GENETIC DISSECTION OF THE TRANSCRIPTIONAL HYPOXIA RESPONSE AND
GENOMIC REGIONAL CAPTURE FOR MASSIVELY PARALLEL SEQUENCING
by
DOUGLAS WILLIAM TURNBULL
A DISSERTATION
Presented to the Department of Biology
and the Graduate School ofthe University of Oregon
in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
September 2008
11
University of Oregon Graduate School
Confirmation of Approval and Acceptance of Dissertation prepared by:
Douglas Turnbull
Title:
"Genetic Dissection of the Transcriptional Hypoxia Response and Genomic Regional Capture for
Massively Parallel Sequencing"
This dissertation has been accepted and approved in partial fulfillment of the requirements for
the Doctor of Philosophy degree in the Department of Biology by:
Karen Guillemin, Chairperson, Biology
Eric Johnson, Advisor, Biology
Bruce Bowerman, Member, Biology
Christopher Doe, Member, Biology
Kenneth Prehoda, Outside Member, Chemistry
and Richard Linton, Vice President for Research and Graduate Studies/Dean of the Graduate
School for the University of Oregon.
September 6, 2008
Original approval signatures are on file with the Graduate School and the University of Oregon
Libraries.
© 2008 Douglas William Turnbull
iii
iv
An Abstract of the Dissertation of
Douglas William Turnbull
in the Department of Biology
for the degree of
to be taken
Doctor of Philosophy
September 2008
Title: GENETIC DISSECTION OF THE TRANSCRIPTIONAL HYPOXIA RESPONSE
AND GENOMIC REGIONAL CAPTURE FOR MASSIVELY PARALLEL
SEQUENCING
Approved: _
Dr. Eric Johnson, Advisor
When cells are faced with the stress of oxygen deprivation (hypoxia), they must
alter their physiology in order to survive. One adaptation cells make during hypoxia entails
the transcriptional activation of specific groups ofgenes as well as the concurrent
repression ofother groups. This modulation is achieved through the actions of
transcription factors, proteins that are directly involved in this transcriptional activation and
repression. I studied the transcriptional response to hypoxia in the model organism
Drosophila melanogaster utilizing DNA microarrays to examine the transcriptomes of five
different mutant Drosophila strains deficient in the hypoxia-responsive transcription factors
HIF-l, FOXO, NF1d3, p53, andMTF-l. By comparing hypoxia responsive gene
expression in these mutants to that of wild type flies and subsequently identifYing binding
sites for each transcription factor near putative target genes, I was able to identifY the
v
transcripts regulated by each transcription factor during hypoxia. I discovered thatFOXO
plays an unexpectedly large role in hypoxic gene regulation, regulating a greater number of
genes than any other transcription factor. I also identified multiple interesting targets of
other transcription factors and uncovered a potential regulatory link: between HlF-l and
FOXO. This study is the most in-depth examination of the transcriptional hypoxia
response to date.
I was also involved in additional research on transcriptional stress responses in
Drosophila. Also included in this dissertation are two papers on which I was the second
author. One paper identified a regulatory link: between the transcriptional responses to
hypoxia and heat-shock. The other examined elevatedC~ stress (hypercapnia) in
Drosophila, showing that this stress causes the down-regulation ofNFKB-dependent
antimicrobial peptide gene expression.
My studies of stress responses would not have been possible without well-described
mutant fly strains. Another part of my dissertation research involved the creation of a
method for characterizing new mutants for future studies. When researchers seek to
identify the molecular nature of a mutation that causes an interesting phenotype, they must
ultimately determine the specific responsible genomic sequence change. While classical
genetic methods and other techniques can easily be used to roughly map the location ofa
mutation in a genome, regions identified by these means are usually so large that
sequencing them to precisely identify the polymorphism is laborious and slow. I have
developed a technique that makes sequencing genomic regions ofthis size much easier.
My technique involves capturing genomic regions by hybridization of fragmented genomic
target DNA to biotinylated probes generated from fosmid DNA, which are subsequently
vi
immobilized and washed on streptavidin beads. Genomic DNA fragments are then
eluted by denaturation and sequenced using the latest generation ofmassively parallel
sequencing technology. I have demonstrated the effectiveness of this approach by
sequencing a mutation-containing 336-kilobase genomic region from a Caenorhabditis
elegans strain. My entire protocol can be completed in two days, is relatively inexpensive,
and is broadly applicable to any situation in which one wants to sequence a specific
genomic region using massively parallel sequencing.
This dissertation includes both my previously published and my coauthored
materials.
CURRICULUM VITAE
NAME OF AUTHOR: Douglas William Turnbull
PLACE OF BIRTH: LaGrande, Oregon
DATE OF BIRTH: March 18th, 1979
GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED:
University of Oregon, Eugene, Oregon
University ofPuget Sound, Tacoma, Washington
DEGREES AWARDED:
Doctor of Philosophy in Biology, 2008, University of Oregon
Bachelor of Science in Biology, 2001, University of Puget Sound
AREAS OF SPECIAL INTEREST:
Gene Regulation
Genomics
PROFESSIONAL EXPERIENCE:
Graduate Research Fellow, Department of Biology, University of Oregon,
Eugene, Oregon, 2003-2007
Graduate Teaching Fellow, Department of Biology, University of Oregon,
Eugene, Oregon, 2002-2003
vii
GRANTS, AWARDS AND HONORS:
Keck Foundation Training Grant, University of
Oregon, 2007-2008
NIH Genetics Training Grant, University of
Oregon,200S-2007
NIH Molecular Biology Training Grant, University of
Oregon, 2003-2004
Murdock Summer Research Fellowship, University of Puget Sound, 2000
PUBLICATIONS:
Baird, N.A., D.W. Turnbull, and E.A. Johnson. 2006. Induction of heat shock
pathway during hypoxia requires regulation of Heat shock factor by Hypoxia-
inducible factor-I. J Biol Chem 281: 38675-38681.
viii
IX
ACKNOWLEDGMENTS
I thank my advisor, Dr. Eric Johnson, for allowing me the freedom to design my
own projects and experiments and for his seemingly endless supply of new scientific
ideas. I also thank my family for fostering my curiosity and lifelong love of learning.
Finally, I thank my wife Lisa for her love and support and for helping me to live each day
to its fullest.
xI dedicate this document to my father Valiant Richard Turnbull for the perfectionism and
attention to detail I inherited from him. For this reason lowe my success in science and
in life to him. Rest in peace Dad.
xi
TABLE OF CONTENTS
Chapter
I. INTRODUCTION TO HYPOXIC GENE REGULATION AND NEXT
GENERATION SEQUENCING .
II. GENETIC DISSECTION OF THE TRANSCRIPTIONAL HYPOXIA
RESPONSE IN DROSOPHILA MELANOGASTER .
Page
1
11
Materials and Methods .
Results .
13
15
Discussion 23
III. HEAT SHOCK PATHWAY IS ACTIVATED BY THE DIRECT REGULATION 29
OF HEAT SHOCK FACTOR BY HIF-l DURING HYPOXIA ..
Results 31
Discussion 41
Methods 44
IV. ELEVATED C02 SUPPRESSES SPECIFIC DROSOPHILA INNATE IMMUNE 49
RESPONSES DOWNSTREAM OF NF-kB PROTEOLYTIC ACTIVATION
Results and Discussion 50
Concluding Remarks..... 63
Materials and Methods...... 63
V. GENOMIC REGIONAL CAPTURE FOR NEXT GENERATION SEQUENCING 69
Materials and Methods.......................................................................................... 71
Results 74
Discussion 81
VI. CONCLUSION........................................................................................................... 84
APPENDIX: ONLIJ'JE SUPPLEMENTAL MATERIAL FOR CHAPTER IV............. 87
xii
Chapter Page
REFERENCES.................................................................................................................. 90
--_ .._._-_._----------------
xlii
LIST OF FIGURES
R~ ~~
Chapter II
1. Example data plot from BAMarrayTM Bayseian ANOVA data analysis
comparing the expression of 435 hypoxia-regulated transcripts in sima-
and WT flies............................ 17
2. Hierarchical clustering of hypoxia modulated transcripts with significantly
perturbed expression levels in at least one mutant strain 18
3. Examples of computationally identified conserved binding sites for
putative hypoxia-responsive transcription factor targets 23
4. Schematic representation of a potential regulatory mechanism by which
Sima likely contributes to the activation of dFOXO during hypoxia
through transcriptional upregulation of Imp-L2 26
Chapter III
1. Hsftranscript levels are increased in a HIF-la-dependent manner.................. 33
2. Hsf gene region has conserved HREs and is a direct target of HIF-l............... 35
3. Hsp transcript levels are increased in an Hsf and HIF-la-dependent
manner during hypoxia 37
4. Up-regulation of Hsps is Hsf dosage-dependent 38
5. Up-regulation of Hsps after reoxygenation is HIF-la-dependent and
critical to survival 40
Chapter IV
1. Hypercapnia affects Drosophila development and physiology
independently of neuronal CO2 sensing 52
2. Hypercapnia down-regulates specific antimicrobial peptides in Drosophila... 54
3. Hypercapnia increases mortality of bacterial infection in Drosophila 56
4. Hypercapnia suppresses antimicrobial peptide induction with rapid onset
and recovery................................................................................ 59
5. Hypercapnia suppresses innate immune responses via a novel pathway......... 60
Chapter V
1. Schematic representation of regional capture protocol..................................... 76
2. Verification of captured library prior to NGS................................................... 79
3. Graphical representation of sequences from regionally captured samples
aligned to a portion of the C. elegans genome............................................ 81
XIV
Figure Page
Appendix
S1. Absolute levels of antimicrobial peptide RNAs in S2* cells, normalized to
RP49............................................................................................................ 87
xv
LIST OF TABLES
Table Page
Chapter II
1. Hypoxia modulated transcripts with significantly altered hypoxia
expression levels in mutant strains , 19
Chapter V
1. Fosmid clones used for preparation of biotinylated probes 77
Appendix
S1. Hypercapnia strongly down-regulates egg formation genes, but does not
up-regulate genes induced by hypoxic, heat-shock, or oxidative stress
responses , 88
S2. Hypercapnia does not affect cell viability........................................................ 89
CHAPTER I
INTRonUCTION TO HYPOXIC GENE REGULATION AND NEXT
GENERATION SEQUENCING
Background of hypoxic gene regulation
Molecular oxygen plays a multitude of essential roles in eukaryotic cellular
physiology, perhaps most notably as the terminal electron acceptor in the electron
transport chain of cellular respiration. Because of this critical role in cellular energy
harvest, eukaryotic organisms have evolved several mechanisms to deal with situations
where the supply of oxygen becomes scarce (hypoxia). One of the central hypoxic
adaptations made by eukaryotic organisms .is the differential regulation of specific sets of
transcripts. By varying the amounts of transcripts for various genes, cells are able to alter
quantities of assorted proteins, and by doing so, change their physiology in ways that
allow them to better cope with hypoxia. This hypoxia-induced transcriptional modulation
accomplished through ihc action of hypoxia-responsi ve transcription factors, proteins that
are directly involved in regulating the quantities of transcripts that are produced from
specific sets of genes.
----------
2
The transcription factor with the best-characterized role in hypoxic gene
regulation is Hypoxia Inducible Factor-l (HIF-l) (1). HIF-l is a heterodimeric
transcription factor composed of two subunits known as HIF-1 a and HIF-1~. HIF-1 ~ is
a protein that is constitutively present in many cells, and dimerizes with several other
transcription factors other than HIF-la in order to regulate transcription of genes
involved processes as diverse as cellular responses to environmental toxins, to neural
development (2). HIF-1 ~ is absolutely essential for the function of HIF-1 as a hypoxia-
responsive transcription factor, but plays little role in the regulation of its activation.
HIF-1 a, on the other hand, is a protein that is tightly regulated by cellular oxygen
levels, and this regulation is the key step in the regulation of HIF-1 activity. When
oxygen is present in a cell at normal levels (nonnoxia), HIF-la is hydroxylated at a
proline residue by an enzyme known as HIF-l Proline Hydroxylase (HPH). This
hydroxylation reaction is dependent on the presence of molecular oxygen, and serves as
the direct link between oxygen abundance and HIF-Ia activity (3). When HIF-la is
proline hydroxylated, it is recognized by Von Hippel Lindau tumor suppressor protein
(VHL), which facilitates the ubiquitination and rapid proteasomal degradation of the HIF-
lao This oxygen-dependent pathway prevents HIF-la protein from ever being present at
normal oxygen concentrations long enough to dimerize with HIF-1 ~ and take part in
transcriptional regulation. During hypoxia, however, HIF-1 a is not degraded, dimerizes
with HIF-1~, and the dimeric HIF-1 transcription factor activates the expression of its
target genes.
3
When it is acti ve in hypoxia, HIF-1 binds DNA sequence motifs with the core 5'-
TRCGTG-3'. These elements are present, often in clusters, near genes that are regulated
by HIF-1 under hypoxia. A number of genes have been identified as HIF-1 targets in
mammalian and other model systems. HIF-1 is responsible for the transcriptional up-
regulation of genes such as erythropoietin (EPO), and Vascular endothelial growth factor
(VEGF) which increase oxygen delivery to hypoxic areas by stimulating red blood cell
production, and the recruitment of blood vessels respectively in mammals(4). In the fruit
fly Drosophila rnelanogaster, RIF-1 has been shown to regulate HPH, providing a
feedback mechanism by which HIF-l activity is attenuated during hypoxia (5). Nathan
Baird and J showed that in Drosophila, HIF-1 directly regulates the transcription of Heat
shockfactor (HSF), a transcription factor that activates the transcription of heat shock
protein genes (6). The full complement ofHIF-l regulated genes in Drosophila has not
been exhaustively cataloged yet, however.
Another transcription factor that has been shown to regulate gene expression
during hypoxia is the Forkhead transcription factor FOXO. FOXO activity is inhibited
by Insulin signaling, causing FOXO to activate gene expression during starvation
conditions. Mechanistically, FOXO is phosphorylated by the kinase AKT in response to
Insulin or other gro"'ih factors. This phosphor)' lation causes the export of FOXO from
the nucleus, thereby inhibiting FOXO-mediated transcriptional activation (7).
Interestingly, elevated FOXO activity is linked to increased longevity in Drosophila and
Caenorhabditis elegans, suggesting that the genes regulated by this transcription factor
have significant protective functions for cells (7). Indeed, several genes involved in
4
reactive oxygen species detoxification have been described as FOXO targets, in addition
to a number of genes involved in protecting cell from other stresses (8).
Recently, FOXO has been shown to activate gene expression in response to
hypoxia in human cells (9). This study found that FOX03a, one of the four FOXO
transcription factors in humans, is transcriptionally activated by HIF-I during hypoxia.
This transcriptional up regulation contributes to the activation of FOXO-dependent gene
expression during hypoxia. Interestingly, FOX03a was found to activate transcription of
Cited2, gene that is known to down-regulate HIF-I activity, demonstrating that HIF-I
and FOX03a regulate each other in a negative feedback loop.
Nuclear factor kappa-B (NFKB) is another transcription factor that has been
shown to activate gene expression in response to hypoxia. NFKB has been most
thoroughly characterized as a central transcriptional regulator of the inflammatory
response in mammals (10). Under normal conditions, NFKB is bound by the repressor
protein IKB, which masks the nuclear localization sequence (NLS), prevent nuclear
accumulation of the transcription factor. NFKB can be activated by a number of stimuli,
including bacterial antigens, proinflammatory cytokines, oxidative stress, and ultraviolet
radiation. Upon activation, IKB is phosphorylated, ubiquitinated, and degraded, exposing
the NLS ofNFKB, which translocates in to the nucleus and activates transcription.
Numerous NFKB target genes have been identified in multiple organisms, including those
encoding inflanlmatory cytokines, chemokines, and cell adhesion molecules. In.
Drosophila, there are three NFKB proteins, Dorsal (Dl), Relish (ReI), and Dorsal-related
immunity factor (Dit) (11). Dl has been extensively studied for its role in development.
5
ReI and Dif play key roles in the innate immune system of the fly, where they transcribe
genes encoding anti-microbial peptides in response to bacterial and fungual infection.
Although numerous studies have shown that NFKB is activated by hypoxia, the
mechanism by which this occurs is unclear (12) (13). There is evidence that hypoxia can
induce the production of Nitric Oxide (NO) as well as other reactive oxygen species
(ROS), and activation ofNFKB by ROS has been well documented (14) (15). These
observations seem to support the hypothesis that NFKB activation by hypoxia takes place
because of a mechanism involving some sort of hypoxia-related change in the redox state
of the cellular environment. Although we are not sure of the mechanism responsible, our
lab has observed multiple targets of ReI, as well as ReI itself being transcriptionally up
regulated by hypoxia in Drosophila (16).
Another stress-responsive transcription factor has been implicated in the hypoxia
response is p53. This transcription factor is mainly known as a transcriptional activator
of pro-apoptotic genes that is activated by DNA damage (17). A number of other stimuli
can activate p53, including ultraviolet radiation, X-rays, low pH, and heat shock. In the
absence of activating stimuli, p53 is ubiquitinated and targeted for degradation by the
ubiquitin ligase MDM2. Hypoxia has been shown to activate p53 through direct binding
and inhibition ofMDM2 by HIF-lu (18). Other researchers have reported that ROS play
a role in the hypoxic activation of p53 (19). The role of p53 in Drosophila hypoxic gene
regulation has not been previously examined. Because Drosophila possesses a p53
homologue, and ROS are known to be produced during hypoxia in the fly, it seems likely
that p53 plays a role in hypoxic gene regulation in Drosophila.
6
Another hypoxia-responsive transcription factor that is likely to playa role in the
hypoxia response of Drosophila is the Metal-responsive transcription factor-1 (MTF-1).
MTF-1 is an evolutionarily-conserved transcription factor that possesses DNA binding
zinc fingers which change conformation in response to fluctuations in cellular zinc
concentration (20). MTF-1 activates the transcription of Metallothionein genes in
response to increases in cytoplasmic metal ion levels. Metallothioneins are small,
cysteine-rich proteins that bind zinc under physiological conditions. When other metal
ions such as copper or cadmium enter the cytoplasm, Metallothioneins release their
bound zinc and bind these more toxic ions with higher affinities. This mechanism allows
MTF-1 to respond to a multitude of metals despite the fact that only zinc directly binds
the transcription factor (21). Although the vast majority of work on MTF-1 has been
directed toward understanding its role in cellular metal homeostasis, it has become clear
that MTF-1 directs transcriptional changes in response to hypoxia as well. In human
cells, the hypoxic transcriptional induction of Placenta growth factor (PIGF) was shown
to be dependent on MTF-1 (22). Metallothionein genes have also been shown to be
transcribed in response to hypoxia in an MTF-1-dependent manner (23). In Drosophila,
our lab has observed transcriptional activation of Metallothionein genes during hypoxia,
which is highly likely to require MTF-l (16).
Drosophila has been well established as an extremely powerful model system for
uncovering fundamental new facets of genetics and molecular biology. Numerous
cellular pathways that have key roles in human disease are also present in Drosophila,
and in-depth molecular biological characterization of these pathways is much easier and
more rapid in the fly due to its short generation time and widely available genetic tools.
7
Each of the five hypoxia-responsive transcription factors that I described in the preceding
pages is present in Drosophila. In this dissertation, I describe my work on understanding
the transcriptional response to hypoxia using the Drosophila model system. I examined
hypoxia-responsive gene expression in five different tly lines, deficient in HIF-l, FOXO,
NFKB, p53, and MTF-l. By combining this analysis with bioinformatics, I was able to
determine, to a large extent, the role of each transcription factor in the broader
transcriptional hypoxia response. My work represents the first dissection of the
transcriptional hypoxia response to this level of detail. This would not have been
possible without use of the Drosophila model system.
Background of genomic regional enrichment for sequencing
It could be said that the blueprint for an organism lies in the sequence of its
genome. Because the genomic sequence of any living thing contains so much useful
information about the biology of that organism, a great deal of effort has been devoted to
genome sequencing projects. The genome sequences produced by these projects make
molecular biological studies enormously easier than they would be in the absence of
reference genome sequences for the organisms being studied.
The bulk of existing genomic sequence data has been produced using variations of
the chain-terminator method ofDNA sequencing developed by Frederick Sanger and
colleagues in the 1970's (24). In this method, DNA polymerase synthesizes a new strand
of DNA from a specific primer that has been annealed to the DNA template that is to be
sequenced. This synthesis takes place in the presence of a small concentration of
dideoxynucleotides, which, when incorporated in to the newly synthesized strand,
8
prevent DNA polymerase from adding additional nucleotides and thus terminate the
synthesis of the new chain ofnucleotides. This results in the synthesis of new DNA
strands from the sequencing primers that are a multitude of lengths. In modem Sanger
sequencing, the dideoxy terminator nucleotides also incorporate specific fluorophore
moieties for each of the four dideoxynucleotides in the reaction. When the DNA
synthesis is complete, the mixture of product strands is separated by size using
electrophoresis to a resolution that allows the researcher to discern size differences of one
nucleotide. By observing the size of each fragment and taking note of the fluorescent
color ofthe chain-terminator nucleotide that was incorporated, the researcher can deduce
the sequence of the newly synthesized DNA strand beginning at the 3' end of the
sequencing primer.
