004). Beclin-1, the mammalian homolog of Atg6,
was found to be a tumor suppressor since beclin1+/− mutant mice suffered from a high
incidence of spontaneous tumors (Yue et al., 2003). However, the role of autophagy in
immunity is complex since autophagy responds differently towards different pathogens.
The role of autophagy in immunity can be divided to two groups: (1) Autophagy can
serve as a defense mechanism to target pathogens for degradation. (2) The pathogens can
subvert autophagic machinery for their own benefit so autophagy can facilitate pathogen
replication and expansion (Lin et al., 2010).
Autophagy as a defense mechanism Autophagy has been shown as antiviral against
some viruses. Beclin-1, the Atg6 homolog, was shown to be antiviral when mice were
30
infected with the Neurotropic Sindbis Virus (SINV) (Liang et al., 1998). Overexpres-
sion of Beclin-1 increased the viability of mice infected with SINV, and also reduced the
neuronal apoptosis and viral titers in the mouse brain (Liang et al., 1998).
In Drosophila, autophagy was also found to be important in restricting the infec-
tion of intracellular pathogen Listeria monocytogenes in a PGRP-LE (pattern-recognition
receptor)-dependent manner (Yano et al., 2008). However, the mechanisms underlying
cytoplasmic infection-induced autophagy and the function of autophagy in host survival
after infection with intracellular pathogens (especially viruses) are far from clear.
Pathogen manipulation of autophagy for their own benefit Previous studies suggest
that autophagy is a critical antiviral defense mechanism (Orvedahl et al., 2010; Tallo´czy
et al., 2006). However, the role of autophagy in virus infection is complicated and it may
facilitate viral pathogenesis. Many viruses manipulate autophagy for their own benefit by
one of the following mechanisms.
1. Using Membrane-Bound Replication Compartments for Viral Replication
The subversion of autophagy by poliovirus (PV) is a classic model of viral exploitation of
the autophagy pathway (Suhy et al., 2000). Polio virus infection can induce autophago-
some formation, and knockdown of Atg genes (Atg12, LC3) reduces viral titers (Jackson
et al., 2005). Many other RNA viruses including Coxsackievirus B3 (CVB3), Japanese
encephalitis virus (JEV), HCV, Coronavirus mouse hepatitis virus (MHV), vesicular stom-
atitis virus (VSV), and rhinoviruses 2 and 14 also exploit autophagic membrane scaffolds
for RNA replication (Jackson et al., 2005; Jounai et al., 2007; Ke and Chen, 2011; Li et al.,
31
2012b; Prentice et al., 2004; Tang et al., 2007). In addition, the physical structure of a
double membrane compartment is proposed to allow efficient fusion of the autophago-
somal membrane with the cytoplasmic membrane. Thus, an emerging concept is that
autophagy may play a role in the nonlytic release of cytoplasm during autophagosome
maturation, namely autophagic exit without lysis (AWOL). This machinery can also be
used in the release of PV (Kirkegaard and Jackson, 2005; Taylor et al., 2009).
2. Increased Viral Infectivity by Blocking Autophagic Flux Virus can induce
incomplete autophagy by blocking the later stage of autophagic fusion with the lyso-
some. This was reported in cells infected with CVB3, rotavirus, and Influenza A Virus
(IAV) (Alirezaei et al., 2012; Crawford et al., 2012; Gannage´ et al., 2009; Kemball et al.,
2010). In CVB3-infected pancreatic acinar cells, an increase in the number of double-
membraned autophagic-like vesicles was observed upon infection. However, the accu-
mulation of the autophagic substrate p62 and the formation of large autophagy-related
structures named megaphagosomes indicate that CVB3 blocks a later stage of the au-
tophagic pathway (Kemball et al., 2010). This induction of autophagosome serves as a
niche for CVB3 RNA replication and translation (Alirezaei et al., 2012). In rotavirus
infected cells, it was reported that the NSP4 viroporin releases endoplasmic reticulum
calcium into the cytoplasm, thereby activating a CaMKK-βAMPK pathway to initiate
autophagy (Crawford et al., 2012). However, autophagosome maturation is impeded. By
hijacking this membrane trafficking pathway, rotavirus transport viral proteins from the
ER to the site of infection to produce viral particles. In addition, several studies suggest
that M2, HA, and NS1 proteins of Influenza A Virus (IAV) are involved in the induction
32
of autophagy, but only M2 has been identified as a critical factor in preventing fusion
of autophagosomes with lysosomes (Gannage´ et al., 2009; Sun et al., 2012; Zhirnov and
Klenk, 2013).
3. Escaping the Host Immune Response Viruses like VSV, HCV, DENV, and
JEV can evade the host immune response by activating autophagy to target immune com-
ponents (Jin et al., 2013; Jounai et al., 2007; Ke and Chen, 2011). For example, in VSV
infection, the Atg5-Atg12 conjugate targets RIG-I/MDA5-MAVS-dependent type I IFN
production by directly interacting with MAVS and RIG-I. This results in a suppression of
MAVS-mediated NF-κB and type I IFN promoters, and permits VSV replication. Further-
more, through an unidentified mechanism, HCV- or DENV-induced autophagy negatively
regulates type I IFN production and promotes HCV replication (Ke and Chen, 2011). Ad-
ditionally, JEV replication is impeded in autophagy-deficient cells in vitro. Upon infec-
tion, lack of autophagy also results in mitochondrial antiviral signaling protein (MAVS)
aggregation and activation of IFN regulatory factor 3 (IRF3), markers for innate immune
activation (Jin et al., 2013). This suggests that autophagy can be exploited to facilitate
virus replication, partly through suppression on antiviral responses.
1.3.6.4 Crosstalk between autophagy and other immune signaling
Toll signaling and autophagy Multiple studies suggested that the immune function of
autophagy is closely associated with the pattern recognition receptors (PRRs) (Oh and
Lee, 2014). Specifically, PRRs are not only involved in autophagy induction but can also
promote phagosomal maturation mediated by Atg proteins when pathogenic bacteria in-
33
vade host cells. In addition, autophagy facilitates the delivery of both viral PAMPs and
TLR9 that lead to type I IFN production. Autophagy also regulates PRR-induced in-
flammation in various ways to prevent excessive inflammatory responses, and conversely,
PRR signaling also controls autophagy.
Among all the PRRs, Toll-like receptors (TLRs) are the best characterized. The
first evidence to link TLRs with autophagy is a study of TLR4, whose activation by
lipopolysaccharide (LPS) induces autophagy to enhance elimination of phagocytosed my-
cobacteria (Xu et al., 2007). In addition to LPS-induced autophagy, ligands of TLR3
and TLR7 also induce autophagy. Single-stranded RNA (ssRNA) and imiquimod, two
different ligands of TLR7, promote autophagosome formation in murine macrophages
(Delgado et al., 2008). The activation of TLR7 ligand-induced autophagy results in a de-
creased load of M. tuberculosis var. bovis Bacille Calmette-Gurin (BCG) (Delgado et al.,
2008).
TLR-activated autophagy can promote pathogen clearance through autophagosome
degradation. In addition, autophagy also enhances antiviral defenses by facilitating de-
livery of cytosolic viral PAMPs to endosomal TLRs. In response to vesicular stomatitis
virus (VSV) infection in plasmacytoid dendritic cells (pDCs), endosomal TLR7 recog-
nizes the replication intermediates rather than the viral genome. The replication interme-
diates (recognized as PAMPs) are then delivered to the lysosomes by autophagy, further
activating TLR7 signaling (Lee et al., 2007). pDCs that lack Atg5 fails to produce IFN-α
or IL-12p40 following VSV infection. Furthermore, Atg5 deficient mice are also more
suceptible to VSV infection compared to WT (Lee et al., 2007). In Drosophila, Toll-
7, was shown as the PRR to activate autophagy during vesicular stomatitis virus (VSV)
34
infection. Toll-7 interacted with VSV at the plasma membrane and induced antiviral au-
tophagy independently of the canonical Toll signaling pathway (Nakamoto et al., 2012).
These data indicate a conserved linkage between Toll signaling and autophagy in both
mammals and invertebrates.
Autophagy and inflammation Autophagy can negatively regulate inflammatory re-
sponses (Jounai et al., 2007; Nakahira et al., 2011; Saitoh et al., 2008; Shi et al., 2012;
Tal et al., 2009; Zhou et al., 2011). For example, a lack of Atg16L1, an essential compo-
nent of the autophagosome, results in increased production of IL-1β and IL-18 following
LPS stimulation. Since Atg16L1 plays an important role in the development of Crohn’s
disease, it is possible that the endotoxin-induced inflammasome activation in Atg16L1-
deficiency could be involved in the occurrence of Crohn’s disease (Saitoh et al., 2008).
Another study indicates that autophagy can suppress the NLRP3 inflammasomes,
which play a major role in innate immunity by activating caspase-1 and mediating the pro-
cessing and release of the leaderless cytokine IL-1β(Baroja-Mazo et al., 2014; Nakahira
et al., 2011; Zhou et al., 2011). Blockage of autophagy results in the accumulation of
damaged, reactive oxygen species (ROS)-generating mitochondria, which in turn acti-
vates NLRP3 inflammasomes. Furthermore, depletion of the autophagic proteins LC3B
and beclin-1 also lead to excessive secretion of IL-1β and IL-18 (Nakahira et al., 2011).
In addition, autophagy can negatively regulate RLR signaling (Jounai et al., 2007;
Tal et al., 2009). The RLR signaling pathway is important for cytoplasmic pathogen
35
recognition, which is mediated by cytosolic sensors such as RIG-I and MDA-5 (Reikine
et al., 2014). In both Atg5- and Atg7-deficient mouse embryonic fibroblasts (MEFs),
where the Atg5-Atg12 association is disrupted, type I IFNs are overproduced following
VSV infection. In contrast, overexpression of Atg5 or Atg12 results in suppression of
IFN signaling. The Atg5-Atg12 conjugates directly interact with the CARD domains of
RIG-I and IPS-1, inhibiting subsequent RLR signaling (Jounai et al., 2007). Similarly,
Atg5-deficient cells overproduce IFNs through enhanced RLR signaling in response to
VSV infection (Tal et al., 2009).
A recent study showed that autophagy induced by inflammatory signals targets
ubiquitinated inflammasomes, thereby limiting IL-1β production through inflammasome
destruction (Shi et al., 2012).
In Drosophila, inflammation is best characterized in the intestine with a phenotype
of overexpressed antimicrobial peptides (AMP) (Lee and Lee, 2014). A low AMP ex-
pression is stimulated by the gut microbiota, which is beneficial for the preservation of
community structure. However, overexpression of antimicrobial peptides might be detri-
mental to the fly health. This is demonstrated in the caudal-silenced flies with a phenotype
of gut cell apoptosis and early host death (Ryu et al., 2008). This is because caudal, an
intestine-specific homeobox transcription factor, acts as a repressor of Relish-dependent
AMP genes. Lack of caudal will results in AMP overexpression that further destroys the
gut microbiota balance.
Similar to caudal knockdown flies, flies that carry mutations in negative regulators
36
of the Imd pathway and thus are constitutively overexpressing AMPs have a reduced sur-
vival rate in a conventional environment, when microbiota exist in flies. However, when
flies were raised in a germ free environment, these mutant flies have a normal survival rate
(Bischoff et al., 2006; Lhocine et al., 2008; Paredes et al., 2011). This indicates that the
Imd pathway tightly controls the expression levels of AMPs stimulated by the gut micro-
biota. So far, it is unclear whether autophagy could play a role in causing inflammation
in flies.
1.4 Lipid Metabolism
1.4.1 Lipid droplets as energy storage compartments
In recent years, Drosophila melanogaster has proven to be a powerful model for the
studies of lipid metabolism and energy homeostasis (Liu and Huang, 2013; Schlegel and
Stainier, 2007). The anatomy of organs and cell types, the signaling pathways and the
genes involved are highly conserved between mammals and Drosophila. The Drosophila
fat body, the equivalent of mammalian adipocytes, is the major organ for lipid storage in
the form of lipid droplets (LDs). The major components of LDs are triacylglycerol (TAG)
and cholesterol ester, the functions of which are storing energy, composing the cellular
membranes, and serving as precursors of hormones and vitamins (Figure 1.8).
In addition, the recent evidence has revealed oenocytes, a cluster of large secre-
tory cells underlying the epidermis of abdominal segments, along with the fat body are
critical for the regulation of lipid metabolism (Gutierrez et al., 2007). During starvation,
Drosophila larve release large quantities of lipid from the fat body while lipid accumu-
37
Figure 1.8: Schematic representation of the lipid storage tissues in the Drosophila.
Red indicates larval and adult fat body (Liu and Huang, 2013).
lates in the oenocytes. Disruption of oenocyte function prevents the depletion of lipid in
the fat body. This indicates that the fat body and oenocytes coordinate in lipid mobiliza-
tion and that oenocytes act downstream of the fat body. It is not fully understood how the
fat body communicates with oenocytes. The fat body and oenocytes also express different
lipid-metabolizing proteins. For example, in the fat body, lipases such as Brummer, and
lipase regulators such as Lsd-1, Lsd-2 are highly expressed. In oenocytes, Cyp4g1, an
omega-hydroxylase regulating triacylglycerol composition was found to be particularly
important (Gutierrez et al., 2007) (Figure 1.9).
The LD homeostasis is achieved by a balanced control of lipogenesis and lipolysis.
Activation of lipogenesis results in an increase of LD size, which is mainly controlled
by the lipid synthesis enzyme diacylglycerol O-acyltransferase 1 (DGAT1) in mammals.
38
Figure 1.9: Schematic representation of the core enzymes in the Drosophila lipid
metabolism.
Lipid metabolism is a balance between lipid synthesis and lipid mobilization. Repre-
sented are lipogenic enzymes of the glycerol-3-phosphate pathway (blue), lipases (red),
and modulatory LD-associated proteins of the Perilipin family (green). The proteins
marked by a question mark (?) are identified with sequence homology, while the func-
tions of those proteins are not yet confirmed. An asterisk (*) indicates that in vitro evi-
dence supports the involvement of the Manduca sexta CG8552 ortholog MsTGL in stor-
age fat mobilization from LDs. ATGL: adipose triglyceride lipase; FA-CoA: fatty acid
CoA ester; HSL: hormone sensitive lipase; MG: monoacylglycerol; PC: phosphatidyl-
choline; PE: phosphatidylethanolamine; PG: phosphatidylglycerol; PI: phosphatidylinos-
itol; PLIN1, 2: Perilipin1, 2; PS: phosphatidylserine; TGL: triglyceride lipase (Ku¨hnlein,
2012).
In contrast, a facilitated lipolysis will decrease the LD size, with the adipose triglyceride
lipase (ATGL/PNPLA2) degrading stored lipids in the LDs. In Drosophila, DGAT1 and
ATGL are encoded by midway and brummer, respectively (Buszczak et al., 2002; Gro¨nke
et al., 2005). Loss-of-function of brummer generates an obese phenotype with enlarged
LDs in the fat body. In contrast, the midway null mutant displays a lean phenotype.
The detailed regulation of LD homeostasis is not yet well understood. Among all
the regulators, the Perilipin family are the best characterized LD surface proteins (Bickel
et al., 2009; Londos et al., 2005).
39
1.4.2 The Perilipin family proteins in the regulation of LDs
The mammalian perilipins (PLINs) include several sequence-related and evolution-
arily conserved LD proteins, which have been extensively studied for their roles in LD
regulation (Bickel et al., 2009; Brasaemle, 2007; Brasaemle et al., 2009; Londos et al.,
2005). Among the perilipins, perilipin A is the most abundant protein on the surface
of LD (Brasaemle et al., 2009). Perilipin A acts as a protein coating to prevent the ac-
cess of hormone-sensitive lipase (HSL) to neutral lipids. Following β-adrenergic receptor
activation, however, perilipin is phosphorylated and changes its protein conformation ac-
cordingly (Miyoshi et al., 2006). This change in conformation promotes the accessibility
of HSL for lipid lipolysis (Brasaemle et al., 2009).
In Drosophila, only two perilipins are encoded: lipid storage droplet-1 (lsd-1) and
lipid storage droplet-2 (lsd-2) (Beller et al., 2010; Gro¨nke et al., 2003; Lu et al., 2001).
Due to the recent nomenclature revision, lsd-1 and lsd-2 are also called perilipin1 (plin1),
perilipin2 (plin2), respectively (Kimmel et al., 2010). Fly perilipins not only share se-
quence homology with mammalian perilipins, they were also shown to localize to the
surface of LD by multiple studies. GFP-tagged Lsd-1 and Lsd-2 fusion proteins localize
to the LD in the larval fly fat body and CHO cells (Miura et al., 2002). The lipid droplet
association with Drosophila Perilipins has also been found in the lipid droplet fraction of
fat body cells after density gradient fractionation (Beller et al., 2010; Gro¨nke et al., 2003;
Welte et al., 2005). An LD proteomics study also confirms the association of PLINs with
LD (Beller et al., 2006; Cermelli et al., 2006).
Drosophila plin1 gene is expressed during all ontogenetic stages from late embryo-
40
genesis to adult flies. Although present in neuroendocrine cells of the ring gland during
embryogenesis, plin1 is predominantly expressed in both larval and adult fat body (Beller
et al., 2010; Chintapalli et al., 2007). In addition, microarray data indicates that plin1
is also expressed moderately in the adult gut, heart and spermatheca (Chintapalli et al.,
2007).
Drosophila PLIN1 is only found in the lipid droplet fraction of fat body cells (Beller
et al., 2010). GFP-tagged PLIN1 shows exclusive localization on the surface of LD. The
level of PLIN1 protein also correlates with the total surface area of LDs (Beller et al.,
2010). The mRNA transcripts and protein levels of PLIN1 are both sensitive to changes
in lipid storage. However, pieces of data suggests that not only the transcriptional level of
plin1 is critical, but the post-translational modification of PLIN1 is essential for normal
activity (Beller et al., 2010).
plin1 null mutants develop early onset obesity as adult flies. The fat content of the
mutants is doubled compared to wildtype (Beller et al., 2010). The food intake of plin1
mutants increase significantly when the files are on a high-sugar diet. However, on a low-
sugar diet, mutant flies eat an equivalent amount of food as wildtype but still have a higher
fat content. These data indicates that the obesity is not only attributed to an increased food
intake but is also due to a slower metabolism.
