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Journal of Virology, May 2001, p. 4321-4331, Vol. 75, No. 9
0022-538X/01/$04.00+0 DOI: 10.1128/JVI.75.9.4321-4331.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
Global Impact of Influenza Virus on Cellular
Pathways Is Mediated by both Replication-Dependent and
-Independent Events
Gary K.
Geiss,1,*
Mahru C.
An,2
Roger E.
Bumgarner,1,2
Erick
Hammersmark,1
Dawn
Cunningham,2 and
Michael G.
Katze1,2
Department of Microbiology, School of
Medicine,1 and Washington Regional
Primate Research Center,2 University of
Washington, Seattle 98195
Received 20 November 2000/Accepted 29 January 2001
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ABSTRACT |
Influenza virus, the causative agent of the common flu, is a
worldwide health problem with significant economic consequences. Studies of influenza virus biology have revealed elaborate mechanisms by which the virus interacts with its host cell as it inhibits the
synthesis of cellular proteins, evades the innate antiviral response,
and facilitates production of viral RNAs and proteins. With the advent
of DNA array technology it is now possible to obtain a large-scale view
of how viruses alter the environment within the host cell. In this
study, the cellular response to influenza virus infection was examined
by monitoring the steady-state mRNA levels for over 4,600 cellular
genes. Infections with active and inactivated influenza viruses
identified changes in cellular gene expression that were dependent on
or independent of viral replication, respectively. Viral replication
resulted in the downregulation of many cellular mRNAs, and the effect
was enhanced with time postinfection. Interestingly, several genes
involved in protein synthesis, transcriptional regulation, and cytokine
signaling were induced by influenza virus replication, suggesting that
some may play essential or accessory roles in the viral life cycle or
the host cell's stress response. The gene expression pattern induced
by inactivated viruses revealed induction of the cellular metallothionein genes that may represent a protective response to
virus-induced oxidative stress. Genome-scale analyses of virus infections will help us to understand the complexities of virus-host interactions and may lead to the discovery of novel drug targets or
antiviral therapies.
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INTRODUCTION |
Although it has been nearly 7 decades since the isolation of human influenza virus (34),
it remains a world health threat with large economic consequences
(28, 43, 52). Although vaccine and drug strategies have
managed to contain the spread of the disease and the severity of its
symptoms, recent outbreaks, such as the one in Hong Kong in 1997, emphasize the need for continued research efforts for influenza
prevention. An abundant but often overlooked source of potential
antiviral targets are those cellular genes whose expression is most
affected by viral infection. With DNA microarray technology it
is now possible to measure the mRNA levels of thousands of cellular
genes under a variety of experimental conditions. This approach is
increasingly being used to monitor cellular gene expression in response
to viral infections (5, 19, 20, 25, 30, 55, 59),
expression of viral genes (21, 31, 58), or treatment with
antiviral compounds such as interferon (12).
Influenza virus is a negative-stranded RNA virus that induces a
profound inhibitory effect on the synthesis of cellular proteins. Much
of this effect occurs at a posttranscriptional level, as viral RNAs are
selectively translated while the initiation and elongation of cellular
proteins are inhibited (15). On the other hand, viral
proteins carry out a variety of functions within the nucleus, such as
removing 5' methyl caps from host cell mRNAs (50),
blocking mRNA export (32), and inhibiting mRNA splicing (38) that could profoundly alter the steady-state levels
of cellular mRNAs. Despite these characteristics, studies aimed at determining the effect of influenza virus infection on cellular mRNA
levels have been limited to the analysis a few selected cellular mRNAs
(3, 23, 27). A comprehensive large-scale analysis of host
cell mRNAs during influenza virus infection has not been performed
until now.
The expression of more than 4,500 cellular genes during the course of
influenza virus infection was examined by cDNA microarrays. As a
control to determine if viral replication was required to alter
cellular gene expression, infections with an inactivated and
replication-incompetent virus were performed. The pattern of gene
expression was used to identify changes that were either dependent or
independent of viral replication. At 4 h postinfection (p.i.),
cellular genes were altered in both a replication-independent and a
replication-dependent manner. However, as infection proceeded, changes
in cellular mRNA levels were almost exclusively dependent on viral
replication. These results suggest that early events in the viral life
cycle are capable of inducing a change in host cell mRNA levels,
possibly by attachment or fusion to the host cell. Although the
relationships between cellular mRNA levels and cellular protein
synthesis remain unclear, the findings are discussed in the context of
host cell response and the viral replication cycle.
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MATERIALS AND METHODS |
Cell line and infection conditions.
HeLa cells were grown as
monolayers in Dulbecco's modified Eagle's medium supplemented with
10% fetal bovine serum, 2 mM L-glutamine, 100 U of
penicillin/ml, and 100 µg of streptomycin sulfate/ml at 37°C. When
the cells were approximately 80% confluent, supernatant was removed
and replaced with either medium alone (Dulbecco's modified Eagle's
medium with 2% calf serum), medium containing untreated influenza
virus (48), or medium containing inactivated virus (see
below). The multiplicity of infection was approximately 50 PFU/cell.