The reads produced by Sanger sequencing are typically less than one kilobase
(kb) in length. This read length is 3,000,000 times smaller than the length of the human
genome, for example, making the task of sequencing entire genomes using the Sanger
approach laborious and costly. The desire to sequence new genomes more quickly as
well as a need to determine sequence polymorphisms at the root of genetic diseases has
led to the development of several types next generation DNA sequencing (NGS)
technology. These NGS approaches all are based on the concept of sequencing massive
numbers of random DNA fragments in paralleL Each sequencing read from a NGS run is
much shorter than those produced in a Sanger sequencing run; the advantage that NGS
provides lies in the number ofreads produced by these approaches. For example, the
individual sequencing reads produced by Illurnina Genome Analyzer II NGS instrument
9
are 50 nucleotides or less in length, hbwever, this instrument produces roughly
96,000,000 of these reads which results in 4.8 gigabases ofraw sequence information.
Due to sequencing errors and the necessity of significant overlap betweeQ.
individual reads for assembly into a long continuous sequence, NGS instruments can still
not sequence entire genomes of higher eukaryotes in single runs. In many cases,
researchers are only interested in sequencing portions of a genome, such as when
identifying disease related single nucleotide polymorphisms (SNPs) in clinical samples,
or when attempting to identify the SNP responsible for an interesting phenotype in a
mutant identified in a genetic screen. NGS instruments, however, do not sequence
targeted genomic regions; random DNA fragments serve as the sequencing template
resulting in reads that are scattered throughout the genome. Sanger sequencing allows
researchers to select the region they would like to sequence based on the location of the
sequencing primer, but sequencing large regions requires the design of numerous primers
that each must be run in a separate sequencing reaction making the targeted sequencing
oflarge n'gions with Sanger technology both slow and expensive. For these reasons,
several groups are pur,;uing methods for selecting large genomic regions for sequencing
byNGS.
Two independent groups published methods for targeted sequencing of large
genomic regions by NGS last year (25,26). Both ofthese papers describe essentially the
same protocol in'Nhich custom DNA microarrays are designed and constructed to contain
oligonucleotide sequences that are homologous to a large genomic region of interest.
Genomic DNA is randomly sheared and hybridized to the custom microarray, and the
sequences that are not homologous to those represented on the array are washed away.
10
The genomic fragments that hybridized to the array are then eluted by denaturation,
amplified, and sequenced by NGS. This approach has proven to be an effective method
of isolating and sequencing specific large genomic regions.
I have developed an alternative method to the custom microarray based approach
for capturing large genomic regions for sequencing by NGS. My method uses fosmid or
bacterial artificial chromosome (BAC) DNA that is homologous to the genomic region of
interest in place of the custom microarrays that are used in the previously published
protocols. My method is significantly faster, and can be completed for a small fraction of
the cost of microarray-based methods. Furthermore, no complete reference genome
sequence for the target region is required by my technique. The low cost and simplicity
of my technique will make current NGS technology an effective means of sequencing
large genomic regions for more than only the most well funded research groups in the
world.
P.D. Etter contributed to the research described in Chapter II as a second author.
N.A Baird was the first author of Chapter III. LT. Helenius and T. Krupinski were co-
first authors and Y. Gruenbaum was the third author of Chapter IV.
Bridge to Chapter II
In the previous chapter I described what is known about the hypoxia-responsive
transcriptional regulatory activities ofHIF-I, FOXO, NFKB, p53, and MTF-l. In
Chapter II, I will report the identities of numerous genes that are regulated by each
transcription factor during hypoxia in Drosophila melanogaster.
11
CHAPTER II
GENETIC DISSECTION OF THE TRANSCRIPTIONAL HYPOXIA RESPONSE
IN DROSOPHILA MELANOGASTER
P.D. Etter contributed to this work by performing the genetic crosses and
microarray experim~nts for two of the six genetic strains used. I conducted the remainder
ofthe experiments as well as the data analysis and writing.
Oxygen is required by all multicellular organisms for survival. When animals are
deprived of oxygen, they undergo a complex series of behaviorial, developmental,
physiological and molecular changes in order to survive the challenge. Hypoxia is also
encountered by cells in tissues affected by heart attack, stroke, wounding, as well as
within poorly vascularized tumors. Hypoxic tumors are often resistant to treatment, and
indicate a poor prognosis for cancer patients (27). Due to the medical importance of
hypoxia, a great deal of research has been devoted to understanding the mechanisms cells
utilize to survive this stress.
One mechanism widely utilized by organisms to cope with oxygen deprivation
involves the modulation of gene expression in response to hypoxia. The transcription
factor HIF-l has been well characterized as a regulator of hypoxia-responsive gene
12
expression (1). HIF-l is a heterodimeric transcription factor composed of an a and a ~
subunit. HIF-la is rapidly ubiquitinated and degraded in the presence of oxygen, but
during periods of hypoxia the protein dimerizes with HIF-1~, which is constitutively
expressed, to form the active HIFI transcription factor complex. HIF-l binds genomic
regulatory elements known as hypoxia response elements (HREs), where it is
transactivated and facilitates the transcriptional activation of genes that help cells survive
hypoxia by either decreasing their demand for oxygen by shifting to glycolytic
metabolism, increasing the supply of oxygen by recruiting blood vessels to the hypoxic
area, or by stimulating the production ofred blood cells (1).
In addition to HIF-l , other transcription factors are known to take part in
regulating hypoxic gene expression. Nuclear factor kappa-B (NFKB) transcription
factors playa large part in regulating gene expression in response to inflammatory stimuli
and are also known to be activated by hypoxia (12) (13). Another transcription factor
that has been shown to regulate gene expression in response to hypoxia is p53, which is a
key regulator of genes that are responsible for the induction of apoptosis (28). The
FOXO family of Forkhead transcription factors regulate gene expression in response to a
multitude of stresses, and have recently been shown to be involved in regulating the
transcriptional response to hypoxia in human cells(9). Although primarily characterized
as a regulator of genes involved in the cellular response to heavy metal stress, the metal
response element (MRE) binding transcription factor (MTF-l) has also been shown to
respond to nitric oxide as well as hypoxia (29) (23). While NFKB, p53, FOXO, and
13
MTF-1 are all known to be hypoxia-responsive transcription factors, the comprehensive
set of genes regulated by each during hypoxia have yet to be determined.
Much of what is currently known in the field of genetics was gleaned from the
study of the fruit fly Drosophila melanogaster. Drosophila has also proven to be an
effective model system for the study of hypoxia. The Drosophila proteins Similar and
Tango have been identified as functional homologues of mammalian RIF-1 a and RIF-1 ~
(30,31). Functional homologues of the hypoxia-responsive transcription factors NFKB,
p53, FOXO, and MTF-1 are also present in Drosophila. Previously, our lab used
microarrays to identify Drosophila genes that are transcribed in response to hypoxia (16).
In the present study, we used available mutant Drosophila strains deficient in RIF-l
to identify hypoxia-responsive transcripts that are regulated by each respective
transcription factor.
Materials and Methods
Drosophila strains. In order to minimize differences in gene expression due to
variations in the genetic backgrounds of the strains that were studied, both the wild type
Oregon R control strain and each mutant were crossed to a strain bearing visible markers
on each chromosome (yw;Sp/cCyO;dDp/TM3-Sb). The genotype of the wild type control
strain wasyw;Sp/CyO;+. The genotypes of the mutant strains studied were as follows:
yw;Sp/CyO;sima7607, yw;Sp/CyO,foxo21/foxo25, yw;Sp/CyO;p535A-1-4, yw;Sp/CyO;reze20,
and yw;Sp/CyO/mtj-1140-IR.
14
Hypoxia treatment. Twenty male and female adult flies between the ages of 1 and
5 days post-ec1osion were collected from each genotype and placed in separate vials
containing standard Drosophila medium for each hypoxia experiment. Six replicate
populations were collected for each genotype. After a 24-hour recovery period following
counting, three of the vials for the wild type strain and each mutant were placed in a
sealed chamber at room temperature that was then flushed with a mixture of 0.5% O2 and
99.5% N2 (hypoxia). The three other vials for each respective genotype were placed in an
identical chamber containing normal atmospheric oxygen concentrations (normoxia).
The flies were incubated in the hypoxia and normoxia chambers for six hours, following
which they were rapidly removed and frozen in liquid N2•
RNA extraction and DNA microarray experiments. Following either hypoxia or
normoxia treatment, frozen flies were homogenized in TRIzol reagent and RNA was
extracted according to the manufacturer's protocol (Invitrogen). For each strain, 20 I-lg of
RNA from each normoxia and hypoxia treatment were labeled for microarray
hybridization using the SuperScript Direct cDNA Labeling System (Invitrogen),
incorporating either Cyanine 3 (normoxia) or Cyanine 5 (hypoxia) conjugated dUTP
(Perkin Elmer). In each strain, gene expression during hypoxia was assayed by
combining labeled normoxia and hypoxia eDNA, and hybridizing the samples to DNA
microarrays containing 16,416 oligonucleotides from the INDAC set representing the D.
melanogaster transcriptorne (Illumina). Three microarrays were hybridized for each
mutant, representing three independent biological repeats. Microarrays were scanned and
analyzed using Gene Pix Pro 6.0 software (Molecular Devices).
15
Microarray Data Analysis. In order to identify the complete set of hypoxia-
modulated transcripts, we identified transcripts in wild type flies that increased or
decreased greater than 1.6 fold during hypoxia (average of 3 microarray data sets). We
then examined the expression level of this set of transcripts in each mutant strain. In
order to identitY genes that were differentially regulated during hypoxia in each mutant
strain, we used BAMarrayTM 2.0 software, which utilizes Bayesian ANOVA analysis to
identitY significant differences in transcript levels between multiple microarray data sets
(32). Conserved binding sites for each transcription factor were identified using our own
software (http://genomix.uoregon.edu/~eric-johnsonlcgi-binlGAS.pl). The motifs used
for each transcription factor binding site search are as follows: Sima - TACGTG;
dFOXO - RTAAACAA; Rel- GGGAHNYMY; p53 - RRRCWWGYYY; MTF-1 -
TGCRCNC. Groups of2 or more binding sites within 2 kb of an open reading frame that
are conserved in D. melanogaster as well as at least three other Drosophilid genomes
were considered likely to be functional binding sites for the transcription factor being
queried.
Results
Hypoxia-induced changes in gene expression-DNA microarrays were used to
compare transcriptional profiles of adult Drosophila melanogaster flies that had been
exposed to hypoxia to those of flies that were kept at atmospheric oxygen concentrations.
Upon treatment with hypoxia, 435 transcripts were either up- or down-regulated greater
than 1.6 fold (3.05% of the transcripts represented on the array). These results are in
agreement with the set of transcripts that we previously identified as being induced by
16
hypoxia (16). In order to identify transcription factors involved in the regulation of the
identified hypoxia modulated transcripts, we examined hypoxia-responsive gene
expression in mutant strains deficient in Sima, dFOXO, NFKB, p53, and MTF-l.
Hypoxia-responsive gene regulation in mutanr strains-- In order to minimize
differences among the mutants due to variation in their genetic backgrounds, we
performed a series of crosses to make each mutant genetically identical to the wild-type
strain, with the exception of the chromosome containing each respective mutation. We
then exposed each mutant strain to the same hypoxia treatment as the wild type flies and
identified transcripts that were either induced or repressed by hypoxia using microarrays.
Baesian ANOVA analysis was then used to identify transcripts that were differentially
regulated under hypoxia between wild type flies, and each mutant strain (Figure 1) (32).
Of the 435 transcripts that were induced or repressed 1.6 fold or greater during
hypoxia in wild type flies, 190 were identified as significantly different in anyone of the
mutant strains by Bayesian ANOVA analysis. We analyzed the expression or each of
these 190 genes by hierarchical clustering (Figure 2). Interestingly, dFOXO mutants
exhibit the greatest difference in hypoxia-induced gene regulation when compared to
wild type flies by hierarchical clustering. Sima mutants exhibit the second largest
difference in hypoxic gene regulation from wild type flies, while p53, rei, and MTF-l
exhibit patterns in gene regulation that are more similar to wild type.
Figure J. Example data plot from BAMarrayTM Bayseian ANOYA data analysis comparing the expression
of 435 hypoxia-regulated transcripts in simo- and WT fl ies. Genes are class ified as significantly
di fferentially expressed between two data sets when their Zcut values are large, and their posterior
variances are close to I. BAMarray™ automates the selection of such genes using a rule based on the
specific data set. For details of BAMarra/M statistics, please see {Ishwaran et aI., 2006, BMC
Bioillfonnatics, 7, 59}.
18
I~.
000
0000000
.. '0000~N.---i ....
! I IO...--if'JM
;J~~~::f;;'j::::::.
...''';.·.1·... ·111·
.1.'.' '.', _. ~~u...
- ",'- 'I'· , ......I' ,. ,. ",. II"",,·,
'''.1','
... ,q ....... ,
,.,.. .. ' "",., , . . ~,.. .'.....,," ~; ".
,!~:~~*~~;~;,:,.'
Figure 2. Hierarch ical clustering of hypox ia modulated transcripts with significantly perturbed expression
levels in at least one mutant strain. Rows represent the average expression levels of transcripts from 3
biological replicates for the strain indicated in each column. During hypoxia, dFOXO mutants exhibit the
greatest difference from wild type flies, followed by sima mutants.
19
In order to refine the lists of target genes to include only direct transcriptional
targets of each transcription factor under hypoxia, and to eliminate genes indirectly
affected by the loss of that factor, we used a bioinformatic approach (Figure 3). Genes
were considered strong candidate targets of a given transcription factor under hypoxic
conditions if they showed significantly altered expression levels in that mutant under
hypoxia and also contained two or more conserved binding sites for the affected
transcription factor within two kilobases of the open reading frame. Based on these
criteria, we identitied 47 hypoxia modulated target genes for dFOXO, 24 targets of Sima,
16 targets of Relish, 14 targets ofp53, and 14 MTF-I targets (Table 1). These numbers
are in accordance with the differences in hypoxic transcriptional modulation between
each mutant strain and wild type based on hierarchical clustering, with FOXO being
required for the hypoxic modulation of the greatest number of transcripts, followed by
Sima, ReI, p53, and MTF-1 (Figure 2).
Table 1. Hypoxia modulated transcripts with significantly altered hypoxia expression levels in mutant
strains
Transcript
dFOXO-dependent transcripts
CG18466 NAD-dependent methylenetetrahydrofolate
dehydrogenase
CGl0383
CG7219 Proteinase inhibitor 14
CG5966 lipid metabolism
CG8709 lipid metabolism
CG7850 puckered
CG5953
Fold
hypoxic
induction
inWT
3.94
3.66
336
3.01
2.93
2.85
2.77
Fold
hypoxic
induction
in mutant
1.06
1.25
0.00
1.00
1.42
1.93
0.00
Conserved
binding
sites
4
5
2
2
6
8
8
20
CG3090 Sox box protein 14 2.66 0.00 2
CG13868 2.39 0.79 7
CG3705 astray 2.39 1.05 2
CG14801 nucleic acid metabolism 2.22 0.00 8
CG32369 carbohydrate metabolism 2.22 0.00 6
CG15423 2.22 1.24 2
CG11796 amino acid catabolism 2.20 0.69 3
CG7554 comm2 2.17 0.00 2
CG5295 2.13 1.41 3
CG 12489 Defense repressor 1 2.04 0.00 10
CG 1600 Zinc-containing alcohol dehydrogenase
superfamily 2.01 1.15 6
CG31811 centaurin gamma 1A 1.96 1.29 5
CG5467 1.93 0.00 3
CG5246 proteolysis Peptidase S1 1.93 1.13 2
CG16926 1.91 0.91 2
CG12358 polyA-binding protein interacting protein 2 1.89 1.03 3
CG32972 cell adhesion 1.84 0.00 4
CGl0580 fringe 1.82 0.00 6
CG7525 Tie-like receptor 1.78 0.00 2
CG15162 Misexpression suppressor ofras 3 1.73 0.96 3
CG12845 Tetraspanin 42Ef 1.73 1.07 2
CG8222 PDGF- and VEGF-receptor related Pvr 1.72 1.03 2
CG31319 RhoGAP88C 1.71 0.00 3
CG3234 timeless 1.71 0.00 2
CG5248 locomotion defects 1.69 0.00 3
CG13388 A kinase anchor protein 200 1.67 1.13 2
CG1921 sprouty 1.66 1.04 3
CG5461 bunched 1.64 1.00 19
CG4993 PRL-l 1.59 1.03 5
CG8095 scab 1.59 1.06 2
CG8127 Ecdysone-induced protein 75B 1.57 1.05 24
CG14401 1.57 0.83 3
CG10637 Numb-associated kinase 1.53 0.00 3
CG7210 kelch 1.53 0.92 2
CG17278 Proteinase inhibitor] I 1.52 0.00 2
CG8098 Picot 0.63 0.93 4
CG13607 0.62 1.06 2
CG7720 cation transporl 0.54 1.01 7
CG12602 cation transport 0.42 0.74 2
Sima-depenndent transcripts
CG31449 Heat-shock-protem-70Ba 4.91 3.89 2
CGI0160 Ecdysone-inducible gene L3 3.06 1.09 2
CG15009 Ecdysone-inducible gene L2 2.13 0.92 6
CG7224 1.60 0.30 3
CGl1652 DPH2 protein 68D2 1.56 0.15 2
CG31769 1.29 -0.07 2
21
CG31543 HIF prolyl hydroxylase 1.16 -0.02 9
CG1333 ErolL 1.05 -0.04 2
CG5467 0.95 0.10 4
CG6246 nubbin 0.95 0.41 4
CG 12358 polyA-binding protein interacting protein 2 0.92 0.27 5
CG32972 Beta-Ig-H3/fasciclin Protein kinase-like
35Al 0.88 0.00 5
CG5748 Heat shock factor 0.86 0.08 3
CG30069 cell cycle 0.85 -0.05 3
CG 10746 fledgling ofKlp38B 0.84 -0.22 4
CG8913 oxygen and reactive oxygen species
metabolism 0.83 0.22 3
CG3234 timeless 0.77 -1.20 2
CG9078 infertile crescent 0.66 0.11 2
CG 10827 Alkaline phosphatase -0.66 0.00 2
CG12385 Peptidase SI -0.70 -0.17 2
CG13997 -1.21 -0.06 2
CG12602 cation transport -1.24 -0.63 3
CGI0129 nudel -1.97 0.00 3
Relish-dependent transcripts
CG32130 apoptosis proteolysis Peptidase SI 4.72 7.00 5
CG 18466 NAD-dependent methylenetetrahydrufolate
dehydrogenase 3.94 1.75 4
CG3896 defense response 2.11 1.41 2
CG 11992 Relish 2.03 1.00 4
CG5467 1.93 1.08 3
CG32972 cell adhesion 1.84 1.00 3
CGI7631 1.64 1.06 2
CG 10248 Cytochrome P450-6a8 1.53 2.00 6
CG170i 0.64 0.94 3
CG8098 Picot 0.63 0.98 2
CGI0827 0.63 1.00 2
CG 12387 Trypsin 0.58 0.88 2
CG 17876 Amylase distal 0.55 1.00 4
CG8083 cation transport 0.48 0.70 2
CG 12602 cation transport 0.42 0.67 2
MTF-l-dependent tra!1scripts
CG4463 Heat shock protein 23 5.13 10.80 3
CG5550 defense response 5.10 8.30 2
CG14847 1.84 0.95 3
CG4919 Glutamate-cysteine ligase modifier 1.82 1.00 2
CG 1399 Leucine-rich repeat 1.77 1.88 8
CG3234 timeless 1.71 1.13 5
CG32041 Heat shock gene 67Bb 1.69 4.63 5
CG9568 1.67 2.63 2
CG4312 Metallothionein B 1.59 0.00 5
-------~--~ ~--
22
CG5059 L56 2.35 4
CG10725 chitin metabolism 1.53 0.00 2
CG 10827 Alkaline phosphatase 0.63 0.94 4
CGl941 0.60 0.99 2
CG17876 Amylase distal Amy-d 0.55 0.00 3
p53-dependent transcripts
CG8127 Ecdysone-induced protein 75B 1.53 2.27 12
CG30069 cell proliferation 1.80 0.94 8
CG7720 cation transport 0.54 0.91 5
CG 10827 mesoderm development 0.63 1.03 4
CG32972 cell adhesion 1.84 0.00 3
CG 10580 fringe 1.82 0.00 3
CG13503 actin filament organization 1.73 0.00 3
CG31319 RhoGAP88C 1.71 0.00 3
CG 12602 cation transport 0.42 0.63 3
CG5246 proteolysis Peptidase 81 1.93 0.00 2
CG18402 Insulin-like receptor InR 1.91 1.30 2
CG6437 GlcT-l 1.68 1.11 2
CG1941 0.60 0.00 2
CG 10129 nudel Toll signaling pathway 0.26 0.00 2
23
A
.ll:l)h
..<.
;;Lw
r~<.