PLIN1 is the downstream effector of the pro-lipolytic adipokinetic hormone (AKH)/AKH-
receptor (AKHR) pathway at the LD surface. The AKH signaling is the equivalent of the
mammalian β-adrenergic signaling pathway that regulates lipid mobilization. Once the
AKH receptor (AKHR) is activated in the fat body, the second messenger cAMP is re-
leased to activate protein kinase A (PKA). Phosphorylation of PLIN1 by PKA is proposed
41
to be essential for the initiation of lipid lipolysis, however, the details needs to be further
clarified. On one hand, phosphorylation of PLIN1 by PKA has been shown in both the ex
vivo Manduca fat body and Drosophila PLIN1 decorated liposome in vitro (Arrese et al.,
2008a; Patel et al., 2005). On the other hand, mutated PLIN1 with defective phosphoryla-
tion sites still functions normally in Drosophila in vivo. This might be due to the fact that
multiple redundant phosphorylation sites exist in PLIN1. Mutation of the classical phos-
phorylation sites identified in Manduca might not be sufficient to turn off PLIN1 (Arrese
et al., 2008b).
Recent studies indicate that PLIN1 is necessary to recruit hormone-sensitive lipase
(HSL) to facilitate lipid mobilization during starvation (Bi et al., 2012). HSL (Drosophila
dHSL) , along with ATGL (Drosophila Brummer), is one of the two key lipases for basal
and stimulated lipolysis, respectively (Bi et al., 2012; Gro¨nke et al., 2005; Schweiger
et al., 2006). In normal conditions, dHSL is largely dispersed in the cytoplasm, with
little dHSL localized to the surface of LD. However, in starved conditions, more dHSL
localizes to the surface of LDs and appears as ring structures around the lipid droplets.
This data is consistent with the role of HSL in mammals (Egan et al., 1992; Sztalryd
et al., 2003). Strikingly, the localization of dHSL is abrogated in the plin1 mutants, which
indicates that PLIN1 is required for dHSL to target to the lipid droplet. Since dHSLb24
null mutants accumulate more body fat with enlarged lipid droplets, it is highly possible
that PLIN1 is facilitating dHSL lipase function (Bi et al., 2012).
Although a defect in PLIN1 displays impairment of LD lipolysis and fat mobiliza-
tion, PLIN1 is not required for this process since fat mobilization is not totally blocked
in the plin1 mutant (Beller et al., 2010). Starvation-resistance is correlated to body fat
42
content and serves as a hallmark for fat storage. Null homozygous and heterozygous
plin1 mutants or RNAi knowdown flies are all resistant to starvation compared to WT
(Beller et al., 2010). This indicates there are other parallel signaling pathways regulating
a PLIN1-independent fat mobilization.
Genetic studies in Drosophila also reveal that the interaction between BMM, PLIN1
and dHSL are not exclusive (Bi et al., 2012). First, dHSLb24;bmm1 double mutants
have larger lipid droplets than any of the single mutants. This indicates that BMM
and dHSL act in parallel pathways. Second, the lipid droplets in plin11 are larger than
dHSLb24;bmm1 double mutants. This indicates that PLIN1 has other functional targets
rather than BMM and dHSL. Third, both plin11;bmm1 and plin11;dHSLb24double mu-
tants have larger droplets compared to plin11, indicating the existence of other compo-
nents controlling BMM and dHSL rather than PLIN1 (Bi et al., 2012).
Other than controlling lipid mobilization, PLIN1 is also critical for maintaining
the lipid droplet structure in vivo (Beller et al., 2010). Lack of PLIN1 transforms the
heterogeneously sized LD population of the wild-type fat body cell into a single giant
LD accompanied by few small satellite droplets (Beller et al., 2010). However, the en-
largement of the lipid droplets are not likely due to a lack of surface protein coverage.
Overexpression of human PLIN1a, EGFP-tagged CG2254, or PLIN2 in plin11 mutant
does not modify the size of lipid droplet (Beller et al., 2010). Additionally, it is contro-
versial whether the enlarged size of lipid droplet in plin11 mutant is due to accumulated
fat content. In the mdy−;plin1 double mutants, the fat content is reduced compared to
plin11 mutants whereas enlarged lipid droplets can still be observed. Moreover, plin11
mutants only display obesity in early adult stages but the enlarged droplets appear in the
43
larval stage (Beller et al., 2010). In summary, it is difficult to argue whether the change
of LD structures results in the increased fat content or vice versa. In order to understand
this, more experiments need to be done.
plin2 also expresses in all developmental stages in Drosophila. However, the distri-
bution of plin2 mRNA varies at different developmental stages. During the early embryo-
genesis, plin2 is ubiquitously and uniformly present in the embryo, which is maternally
provided (Edgar and Schubiger, 1986). At later stages of embryogenesis, plin2 is pre-
dominately expressed in germline cells, specifically in the female (Teixeira et al., 2003).
However, at the later stage of embryogenesis, larval and adult stages, plin2 is relatively
broadly expressed with an enrichment in the fat body and the midgut (Teixeira et al.,
2003).
Although present at different tissues across developmental stages, PLIN2 is in-
volved in regulating lipid droplet within all these tissues. For example, PLIN2 plays
an important role in controlling of the movement of embryonic LDs along microtubules.
Additionally, abnormal accumulation of neutral lipids is observed in the germline and
eggs of lsd21 females (Teixeira et al., 2003). In larval and adult flies, lack of PLIN2 re-
sults in decreased lipid accumulation, as characterized by a 50% and 27% decrease in the
triglyceride level of the plin2 mutant compared to WT in larvae and adults, respectively
(Teixeira et al., 2003). Accordingly, the size of the lipid droplet is also smaller compared
to wildtype in the fat body (Teixeira et al., 2003). These data shows the significance of
PLIN2 in lipid metabolism.
However, in contrast to PLIN1, PLIN2 is required for lipid droplet transport in
Drosophila embryos and is important for protecting lipid droplets from lipolysis (Fauny
44
et al., 2005; Gro¨nke et al., 2003; Welte et al., 2005). Additionally, unlike PLIN1, PLIN2
can either localize to the surface of lipid droplet or remains in the cytoplasm (Gro¨nke
et al., 2003). However, the regulation of the exchange in localization of PLIN2 is not well
known.
In Drosophila nurse cells, PLIN2 levels depend on the activity of cytoplasmic phos-
phorylated Akt in nurse cells (Vereshchagina et al., 2008; Vereshchagina and Wilson,
2006). Specifically, pten mutants show enlarged lipid droplets in nurse cells, an increase
in activated Akt, and a high expression of plin2 (Vereshchagina and Wilson, 2006). Fur-
thermore, an increase in lipid droplet size was observed in the mutants of the phosphatase
PP2A-B regulatory subunit Widerborst (Wdb), a negative regulator of cytoplasmic acti-
vated Akt. The phenotype in theWdb mutant is abrogated in the Akt mutant (Vereshchag-
ina et al., 2008).
PLIN2 is proposed to regulate adipose triglyceride lipase BMM, but not HSL, for
lipid degradation. Evidence shows that bmm− plin2− double mutants have wildtype TAG
levels, indicating that loss of PLIN2 activity compensates for the lack of BMM (Gro¨nke
et al., 2005). Conversely, overexpression of both bmm and plin2 in the fat body can par-
tially revert the phenotypes generated by overexpressing either of the single genes. These
data demonstrate that both BMM and PLIN2 play important roles in lipid metabolism,
but in opposite direction.
Although PLIN1 and PLIN2 were previously demonstrated to have opposite func-
tion. A new piece of data suggests they may also have redundant roles. Overexpression of
plin1 in plin2 mutants do not enhance the large lipid droplet phenotype but instead mildly
suppresses the size of lipid droplets (Bi et al., 2012). Furthermore, plin138;plin2KG00149
45
double mutant larvae were found to display an even smaller lipid droplet phenotype than
the plin2KG00149 single mutant (Bi et al., 2012). Domain deletion and swapping experi-
ments indicate that the C-terminal of PLIN1 is critical in determining the localization and
functional difference between PLIN1 and PLIN2 (Bi et al., 2012).
46
Figure 1.10: Diagram of Lipid droplet metabolism.
Under normal conditions, lipases Brummer and dHSL are inactivated. dHSL is distributed
in the cytoplasm without access to the lipid droplet. Brummer is functionally inhibited
by Lsd-2/PLIN2. When lipolysis is activated, Brummer and dHSL are activated through
phosphorylation. This results in the degradation of triglyceride in the lipid droplet. The
free fatty acids generated by lipolysis can be transported into mitochondria for ATP pro-
duction.
47
1.4.3 β-Oxidation
Mitochondrial β-oxidation is an important system involved in the energy produc-
tion of various cells. β-oxidation is a process when free amino acids are oxidized through
a chain of reactions, resulting in ATP production. Disorders of β-oxidation are believed to
cause about 1-3% of unexplained sudden infant deaths (SIDS). Acute fatty liver of preg-
nancy (AFLP) and the syndrome of hemolysis, elevated liver enzymes, and low platelets
(HELLP syndrome), which have significant neonatal and maternal morbidity and mor-
tality, have also been associated with β-oxidation deficiency in fetuses. In adults, dys-
function of β-oxidation also cause multiple heart diseases such as cardiomyopathy (Fig-
ure 1.11).
β-oxidation is closely associated with lipid metabolism, since the free fatty acids
generated by lipid metabolism are the major substrates for β-oxidation. The free amino
acid is produced through two steps of lipase hydrolysis reactions. Specifically, triglyc-
eride is degraded by ATGL into diglyceride, which is further hydrolyzed into monoglyc-
eride by HSL. Consistent with the mammalian system, in Drosophila, the hydrolysis re-
actions are executed by BMM and dHSL, respectively. Before translocation to the mito-
chondria, free fatty acids will be oxidated to acyl-CoA by acyl-CoA synthetase.
One of the rate limiting step is the transportation of acyl-CoA across the inner mi-
tochondrial membrane from the cytoplasm. This process is controlled by the carnitine
transporter system, which is comprised of Carnitine acyl transporter (CPT), Carnitine
acyl transporter and L-carnitine (Bremer, 1983; Vaz and Wanders, 2002). Specifically,
48
Figure 1.11: Lipid metabolism and β-oxidation.
This diagram shows the core enzymes in the β-oxidation pathway. Free fatty acids are
converted to acyl-CoA by AcCoAs before entering into mitochondria. The transportation
of acyl-CoA is facilitated by the carnitine carrier system. As a result of a series of oxida-
tion reactions, two carbon bonds are oxidized in the acyl-groups. This cycle will go on
until the total carbon chain is degraded (Palanker et al., 2009).
49
carnitine serves as a carrier to facilitate the transportation of acyl-CoA. Accumulated car-
nitine within the cell is conjugated with fatty acids to form acylcarnitine by carnitine
palmitoyl transferase 1 (CPT1). The transfer of the acylcarnitine across the inner plasma
membrane is facilitated by carnitine-acylcarnitine translocase (CACT). Once the acylcar-
nitine crossed the inner membrane, carnitine palmitoyl transferase 2 (CPT2) facilitates the
disassociation between acyl group and carnitine and promotes the conjugation of the fatty
acid back to Coenzyme A for subsequent β-oxidation. The freed carnitine is cycled out
of the mitochondria inner membrane by the carnitine transporter (OCTN2). Deficiency
in any part of this system can lead to multiple diseases due to β-oxidation dysfunction
(Longo et al., 2006).
For example, deficiency of the OCTN2 carnitine transporter causes primary carni-
tine deficiency, characterized by increased loss of carnitine in the urine and decreased
carnitine accumulation in tissues. Patients can develop hypoketotic hypoglycemia and
hepatic encephalopathy, or with skeletal and cardiac myopathy (Scaglia et al., 1998). De-
fects in the liver isoform of CPT1 present with recurrent attacks of fasting hypoketotic
hypoglycemia, a disease with symptoms of low levels of ketones and low blood sugars.
CACT deficiency in patients results in the neonatal period with hypoglycemia, hyperam-
monemia, and cardiomyopathy with arrhythmia leading to cardiac arrest (Rubio-Gozalbo
et al., 2004). Deficiency of CPT2 present more frequently in adults with rhabdomyolysis
triggered by prolonged exercise. More severe variants of CPT2 deficiency present in the
neonatal period similarly to CACT deficiency associated or not with multiple congenital
anomalies (Bonnefont et al., 2004).
50
Chapter 2: Atg1 plays an antiviral role against Drosophila X Virus, but
this effect appears to be independent of classical autophagy
2.1 Results
2.1.1 Atg1 plays an antiviral role against DXV infection in the fat body
To examine whether autophagy plays a role in the immune response against DXV
infection, we used RNA interference to silence autophagy genes in different adult tis-
sues with tissue specific Gal4 drivers. Among all the Atg genes, Atg1 is upstream in
the autophagy signaling pathway and is a critical initiator for autophagy (Chang and
Neufeld, 2009). When Atg1 is silenced in multiple tissues including the fat body and
hemocytes, the major immune tissues,(Hultmark, 1993; Lemaitre and Hoffmann, 2007),
flies are more susceptible to DXV infection (Figure 2.1 A). Atg1 RNAi flies are not sus-
ceptible to wounding as injection of PBS does not cause fly death (Figure 2.1 A). Three
survival experiments have been done and the median survival is 12 days and 10 days for
WT and IR-Atg1 flies, respectively (Figure 2.1 B). This suggests that Atg1 is required for
maintaining fly survival against DXV.
Upon infection, host can resist a pathogen by initiating an immune response to clear
the pathogen or develop tolerance to the pathogen without clearing them (Schneider and
51
Ayres, 2008). To examine whether the immune function of Atg1 associates with pathogen
resistance or host tolerance, we examined the viral mRNA levels in both wildtype and
Atg1 RNAi flies upon infection. At day 3 post infection, VP1 (the RNA-dependent RNA
polymerase ) mRNA levels are ∼50 fold higher than in wildtype. At day 7 post infection,
there is a ∼10 fold increase of VP1 mRNA levels (Figure 2.1 C). Atg1 RNAi flies also
show a higher viral protein levels by Western blot (Figure 2.1 D). This indicates that Atg1
plays an antiviral role to fight against DXV.
52
Figure 2.1: Silencing of Atg1 renders flies more susceptible to DXV.
(A) Survival curves of wildtype and Atg1 RNAi flies injected with PBS (dotted lines, n >
90 flies) or DXV (solid lines, n > 90 flies). Wildtype and Atg1 RNAi lines are represented
in black and red, respectively. C564-Gal4 is used for gene silencing in multiple tissues
including fat body, hemocytes.. ∗∗∗P < 0.001, (log-rank analysis). (B) Medial survival
of WT and Atg1 RNAi flies. n=3. (C) Quantitative PCR of viral RNAs in wildtype and
Atg1 RNAi flies. (D) Representative western blot of viral proteins in both wildtype and
Atg1 RNAi flies at day 3 and day 6 post infection. Proteins are extracted out of a pool of
three flies. Student’s t-test are used for statistical analysis in (B) and (D). All experiments
were repeated at least three times.
53
To identify whether a particular tissue is important for this Atg1-dependent antiviral
response, Atg1 was silenced specifically in the hemocytes or fat body or central nervous
system (CNS) using tissue specific Gal4 drivers (Georgel et al., 2001; Luo et al., 1994;
Sinenko and Mathey-Prevot, 2004). Silencing of Atg1 in the fat body results in an in-
creased susceptibility against DXV. In contrast, silencing of Atg1 in the hemocytes or
CNS does not change fly survival. These results indicate that Atg1 is necessary in the fat
body, but not in the hemocytes or CNS for this antiviral immune response (Figure 2.2
A-C). To further identify the role of fat body in this Atg1-dependent response, adult fat
tissues were dissected out for immunostaining of the virus. Our result shows that a higher
level of viral proteins were detected in the fat body of Atg1 RNAi flies compared to
wildtype (Figure 2.2 D). The virus particles were quantified based on the fluorescence
intensities. Our result shows ∼ 2 fold more viral particles in the Atg1 RNAi flies com-
pare to wildtype (Figure 2.2 E).
54
Figure 2.2: Fat body is the tissue important for the Atg1-dependent immune response.
(A) Atg1 was silenced in the fat body by driving the expression of IR-Atg1 fragment
using Yolk-Gal4 in female flies. (B) Atg1 was silenced in the hemocytes, the cellular
immune cells, by hml∆-Gal4. (C) Atg1 was silenced in the central nervous system by IR-
Atg1 fragment driven by Elav-Gal4. All experiments were repeated at least three times.
n > 90 flies. Log-rank tests were used for survival analysis. (D) Confocal imaging of
viral proteins in the adult fat body. Nuclei are stained with DAPI (blue). DXV is im-
munostained with anti-DXV primary antibody that was probed by Alexa-594 secondary
antibody (red). (E) Quantification of viral proteins in adult fat body. Viral proteins lev-
els are determined by fluorescence intensity using ImageJ software. n > 100 cells are
examined per genotype. Student’s t-test was used for statistical significance.
55
To further characterize the tropism of DXV in wildtype and Atg1 RNAi flies, flies
of both genotypes were infected with DXV and then were dissected into different com-
ponents (head, thorax, gut, ovary, abdominal wall) at 4 and 6 days post infection. These
tissues were examined for viral protein levels by Western blot. Among the five different
components, abdominal wall, to which most fat body tissue attaches, shows the most viral
proteins. There is also a significantly higher load of virus in the Atg1 RNAi flies com-
pared to wildtype (Figure 2.3). These data all suggest that fat body is important for the
Atg1-dependent antiviral response against DXV.
56
Figure 2.3: Western blots of DXV viral proteins in the dissected tissues of adult flies.
WT and IR-Atg1 flies were sacrificed, dissected into different components at day 4 and
day 6 post DXV infection, respectively. Total protein from each component was ex-
tracted and western blots were performed by polyclonal antibody against DXV. In all
components, a higher load of viral protein was observed in the IR-Atg1 flies compared to
wildtype. Six flies were pooled for one experiment. This experiments has been done for
three times.
57
To test whether this protective role of Atg1 is specific to DXV, we also infected the
flies with Drosophila C virus or Cricket Paralysis virus (Jousset et al., 1977; Reinganum,
1975), both of which are single stranded RNA viruses. Reduced levels of Atg1 do not
render flies more susceptible to either virus (Figure 2.4 A-B). This indicates the Atg1-
dependent protective effect is not a general antiviral mechanism but rather is specific to
DXV.
Figure 2.4: Atg genes are not important for the host response against CrPV and DCV.
(A) Survival analyses for adult flies with Atg1, Atg7, Atg8 silenced upon infection with
Cricket Paralysis Virus (CrPV). n > 90. (B) Silencing of Atg1 does not result in a sur-
vival phenotype of the flies infected with Drosophila C virus (DCV). n > 90. Log-rank
statistics are used. There is no statistical difference between these lines.