Viral attachment was carried out at 4°C for 45 min with gentle
agitation. Cells were then incubated at 37°C for 4 or 8 h. At
the indicated times, medium was removed, the cells were harvested, and
the total RNA was extracted using the method of Chomczynski and Sacchi
(7).
Inactivation procedures and agglutination assay.
Heat
inactivation of influenza virus stocks was performed by incubating the
virus stock at 56°C for 90 min. When incubated under these
conditions, the virus was rendered unable to inhibit host cell protein
synthesis as monitored by
[35S]methionine-cysteine labeling experiments
(Fig. 1A). UV inactivation of influenza
virus stocks was performed in a Stratalinker 2400 (Stratagene, La
Jolla, Calif.) with increasing amounts of energy. Optimal inactivation
was determined by [35S]methionine-cysteine
labeling of infected cells to be 80 mJ (data not shown; see Fig. S1 at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm). To determine if the viral hemagglutinin (HA) protein was still active
after inactivation procedure, agglutination assays of chicken red blood
cells were performed. Medium alone (for mock infection), untreated
influenza virus, or inactivated virus was diluted (1:2 to 1:2,048) in
ice-cold phosphate-buffered saline in a conical-bottom 96-well plate.
An equal volume of chicken red blood cells was mixed with the dilutions
and incubated at 4°C for 30 min. Loss of HA activity was monitored by
the formation of a visible pellet of blood cells.
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FIG. 1.
Effect of inactivation of influenza virus on the
synthesis of host cell proteins and viral hemagglutination titers. (A)
Protein extracts from mock-infected cells (lanes 1 and 4), cells
infected with heat-inactivated virus (lanes 2 and 5), and cells
infected with active influenza virus (lanes 3 and 6) were examined by
[35S]methionine-cysteine pulse-labeling at 4 and 8 h
p.i.. The relative positions of four viral proteins, HA, nucleoprotein
(NP), membrane protein 1 (M1), and NS1, are indicated by arrows. (B)
Comparison of HA titers. Serial dilutions of medium alone (mock),
FACT virus, and FHT virus were mixed with
chicken red blood cells to determine relative HA titers. As an
additional negative control, an influenza virus preparation was heated
in a boiling water bath for 90 min (boiled). All reactions were
performed in triplicate in 96-well plates; one replica is shown here.
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Analysis of cellular and viral protein synthesis.
The extent
of influenza virus infection and the effectiveness of the inactivation
procedure were monitored by examining host cell protein synthesis after
infection. Mock- or virus-infected HeLa cells were labeled at the
indicated times with [35S]methionine-cysteine
(NEN, Boston, Mass.), and protein extracts were examined on 14% sodium
dodecyl sulfate (SDS)-polyacrylamide gels as described previously
(17).
RNA isolation, first-strand cDNA synthesis, and Northern blot
analysis.
Total RNA preparations were performed essentially as
described previously (7) with an additional
phenol-chloroform (49:1) extraction. Poly(A)+ RNA
was isolated from total RNA using an Oligotex mRNA purification kit
(Qiagen, Valencia, Calif.). Northern blots were performed as described
previously (1). Radioactive probes for Northern analysis
were generated from double-stranded (ds) PCR products by either
Ready-to-go beads (AP Biotech, Little Chalfont, Buckinghamshire, United
Kingdom) or by amplification of the minus strand with Taq polymerase using strand-specific primers.
Fluorescently labeled cDNA probes were generated by reverse
transcription as follows. Two micrograms of
poly(A)
+ RNA from mock- or influenza
virus-infected HeLa cells, 2.5 ng
of green fluorescent protein
poly(A)
+ RNA, 8 pmol of anchored dT primer, and 1 µg of random nonamers
were combined in a 10.5-µl reaction volume.
The solution was heated
to 70°C for 10 min, chilled briefly on ice,
and centrifuged. Reverse
transcription was performed in a 20-µl
reaction volume. Final
concentrations were 1× first-strand buffer
(Life Technologies,
Rockville, Md.), 10 mM dithiothreitol, 100 nM dATP,
dGTP, and
dTTP, 50 nM nonlabeled dCTP, 50 nM FluoroLink-dCTP (either
Cy3
or Cy5 labeled; AP Biotech), and 0.5 U of placental RNase inhibitor
(Promega, Madison, Wis.)/µl. The contents were mixed and incubated
at
room temperature for 10 min. Superscript II RT (Life Technologies)
was
added (200 U), and the reaction mixtures were incubated at
42°C for
2 h. RNA was hydrolyzed with sodium hydroxide (0.25 N
final
concentration) for 15 min at 37°C. Samples were neutralized
by
addition of 2 M MOPS (morpholinepropanesulfonic acid) buffer
to 0.4 M. Unincorporated nucleotides were removed using 96-well
multiscreen-FB
filter plates (Millipore, Bedford, Mass.) followed
by G-50 ProbeQuant
columns (AP
Biotech).
Microarray construction, hybridization, and detection.