Lo",~\
~tr('
-;-:l""'~ ;rt,o,
.:":':.' ~ ;,. .
:-":l.' ~:.:o:...
:-: ..•.t;. ,'-XI,
.•;"\7,t
n.~. ,";1:.'
{N,... I"XI.
:.J.; ""r..
: ~. to",
." .
..~I~··'n...t'I.P::··': .:':·':""J,·IJJ>J,J.l;/.~
r~:·"""\I:';";I.·.~·".:~~·.J;J...·~'.·.:·".'. -
·:';:"~v..\;;·tC' ·.t""7ia,lMI·"_'J;"~M
;(;I·.M.\;t::' ·,~,,:-~ /..·.':·J..O.....'.,
:t\·)J\.\::'·... :·.v.I·f.~ '.A~U.I..~.~·
.~~·A"'·.: ll.", ':.
:U'~'lA: tt I ..•••.!:;.'.\
:-:.1..J.....-;- rt " r~·J<.·.,
~;"l ..Jl,,'r;:; h
'" '" -
("'i':!:~... :~ ·1"l:'~.U•• :l.1.';\:-.•~ ...
::.l.:"'....o;-:; :·~~: , .(}.M·~':":.A
.(',":-"-":':\': : V p .....·l"cn·...
:::.::1.::: ..•:I.'.~,·_,:::":"~...
::Io~-: r;,." =re.7:' ...
':..' .:,,~ ~:: ..~
__ l";-to:n:_'~.'.rl.·.· .. ~ "'c
:::t~,"':';:::I.J.r:.·x.'V.·.·.;·.·.:..·.:
;;'.'·'.7...: O'-:.·.·.{·.·N,·r."..;;
:::r.·.:~·::(,· r.ru:J.-;I.:::.:
~-:;;,••\7'l;';.,\.:.J.r.\V; 1::1;:;.
r ". ;':.1. \,1, \1.·.r:_~ II
;,. ~:t 1:\.... :U•••t,'1.')''''~ 1,
':tA ::. ... fI.1ti.l>." .,
\. :~~. ~'X'" (':.I.G.'~ ::, ,-J, ...
.~~ ~ \..... :
;-. t ( : ~:. ~ ::-
r ~. r.·. ::;l.' l. :.'\ L·~• ....:;.
--
_•.
B
.... -.
Hsf-RA
lill:r.
"It!. ~-;:; :t~,~••J.":'.. :-:-:;1.'."1.'".,1.'.;-'.,,
..~:f ~-:-;t: ......" -:':":\....'7-:.'I,••~;~., ..
tH~ '.7:'\:",' ,' '7'\.\ .. ::. '''',1.1..' '.
~ ,.1 -;::1,,: Il}.·.'~. ~.·.... -;-::..u~IJo7 :
:<fl', ~";':'t; '''J., :7" ::),1.:;"'.. :
.) :: I:....•...::
:: :':':;-:::~":.\~'.',:'.;"':,:I.~~';,·.:'.-;-"J~·."~·l;...~:.;- ;."'":I.','••• ~ :,":,:':-::;( :.'.-: ••••• ~I:':~
~"l"':':"~..··:t"';";'·:.'.. t .. •.. I.l'-:.U.;-:I'.!.r1 .\.:•."":'.. ,..... ll,._.·.. :t:.l • .-: ... ~:•.-'.. t
:-:",,":,: •. 1.::"1:::.'•...,:.:(:'..11."" t':'l:.JVJ.lI 1,.,\ •• 1: .·~tl .\~~ •. t::,._:~,.~tL
7"": ::":'7":":... :-l.-: o..A, :;.•• :" : i." "-',.".~" ":'\-:'~ A-..x'< T."'".:~.I.Ir:. ••'i.~~'.Ll·
-: :: :·~"l:I.;fJ-' :1. ..."..~.~'\ " .... :'"-1 , t', II •••• ;11':;:::':T".~sn.
I_".<j,,: ~;-: ~ C":-::-:7:':~;;
".:~ . ."
r-~. : I;
~d: ,'....
~c'J~' _,~ I.•
.....-
:: .\~,..}o.;~:: j, ',:.-:-;
":J.; ;C --::-:(
v.:"-' :t -:0::..
---
ImpL2-RA
.\.-: r.c· l
r-::. I'
......~~
( :,' t·::.::t .: .. :-:-.:.;.,'.; ~', •;- r. ::.::-:. I.·J,;~ ..N. !'
,!·.f ... ·.: ... ~:i..·:(l·.·..."t~r.-.\f~.-:-:-. ......\;t.\.·"':
1:\.I"-A-;-:-;'~I":'~\\':-:·:::-:1l:I.:-.'''''''·.'l.'''t.\.\.,;
ltn-"", 'l'\;--::-t:~~t·lt:n.:-f... :,r:·:/t..'.\:-....UC"
L.'.\'~·.~.... -:l:t"L...-'.~-:r·':\··"",';:"'_\:.:­
tr.'\.\~·:-:o:-t;·""~TJ;:-:;·I'}''''''','.)'::
~7'"ll'::-:-:--':: :'l~--'~I,;"':·","""'\~\.t:
_:-:: ; l .'.~ ..• , ~ ':....'t';; •• ,I;,'... '1J...\,...-....'IM..
;-IIt.\:~ l.M
.t l;. ;;11 .'.:";'. /..M.. •.... ~
...-' :~:-.:, .·.-,-;f."f.l......:~.·_""i;
7. "''XI.,:.-r. '.":-l .....U ...; ...~
----
~e--------------+--------o
figure 3. Examples of computationally identified conserved binding sites for putative hypoxia-responsive
transcription factor targets. Transcripts identified as differentially expressed during hypoxia in sima
mutants contained both known Sima targets such as Heat shockfaclor (A), and transcripts that have
previollsly been uncharacterized as targets of Sima, such as Imp-L2 (B). Both transcripts contain Sima
binding sites within introns that are conserved in multiple Drosophila species.
Discussion
The transcriptional regulation of genes during hypoxia has been examined in
many model systems. However, the exact roles of many of the transcription factors
known to be involved in the hypoxic response and how they regulate gene expression
during hypoxia have not been comprehensively determined until now. We identified 435
transcripts that are either induced or repressed greater than 1.6 fold by hypoxia in wild
24
type D. melanogaster. Of these genes, 190 (43.7%) are significantly differentially
regulated during hypoxia in flies that are deficient in at least one of the transcription
factors we examined. In order to separate genuine targets of the hypoxia-responsive
transcription factors from transcripts that change in abundance due to indirect effects, we
computationally identified conserved transcription factor binding sites near putative
target genes identified in each mutant strain. Following this analysis, we identified 88
transcripts as being regulated by at least one of the transcription factors we examined.
Interestingly, the vast majority of hypoxia-modulated transcripts we were able to
assign to a particular transcription factor were those that were up-regulated by hypoxia.
Relish was the only transcription factor out of the five we studied that seems to playa
substantial role in transcriptional repression during hypoxia; 43.75% (7/16) of the genes
regulated by ReI during hypoxia are down-regulated by the stress in wild type flies.
Interestingly, in the absence of ReI, several·hypoxia modulated transcripts are more
highly transcribed, supporting the potential role of ReI as a hypoxia-responsive
transcriptional repressor. Several j\1TF-l-dependent transcripts are more highly induced
by hypoxia in the transcription factor's absence, suggesting that MTF-1 functions as a
repressor as well. Each of the other transcription factors appear to function more as
transcriptional activators during hypoxia; of the dFOXO regulated genes, 91.49% were
up-regulated, 79.17% of Sima dependent genes were up-regulated, and 64.3% of p53
dependent genes were up-regulated.
To our surprise, dFOXO appears to regulate the expression of the greatest number
of hypoxia-modulated transcripts of all the transcription factors we examined, including
Sima, the homologue of HIF-1 U, which is widely considered to be the master regulator of
25
hypoxia-induced gene expression. While this may be partially due to the relative strength
of the alleles we used, it is still an unexpected result. This study constitutes the first
characterization of dFOXO as a major regulator of hypoxia-induced gene expression. It
will be interesting to detennine the extent to which FOXO transcription factors function
during hypoxia in other organisms. In future experiments, we plan to examine dFOXO
mutants that have been raised at non-lethal levels of hypoxia and determine whether they
exhibit observable developmental or reproductive phenotypes. These experiments will
further validate our identification of dFOXO as a major regulator of gene expression
during hypoxia.
Sima, the Drosophila homologue ofHIF-1a, has previously been characterized as
a regulator of hypoxic gene expression (31). Our experiments have shown that Sima
does indeed regulate a large number of genes during hypoxia. The set of Sima-regulated
transcripts that we identified includes genes such as Heat shock/actor (Hsj) and HIF
prolyl hydroxylase (HPH) , which have been previously characterized as direct targets of
Sima (Table 1)(5,6). We also identified several transcripts that have never been
previously described as Sima targets. One of these encodes Imaginal morphogenesis
protein-Late 2 (lmp-L2), a protein that has recently been characterized as a negative
regulator of the insulin signaling pathway in Drosophila (33). This is an intriguing result
because it may prove to be a link between the activities Sima and dFOXO during
hypoxia. Because Imp-L2 inhibits the activity of Insulin-like peptide, which participates
in a pathway that in turn inhibits the activity of dFOXO, the Sima-dependent up-
regulation oflmp-L2 may result in the up-regulation of dFOXO activity during hypoxia
26
(Figure 4). We are currently conducting experiments to explore this regulatory link, as
well as a possible role of Imp-L2 as a hypoxia-induced inhibitor of growth.
Sima Imp-L2 Insulin-like peptide
dFOXO
Figure 4. Schematic representation ofa potential regulatory mechanism by which Sima likely contributes
to the activation of dFOXO during hypoxia through transcriptional up-regu lation of Imp-L2.
There are several other interesting results from our analysis. Relish, a
transcription factor that has been previously identified as a regulator of the transcriptional
response to Gram-negative bacterial infection, also regulates the expression of several
genes in response to hypoxia. This may serve to sensitize the innate immune system of
flies during hypoxia, a condition that likely coincides with areas containing large
amounts of microbes when encountered by flies in their natural enviroiUuent. We have
frequently observed stochastic transcriptional activation of antimicrobial peptide genes in
wild type flies exposed to hypoxia (unpublished data). This may be due to hyper-
sensitization of the fly's innate immune system by hypoxic activation of Relish.
MTF-1 and p53 have both been implicated in hypoxia-induced gene regulation in
mammalian systems (34,35). Our data indicates that these transcription factors are
active under hypoxia in Drosophila as well. It has been previously demonstrated that
MTF-l activates the transcription of lVfetallothionein genes in response to hypoxia (23),
27
and we have shown that this also occurs in Drosophila in the case of Metallothionein B.
We have also identified several other hypoxia-induced targets ofMTF-l. The group of
p53-dependent hypoxia modulated transcripts we identified contains several interesting
genes. We identified Fringe, a regulator of the Notch signaling pathway, as a target of
p53 during hypoxia. Activation ofthe Notch pathway during hypoxia has been observed
in other organisms (36). It will be interesting to determine whether the Notch pathway is
activated by hypoxia in Drosophila as well.
In order to survive hypoxic stress, organisms undergo a complex series of both
behavioral and physiological adaptations, including the transcriptional activation and
repression of sets of genes. In addition, cells within dense and rapidly growing tumors
frequently encounter hypoxia, and must alter their transcriptomes in order to survive. For
these reasons, understanding the mechanisms by which transcription is altered during
hypoxia not only broadens our understanding of how organisms endure physiological
stress, but also provides information that may be useful in designing strategies to prevent
cells within hypoxic tumors from surviving and proliferating. Our group and others have
previously identified transcripts that are induced and repressed by hypoxia in Drosophila
and other organisms. In the present study, we illuminated the roles of five different
hypoxia-responsive transcription factors in the broader transcriptional hypoxia response.
This represents the most in depth dissection of the transcriptional hypoxia response to
date. We have shovm that dFOXO regulates a surprisingly large number of transcripts
during hypoxia, and have identified several interesting targets of Sima, ReI, MTF-1, and
p53 that likely playa multitude of important functional roles during hypoxia, and warrant
further in-depth investigation.
28
Bridge to Chapter III
In the previous chapter, I described experiments that identified genes that are
regulated by RIF-I, FOXO, NFKB, p53, and MTF-l during hypoxia. In Chapter III, I
report data from a published paper, of which I was the second author, that examined one
HIF-l target gene in detail. We show how this regulation constitutes cross-talk between
two stress response pathways, and that it is important for viability under hypoxia.
29
CHAPTER III
HEAT SHOCK PATHWAY IS ACTIVATED BY THE DIRECT REGULATION
OF HEAT SHOCK FACTOR BY HIF-l DURING HYPOXIA
Reproduced with permission from Baird, N. A., Turnbull, D. W., Johnson, E. A. J Bio!.
Chern. 2006,281,38675-38681. Copyright 2006, Journal of Biological Chemistry.
I performed the chromatin IP experiments described in this paper, which showed
that HIF-1 directly binds the regulatory region of the HSF gene. I also conducted DNA
microarray experiments prior to the research described that allowed us to identify HSF as
a likely HIF-ltarget.
In order to endure oxygen deprivation, most eukaryotes utilize a conserved set of
cellular adaptations (37). Many of these changes are brought about by the activation of
the transcription factor HIF-l, a heterodimeric complex composed ofHIF-la and HIF-l~
subunits. When this complex is formed it binds to specific DNA enhancer sequences and
regulates the activity of target genes. Both HIF-la and HIF-l~ are constitutively
expressed in normal oxygen conditions (normoxia), but HIF-la protein is quickly
degraded before dimerization can occur with HIF-l ~ (3). Normoxic HIF-la degradation
30
is mediated by a series of hydroxylations and ubiquitinations, which tag HIF-la for
disposal through the proteasome (38) (39) (40) 041
The HIF-l complex transcriptionally regulates a wide array of genes involved in
anaerobic metabolism, growth, proliferation, angiogenesis, and cell death (42) (43). This
multifaceted control of cellular and organismal physiological pathways is exploited by
solid tumors through the natural hypoxic environment caused by rapid growth or genetic
alterations which stabilize HIF-la (44). Overexpression or activation ofHIF-la is often
seen in a wide array of cancers and is correlated with patient survival (45), and studies
have shown that targeting the HIF-1 pathway is a promising means of cancer therapy
(46). Thus, HIF-l is a central regulator of normal and pathological changes in response
to low oxygen
While many genes that are up-regulated during hypoxia are known to be regulated
by HIF-I, there are also diverse sets of genes up-regulated that have not been linked to
the actions of HIF-1. Among these are the highly conserved heat shock proteins (Hsps)
that are highly up-regulated during hypoxia but have not been linked to HIF-l regulation
(47). Hsps are known to act as cellular chaperones for proteins that are misfolded by
cellular stresses (48). Heat shock factor (Hst) was one ofthe first studied transcription
factors and its activation by stresses that promote the unfolding of proteins has been well
characterized. When cells are unstressed Hsf is in a monomeric state, but cellular stress
induces trimerization of the protein(49) (50). The trimeric form ofHsf activates
transcription of do~nstreamgenes, such as H"ps (51) (52). However, this study
identifies a novel mode of regulation of heat shock pathway activity during hypoxia
through a HIF-l-dependent increase in Hsftranscript levels. This up-regulation of Hsfis
31
necessary for the full increase of Hsp transcripts normally observed during hypoxia and
also during reoxygenation. These findings establish a novel regulatory link between two
stress pathways previously thought to be independent in responding to hypoxia.
Results
Hsftranscript levels increase during hypoxia in a HIF-l a-dependent manner
Drosophila melanogaster KC167 cells were treated with GFP control double-
stranded RNA (dsRNA) or dsRNA directed to eliminate transcripts of the Drosophila
HIF-la homologue, similar (31) (53), through RNAi. After exposure of the treated cells
to normoxic or hypoxic conditions, total RNA was isolated for semi-quantitative reverse
transcription-PCR in order to characterize the transcript levels of HIF-la, Hsf, and
Actin5c as a control (Fig. lA). Interestingly, we found that transcript levels of Hsf
increased during hypoxia, and that this up-regulation was HIF-la-dependent. Cells
lacking HIF-l a due to RNAi did not display a hypoxic increase in Hsf, instead
maintaining Hsf levels more similar to control normoxic cells. Real-time PCR was then
used to more accurately characterize these results (Fig. IB) and further corroborated that
Hsf transcripts increase under hypoxic conditions in a HIF-l a-dependent manner. As an
additional control we repeated the RNAi with an alternate dsRNA sequence targeting
another area of HIF-la, which also showed a HIF-l a-dependent hypoxic increase of Hsf
(Fig. lC). This control experiment confirms our results were not due to off-target effects
of the original RNAi.
32
The increase in lL~ftranscript levels during hypoxia is directly regulated by HIF-l a
The DNA recognition element to which HIF-l binds during hypoxia contains a
core 5' -RCGTG sequence (54) We had identified multiple instances of a related motif,
5'-TACGTGC, in the intron of the known HIF-l target gene (55) HIF-l prolyl
hydroxylase (PHD), and searched for this motif in the Hsfgene region. We identified two
ofthese putative hypoxia response elements (HREs) in close proximity to one another in
the second intron of Hsf The two sites were 923 and 992 base pairs (bps) downstream
from the transcriptional start site of li~frespectively. When this genomic region was
aligned (56) with seven other Drosophila species these potential HREs were perfectly
conserved (Fig. 2A). The sequence conservation of the two HRE motifs strongly
suggests that there is evolutionary pressure to maintain these specific sequences.
We next tested if these conserved HRE motifs had a regulatory function during
hypoxia. A portion of the second intron of Hsfcontaining the potential HREs was cloned
upstream of a minimal promoter driving GFP in the Green H Pelican reporter vector (57).
This reporter construct was then transfected into the KCl67 cell line and put under
normoxic or hypoxic conditions. The hypoxic cells showed a dramatic increase in GFP
fluorescence compared to the nOlmoxic cells (Fig. 2B). The hypoxic increase in GFP
expression was eliminated by HIF-lu RNAi treatment. The original
33
A B
Hypoxia
HIF-1a RNAi
+ +
+ 3
Actin5cll!'it ... rfb
HIF-1a ¥%ccc
2
1
0.5
1.5
o
Q)g 2.5
Cl1
"0
C
:J
.D
Cl1
Q.
'I::
U
til
C
Cl1
....
.....
't?
I
"0
"6
LL
+
+
+
, Hsfffllr,
HIF-1a
Hypoxia
HIF-1a RNAi
c
Hsf
Actin5c
Normoxia Hypoxia Hypoxia
+ HIF-1a
RNAi
Figure 1. H~ftranscript levels are increased in a HIF-Ia-dependent manner. A) RT-PCR analysis of the
abundance of transcripts encoding HIF-Ia, Hsfand Actin5c (control) during normoxia or hypoxia in KCJ67
cells. H~fis up-regulated after hypoxia and RNAi inactivation ofHIF-la eliminates this up-regulation. B)
Real-time PCR experiments confirm that RNAi inactivation ofHIF-la reduces the up-regulation of H~f
after hypoxia. Standard error ofthe mean is shown. Transcript changes in each condition are significantly
different (p < 0.05). C) RT-PCR analysis of the abundance of transcripts during normoxia or hypoxia. An
alternate dsRNA sequence targeting HIF-Ia for RNAi showed similar results as in (A) reducing the
possibility that results from the original RNAi were due to non-specific effects.
34
reporter vector lacking the Hsfintron showed no hypoxic activation ofGFP (data not
shown), confirming that it was the cloned intronic region ofHsfthat was leading to the
HIF-I-dependent induction of the reporter during hypoxia.
The HRE-containing region was also tested for direct binding by HIF-1 by
chromatin immunoprecipitation. We transfected KC167 cells with a epitope-tagged HIF-
Ia expression vector (58). DNA bound to HIF-I protein during hypoxia was
immunoprecipitated and the Hsfintron genomic region as well as control genomic
regions were PCR amplified to test for enrichment compared to DNA
immunoprecipitated from a mock transfection. We found distinct enrichment of a 260-bp
fragment encompassing the two HREs ofthe Hsfintron (Fig. 2e). As a positive control
we showed an enricrill1ent of a genomic region containing an HRE within the intron of
the knovm HIF-1 target Hph. A negative control fragment located in an Actin5c intron
showed no enrichment between the HIF-Ia pull-down and the untransfected pull-down.
These data indicate that Hsf is a direct target of HIF-1 a through the binding of an intronic
region containing two HREs that act as an enhancer of transcription during hypoxia.
Full induction ofHsps during hypoxia is dependent on HIF-l a regulation ofHsf
The functional impact of the up-regulation of Hsfby HIF-Ia on Hsp induction
during hypoxia was then assayed. KC167 cells were exposed to normoxia and hypoxia
after treatment with control and HIF-Ia RNAi and reverse transcription-PCR assayed
35
transcript levels of various Hsps. All Hsps examined were dramatically up-regulated
under hypoxia and this increase was partly HIF-1 a-dependent (Fig. 3A). I-Isp transcripts
A
D,mel CM';7:'C/ICGCACGTJ\CCCI\
D.yak ClIc_-:rJ\cGCACGTAccc/,
1J.:ma ,. - (, C CiiCGCACGTAccC/,
D.am Gl~C';:-CM'GCACGT1\(,C:Cl\
D. moj CIIG CI\cGCJ\CGTACGCil
D.p.'iO C/\C CiICGCJ\CGTJ\(GCl.