58
2.1.2 Autophagy does not appear to be specifically activated by DXV in
both larval hemocytes and adult fat body
In order to examine whether autophagy plays a role in the immune response against
DXV, other than Atg1, we also examined other core autophagy genes, such as Atg7 and
Atg8. Atg7 is a ubiquitin ligase that functions in the Atg7-Atg5-Atg12 conjugation system.
The Atg7 null mutant shows a defect in starvation-induced autophagy in the larval fat body
(Juha´sz et al., 2007). Upon infection with DXV, Atg7 null mutants are not susceptible
to DXV compared to wildtype (Figure 2.5 A). We also examined another critical Atg
gene Atg8. Atg8 is a small protein that is lipidated upon autophagy induction and thus
localizes to the surface of autophagosomes. Atg8 is not only essential for autophagy but
also serves as a marker for autophagy induction when lipidated (Scott et al., 2004). When
Atg8 is silenced in the fat body, the flies only result in a slightly increased fly susceptibility
(Figure 2.5 B). This indicates that Atg1, but not other core autophagy genes are important
for the immune response against DXV.
If autophagy is actively playing a role during DXV infection, cellular markers for
autophagy induction should be apparent in DXV infected cells. When GFP-Atg8 trans-
gene is expressed in the fat body, under normal condition, Atg8 is evenly distributed in the
cytoplasm (Juha´sz et al., 2008; Scott et al., 2004). Upon induction of autophagy, GFP-
Atg8 will form puncta. Our results show that the number of autophagosomes does not sig-
nificantly increase as infection progresses (Figure 2.5 C). Autophagy puncta also do not
specifically colocalize with DXV in the fat body cells (Figure 2.5 C). Once the fat body
cells are overwhelmingly infected with virus, there is an increase of GFP-Atg8 puncta,
59
but no colocalization of the GFP-Atg8 puncta with DXV was observed (Figure 2.5 D).
Autophagy is responsive to multiple kinds of stresses, including starvation, heat, reactive
oxidation species (Murrow and Debnath, 2013). The activation of autophagy during the
late stage of infection may be due to cellular stress induced by active viral replication,
and may not be a direct immune response specific against the virus. This speculation is
confirmed in electron microscopy, when autophagosomes were only observed in heavily
infected S2 cells. Specifically, at the late stage of infection, mitochondria was observed
in autophagososomal-like structures (Figure 2.10).
60
Figure 2.5: Autophagy does not appear to be activated directly by DXV.
(A,B) Survival analysis of Atg7 mutant (A) and Atg8 RNAi lines (B). Wounding controls
by injection of PBS are represented by dotted lines. The solid lines represent survival
curves of wildtype and Atg7 mutants, Atg8 RNAi lines upon DXV infection. (C,D) GFP-
Atg8 puncta were examined in the fat body of C564 > GFP-Atg8 flies. (C) In WT flies,
at day 5 post infection, GFP-Atg8 puncta does not seem to be induced by DXV infection.
(D) In WT flies, at day 9 post infection, Atg8 puncta are induced. The viral particles are
shown in red. Nuclei are stained with DAPI (blue). Atg8 puncta are represented by GFP
(green) fluorescence. DXV is immunostained with anti-DXV primary antibody that was
probed by Alexa-594 secondary antibody (red).
61
2.1.3 Autophagy is not induced in ex vivo hemocytes upon DXV infec-
tion
Hemocytes are the major immune cells responsible for cellular immune response.
The plasmatocytes are professional phagocytes most similar to the mammalian mono-
cyte/macrophage lineage and represent 95% of the total hemocyte populations (Williams,
2007). Phagoctytosis of various bacteria, such as E.Coli and Staphylococcus aureus, has
been well characterized in plasmatocytes (Ulvila et al., 2011). Recently, plasmatocytes
have also been shown critical for the antiviral immune responses (Costa et al., 2009; Honti
et al., 2014; Shelly et al., 2009). Specifically, blocking phagocytosis prior to CrPV infec-
tion results in an increased fly sensitivity (Costa et al., 2009; Elrod-Erickson et al., 2000).
Ex vivo larval hemocytes are also found important for the autophagic response against
VSV (Shelly et al., 2009). Since DXV virus particles are taken up by larval and adult
hemocytes rapidly after DXV exposure (within 30 min), we were interested in examining
whether autophagy is induced in larval hemocytes upon DXV infection.
The larval hemocytes are bled out from hml∆>GFP-Atg8 transgenic flies followed
by ex vivo infection with either live DXV virus or Texas red-tagged DXV (TR-DXV)
particles. Due to a short life window for the ex vivo hemocytes, experiments can only be
conducted within 3 hours after hemocytes isolation. Ex vivo hemocytes were first infected
with the Texas Red fluorescent tagged virus (TR-DXV). The tagged virus particles appear
in hemocytes within 30 minutes post infection. However, within a 3 hour time (checked
at 30 mins, 1 hour, 2 hour, 3 hour), no autophagy puncta was observed (Figure 2.6).
It is possible that replication intermediates, produced by active virus, are required for
62
autophagy induction. TR-DXV might be inactive after conjugation with Texas Red flu-
orescein. This could be the reason why GFP-Atg8 pucta was not observed. To evaluate
whether live viruses can induce autophagy, GFP-Atg8 hemocytes were infected with live
DXV particles. Within 3 hours, the number of GFP-Atg8 puncta were also not signifi-
cantly increased (data not shown). Thus, we confirm that DXV, either live or not, does
not induce autophagy in larval hemocytes, at least in the early stage of infection.
63
Figure 2.6: Autophagy is not induced in ex vivo hemocytes upon TS-DXV infection.
A representative fluorescent microscopy picture showing bled-out larval hemocytes con-
taining Texas-Red tagged DXV particles. Hemocytes from six larve were bled out into
Schneider’s Drosophila media for temporary culture followed by Texas-Red tagged DXV
treatment for 2 hours. green, GFP-Atg8; red, TR-DXV; blue: DAPI. Experiments have
been performed more than three times. n> 300 cells for each condition.
64
2.1.4 Autophagy is not induced in Drosophila S2 cells
Drosophila S2 cells were examined by Transmission Electron Microscopes (TEM)
following infection with live DXV virus at several doses and for different periods of
time. Autophagosomes appear at a very low frequency in WT S2 cells (data not shown).
Infection with DXV does not increase the number of autophagosomal structures. The
virus appears in various vesicles, such as lysosome and some single membraned or multi-
membraned vesicles (Figure 2.7, Figure 2.8). At the late stage of infection, DXV repli-
cates massively, and forms array-like structures that occupies the cytoplasm (Figure 2.9).
However, at the late stage of infection, one autophagosome structures with mitochondria
within them was observed, indicating dysfunctional mitochondria are being degraded
(Figure 2.10). This data is in agreement with our previous observation of GFP-Atg8
puncta only in heavily infected Drosophila adult fat cells (Figure 2.5 D).
65
Figure 2.7: A representative picture showing DXV in multi-membraned vesicles by elec-
tron microscopy-I.
S2 cells were infected with 10−4 DXV for 24 hours followed by fixation, ultra-thin
subsection and staining of the sample. Arrows indicate structures like DXV, multi-
membraned vesicles. > 60 cells were examined. The picture is taken at X 40,000 magni-
fication.
66
Figure 2.8: A representative picture howing DXV in multi-membraned vesicles by elec-
tron microscopy-II.
S2 cells were infected with 10−4 DXV for 24 hours followed by fixation, ultra-thin sub-
section and staining of the sample. Arrows indicate structures like nucleus, mitochondria,
multi-membraned vesicles. The picture is taken at X 5,000 magnification.
67
Figure 2.9: DXV replicates in S2 cells and forms array-like structures.
DXV replicates in the cytoplasm of S2 cells until they reach high concentrations that
can occupy the cytoplasm. When isometric particles are crowded, they form arrays. The
picture is taken at X 5,000 magnification.
68
Figure 2.10: Autophagy is induced to eliminate mitochondria.
Mitochondria were digested in the autophagosomal-like structures. Arrows indicates spe-
cific structures such as mitochondria, autophagosomal-like structures, rough ER, and var-
ious vesicles. The picture is taken at X 5,000 magnification.
69
Interestingly, one EM picture shows contact of DXV viral particles with the endo-
plasmic reticulum (ER). Specifically, DXV seems to appear within the lumen of the ER
(Figure 2.11, Figure 2.12). However, due to the limited number of observations of this
contact, it is premature to conclude that the ER is involved in the DXV infection process.
Figure 2.11: Electron Microscopy of an S2 cell upon DXV infection: Virus particles
within ER-like structures.
Virus particles closely associate with ER-like structures. Arrows point to specific struc-
tures such as nucleus, ER, mitochondria. The picture is taken at X 5,000 magnification.
70
Figure 2.12: Magnified picture from Figure 2.11.
Arrows point to specific structures including nucleus, ER and DXV particles. The picture
is taken at X 40,000. magnification.
71
2.1.5 Autophagy is not induced in GFP-Atg8 Drosophila S2R+ cells
To further test whether DXV induce autophagy in other cell lines, we infected GFP-
Atg8-transfected Drosophila S2R+ cells (gift from Eric Baehrecke) with DXV while us-
ing starved cells as the positive control. After 19h of starvation, the number of Atg8
puncta significantly increased (Figure 2.13 A, B, D). However, GFP-Atg8 puncta are not
induced in the DXV-infected cells compared to WT untreated cells (Figure 2.13 A, C, D).
Overall, data from larval hemocytes, adult fat body, S2 cells and S2R+ cells all indicate
that autophagy is not directly induced by DXV.
72
Figure 2.13: DXV does not induce autophagosome formation in S2R+ cells.
GFP-Atg8 S2R+ cells are either fed (A), starved for 19 hours (B) or treated with DXV
while fed (C) for 19 hours. (D) Two-tailed Student’s tests were performed to test the
statistical significance between groups. green: GFP-Atg8; blue: DAPI. n > 250 cells for
each condition.
73
2.1.6 The antiviral function of Atg1 is not through regulating RNA inter-
ference pathway upon DXV infection
RNA interference was previously shown to be important for the immune response
against DXV (Zambon et al., 2005). In S2 cells following DXV infection, reducing the
level of Ago2, a major component of the RNAi pathway, results in an increase level of
DXV virus (Zambon et al., 2005). Silencing of Ago2 specifically in the adult fat body
also increases the fly susceptibility to DXV infection and an increased viral protein lev-
els is also observed (Figure 2.14). A recent study suggests that a select autophagy can
regulate the miRNA pathway by degrading Dicer and Ago2 (Gibbings et al., 2012). We
were interested in determining whether the autophagy pathway can regulate the RNAi
pathway upon DXV infection. By injecting in vitro synthesized viral dsRNA in adult flies
prior to DXV infection, we observed an increased fly resistance to DXV infection com-
pared to flies pre-injected with PBS (Figure 2.15 A). A decreased load of virus was also
observed in the dsRNA-injected flies compared to PBS-injected flies (Figure 2.15 B).
This indicates that viral dsRNA can act as a primer to protect DXV infection. However,
c564>IR-Atg1 flies pre-injected with dsRNA are also more resistant to DXV compared
to the flies pre-injected with PBS, indicating that loss of Atg1 does not render the RNAi
pathway ineffective in fighting against DXV (Figure 2.15 B).
74
Figure 2.14: Ago2 is important for antiviral response against DXV.
IR-Ago2 flies accumulated more DXV viral proteins at day 3 and day 6 post infection.
Ago2 is silenced by C564-Gal4 driver in the adult fat body. Western blot of viral proteins
were performed on samples collected at day 3, day 6 post infection. β-actin is the loading
control. Experiments are performed three times.
75
Figure 2.15: Loss of autophagy does not render the RNAi pathway ineffective against
DXV .
(A) Survival curves of WT (black) and Atg1-RNAi flies (red) pre-injected with DXV
dsRNA (dotted lines, n > 90 flies) or PBS (solid lines, n > 90 flies) prior to DXV
infection. Log rank analyses were used to test statistical significance. (B) A representative
western blot of viral proteins in flies in the above-mentioned conditions at day 4 and day
5 post infection. Three flies were pooled for protein extraction.
76
2.2 Materials and methods
2.2.1 Fly stocks.
Flies were maintained on Standard Bloomington Stock Center medium at 25 ◦C.
All experiments were performed with five to seven day old female flies. RNAi flies were
obtained from the Harvard TRiP Stock Center. The Atg7 mutants were kindly provided
by Dr. Tom Neufeld from University of Minnesota (Juha´sz et al., 2007).
2.2.2 Cell lines.
S2 cells were cultured in the Schneider’s Drosophila Medium with 10% FBS and
1xPenicillin/Streptomycin at 25 ◦C (Life Technologies). The Drosophila S2R+ cells with
GFP-Atg8 stable-transfected were kindly provided by Dr. Eric Baehrecke. The cells were
cultured in Schneider’s Medium with 800 µg/mL G418, 10% FBS, 1X GlutaMAX and
1X Penicillin/Streptomycin (Gibco).
2.2.3 Virus infections.
Five to seven days old female flies were used for DXV infection. DXV was aliquoted
and stored at −80 ◦C. Before injection, flies were anesthetized with CO2 followed by an
injection of 27nl of DXV (10−5) using individually calibrated pulled glass needles at-
tached to a Nano injector II (Drummond Scientific). Flies were always injected in the
abdomen, close to the junction with the thorax and just ventral to the junction between
the ventral and dorsal cuticles. Flies were never anesthetized for longer than 10 min.
77
After each injection, all flies were transferred to a new vial and maintained at 25 ◦C.
2.2.4 Survival analyses.
After infection, flies were kept in vials at 25 ◦C and transferred to a new vial every
day. Flies were counted every day post infection. Log-rank survival analyses was used
for statistical analysis (Prism, Graphpad).
2.2.5 RT-PCR.
Flies were challenged as described above and incubated at 25 ◦C for the indicated
time points. At a given time, triplicates of ten flies were anesthetized, placed in 1.5ml
tubes. Total RNA was extracted using the standard RNeasy Mini Kit (Qiagen). Quantitative-
PCR was carried out with a Applied Biosystems 7300 Real-Time PCR Machine using
Maxima SYBR Green qPCR Master Mixes (Thermo Scientific) as directed by the man-
ufacturer. The following primers below were used. Relative RNA quantities were deter-
mined with respect to Drosophila ribosomal protein Rp49, and all levels were normalized
with respect to the zero time point for media injection: VP1 (forward primer: TCAAG-
GCATTCGATCCCT), (reverse primer: GGCTAGCCTCTACGGCTT); Rp49 (forward
primer: GCAAGCCCAAGGGTATCGA), (reverse primer: TAACCGATGTTGGGCATCAG).
2.2.6 Protein extraction and Western blot.
Total protein was isolated from adult flies harvested and protein were denatured af-
ter extraction from a pool of three flies at 100 ◦C for 5 minutes in Laemmli sample buffer
78
containing 62.5 mM Tris, pH 6.8, 2% SDS, 25% glycerol, 0.01% bromophenol blue,
and 5% β-mercaptoethanol. The lysates were centrifuged and the supernatant was used
for western blotting. Samples were subjected to SDS-polyacrylamide gel electrophoresis
and resolved at 150 V over 1.5 hours. Proteins were transferred to a PVDF membrane
(Thermo Scientific) in transfer buffer containing 25 mM Tris, pH 8.3, 192 mM glycine,
0.01% SDS, and 15% methanol using a Bio-Rad Trans-blot SD semidry transfer cell to
which 70V were applied for 60 minutes. Membranes were blocked in 4% nonfat dry
milk in PBS at room temperature for over 2 hours. Membranes were exposed to anti-
bodies that recognized DXV (Zambon et al., 2005) . Equivalent protein loading between
the samples was verified by reprobing membranes for β-actin (Santa Cruz Biotechnol-
ogy). Primary antibodies were used at 1:5,000 to 1:10,000 dilutions in 5% BSA or nonfat
milk for overnight at 4 ◦C. Membranes were exposed to anti-rabbit, anti-mouse, or anti-
goat secondary antibodies conjugated with horseradish peroxidase (HRP) at a dilution of
1:10,000 in 4% nonfat milk PBS for 1 hour at room temperature. Signals were detected
with a chemiluminescence detection system (Amersham ECL Western Blotting System,
GE Healthcare) and exposure to x-ray film.
2.2.7 Staining and confocal imaging.
For confocal imaging, adult fat bodies were dissected in PBS and fixed in 4 %
paraformaldehyde and 0.01% Tween PBS at 4 ◦C overnight. For viral particle staining,
fixed fat body tissues were blocked in PBST (3 % BSA, 0.1%Tween) for 2 hours, followed
by anti-DXV antibody incubation overnight with a dilution at 1:500 at 4 ◦C. Alexa-594
79
or Alexa-488 secondary antibody (Invitrogen) was used at 1:250 dilutions in buffer with
0.5% BSA and 0.1% Tween for an hour. After the PBS washes, the stained fat bodies
were fixed with ProLong Gold Antifade Reagent (Invitrogen). More than 800 cells were
examined from either wildtype or Atg1 RNAi fat bodies.
Confocal fluorescent images were obtained by a Zeiss LSM700 confocal scan head
mounted on a Zeiss Axiovert 200M. Images were analyzed by Zeiss software. The term
colocalization refers to the coincidence of green and red fluorescence, as measured by
the confocal microscope. Fluorescence intensities were quantified using ImageJ software
(NIH).
2.2.8 Electron Microscopy
S2 cells were cultured in 12-well plates. Cells were harvested and centrifuged in an
eppendorf tube. After the PBS washes, cell pellets were fixed with 2.5% glutaraldehyde
in cacodylate buffer at RT for one hour. Cell pellets were post-fixed with 1% OsO4 and
1.5% K3[Fe(CN)6] for one hour at 4 ◦C. Followed by PBS washes, pellets were stained
with 1% uranyl acetate for one hour. After a series of washes and dehydration, samples
were embedded in spurr’s resin.
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Chapter 3: Transcriptome Profiling of WT and Atg1 RNAi flies upon
DXV infection
3.1 Introduction to RNA-seq
RNA sequencing (RNA-seq) is a technique used to sequence the total RNAs of
within a biological sample to determine the primary sequences in the transcriptome and
the relative abundance of each RNA.
Compared with traditional EST sequencing by Sanger technology, which only de-
tects transcripts that are in relative high abundance, RNA-seq can detect the transcrip-
tional RNAs at a higher resolution with unprecedented sensitivity and accuracy, including
the rare transcripts that are in low abundance (Marguerat and Ba¨hler, 2010; Wang et al.,
2009; Wilhelm and Landry, 2009). In contrast to the other high-throughput technologies,
such as microarrays, which rely on pre-constructed oligonucleotide libraries, RNA-seq
displays an absolute advantage by offering a near-complete snapshot of a transcriptome
including transcripts that haven not previously been identified (Ozsolak and Milos, 2011).