The
human cDNA I.M.A.G.E. clones (36) used in this analysis
were obtained from Research Genetics (Huntsville, Ala.) and consisted
of a subset of the Homo sapiens 15K sequence verified set
(UniGene Build 19, plates 1 to 44) plus a 384-well control plate. cDNA inserts for I.M.A.G.E clones and controls were PCR amplified and purified (see protocols at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm). DNA pellets were suspended in a 50% solution of reagent D (AP Biotech)
and deposited on 75- by 25-mm coated glass microscope slides (type VII;
AP Biotech) with the use of a Molecular Dynamics (Sunnyvale, Calif.)
Generation III microarray spotter. After spotting, microarrays were air
dried, cross-linked at 450 mJ, and stored desiccated under liquid
N2 until needed.
Prior to hybridization, microarray slides were pretreated for 20 min at
55°C in 5× SSC-0.2% SDS (1× SSC is 0.15 M NaCl plus
0.015 M
sodium citrate), rinsed briefly in deionized water, and
dried with
compressed air. Fluorescently labeled first-strand
cDNAs were
concentrated by drying and resuspended in 20 µl of
1× hybridization
solution [5× SSC, 5× Denhardt's solution, 0.1%
SDS, 50%
formamide, 0.1 µg of Cot1 DNA/µl, and 20 µg of
poly(A)
72/ml].
The appropriate Cy3- and
Cy5-labeled probes were combined (total
hybridization volume, 40 µl),
denatured by boiling, and applied
to the slides under a 22- by 64-mm
glass coverslip. Microarrays
were hybridized at 42°C in a humidified
chamber for 16 to 20 h.
Following hybridization, slides were
washed briefly in 1× SSC-0.2%
SDS at 55°C to remove the coverslip
and then washed once in 1×
SSC-0.2% SDS for 10 min (55°C), twice
in 0.1× SSC-0.2% SDS for
10 min (55°C°), twice in 0.1× SSC for
1 min (at room temperature),
and once with deionized water for 10 s (at room temperature).
Microarrays were dried with compressed air and
scanned at 532
and 633 nm with an Avalanche dual-laser confocal scanner
(Molecular
Dynamics).
Data analysis and differentially expressed clone selection and
control genes.
Each slide contained 4,608 cDNAs spotted in
duplicate. Included in this number was a set of 384 selected cDNAs that
were spotted on every slide. This set contained four influenza virus
genes used as positive controls, nonhuman genes used as negative
controls, and genes for a variety of selected transcription factors,
ligands, and receptors chosen from the Research Genetic 15K human gene set. A complete list of "control" genes, including intensities, ratios, and errors, can be found at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm. For
each of the three infection conditions (Fig.
2), duplicate slides were hybridized with
the same RNAs but with the fluorescent labels reversed to control for
dye-specific effects as described previously (19). In
addition, each infection was repeated independently for a total of four
slides per condition (eight measurements per gene). Intensity values in
Cy3 and Cy5 channels were extracted from each image, and the Cy3/Cy5
ratio was determined using Spot-on Image software. Data for all
replicates were combined and normalized with custom software, Spot-on
Unite. Briefly, Spot-on Unite normalizes the data, rejects outliers,
and calculates the mean and standard deviation for each replicate
measurement. The normalization method takes into account nonlinearities
in the Cy3/Cy5 ratio as a function of intensity by fitting a
second-order polynomial to Cy3/Cy5 ratio versus Cy3-plus-Cy5 intensity.
This normalization procedure should also compensate for the amount of
viral mRNA in the infected samples that are not present in cells
infected with heat-inactivated virus or mock infection medium. Outlier
rejection was then performed by removing each replicate data point one
at a time and recalculating the mean and standard deviation. If the
removal of a replicate point resulted in a twofold or larger reduction
of the standard deviation, that point was removed from the calculation
of the mean. The resulting mean and standard deviation calculations
were then imported into the program Spot-on SELECT. A clone was
considered differentially expressed if the signal (intensity minus
background) was above 750 (at least three times the standard deviation
of the background; see Fig. S2 at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm) and the ratio plus and minus its standard deviation was at least 1.5-fold. A list of all genes differentially expressed in at least one
experiment was generated for each time point. For each gene that was
differentially expressed in at least one experiment, the mean intensity
and standard deviation were extracted for all experiments (at each time
point) using a program called Spot-on MERGE. Those genes that exhibited
a consistent pattern of expression in both infections are presented in
Results. Genes were then classified as either independent of or
dependent on viral replication based on their pattern of expression.
All image and data analysis programs were designed and written by E. Hammersmark and R. Bumgarner (unpublished data).
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FIG. 2.
Schematic representation of experimental design and
classification of differentially expressed genes. Microarray
experiments were performed in a pairwise fashion with RNA from
mock-infected cells (M), cells infected with FHT virus, and
cells infected with FACT virus. A set of differentially
expressed genes was generated for each of the three possible
comparisons. The whole set of experiments was then repeated with RNA
from independent infections. The lists of differentially expressed
genes for each time point and condition were combined, and the pattern
of expression for each gene was used to determine whether it was
dependent on viral replication. A gene that is dependent on viral
replication should be differentially regulated in the experiments
depicted in the left and center panels but not during experiments
depicted on the right, whereas a replication-independent gene should be
differentially regulated under the conditions shown at center and right
but not in FHT-versus-FACT experiments. The
same pattern of expression had to be observed in both independent
infections in order to be considered in this study (Table 1).