D. slm c../\C-.'"iCiICGCACGTAcOC,\
D. vir Cl\C ct,COCACGTACCCt\
:'CClIlll\TCC Ci\I~ClI';CCl\CM\CM\CI~ /\Ol..!l·
:--cC/IlIJYiGC ClIhCII.:C J\Cii/\Ci\J\Ci\ II 11.,\
':CCNv'l"l." .- C11l\ j\ 1\ ll.
:CClU\lYl.'CC Cld\C!I':"C h 11(:11/\
"cr;'lIhT Uillel'! 1\ 1\tIC;\ C/\ I\ClIi\
:'CCfllv\;' C C
;CC;,'lIlly}:cr; Clv\ClI-"'CCtIC/\lIC/\j\Ci\ .\Ci\l'!.
':'CC/\i\ll;, fll\C ;\C1\/\
Cl.....·C'IC:'C ~·C- ':CCCCAlICCC,/"v\C-::-CCIITACGTGC-e ~CI\7C
C.Y:'C;\ .: ':C GCCC'u\CCChbC::cu,TACGTGC· ~Cl\-=-C
Col, CIIC -,'cciITACGTGC- ·_~C
(,11.:( CClu\c:cCi\l\r:-:"~cCI\TACGTGC- ·-Cl,?C
1\ .\G (". C 1\ (; C::: C'I\TACGTGC c/\
Cni. -,":::;c"TACGTGC ell
,;/\:'C/\C'~'C C ·'~CCCClv\CC::'i\AC·_ cCiITACGTGC. CI.?G
l.": h eel. c c:-:'cc/\TACGTGC GIl
HSI
PHD
D
Inpul Mock IP= 7
~ 6
" 5ua; ~
l;l 3
~ 2
~I
1.L
0-1--'-'--.-- '"
";;
o
0.
>-
I
c
Hypoxill
... HIF-lII.RNAi
HypoxiaNonnoxia
B
Figure 2. The I-Isfgene region has conserved I-IREs and is a direct target of I-lIF-I. A) An alignment of the
nucleotide sequence ofthe second intron the H.~lgene in eight Drosophila species shows two HREs are
fully conserved. B) The genomic sequence containing the two HREs in the H4intron were cloned up-
stream of the minimal promoter of the Green ]-I Pel ican reporter vector. This construct was transfected into
[(c I C>7 cells split into three conditions: normoxia, hypoxia or hypoxia with H1F-1 a RNAi. The reporter was
not activated by normox ia but hypox ia induced expression of the GFP reporter. The hypoxia activation of
the reporter was eliminated by the addition of 1-1 IF- Ia RNAi. C) Fluorescence was measured using an ISS
PC I spectrofluorometer and normalized by cell number. Quantification confirmed a significant increase in
fluorescence during hypoxia and a significant decrease from the hypoxic induction when HIF-l a RNAi
was added to hypoxic cells. D) Chromatin immunoprecipitation and PCR showing enrichment of the
genomic region contain ing the two f-l REs within the Hsfgene in epitope-tagged HIF-I a transfected versus
untransfected KC167 cells. The Hph gene and AClin5c genes were used as positive and negative controls
respectively.
36
were not completely eliminated in hypoxic cells treated with HIF-1u dsRNA, presumably
because the hypoxic stress activated the basal (normoxic) levels of Hsf protein already
present in the cells. No HREs were found near any of the Hsp genes, therefore it is
unlikely that HIF-1 was directly up-regulating these genes during hypoxia.
We tested if the up-regulation of Hsps during hypoxia was dependent on Hsf.
Cells were treated with control or HsfRNAi and placed in normoxic and hypoxic
conditions. When Hsf was removed through RNAi, Hsp transcripts were eliminated
completely, compared to the strong induction seen in cells treated with control dsRNA
(Fig.3B). Real-time peR was used to more accurately quantify the results from both of
the RNAi experiments. HIF-lu RNAi reduced the up-regulation ofHsps during hypoxia,
yet Hsf RNAi completely removed Hsp transcripts (Fig. 3C). From these results, we can
discern that Hsfregulates Hsps, while HIF-l regulates Hsf
The lack of strong Hsp up-regulation in hypoxic HIF-1 knockdown cells suggests
that the HIF-l-mediated increase in Hsftranscript levels is an important step in regulating
the sensitivity and activity of the heat shock response pathway. The functional impact of
an increase in Hsf transcript levels in hypoxia was tested by assaying the response to
hypoxia of a fly heterozygous for the null Hsj mutation (59), and therefore containing
only a single wild-type copy of Hsf After exposure to hypoxia, these flies had reduced
levels of Hsftranscripts compared to wild-type Oregon R flies as measured by real-time
PCR (Fig. 4). The heterozygous flies with a reduction in Hsftranscripts also showed a
strong reduction in Hsp26, Hsp27 and Hsp68 transcript levels compared to the control
flies, although two Hsp 70 genes had normal levels of induction.
37
A
Hypoxia + ...
HIF· Ia. RNAi +
HIF-1<x
Hsp26
-
Hsp27 ....
Hsp68 ~
Hsp70Ab ......
Hsp70Ba ........
Actin5c
- -
....
C
60
50
Q)
\) I~ 40 1
u l
C I::l
.D i(\j0. 30'C0C/)c(\j
~
u 20
"0
u..
10
o
B
Hypoxia + +
Hst RNAi +
Hsf ~
Hsp26 ....
Hsp27 ~.
Hsp68 ~"''<;,
Hsp70Ab ....
Hsp70Ba -
Actin5c-_ ....
Hsp26 Hsp27 Hsp68 Hsp70Ab Hsp70Ba
Figure 3. H~~p transcript levels are increased in an Hsf and HIF-l (X-dependent manner during hypoxia. A)
RT-PCRs oftran~crjpts involved in the heat shock pathway are up-regulated after hypoxia. Inactivation of
HIF··J a reduces the increase in Hsp transcripts. Actin5c is used as a control. B) RNAi of Hsf eliminates
up-regulation of H'Jps completely. C) Real·time peR analysis of transcripts from normoxic cells (black
bars), hypoxic cejb (dark grey bars), hypoxic cells treated with HIF-la RNAi (light grey bars) and hypoxic
cells treated with llsfRNAi (white bars). Hsp transcripts were normalized to normoxic levels. RNAi
treatment<: reduced the transcripts Hsps GOmpared to hypoxia alone (p < 0.05).
38
120% .
c
0
n 100%:::l
"0
C
0. 80'%
';:: T()
(/)
c 60%~
-(I)
a. 40%>.
-:9
.~
20%
'0
~0
0%
Hsf Hsp26 Hsp27 Hsp68 Hsp70Ab Hsp70Ba
Figure 4. L'p-rcguiation ofJr'Ps is Hsf dosage..dependent. Re,1!-time peR of flies heterozygous for a null
Hs/mutation shov, a sigwfk,mt cC'du.;tion ill }h[ Hsp26, lIsp2} and Hsp68 transcript abundance compared
to wild-type flies ;tIter hypoxia \1' < 0.05). Tris demonstrates that l-hftranscript abundance is critical to the
magnituck of Hsr 1" o\luct'on. Standard ern,!, of the mean is shown.
These findings suggest that Hsf abundance impacts the up-regulation of some Hsps in a
dose-dependent manner during hypoxia. Lower Hsf transcript abundance than the levels
normally achieved during hypoxia are insufficient for the full up-regulation of Hsps.
Full inducTion of/i<;ps und viability during reoxygenatioll is dependent on increased Hsf
levels
During the return. to normal oxygen conditions., Hsp levels remain high and are
critical to tissue survival during this reoxygt~natioll (60) (61). The effect of the HIF-I-
dependent increase in Hsf level on I-lsI' expression persists during reoxygenation. KC167
39
tissue culture cells \vith HIF- i a knocked down by RNAi had little increase 'in Hsp
expression after hypoxia treatment a..1'J.d a reoxygenation period (Fig. 5A). Thus, the up-
regulation of lisjduring hypoxia is critical to the high levels of Hsp transcripts during
reoxygenation, as well as hypoxia.
Furthermore, we examined the functional importance in vivo of increased Hsf transcript
abundance by assaying larval survival under hypoxia and reoxygenation stress. First
instal' larvae were reared in a regime of alternating hypoxia and reoxygenation. The lis!
heterozygotes had greatly reduced survival compared to larvae reared in normoxia (Fig.
5B). Control wild-type larvae showed no significant difference in survival between
normoxia and the hypoxia and reoxygenation environments. These findings demonstrate
the dosage importance of HsE transcript levels for coping with hypoxia and reoxygenation
at the organismalleveL
40
Hsp27 ....•............•.
Actin5c ........
wt Hsf +/-
0%
100%
80%
co
>
-;:; 60%
...
::J
(j)
co
>
...
~ 40%
;:R0
20%
B
+ +
+
....
Hsp68
Reoxygenation
HIF-1a RNAi
r----------..,
Hsf
Hsp26
A
Figure 5. Up-regulation of Hsps atter reoxygenation is HIF-la-dependent and critical to survival. A) RT-
peR of transcripts involved in the heat shock pathway are up-regulated after hypoxia. HIF-la RNAi
reduces the increase in Hsp transcripts. Actin5c used as a control. B) Larvae reared in either normoxia Or
hypoxia with a reoxygenation period each day were allowed to develop into pupae. Development of wild-
type larvae was minimally affected by the hypoxic and reoxygenation stress. However, half as many Hsf-
larvae reached the pupal stage when faced with repetitive hypoxia and reoxygenation compared to
normoxic larvae_ The reductiOn in survival was significant (p < 0.05). Standard error of the mean is
shown.
41
Taken together these experiments show the sequential order and importance of the
hypoxia response. During hypoxia, HIF-I directly up-regulates Hsj, which in tum up-
regulates the whole family of Hsps. Without the HIF-I regulated increase in Hsf, Hsps
transcript levels never reach full induction during hypoxia or reoxygenation, and
organismal viability is reduced.
Discussion
Up-regulation of Hsps during hypoxia is part of the canonical low-oxygen stress
response seen in Drosophila (16), C. elegans (47), and mammalian tissues (62). This
study provides evidence that the up-regulation of Hsfduring hypoxia surprisingly
requires the activity of HIF-1, the effector of the low oxygen response. The
transcriptional control of Hsfby HIF-1 has a functional impact on the activity of the heat
shock response during hypoxia and the return to normal oxygen levels. Cells lacking
HIF-lor with reduced dosage of Hsf only increase Hsp transcript production slightly
during low oxygen exposure and reoxygenation. The decreased production of Hsps
reduces viability in flies experiencing hypoxia and reoxygenation, demonstrating that the
full induction of the heat shock response is essential to counter the diverse physiological
stresses associated with low oxygen.
Thus, we propose a model where HIF-l directly up-regulates Hsfduring hypoxia,
and the increased Hsf abundance in tum allows Hsfto further up-regulate Hsps during
low oxygen exposure and also after the return to normal oxygen levels. The regulation of
Hsfby HIF-l provides a clear example of how cross-regulation between physiological
42
stress response pathways can allow one pathway to sensitize the second and elicit a
response under conditions where normally it would not be activated.
Complex regulation o/physiological re.sponse pathways
Cross-regulation bct\veen physiological pathways appears to be a feature of the
low oxygen response. It has been shown that the insulin pathway can dramatically affect
the HIF-l pathway (63). Through the actions of the phosphatidylinositol 3-kinase/Akt
pathway, HIF-Io. translation is increased in a manner that outpaces the naturally
normoxic degradation ofHIF-la (64). This leads to HIF-l activation even when oxygen
is present and up-regulating its downstream targets. Recently, it has been shown that
transforming gro~ih factor-(31 activates the HTF-l pathway by reducing the levels of
prolyl hydroxylases that tag HIF-IO'. fi)r degradation. Interestingly, it is also known that
Hsp90 plays are role in stabilizing H1.F-la (65) (66). This mechanism is independent of
the canonical oxygen-dependent regulation of HIF-1 a and was the first evidence of any
link between the heat shock and hypoxia stress pathways.
The cross-regulation between HIF-1 and Hsf found here is a new type of control,
where the transcriptional eilector of the low oxygen response directly regulates the
transcript level of the effector of the heat shock response in order to sensitize the
pathway. Interestingly, it has been already shown that HIF-l and Hsfpathways have
regulatory interactions, hut in response to heat. Studies using C. elegans and rats showed
that HLF··J aCiivity was essential for heat acclimation (67) (68). Our findings may
explain the mechanism behind this phenomenon, in that the increase in metabolic activity
43
during high temperature may cause oxygen scarcity, thus stabilizing HIF-I and increasing
Hsf transcript levels.
Transcriptional control o.lthe heat shock response
The activity of the heat shock pathway has been shown to be controlled by the
trimerization and post-translationalmoditication of Hsfprotein subunits (51). Our results
indicate that transcriptional control of Hslis a means of further regulation of heat shock
pathway activity. This transcriptional regulatory step is controlled by HIF-1, supporting
a model in which the HIF-l pathway causes increased H.sftranscription during hypoxia as
a means to increase the cellular abundance of Hsf and increase the sensitivity of the heat
shock pathvvay. In addition. the control of heat shock response sensitivity by HIF-I, the
regulator of the low oxygen response. suggests that strcss response pathways can
assimilate complex new fWlctions by regulating the transcriptional activators of other
stress pathways,
Disease implications
It has been shmvn that the increase in Hsp levels are critical for cell survival
during hypoxia and the subsequent reoxygenation (61) (69). Our results indicate that it is
through the fHF-l padmay that tht~ cell achieves this IIsp increase and is a means to
protect against tnr stress of hypoxia. HIF-I ac(;umulation and activity have been linked
to tumor progression and various Hsps have also been shown to be crucial to cancer
survival (70), thus, the hypoxic and beat shock response pathways play important roles in
the pathophysIOlogy of cancer. Our finding that the activity ofHIF-I controls the output
44
of the heat shock pathway offers possible therapeutic approaches for mitigating hypoxic
tissue damage and tumor growth by targeting this novel regulatory link.
Methods
Cell culture and hypoxia treatments
Drosophila melanogaster KC167 tissue culture cells were obtained from the
Drosophila Genomics Resource Center. Cells were maintained in Schneider's
Drosophila Medium (Gibco), supplemented with 5% heat-inactivated Fetal Bovine
Serum (Gibco). For hypoxia experiments, cells were incubated for 6 hours in chambers
flushed with 0.5% 02 gas. The reoxygenation step consisted of a 15 minute return to
normal oxygen levels.
RNA interference
RNAi was performed as previously reported (71). The following primer pairs
were used to generate template DNA: control Green Fluorescent Protein (GFP) (5'-
GCCACAAGTTCAGCGTGTCC and 5'-GCTTCTCGTTGGGGTCTTTC), HIF-la
(sima) (5'-CTGCGGGACTATCATAACAACC and 5'-
AGGCTCAAAATCAATCTTTTGG), alternate HIF-la (5'-
GCATCACATCAAAGAGTCCCGAG and 5'-TCCGCAACCGTAACACCACTAC) and
Hsf (5'-TGCCAAACAGTCCGCCTTATTAC and 5'-TGCTTTCCAAGTGCCCGTG).
The T7 promoter sequence (5'-TAATACGACTCACTATAGGGAGA) was added 5' to
all above primers when ordered (IDT).
45
Reverse Transcription-PCR
Total RNA was isolated using standard Trizol protocols. Instructions from
Superscript III One-Step RT-PCR System with Platinum Taq (Invitrogen) were followed
using 1 !lg of total RNA and 21 cycles of amplification were used for each test. The
following primer pairs were used: HIF-1u (5'-CGAACTCGGTACTAAAGAACCTGC
and 5'-GGGTCCTACTTTCACGCAAGG), Hsf (5'-ATCTGCTGCGTGGCGATG and
5'-CGTCCGTGTCCAAAATGTCG), Hsp26 (5'-ATGGCGTGCTCACCGTCAGTATTC
and 5'-GGATGATGTTGGATGATGATGGCTC), Hsp27 (5'-
AGGAGGAAGAAGACGACGAGATTCG and 5'-
CATTGGGTGTGTTGTGGTGTGTCC), Hsp68 (5'-
TTCACCACCTATGCCGACAACCAG and 5'-
TCACATTCAGGATACCGTTTGCGTC), Hsp70Ab (5'-
TCCATTCAGGTGTATGAGGGCG and 5'-CGTTCAGGATTCCATTGGCGTC),
Hsp70Ba (5'-ACGATGCCAAGATGGACAAGGG and 5'-
CGTCTGGGTTGATGGATAGGTTGAG) and Actin5c (5'-
GGATGGTCTTGATTCTGCTGG and 5'-AGGTGGTTCCGCTCTTTTC).
Real-time PCR
Total RNA was isolated using standard Trizol protocols. cDNA was synthesized
following the SuperScript III Reverse Transcriptase protocol (Invitrogen). Real-time
PCR was performed using the Sybr Green PCR Master Mix (Applied Biosystems) and an
46
ABI PRISM 7900HT detection system (Applied Biosystems). The supplied analysis
software was used for data interpretation. The following primer pairs were used: Hsf (5'-
ACACCGCAGCCTCACATTATGACC and 5'...
ATTTCCCTGGAGCAGCAAGTCCTC), Hsp27 (5'-
AGGAGGAAGAAGACGACGAGATTCG and 5'-
CATTGGGTGTGTTGTGGTGTGTCC), Hsp68 (5'-
TTCACCACCTATGCCGACAACCAG and 5'-
TCACATTCAGGATACCGTTTGCGTC), Hsp70Ab (5'-
TCCATTCAGGTGTATGAGGGCG and 5'-CGTTCAGGATTCCATTGGCGTC),
Hsp70Ba (5'-ACGATGCCAAGATGGACAAGGG and 5'-
CGTCTGGGTTGATGGATAGGTTGAG) and Actin5c (5'~
TGCTGGAGGAGGAGGAGGAGAAGTC and 5'-
GCAGGTGGTTCGCTCTTTTCATe).
Hypoxia reporter construction
A small region of the Hsfintron containing the possible hypoxia regulatory
elements was cloned into the Green H Pelican reporter vector. KC167 cells were
transfected using the Effectene kit (Qiagen) with this reporter and put in normoxia,
hypoxia or hypoxia with HIF-la R.~Ai. Images were taken using a Nikon Eclipse
TE2000-U microscope and MetaVue image capture software.
47
Chromatin immunoprecipitation
KCl67 cells were transfected using the Effectene kit (Qiagen) with a pAc5.1/Sima
plasmid, which contains the full-length HIF-Iu (sima) cDNA sequence with a c-terminal
V5 epitope tag under the control of the Actin5c promoter. An equal quantity of mock
transfected cells was used as a control and all purification steps were carried out in
parallel with the control and experimental cells. 24 hours after transfection, control and
experimental cells were incubated in hypoxia at room temperature for 24 hours. DNA
isolation and purification procedures followed standard V5 protocol.
PCR was used to detect Hsj, Hph, and Actin5c genomic regions in each of the
samples of isolated DNA. 35 cycles were used to amplify 2 fll of template from each
sample using the following primers: Hsf(5/-CTCCCACCACATACCGCTAATC and 5/-
AAAAGCCAACTGAATGACCAAGG), Hph (5/-CCTTCTCACACTCCCTTCGCTG
and 5/-CACTCTCTGCCAAGCCAAACC), Actin5c (5'-
TGTGTGTGAGAGAGCGAAAGCC and 5'-CTGGAATAAACCGACTGAAAGTGG).
Larval survival
First instar larvae of wild-type or flies with only one copy of the Hsfgene were
counted and placed 10 per vial of food. These vials were split and placed into groups for
normoxic or hypoxia and reoxygenation stresses. This experimental group was
maintained at 0.5% oxygen for 23 hours, then placed in ambient oxygen for one hour,
before returning to hypoxia. This cycle was repeated daily and after two weeks the vials
were scored for survival.
48
Bridge to Chapter IV
By identifying HSF as a target of HIF-1, we described a mechanism by which
hypoxia activates a transcriptional response more commonly associated with heat stress.
In Chapter IV, more cross talk between stress pathways is reported. Here, we showed
that hypercapnia causes the repression ofNF-KB-dependent antimicrobial gene
expression.
-~~-----------------
49
CHAPTER IV
ELEVATED C02 SUPPRESSES SPECIFIC DROSOPHILA INNATE IMMUNE
RESPONSES DOWNSTREAM OF NF-kB PROTEOLYTIC ACTIVATION
Reproduced from LT. Helenius·, T. Krupinski·, D.W. Turnbull, Y. Gruenbaum, N.
Silverman, E.A. Johnson, P.H.S. Sporn, 1.1. Sznajder, G.J. Beitel. Submitted to Journal of
Experimental.Mediczne, 2008
*Authors contributed equally to this work
I performed the experiments and analyzed the data for the gene expression
microarray results described in this paper. I also helped with interpretation of the data
and the design of subsequent experiments based on it.
