Download Functional classification of interferon-stimulated genes

Functional classification of interferon-stimulated genes
identified using microarrays
Michael J. de Veer,* Michelle Holko,*,† Mathias Frevel,* Eldon Walker,‡ Sandy Der,§
Jayashree M. Paranjape,* Robert H. Silverman,* and Bryan R. G. Williams*
*Department of Cancer Biology, Lerner Research Institute, and ‡Computer Core, Cleveland Clinic Foundation, Ohio;
†
Department of Genetics, Case Western Reserve University, Cleveland, Ohio; and §Department of Laboratory Medicine
and Pathobiology, University of Toronto, Ontario, Canada
Abstract: Interferons (IFNs) are a family of multifunctional cytokines that activate transcription of
subsets of genes. The gene products induced by
IFNs are responsible for IFN antiviral, antiproliferative, and immunomodulatory properties. To
obtain a more comprehensive list and a better understanding of the genes regulated by IFNs, we
compiled data from many experiments, using two
different microarray formats. The combined data
sets identified >300 IFN-stimulated genes (ISGs).
To provide new insight into IFN-induced cellular
phenotypes, we assigned these ISGs to functional
categories. The data are accessible on the World
Wide Web at http://www.lerner.ccf.org/labs/williams/,
including functional categories and individual
genes listed in a searchable database. The entries
are linked to GenBank and Unigene sequence information and other resources. The goal is to eventually compile a comprehensive list of all ISGs.
Recognition of the functions of the ISGs and their
specific roles in the biological effects of IFNs is
leading to a greater appreciation of the many facets
of these intriguing and essential cytokines. This
review focuses on the functions of the ISGs identified by analyzing the microarray data and focuses
particularly on new insights into the protein kinase
RNA-regulated (PRKR) protein, which have been
made possible with the availability of PRKR-null
mice. J. Leukoc. Biol. 69: 912–920; 2001.
Key Words: cytokine 䡠 Janus kinase 䡠 protein kinase RNA-regulated
INTRODUCTION
The interferons (IFNs) are a diverse family of pleiotropic
cytokines consisting in humans of the type I species with 12
IFN-␣ subtypes, IFN-␼ and IFN-␤, and the type II species
IFN-␥. IFNs play an essential role in innate immunity by
inhibiting the replication and spread of viral, bacterial, and
parasitic pathogens. They also modulate immune responses
and exert antiproliferative effects in some cell types. As a
result of these functions, IFNs are used in the clinic to treat
certain viral infections, some cancer types, and multiple sclerosis [reviewed in ref. 1]. IFNs mediate their effects by binding
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Journal of Leukocyte Biology Volume 69, June 2001
to cell surface receptors activating members of the JAK kinase
family of proteins. Activated JAK kinases phosphorylate the
signal transducers and activators of transcription (STAT) family of transcription factors. The STAT proteins homo- or heterodimerize and form complexes with other transcription factors to activate transcription of IFN-stimulated genes (ISGs)
[2]. The gene products regulated by IFNs are the primary
effectors of the IFN response. Although the functions of most
ISGs remain to be elucidated, some of the best studied ISGs
play pivotal roles in host defense.
Experiments using mice indicate the IFNs are essential for
innate immunity against viral infections. Mice with a targeted
disruption of the type I or II IFN receptor genes are extremely
susceptible to viral infections. These mice have multiple defects in host defense and show enhanced viral replication in
many tissues [3–5]. IFNs induce the production of several
known antiviral proteins including the double-stranded RNAdependent kinase “protein kinase RNA-regulated” (PRKR), a
family of 2⬘,5⬘-oligoadenylate synthetases that lead to the
activation of RNase L and the Mx proteins, all of which have
been shown to restrict the growth of certain viruses [1]. However, the inhibition of viral replication induced by IFNs is only
partially dependent on these particular ISGs, because mice
triply deficient for PRKR, RNase L, and Mx1 genes retain
partial responsiveness to the antiviral effects of IFNs [6]. These
results imply that other as-yet-unidentified ISGs are also potent
antiviral effectors.