The novel transcripts can be either newly-identified splice forms from a known gene or
from novel genes. Due to a high sequencing depth (100-1,000 reads per base pair of a
transcript), small RNAs such as micro RNAs, PIWI-interacting RNAs (piRNAs) small
81
nucleolar (snoRNAs) and small interfering (siRNAs) can also be identified.
Despite all the advantages, however, the analysis of RNA-sequencing data remains
a challenge, specifically the reconstruction of the transcriptome. The sequence reads
generated by the common sequencing platforms, such as Illumina, SoLid and 454, are
very short, between 35-500bp (Metzker, 2010). These short sequence fragments needed
to be assembled and mapped to the genome annotation before quantification. Although
several assembler programs, including Velvet and ABYSS have proven powerful in as-
sembling genomes, they are not sufficient for transcriptome assembly (Simpson et al.,
2009; Zerbino and Birney, 2008). The underlying reasons are: (1) The DNA sequenc-
ing depth is the same across the whole genome. Yet, the sequencing depth of transcripts
could be very different since transcripts have difference abundance. (2) The existence
of overlapping genes, different isoforms, strand-specific genes, and repetitive sequences
adds another level of complexity in mapping the transcriptome. To tackle these problems,
a complete and accurate reference genome, longer sequencing fragments, or pair-end se-
quencing technology can be used to resolve the ambiguities.
3.1.1 Pipeline for RNA seq analysis
RNA-seq experiments in general comprise of three steps: generation of RNA seq
data set, RNA seq data analysis and statistical modeling analysis. The flow chart of the
pipeline is shown in Figure 3.1.
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Figure 3.1: Diagram of RNA-seq analysis workflow .
Total RNAs are extracted and converted to a cDNA library. cDNAs are broken down into
small fragments (200-300bp) before high throughput sequencing. The raw sequences will
be either mapped to the reference genome or be assembled de novo into a transcriptome.
The gene or transcript levels can be quantified based on how many sequences mapped to
a particular gene or transcript (Martin and Wang, 2011).
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3.1.1.1 Generation of RNA seq data set
Generating an RNA-sequencing data set comprises of three steps: RNA isolation,
cDNA library construction and sequencing. Depending on the type of RNAs to be se-
quenced, the extraction procedure can vary. For example, for gene transcript sequencing,
total mRNAs are extracted, followed by a poly-A tail enrichment to exclude the non-
polyA transcripts. For small RNA sequencing, the RNA 3’ adapter is specifically modi-
fied to target microRNAs and other small RNAs that have a 3’ hydroxyl group resulting
from enzymatic cleavage by Dicer or other RNA processing enzymes.
cDNA library construction is to convert RNAs to DNAs for sequencing. Due to the
high representation of rRNA in a transcriptome, detection and assembly of rare transcripts
could be affected. To avoid rRNA contamination, hybridization-based depletion methods
can be used to eliminate the rRNAs (Chen and Duan, 2011; He et al., 2010). Another
concern for library construction is whether PCR amplification should be involved. PCR
amplification of the initial cDNA sample could result in a low sequencing coverage for
transcripts that have a high GC-content (Kozarewa et al., 2009). This could further cause
gaps in transcriptome assembly. To overcome this problem, amplification-free protocols
have been developed (Kozarewa et al., 2009; Mamanova et al., 2010). One example is
the single molecular sequencing technology, which does not require PCR-amplification
(Sam et al., 2011). However, the sequencing rate is much higher compared to the PCR-
dependent sequencing technology.
The final step is sequencing the cDNAs. To ensure a high-quality transcriptome
assembly, there are several factors to consider: the length of the reads and a single-end or
84
pair-end protocol. Longer reads are generally more favorable for transcriptome assembly
as less assembly events are needed to construct the contigs. To regions that have repetitive
sequences or are at the junction sites between exons, longer reads are specifically useful.
Paired-end strategy can also improve the accuracy for mapping and assembly (Rodrigue
et al., 2010). However, the choice between these sequencing options largely depends
on the purpose of the experiment and the budget. The longer reads and paired-end se-
quencing are generally more expensive. For experiments targeting for gene-level mRNA
quantification, single end and shorter reads might be sufficient since distinction of various
transcripts or discovery of novel transcripts are not the major concern.
3.1.1.2 RNA seq data analysis
cDNA sequencing data needs to be pre-processed to remove artifact reads such as
sequencing adapters and low-complexity reads (Falgueras et al., 2010; Lassmann et al.,
2009). Adapter sequences and low-complexity reads can result in misassembly of the
transcriptome.
To ensure the quality of the sequencing data, quality score and k-mer (all the pos-
sible sub sequences (of length k)) frequency should be closely monitored. Low quality
scores are usually associated with sequencing errors. The sequencing error rate can be
inferred from the frequency of k-mers. Assuming there are no sequencing errors, a k-
mer should occur multiple times as the same fragment is sequenced many times. When
sequencing errors happen, there will be k-mers with very low frequencies. In order to
correct the sequencing errors, the transcripts containing these low-frequency k-mers can
85
be removed, trimmed or corrected (Kelley et al., 2010; Miller et al., 2010). However,
the side effect of removing these transcripts is eliminating the rare transcripts which also
appear at low frequencies.
After trimming the data set, short sequences need to be assembled into transcrip-
tomes. There are three strategies for transcriptome assembly: a reference-based strategy,
a de novo strategy or a combined strategy. When there is a complete and accurate genome
annotation, a reference-based method provides a reliable and accurate assembly. When
no genome annotation is available, a de novo method is used to assemble transcripts by
the overlapping sequences in the short reads. When there is an incomplete genome anno-
tation, the combined strategy is more effective. For the sake of relevance to this research,
we will only discuss the reference-based strategy.
There are three steps for a reference-based assembly. First, RNA-seq reads are
aligned to a reference sequence genome using a splice-aware aligner, such as TopHat,
SpliceMap, MapSplice and Blat (Au et al., 2010; Kent, 2002; Trapnell et al., 2009; Wang
et al., 2010). Second, overlapping reads from each locus are clustered to build up a graph
with all possible splice forms. Finally, real transcripts are resolved by eliminating the
non-existent ones from the total possible transcripts. Cufflinks and Scripture are some of
the programs using the reference-based strategy to assemble transcripts (Guttman et al.,
2010; Trapnell et al., 2010). However, if the purpose of RNA-seq is to quantify the RNA
expression on the gene level rather than on the transcript level, a splice-aware alignment
by itself is sufficient.
After the sequencing reads are aligned to the reference genome, quantification of
the transcripts or genes can be achieved by counting for each gene how many reads are
86
mapped to the reference genome. HT-seq is one of the programs using this strategy (An-
ders et al., 2014). Since the initial aim of the program is for gene expression quantifica-
tion, the reads that map to multiple genes are not counted (Anders et al., 2014).
Recently, however, RNA-seq quantification algorithms that are alignment-free have
been developed, such as Sailfish and RNA-skim (Patro et al., 2014; Zhang and Wang,
2014). This substantially decreases the amount of computing time while maintaining a
comparable quantification accuracy. However, these programs can only be used to quan-
tify the previously annotated RNA isoforms. For organisms that do not have a complete
annotation, an alignment-based de novo strategy is required.
3.2 Differential expression analysis
RNA-seq technology enables the detailed identification of novel genes, gene iso-
forms, translocation events, nucleotide variations and post-transcriptional base modifica-
tions (Wang et al., 2009). However, one of the major goals of RNA-seq is to quantify
differentially expressed genes. Such genes are usually selected based on the fold change
of the gene expression levels and statistical significance (p value).
Since the expression signal of a gene is largely dependent on the sequencing depth
and the expression levels of other transcripts, multiple statistical models and algorithms
have been developed for normalization and differential expression (DE). Currently, most
algorithms are based on Poisson or negative binomial distributions to model the gene
count data (Auer et al., 2012; MD, 2012). Specifically, the most frequently used packages
are: DESeq, PoissonSeq, edgeR, Cuffdiff, baySeq and limma (Anders and Huber, 2010;
87
Hardcastle and Kelly, 2010; Li et al., 2012a; Robinson et al., 2010; Smyth, 2004; Trap-
nell et al., 2013). These software packages mainly differ in (1) normalization methods;
(2) statistical modeling of gene expression; and (3) the test for differential expression.
Since this research only involves DE-seq and Limma packages, we will only review and
compare these two.
3.2.1 Normalization
Due to the variability caused by RNA sample preparation, cDNA library prepara-
tion, sequencing run and nucleotide compositions, a normalization procedure is required
for accurate comparisons between samples. One intuitive strategy is to divide each gene
count by the total number of sequencing reads in the library, named per kilobase per mil-
lion reads (RPKM) (Mortazavi et al., 2008). This method assumes that each gene count
is independent of each other, which does not accurately represent the situation. Often a
small fraction of genes can generate a large proportion of sequencing reads and a small
expression change in these genes will affect the counts of lowly-expressed genes. This
will skew the DE results (Bullard et al., 2010; Robinson and Oshlack, 2010). To consider
the gene coverage differences, DESeq computes a scaling factor for a given sample by
computing the median of the ratio, for each gene, of its read count over its geometric
mean across all samples. It then uses the assumption that most genes are not DE and
uses this median of ratios to obtain the scaling factor associated with this sample (An-
ders and Huber, 2010). The limma package uses quantile normalization to ensure that the
counts across all samples have the same empirical distribution (Bolstad et al., 2003). An-
88
other normalization function termed voom, performs a LOWESS regression to estimate
the mean-variance relation and transforms the read counts to the appropriate log form for
linear modeling (Law et al., 2014).
3.2.2 Statistical modeling of gene expression
If each sequencing experiment of a biological sample is considered a random sam-
pling event of a fixed group of genes, the distribution of a particular gene counts across all
biological samples should conform to a Poisson distribution. Specifically, in the equation
P (x) = e
−λλx
x! , x represents the frequency of a certain read count and λ represents the
expected read counts of that particular gene. An important feature of a Poisson distribu-
tion is that the variance equals the mean (λ). However, the variance of a particular gene’s
expression level across biological replicates is often larger than the gene expression mean.
To combat this problem, DESeq uses negative binomial distribution for modeling the gene
expression, where variance is equal to the Poisson estimate (λ) plus a second term rep-
resenting the biological expression variability. The equation is shown as v = µ + αµ2
(Anders and Huber, 2010).
3.2.3 Test for differential expression
To test whether a gene is expressed differentially between two conditions, Fisher’s
exact test is used to calculate a p-value in the DESeq package. Limma packages, however,
89
uses a moderated t-statistic to compute p-values in which both the standard error and the
degrees of freedom are modified (Smyth, 2004). The standard error is moderated across
genes with a shrinkage factor, which effectively borrows information from all genes to
improve the inference on any single gene. The degrees of freedom are also adjusted by a
term that represents the a priori number of degrees of freedom for the model. Since there
are a pool of genes to be tested, standard multiple hypothesis testing is used to control the
false discovery rate (FDR), for example, by the Benjamini-Hochberg method (Benjamini,
1995).
3.3 Exploratory studies
Since the RNA-seq experiment generates a large amount of gene expression profiles
from different samples, exploratory studies usually provide big pictures with critical in-
formation in terms of expression patterns within and among the biological samples. Some
of the useful diagnostic and visualization tools are scatter plots, various clustering plots
and principle component analysis (PCA) plots.
3.3.1 Scatter plot
A scatter plot is a direct visualization tools. In a scatter plot, usually a sample or
an mRNA subject is represented by a dot on a 2D/3D coordinate systems. Some typical
coordinates would be gene expression levels, p-values, time, drug load or other variables.
Since such a plot usually does not accommodate many coordinates, some data feature
90
might not be best presented using this method.
3.3.2 Clustering and visualization
Clustering is a approach based on the similarity between samples. By building
up the clustering tree, one can easily observe the similarity between different samples.
One intuitive strategy is to measure the distance between two samples. The two groups
that have the closest distance can be grouped together. As these distances are calcu-
lated between multiple pairs of samples, a clustering map can be constructed. The dis-
tance between two RNA-seq samples can be determined using this equation: D12 =
√
n∑
i=1
(x1i − x2i)
2
. D12 represents the distance between two samples; x1i and x2i repre-
sent the ith gene in each sample, respectively.
Another measurement for similarity is the correlation coefficient. The bigger the
number is, the more the two samples are correlated. This number can either be positive
or negative, whereas 0 indicates no correlation. The correlation coefficient is calculated
as below: rxy =
n
n∑
j=1
xy−
n∑
j=1
x
n∑
j=1
y
√
[n
n∑
j=1
x2−(
n∑
j=1
x)2][n
n∑
j=1
y2−(
n∑
j=1
y)2]
Here rxy is the correlation between genes expression profiles of samples x and y;
x1i and x2i represent the gene counts of the ith gene in the samples x and y, respectively.
The visualization of the clustering can be represented as a heatmap, where the sam-
ples are grouped based on the similarities of their gene expression profiles. The levels of
91
expression counts per gene are colored in a gradient.
3.3.3 Principle component analysis and its visualization
Principle component analysis is a powerful approach to present the samples in a
scatter plot format. The traditional scatter plot is supposed to contain all the variables
such that an n-dimensional coordinates system (n variables) needs to be built up. While it
is not possible to visualize a n-dimensional coordinates system, PCA plots can be gener-
ated instead to identify the most important principle components and projects the samples
into a newly-constructed coordinate system with such principle components. The first
principle component determines the most important factor that affects the gene expres-
sion profile among the samples. The second principle component is the next important
factor. The other components have decreasing weights in affecting the expression profile
and the least principle component can be regarded as noise. PCA is a great tool to identify
the most critical factors in differentiating transcriptomes by eliminating the noise, and it
can also create an accurate representation of the similarities between samples.
3.4 Results
In order to identify the genes that are important for the antiviral response against
Drosophila X virus (DXV), we were interested to analyze the transcriptomes of the wild-
type flies that are either uninfected or infected. In addition, we were interested in explor-
ing the novel role of Atg1 in response to DXV since classical autophagy does not seem to
92
Figure 3.2: Experimental design of the RNA seq experiment.
Wildtype and Atg1 RNAi flies were either uninfected or infected with DXV and sacrificed
for total RNA isolation at different time points. For each of the eight conditions, three
biological replicates were used. Each replicate contains mRNAs from a pool of six flies.
be involved.
Atg1 is the only kinase among all of the Atg genes. It is possible that Atg1 protects
flies against DXV infection by phosphorylating substrates other than classical autophagy
proteins. In order to identify pathways affected in an Atg1-dependent manner, we used
RNA-Seq to analyze the transcriptomes of wildtype and Atg1 RNAi flies without infection
or with infection at day 3, day 5, and day 7 (Figure 3.2). The time points were chosen
to take into account the kinetics of virus replication such that the relationship between
the differentially expressed genes and the viral load can be determined. Three biological
replicates were performed for each of the eight conditions such that the results could be
presented with statistical power.
93
Following data pre-processing, data quality is verified by FASTQC (Andrews S.,2010).
The short reads are further mapped to the Drosophila reference genome using Tophat
(Trapnell et al., 2009). The mapped reads are quantified using HT-seq (Anders et al.,
2014). Differential expression analysis was performed using the Limma and DEseq pack-
ages.
3.4.1 Statistics diagnostics
Following HT-seq count, a data matrix containing total transcriptome gene counts
for 24 samples were generated. In order to minimize the variability due to the difference
of library size, quantile normalization was applied to the data set. Boxplots of counts
across samples in each data set, both before and after normalization should be examined
to ensure the effectiveness of normalization, which should result in a stabilization of read
count distributions across replicates (Dillies et al., 2013). The counts for each gene in
24 samples are transformed to its log forms. The log2(counts + 1) of each gene is pre-
sented in the box plots (Figure 3.3A-B). Before normalization, there is slight variability
of the average log2(counts+ 1) between samples. The average log2(counts+ 1) number
is ∼ 6.2 for all the samples (Figure 3.3A). After normalization, the distribution of the
log2(counts + 1) is stabilized and the average log2(counts + 1) is even for each sample
(Figure 3.3B).
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Figure 3.3: Normalization of RNA-seq data sets.
Quantile normalization to adjust the library size of 24 samples. (A) Before normaliza-
tion. (B) After normalization. The x-axis represents the biological samples. The y-axis
represents the box plot of all the gene read counts (in a log form) for each sample.
95
In the voom plot, the relationship between log2(counts+ 1) and standard deviation
is presented. Due to the variability of gene expression levels, the standard deviation
of each gene displays a distribution that the higher the gene counts are, the smaller the
standard deviations are (Figure 3.4). To estimate the mean-variance relation, a LOWESS
regression was performed to transform the read counts to the appropriate log form for
linear modeling (Figure 3.4).
96
Figure 3.4: Voom.
The LOWESS regression is fitted into the data set. The x-axis represents the log2 counts
for each gene (11140 genes) in the data set. The y-axis represents the standard deviation
of each gene. The red line represents the LOWESS regression line.
97
In order to determine the general trend and pattern of all 24 samples, principle com-
ponent analysis was performed. Each dot in the PCA plots represents one transcriptome.
The relative position between two dots in the PCA plot indicates the degree of similarity
between the two transcriptomes. There are four observations from the PCA plots. First,
all the biological replicates cluster, indicating that the reproducibility of the biological
replicates is high. Second, the dots representing the wildtype and Atg1 RNAi flies sep-
arate, indicating there is a significant difference between the transcriptomes. Third, the
x-axis, representing the first principle component, corresponds to the level of DXV in-
fection, whereas the y-axis, representing the second principle component, corresponds to
genotype. Lastly, the transcriptome of the Atg1 RNAi flies are significantly different than
other transcriptomes (especially on the x-axis), and this indicates a high level of infection
(Figure 3.5).
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Figure 3.5: Principle component analysis.
The first principle component represents the degree of infection. The second principle
component is associated with the genotype. Each biological sample is represented as a
dot in the PCA plot and each color is associated with one experimental condition.
99
3.4.2 Differential expression analysis in wildtype flies upon DXV infec-
tion at day 3 and day 5
To identify genes important during DXV infection, differential expression analysis
was applied for pairwise comparisons between infected and uninfected WT flies. Few
changes in gene expression were detected in flies at days 3 and 5 post infection (dpi),
compared to uninfected flies (Figure 3.6A-B). This might be due to the low level of DXV
virus present at day 3 and day 5 in the fat body (data not shown). Specifically, there
are only 4 and 3 genes that are differentially expressed in the wildtype flies upon DXV
infection at 3 dpi and 5 dpi, respectively (Table 3.1, Table 3.2). In order to determine
the expression of the differentially expressed genes across 24 samples, heatmaps were
generated based on gene clustering (Figure 3.6C-D).