Although an average human produces 450 liters of CO2 per day (72) and elevated
levels of CO2 (hypercapnia) in the pulmonary and/or circulatory system are associated
with worse outcomes in chronic obstructive pulmonary disease (COPD) (73) (74), the
cellular pathways that sense and respond to C02 are poorly understood (reviewed in (75)
(76») and a comprehensive description of a CO2 response pathway is lacking in any
system. We sought to develop a genetically and molecularly tractable system for defining
non-neuronal CO2 response pathways and chose Drosophila because of extensive
conservation of the hypoxia (31), nitric oxide (NO) (77) and nearly all other major signal
50
transduction pathways between Drosophila and mammals. Further, Drosophila possess a
well-characterized, multi-component innate immune system controlled by conserved
signaling pathways that include NF-kB-family transcription factors (78). Genetic and
high throughput RNAi studies using whole Hies and S2 cell culture have contributed to
understanding the human innate immune system (reviewed in (79)), including
identification of Toll, the founding member of the Toll-like-receptor (TLR) family, and
most recently of the Akirin proteins that are required for NF-kB-dependent gene
expression (80).
In this report we show that Drosophila have specific physiological responses to
hypercapnia which include a suppression of particular NF-kB-regulated antimicrobial
peptides that is remarkably parallel to the suppression of specific NF-kB-regulated innate
immune/inflammatory cytokines in human macrophages (see accompanying report by
Wang et al. [refj). These results establish Drosophila as a general model for defining
non-neuronal C02 signaling pathways and as a specific model for investigating
suppression ofNF-kB effectors by hypercapnia.
Results and Discussion
Hypercapnia causes specific effects on Drosophila physiology independent of known
neuronal CO2 sensing pathways.
To determine whether hypercapnia affects Drosophila physiology, we exposed
Hies to 13% and 19.5% CO2 (~pC02 94 and 140 mmHg), while maintaining 02 at 21%
(see Materials and methods). These CO2 levels are well below the ~30% concentration at
which CO2 becomes anesthetic. Hypercapnia causes dose-dependent defects in
51
embryonic development. 13% C02 results in 20% of embryos having moderate defects
such as malformations of the airway system (compare Fig. IA and B) and also
significantly slows hatching of eggs laid by normocapnic mothers (Fig. ID,E). 19.5%
CO2 severely disrupts embryonic development with ~30% of exposed embryos showing
large-scale patterning and morphogenesis defects (Fig. IC), and more than 70% of eggs
failing to hatch (Fig. IE). In contrast, adult Drosophila survive exposure to 13% and
19.5% CO2 for many weeks (data not shown), consistent with the essentially normal
postnatal development of mice exposed to 12% CO2 (81). The effects of hypercapnia on
vertebrate embryonic development have not been reported, but these results suggest that
embryonic and adult metazoans may have different sensitivities to hypercapnia.
However, exposure of adult flies to hypercapnia is not without adverse consequences, as
hypercapnia causes a dose-dependent reduction in the number of eggs females lay (Fig.
1F). Importantly, flies homozygous for a null mutation in the neuronal CO2 receptor
Gr63a (82) (83) are as sensitive to hypercapnia as wild-type flies in the egg hatching and
laying assays (Fig. ID-F). Thus, many physiological effects of hypercapnia are mediated
by an as yet uncharacterized CO2 response pathway(s).
52
I:IWT
_Gr63.
Egg laying
Blot anti.AT!'"
air 13% 19.5%
lC02l
ATP" endocytosis in S2 cellsGElI9 hatching (48hj
• I::::IWT
-.sr63a
Egg hatcbing 124nj F
air 13% 195%
[C~l
D
1
E
Figure 1. Hypercapnia affects Drosophila development and physiology independently of neuronal
CO2 sensing. (A-C) Drosophila embryonic development is disrupted by hypercapnia. The embryonic
tracheal (airway) system, visualized by a lumenal stain, is an excellent readout of general morphogenesis
because tracheal branch guidance requires interaction with multiple tissues. Culturing wild-type embryos in
13% CO2 causes moderate defects in 20% of embryos (B, n=111), while 19.5% CO2 causes severe defects
in 28% of embryos (C, n=71) and increases the prevalence of moderate defects to 41%. Moderate defects
(B) include breaks in the main airways (arrowheads) and missing or ectopic interconnections of dorsal
branches (arrows), while severely abnormal tracheal development reveals gross embryonic patterning
and/or morphogenesis defects (C). (D,E) Hypercapnia (24h, D) decreases hatching of eggs laid in
normocapnia by wild-type (WT) or mutant flies homozygous for a null allele in the neuronal CO2 receptor,
Gr63a. In 13% CO2 over 90% of WT embryos hatch after 48h (E), but in 19.5% CO2 only ~30% hatch. (F)
Hypercapnia reduces the number of eggs laid in 48h by WT and Gr63a females mated in normocapnia. (G)
As in mammalian cells hypercapnia (lh) causes endocytosis of the Na,K-ATPase in 82 cells. * = p < 0.05,
** = P < 0.005 between CO2 condition and air; error bars are standard deviation.
We next asked whether Drosophila share with mammals any specific response to
hypercapnia. We previously reported that in human alveolar epithelial cells, hypercapnia
causes endocytosis of the Na,K-ATPase (84). Analysis of surface abundance of Na,K-
ATPase in Drosophila 82 cells reveals that hypercapnia also causes dose-dependent
endocytosis of the sodium pump in 82 cells (Fig. 1G). As Gr63a is expressed only in
specific CO2-sensing olfactory neurons (82), the responsiveness of the hematopoietic 82
cell line to hypercapnia further supports the existence of cell autonomous C02 responses
that do not depend on the neuronal Gr63a sensing pathway. That both Drosophila and
53
human cells endocytose their Na,K-ATPase in response to hypercapnia suggests that
some C02 responses are conserved between mammals and flies.
Hypercapnia causes specific effects on gene expression
To investigate the molecular basis of the physiological effects of hypercapnia and to
identify CO2-responsive promoters to use as markers for dissecting C02 signaling
pathways, we next performed microarray analysis on adult flies. Similar to the limited
changes in gene expression seen in neonatal mice raised in CO2 (81), exposure of adult
flies to 24h of 13% CO2 causes fewer than 500 genes to be up- or down-regulated more
than 1.5-fold, and fewer than 10 by more than 10-fold (data not shown). Therefore, in
Drosophila CO2 causes specific rather than global changes in gene expression. Consistent
with the dramatic decrease in fecundity of female flies during hypercapnia, the chorion
and vitelline membrane genes required for egg production are among the genes most
highly down-regulated by elevated C02 (Table Sl). Notably, C02 does not induce genes
characteristic of hypoxic, heat shock or oxidative stress responses (Table Sl), indicating
that the responses to elevated C02 are mediated by distinct pathways. Most intriguingly,
a subset of the antimicrobial peptide (AMP) genes that are effectors of the Drosophila
innate immune system are down-regulated by elevated C02 levels (Fig. 2A). Given the
prevaLence of pulmonary infections in hypercapnic COPD patients (85), we focused on
defining the effects of hypercapnia on the highly conserved NF-kB-regulated innate
immune system of Drosophila and determining whether hypercapnia could render a host
more susceptible to infection by a pathogen.
54
E
8' 2. Mult fly q?CR
;!
~;- 1.
" m
- Qi i 1.
~~
.~ ,., 0.•
n.
~ 0 <I' ? <I" ",q,(z <:l Q~ +~
D
B
52' cell qPCR
!tlllchallenged}
Adult fly microarray
Figure 2. Hypercapnia down-regulates specific antimicrobial peptides in Drosophila
(A-B) Hypercapnia (24h) suppresses specific antimicrobial peptides (AMPs) in vivo in unchallenged adults
as shown by microarray analysis (A) and confirmed by quantitative pCR (B). (C-D) Hypercapnia
suppresses specific AMPs in vitro (lOh CO2 for C,D; pGN challenge at 5h in D). (E) Suppression of the
AMP Diptericin by hypercapnia in S2* cells is dose-dependent. Protein levels of AMPs were not assessed
because antibodies against Drosophila AMPs are not available. * = p < 0.05, ** = P < 0.005 between CO2
condition and air; error bars are standard deviation. pGN = E. coli peptidoglycan; Drs = Drosocin; Att =
Attacin; Dpt = Diptericin; Cec = Cecropin; Def = Defensin; Drm = Drosomycin; Mtk = Metchnikowin.
Hypercapnia suppresses select antimicrobial peptide genes in vivo and in vitro
We confirmed and extended our microarray results using quantitative real-time
reverse transcriptase PCR (qPCR) and found that hypercapnia indeed suppresses
expression of many AMPs in adult flies (Fig. 2B). To identify a simplified in vitro system
for investigating hypercapnic immune suppression, we tested the effects of hypercapnia
on the expression and induction of AMPs in Drosophila S2* cells, a macrophage-like cell
line whose viability is unaffected by 13% C02 (Table S2). Consistent with the adult fly
data, exposing S2* cells to 13% CO2 in the absence or presence of immune challenge
significantly suppresses the expression of a subset of AMPs (Figs. 2C,D,Sl). Notably,
55
Diptericin (Dpt) and Attacin, canonical effectors of the TNF-like IMD pathway (78), are
consistently suppressed 3- to 5-fold. In contrast, Metchnikowin is not suppressed by
hypercapnia. The effects of hypercapnia on AMPs are dose-dependent, and even a
modest 5% C02 causes a 50% suppression of Dpt induction in response to PGN (Fig.
2E). As the bulk of AMPs in adult flies are produced by the fat body, the fly's functional
equivalent of a liver, the similar effects of hypercapnia on AMP expression in whole flies
and in S2* cells suggests that the effects of hypercapnia on the innate immune system
affect multiple cell types. Thus, these results establish S2* cells as a powerful in vitro
tool for defining the molecular details of CO2response pathways.
Hypercapnia increases susceptibility ofDrosophila to specific bacterial infections
Antimicrobial peptides are a crucial element in the fly's defense against
pathogens as mutations that decrease AMP production decrease resistance to infection
(86). We therefore asked if hypercapnic suppression of AMPs reduces the ability of flies
to survive infection with bacteria, including the natural Drosophila pathogen E. faecalis
(87), and a human pathogen commonly isolated from COPD patients, S. aureus (88).
Elevated C02 does not decrease survival from inoculation with sterile PBS (Fig. 3A) or
with E. coli, which is not a Drosophila pathogen (Fig. 3B). However, at 13% CO2, flies
show significantly increased mortality when infected with E. faecalis (Fig. 3C).
Strikingly, C02 levels as low as 7% increase mortality to S. aureus (Fig. 3D).
56
,..... air
~7%CC:l
Staphylococcus aurelJS
(p .. c,tJtI8lij
10 20 30 ~ 50 til
Hours al:H inCtul3'U".•
1011'....--..
o
--------I
EIJteroroccus fsecaNs
lP"tHJ03t)
• ....·ar
~n%C~
10 20 30 40 50 GO 10
HOllU a!tl!r inOOJat.,.
B Escherichia coli CPBS
--01
-""alr
.... ')%:0"
.,.,..,
Oatli aller Ir~."\JU:lll
A
1,
H>
0.11
i
eM
o
StaphyIOCf)CCUS 8ureus
19r11Wtll1n l.!IJ
GEnterococcus faecafis
(grOWt!lIll1.6)
••" )V rr'Il
Q I"SOO, //)7....
.>rt
.;/;"
::: •.A.C'-f::::
Y
<l~~:;"-;-.--.--....---.-...,--.-­
2 4 , U d U U
Ttme {houts)
F
.....1'
"'~4COz~alf
~ ~ GO 80100
>llI,t& ..ner VlOCJ\a~OO
StaphylocCK;CIIS aUfQUS
(P"U.00811
E
Figure 3. Hypercapnia increases mortality of bacterial infection in Drosophila. (A-D) Hypercapnia
does not increase mortality of flies inoculated with PBS (A) or with E. coli (B), which is not a Drosophila
pathogen, but does significantly increase mortality after inoculation with the Drosophila natural pathogen
E. faecalis (C), or the human opportunistic pathogen S. aureus (D) at CO2 levels as low as 7%. Unless
otherwise noted, flies were exposed to indicated CO2 level for 24h before inoculation and returned to
hypercapnia until end of assay. We show representative results for the lowest CO2 levels at which
significant effects on mortality were consistently observed. All experiments were done in triplicate and the
trial with the middle p-value is shown. (E) Pre-treatment of flies with 9% CO2 prior to S. aureus infection
in air is sufficient to increase mortality even when flies are cultured in air during and after inoculation.
(F,G) Effects of hypercapnia on bacterial growth. Note that S. aureus growth is dramatically reduced in 7%
CO2 even though hypercapnia increases mortality of flies infected with S. aureus. Error bars smaller than
the data-point symbols are not shown. (A-E) Kaplan-Meier survival curves, p-value determined by log-rank
analysis.
----------------
57
Although the above results demonstrate that hypercapnia can decrease survival
during infection, they do not distinguish whether decreased survival results from
suppression of host defense, from enhanced bacterial pathogenicity, or both. We
definitively demonstrate that increased mortality results at least in part from direct effects
of CO2 on the fly immune system by exposing flies to 9% CO2 for 24h and returning
them to normocapnia after inoculation with S. aureus. This hypercapnic pre-treatment of
the host significantly increases mortality after pathogen infection although the pathogen
was never exposed to hypercapnia (Fig. 3E).
We also investigated whether hypercapnia alters bacterial growth rates. Growth of
E. faecalis in media is not significantly affected by elevated CO2 (Fig. 3F). However,
despite the markedly increased mortality of S. aureus-infected flies, growth of S. aureus
is actually reduced several-fold by even 7% CO2 (Fig. 3G). As decreased bacterial
growth would have been expected to improve fly survival, the observed increase in
mortality further supports the conclusion that hypercapnia suppresses Drosophila innate
immune responses.
Importantly, the decreased growth rates of some bacteria in elevated CO2 may
provide an explanation for the existence of hypercapnic immune suppression. Because
hyperactivation of immune responses in Drosophila or humans can cause pathology
beyond that of an infection alone, immune responses may be deliberately attenuated
during hypercapnia to prevent overreaction when pathogen growth is reduced. The
observed increase in mortality during hypercapnia could result from an imperfect match
between the degree of immune suppression and changes in bacterial growth. Another, and
not exclusive, possibility is that CO2 serves as a readout of metabolic activity with
58
elevated C02 indicating excessive metabolic load. Thus, hypercapnia may suppress select
metabolically demanding functions including immune responses (89) and egg-laying to
allocate energy to more immediate needs such as powering the flight muscles, which are
among the most metabolically active tissues known.
Hypercapnia suppresses, not delays, innate immune responses
To further investigate the nature of hypercapnic immune suppression, we determined how
hypercapnia affects the kinetics of innate immune responses. Hypercapnia does not alter
the timing of AMP induction in PGN-challenged S2* cells, but the magnitude of the
responses at any time is reduced (Fig. 4A). Thus, hypercapnia suppresses rather than
delays innate immune responses. In S2* cells, this suppression exhibits rapid onset and
recovery, as shifting cells from air to 13% C02 simultaneous with PGN challenge causes
a two-fold reduction in Dpt induction, and increasing the pretreatment time beyond 5h
does not further increase the suppression (Fig. 4B). Conversely, CO2-treated cells
challenged upon shift to normocapnia have normal induction of Dpt (Fig. 4C). The
effects of hypercapnia on AMP induction in S2* cells closely parallel the effects on
cytokines in human macrophages as detailed in the accompanying report by Wang et al
[ref], supporting the existence of a conserved molecular mechanism of hypercapnic
. .Immune suppreSSIOn.
A S2"ceUs
(PGN·cbattenged)
-.-air
-Ir13% C02
59
1 3 5
Hours of PGN challenge
1
B 52" celts
{PGW-cball@nged)
iij U)
sa
III
'i en
.\It'
~
_ \)I\!'ill~
~~ "'co.
~
c
i
.si 1.004.....
~.....
52" cells
fPGN-cbalie ng@d)
Figure 4. Hypercapnia suppresses antimicrobial peptide induction with rapid onset and recovery. (A)
Hypercapnia suppresses, not delays, antimicrobial peptide (AMP) induction in vitro as assayed by qPCR of
Diptericin (Dpt) in PGN-challenged S2* cells. (B) qPCR of Dpt in PGN-challenged S2* cells shows that
the onset of immune suppressive effects of hypercapnia is rapid as shifting cells to hypercapnia at the time
of PGN challenge results in ~50% suppression of Dpt with maximal suppression reached by 5h. (C, Left)
Recovery from immune suppression is also rapid as challenging S2* cells with PGN in air immediately
after removal from CO2 results in full induction of Dpt. (C, right) As in unchallenged flies and S2* cells,
Drosomycin is not strongly regulated by hypercapnia. * = p < 0.05, ** = P < 0.005; error bars are standard
deviation.
60
PGN 110 O,~ll 1Ii no 0511 111
, "," r...;,;,,:,.-.:.:..:...;--'-"-----------.k-I50kD
tjrp-R~. I I
(~"oll'","' i S:ICO:<lJGrPIl'If- _
I\lllf')
L-' t-JlkD
Dair
.13%CO,
D
100
Q)
Ol
~ r ..... ().75
£: .=
U ro
u 0
~ ~ 0.50
.~ .i;
u ro
";:: Qj 025
Q) ~
0.-
o
Btz:aL-NAME
R:'IDSNAP
air
100
Q)
Ol
C
~ :;- 0 7~
u ro
u 00-
E.~ 050
u ro
";:: ill
Q) ~
a -- 0.25
o
c
A
Figure 5. Hypercapnia suppresses innate immune responses vill II novel pathwlly.
(A) Suppression of Oiptericin (Opt) in PGN-challenged S2* cells is not attenuated 01' enhanced by the NO
synthase inhibitor L-NAME (ImM) or the NO donor SNAP (lmM). (8) Hypercapnia suppresses Opt
induction five-fold wilen the culture medium is adjusted to pH7.0 with NaOH, but Opt induction is only
suppressed -two-fold by MOPS at pH6.5 and by NaC] and HEPES at pH7.0. (C) Hypercapnia does not
alter Relish (ReI) cleavage in response to PGN challenge. GFP immunoblot of whole-cell Iysates of S2
cells expressing GFP-Rel. (0) Models for hypercapnic suppression of innate immune effectors. Model I: a
hypercapnia response pathway modulates the nuclear import or activity of either Rei or a component of the
Rei transcriptional complex, thus reducing Opt expression. Model 2: analogous to the regulation of hypoxia
responsive genes by the HIF transcription factor {Semenza, 2007, Sci STKE, 2007, cm8}, a COz response
pathway activates a hypothetical COz responsive factor (C02RF) that binds specific CO2 response elements
(COzRE), inhibiting the Rei complex 01' directly suppressing Opt expression. The conceptual difference
between the two models is that, in model I, genetic removal of the component of the ReI complex targeted
by the CO2 response pathway would significantly alter Dpt transcription even in the absence of COz. In
contrast, remova'i of the COz responsive factor in model 2 wou Id render the promoter unresponsive to COz
regulation without affecting basal or induced transcription of Opt in ambient air. * = p < 0.05, ** = P <
0.005 between COz condition and equivalently treated ail' condition (A) or untreated ail' condition (8); error
bars are standard deviation.
61
Hypercapnic immunosuppression is not mediated by nitric oxide or acidosis
As discussed above, the effects of hypercapnia on Drosophila physiology and the
immune responses of S2 cells are not mediated via neuronal CO2 sensing, and
hypercapnia acts via pathways distinct from hypoxia, heat shock and oxidative stress.
Because no non-neuronal CO2 sensing pathways have been defined, we tested possible
roles of candidate signaling pathways in hypercapnic immune suppression. NO has
important roles in innate immune function in Drosophila and mammals (77) (90), and
hypercapnia has been proposed to modulate NO-dependent pathways and inflammatory
oxidants (91) (92). However, NO does not mediate hypercapnic immune suppression
because hypercapnic suppression of Dpt in S2* cells is unaffected by either an NO
synthase inhibitor or an NO donor (Fig. 5A).
Another candidate mediator of hypercapnic effects is acidosis, because C02 reacts
with water to form carbonic acid (H+C03} However, hypercapnic immune suppression
is unlikely to result from sustained intracellular acidosis as we have previously shown
that in primary mammalian alveolar epithelial cells hypercapnia causes only a minutes-
long drop in intracellular pH (93). Here we show that extracellular acidosis does not
account for hypercapnic immune suppression in S2* cells because acidifying S2* culture
media with 19mM MOPS in normocapnia causes only a ~two-fold suppression of Dpt
expression that is less than half that caused by 13% CO2 (Fig. 5B). Notably, 25mM NaCl
or HEPES at neutral pH causes suppression equal to that of acidosis, making it difficult to
discern acidotic from ionic or non-specific effects. We definitively demonstrate effects of
CO2 independent of extracellular acidosis by maintaining the S2* cell media at neutral
62
pH during exposure to 13% C02 and finding that hypercapnia suppresses Dpt induction
to the same extent seen in hypercapnic acidosis (Fig. 5B, black bars). Similarly, Wang et
al. [ref] show that hypercapnia suppresses innate immune responses of human alveolar
macrophages even when extracellular acidosis is neutralized. Thus, in both flies and
mammals, hypercapnia has a unique and consistent pattern of immune suppression
distinct from the effects of extracellular acidosis.