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RESULTS |
Generation of a replication-incompetent influenza virus that
retains cell binding activity.
Before microarray analysis was
performed it was necessary to generate an inactive viral preparation to
compare with infected cells. For the purposes of these experiments,
inactivated virus was defined as one that could not replicate or
express viral mRNAs or proteins. Replication-deficient viruses were
generated by either heat treatment or exposure to UV light. The
effectiveness of the inactivation procedures was monitored by examining
the rate of cellular protein synthesis with
[35S]methionine-cysteine pulse-labeling of
infected cells. As Fig. 1A shows, a 90-min treatment at 56°C was
sufficient to inactivate influenza virus' ability to inhibit host cell
protein synthesis at 4 and 8 h p.i. (compare heat-treated virus
[FHT virus] [lanes 2 and 5] with mock
infection [M] [lanes 1 and 4] and untreated influenza virus
[FACT virus] [lanes 3 and 6]). In addition,
viral proteins were not synthesized in cells infected with
FHT virus but are clearly visible in extracts
from influenza virus-infected cells (Fig. 1A). Moreover, Northern blots
with probes specific for viral mRNA failed to detect transcription of
viral genes in cells infected with heat-treated virus. However, the
possibility that some viral mRNAs are transcribed below our detection
limits remains has not been completely ruled out. The results were
similar for cells that were infected with virus inactivated by 80 mJ of UV energy (data not shown; see Fig. S1 at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm). These results indicated that inactivation procedures were sufficient to
inhibit viral replication at both the 4- and 8-h time points.
In order to determine if either inactivation procedure was destroying
the virus' ability to interact with the cellular membrane,
the
activity of viral HA was examined. HA is the viral glycoprotein
that
mediates attachment and fusion of the virus with the host
cell
(reference
53 and references therein). Serial dilutions
(1:2 to 1:2,048) of medium alone, untreated virus,
F
HT virus,
and an aliquot of influenza virus that
was boiled for 90 min (which
presumably destroys the structure of the
virion and was therefore
used as a negative control) were incubated
with chicken red blood
cells to determine relative HA titer. The red
blood cells in wells
that contain sufficient HA activity will not form
a pellet upon
centrifugation. As Fig.
1B shows, the HA activity of
F
HT virus
was very similar to that of untreated
virus while medium alone
and boiled virus showed no activity. The HA
activity of UV-inactivated
(F
UVI) virus was also
similar to that of untreated virus (data
not shown; see Fig. S3
at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm).
We concluded that replication of inactivated virus is blocked
at a step
after interacting with the host cell since HA retained
structural
conformation and ability to interact with cells whereas
a completely
denatured sample (boiled) or medium alone did not.
For the bulk of this
work, data from F
HT influenza virus was used
as a
primary source of inactivated virus. Results from cells infected
with
F
UVI virus at 4 h p.i. were used to
corroborate some of our
findings.
Microarray analysis was used to identify replication-dependent and
-independent changes in gene expression.
The major goal of this
study was to identify global changes in host cell gene expression that
occur during influenza virus infection. For all of this work, we
utilized cDNA microarrays that were constructed at the University of
Washington's Center for Expression Arrays. A distinct advantage of
using an "in-house" microarray system is that multiple conditions
can be compared in parallel and repeated as many times as necessary
without the cost restraints of commercial microarray sources. In
addition, controls for viral infection, fluorescent dye incorporation,
and nonspecific hybridization can be routinely performed and
statistically analyzed. This feature, along with experiments
specifically designed to distinguish between replication-dependent and
independent changes in gene expression (see below and Fig. 2), provides
an extra degree of confidence in the interpretation of our large-scale
gene expression data.
The use of a replication-incompetent virus that retains cell-binding
activity had two advantages. First, it allowed us to
compare cells
infected with F
HT virus with those infected with
F
ACT virus (Fig.
2, left). Changes in gene
expression observed
in
F
HT-versus-F
ACT microarray
experiments are likely due to viral
replication, since
F
HT virus cannot replicate (Fig.
1A) but other
factors (e.g., attachment) remain roughly constant. Second, RNA
from
cells infected with F
HT virus can be compared to
that of
mock-infected cells (Fig.
2, right) to identify changes in gene
expression that were totally independent of viral replication.
Finally,
since untreated influenza virus retains both its replication-dependent
and -independent properties, the comparison of mock-infected cells
with
cells infected with active influenza virus (Fig.
2, center)
should
yield a composite of differentially regulated gene sets
observed
in the other two conditions. Therefore, changes in gene
expression that
are dependent upon virus replication are defined
as those coordinately
regulated in
F
HT-versus-F
ACT and
M-versus-F
ACT experiments but not
M-versus-F
HT experiments. Accordingly, a gene
that is regulated in the M-versus-F
HT and
M-versus-F
ACT conditions
but not during
F
HT-versus-F
ACT experiments
is defined as replication
independent. This method should minimize
potential artifacts and
false positives due to experimental variation.
However, a potential
consequence of this conservative approach is that
host cell genes
regulated by inactivated influenza virus that are
otherwise inhibited
by viral replication will not be
scored.