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Figure 3.6: Differential expression analysis in wildtype flies upon DXV infection at day
3 and day 5.
(A-B) Scatter plots for the differential expressed genes by comparing wildtype uninfected
flies vs DXV-infected flies at day 3 (A), day 5 (B). (C-D) Heatmaps for the differential
expressed genes by comparing wildtype uninfected flies vs DXV-infected flies at day 3
(C), day 5 (D). The grey dots represent the total (11140) genes that are included in the
analysis. The genes marked in red are differentially expressed with a log2 fold change >
0.5 and a p value < 0.05.
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ID Gene Symbol log2FC AveExpr adj.P.Val
1 FBgn0014396 tim 1.454150316 10.07824969 0.000143115
2 FBgn0004919 gol 1.07083281 7.408629419 0.000245741
3 FBgn0035290 dsb -1.07100695 8.089983163 0.0008206
4 FBgn0033936 CG17386 -2.034002955 4.862512709 0.000143115
Table 3.1: Differentially expressed genes between uninfected WT flies and DXV-infected
flies at day 3 post infection. Genes presented in the table are selected by log2 fold Change
> 0.5 and p value < 0.05. Gene list is sorted by log2 fold Change from the largest to the
smallest.
ID Gene Symbol log2FC AveExpr adj.P.Val
1 FBgn0031805 CG9505 1.317918736 7.485067178 0.009868327
2 FBgn0035348 CG16758 1.166306292 11.74399685 0.04215132
3 FBgn0033936 CG17386 -1.407439117 4.862512709 0.009868327
Table 3.2: Differentially expressed genes between uninfected WT flies and DXV-infected
flies at day 5 post infection. Genes presented in the table are selected by log2 fold Change
> 0.5 and p value < 0.05. Gene list is sorted by log2 fold Change from the largest to the
smallest.
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3.4.3 Differential expression analysis in wildtype flies upon DXV infec-
tion at day 7
At 7dpi in the wildtype flies, there were 714 genes whose change in gene expression
is both large (absolute log2 fold change >0.5) and significantly different (p<0.05) from
WT flies without infection (Figure 3.7A).
To explore the signaling events induced by DXV infection, Gene Ontology analyses
were used to perform biological function enrichment analysis in the list of differentially
expressed genes (Alexa et al., 2006). In the comparison between uninfected and 7 dpi
flies, the top 20 Gene Ontology Terms (GO terms) overrepresented associate with clas-
sic immune signaling pathways such as wound healing, humoral immune response, and
innate immune response (Figure 3.7C). The genes associated with these GO terms are an-
timicrobial peptides, pattern associated microbial patterns (PAMPs), Toll, IMD and JAK-
STAT pathway genes (Table 3.3). These findings are consistent with the transcriptional
profiling of Drosophila infected with other viruses such as Flock House Virus (FHV; No-
daviridae), Sindbis virus (SINV; Alphaviridae) and Drosophila C Virus (DCV; Dicistro-
viridae) (Dostert et al., 2005; Kemp et al., 2013). A group of β-oxidation and lipid droplet
metabolism genes are specifically upregulated in DXV-infected flies, but not in flies in-
fected with the above mentioned viruses. For example, lipid storage droplet-1 (lsd1), a
positive regulator of lipid droplet lipolysis, is upregulated ∼2.1 fold. AkhR (Adipokinetic
hormone receptor) and pka (protein kinase A), which are the upstream regulators of lsd-
1 are also upregulated more than 1.5 fold. Other upregulated genes include triglyceride
lipases (CG5966, CG6295), Acyl-CoA dehydrogenase/oxidase (CG9527), and gamma-
103
butyrobetaine dioxygenases (CG10814, CG5321, CG4335) (Figure 3.7D-E). Triglyc-
eride lipases can break down lipid into free fatty acids, which are then transported into mi-
tochondria for β-oxidation by carnitine as a carrier (Bremer, 1983; McGarry and Brown,
1997; Ramsay et al., 2001; van Vlies et al., 2007). Gamma-butyrobetaine dioxygenase is
the rate-limiting enzyme for carnitine synthesis (Vaz and Wanders, 2002). Since several
genes in these biological processes were transcriptionally upregulated, we suspected that
lipolysis and β-oxidation are induced upon DXV infection.
104
Figure 3.7: DXV infection induces genes important for lipid droplet lipolysis and β-
oxidation.
(A) Scatter plots of significantly changed genes in wildtype flies upon DXV infection at
day 7 post infection. Differentially expressed genes were selected by both p < 0.05 and
absolute log2fold change > 0.5. Significant genes are represented in red dots. Gray dots
indicate all the genes involved in the analysis. (B) Heatmap of significant genes upon
infection across all samples. Heat intensity is indicated by a color gradient. (C) Gene
Ontology Analysis of virus induced genes. Top20 GO terms are shown in the table. (D,E)
Fold change of lipid metabolism genes (D) and β-oxidation genes (E) from the RNA-seq.
105
Signaling pathways Gene name Fold change (5dpi) Fold change (7dpi)
NF-κB pathways
Dif 1 1.43
Imd 1 1.28
Relish 1 1.5
Ird1 1 0.28
PGRP-LC 1 0.626
JAK-STAT
Upd3 1 2.64
Socs36E 1 2.46
Upd2 1 2.99
Diedel 1 11.88
TotC 1 5.13
Tep4 1 1.43
Antiviral proteins
Vago 1 2.85
Listericin 1 2.17
Defense against Bacterium
NimB2 1 1.71
NimC3 1 0.37
PPO1 1 1.5
PPO2 1 1.58
Table 3.3: Immune genes that are responsive to DXV infection in WT flies.
All the genes shown in the table are significantly upregulated compared to uninfected
WT flies. p < 0.05.
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3.4.4 Differential expression analysis between wildtype and Atg1 RNAi
flies without infection
In order to identify genes that are important for the Atg1-dependent immune re-
sponse, we compared the transcriptomes of wildtype and Atg1 RNAi flies. The differen-
tial expression analysis indicates that 381 genes are differentially expressed significantly
(Figure 3.8A). The heatmap is shown to examine the expression pattern of these 381
genesFigure 3.8B. Interestingly, a subset of these DE genes (upregulated in Atg1 RNAi
flies) also have upregulated expression levels as infection progresses in both the wild-
type and Atg1 RNAi flies. This indicates that a subset of the DE genes regulated in the
Atg1 RNAi flies might be overlapping with the DE genes induced by DXV infection.
The top Gene Ontology term includes lipid metabolism and oxidation-reduction process
(Figure 3.8C). In order to determine the biological events caused by the lose of Atg1,
the differential expressed genes associated with the GO terms were examined. The lipid
metabolism and β-oxidation genes are upregulated in Atg1 RNAi flies compared to wild-
type in the absence of DXV infection. These genes encode the well-characterized triglyc-
eride lipase brummer (bmm), predicted triglyceride lipase CG5966, Acetyl Coenzyme A
synthase (AcCoAS) and Ketoacyl-CoA thiolase (Yip2) (Figure 3.8D), indicating that the
lipid metabolism and β-oxidation are upregulated in the Atg1 RNAi flies.
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Figure 3.8: Silencing of Atg1 in the fat body induces lipid droplet lipolysis and β-
oxidation genes in the absence of infection.
(A) Scatter plots of significantly changed genes in Atg1 RNAi flies compared to wildtype
flies. Significant genes are selected by both p < 0.05 and absolute log2 fold change> 0.5.
Significant genes are represented in red. Gray dots indicate all the genes involved in the
analysis. (B) Heatmap of significant genes dependent on Atg1 across all samples. Heat
intensity is indicated by a color gradient.(C) Gene Ontology Analysis of Atg1-dependent
genes prior to infection. Top20 GO terms are shown in the table. (D) Fold change of lipid
metabolism genes and β-oxidation genes from the RNA-seq experiment.
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Brummer is a triglyceride lipase (ATGL) that can break down diglycerides into
monoglycerides (Gro¨nke et al., 2005). An increased transcriptional level of bmm might
indicate an increased lipolysis rate. Following lipolysis, free fatty acids are produced
that can be transported into the mitochondria for β-oxidation. Acyl-CoA synthetase (Ac-
CoAS) is an catalytic enzyme that resides in the outer mitochondrial membrane, As a rate
limiting factor for β-oxidation, AcCoAS is responsible for converting the free fatty acids
into acyl-CoA before entry into the β-oxidation pathway. The acyl-CoA is transported
through the outer and inner mitochondrial membranes via a carnitine intermediate, and
then processed through four enzymatic steps for generation of ATP. The upregulation of
a series of β-oxidation genes indicates that it is possible that the Atg1-deficient fat cells
have an elevated metabolism.
3.5 Materials and methods
A pool of six flies were collected for total RNA extraction (Qiagen RNeasy kit).
Three biological replicates were used for each condition. cDNA libraries were con-
structed using TruSeq RNA Sample Preparation Kit v2 (Illumina). 50 bp, single end
sequencing was performed by Illumina HiSeq 1500 in high output mode. Six samples
were sequenced per lane with an estimate of 180M reads in total. Raw sequences were
aligned using the Tophat package on linux system (Trapnell et al., 2009). HTseq was used
to count the the frequency of sequences that are mapped to a certain gene. Differential
expression analyses were applied by using both DESeq (Anders and Huber, 2010) and
Limma package (Bioconductor). The data shown were results generated by the Limma
109
package. Raw gene counts were log2 transformed for linear regression modeling. TopGo
R package was used for Gene Oncology analysis (Bioconductor) (Alexa et al., 2006).
110
Chapter 4: Atg1 is playing an antiviral role against DXV by regulating
lipid metabolism
4.1 Results
4.1.1 DXV infection induces lipolysis in the lipid droplet
In Chapter 3, we have identified that the lipid metabolism and β-oxidation are sig-
nificantly upregulated in wildtype flies upon DXV infection, specifically at 7 dpi. As
lipid metabolism and β-oxidation are critical processes to control lipid mobilization and
energy production, we were interested to determine whether lipolysis is induced in the
adult fat cells upon DXV infection.
The degradation of neutral lipid is mainly controlled by triglyceride lipases and by
the surface proteins that control the accessibility of lipases to the lipids (Thiam et al.,
2013). When lipases are activated, lipid droplets decrease in size, and this is a key char-
acteristic for lipolysis. As genes related to lipolysis are upregulated in DXV infected flies,
we were interested to examine whether the size of lipid droplets changes over the course
of infection. We stained the lipid droplets of adult fat body with Bodipy 493/503 and
measured the sizes of the droplets in both uninfected and infected flies. In WT infected
flies, the sizes of the lipid droplets are smaller than the ones in uninfected flies. At day
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3 post infection, the number of droplets with a diameter of at least 10 µm has decreased
by 50%. At day 7 post infection, almost no droplets have a diameter that is larger than 10
µm (Figure 4.1 A-D). Furthermore, a fat tissue infected with heterogeneous loads of virus
shows a correlation between the amount of virus in a cell and the size of the lipid droplets.
Individual cells with heavy infection of DXV exhibit smaller lipid droplets compared to
the cells with less virus (Figure 4.1 E). These data suggest that lipolysis is affected during
DXV infection.
112
Figure 4.1: DXV infection decreases lipid droplet size.
(A-C) Lipid droplet (LD) sizes of wildtype adult fat bodies without infection, and with
infection at day 3 and day 7. Lipid droplets were stained with Bodipy (Green). Nuclei are
stained with DAPI (Blue). (D) Quantification of the lipid droplets sizes shown in (A-C).
The number of LDs with a diameter larger than 10 µm were quantified. More than 700
cells were examined. Student’s t-tests were used to test the significance. (E) Wildtype
adult fat tissues were costained for DXV (red) and lipid droplets (green).
113
4.1.2 Atg1 regulates lipid metabolism even in the absence of DXV infec-
tion
As shown in chapter 3, prior to DXV infection, the lipid metabolism and β-oxidation
genes are significantly upregulated (Figure 3.8). This might indicate that loss of Atg1 re-
sults in an elevation of lipid metabolism and β-oxidation. To confirm our hypothesis, we
stained the lipid droplets from adult fat body with Bodipy 493/503 and measured the sizes
of the droplets. In the WT flies, the sizes of lipid droplets are mostly homogeneous with 4
to 6 large droplets in each cell. In contrast, the lipid droplets in Atg1 RNAi flies are much
smaller and the number of lipid droplets in each cell has increased (Figure 4.2 A-C).
This suggests that reduced levels of Atg1 facilitate lipolysis by inducing lipase activity.
When flies were infected with DXV, the difference of lipid droplet size persists between
WT and Atg1 RNAi flies (Figure 4.2 D-K). This indicates that Atg1 can facilitate lipid
degradation and β-oxidation to produce energy.
114
Figure 4.2: Loss of Atg1 decreases lipid droplet size.
(A, B, D, E, G, H) Lipid droplet sizes of wildtype and Atg1 RNAi adult fat bodies without
infection (A-B), with infection at day 3 (D-E) and day 7 (G-H). Lipid droplets are stained
with Bodipy (green). Nuclei are stained with DAPI (blue). (C, F, I, J, K) Quantification
of the number of LDs with a diameter larger than 10 µm. More than 700 cells were
examined. Student’s t-tests were used to test for the significance.
115
4.1.3 Lipid metabolism is important for DXV infection
We have previously shown that the size of lipid droplets become smaller upon in-
fection. We hypothesize that the lipid stored in the lipid droplets is released into the
cytoplasm to generate energy.
The energy released from the lipid droplet could be utilized in two ways:1) to help
initiate systemic immune responses against DXV; or 2) to facilitate DXV replication.
To differentiate between these two possibilities, we applied a genetic approach to mod-
ify lipolysis and examine the effect on fly survival and DXV replication. Perilipins are
proteins localized on the surface of the lipid droplets. In mammals, perilipin prevents
triglyceride from being degraded by lipases (Londos et al., 2005). In Drosophila, two
homologs, lsd1/plin2 and lsd2/plin2 have been characterized (Beller et al., 2010; Teixeira
et al., 2003). plin1 is required for lipolysis and a null mutant has an obese phenotype with
larger lipid droplets than WT flies (Bi et al., 2012) (Figure 4.3 A-B). plin2 functions as
a negative regulator of lipolysis like the mammalian perilipins. plin2 mutants have a lean
phenotype together with smaller lipid droplets compared to wildtype (Beller et al., 2010).
To test our hypothesis, plin1 null mutants were infected with DXV for survival analyses.
plin138 survive better compared to the wildtype flies upon DXV infection (Figure 4.4 A).
Furthermore, less viral protein was detected in the plin1 null mutant compared to wildtype
by western blot (Figure 4.4 C). In contrast, when plin2 was silenced in the adult fat body
by C564-Gal4, flies become more susceptible and showed an increased amount of viral
VP1 mRNA (Figure 4.4 B,D). Despite an increased resistance to DXV, the plin1 null
mutants were not resistant to DCV infection (Figure 4.3 C). This indicates that lipolysis
116
is facilitating viral infection, possibly by providing more energy for DXV replication.
117
Figure 4.3: plin138 null mutants have bigger lipid droplet size and are not susceptible to
Drosophila C virus.
(A) In WT fat cells, most lipid droplets have a diameter of less than 10 µm. (B) In plin138
mutant fat body cells, most lipid droplets have a diameter of more than 20 µm. More than
100 cells were measured per genotype. (C) plin138 null mutants were not susceptible to
Drosophila C virus in either the 10−3 or the 10−4 infectious doses. n> 90 flies for each
experiment.
118
Figure 4.4: Lipid metabolism genes are important regulators for fly survival upon DXV
infection.
(A-B) Survival analysis of (A) plin138 null mutants and (B) flies with the lsd2 gene si-
lenced. lsd2 was silenced in the fat body using the C564-Gal4 driver. n> 90. Log-rank
tests were used for the survival analyses (C) Western blot of DXV viral proteins in wild-
type and plin138 mutant. (D) Quantitative PCR of the viral mRNAs in wildtype and lsd2
RNAi flies. Six flies were pooled for mRNA extraction. All experiments were repeated at
least three times. Student’s t-tests were used for testing statistical significance.
119
4.2 Materials and Methods
4.2.1 Fly stocks.
plin138 was a generous gift from Dr. Xun Huang from the Chinese Academy of
Sciences (Bi et al., 2012). IR-lsd2 RNAi flies (stock number: 32846) were obtained from
the TRiP Drosophia stock center.
4.2.2 Lipid staining and confocal imaging.
For confocal imaging, adult fly fat bodies were dissected in PBS and fixed in 4 %
paraformaldehyde and 0.01% Tween PBS at 4 ◦C overnight. After three PBS washes,
fixed fat bodies were incubated in BODIPY 493/503 fluorescent stain at a concentration
of 10µg/ml for an hour at room temperature (Invitrogen). The size of lipid droplets were
measured by the ruler in the Zen software.
120
Chapter 5: Conclusions and discussions
Due to the lack of mutants available in mouse, most of the autophagy studies have
focused on Atg5, Atg6, Atg7 and Atg8 (Gutierrez et al., 2004). If a phenotype was ob-
served when Atg5, Atg7 and Atg8 were silenced or in these null mutants, it is generally
recognized that autophagy is playing a role. However, recently, more studies have shown
that autophagy genes might have roles in processes other than autophagy such as cell sur-
vival and apoptosis, modulation of cellular trafficking, protein secretion, cell signaling,
transcription, translation and membrane reorganization (Subramani and Malhotra, 2013).
Here we identify a novel function for Atg1 in regulating lipid droplet metabolism in the
Drosophila fat body. This change of lipid metabolism caused by Atg1 is closely related
to the antiviral role of Atg1. Additionally, other than Atg1, the major critical regulators of
lipid droplet metabolism also play important roles in controlling the DXV viral infection.
The non-canonical role of Atg1 was first discovered by the different phenotypes
shown in the IR-Atg1 and other IR-Atg flies upon DXV infection. Specifically, loss of
Atg1 in the fat body resulted in an increased susceptibility to DXV. In contrast, silenc-
ing of Atg7 and Atg8 did not lead to an increased susceptibility phenotype. Atg1, Atg7
and Atg8 act in the same autophagy signaling pathway. If the increased susceptibility ob-
served in the IR-Atg1 is attributed to autophagy, a similar effect should be observed in all
121
three RNAi-silenced lines. Thus, we hypothesized that Atg1 might have other functions
in addition to autophagy. This hypothesis was also supported by other studies. ULK1/2,
the mammalian homologs of Atg1, mediates a non-clathrin-coated endocytosis in sen-
sory growth cones. Silencing of either ulk1 or ulk2 causes a defect in endocytosis of
Nerve Growth Factor (NGF) that prevents filopodia extension and branching of sensory
axons (Zhou et al., 2007). Furthermore, the fact that Atg1 is the only serine/threonine
kinase among all the Atg proteins (e.g., 20 in Drosophila, and 31 in Saccharomyces
cerevisiae) also suggests a possible role for Atg1 in phosphorylating multiple protein
targets. However, as over-expression of myc-Atg1 in the adult fat body results in fly
lethality, we were not able to identify possible phosphorylation targets of Atg1 through
co-immunoprecipitation (Co-IP).