Hypercapnia acts downstream of, or in parallel to, NF-kB activation
How does hypercapnia modulate innate immune responses? In S2 cells, induction
of AMPs by E. coli PGN is entirely dependent on the TNF-like IMD pathway Rel/NF-kB
factor Relish (ReI) (94) (95). ReI contains an N-terminal ReI homology domain (RHD),
which is endoproteolytically cleaved from a C-terminal inhibitory IkB-like region in
response to PGN stimulation (96). In S2 cells exposed to 13% hypercapnia, there is no
reduction in the PGN-induced proteolytic cleavage of ReI (Fig. 5C). Therefore,
hypercapnia inhibits expression of ReI targets downstream of proteolytic activation either
by acting on one of the components of the ReI transcription complex (Fig. 5D, model 1),
or on unidentified transcriptional complexes acting in parallel to ReI (Fig. 5D, model 2).
Although further work is required to elucidate the exact mechanism, together with the
work of Wang et al. in mammalian macrophages showing that hypercapnia suppresses
NF-kB targets without blocking proteolytic activation of ReIA, our combined results
suggest that this mechanism is conserved.
63
Concluding Remarks
Hypercapnic suppression of innate immune and inflammatory responses has
important implications for human health. Previous reports have suggested that
hypercapnia can have beneficial effects when "permissive hypercapnia" regimens are
used during mechanical ventilation (97), and hypercapnic suppression of NF-kB-
mediated inflammatory responses may account for the favorable outcomes observed
when C02 is used as an inflatant in laparoscopic surgery (98). However, suppression of
innate immune responses by hypercapnia in Drosophila as well as in a recent bacterial
pneumonia model in rats (99) is highly suggestive of elevated C02 causally contributing
to worse outcomes of hypercapnic COPD patients by promoting bacterial infections and
exacerbations. The current lack of understanding of the molecular pathways that mediate
C02 responses severely limits our ability to assess and intervene in pathological
situations involving hypercapnia. The extensive conservation of innate immune and other
biologically important cellular pathways between Drosophila and vertebrates, and the
findings presented in this report, establish adult Drosophila and Drosophila S2 cells as
genetically and molecularly tractable systems in which to identify, and define the
functions of, components of non-neuronal C02 response pathways.
Materials and Methods
C02 exposure. C02 treatments on flies and cells were performed in a BioSpherix
C-Chamber (BioSpherix Ltd, Redfield, NY) fitted with a ProC02 regulator using 20%
C02121 % 0 21 59% N2 gas input. C02 concentrations for in vivo assays were confirmed
using a FYRITE gas analyzer with C02 Indicator (Bacharach Inc., New Kensington, PA).
64
For cell culture experiments, pC02 and pH of media were measured using a STAT
PROFILE pHOx Plus blood gas analyzer (Nova Biomedical, Waltham, MA). Culture
medium was pre-equilibrated at desired C02 concentration overnight into which cells
were then resuspended. Unless otherwise noted, cells were exposed to CO2 for 5h prior to
PGN challenge.
Cell culture, Western blotting and reagents. Drosophila S2 and S2* cells were
grown in Schneider's insect medium (GIBCO, Carlsbad, CA, cat. #11720) with 10% FBS
(Valley Biomedical, Winchester, VA, cat. #BS3032), supplemented with 0.2% Penicillin-
Streptomycin (GIBCO, cat. #15140) and GlutaMAX (GIBCO, cat. #35050) at room
temperature. S2 GFP-Relish cells were a gift from PH O'Farrell (UCSF). Mouse
monoclonal anti-GFP antibody was used at 1:200 (Santa Cruz Biotechnology, Santa
Cruz, CA, cat. #sc-9996). Anti-actin antibody from Sigma (cat. #A2066) was used at
1:2000. Priming of immune response in cell culture was achieved by treatment with ImM
final concentration 20-hydroxyecdysone (Sjgma, St. Louis, MO, cat. #H5142) for 15h
prior to exposure to C02 or other experimental condition (fresh ecdysone was added upon
resuspension of cells in CO2-pre-equlibrated medium). Purified E. coli peptidoglycan (E.
coli 0111:B4, InvivoGen, San Diego, CA, cat. #tlrl-pgnec) was used at 100ng/mL final
concentration for 5h (Fig.2D) or 2.5h (Figs. 2E, 4B,C, 5A,B). 1mM L-NAME (Nw-Nitro-
L-arginine methyl ester hydrochloride, Sigma cat. #N5751) was added 10h before C02
treatment, ImM SNAP (S-nitroso-N-acetyl-DL-penicillamine, Sigma cat. #N3398) was
added 3h before CO2treatment. For pH adjustment of Schneider's medium, the indicated
reagents (Fig. 5B) were added directly to the medium to the following final
concentrations: 19mM MOPS (3-(N-morpholino)propanesulfonic acid, Sigma cat. #M-
65
8899), 25mM NaCl, l2.5mM HEPES (free acid, Cellgro cat. #25-060-Cl, Manassas,
vA), 25mM NaOH (pre-equilibrated in C02 overnight before experiment). pH of
solutions remained constant for the duration of treatment of cells.
Cell surface protein biotinylation assay. Surface biotinylation was performed as
previously described (93). In brief, after treatment for lh in indicated C02 conditions
(Fig. 1G), S2 cells were labeled for l5min using 1 mg/ml EZ-link NHS-SS-biotin (Pierce
Chemical Co. cat. #21331, Rockford, IL). Cells were then washed, lysed and 40mg of
protein as measured by Bradford assay per sample were incubated overnight at 4°C with
end-over-end shaking in the presence of streptavidin beads (Pierce Chemical Co.). The
beads were thoroughly washed, resuspended in Laemmli sample buffer solution and
loaded onto SDS-PAGE gel with 30mg total cell lysate. a5 anti-Na,K-ATPase antibody
was obtained trom the Developmental Studies Hybridoma Bank (U. of Iowa, IA) and
used at 1:100 dilution.
Bacterial infection protocol. Fly stocks were raised on standard cornmeal-agar
medium at 25°C. Batches of 25-30 C02-anesthetized (for <10 min) Oregon-R flies were
challenged, in triplicate using a blinded protocol, by septic injury in the dorsal thorax
with a sharpened tungsten needle previously dipped in a PBS solution containing the
bacteria of interest. The vials were then exposed to C02 as described above at room
temperature and the surviving flies counted at indicated intervals. S. aureus and E.
faecalis (87) were a gift from DS Schneider (Stanford). All bacteria were grown on LB.
Kaplan-Meier survival plots were generated from combining the triplicates for a total of
~75 flies per survival curve.
66
Drosophila developmental and fecundity assays. Developmental defects were
assessed in embryos expressing GFP in the tracheal system (btl-GaI4 UAS-GFP/+) that
were collected in air for 2h, transferred to experimental chambers containing air or
elevated C02 for 19h and then evaluated for phenotypes. Defects were scored as
moderate if embryos had 5 or fewer dorsal trunk: breaks, and severe if there were more
than 5 breaks. No embryos cultured in 13% CO2 or ambient air displayed severe defects,
and the frequency of moderate defects was 20% and 4%, respectively. For the egg
hatching assay, eggs laid by flies in ambient air were collected on grape juice plates
covered with yeast for 1-2 hours. The eggs from 3 separate experiments of at least 60
eggs each were then counted and transferred to various C02 conditions. 24 and 48h later,
the hatched and unhatched eggs were counted. Gr63a l mutants were a gift from L
Vosshall (Rockefeller University). Fecundity was determined as per the protocol of Wang
et al. (Wang, B.D. et al., PNAS 101:12610-12615). In brief, 20 age-matched females and
males were mated for 2 days in air at 25°C. The females were then transferred to the
experimental conditions and eggs collected at approximately 8h intervals on standard
yeasted grape juice plates. Each experiment was done in triplicate.
Quantitative peR. 10mg of Trizol (Invitrogen, Carlsbad, CA, cat. #15596)
extracted m&""JA was treated with DNA-free (Ambion, Austin, TX, cat. #1906) according
to manufacturer's directions. 1mg of DNA-free-treated mRNA was used in reverse-
transcription reaction using Bio-Rad's iScript cDNA Synthesis Kit (Hercules, CA, cat.
#170-8891). The cDNAs were diluted in water to ~lng/ml. PCR reactions were
performed using iQ SYBR Green Supermix from Bio-Rad (cat. #170-8882) according to
manufacturer's directions. Real-time PCR was performed in 96-well plates on an iCycler
67
iQ (Bio-Rad). PCR conditions were: preincubation at 95°C for 3 min, 40 cycles: 30 sec at
95°C, 30 sec minute at 58°C, and 30 sec at 72°C. Quantitative analysis was performed
using the iCycler iQ Optical System Software v3.0a. PCR reactions were performed in
triplicate. The levels of expression of the gene of interest were normalized against
ribosomal protein 49 (RP49) transcript levels in each sample. Primers used were as
follows: RP49: forward 5'-GACGCTTCAAGGGACAGTATCTG-3', reverse 5'_
AAACGCGGTTCTGCATGAG-3' (Gottar, M. et at., Nature 416:640-644),
Drosomycin (Drm): forward 5'-CGTGAGAACCTTTTCCAATATGATG-3',
Reverse 5'TCCCAGGACCACCAGCAT-3 ';
Diptericin (Dpt): forward S'-GCTGCGCAATCGCTTCTACT-3',
reverse 5'-TGGTGGAGTGGGCTTCATG-3' (Gobert, V. et at., Science (New York, NY
302:2126-2130);
Attacin (Att): forward 5'-CTGACCAAGGGCATTGGCAATC-3',
reverse 5'-CTCCGGGCGTGTGTGTTTTGGTC-3';
Cecropin (Cec): forward 5'-CCATGAACTTCTACAACATCTTC-3',
reverse 5' -GCGATTCCCAGTCCCTGGATTG-3';
Metchnikowin (Mtk): forward 5'-GTGTGATGGCCACGGCTACATC-3',
reverse 5'-CGACATCAGCAGTGTGAATTTC-3';
Drosocin (Drs): forward 5'-CGCCTAAAGATGTGTGCATAC-3' ,
reverse 5' -CTCATTGTGTCACAATTGATTG-3';
Defensin (De£): forward 5'-CATGTCCTGGTGCATGAGGATG-3',
reverse: 5' -CGTTGCAGTAGCCGCCTTTGAAC-3'.
68
Whole fly microarrays. RNA was extracted from a minimum of 30 flies per
condition using Trizol according to manufacturer's instructions. Microarray analysis was
performed as previously described (Liu, G. et al., Physiological genomics 25:134-141).
Bridge to Chapter V
In Chapters II-IV, I discussed studies in which genetic approaches were utilized to
study physiological stress responses, making use of mutant Drosophila strains. Chapter
VI describes a technique I have developed that makes the characterization of mutants
such as these much faster and easier than is possible with current methodology.
69
CHAPTER V
GENOMIC REGIONAL CAPTURE FOR NEXT GENERATION SEQUENCING
In recent years, enormous leaps have been made in DNA sequencing technology,
enabling researchers to collect immense amounts of sequence data in much less time and
at a lesser cost than has previously been possible (l00) (101). Despite the low cost and
high throughput of these next generation sequencing (NGS) approaches, when seeking to
locate the genomic identities of uncharacterized mutations it is still not feasible to
sequence entire higher eukaryotic genomes. This fact necessitates a means by which
smaller genomic regions of interest can be isolated for in depth analysis.
Traditionally, DNA polymerase chain reaction (PCR) has been the technique
researchers have turned to for targeted enrichment of a particular genomic region. The
upper size limit of products that PCR can reliably generate (5-50 kilobases (kb)) is
considerably smaller than the genomic intervals typically identified by classical genetic
techniques as containing mutations of interest however, and is orders of magnitude
smaller than the amount of sequence data that can be generated in a single NGS run (up
to 420 rnegabases (mb) at 6x coverage). The inadequacy of PCR for isolating large
70
genomic regions for NOS has led to the development of a number of new techniques
designed to enrich for genomic regions that are hundreds or thousands of kb in size.
Recently, two separate research groups described techniques by which large
regions of a target genome can be captured for NOS by hybridization of target DNA to
custom DNA microarrays(26) (25). These approaches have been used to successfully
capture regions as large as 5 mb. Despite their effectivenes, array based genomic
regional capture approaches have some drawbacks. For one, complete sequence data
must be available for the genome being captured in order to design the custom
oligonucleotide probes contained on the array. These oligo sequences must also be
carefully optimized to have highly similar melting temperatures, which can be a laborious
process (26). Finally, custom high-density arrays are expensive, making the design of a
new array for each new genomic region of interest impractical for smaller laboratories
attempting to characterize mutants from genetic screens.
Other hybridization-based strategies have been used to enrich for specific
sequences within larger complex DNA samples. Hybridization of DNA prepared from
mixed bacterial populations to biotinylated oligonucleotide probes followed by capture
with streptavidin-conjugated beads has been used to enrich for and identify species-
specific sequences in both clinical and environmental microbiological samples (102)
(103). Another approach based on hybridization ofbiotinylated bacterial artificial
chromosome (BAC) DNA to homologous genomic DNA target regions of interest has
been shown to be an effective strategy for resequencing large segments of human DNA
by traditional Sanger DNA sequencing techniques (104). None of these
71
biotinlstreptavidin-based approaches have been utilized to target genomic regions for
sequencing by NGS, however.
In this chapter, I describe a method that I developed for isolating large genomic
regions of interest for NGS. My technique involves the hybridization of sheared target
genomic DNA to biotinylated homologous fosmid DNA. Following hybridization,
biotinylated probe / target genomic DNA hybrids are captured with streptavidin-
conjugated paramagnetic beads, and the target genomic DNA is eluted by heat
denaturation. The eluted DNA is then amplified and sequenced using Solexa massively
parallel sequencing. I have used this protocol to isolate a 336 kb genomic region that was
determined to contain a mutation of interest from a Caenorhabditis elegans strain
identified in a genetic screen. This captured DNA was sequenced using a fraction of a
lane in an Illumina Genome Analyzer II (GA-II) Solexa instrument, resulting in 863,420
reads of 31 bases each that aligned to the C. elegans genome, 31% of which were from
the targeted region. This represents 8,317,486 bases of sequence information from the
336 kb region of interest (~24x average coverage).
Materials and Methods
Genomic target DNA preparation. 5fAg of C. elegans genomic DNA was sheared
to an average size of 500 bp by sonication in a Bioruptor (Diagenode). The ends of the
sheared DNA fragments were then blunted using a QuickBlunt kit (Invitrogen), following
which, the fragments were purified with a peR purification kit (Qiagen). A-overhangs
were then added to the genomic fragments by incubation ofthe purified, blunted DNA
with 150 units of Klenow DNA polymerase exo- (New England Biolabs) and dATP at
72
37°C for 30 minutes. The fragments were then purified with a mini-elute PCR
purification kit (Qiagen). 7[A,L of 200 [A,M modified Solexa sequencing adapters
(Top strand:
5'AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTT
CCGATCTxxxxT 3'
Bottom strand:
5' phosphate xxxxAGATCGGAAGAGCTCGTATGCCGTCTTCTGCTTGC 3' (x -
barcode bases),
were then ligated the sheared genomic fragments at 16°C for 2 hours using 2000 units of
T4 DNA ligase (New England Biolabs). The ligation reaction was size separated by
agarose gel electrophoresis, and fragments between 150-500 bp in size were purified
from the gel using a Gel Extraction kit (Qiagen). The purified ligation products were
then PCR amplified using Phusion high fidelity DNA polymerase (New England Biolabs)
and the Solexa amplification primers
P5: 5'AATGATACGGCGACCACCGA3' and
P7: 5'CAAGCAGAAGACGGCATACGA3'. The following cycling conditions were
used for PCR: 98°C for 2 minutes, 15 cycles of 98°C for 10 seconds, 65°C for 30 sec,
72°C for 15 seconds. Following amplification, samples were size separated by agarose
gel electrophoresis, and fragments between 150 and 500 bp were purified with a Gel
extraction kit (Qiagen).
Biotinylatedprobe preparation. 11 fosmids which, taken together, are
homologous to a 336 kb region ofthe C. elegans genome, were pooled in equal amounts.
100 ng of this mixture were combined with 20 [A,L of2.5x random octamer solution
73
(Invitrogen) and heated to 100°C for 5 minutes. The mixture was rapidly cooled in an
ice/water bath, following which 5 ~L of biotin dNTP mixture (1mM biotin-14-dCTP,
ImM dCTP, 2mMdATP, 2mM dGTP, 2 mM dGTP, in 10 mM Tris-HCI (pH 7.5), ImM
Na2EDTA) (Invitrogen) was added, along with 1 ~L of Klenow fragment DNA
polymerase (Invitrogen) and ultra pure water to bring the reaction volume to 50 ~L. The
reaction was then incubated at 37°C for 1 hour, following which, the products were size-
separated on an agarose gel, and the predominant 100 bp product was purified with a Gel
extraction kit (Qiagen).
Streptavidin bead preparation. 50 ~L ofM270 streptavidin Dynabeads
(Invitrogen) were washed 3 times with 100 ~L of 6x SSC and resuspended in 100 ~L of
bead block buffer (2% I-Block (Tropix), 0.5% SDS, Ix PBS) (105). Beads were
incubated at room temperature for 30 minutes with occasional mixing, and were then
magnetically captured, and washed 3x with 6x SSC.
Hybridization, immobilization, elution and sequencing. 5 ~g of adapted, purified
genomic DNA was combined with 150 ng of purified biotinylated probe in 300 ~L of
hybridization buffer (54% formamide, Ix SSC, 1% SDS, SAx Denhardt's solution
(Sigma), Img/ml Salmon sperm DNA (Invitrogen). The mixture was heated to 100°C for
2 minutes, then transferred to a 42°C incubator, where it was incubated with mixing
overnight. Following overnight incubation, biotinylated probe/genomic DNA fragment
hybrids were immobilized by binding to prepared blocked and washed streptavidin beads
by combining the hybridization mixture (300 ~L) with the bead/SSC mixture (100 ~L),
and incubating at room temperature for 15 minutes with occasional mixing. Beads were
74
then magnetically captured, and washed 3x with wash solution 1 (Ix SSC, 0.15% SDS),
3x with wash solution 2 (.2x SSC), and 3x with wash solution 3 (0.05x SSC). After the
final wash step, the beads were resuspended in 200 !!L of ultra pure water, and heated to
100°C for 2 minutes, and quickly magnetically captured. The supernatant was carefully
collected and concentrated to a volume of20 !!L in a speedvac concentrator (Savant).
10!!L of the concentrated supernatant was then used as template for a PCR reaction
utilizing Solexa P5 and P7 primers and Phusion high fidelity DNA polymerase (l\few
England Biolabs) (2 minutes 98°C, 24 cycles of 98°C for 10 seconds, 65°C for 30 sec,
72°C for 15 seconds). The PCR product was purified with a PCR cleanup kit (Qiagen),
quantified, and submitted for Solexa sequencing on an Illumina Genome Analyzer II.
Results
Sequencing targeted genomic regions by NGS has previously been an expensive
proposition requiring the design and manufacture of custom DNA microarrays. The
method described here is considerably cheaper, easier, and faster than existing techniques
because readily available fosmid clones are used as the capture probe in place of the
microarrayed custom oligonucleotides (Figure I). In this pilot experiment, DNA from 11
fosmid clones representing 336,224 bases of the C. elegans were combined and used as
the capture probe (Table 1). The addition of biotin to the probe DNA allowed the
subsequent capture of hybridized target fragments using streptavidin-conjugated
paramagnetic beads. Biotin addition was carried out by the replication of the probe DNA
from random primers in the presence ofbiotinylated nucleotides. The extremely high
affinity of the biotin-streptavidin bond allowed stringent washing of the beads to
minimize the isolation of non-target DNA.
75
76
Genomit: DNA with region of interesl (red)
FOSl11id Jlool homologous 10 rcgion of intcrcst
<go 0
o
O:~II-II~ t~~ !~
-= 'f!!?
lin 2.B) Random prImc, synlhcsil:c
IlU (;1;- incorporating biotin, purify
/-
"
f I) She",
...... , ,;,.,
- /;" ..........
....... " -\ ,II 2.A) Ligmc Solcxa adaptcrs,
, purify, amplify
/,.,
"_//
........ \
//
-""".
..........
,
/' ,/ ".,..