To identify ISGs and perhaps elucidate new functions for
IFN, we undertook extensive microarray analysis of RNA samples collected from experiments on human and murine cell
lines treated with IFN-␣, IFN-␤, or IFN-␥. Previous work from
our laboratory identified 122 ISGs, using HT1080 as the cell
type and oligonucleotide microarrays [7]. Here we extend and
confirm these results using other microarray-screening methods. By combining data from all sources listed in Table 1, we
have screened several thousand individual sequences and have
extended the initial 122 ISGs to over 300. To uncover new IFN
functions, each ISG was assigned to a series of defining functional categories. Furthermore, the categorized ISGs were as-
Correspondence: Bryan R. G. Williams, Ph.D., Chairman, Department of
Cancer Biology NB 40, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH 44195. E-mail: [email protected]
Received January 8, 2001; revised April 16, 2001; accepted April 19, 2001.
http://www.jleukbio.org
TABLE 1.
Array method
Affymetrix Hu 6800
Affymetrixs Mu 6500
Affymetrixs Hu 6800
INCYTE uniGEM I
INCYTE uniGEM V
Microarray Formats and Treatments Screened
cDNA sample
Treatment conditions
Time point
HT 1080 cells
MEFs
Hu Dendritic cells
HT1080 cells
HT1080 cells
1,000 IU/ml IFN-␣, ␤ or ␥
1,000 IU/ml IFN-AD
1,000 IU/ml IFN-␣
1,000 IU/ml IFN-␣
100 IU/ml IFN-␤
6h
6, 18 h
6h
6h
1, 3, 6, 24 h
sembled into a database that contains gene names, descriptions, accession numbers, and links to other databases containing nucleotide and protein sequence information. The
genes were placed into progressively more specific functional
categories that are fully searchable. Our laboratory is drawing
from the microarray data to construct a cDNA microarray of
human and murine ISGs.
CONSTRUCTION OF THE ISG DATABASE
To determine whether a gene is an ISG, a unique induction
cutoff score for each array format was established. All mRNAs
identified using Affymetrix (Santa Clara, CA) human 6800 and
murine 6500 Genechips were analyzed and compiled with
defined cutoff scores as described previously [7]. For an individual gene to be included in the database, it must have been
induced by IFN-␣ at least twofold in two IFN-treated samples
or threefold in a single treated sample. We found that this
subset includes nearly all the known ISGs that were included
on the Affymetrix arrays [7]. Only experiments that showed at
least 1 in 3 genes to be present and a detection limit of between
1 and 5 pM, determined using spiked RNA controls, were
included in the analysis. The INCYTE data sets were obtained
using the UniGEM V microarrays (INCYTE, St Louis, MO).
The sample RNA was harvested from cells treated as outlined
in Table 1, by Trizol extraction according to manufacturer
instructions (Life Technologies, Gaithersburg, MD). The purified RNA was then shipped to INCYTE Corp. for hybridization
and analysis. To be included in the database, genes must have
increased in expression at least 2-fold in the IFN-␣-treated
sample or 1.5-fold by IFN-␤ twice during treatment. In general, the INCYTE method underestimated the fold induction of
ISGs compared with the Affymetrix method, even when identical RNA templates were analyzed (R. H. Silverman, unpublished results). The array format, cell type, and treatment
applied are detailed in Table 1.
The Affymetrix human 6800 chip set identified 122 ISGs [7]
that were subsequently included in the functional groupings.
This study showed that IFN positively influenced the expression of approximately 1 in every 55 human genes represented
on the chip. By extrapolation, this suggests that there may be
as many as 636 –2,180 ISGs, assuming that the number of
genes in the human genome is between 35,000 and 120,000.
Because all the data sets are redundant and include both
human and murine genes, no overall figure on the number of
individual genes screened was determined. Combining the
results from the three array experiments and applying the
above cutoff criteria, 335 unique ISGs with high homology to
known genes and 78 expressed sequence tag (EST) sequences
were identified.