Another result indicates that the the antiviral function of Atg1 is not due to au-
tophagy. Autophagy induction is characterized by GFP-Atg8 puncta formation or an in-
creased number of autophagosomal structures by EM (Swanlund et al., 2010). Although
multiple attempts have been made by changing the infection dose or incubation time,
GFP-Atg8 puncta is not significantly increased in ex vivo larval hemocytes or S2R+ cells
upon DXV infection. Additionally, in adult fat body and S2 cells, autophagy is not in-
duced at the early stage of infection, only at the late stages of infection. However, we
come to the conclusion that the late stage induction of autophagy is not directly associ-
ated with DXV viral particles. First, in the adult fat body, GFP-Atg8 puncta only appear
when the fat cells are mostly occupied by DXV particles. There is no colocalization be-
tween the virus and GFP-Atg8 puncta. This indicates that the induction of autophagy is
not directly associated with DXV but rather appears to be a secondary effect (Figure 2.5
122
C,D). Second, autophagosomal-like structures are only observed in heavily infected S2
cells by EM, where mitochondria are engulfed in the autophagsosome (Figure 2.10).
To identify the non-canonical role of Atg1 in the antiviral response, transcriptomes
of WT and IR-Atg1 flies were compared by an RNA-seq experiment. A group of lipid
metabolism and β-oxidation genes were found to be differentially expressed. For exam-
ple, the adipose triglyceride lipases brummer and CG5966 are upregulated in the IR-Atg1
flies, suggesting an increased level of lipid hydrolysis. On the cellular level, lipid staining
also shows a decreased size of the lipid droplets in IR-Atg1 flies. Additionally, critical
β-oxidation genes such as AcoCOA are also upregulated. As lipid droplets are the major
energy storage organelles in flies, an elevation of lipid lipolysis and β-oxidation would
result in an increased production of ATP for energy consumption.
We find that the facilitated lipid metabolism and β-oxidation are favorable for DXV
replication based on three pieces of evidence. First, loss of Atg1 increases the fly sensi-
tivity to DXV infection. This indicates that the increased production of ATP as a result of
Atg1 silencing was not used against viral replication. Although previous data has shown
the importance of β-oxidation in activating the immune system, the possibility of ATP be-
ing consumed by replicating pathogen should not be ignored. For example, dengue virus
can exploit autophagy to produce energy for its replication in Huh-7.5 cells (Heaton and
Randall, 2010). Additionally, the expression of immune related genes are significantly
higher in the IR-Atg1 flies compared to WT upon DXV infection, indicating at least loss
of Atg1 does not block immune responses at the transcriptional level. Second, increased
lipolysis and β-oxidation are observed when flies are infected with DXV. Specifically, the
123
upregulation of perilipin (plin1), triglyceride lipases, Acyl-CoA dehydrogenase/oxidase
and gamma-butyrobetaine dioxygenase provides evidence on the transcriptional level.
On the cellular level, the fact that the lipid droplets decrease in size as DXV infection
progresses is also in agreement with our conclusion. Finally, manipulation of the lipid
metabolism process by adjusting perilipin function results in changes in fly survival to
DXV infection. Specifically, loss of the positive regulator, plin1 causes the fly to be resis-
tant to DXV. In contrast, silencing of the negative regulator plin2 results in an increased
susceptibility to DXV.
So far, we have demonstrated (1) the importance of lipid metabolism (specifically
perilipins) in affecting DXV infection in the adult fat body and (2) how Atg1 regulates
lipid metabolism. However, whether Atg1 and perilipin act in the same pathway is not
known. Due to limited resources, epistasis experiments have not been performed. How-
ever, we speculate that Atg1 and PLIN1 acts in parallel pathways. There are two major
signaling pathways controlling lipid metabolism: the pro-lipolytic adipokinetic hormone
(AKH)/AKH-receptor (AKHR) pathway and the adipocyte triglyceride lipase (ATGL)-
dependent pathway (Bi et al., 2012; Gro¨nke et al., 2005, 2007). In the AKHR pathway,
PLIN1 facilitates the hormone sensitive lipase (HSL), which goes on to hydrolyze lipid. In
contrast, PLIN2 negatively regulates the ATGL-dependent pathway by preventing Brum-
mer from hydrolyzing lipids. The fact that loss of Atg1 upregulates bmm expression
suggests that Atg1 might play a role in the Brummer-dependent lipolytic pathway. How-
ever, whether bmm acts in the same pathway as plin1 should be confirmed by an epistasis
experiment. Due to the weakness of adult bmm mutants, we were not able to perform this
experiment in our system.
124
In summary, we have defined a novel role for Atg1 in regulating lipid metabolism
in the adult fat body. We also establish a model of how lipid metabolism affects DXV
infection by identifying the critical players Atg1, PLIN1 and PLIN2. Our study provides
a good example of how autophagy genes could play non-autophagic roles in an immune
response. As more exceptions are identified for the functions of autophagy genes, it is
possible that the autophagy system is much more complex than what we already know.
Furthermore, the link between lipid droplet metabolism and DXV also brings our at-
tention to the basic metabolic processes and demonstrates how non-traditional immune
pathways can play a role in host-pathogen interactions. Finally, we have proven RNA-
seq as a powerful tool in identifying critical genes and pathways in the host upon viral
infection.
125
Chapter A: Appendix A
A.1 Differentially expressed genes between uninfected WT flies and DXV-
infected flies at day 7 post infection
ID Gene Symbol AveExpr log2FC adj.P.Val
1 FBgn0039666 Diedel 2.52E+00 3.57E+00 1.99E-02
2 FBgn0034173 CG9010 1.84E+00 3.44E+00 4.99E-02
3 FBgn0031805 CG9505 7.49E+00 3.38E+00 0.00E+00
4 FBgn0005660 Ets21C 5.05E+00 3.31E+00 2.00E-04
5 FBgn0037783 Npc2c 2.35E+00 3.18E+00 3.56E-02
6 FBgn0013278 Hsp70Bb 3.51E+00 3.09E+00 3.01E-02
7 FBgn0001230 Hsp68 8.74E+00 3.05E+00 2.70E-03
8 FBgn0052368 CG32368 8.25E+00 2.95E+00 6.50E-03
9 FBgn0033830 CG10814 7.08E+00 2.76E+00 6.00E-04
10 FBgn0034480 CG16898 8.96E+00 2.74E+00 0.00E+00
11 FBgn0037083 CG5656 1.03E+00 2.54E+00 5.80E-03
12 FBgn0010041 GstD5 5.72E+00 2.48E+00 0.00E+00
126
13 FBgn0002632 E(spl)m6-BFM 9.23E-01 2.43E+00 4.73E-02
14 FBgn0034741 CG4269 6.65E+00 2.43E+00 2.00E-04
15 FBgn0044812 TotC 8.24E+00 2.36E+00 3.31E-02
16 FBgn0036949 CG7290 1.03E+00 2.28E+00 1.25E-02
17 FBgn0031470 CG18557 3.43E+00 2.26E+00 3.20E-03
18 FBgn0051259 CG31259 5.65E+00 2.16E+00 1.13E-02
19 FBgn0031034 CG14205 6.92E+00 2.13E+00 1.13E-02
20 FBgn0053510 CG33510 3.03E+00 2.10E+00 2.28E-02
21 FBgn0039685 Obp99b 4.79E+00 2.09E+00 3.90E-03
22 FBgn0051041 CG31041 6.04E+00 1.97E+00 3.06E-02
23 FBgn0052107 CG32107 5.17E+00 1.96E+00 6.00E-03
24 FBgn0063388 snoRNA:U27:54Ea 8.72E-01 1.96E+00 1.75E-02
25 FBgn0003961 Uro 8.25E+00 1.91E+00 4.40E-03
26 FBgn0038526 CG14327 1.46E+00 1.87E+00 4.51E-02
27 FBgn0013277 Hsp70Ba 1.28E+00 1.86E+00 4.78E-02
28 FBgn0034289 CG10910 9.29E+00 1.86E+00 9.30E-03
29 FBgn0031490 CG17264 7.35E+00 1.80E+00 3.60E-03
30 FBgn0002732 E(spl)malpha-BFM 2.58E+00 1.79E+00 2.85E-02
31 FBgn0033355 CG13748 1.08E+00 1.77E+00 4.13E-02
32 FBgn0052751 CG32751 2.85E+00 1.77E+00 3.16E-02
33 FBgn0037225 TwdlG 2.71E+00 1.73E+00 4.36E-02
127
34 FBgn0038083 CG5999 7.28E+00 1.73E+00 1.00E-04
35 FBgn0033215 CG1942 6.06E+00 1.68E+00 4.00E-04
36 FBgn0034296 CG10912 1.06E+01 1.67E+00 7.00E-04
37 FBgn0038795 CG4335 6.80E+00 1.62E+00 0.00E+00
38 FBgn0035176 CG13905 8.67E+00 1.61E+00 1.43E-02
39 FBgn0030904 upd2 1.53E+00 1.58E+00 3.56E-02
40 FBgn0033388 CG8046 4.68E+00 1.58E+00 1.00E-03
41 FBgn0034880 ItgalphaPS5 3.46E+00 1.57E+00 3.92E-02
42 FBgn0036723 CG12229 3.73E+00 1.57E+00 1.98E-02
43 FBgn0033926 Arc1 1.17E+01 1.55E+00 0.00E+00
44 FBgn0038973 Pebp1 9.88E+00 1.52E+00 2.84E-02
45 FBgn0014396 tim 1.01E+01 1.51E+00 0.00E+00
46 FBgn0030262 Vago 8.70E+00 1.51E+00 1.00E-02
47 FBgn0030438 CG15721 1.12E+01 1.49E+00 3.00E-04
48 FBgn0030575 CG5321 8.25E+00 1.47E+00 0.00E+00
49 FBgn0033365 CG8170 3.82E+00 1.46E+00 8.10E-03
50 FBgn0037562 CG11671 6.14E+00 1.46E+00 4.92E-02
51 FBgn0039396 CCAP-R 2.71E+00 1.46E+00 2.24E-02
52 FBgn0033153 Gadd45 7.80E+00 1.44E+00 0.00E+00
53 FBgn0053494 CG33494 7.87E+00 1.42E+00 0.00E+00
54 FBgn0003067 Pepck 1.32E+01 1.40E+00 2.00E-04
128
55 FBgn0026403 Ndg 7.39E+00 1.37E+00 0.00E+00
56 FBgn0053542 upd3 3.86E+00 1.36E+00 3.20E-02
57 FBgn0003996 w 8.81E+00 1.34E+00 1.00E-03
58 FBgn0046878 Obp83cd 5.73E+00 1.34E+00 6.60E-03
59 FBgn0015035 Cyp4e3 8.20E+00 1.33E+00 4.10E-03
60 FBgn0033792 CG13325 6.26E+00 1.33E+00 1.77E-02
61 FBgn0034290 CG5773 8.52E+00 1.33E+00 5.00E-04
62 FBgn0039152 Rootletin 6.38E+00 1.33E+00 1.10E-03
63 FBgn0039319 CG13659 6.19E+00 1.32E+00 1.00E-04
64 FBgn0051901 Mur29B 8.34E+00 1.32E+00 2.06E-02
65 FBgn0041184 Socs36E 8.24E+00 1.30E+00 0.00E+00
66 FBgn0031801 CG9498 9.26E+00 1.29E+00 0.00E+00
67 FBgn0040069 vanin-like 6.89E+00 1.28E+00 1.70E-03
68 FBgn0035665 Jon65Aiii 1.29E+01 1.27E+00 1.30E-03
69 FBgn0039593 CG9989 5.43E+00 1.27E+00 6.90E-03
70 FBgn0032004 CG8292 3.52E+00 1.24E+00 3.47E-02
71 FBgn0036627 Grp 5.68E+00 1.24E+00 7.70E-03
72 FBgn0086666 snoRNA:Psi28S-2179 3.67E+00 1.24E+00 4.80E-02
73 FBgn0004919 gol 7.41E+00 1.22E+00 0.00E+00
74 FBgn0032377 CG14937 3.51E+00 1.22E+00 2.90E-02
75 FBgn0037850 CG14695 3.60E+00 1.22E+00 4.31E-02
129
76 FBgn0250815 Jon65Aiv 1.38E+01 1.22E+00 1.80E-03
77 FBgn0015037 Cyp4p1 8.80E+00 1.21E+00 0.00E+00
78 FBgn0035619 CG10592 8.76E+00 1.20E+00 4.83E-02
79 FBgn0259998 CG17571 1.06E+01 1.20E+00 1.94E-02
80 FBgn0038299 Spn88Eb 7.87E+00 1.17E+00 3.07E-02
81 FBgn0038717 CG17751 7.77E+00 1.17E+00 4.80E-03
82 FBgn0031645 CG3036 1.01E+01 1.15E+00 2.00E-04
83 FBgn0034295 CG10911 1.21E+01 1.15E+00 2.70E-03
84 FBgn0039316 CG11893 4.15E+00 1.14E+00 3.55E-02
85 FBgn0015038 Cyp9b1 6.01E+00 1.12E+00 4.40E-03
86 FBgn0032381 Mal-B1 1.02E+01 1.12E+00 1.21E-02
87 FBgn0020762 Atet 9.93E+00 1.10E+00 2.70E-03
88 FBgn0031249 CG11911 9.56E+00 1.10E+00 3.82E-02
89 FBgn0085419 Rgk2 5.46E+00 1.10E+00 4.70E-03
90 FBgn0261113 Xrp1 1.19E+01 1.10E+00 0.00E+00
91 FBgn0046876 Obp83ef 6.15E+00 1.09E+00 5.00E-04
92 FBgn0040060 yip7 1.37E+01 1.08E+00 2.30E-03
93 FBgn0029831 CG5966 1.09E+01 1.07E+00 4.00E-04
94 FBgn0031888 Pvf2 5.28E+00 1.07E+00 2.09E-02
95 FBgn0035623 mthl2 6.60E+00 1.06E+00 4.10E-03
96 FBgn0031520 CG8837 7.00E+00 1.05E+00 4.10E-03
130
97 FBgn0034335 GstE1 9.88E+00 1.05E+00 0.00E+00
98 FBgn0053281 CG33281 5.14E+00 1.05E+00 8.50E-03
99 FBgn0024290 Slob 7.72E+00 1.04E+00 7.50E-03
100 FBgn0037391 CG2017 9.62E+00 1.04E+00 0.00E+00
101 FBgn0035348 CG16758 1.17E+01 1.02E+00 4.80E-03
102 FBgn0035049 Mmp1 8.22E+00 1.01E+00 1.10E-03
103 FBgn0039798 CG11313 6.41E+00 1.01E+00 2.70E-03
104 FBgn0050280 CG30280 4.90E+00 1.01E+00 3.60E-02
105 FBgn0036321 CG14120 9.14E+00 9.92E-01 1.82E-02
106 FBgn0002543 lea 6.02E+00 9.91E-01 1.13E-02
107 FBgn0042627 v(2)k05816 1.10E+01 9.87E-01 5.50E-03
108 FBgn0032669 CG15155 8.22E+00 9.86E-01 1.89E-02
109 FBgn0015010 Ag5r 1.11E+01 9.79E-01 2.00E-04
110 FBgn0016076 vri 9.28E+00 9.76E-01 2.00E-04
111 FBgn0038658 CG14292 1.10E+01 9.76E-01 3.30E-03
112 FBgn0051233 CG31233 9.28E+00 9.71E-01 4.91E-02
113 FBgn0033913 CG8468 9.31E+00 9.66E-01 8.10E-03
114 FBgn0050277 Oatp58Da 6.77E+00 9.66E-01 7.20E-03
115 FBgn0036362 CG10725 6.39E+00 9.65E-01 1.57E-02
116 FBgn0033438 Mmp2 8.53E+00 9.63E-01 5.30E-03
117 FBgn0030261 CG15203 7.26E+00 9.61E-01 5.00E-04
131
118 FBgn0029896 CG3168 1.19E+01 9.59E-01 5.70E-03
119 FBgn0032136 Apoltp 1.08E+01 9.58E-01 4.40E-03
120 FBgn0039114 Lsd-1 1.07E+01 9.58E-01 4.41E-02
121 FBgn0028420 Kr-h1 8.84E+00 9.55E-01 3.50E-03
122 FBgn0010019 Cyp4g1 1.59E+01 9.50E-01 1.11E-02
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677 FBgn0036324 CG12520 6.55E+00 -1.19E+00 3.03E-02
678 FBgn0053481 dpr7 6.85E+00 -1.19E+00 1.94E-02
679 FBgn0029690 CG6414 4.88E+00 -1.21E+00 2.06E-02
680 FBgn0050285 CG30285 7.44E+00 -1.21E+00 3.37E-02
681 FBgn0040575 CG15922 8.29E+00 -1.22E+00 2.47E-02
682 FBgn0016053 pgc 1.35E+01 -1.23E+00 1.89E-02
683 FBgn0037742 Rpt3R 4.78E+00 -1.23E+00 4.91E-02
684 FBgn0038481 CG17475 8.57E+00 -1.23E+00 3.66E-02
158
685 FBgn0041005 pncr013:4 1.05E+01 -1.25E+00 2.70E-02
686 FBgn0020506 Amyrel 5.75E+00 -1.26E+00 1.65E-02
687 FBgn0031957 TwdlE 5.00E+00 -1.26E+00 2.56E-02
688 FBgn0051029 CG31029 3.79E+00 -1.26E+00 3.93E-02
689 FBgn0026563 CG1979 7.27E+00 -1.27E+00 1.61E-02
690 FBgn0085475 CG34446 5.49E+00 -1.27E+00 1.13E-02
691 FBgn0040349 CG3699 1.07E+01 -1.28E+00 5.40E-03
692 FBgn0052448 CG32448 7.02E+00 -1.28E+00 1.25E-02
693 FBgn0039761 CG18404 8.48E+00 -1.30E+00 2.10E-02
694 FBgn0040733 CG15068 1.17E+01 -1.31E+00 6.30E-03
695 FBgn0033936 CG17386 4.86E+00 -1.33E+00 9.00E-04
696 FBgn0013348 TpnC41C 8.47E+00 -1.35E+00 7.40E-03
697 FBgn0040634 CG4186 6.98E+00 -1.36E+00 4.80E-02
698 FBgn0085201 CG34172 9.20E+00 -1.38E+00 4.20E-03
699 FBgn0002565 Lsp2 1.26E+01 -1.39E+00 4.80E-03
700 FBgn0001967 NimC3 1.38E+00 -1.41E+00 4.89E-02
701 FBgn0058298 CG40298 5.16E+00 -1.46E+00 2.06E-02
702 FBgn0046776 CR14033 7.01E+00 -1.47E+00 4.80E-03
703 FBgn0039676 ppk20 4.33E+00 -1.49E+00 4.01E-02
704 FBgn0051636 CG31636 5.66E+00 -1.62E+00 2.50E-03
705 FBgn0061196 SIP3 1.08E+00 -1.63E+00 4.59E-02
159
706 FBgn0085209 CG34180 4.39E+00 -1.66E+00 6.40E-03
707 FBgn0036656 CG13026 2.66E+00 -1.71E+00 4.02E-02
708 FBgn0052831 CG33695 1.63E+00 -1.75E+00 4.15E-02
709 FBgn0051279 CG31279 5.66E+00 -1.81E+00 3.50E-03
710 FBgn0050334 CG30334 6.03E+00 -1.83E+00 2.71E-02
711 FBgn0040705 CG15434 7.73E+00 -1.84E+00 4.00E-04
712 FBgn0039002 CG17625 2.45E+00 -1.92E+00 3.54E-02
713 FBgn0039811 CG15550 9.95E-01 -1.92E+00 3.31E-02
714 FBgn0028853 CG15263 6.69E+00 -2.08E+00 5.40E-03
715 FBgn0054040 CG34040 8.84E+00 -2.14E+00 2.70E-03
716 FBgn0051225 Ir94f 1.46E+00 -2.36E+00 3.68E-02
717 FBgn0031554 CG15418 3.61E+00 -2.37E+00 3.20E-02
718 FBgn0034052 CG8299 5.51E+00 -2.47E+00 1.47E-02
Table A.1: Differentially expressed genes between uninfected WT flies and DXV-infected
flies at day7 post infection. Genes presented in the table are selected by log2 fold Change
> 0.5 and p value < 0.05. Gene list is sorted by log2 fold Change from the largest to the
smallest.