~
-\ I' /1/)
.........
lj//"
4) Incubate wilh streplavidin/
paramagnctic bcads / ~
- /' .",
....... 1- ,;
\ -
•
5) Magnctically caplure bcads and \v.lsh
6) Elutc caplllrcd fragmcnts by hcal dcnaturation
.- "., ........-----------17) peR amplify with Solcxa adaptcr primcrs
..; -
-.-
I
\
IOOC
8) Submit fragmenl library lor Solexa sequencing
Figure I. Schematic representation of regional capture protocol. Genomic DNA containing a region of
interest is (I) sheared, and Solexa adapters are added by ligation and amplification (2.A). A pool of
Fosmids which is homologous to the region of interest is (2.B) converted to biotinylated probe by Klenow
polymerase-mediated synthesis incorporating biotinylated nucleotides. Sheared genomic DNA and
biotillyJated probe are hybridized (3), and the probe/target DNA hybrids are by the addition of streptavidill
paramagnetic beads (4), which are magnetically captured and washed (5). Genomic target fragments are
then eluted by heat denaturation (6), and amplified by peR with adapter-specific primers (7). The library
of target region DNA fragments is then submitted for Solexa sequencing.
77
Table 1. Fosmid clones used for preparation ofbiotinylated probes. The genomic coordinates of the
fosmids are listed. Taken together, these fosmids cover a genomic region of 336,224 bases.
Fosmid clone
WRM0613cF02
WRM0610dB09
WRM0629dDO I
WRM0619aCI0
WRM0636cG07
WRM0617aH02
WRM0630cB06
WRM0634aD 11
WRM0627aC04
WRM061aB03
WRM0615aE07
Chromosome
v
V
V
V
V
V
V
V
V
V
V
Clone start
14251674
14270881
14301880
14322557
14367151
14385040
14420643
14453407
14504084
14522269
14558577
Clone stop
14290493
14304812
14332947
14352744
14399508
14425855
14460182
14491275
14537563
14560418
14587898
Five !J.g of genomic target DNA was used as starting material. This DNA was
sheared and asymmetric Solexa sequencing adapters were attached by ligation.
Following ligation, the reaction was gel extracted to select the appropriate fragment size
for Solexa sequencing, as well as to remove adapter dimers. The fragments were then
amplified by PCR using an extremely high fidelity polymerase. The sheared, adapted
genomic target DNA was then hybridized overnight to the previously biotinylated fosmid
DNA. After hybridization, target DNAIbiotinylated fosmid DNA hybrids were purified
using streptavidin beads, which were stringently washed to remove un-hybridized
background DNA. Finally, the captured genomic DNA was eluted from the beads by
heat denaturation, and PCR amplified. After a final purification, the DNA sample was
ready to be Solexa sequenced. This entire protocol, excluding sequencing, can be
completed in two days.
Prior to submitting samples for NGS, I cloned several fragments in the library and
sequenced the inserts using Sanger sequencing (Figure 2A). This allowed me to verify
that the Solexa amplification and sequencing primers would bind in the proper locations
78
for NGS, and verified that many of the clones contained DNA that aligns to the C.
elegans genome in the target region. In order to provide additional verification of
enrichment of the target genomic region in the purified samples, I designed two sets of
PCR primers to amplify sequences within the captured DNA area (Figure 2B). Equal
quantities of pre-hybridization input DNA and purified DNA were used as template for
PCR. This reaction showed significant enrichment of the targeted region in the purified
sample.
79
A GAGATCTACACTCTn'CCCTACACGACGCTCnCCGATCTAAGCTA
GAGCTCGTATGCCGTCTTCTGCTTGCATGATACGG
CGACCACCGAGATCTACACTCTTTCCCTACACGAC
GCTCTTCCGATTTAACTTTGATTCTGGCAAATTTCT
GTGTTTCTCAAACGAATTCCAAAAAATAAAAACAAC
TTACCACTGCATTTTTGCATATTTCAAGAGTGTACT
CATAAGCGGGATAACTACTAATCGTATTTTCAGAAA
CAATAAATGCAAATTCATCGACAATTGATGCACATG
TTCCAACTTCTGAATTGACTTGAATGGGGGAACGT
AACGCTCCATACAGliCAATAATTGTTGAATTTCCA
CTTGTTTCTGTATCATTTAGCTTATATCGGAAGAGC
B Input Capture Input Capture
I II III IV V x
Prilller pair I Prj 111Cr pair 2
I'igure 2. Verification of captured library prior to NGS. Before submitting the captured libraly of
fragments for Solexa sequencing, fragments were cloned and sequenced. A) An example clone sequence
shows the correct Solexa P5 (orange) and P7 (green) amplification primer sequences, Solexa sequencing
primer sequence (blue), barcode (red), and an insert sequence that aligns to the target region of the C.
elegans genome (highlighted in red) by BLAST. B) Equal amounts of input and region-captured DNA
fragments were used as template for peR using two primer sets that amplify products within the target
region. Much more product is generated using the captured template indicating enrichment of the target
region.
Because barcoded Solexa adapters were used in the preparation of the DNA
sample, samples prepared with other barcodes by other researchers could be combined
with my sample in the same lane of the GA-II so that the cost ofthe sequencing run could
80
be shared. The GA-II data analysis software aligns the output of the sequencing run to
available reference genomes. This alignment yielded 863,420 reads of 31 bases each that
aligned to unique locations in the C. elegans genome. Of these reads, 268,305 (31 %)
were from within the targeted region. These reads provide 8,317,455 bases of data,
which provides an average 24x coverage of the targeted region (Figure 3). The
variability in coverage at different locations within the region may be related to GC
content variations within the region, or could be related to the number ofcycles used in
the amplification reactions in the protocol. Future optimization of the protocol will
probably be able to provide more consistent depth of coverage throughout the captured
regIOn.
81
( ) :<
:>
:>
...
...
...
...
( ) ......
( ) ...
336 kb
Figure 3. Graphical representation of sequences from regionally captured samples aligned to a portion of
the C. e/egans genome. The area of chromosome V that was targeted for capture is indicated in red
bordered by blue Iines. Black bars beneath the zoomed in area of chromosome V indicate sequence reads.
31% of the total sequence reads that aligned uniquely to the genome were within the target region. Other
reads were scattered randomly throughout the genome.
Discussion
One major drawback of NOS approaches is that they do not provide an easy
means of sequencing specific genomic areas since the sequencing reaction does not
proceed from a fixed gene-specific primer as it does in Sanger sequencing. Because of
this limitation, a method for capturing specific genomic regions for NOS by DNA
microarray hybridization was previously developed. Here, I have described an
alternative to the array-based capture method, which is cheaper, faster, and easier to
perform.
82
I have demonstrated the effectiveness of this protocol by capturing and
sequencing a 336 kb region of the C. elegans genome from a mutant strain isolated in a
genetic screen. Despite the fact that a large number of sequences obtained were from
outside the region of interest, the enrichment was sufficient to sequence the mutation-
containing region with an average 24x coverage. The data from the sequencing run was
obtained quite recently, so we have only begun to analyze the data. In fairly short order,
we will be able to visualize SNPs between the obtained aligned sequences and the C.
elegans reference genome, which should make identification ofthe SNP responsible for
the mutant phenotype quite easy.
This regional capture technique has applications beyond the characterization of
mutants from genetic screens in model organisms. Using a fraction of a lane in a GA-II
NGS instrument, my protocol was able to provide 24x coverage of the targeted region.
This depth of coverage would have increased substantially if the entire lane had been
dedicated to sequencing the captured region. With increased depth of coverage, the
captured region could be assembled without the aid of a reference genome, and this
protocol would be a viable method of capturing and sequencing genomic regions of non-
model organisms with incompletely sequenced genomes. A variation on this protocol is
currently being evaluated as a means of mapping transposon insertion sites in maize.
Indeed, researchers should be able to make use of this protocol, or a modification of it,
for any situation in which they would like to enrich for a specific genomic location for
NGS.
83
Bridge to Chapter VI
In Chapter V I described a technique I have developed for sequencing specific
DNA regions using NGS. This protocol is considerably faster and less expensive than
existing techniques. In Chapter VI I will summarize the findings from Chapters II, III,
IV, and V, and highlight the broader significance of these results
84
CHAPTER VI
CONCLUSION
Stress-responsive gene regulation
A key adaptation organisms make in order to survive physiological stresses is the
differential regulation of gene expression. This regulation is directly carried out by
stress-responsive transcription factors. One physiological stress that all eukaryotic
organisms must cope with is hypoxia. In Chapter II I have identified genes that are
regulated by hypoxia, and have determined the roles of five different hypoxia-responsive
transcription factors in the regulation of this set of hypoxia-modulated transcripts. This
was achieved by examining hypoxic gene expression in D. meianogaster mutants lacking
the hypoxia-responsive transcription factors HIF-l, FOXO, Rei, p53, and MTF-l , and by
refining the lists of affected transcripts using bioinformatic analysis. I have identified
several previously characterized target genes of the five hypoxia-responsive transcription
factors, as well as genes that have never been previously described as targets. I have also
identified a mechanism by which HIF-l can contribute to the activation of FOXO during
hypoxia through the HIF-l-dependent transcriptional up regulation of Imp-L2. Finally, I
85
have determined that FOXO plays a much larger role in hypoxic gene regulation than has
previously been thought.
One set of genes that are highly transcriptionally up regulated in response to
hypoxia are those encoding heat shock proteins. In Chapter III, we showed that HIF-l
plays a significant role in the transcriptional up regulation of Heat shock protein (HSP)
genes during hypoxia. This occurs because HIF-l directly up regulates the transcription
of the Heat shock factor (HSF) gene during hypoxia, and this up regulation is necessary
for the full transcriptional induction of HSP's. We showed that this HIF-I-dependent up-
regulation of HSF during hypoxia is functionally significant because flies with reduced
HSF levels during hypoxia are less viable than wild-type flies.
Exposure to elevated CO2 levels (hypercapnia) is another stress that eukaryotes
adapt to by modulating gene expression. In Chapter IV, we identified genes that are
differentially regulated during hypercapnia in Drosophila. We found that the
transcription of immune response genes that are known targets of the transcription factor
Relish (ReI) is significantly repressed by hypercapnia. Interestingly, we showed that this
transcriptional repression occurs by a pathway that is either downstream or in parallel to
the canonical ReI activation pathway. Our results have medical relevance because
hypercapnia is frequently experienced by patients suffering from chronic pulmonary
obstructive disease and the suppression of immune response genes may promote bacterial
infections in patients experiencing this physiological stress. This study also establishes
Drosophila as a powerful model system for the study of hypercapnia.
86
Regional capture for next generation sequencing
The Sanger method of DNA sequencing is a well-established technology that is
widely available to researchers at numerous institutions across the world. However, the
amount of sequence data produced per Sanger sequencing run is limited to less than 1000
bases, making sequencing large DNA fragments with this technology costly and slow.
To address this limitation, next generation sequencing (NGS) techniques have been
developed in recent years that produce much more sequence data per run. The downside
of these new approaches is that the sequence reads produced by NGS instruments are not
targeted to a specific region of the template DNA making the sequencing of specific
regions of complex genomic DNA samples impossible with these techniques. Custom
DNA microarray-based regional capture schemes have been developed to facilitate the
enrichment of specific DNA regions for NGS, but these approaches are slow and costly.
In Chapter V I have described a regional capture protocol that provides enrichment of
target DNA sequences that is comparable to the array-based schemes. My approach is
considerably faster and less expensive, however because biotinylated fosmid DNA is
used in place of custom DNA microarrays as the probe for the hybridization-based
capture. I have demonstrated the effectiveness of my protocol by capturing a genomic
DNA region from a C. elegans strain that contains a mutation causing SNP. Sequencing
this region by NGS provided an average of 24x coverage for every base in the targeted
region, which was more than sufficient for identifying the SNP. My regional capture
protocol is broadly applicable to any situation in which a researcher desires to enrich for
a specific region of a complex DNA sample for sequencing by NGS.
87
APPENDIX
ONLINE SUPPLEMENTAL MATERIAL FOR CHAPTER IV
..Air; uninduced
IIIlIAir; PGN
fZ?JC02; uninduced
mC02 ;PGN
30
26
22
18
14
10
< 6z
0:::
0> 2~ 1.25a..
0:::
-a..
~ 1.00
0.75
0.50
0.25
0.00
Drs Att Opt Cee Def Orm Mtk
Figure 81. Absolute levels of antimicrobial peptide RNAs in 82* cells, normalized to RP49. CO2 = 13%,
1Dh duration before RNA extraction; PON = 5h before RNA extraction. This is the same experiment as in
Figure 2C and D. AMP abbreviations are as in Figure 2.
88
Table 81. Hypercapnia strongly down-regulates egg fonnation genes, but does not up-regulate genes
induced by hypoxic, heat shock, or oxidative stress responses.
CG6517 Chorion protein 18
CG6524 Chorion protein 19
CG6533 Chorion protein 16
CG 11213 Chorion protein 38
CG 1778 Chorion protein 36
CG6519 Chorion protein 15
CG2175 Defective chorion 1
CG9271 Vitelline membrane 34Ca
CG9048 Vitelline membrane 26Aa
CG 15349 Chorion protein a
CG15351 Chorion protein c
CG9046 Vitelline membrane 26Ab
B. H oxia-induced enes§
CG 11765 Peroxiredoxin
CG 11949 Coracle
CGl2896
CG 10160 ImpL3
CG6494 Hairy h
CGl3499
CG32130
CG 10078 Phosphoribosylamidotransferase 2
CGl600
CG8846 Thor
Fold
induction
13%
CO2
C. Heat shock-induced enes~
-42.3 CG16749
-38.5 CG18493
-28.1 CG9470 Metallothionein A
-26.5 CG3106
-20 CG6730 Cyp4d21
-lOA CG1946
-9.8 CG9080
-7.2 CG6602
-5.9 CG3360 Cyp313al
CG13833 (up 2.9-fold in heat
-5.8 shock)
-4.8
-4
D. Oxidative stress-induced
enest
-1.2 CG4533 1(2)efl
-1. 3 CG5164 GstEl
1.1 CG 10794 Diptericin B
1 CG31628 ade3
-1.3 CG18466 Nmdmc
-1.1 CG7632
-1.2 CGI0146 Attacin A
-1.6 CG8772 nemy
1.1 CG 14121
1.2 CG1383 Defensin
Fold
induction
1
1.1
1
-1
-1.2
1
-1.1
-1.1
-1.1
1.1
-1.2
1
-104
-1
1.1
-1.1
-2.3
1
-1
-2.1
Average fold induction in 13% CO2 (24h, CO2 versus air) of genes specific for egg fonnation (A) and
selected genes reported to be highly induced by previously characterized stresses (B-D). Fold
induction is the average of 3 separate microarray experiments of 5-day old flies. Stress genes are
shown in decreasing order of induction magnitude for particular stresses with maximal and minimal
values shown in parentheses. See references below for fold up-regulation by hypoxia§ (range:
CG8846 Thor 4.7-fold to CG 11765 Peroxiredoxin 8A-fold), heat shock~ (range: CG13833 2.9-fold to
CG 16749 41-fold) and oxidative stresst (range: CG 1383 Defensin 1.6-fold to CG4533 1(2)efl 7-fold).
§Hypoxia-induced genes reported by G. Uu et al., Physiol. Genomics 25: 131-141,2006
~Heat shock-induced genes reported by lG. Sorensen et al., Cell Stress & Chaperones, (2005) 10 (4) 312-
328
tOxidative stress-induced genes reported by G.N. Landis et al., PNAS 2004;101(20):7663-8
Table S2. Hypercapnia does not affect cell viability. Trypan blue dye exclusion assay
performed with S2* cells shows that hypercapnia and the other experimental conditions used
do not significantly alter cell viability. If indicated, cells were primed for immune response
with 20-hydroxyecdysone (ecd) at Oh, placed in 13% CO2 at 15h, and/or stimulated with E.
coli PON at 20h. Rapid cell death by strong oxidative stress shown for comparison.
t standard deviation of triplicate experiments in parentheses.
Time at
Experimental condition measurement (h) % cells containing dye t
Air 0 6.9 (0.13)
Air 23 5.6 (0.74)
Air + ecd 15 5.6 (0.82)
Air + ecd 23 4.4 (1.07)
13% C02 + ecd 23 5.7 (1.30)
air + ecd + PGN 23 5.2 (1.54)
13% C02 + ecd + PGN 23 6.0 (0.98)
Air + 100mM t-butyl hydroperoxide 1.5 31
Air + 1M t-but I h dro eroxide 1.5 100
89
90
REFERENCES
1. Semenza, G.L. 2007. Hypoxia-inducible factor 1 (HIF-l) pathway. Sci STKE.
2007:cm8.
2. Kewley, R.J., M.L. Whitelaw, and A. Chapman-Smith. 2004. The mammalian basic
helix-loop-helixlPAS family of transcriptional regulators. Int J Biochem Cell Bioi.
36:189-204.
3. Wang, G.L., B.H. Jiang, E.A. Rue, and G.L. Semenza. 1995. Hypoxia-inducible
factor 1 is a basic-helix-loop-helix-PAS heterodimer regulated by cellular 02
tension. Proc Natl Acad Sci USA. 92:5510-5514.
4. Stockmann, C., and J. Fandrey. 2006. Hypoxia-induced erythropoietin production: a
paradigm for oxygen-regulated gene expression. Clin Exp Pharmacol Physiol.
33:968-979.
5. Centanin, L., P.l. Ratcliffe, and P. Wappner. 2005. Reversion oflethality and
growth defects in Fatiga oxygen-sensor mutant flies by loss of hypoxia-inducible
factor-alpha/Sima. EMBO Rep. 6:1070-1075.
6. Baird, N.A., D.W. Turnbull, and E.A. Johnson. 2006. Induction of the heat shock
pathway during hypoxia requires regulation of heat shock factor by hypoxia-
inducible factor-I. J Bioi Chern. 281 :38675-38681.
7. Accili, D., and K.C. Arden. 2004. FoxOs at the crossroads of cellular metabolism,
differentiation, and transformation. Cell. 117:421-426.
8. Brunet, A., L.B. Sweeney, l.F. Sturgill, K.F. Chua, P.L. Greer, Y. Lin, H. Tran, S.E.
Ross, R. Mostoslavsky, H.Y. Cohen, L.S. Hu, H.L. Cheng, M.P. Jedrychowski, S.P.
Gygi, D.A. Sinclair, F.W. Alt, and M.E. Greenberg. 2004. Stress-dependent
regulation ofFOXO transcription factors by the SIRTI deacetylase. Science.
303:2011-2015.
9. Bakker, W.J., 1.S. Harris, and T.W. Mak. 2007. FOX03a is activated in response to
hypoxic stress and inhibits HIFl-induced apoptosis via regulation ofCITED2. Mol
Cell. 28:941-953.
91
10. Chen, F., V. Castranova, X. Shi, and L.M. Demers. 1999. New insights into the role
of nuclear factor-kappaB, a ubiquitous transcription factor in the initiation of
diseases. Clin Chem. 45:7-17.
11. Minakhina, S., and R. Steward. 2006. Nuclear factor-kappa B pathways in
Drosophila. Oncogene. 25:6749-6757.
12. Chandel, N.S., W.C. Trzyna, D.S. McClintock, and P.T. Schumacker. 2000. Role of
oxidants in NF-kappa B activation and TNF-alpha gene transcription induced by
hypoxia and endotoxin. J Immunol. 165:1013-1021.
13. Koong, A.C., E.Y Chen, and A.J. Giaccia. 1994. Hypoxia causes the activation of
nuclear factor kappa B through the phosphorylation of I kappa B alpha on tyrosine
residues. Cancer Res. 54:1425-1430.
14. Wingrove, J.A., and P.B. O'Farrell. 1999. Nitric oxide contributes to behavioral,
cellular, and developmental responses to low oxygen in Drosophila. Cell. 98:105-
114.
15. Galkin, A., A. Higgs, and S. Moncada. 2007. Nitric oxide and hypoxia. Essays
Biochem. 43 :29-42.
16. Liu, G., J. Roy, and E.A. Johnson. 2006. Identification and function of hypoxia-
response genes in Drosophila melanogaster. Physiol Genomics. 25: 134-141.
17. Slee, E.A., D.J. O'Connor, and X. Lu. 2004. To die or not to die: how does p53
decide? Oncogene. 23:2809-2818.
18. Chen, D., M. Li, J. Luo, and W. Gu. 2003. Direct interactions between HIF-l alpha
and Mdm2 modulate p53 function. J Bioi Chem. 278:13595-13598.
19. Chandel, N.S., M.G. Vander Heiden, C.B. Thompson, and P.T. Schumacker. 2000.
Redox regulation ofp53 during hypoxia. Oncogene. 19:3840-3848.
20. Li, Y, T. Kimura, J.B. Laity, and G.K. Andrews. 2006. The zinc-sensing
mechanism of mouse MTF-l involves linker peptides between the zinc fingers. Mol
Cell Bioi. 26:5580-5587.
21. Zhang, B., O. Georgiev, M. Hagmann, C. Gunes, M. Cramer, P. Faller, M. Vasak,
and W. Schaffner. 2003. Activity of metal-responsive transcription factor 1 by toxic
heavy metals and H202 in vitro is modulated by metallothionein. Mol Cell Bioi.
23:8471-8485.
22. Green, C.J., P. Lichtlen, N.T. Huynh, M. Yanovsky, K.R. Laderoute, W. Schaffner,
and B.J. Murphy. 2001. Placenta growth factor gene expression is induced by
92
hypoxia in fibroblasts: a central role for metal transcription factor-I. Cancer Res.
61 :2696-2703.