Microarray experiments were performed on cells infected at both 4 and
8 h under the three pairwise conditions described above.
Fluorescently labeled first-strand cDNAs were generated
(
19)
from poly(A)-selected RNA from mock-infected cells or
cells infected
with F
HT virus,
F
UVI virus, or untreated influenza virus. The
appropriate probes were combined and hybridized to replica slides
containing 4,608 cDNAs (spotted in duplicate). To control for
dye-specific incorporation effects and differences in the saturation
curves for the two dyes, all experiments were done on duplicate
slides
where the labeling scheme was reversed. In addition, microarray
experiments were repeated with mRNA from independent infections
with a
different viral stock. Therefore for each gene on the array,
eight
separate measurements (four from each infection) were made
per time
point (see Materials and
Methods).
Hybridization signals were quantitated using the Spot-on software
package, and a set of differentially expressed genes was
generated for
each condition and time point described in Materials
and Methods. Host
cell protein synthesis (Fig.
1A) and influenza
virus gene
expression (see Fig. S4 at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm)
was monitored for every experiment. An example of the microarray
results is shown in Fig.
3A and B. Between one-third and two-thirds
of the 4,608 cDNAs were above the
minimal intensity value for
any given experiment (see Table S1 at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm).
Ratios for genes below this threshold could not be measured accurately
and therefore were eliminated from further consideration. Three
general
points were extracted from this analysis: (i) more changes
in cellular
gene expression were dependent on the presence of
replication competent
virus than not, (ii) inactivated virus alone
was capable of affecting
host cell gene expression, and (iii)
more cellular genes were
downregulated by influenza virus infection
than were induced. The
numbers of replication-dependent and -independent
genes identified by
the analysis described above are summarized
in Table
1. The raw microarray images,
quantitation results (pre-
and postnormalization), and selected sets
of differentially regulated
genes are available at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm and will be submitted to the first publicly available gene expression
database.
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FIG. 3.
Example of microarray results and determination of
replication-dependent and -independent differentially expressed genes.
False-color images for a portion of the microarray for the three
pairwise conditions using FHT virus are shown in panels A
(4 h p.i.) and B (8 h p.i.). Replica microarrays were hybridized with
FHT virus (green) versus FACT virus (red), mock
infection (green) versus FACT virus (red), and mock
infection (green) versus FHT virus (red). A list of
differentially expressed genes was generated for each set of samples
using Spot-on software (see Materials and Methods). Examples of
differentially expressed genes that are independent of or dependent on
viral replication are shown (arrows 1 and 2, respectively). (C) RNA
from mock-infected cells (M) or cells infected with FHT
virus or FACT virus were run in 1% agarose gels under
denaturing conditions and transferred to nylon membranes. Blots were
hybridized with 32P-labeled DNA specific for IL-6 or
metallothionein IG (MT-IG). Northern analysis was performed on RNA from
the 4-h time point.
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Changes in cellular gene expression reveal pathways that may be
important for viral replication.
Viral replication resulted in the
differential expression of 61 and 329 genes at the 4- and 8-h time
points, respectively (Table 1). A partial list based on the levels of
expression at the 8-h time point is presented in Table
2. A complete list is available at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm. The cellular processes most affected by viral replication include transcriptional regulation (38 genes), interleukin and growth factor
signaling (32 genes), mRNA processing (15 genes), protein synthesis (9 genes), and protein degradation (6 genes). While a large percentage of
these genes (351) decreased in expression during influenza virus
infection, the expression of 39 genes was upregulated by viral
replication. As one might predict of changes in mRNA expression that
were dependent on viral replication, 39 of the 61 genes regulated at
the 4-h time point were also regulated at 8 h (Table 2 and see
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm).
Influenza virus replication induced the expression of the
interleukin 6 (IL-6) gene (present in two copies) at 4 h p.i. and
37 genes at 8 h p.i. Given that influenza virus generally
represses
the synthesis of cellular proteins (Fig.
1A) and gene
expression
(see below), upregulated genes are attractive candidates for
future
functional studies provided that their protein levels increase
accordingly. IL-6 was the only gene upregulated by viral replication
at
the 4-h time point. Its mRNA expression levels increased almost
eightfold at 8 h p.i. (Fig.
3B and Table
2). This proinflammatory
cytokine is regulated by a variety of extracellular stresses,
including
bacterial and viral infections (
2,
51). Northern
blot
analysis of the three RNA samples with an IL-6-specific probe
detected
two bands and confirmed the increase in IL-6 signal (lower
band) only
in cells infected with active influenza virus (Fig.
3C, left, lane F).
The significance of the two transcripts is
not known at this time, but
one band may represent an alternatively
spliced IL-6 product
that has been detected in another system
(
29) which is not
induced by infection. The induction of IL-6
mRNA is specific, since a
similar increase in mRNAs encoding other
interleukin genes present on
the array was not upregulated by
influenza virus. For example, mRNA
levels for IL-15 and IL-11
as well as interleukin enhancer binding
factor 3 (Table
2) were
actually downregulated. Although IL-6 protein
levels were not
directly addressed in this study, the microarray data
are consistent
with reported IL-6 increases in cell lines (
41,
57) and in
mice (
42). Finally, the induction of
IL-6 mRNA was also observed
in other microarray-based studies during
cytomegalovirus (
59),
poliovirus (
25), and
coxsackievirus (
55) infections. If the
IL-6 protein
expression verifies the expression levels in this
system, it may hint
at a potential role for IL-6 in the host's
innate antiviral
response.