160
A.2 Differentially expressed genes between wildtype flies and IR-Atg1
flies without DXV infection
ID Gene Symbol AveExpr log2FC adj.P.Val
1 FBgn0011832 Ser12 3.18E+00 5.88E+00 1.51E-06
2 FBgn0040725 CG13946 2.43E+00 4.72E+00 1.41E-02
3 FBgn0032549 CG4650 3.23E+00 4.69E+00 2.47E-02
4 FBgn0031276 CG12506 2.14E+00 4.39E+00 2.55E-03
5 FBgn0052198 CG32198 2.49E+00 4.31E+00 3.40E-03
6 FBgn0036068 CG42825 7.59E+00 3.96E+00 1.67E-02
7 FBgn0036068 CG42826 7.59E+00 3.96E+00 1.67E-02
8 FBgn0052071 CG32071 2.20E+00 3.75E+00 1.31E-02
9 FBgn0013278 Hsp70Bb 3.51E+00 3.54E+00 1.66E-02
10 FBgn0010197 Gyc32E 3.41E+00 3.24E+00 1.48E-04
11 FBgn0035941 CG13313 3.58E+00 3.03E+00 8.93E-05
12 FBgn0036468 CG13461 2.43E+00 2.99E+00 2.76E-03
13 FBgn0052302 CG32302 8.24E+00 2.85E+00 7.19E-05
14 FBgn0034296 CG10912 1.06E+01 2.54E+00 8.20E-06
15 FBgn0051809 CG31809 2.30E+00 2.48E+00 3.25E-02
16 FBgn0040503 CG7763 4.99E+00 2.36E+00 1.58E-03
17 FBgn0037724 Fst 8.62E+00 2.31E+00 2.16E-04
161
18 FBgn0036466 CG18581 1.56E+00 2.28E+00 3.79E-02
19 FBgn0015035 Cyp4e3 8.20E+00 2.22E+00 2.01E-05
20 FBgn0034288 CG5084 1.70E+00 2.16E+00 2.76E-02
21 FBgn0036467 CG12310 5.32E+00 2.15E+00 9.33E-03
22 FBgn0003067 Pepck 1.32E+01 2.13E+00 3.23E-06
23 FBgn0040730 CG15127 4.08E+00 2.12E+00 5.63E-04
24 FBgn0013772 Cyp6a8 5.49E+00 2.08E+00 7.19E-05
25 FBgn0036226 CG7252 1.98E+00 2.04E+00 3.49E-02
26 FBgn0039694 fig 2.95E+00 2.03E+00 2.06E-03
27 FBgn0083972 CG34136 7.49E+00 2.00E+00 4.71E-05
28 FBgn0085319 CG34290 4.36E+00 1.97E+00 5.81E-03
29 FBgn0031412 CG16995 4.06E+00 1.95E+00 1.53E-02
30 FBgn0036419 CG13482 7.61E+00 1.94E+00 2.69E-03
31 FBgn0032381 Mal-B1 1.02E+01 1.91E+00 8.93E-05
32 FBgn0035348 CG16758 1.17E+01 1.89E+00 2.01E-05
33 FBgn0034279 CG18635 6.01E+00 1.85E+00 8.93E-05
34 FBgn0259232 Dscam4 6.80E+00 1.84E+00 2.55E-03
35 FBgn0036181 Muc68Ca 2.98E+00 1.83E+00 1.14E-02
36 FBgn0033789 CG13324 8.27E+00 1.82E+00 1.60E-03
37 FBgn0030928 CG15044 6.06E+00 1.81E+00 6.26E-04
38 FBgn0051427 CG31427 3.44E+00 1.81E+00 1.15E-02
162
39 FBgn0035949 CG13314 5.54E+00 1.79E+00 7.06E-05
40 FBgn0003719 tld 6.25E+00 1.67E+00 5.63E-04
41 FBgn0038074 Gnmt 9.48E+00 1.66E+00 9.53E-04
42 FBgn0036836 CG11619 6.57E+00 1.66E+00 2.51E-04
43 FBgn0033659 Damm 8.87E+00 1.63E+00 4.65E-03
44 FBgn0035790 Cyp316a1 2.90E+00 1.62E+00 2.10E-02
45 FBgn0037684 CG8129 9.21E+00 1.60E+00 6.58E-06
46 FBgn0028899 CG31817 4.00E+00 1.60E+00 6.01E-03
47 FBgn0040958 Peritrophin-15b 4.98E+00 1.58E+00 1.36E-02
48 FBgn0034160 CG5550 5.65E+00 1.56E+00 2.88E-02
49 FBgn0033521 CG12896 5.67E+00 1.55E+00 8.48E-04
50 FBgn0033065 Cyp6w1 1.17E+01 1.53E+00 1.34E-03
51 FBgn0013811 Dhc62B 5.44E+00 1.53E+00 2.55E-03
52 FBgn0085285 CG34256 4.83E+00 1.52E+00 4.57E-03
53 FBgn0038172 Adgf-D 9.58E+00 1.51E+00 7.44E-05
54 FBgn0024740 Lip2 6.27E+00 1.49E+00 1.15E-02
55 FBgn0035176 CG13905 8.67E+00 1.48E+00 2.77E-02
56 FBgn0038353 CG5399 1.11E+01 1.48E+00 1.05E-04
57 FBgn0030774 spheroide 9.48E+00 1.46E+00 3.44E-04
58 FBgn0030929 CG15043 1.00E+01 1.45E+00 2.06E-03
59 FBgn0051004 mesh 1.01E+01 1.44E+00 2.63E-03
163
60 FBgn0063499 GstE10 7.02E+00 1.42E+00 1.86E-04
61 FBgn0085261 CG34232 7.33E+00 1.41E+00 1.77E-04
62 FBgn0035663 CG6462 4.29E+00 1.41E+00 4.30E-03
63 FBgn0032049 Bace 1.22E+01 1.40E+00 3.64E-02
64 FBgn0031906 CG5160 4.16E+00 1.39E+00 1.74E-02
65 FBgn0034758 CG13510 8.74E+00 1.39E+00 9.08E-04
66 FBgn0030999 Mur18B 1.39E+01 1.37E+00 7.19E-05
67 FBgn0062961 pncr016:2R 5.33E+00 1.36E+00 3.14E-03
68 FBgn0033190 CG2137 3.99E+00 1.35E+00 2.06E-02
69 FBgn0051106 CG31106 8.69E+00 1.35E+00 7.07E-05
70 FBgn0085419 Rgk2 5.46E+00 1.35E+00 4.86E-04
71 FBgn0024361 Tsp2A 8.36E+00 1.34E+00 8.48E-04
72 FBgn0041194 Prat2 1.16E+01 1.34E+00 7.19E-05
73 FBgn0025454 Cyp6g1 1.18E+01 1.34E+00 3.36E-04
74 FBgn0037727 CG8358 6.84E+00 1.31E+00 2.41E-04
75 FBgn0031690 CG7742 3.38E+00 1.31E+00 2.65E-02
76 FBgn0051445 CG31445 8.24E+00 1.30E+00 3.97E-04
77 FBgn0029091 CS-2 9.14E+00 1.29E+00 2.48E-04
78 FBgn0033926 Arc1 1.17E+01 1.28E+00 1.98E-04
79 FBgn0038658 CG14292 1.10E+01 1.27E+00 2.34E-04
80 FBgn0039809 CG15547 6.84E+00 1.27E+00 3.18E-03
164
81 FBgn0035933 CG13309 8.70E+00 1.26E+00 4.65E-03
82 FBgn0054054 CG34054 6.41E+00 1.25E+00 1.64E-02
83 FBgn0041337 Cyp309a2 8.62E+00 1.24E+00 4.52E-03
84 FBgn0033271 CG8708 6.39E+00 1.23E+00 4.08E-04
85 FBgn0039452 CG14245 1.04E+01 1.23E+00 2.51E-04
86 FBgn0034291 CG5770 6.76E+00 1.23E+00 2.76E-02
87 FBgn0038194 Cyp6d5 1.18E+01 1.23E+00 2.48E-04
88 FBgn0037065 CG12974 8.73E+00 1.22E+00 4.28E-04
89 FBgn0032613 CG13283 6.97E+00 1.22E+00 1.70E-04
90 FBgn0030157 CG1468 9.75E+00 1.21E+00 5.58E-04
91 FBgn0033787 CG13321 9.81E+00 1.21E+00 6.80E-04
92 FBgn0035439 CG14961 4.59E+00 1.21E+00 4.86E-02
93 FBgn0054040 CG34040 8.84E+00 1.19E+00 1.03E-02
94 FBgn0035360 CG1246 8.43E+00 1.19E+00 2.46E-04
95 FBgn0013771 Cyp6a9 7.85E+00 1.18E+00 1.03E-02
96 FBgn0085265 CG34236 5.51E+00 1.18E+00 1.80E-02
97 FBgn0031432 Cyp309a1 9.67E+00 1.18E+00 4.08E-04
98 FBgn0029507 Tsp42Ed 9.24E+00 1.17E+00 1.98E-04
99 FBgn0034140 CG8317 1.04E+01 1.15E+00 7.44E-05
100 FBgn0029826 CG6041 6.94E+00 1.14E+00 8.98E-04
101 FBgn0034294 Muc55B 4.01E+00 1.14E+00 2.03E-02
165
102 FBgn0029831 CG5966 1.09E+01 1.13E+00 1.70E-04
103 FBgn0039629 CG11842 7.34E+00 1.13E+00 3.02E-03
104 FBgn0024293 Spn43Ab 1.16E+01 1.13E+00 4.28E-04
105 FBgn0031580 CG15423 7.88E+00 1.13E+00 1.08E-03
106 FBgn0085227 CG34198 6.14E+00 1.12E+00 2.60E-03
107 FBgn0032908 CG9270 5.44E+00 1.11E+00 2.81E-02
108 FBgn0085205 CG34176 6.92E+00 1.11E+00 1.26E-02
109 FBgn0033788 CG13323 1.10E+01 1.11E+00 1.63E-03
110 FBgn0035583 CG13704 7.49E+00 1.10E+00 1.09E-02
111 FBgn0033820 CG4716 1.30E+01 1.09E+00 4.36E-04
112 FBgn0035094 CG9380 9.62E+00 1.09E+00 3.40E-03
113 FBgn0000406 Cyt-b5-r 1.27E+01 1.08E+00 4.28E-04
114 FBgn0034295 CG10911 1.21E+01 1.07E+00 3.40E-03
115 FBgn0028518 CG18480 7.53E+00 1.07E+00 5.56E-03
116 FBgn0040733 CG15068 1.17E+01 1.06E+00 2.17E-02
117 FBgn0040723 CG5011 9.60E+00 1.06E+00 5.40E-03
118 FBgn0026760 Tehao 5.92E+00 1.05E+00 1.84E-02
119 FBgn0053307 CG33307 8.21E+00 1.05E+00 2.96E-04
120 FBgn0028940 Cyp28a5 9.34E+00 1.05E+00 1.44E-03
121 FBgn0034480 CG16898 8.96E+00 1.04E+00 1.02E-02
122 FBgn0031910 CG15818 7.11E+00 1.04E+00 1.98E-02
166
123 FBgn0037090 Est-Q 8.48E+00 1.03E+00 1.98E-04
124 FBgn0046689 Takl1 6.11E+00 1.03E+00 4.98E-02
125 FBgn0033786 CG44250 1.02E+01 1.03E+00 3.36E-03
126 FBgn0033786 CG44251 1.02E+01 1.03E+00 3.36E-03
127 FBgn0030985 Obp18a 8.34E+00 1.03E+00 6.61E-03
128 FBgn0031695 Cyp4ac3 5.61E+00 1.03E+00 5.95E-03
129 FBgn0051272 CG31272 8.92E+00 1.03E+00 6.39E-04
130 FBgn0035623 mthl2 6.60E+00 1.02E+00 4.53E-03
131 FBgn0032088 CG13102 7.05E+00 1.02E+00 2.69E-03
132 FBgn0050489 Cyp12d1-p 7.19E+00 1.01E+00 6.15E-04
133 FBgn0043806 CG32032 8.15E+00 1.01E+00 2.48E-04
134 FBgn0033815 CG4676 7.16E+00 1.00E+00 2.79E-03
135 FBgn0259714 CG42368 5.92E+00 1.00E+00 1.16E-02
136 FBgn0033124 Tsp42Ec 8.21E+00 9.96E-01 9.24E-03
137 FBgn0031976 CG7367 4.68E+00 9.92E-01 2.89E-02
138 FBgn0040732 CG16926 1.21E+01 9.84E-01 6.35E-04
139 FBgn0028542 NimB4 7.10E+00 9.84E-01 3.44E-04
140 FBgn0040256 Ugt86Dd 7.97E+00 9.83E-01 3.32E-03
141 FBgn0028543 NimB2 1.05E+01 9.78E-01 1.52E-03
142 FBgn0032235 CG5096 9.46E+00 9.74E-01 3.53E-03
143 FBgn0032435 Oatp33Eb 7.76E+00 9.70E-01 3.93E-03
167
144 FBgn0051326 CG31326 9.47E+00 9.68E-01 4.82E-04
145 FBgn0014396 tim 1.01E+01 9.66E-01 1.45E-03
146 FBgn0051704 CG31704 7.05E+00 9.65E-01 2.67E-02
147 FBgn0039241 CG11089 1.22E+01 9.62E-01 4.18E-05
148 FBgn0032436 CG5418 3.79E+00 9.51E-01 3.98E-02
149 FBgn0036945 Ssk 9.77E+00 9.48E-01 3.27E-03
150 FBgn0030747 CG4301 7.90E+00 9.47E-01 2.73E-02
151 FBgn0034512 CG18067 1.23E+01 9.45E-01 3.21E-02
152 FBgn0033047 CG7882 9.37E+00 9.39E-01 2.11E-03
153 FBgn0037683 CG18473 6.95E+00 9.38E-01 4.61E-03
154 FBgn0038466 CG8907 8.43E+00 9.33E-01 1.44E-02
155 FBgn0042105 CG18748 7.07E+00 9.31E-01 4.37E-03
156 FBgn0000053 ade3 1.24E+01 9.31E-01 9.17E-05
157 FBgn0040299 Myo28B1 7.73E+00 9.28E-01 1.23E-02
158 FBgn0004885 tok 9.69E+00 9.23E-01 2.32E-02
159 FBgn0028936 NimB5 7.55E+00 9.14E-01 8.93E-05
160 FBgn0011822 pcl 6.70E+00 9.13E-01 4.19E-02
161 FBgn0037751 topi 4.69E+00 9.11E-01 3.25E-02
162 FBgn0040850 CG15210 8.08E+00 9.10E-01 2.32E-02
163 FBgn0030334 Karl 8.02E+00 9.04E-01 2.16E-04
164 FBgn0037548 CG7900 8.17E+00 9.03E-01 5.69E-03
168
165 FBgn0031579 CG15422 7.89E+00 8.99E-01 7.47E-04
166 FBgn0051414 CG31414 8.23E+00 8.85E-01 1.30E-03
167 FBgn0259896 NimC1 8.14E+00 8.84E-01 5.44E-03
168 FBgn0029766 CG15784 1.06E+01 8.79E-01 3.20E-03
169 FBgn0030160 CG9691 1.10E+01 8.77E-01 2.06E-03
170 FBgn0000473 Cyp6a2 7.55E+00 8.70E-01 7.47E-04
171 FBgn0054043 CG34043 7.43E+00 8.66E-01 2.67E-02
172 FBgn0039023 CG4723 7.05E+00 8.64E-01 2.67E-02
173 FBgn0038346 CG44014 9.25E+00 8.62E-01 1.16E-02
174 FBgn0038346 CG44013 9.25E+00 8.62E-01 1.16E-02
175 FBgn0039307 CR13656 5.36E+00 8.61E-01 1.90E-02
176 FBgn0041182 Tep2 1.08E+01 8.50E-01 2.26E-02
177 FBgn0034664 CG4377 1.02E+01 8.49E-01 2.47E-03
178 FBgn0033764 nemy 8.30E+00 8.49E-01 2.09E-02
179 FBgn0035587 CG4623 5.81E+00 8.43E-01 1.26E-02
180 FBgn0028381 Decay 8.42E+00 8.43E-01 2.83E-03
181 FBgn0044047 Ilp6 7.13E+00 8.39E-01 2.19E-02
182 FBgn0016684 NaPi-T 9.34E+00 8.37E-01 1.70E-04
183 FBgn0034493 CG8908 7.15E+00 8.36E-01 2.67E-02
184 FBgn0034717 CG5819 9.61E+00 8.35E-01 7.81E-03
185 FBgn0038465 Irc 1.13E+01 8.33E-01 4.19E-04
169
186 FBgn0030482 CG1673 1.03E+01 8.26E-01 6.29E-03
187 FBgn0033913 CG8468 9.31E+00 8.17E-01 2.73E-02
188 FBgn0030040 CG15347 9.73E+00 8.12E-01 1.43E-02
189 FBgn0032282 CG7299 6.63E+00 8.10E-01 4.57E-03
190 FBgn0036968 Spn77Ba 8.95E+00 8.09E-01 2.37E-03
191 FBgn0031012 CG8051 7.85E+00 8.09E-01 4.49E-03
192 FBgn0029896 CG3168 1.19E+01 8.08E-01 1.78E-02
193 FBgn0050026 CG30026 7.72E+00 7.97E-01 4.87E-02
194 FBgn0085313 CG34284 7.46E+00 7.93E-01 3.41E-03
195 FBgn0010225 Gel 1.23E+01 7.87E-01 7.93E-04
196 FBgn0040350 CG3690 8.49E+00 7.86E-01 1.31E-02