The genes were grouped into broad functional families and
then further categorized into more specific groups. For example, an IFN-induced protease forming part of the proteosomal
subunit was placed in the broad category “Host Defense” and
then in the more specific group “Antigen Processing,” then
“Proteosome Subunit,” and finally “Protease.” Table 2 outlines the functional categories containing over 4 members,
defined for the 335 unique ISGs identified by the microarrays.
These groups were assigned by using resources from the following databases: GenBank (http://www.ncbi.nlm.nih.gov/
Genbank/index.html), PubMed (http://www4.ncbi.nlm.nih.gov/
entrez/query.fcgi), and Gene Cards (http://bioinfo.weizmann.
ac.il/cards) [8].
The genes have been assembled into a fully searchable
database available at the following site: http://www.lerner.
ccf.org/labs/williams/. The search options are keyword, accession number, human genome organization (HUGO) gene symbol, or category. A pull-down list of the categories is available
for easy searching. Figure 1 outlines the search results obtained when the proteosome subunit category is selected. This
TABLE 2.
ISG Categories Containing More Than Four Members
Adhesion
Amino acid metabolism
Angiogenesis
Antigen presentation
Antigen processing
Antiviral
Apoptosis
Blood clotting
Cancer
Cell-cell adhesion
Cell cycle
Chemokine
Complement
Cytoskeleton
Development
DNA replication/repair
Extracellular matrix
G-protein signaling
Growth factor
GTP-binding
Hormone
Host defense
ISG-unknown
Immune modulation
Inflammation
Ion transport
13
7
8
6
8
8
19
5
6
6
7
6
5
16
18
8
8
8
15
7
11
51
10
39
16
5
Kinase
Lipid metabolism
Lymphocyte adhesion
Metabolic enzyme
Muscle contraction
Nucleotide metabolism
Oncogene
Protease
Protease inhibitor
Proteosome subunit
Protective
Protein folding
Receptor
RNA splicing
RNA binding
Signaling
Transcription factor
Transcriptional activator
Transcriptional repressor
Translation
Tumor supressor
Ubiquitination
Unknown
Vesicle transport
7
10
4
16
4
9
6
7
6
6
8
4
16
4
5
53
37
30
8
11
4
5
12
5
Others
ESTs
22
78
de Veer et al. Mediators of interferon action
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Fig. 1. ISG database search result from the proteosome subunit category. The accession numbers, Unigene number, and HUGO gene symbol all link to relevant
databases. The database is accessible from http:www.lerner.ccf.org/labs/williams/.
search returned six entries and listed the GenBank accession
number, Unigene number, a brief gene description, the HUGO
gene symbol, and the chromosomal location of the gene if
known. The accession number, if selected, will link to the
sequence information contained in the GenBank database. The
Unigene number links to the Unigene database, and the HUGO
gene symbol links to the relevant gene card entry, where some
basic functional data are available and alternative nomenclature is listed. The gene symbols and abbreviations used
throughout this manuscript conform to HUGO gene symbols.
The largest functional category of genes identified from the
microarray analysis contained a total of 53 genes, all of which
have roles in cellular-signaling pathways. Fifty-one genes have
been implicated in contributing to host defense; 39 genes have
roles in immune modulation; and 16 genes have roles in
inflammatory responses. It is also interesting that 37 transcrip914
Journal of Leukocyte Biology Volume 69, June 2001
tion factors were induced by IFN, 30 of which activate transcription, while 8 repress transcription and some are both
activators and repressors. IFN has been implicated in the host
apoptotic response to viral infection [9 –11], which is supported
by the identification of 19 ISGs with roles in apoptosis. IFN
may also alter the translation or stability of proteins and not
just the transcriptional profile of cells, because 11 genes with
roles in translation, 7 proteases, and 5 genes in the ubiquitination
pathway were identified. Thirteen genes coding for proteins involved in cellular adhesion events and 15 growth factors and 18
genes involved in development and 16 cytoskeletal proteins were
identified. Table 2 lists the categories with over four members and
the numbers of genes contained within each category. The following sections discuss the functions of the ISGs, with a focus on
PRKR and new insights into IFN biology that have been elucidated using the microarrays.