The mRNAs for several genes involved in the process of protein
synthesis were also induced in a replication-dependent manner.
These
include mRNAs for the ribosomal subunit S6, translation
elongation
factor 1-gamma, a homolog of the recently identified
ribosome
biogenesis regulatory protein, and several other ribosomal
proteins
(Table
2). The upregulation of mRNAs for ribosomal proteins,
especially
S6 (
13) and RRS1 (
56), was intriguing given
that
viral mRNAs were selectively translated in infected cells, despite
an overall inhibition of the synthesis of cellular proteins.
Interestingly,
not all the mRNAs for genes in this pathway are
upregulated, indicating
that influenza virus may recruit specific
components of the translation
machinery. The concomitant downregulation
of translation factors
by influenza virus could account for the
similarity of polysome
profiles in mock- and influenza virus-infected
cells despite an
obvious decrease in the synthesis of cellular proteins
(
26).
It is also possible that some of these genes
cooperate with additional
cellular or viral proteins such as GRSF-1
(
49) or nonstructural
protein 1 (NS1) (
11),
respectively, in the selective translation
of viral mRNAs. Of course,
follow-up studies will be required
to verify the protein levels,
phosphorylation states, enzyme activities,
and functional significance
of key translation components to demonstrate
a potential role in
selective translation of influenza virus
mRNAs.
Induction of cellular genes potentially involved in transcriptional
regulation was also observed at the 8-h time point. These
genes could
have several roles in an infected cell such as maintaining
the pool of
capped RNA polymerase II transcripts for viral mRNA
synthesis or
controlling expression of other genes that mediate
the cellular stress
response. Two of these genes encode zinc finger
proteins, A20 and the
product of the transforming growth factor
(TGF)
-inducible
early gene, that are normally induced by tumor
necrosis factor and TGF,
respectively. This result is the opposite
of what was observed for
mRNAs encoding other transcriptional
regulators that were shown to
decrease during infection (see below
and Table
2). The induction of A20
or TEIG amidst the broad downregulation
of many other cellular genes
may indicate that they are directly
induced by viral components, such
as viral dsRNAs. Indeed, the
level of A20 mRNA has been shown to be
upregulated by pI-pC treatment
of GRE cells used as a model
system for dsRNA signaling (G. K.
Geiss and G. C. Sen,
submitted for
publication).
In contrast to the examples of cellular genes induced by influenza
virus infection, the major cellular response was the downregulation
of
genes from a diverse set of cellular pathways. The number of
downregulated cellular genes increased from 61 genes at 4 h p.i.
to 329 at 8 h p.i.. The number of downregulated genes represents,
on average, less than 4% of the total number of genes detected
at the
4-h time point and less than 17% of the number detected
at 8 h
p.i., suggesting that a complete degradation of cellular
mRNAs did not
occur. However, the potential effects that influenza
virus infection
has on mRNA processing and stability, especially
considering the
removal of the 5' cap and inhibition of cellular
protein synthesis,
could, in part, explain the downregulation
of some genes, since
uncapped messages are quickly degraded (
24).
Whether the
mechanisms behind the decrease in steady-state mRNA
levels in influenza
virus-infected cells are due to transcriptional
regulation, the
degradation of aberrantly processed mRNAs, altered
mRNA stability, or
some combination of these events has not yet
been addressed on a
genome-wide
scale.
The identities of the replication-dependent downregulated genes
indicated that a broad array of cellular genes were affected.
The
classes with the highest number of genes downregulated include
those
involved in growth factor and cytokine signaling, those
encoding DNA
binding proteins, extracellular and cytoskeletal
genes, and genes
involved in mRNA processing and export. The latter
class of genes is
interesting in the context of influenza virus
infection, given the role
of the viral NS1 protein in inhibiting
mRNA polyadenylation, splicing,
and export of cellular mRNAs within
the nucleus. Interestingly, a
direct interaction between the influenza
virus NS1 protein and cellular
cleavage and polyadenylation specificity
factor has recently been
reported (
45). Whether NS1-induced
perturbations in mRNA
processing are responsible for the decrease
in cellular mRNA levels,
potentially through its interactions
with cellular cleavage and
polyadenylation specificity factor,
could be addressed with
infections using mutant influenza viruses
lacking the NS1 gene
(
16).
Cellular genes regulated independently of viral replication
identify a potential antiviral pathway.
It is clear from the
microarray data that the steady-state level of cellular mRNAs was also
altered by events that do not require viral replication. Since
heat-inactivated virus retains HA activity similar to that of untreated
virus, it is likely that replication-independent steps of the viral
life cycle, for example, attachment or fusion to the host cell, were
responsible for inducing most of these changes. In contrast to the
replication-dependent changes in gene expression, the number of
replication-independent changes decreased dramatically over the course
of infection, from 84 genes at 4 h p.i. (58% of all genes that
change at that time point) to 13 at 8 h p.i. (4% of the genes
regulated). This result is consistent with the notion that contact or
fusion of the virus with the host cell transmits signals that alter
host cell gene expression.