23. Murphy, RJ., G.K. Andrews, D. Bittel, D.J. Discher, J. McCue, C.J. Green, M.
Yanovsky, A. Giaccia, R.M. Sutherland, K.R. Laderoute, and K.A. Webster. 1999.
Activation of metallothionein gene expression by hypoxia involves metal response
elements and metal transcription factor-I. Cancer Res. 59:1315-1322.
24. Sanger, F., S. Nicklen, and A.R. Coulson. 1977. DNA sequencing with chain-
terminating inhibitors. Proc Nat! Acad Sci USA. 74:5463-5467.
25. Okou, D.T., K.M. Steinberg, C. Middle, D.J. Cutler, T.J. Albert, and M.E. Zwick.
2007. Microarray-based genomic selection for high-throughput resequencing. Nat
Methods. 4:907-909.
26. Albert, T.J., M.N. Molla, D.M. Muzny, L. Nazareth, D. Wheeler, X. Song, T.A.
Richmond, C.M. Middle, M.J. Rodesch, C.J. Packard, G.M. Weinstock, and R.A.
Gibbs. 2007. Direct selection ofhuman genomic loci by microarray hybridization.
Nat Methods. 4:903-905.
27. Brown, J.M., and A.J. Giaccia. 1998. The unique physiology of solid tumors:
opportunities (and problems) for cancer therapy. Cancer Res. 58: 1408-1416.
28. Hammond, E.M., and A.J. Giaccia. 2005. The role ofp53 in hypoxia-induced
apoptosis. Biochem Biophys Res Commun. 331 :718-725.
29. Chung, M.J., C. Hogstrand, and S.J. Lee. 2006. Cytotoxicity of nitric oxide is
alleviated by zinc-mediated expression of antioxidant genes. Exp Bio! Med
(Maywood). 231:1555-1563.
30. Bacon, N.C., P. Wappner, J.F. O'Rourke, S.M. Bartlett, R Shilo, C.W. Pugh, and
P.J. Ratcliffe. 1998. Regulation of the Drosophila bHLH-PAS protein Sima by
hypoxia: functional evidence for homology with mammalian HIF-1 alpha. Biochem
Biophys Res Commun. 249:811-816.
31. Lavista-Llanos, S., L. Centanin, M. Irisarri, D.M. Russo, J.M. Gleadle, S.N. Bocca,
M. Muzzopappa, P.J. Ratcliffe, and P. Wappner. 2002. Control of the hypoxic
response in Drosophila melanogaster by the basic helix-loop-helix PAS protein
similar. Mo! Cell Bio!. 22:6842-6853.
32. Ishwaran, H., J.S. Rao, and D.R Kogalur. 2006. BAMarraytrade mark: Java
software for Bayesian analysis ofvariance for microarray data. BMC
Bioinformatics.7:59.
93
33. Honegger, B., M. Galic, K. Kohler, F. Wittwer, W. Brogiolo, E. Hafen, and H.
Stocker. 2008. Imp-L2, a putative homolog of vertebrate IGF-binding protein 7,
counteracts insulin signaling in Drosophila and is essential for starvation resistance.
JBiol.7:10.
34. Murphy, B.J., T. Kimura, B.G. Sato, Y. Shi, and G.K. Andrews. 2008.
Metallothionein induction by hypoxia involves cooperative interactions between
metal-responsive transcription factor-l and hypoxia-inducible transcription factor-
1alpha. Mol Cancer Res. 6:483-490.
35. Krieg, A.J., E.M. Hammond, and A.J. Giaccia. 2006. Functional analysis ofp53
binding under differential stresses. Mol Cell Bioi. 26:7030-7045.
36. Pear, W.S., and M.C. Simon. 2005. Lasting longer without oxygen: The influence
of hypoxia on Notch signaling. Cancer Cell. 8:435-437.
37. Semenza, G.L. 1999. Regulation of mammalian 02 homeostasis by hypoxia-
inducible factor 1. Annu Rev Cell Dev Bioi. 15:551-578.
38. Hon, W.C., M.l. Wilson, K. Harlos, T.D. Claridge, C.J. Schofield, C.W. Pugh, P.H.
Maxwell, P.J. Ratcliffe, D.l. Stuart, and E.Y. Jones. 2002. Structural basis for the
recognition of hydroxyproline in HIF-l alpha by pVHL. Nature. 417:975-978.
39. Huang, L.E., J. Gu, M. Schau, and H.F. Bunn. 1998. Regulation of hypoxia-
inducible factor lalpha is mediated by an 02-dependent degradation domain via the
ubiquitin-proteasome pathway. Proc Natl Acad Sci USA. 95:7987-7992.
40. Ivan, M., K. Kondo, H. Yang, W. Kim, J. Valiando, M. Ohh, A. Salic, J.M. Asara,
W.S. Lane, and W.G.J. Kaelin. 2001. HIFalpha targeted for VHL-mediated
destruction by proline hydroxylation: implications for 02 sensing. Science.
292:464-468.
41. Jaakkola, P., D.R. Mole, Y.M. Tian, M.l. Wilson, J. Gielbert, SJ. Gaskell, A.
Kriegsheim, H.F. Hebestreit, M. Mukherji, C.J. Schofield, P.H. Maxwell, C.W.
Pugh, and P.J. Ratcliffe. 2001. Targeting ofHIF-alpha to the von Hippel-Lindau
ubiquitylation complex by 02-regulated prolyl hydroxylation. Science. 292:468-
472.
42. Huang, L.B., and H.F. Bunn. 2003. Hypoxia-inducible factor and its biomedical
relevance. J Bioi Chern. 278:19575-19578.
43. Pugh, C.W., and P.J. Ratcliffe. 2003. Regulation of angiogenesis by hypoxia: role
of the HIF system. Nat Med. 9:677-684.
94
44. Vogelstein, B., and K.W. Kinzler. 2004. Cancer genes and the pathways they
control. Nat Med. 10:789-799.
45. Semenza, G.L. 2002. HIF-l and tumor progression: pathophysiology and
therapeutics. Trends Mol Med. 8:S62-7.
46. Pouyssegur, 1., F. Dayan, and N.M. Mazure. 2006. Hypoxia signalling in cancer and
approaches to enforce tumour regression. Nature. 441:437-443.
47. Shen, C., D. Nettleton, M. Jiang, S.K. Kim, and J.A. Powell-Coffman. 2005. Roles
of the HIF-l hypoxia-inducible factor during hypoxia response in Caenorhabditis
elegans. J Biol Chem. 280:20580-20588.
48. Welch, W.J. 1993. Heat shock proteins functioning as molecular chaperones: their
roles in normal and stressed cells. Philos Trans R Soc Lond B Biol Sci. 339:327-
333.
49. Westwood, J.T., J. Clos, and C. Wu. 1991. Stress-induced oligomerization and
chromosomal relocalization of heat-shock factor. Nature. 353:822-827.
50. Wu, C. 1995. Heat shock transcription factors: structure and regulation. Annu Rev
Cell Dev Biol. 11 :441-469.
51. Orosz, A., J. Wisniewski, and C. Wu. 1996. Regulation of Drosophila heat shock
factor trimerization: global sequence requirements and independence of nuclear
localization. Mol Cell Biol. 16:7018-7030.
52. Pelham, H.R. 1982. A regulatory upstream promoter element in the Drosophila hsp
70 heat-shock gene. Cell. 30:517-528.
53. Nambu, J.R., W. Chen, S. Hu, and S.T. Crews. 1996. The Drosophila melanogaster
similar bHLH-PAS gene encodes a protein related to human hypoxia-inducible
factor 1 alpha and Drosophila single-minded. Gene. 172:249-254.
54. Semenza, G.L., B.B. Jiang, S.W. Leung, R. Passantino, J.P. Concordet, P. Maire,
and A. Giallongo. 1996. Hypoxia response elements in the aldolase A, enolase 1,
and lactate dehydrogenase A gene promoters contain essential binding sites for
hypoxia-inducible factor 1. J Biol Chem. 271:32529-32537.
55. Aprelikova, 0., G.V. Chandramouli, M. Wood, J.R. Vasselli, 1. Riss, J.K.
Maranchie, W.M. Linehan, and 1.C. Barrett. 2004. Regulation ofHIF prolyl
hydroxylases by hypoxia-inducible factors. J Cell Biochem. 92:491-501.
56. Bray, N., and L. Pachter. 2004. MAVID: constrained ancestral alignment of
multiple sequences. Genome Res. 14:693-699.
95
57. Barolo, S., L.A. Carver, and J.W. Posakony. 2000. GFP and beta-galactosidase
transformation vectors for promoter/enhancer analysis in Drosophila.
Biotechniques. 29:726, 728, 730, 732.
58. GOIT, T.A., T. Tomita, P. Wappner, and H.F. Bunn. 2004. Regulation ofDrosophila
hypoxia-inducible factor (HIF) activity in SL2 cells: identification of a hypoxia-
induced variant isoform of the HIFalpha homolog gene similar. J Bioi Chern.
279:36048-36058.
59. Jedlicka, P., M.A. Mortin, and C. Wu. 1997. Multiple functions of Drosophila heat
shock transcription factor in vivo. EMBO J. 16:2452-2462.
60. Donnelly, TJ., R.E. Sievers, F.L. Vissem, W.J. Welch, and C.L. Wolfe. 1992. Heat
shock protein induction in rat hearts. A role for improved myocardial salvage after
ischemia and reperfusion? Circulation. 85:769-778.
61. Kabakov, A.E., K.R. Budagova, A.L. Bryantsev, and D.S. Latchman. 2003. Heat
shock protein 70 or heat shock protein 27 overexpressed in human endothelial cells
during posthypoxic reoxygenation can protect from delayed apoptosis. Cell Stress
Chaperones. 8:335-347.
62. Morimoto, R.1. 1993. Cells in stress: transcriptional activation of heat shock genes.
Science. 259:1409-1410.
63. Zelzer, E., Y. Levy, C. Kahana, B.Z. Shilo, M. Rubinstein, and B. Cohen. 1998.
Insulin induces transcription of target genes through the hypoxia-inducible factor
HIF-1alpha/ARNT. EMBO J. 17:5085-5094.
64. Mottet, D., V. Dumont, Y. Deccache, C. Demazy, N. Ninane, M. Raes, and C.
Michiels. 2003. Regulation of hypoxia-inducible factor-1alpha protein level during
hypoxic conditions by the phosphatidylinosito13-kinase/Akt/glycogen synthase
kinase 3beta pathway in HepG2 cells. J Bioi Chern. 278:31277-31285.
65. Isaacs, J.S., Y.J. .lung, E.G. Mimnaugh, A. Martinez, F. Cuttitta, and L.M. Neckers.
2002. Hsp90 regulates a von Hippel Lindau-independent hypoxia-inducible factor-l
alpha-degradative pathway. J Bioi Chern. 277:29936-29944.
66. Minet, E., D. Mottet, G. Michel, 1. Roland, M. Raes, J. Remacle, and C. Michiels.
1999. Hypoxia-induced activation ofHIF-l: role ofHIF-lalpha-Hsp90 interaction.
FEBS Lett. 460:251-256.
67. Maloyan, A., L. Eli-Berchoer, G.L. Semenza, G. Gerstenblith, M.D. Stem, and M.
Horowitz. 2005. HIF-lalpha-targeted pathways are activated by heat acclimation
96
and contribute to acclimation-ischemic cross-tolerance in the heart. Physiol
Genomics.23:79-88.
68. Treinin, M., J. Shliar, H. Jiang, lA. Powell-Coffman, Z. Bromberg, and M.
Horowitz. 2003. HIF-1 is required for heat acclimation in the nematode
Caenorhabditis elegans. Physiol Genomics. 14:17-24.
69. Nakano, M., D.L. Mann, and A.A. Knowlton. 1997. Blocking the endogenous
increase in HSP 72 increases susceptibility to hypoxia and reoxygenation in isolated
adult feline cardiocytes. Circulation. 95:1523-1531.
70. Ciocca, D.R., and S.K. Calderwood. 2005. Heat shock proteins in cancer:
diagnostic, prognostic, predictive, and treatment implications. Cell Stress
Chaperones. 10:86-103.
71. Clemens, lC., C.A. Worby, N. Simonson-Leff, M. Muda, T. Maehama, B.A.
Hemmings, and J.E. Dixon. 2000. Use of double-stranded RNA interference in
Drosophila cell lines to dissect signal transduction pathways. Proc Natl Acad Sci U
SA. 97:6499-6503.
72. Cherniack, N.S., and G.S. Longobardo. 1970. Oxygen and carbon dioxide gas stores
of the body. Physiol Rev. 50:196-243.
73. Groenewegen, K.H., A.M. Schols, and E.F. Wouters. 2003. Mortality and mortality-
related factors after hospitalization for acute exacerbation of COPD. Chest.
124:459-467.
74. Connors, A.F.J., N.V. Dawson, C. Thomas, F.E.J. Harrell, N. Desbiens, W.J.
Fulkerson, P. Kussin, P. Bellamy, L. Goldman, and W.A. Knaus. 1996. Outcomes
following acute exacerbation of severe chronic obstructive lung disease. The
SUPPORT investigators (Study to Understand Prognoses and Preferences for
Outcomes and Risks of Treatments). Am J Respir Crit Care Med. 154:959-967.
75. Hetherington, A.M., and J.A. Raven. 2005. The biology of carbon dioxide. Curr
Bioi. 15:R406-10.
76. Bahn, y'S., and F.A. Muhlschlegel. 2006. C02 sensing in fungi and beyond. Curr
Opin Microbiol. 9:572-578.
77. Foley, E., and P.H. O'Farrell. 2003. Nitric oxide contributes to induction of innate
immune responses to gram-negative bacteria in Drosophila. Genes Dev. 17: 115-
125.
78. Lemaitre, B., and J. Hoffmann. 2007. The host defense of Drosophila melanogaster.
Annu Rev Immunol. 25:697-743.
97
79. Hoffmann, J.A., and 1M. Reichhart. 2002. Drosophila innate immunity: an
evolutionary perspective. Nat Immunol. 3:121-126.
80. Goto, A., K. Matsushita, V. Gesellchen, L. El Chamy, D. Kuttenkeuler, O.
Takeuchi, J.A. Hoffmann, S. Akira, M. Boutros, and J.M. Reichhart. 2008. Akirins
are highly conserved nuclear proteins required for NF-kappaB-dependent gene
expression in drosophila and mice. Nat Immunol. 9:97-104.
81. Li, G., D. Zhou, A.G. Vicencio, J. Ryu, J. Xue, A. Kanaan, O. Gavrialov, and G.G.
Haddad. 2006. Effect of carbon dioxide on neonatal mouse lung: a genomic
approach. J Appl Physiol. 101:1556-1564.
82. Jones, W.D., P. Cayirliog1u, LG. Kadow, and L.B. Vosshall. 2007. Two
chemosensory receptors together mediate carbon dioxide detection in Drosophila.
Nature. 445:86-90.
83. Kwon, J.Y., A. Dahanukar, L.A. Weiss, and 1R. Carlson. 2007. The molecular
basis of C02 reception in Drosophila. Proc Natl Acad Sci USA. 104:3574-3578.
84. Vadasz, 1., L.A. Dada, A. Briva, H.E. Trejo, L.C. Welch, J. Chen, P.T. Toth, E.
Lecuona, L.A. Witters, P.T. Schumacker, N.S. Chandel, W. Seeger, and J.L
Sznajder. 2008. AMP-activated protein kinase regulates C02-induced alveolar
epithelial dysfunction in rats and human cells by promoting Na,K-ATPase
endocytosis. J Clin Invest. 118:752-762.
85. Sethi, S., and T.F. Murphy. 2001. Bacterial infection in chronic obstructive
pulmonary disease in 2000: a state-of-the-art review. Clin Microbiol Rev. 14:336-
363.
86. Shirasu-Hiza, M.M., and D.S. Schneider. 2007. Confronting physiology: how do
infected flies die? Cell Microbiol. 9:2775-2783.
87. Cox, C.R., and M.S. Gilmore. 2007. Native microbial colonization of Drosophila
me1anogaster and its use as a model of Enterococcus faecalis pathogenesis. Infect
Immun.75:1565-1576.
88. Sethi, S. 2004. Bacteria in exacerbations of chronic obstructive pulmonary disease:
phenomenon or epiphenomenon? Proc Am Thorac Soc. 1: 109-114.
89. Schneider, D.S. 2007. How and why does a fly turn its immune system off? PLoS
Biol.5:e247.
90. Bogdan, C. 2001. Nitric oxide and the immune response. Nat Immunol. 2:907-916.
98
91. Lang, J.D.J., P. Chumley, J.P. Eiserich, A. Estevez, T. Bamberg, A. Adhami, J.
Crow, and B.A. Freeman. 2000. Hypercapnia induces injury to alveolar epithelial
cells via a nitric oxide-dependent pathway. Am J Physiol Lung Cell Mol Physiol.
279:L994-1002.
92. Lang, J.D., M. Figueroa, K.D. Sanders, M. AsIan, Y. Liu, P. Chumley, and B.A.
Freeman. 2005. Hypercapnia via reduced rate and tidal volume contributes to
lipopolysaccharide-induced lung injury. Am J Respir Crit Care Med. 171:147-157.
93. Briva, A., r. Vadasz, E. Lecuona, L.C. Welch, J. Chen, L.A. Dada, H.E. Trejo, V.
Dumasius, Z.S. Azzam, P.M. Myrianthefs, D. Batlle, Y. Gruenbaum, and J.r.
Sznajder. 2007. High C02 levels impair alveolar epithelial function independently
ofpH. PLoS ONE. 2:eI238.
94. Choe, K.M., H. Lee, and K.V. Anderson. 2005. Drosophila peptidoglycan
recognition protein LC (PGRP-LC) acts as a signal-transducing innate immune
receptor. Proc Natl Acad Sci USA. 102:1122-1126.
95. Ramet, M., P. Manfruelli, A. Pearson, B. Mathey-Prevot, and R.A. Ezekowitz.
2002. Functional genomic analysis of phagocytosis and identification of a
Drosophila receptor for E. coli. Nature. 416:644-648.
96. Stoven, S., r. Ando, L. Kadalayil, Y. Engstrom, and D. Hultmark. 2000. Activation
of the Drosophila NF-kappaB factor Relish by rapid endoproteolytic cleavage.
EMBO Rep. 1:347-352.
97. Ni Chonghaile, M., B. Higgins, and J.G. Laffey. 2005. Permissive hypercapnia: role
in protective lung ventilatory strategies. Curr Opin Crit Care. 11 :56-62.
98. Vittimberga, F.J.J., D.P. Foley, W.C. Meyers, and M.P. Callery. 1998.
Laparoscopic surgery and the systemic immune response. Ann Surg. 227:326-334.
99. O'Croinin, D.F., A.D. Nichol, N. Hopkins, J. Boylan, S. O'Brien, C. O'Connor, J.G.
Laffey, and P. McLoughlin. 2008. Sustained hypercapnic acidosis during
pulmonary infection increases bacterial load and worsens lung injury. Crit Care
Med.36:2128-2135.
100. Margulies, M., M. Egholm, W.E. Altman, S. Attiya, J.S. Bader, L.A. Bemben, J.
Berka, M.S. Braverman, Y.J. Chen, Z. Chen, S.B. Dewell, L. Du, J.M. Fierro, X.V.
Gomes, B.C. Godwin, W. He, S. Helgesen, C.H. Ho, G.P. Irzyk, S.C. Jando, M.L.
Alenquer, T.P. Jarvie, K.B. Jirage, J.B. Kim, J.R. Knight, J.R. Lanza, J.H. Leamon,
S.M. Lefkowitz, M. Lei, J. Li, K.L. Lohman, H. Lu, V.B. Makhijani, K.E. McDade,
M.P. McKenna, E.W. Myers, E. Nickerson, J.R. Nobile, R. Plant, B.P. Puc, M.T.
Ronan, G.T. Roth, G.J. Sarkis, J.F. Simons, J.W. Simpson, M. Srinivasan, K.R.
Tartaro, A. Tomasz, K.A. Vogt, G.A. Volkmer, S.H. Wang, Y. Wang, M.P. Weiner,
99
P. Yu, R.F. Begley, and J.M. Rothberg. 2005. Genome sequencing in
microfabricated high-density picolitre reactors. Nature. 437:376-380.
101. Bennett, S. 2004. Solexa Ltd. Pharmacogenomics. 5:433-438.
102. Mangiapan, G., M. Vokurka, L. Schouls, J. Cadranel, D. Lecossier, J. van Embden,
and AJ. Hance. 1996. Sequence capture-PCR improves detection of mycobacterial
DNA in clinical specimens. J Clin Microbiol. 34:1209-1215.
103. Meyer, Q.C., S.G. Burton, and D.A Cowan. 2007. Subtractive hybridization
magnetic bead capture: a new technique for the recovery of full-length ORFs from
the metagenome. Biotechnol J. 2:36-40.
104. Bashiardes, S., R. Veile, C. Helms, E.R. Mardis, AM. Bowcock, and M. Lovett.
2005. Direct genomic selection. Nat Methods. 2:63-69.
105. St John, J., and T.W. Quinn. 2008. Rapid capture of DNA targets. Biotechniques.
44:259-264.