197 FBgn0031645 CG3036 1.01E+01 7.85E-01 4.65E-03
198 FBgn0039290 CG13654 6.43E+00 7.85E-01 2.59E-02
199 FBgn0053494 CG33494 7.87E+00 7.84E-01 5.23E-03
200 FBgn0015336 CG15865 7.11E+00 7.76E-01 1.23E-02
201 FBgn0002652 squ 1.06E+01 7.70E-01 4.82E-04
202 FBgn0032075 Tsp29Fb 1.08E+01 7.69E-01 2.60E-03
203 FBgn0039564 CG5527 6.53E+00 7.67E-01 2.30E-02
204 FBgn0026721 fat-spondin 1.00E+01 7.61E-01 3.86E-03
205 FBgn0085282 CG34253 6.15E+00 7.60E-01 3.76E-02
206 FBgn0042104 CG18747 6.15E+00 7.57E-01 2.03E-02
170
207 FBgn0034454 CG15120 6.33E+00 7.57E-01 3.76E-02
208 FBgn0038719 CG16727 8.92E+00 7.55E-01 2.22E-02
209 FBgn0029990 CG2233 1.39E+01 7.54E-01 2.74E-02
210 FBgn0053346 CG33346 9.53E+00 7.41E-01 4.30E-02
211 FBgn0032074 Tsp29Fa 9.49E+00 7.41E-01 1.59E-02
212 FBgn0020513 ade5 1.29E+01 7.36E-01 6.39E-04
213 FBgn0027070 CG17322 1.04E+01 7.27E-01 1.30E-03
214 FBgn0034501 CG13868 1.27E+01 7.27E-01 1.97E-03
215 FBgn0001208 Hn 1.15E+01 7.23E-01 1.02E-02
216 FBgn0020416 Idgf1 9.50E+00 7.21E-01 2.27E-02
217 FBgn0046302 CG10650 9.85E+00 7.19E-01 4.92E-02
218 FBgn0086450 su(r) 1.01E+01 7.10E-01 1.11E-03
219 FBgn0028526 CG15293 1.19E+01 6.98E-01 2.05E-02
220 FBgn0032774 CG17549 1.15E+01 6.95E-01 3.70E-02
221 FBgn0035076 Ance-5 8.71E+00 6.91E-01 1.68E-02
222 FBgn0085241 CG34212 1.01E+01 6.88E-01 1.90E-02
223 FBgn0039817 CG15553 6.44E+00 6.85E-01 4.28E-02
224 FBgn0037517 CG10086 7.81E+00 6.82E-01 2.16E-03
225 FBgn0051778 CG31778 8.32E+00 6.81E-01 2.24E-02
226 FBgn0023507 CG3835 1.18E+01 6.81E-01 5.31E-03
227 FBgn0032382 Mal-B2 1.26E+01 6.80E-01 1.19E-02
171
228 FBgn0035431 CG14968 7.83E+00 6.80E-01 3.19E-02
229 FBgn0034356 CG10924 7.49E+00 6.78E-01 4.01E-02
230 FBgn0028540 CG9008 9.30E+00 6.77E-01 7.47E-04
231 FBgn0015040 Cyp9c1 8.65E+00 6.75E-01 4.34E-03
232 FBgn0034005 ItgalphaPS4 4.97E+00 6.74E-01 3.79E-02
233 FBgn0033520 Prx2540-1 6.81E+00 6.69E-01 3.69E-02
234 FBgn0032820 fbp 1.13E+01 6.68E-01 2.27E-03
235 FBgn0045761 CHKov1 6.93E+00 6.67E-01 1.81E-02
236 FBgn0036587 CG4950 7.45E+00 6.56E-01 2.22E-02
237 FBgn0035300 CG1139 7.69E+00 6.56E-01 3.02E-02
238 FBgn0027073 CG4302 9.85E+00 6.55E-01 5.60E-03
239 FBgn0034638 CG10433 1.13E+01 6.51E-01 7.91E-03
240 FBgn0034381 List 6.51E+00 6.51E-01 6.73E-03
241 FBgn0015039 Cyp9b2 1.08E+01 6.49E-01 2.47E-02
242 FBgn0010395 Itgbetanu 7.46E+00 6.49E-01 9.53E-03
243 FBgn0044452 Atg2 1.12E+01 6.48E-01 1.34E-02
244 FBgn0030347 CG15739 8.69E+00 6.47E-01 1.36E-02
245 FBgn0025692 CG3814 8.19E+00 6.46E-01 3.84E-02
246 FBgn0050424 CG30424 6.31E+00 6.46E-01 2.67E-02
247 FBgn0014031 Spat 1.14E+01 6.46E-01 1.08E-02
248 FBgn0034200 CG11395 1.11E+01 6.46E-01 2.76E-02
172
249 FBgn0050269 CG30269 7.06E+00 6.44E-01 3.32E-02
250 FBgn0030775 CG9673 9.47E+00 6.40E-01 1.53E-02
251 FBgn0032935 Atg18b 8.77E+00 6.38E-01 1.48E-02
252 FBgn0031538 CG3246 9.89E+00 6.38E-01 1.08E-02
253 FBgn0034909 CG4797 8.85E+00 6.36E-01 6.01E-03
254 FBgn0038211 CG9649 8.81E+00 6.34E-01 6.01E-03
255 FBgn0052687 CG32687 1.09E+01 6.34E-01 8.73E-03
256 FBgn0031523 CG15408 7.81E+00 6.34E-01 2.86E-02
257 FBgn0025687 LKR 8.66E+00 6.33E-01 2.76E-02
258 FBgn0033093 CG3270 9.51E+00 6.31E-01 1.41E-02
259 FBgn0051769 CG31769 9.66E+00 6.29E-01 1.41E-02
260 FBgn0036368 CG10738 8.32E+00 6.23E-01 2.30E-02
261 FBgn0085360 CG34331 9.11E+00 6.16E-01 1.21E-02
262 FBgn0038088 CG10126 7.85E+00 6.11E-01 1.12E-02
263 FBgn0020385 pug 1.30E+01 6.08E-01 3.79E-02
264 FBgn0034497 CG9090 1.06E+01 6.02E-01 4.31E-02
265 FBgn0002569 Mal-A2 1.03E+01 6.00E-01 3.10E-02
266 FBgn0035091 CG3829 9.67E+00 5.99E-01 3.04E-02
267 FBgn0037714 CG9396 9.06E+00 5.99E-01 1.08E-02
268 FBgn0063497 GstE3 8.86E+00 5.97E-01 1.48E-02
269 FBgn0039927 CG11155 8.74E+00 5.87E-01 6.17E-03
173
270 FBgn0033079 Fmo-2 9.65E+00 5.85E-01 6.89E-03
271 FBgn0003328 scb 1.05E+01 5.76E-01 1.28E-02
272 FBgn0035083 Tina-1 1.09E+01 5.72E-01 4.18E-05
273 FBgn0012034 AcCoAS 1.30E+01 5.70E-01 2.76E-02
274 FBgn0037146 CG7470 1.22E+01 5.68E-01 9.18E-03
275 FBgn0037166 CG11426 8.12E+00 5.68E-01 7.91E-03
276 FBgn0050371 CG30371 8.59E+00 5.66E-01 3.33E-02
277 FBgn0052672 Atg8a 1.23E+01 5.59E-01 3.32E-03
278 FBgn0043841 vir-1 1.25E+01 5.53E-01 2.52E-02
279 FBgn0051663 CG31663 8.16E+00 5.52E-01 1.08E-02
280 FBgn0037007 CG5059 1.12E+01 5.50E-01 4.34E-03
281 FBgn0031381 Npc2a 1.17E+01 5.50E-01 1.30E-03
282 FBgn0037818 CG6465 7.41E+00 5.48E-01 8.10E-03
283 FBgn0037973 CG18547 9.76E+00 5.45E-01 4.37E-03
284 FBgn0038037 Cyp9f2 1.06E+01 5.45E-01 1.91E-03
285 FBgn0051777 CG31777 8.13E+00 5.45E-01 3.82E-02
286 FBgn0040985 CG6115 1.04E+01 5.38E-01 3.97E-04
287 FBgn0023129 aay 1.16E+01 5.34E-01 1.68E-02
288 FBgn0032213 CG5390 1.03E+01 5.27E-01 3.98E-02
289 FBgn0030521 CtsB1 1.30E+01 5.27E-01 5.59E-04
290 FBgn0036831 CG6839 9.41E+00 5.21E-01 1.67E-02
174
291 FBgn0028562 sut2 5.90E+00 5.21E-01 3.96E-02
292 FBgn0027579 mino 1.24E+01 5.20E-01 4.50E-03
293 FBgn0014417 CG13397 1.21E+01 5.19E-01 4.33E-04
294 FBgn0011705 rost 1.09E+01 5.10E-01 1.38E-03
295 FBgn0030594 CG9509 1.03E+01 5.10E-01 2.75E-02
296 FBgn0037872 cu 1.10E+01 5.00E-01 1.62E-02
297 FBgn0029843 Nep1 7.53E+00 -5.39E-01 5.23E-03
298 FBgn0033294 Mal-A4 1.03E+01 -5.54E-01 1.31E-02
299 FBgn0039030 CG6660 8.57E+00 -5.66E-01 3.20E-02
300 FBgn0033627 CG13204 7.51E+00 -5.77E-01 3.65E-02
301 FBgn0259112 CR42254 6.80E+00 -5.83E-01 4.60E-02
302 FBgn0037923 CG6813 6.51E+00 -5.83E-01 4.08E-02
303 FBgn0027513 ana2 9.88E+00 -5.90E-01 3.33E-03
304 FBgn0000615 exu 1.34E+01 -5.90E-01 1.59E-03
305 FBgn0035985 Cpr67B 8.04E+00 -6.16E-01 1.44E-02
306 FBgn0026263 bip1 1.00E+01 -6.39E-01 2.45E-04
307 FBgn0052554 CG32554 6.37E+00 -6.46E-01 3.70E-02
308 FBgn0035490 CG1136 6.42E+00 -6.50E-01 1.95E-02
309 FBgn0034262 swi2 5.84E+00 -6.64E-01 2.04E-02
310 FBgn0034270 CG6401 8.41E+00 -6.65E-01 6.19E-04
311 FBgn0035791 CG8539 9.12E+00 -6.65E-01 8.70E-03
175
312 FBgn0015558 tty 7.46E+00 -6.74E-01 1.75E-02
313 FBgn0002571 Mal-A3 1.00E+01 -7.20E-01 4.19E-02
314 FBgn0083945 CG34109 5.89E+00 -7.42E-01 1.66E-02
315 FBgn0023541 Cyp4d14 6.33E+00 -7.53E-01 1.60E-02
316 FBgn0034312 CG10916 7.60E+00 -7.65E-01 3.40E-03
317 FBgn0032896 CG14400 6.33E+00 -7.69E-01 3.89E-02
318 FBgn0031791 AANATL2 5.50E+00 -7.71E-01 4.99E-02
319 FBgn0040342 CG3706 7.69E+00 -8.14E-01 3.98E-02
320 FBgn0085426 Rgk3 5.85E+00 -8.39E-01 4.01E-02
321 FBgn0033836 CG18278 6.76E+00 -8.44E-01 2.45E-02
322 FBgn0039342 CG5107 1.15E+01 -8.46E-01 2.65E-02
323 FBgn0003390 shf 7.40E+00 -8.73E-01 3.48E-04
324 FBgn0039798 CG11313 6.41E+00 -8.81E-01 2.68E-02
325 FBgn0033817 GstE14 5.83E+00 -9.34E-01 2.32E-02
326 FBgn0004360 Wnt2 4.85E+00 -9.48E-01 2.14E-02
327 FBgn0028534 CG7916 1.14E+01 -9.68E-01 2.76E-02
328 FBgn0031533 CG2772 7.86E+00 -9.71E-01 6.76E-03
329 FBgn0034553 CG9993 6.01E+00 -1.00E+00 2.65E-02
330 FBgn0040823 dpr6 6.99E+00 -1.05E+00 1.63E-03
331 FBgn0011722 Tig 8.49E+00 -1.05E+00 3.93E-03
332 FBgn0053109 CG33109 9.47E+00 -1.05E+00 3.14E-02
176
333 FBgn0039471 CG6295 1.12E+01 -1.06E+00 2.71E-02
334 FBgn0010549 l(2)03659 9.24E+00 -1.06E+00 4.18E-05
335 FBgn0037782 Npc2d 7.48E+00 -1.10E+00 2.77E-02
336 FBgn0042186 CG17239 8.14E+00 -1.10E+00 2.27E-02
337 FBgn0031451 CG9961 5.28E+00 -1.12E+00 4.27E-03
338 FBgn0053301 CG33301 8.20E+00 -1.12E+00 3.49E-04
339 FBgn0039474 CG6283 1.00E+01 -1.15E+00 3.32E-03
340 FBgn0016054 phr6-4 9.01E+00 -1.16E+00 1.53E-02
341 FBgn0050054 CG30054 7.57E+00 -1.18E+00 1.34E-03
342 FBgn0001089 Gal 8.85E+00 -1.19E+00 1.11E-03
343 FBgn0032085 CG9555 7.02E+00 -1.19E+00 6.34E-03
344 FBgn0036024 CG18180 1.24E+01 -1.25E+00 1.14E-02
345 FBgn0040262 Ugt36Ba 5.70E+00 -1.26E+00 4.02E-03
346 FBgn0000055 Adh 6.25E+00 -1.28E+00 3.18E-03
347 FBgn0031930 CG7025 6.34E+00 -1.29E+00 1.65E-02
348 FBgn0053965 CG33965 6.03E+00 -1.30E+00 1.86E-03
349 FBgn0025583 IM2 1.08E+01 -1.31E+00 4.38E-03
350 FBgn0033603 Cpr47Ef 5.21E+00 -1.36E+00 2.10E-02
351 FBgn0034197 Cda9 6.09E+00 -1.41E+00 1.39E-03
352 FBgn0039476 CG6271 5.51E+00 -1.41E+00 3.46E-03
353 FBgn0032891 Oseg5 6.65E+00 -1.46E+00 4.61E-03
177
354 FBgn0039475 CG6277 9.35E+00 -1.46E+00 5.51E-04
355 FBgn0031944 CG46025 3.70E+00 -1.47E+00 3.65E-02
356 FBgn0033090 CG15909 2.99E+00 -1.49E+00 1.84E-02
357 FBgn0001224 Hsp23 9.90E+00 -1.49E+00 8.30E-03
358 FBgn0039152 Rootletin 6.38E+00 -1.49E+00 4.24E-03
359 FBgn0033096 Zip42C.1 3.93E+00 -1.49E+00 7.81E-03
360 FBgn0034318 CG14500 8.01E+00 -1.50E+00 1.96E-02
361 FBgn0038790 MtnC 9.04E+00 -1.51E+00 2.55E-03
362 FBgn0082987 snoRNA:Psi28S-2444 3.07E+00 -1.51E+00 2.57E-02
363 FBgn0052483 CG32483 6.51E+00 -1.64E+00 7.81E-04
364 FBgn0002869 MtnB 8.53E+00 -1.68E+00 5.04E-03
365 FBgn0003313 sala 2.01E+00 -1.69E+00 2.77E-02
366 FBgn0032066 CG9463 9.95E+00 -1.77E+00 2.47E-02
367 FBgn0033216 CG1946 4.83E+00 -1.78E+00 1.32E-02
368 FBgn0035666 Jon65Aii 7.40E+00 -1.85E+00 1.60E-02
369 FBgn0032507 CG9377 5.43E+00 -1.86E+00 1.44E-03
370 FBgn0037183 CG14451 2.07E+00 -1.92E+00 1.66E-02
371 FBgn0004428 LysE 4.95E+00 -1.92E+00 2.88E-02
372 FBgn0002939 ninaD 5.57E+00 -1.95E+00 1.73E-03
373 FBgn0004429 LysP 7.35E+00 -2.02E+00 7.06E-05
374 FBgn0052984 CG32984 5.05E+00 -2.12E+00 3.97E-04
178
375 FBgn0036023 CG18179 7.74E+00 -2.13E+00 6.52E-03
376 FBgn0082940 snoRNA:Me28S-C3227a 9.58E-01 -2.14E+00 1.95E-02
377 FBgn0039684 Obp99d 2.87E+00 -2.14E+00 3.92E-02
378 FBgn0032067 CG9465 6.08E+00 -2.18E+00 4.49E-02
379 FBgn0085358 Diedel3 9.33E+00 -2.25E+00 3.97E-04
380 FBgn0040104 lectin-24A 4.01E+00 -2.67E+00 1.03E-02
381 FBgn0051089 CG31089 3.88E+00 -2.94E+00 2.50E-03
382 FBgn0050091 CG30091 2.07E+00 -3.16E+00 9.53E-03
383 FBgn0035886 Jon66Ci 5.61E+00 -3.18E+00 2.63E-03
384 FBgn0020506 Amyrel 5.75E+00 -3.61E+00 7.19E-05
Table A.2: Differentially expressed genes between wildtype flies and IR-Atg1 flies with-
out DXV infection. Genes presented in the table are selected by log2 fold Change >
0.5 and p value < 0.05. Gene list is sorted by log2 fold Change from the largest to the
smallest.
179
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