There were a total of 97 differentially regulated genes at both time
points that did not require active viral replication.
A partial list is
presented in Table
3, based on their
expression
at the 4-h time point. A complete list is available at
http://thor.csi.washington.edu/katzelab/papers/Geiss2000/index.htm.
The cellular pathway genes affected include the metallothionein
genes (five genes), genes involved in cell cycle progression (five
genes), those encoding transcriptional regulators (eight genes),
those
involved in the ubiquitin/proteasome pathway (six genes),
and those
encoding various cellular kinases (three genes) (Table
3 and see
http://thor.csi.washington.edu/katzelab/paper/Geiss2000/index.htm).
As mentioned above, the majority of these genes were exclusively
regulated at the 4-h time point. Only eight genes (four of the
five
metallothionein genes, the genes for flavin monooxygenase,
cyclin A,
and extracellular regulated kinase-3, and I.M.A.G.E.
ID 299737) were
differentially regulated at both time points.
The mRNAs that most consistently increased in a replication-independent
manner were members of the metallothionein gene family
(encoding
metallothioneins IB, IG, IH, IL, and II) (Table
3).
All five cDNAs
representing metallothionein genes on this array
were induced at the
early time point, and four continue to be
regulated at the later time
point. Northern blot analysis with
a radiolabeled metallothionein IG
probe detected a single band
and confirmed the increased mRNA levels in
cells infected with
F
HT and active influenza
virus (Fig.
3C, right). However, due
to the high sequence homology
among the metallothionein mRNAs,
an increase in a single species cannot
be distinguished on these
cDNA microarrays. Although the exact
physiological role of metallothioneins
has not been fully elucidated,
they are induced by a variety of
extracellular stimuli, including IL-6,
heavy metals, oxidative
stress, and bacterial endotoxins (
10,
14,
22,
35,
37,
47). Indeed, influenza virus infections with either
active or
inactive viral preparations have previously been shown to
induce
oxidative stress (
6,
8). If protein levels confirm
the expression
data, metallothionein induction may represent a host
response
to attachment-induced oxidative stress. It will be extremely
interesting
to examine the outcome of influenza virus infection in cell
lines
by inducing metallothionein gene expression (by treatment with
Zn
or IL-6) before or after infection or in metallothionein knockout
mice
(
44). With regard to the former, the clinical
administration
of Zn at the onset of illness has been shown to reduce
the severity
and longevity of cold-like symptoms (
18,
40)
and has been
shown to reduce herpes simplex virus infectivity in vitro
(
33).
Nearly all of the genes regulated independently of viral replication
were downregulated (82 of 97), most of them exclusively
at the 4-h time
point (74 of 82). Among the pathways most influenced
were those
involved in cell cycle progression, protein degradation,
and
transcriptional regulation. Binding of influenza virus to
the cell
membrane may initiate a cellular response that "sets
up" the host
environment for viral infection or induces extracellular
stress that
results in the downregulation of these genes. For
example, influenza
virus may require a specific stage of the cell
cycle or downregulation
of the ubiquitin pathway in order to achieve
maximum replication
efficiency or synthesis of viral proteins.
Interestingly, several
members of the protein degradation pathway
were upregulated by viral
replication during later times of infection
(Table
2), suggesting that
increased expression of viral proteins
during replication eventually
activates the protein degradation
process. Finally, downregulation of
these genes does not likely
represent a nonspecific effect of cell
death, since inactivated
virus does not inhibit host cell protein
synthesis (Fig.
1A) or
significantly reduce cell viability (data not
shown).
Gene expression analysis of UV-inactivated virus confirms the
replication-dependent and -independent changes in cellular gene
expression.
To provide additional evidence that changes in
steady-state mRNA levels were due to infection, microarray experiments
were repeated with FUVI virus at 4 h p.i. as
described above. We compared the expression level for genes
differentially expressed in the first set of experiments to those
observed during the FUVI microarray experiments.
There were 13 genes that were altered in a replication-independent manner and 142 genes whose changes were dependent on viral replication at 4 h p.i.. Four of the 13 replication-independent genes
(encoding metallothioneins IB, IH, and II and alanine-glyoxylate
aminotransferase) were differentially regulated in the same manner as
FHT virus. Similarly, 96 (4 up and 92 down) of
the 142 replication-dependent genes were also differentially regulated
in UV experiments. These include genes for IL-6 (two copies),
connective tissue growth factor, and transcription factors and genes
involved in mRNA processing (Table 2). In addition, flavin
monooxygenase and metallothionein IG mRNA levels also increased
moderately but not enough to meet our strict cutoff criteria. These
data in conjunction with previous findings in other studies support the
hypothesis that the changes in expression of the metallothionein genes
and others are due to the presence of viral components.