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FUNCTIONS OF ISGS
Host Defense
The PRKR gene is a previously identified ISG involved in host
defense. This gene encodes a cellular double-stranded RNA
(dsRNA)-activated kinase. dsRNA is a common intermediate
in the replication of many viruses [10, 12]. Many viruses have
evolved strategies to inhibit the activation or function of the
PRKR protein, implying that it plays an important part in the
host response to viral infection [13–15]. On activation, PRKR
phosphorylates the translational initiation factor eIF2␣ [10,
16]. Phosphorylation of eIF2␣ halts cellular translation, thus
inhibiting host and viral protein synthesis in infected cells [12,
16]. We recently showed that PRKR also phosphorylates the
B56␣ subunit of pyrophosphatase 2A (PP2A), which reduces
translation of a luciferase reporter, presumably by inhibition of
PP2A dephosphorylation of the eIF4E translation initiation
factor [17]. Thus PRKR might inhibit translation and viral
replication through multiple mechanisms.
Inhibition of translation by dsRNA was the assay used to
clone PRKR [16]; however, the availability of mice with a
targeted deletion in the PRKR gene [18] has allowed new roles
for the PRKR protein to be elucidated. PRKR-null mice develop normally and have no overt defects [18]; however, murine
embryonic fibroblasts (MEFs) derived from PRKR-null mice
are less sensitive to some apoptotic stimuli than those obtained
from isogenic normal mice [19]. Also, overexpression of PRKR
in 3T3 cells induces apoptosis [20, 21]. Recent evidence has
indicated that PRKR is required for the activation of the
nuclear factor-␬B (NF-␬B) transcription factor by some inducers [22, 23]. NF-␬B is a latent transcription factor that is
sequestered in the cytoplasm by the I␬B␣ subunit. The IKK␣
and IKK␤ kinases phosphorylate the I␬B proteins, which targets them for degradation and releases the NF-␬B transcription
factor, which then translocates to the nucleus and activates
gene expression [24 –26].
The genes induced by NF-␬B play an important role in
apoptosis, immune modulation, and induction of inflammatory
cytokines [27]. Others and we have shown that activation of
NF-␬B by dsRNA depends on PRKR activation of the IKK␤
kinase [22]. In PRKR-null cell lines, dsRNA failed to stimulate IKK activity compared with cells from an isogenic background that are wild type for PRKR. Coimmunoprecipitation
assays showed that PRKR was physically associated with the
IKK complex and transient expression of a dominant negative
mutant of IKK␤, or the NF-␬B-inducing kinase inhibited
dsRNA-induced gene expression from an NF-␬B-dependent
reporter construct [22]. Taken together, these results demonstrate that PRKR-dependent dsRNA induction of NF-␬B is
mediated by NF-␬B-inducing kinase and IKK activation.
Whether PRKR kinase activity is required for this function
remains a subject of debate [28, 29].
The PRKR protein is required for efficient activation of some
stress-activated protein kinases (SAPKs). The cytokines tumor
necrosis factor-␣ (TNF-␣) and interleukin (IL)-1␤ and pathogenic factors such as lipopolysaccharide (LPS) and dsRNA
induced phosphorylation and activation of mitogen-activated
protein (MAP) kinase kinase (MKK6), p38␣ MAPK and Jun
N-terminal kinase SAPKs in normal MEFs but not in PRKRnull MEFs [30]. This did not reflect a global failure to activate
the SAPKs in the PRKR-null background because other
physiochemical stressors activated the SAPKs normally [30].
The failure to activate SAPKs also appeared to be important in
vivo, because induction of two SAPK target genes, IL-6 and
IL-12, by LPS was reduced in PRKR-null mice when compared with normal mice [30]. NF-␬B and p38␣ can synergize
to induce inflammatory cytokines, implying that PRKR may be
an important mediator of the inflammatory response induced by
foreign pathogens.
Recent work has identified that PRKR-null mice are highly
sensitive to vesicular stomatitis virus (VSV) or influenza virus.