There were also significant differences in mRNA levels that were
specific to the method of inactivation. First, the absence
of
replication-independent downregulated genes and a increase
in the
number of replication-dependent genes suggests that the
kinetics of
infection were increased slightly in UV experiments,
resulting in a
gene expression profile more similar to that of
8-h
F
HT virus (Table
2). In addition, there were a
number of
genes that were differentially regulated exclusively by UV
and
not F
HT virus and others that were regulated
in the opposite direction,
suggesting that they are regulated by
factors that were affected
differently by the two distinct inactivation
procedures.
| |
DISCUSSION |
Our efforts to control for nonviral factors that may alter host
cell gene expression support the idea that the majority of changes in
mRNA abundance identified here are due to influenza virus infection. We
used a DNA microarray-based approach to show that influenza virus
infection affects the steady-state mRNA levels for a wide variety of
cellular genes in both a replication-dependent and a
replication-independent manner. The mechanisms driving the observed
changes in mRNA abundance for each gene are not known and are probably
due to both altered mRNA stability and transcriptional regulation. We
have attempted to minimize potential problems by (i) requiring a gene
to be regulated similarly in two of the three relevant pairwise
conditions, (ii) performing duplicate infections and selecting only
genes that were consistently regulated, and (iii) preparing inactivated
virus by two different mechanisms. This approach minimizes the
selection of genes whose expression patterns vary from experiment to
experiment and the potential effects of the method of inactivation. The
phenomenon of genes being regulated consistently under all conditions
is almost certainly due to virus-mediated events, since a
nonviral factor would have to resist heat and UV inactivation in order
to induce a similar pattern of expression in both experiments. Finally,
although some of the findings reported here are corroborated by
published reports, it is important to note that replication-dependent
changes in gene expression occur in the presence of an inhibition of
cellular protein synthesis. Therefore, it will be critical to verify
that genes whose steady-state mRNA levels increase during infection do,
in fact, escape the virus-induced inhibition of protein synthesis.
The number of DNA array-based studies aimed at elucidating host cell
gene expression during viral and bacterial infections has increased
substantially in the past 2 years (9, 39). A comparative
analysis of influenza virus infections with gene expression studies on
genetically unrelated viruses revealed some interesting similarities
and differences with influenza virus. For instance, poliovirus and
coxsackievirus also induce mRNAs encoding ribosomal components
(25, 55), while cytomegalovirus induces expression of IL-6
(see above), and metallothionein I RNA is upregulated in coxsackievirus
infections. In addition, both influenza virus and cytomegalovirus
downregulate the expression of genes in the insulin and TGF pathways.
On the other hand, the differences in these viral systems is
exemplified by the finding that cytomegalovirus infection induces a
large set of interferon-induced genes which is not seen in these
influenza virus infections or experiments on papillomavirus-expressing
cell lines in which the interferon response was specifically repressed
(5). As mentioned above, the differences observed in these
large-scale studies are likely due to a number of factors. For
instance, the lack of an interferon-induced gene expression in these
experiments might be attributed to influenza virus' ability to block
interferon signaling via the viral NS1 protein (4, 16, 54)
or the presence or absence of exogenous interferon in the viral
preparation. Alternatively, the timing of the experiments may not be
the optimal system for observing changes in this classic antiviral
system, since this HeLa cell line is at least partially responsive to exogenous interferon treatment (G. K. Geiss and M. G. Katze,
unpublished data).
Microarray-based gene expression studies are especially well suited for
comparing the cellular response to viral infections, viral components
(purified proteins and dsRNA), and treatment with various cytokines or
antiviral compounds such as those approved for influenza treatment and
prevention. The cellular response to influenza virus and other viral
infections will ultimately be defined by comparing the results from
different viruses, host cells, and infection kinetics with the present
knowledge base and future functional analysis. In addition, highly
parallel and genome-wide comparisons might allow us to determine which
cellular genes are essential to virus or host cell survival. This study is the first in what we anticipate will be an ongoing effort to define
global gene expression patterns in response to different influenza
virus strains, mutants, proteins, and host cell types. Furthermore, recent advances in reverse genetic techniques
(46) will allow a systematic analysis of wild-type
influenza virus genes or mutants and their influence on cellular gene
expression. The information obtained from this and related studies will
revolutionize our view of influenza virus infection and its influence
on host cell biology and may eventually lead to novel therapeutic targets.
| |
ACKNOWLEDGMENTS |
This work was supported by Public Health Service grants AI-22646,
AI-41629, and RR-00166 from the National Institutes of Health. Mahru C. An was a recipient of a Howard Hughes Fellowship and an Early
Identification Program Presidential Scholarship.
We acknowledge M. J. Korth and other members of the Katze
laboratory for critical reading and review of the manuscript and A. B. van 't Wout for helpful discussions and for construction and design of the control plate. We also thank Mary Claire King's laboratory for access to the human cDNA I.M.A.G.E clones and the University of Washington's Center for Expression Arrays for providing microarray services and software.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Department of
Microbiology, Box 357242, University of Washington, Seattle, WA 98195. Phone: (206) 732-6155. Fax: (206) 732-6055. E-mail:
geiss{at}u.washington.edu.
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Journal of Virology, May 2001, p. 4321-4331, Vol. 75, No. 9
0022-538X/01/$04.00+0 DOI: 10.1128/JVI.75.9.4321-4331.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