It is interesting that the differences were apparent only when
these viruses were administered intranasally and not when they
were administered systemically [11]. The lungs from infected
PRKR-null mice showed much higher titers of virus compared
with wild-type mice. The reason PRKR-null mice are more
sensitive is not entirely clear; it may be the failure of PRKR to
halt protein synthesis in the lungs of PRKR-null mice, allowing
increased viral replication, although other eIF2␣ kinases are
known to exist. A lack of PRKR could result in a reduction of
apoptosis, allowing the virus to replicate to higher levels in
cells, or stimulation of the immune system may be defective in
PRKR-null mice through reduced NF-␬B or SAPK activity.
Alternatively it has been demonstrated that MEFs derived from
PRKR-null mice fail to produce nitric oxide (NO) in response
to LPS, IFN␣ and dsRNA [31]. NO is an important inhibitor of
VSV replication [32], and a defect in NO production in the
lungs of PRKR-null mice may explain the sensitivity of the
lungs to VSV. We are currently using various methods to
determine the functions of PRKR which are important in host
defense against viral and bacterial infection.
Another transcript encoding the antiviral OAS2 protein was
induced by IFN in the array experiments. The OAS proteins
are activated by dsRNA and produce 2⬘5⬘-oligoadenylates that
bind and activate the latent ribonuclease RNase L. The RNase
L protein cleaves viral and cellular mRNA and ribosomal
RNA, halting cellular production of protein [1]. The OAS
proteins form a gene family that resides on chromosome
12q24.1 and likely evolved from a single ancestral gene
through gene duplication (32a). The p46 (OAS1) and p69
(OAS2) synthetase proteins have antiviral activity through the
activation of RNase L after viral infection. However, the recently identified and largest member of this family, p100
(OAS100), does not activate RNase L but rather potentiates
apoptosis in cells exposed to dsRNA. Overexpression of p100
but not other OAS family members sensitizes cells to apoptosis
by dsRNA [32a].
The array experiments showed induction of a family of large
guanosine triphosphatases (GTPases) known in the mouse as
Mx1 and Mx2 and in the human as MxA and MxB. The Mx
genes are mutated in most inbred laboratory mouse strains that
are highly sensitive to the influenza virus [33]. The Mx proteins
belong to the dynamin family and inhibit the replication of
some RNA viruses by binding to viral ribonucleoprotein structures and preventing transcription of viral RNA or movement of
viral subparticles within the cell [34, 35]. Another recently
described IFN-induced guanylate-binding protein (GBP) 1 was
de Veer et al. Mediators of interferon action
915
identified using the arrays and has been shown to have innate
antiviral activity [36]. Overexpression of GBP1 inhibited the
replication of both VSV and encephalomyocarditis virus in 3T3
cells. Expression of antisense GBP1 also reduced the antiviral
effect of IFN␥ but not IFN␣ [36]. The GBP1 gene was preferentially induced by IFN␥.
The above ISGs are all previously characterized mediators of
innate immunity; other ISGs must be involved in the inhibition
of viral replication induced by IFNs because mice triply deficient for PRKR, RNase L, and Mx1 genes retain partial responsiveness to the antiviral effects of IFNs [6]. The GBP1
gene product also only partially protects cells from viral killing
[36]. The complement of the intracellular innate immune response is the humoral immune response, which is mediated by
immune effector cells that respond to and clear the infectious
agent. The categorization of the ISGs showed a large subgroup
of host defense gene-induced humoral immune responses.
These immunomodulatory genes included the chemokines,
which are small proteins that recruit lymphocytes to sites of
inflammation or infection [37–39]. In all, genes for six chemokines—MIG, EBI1, SCYA2, SCYA5, SCYB10, and IL-8 —
were identified as ISGs. IFN also induced the expression of
four genes—ICAM1, SELL, CD47 (see Fig. 2), and ALCAM—
that promote lymphocyte adhesion to endothelial cells. Adhesion of lymphocytes to vessel walls is an important first step in
the trafficking of lymphocytes to areas of infection. This is the
first report we know of that details the induction of the CD47
protein by IFNs. The CD47 protein associates with integrins
and plays roles in cell adhesion signaling [40, 41], and CD47null mice are extremely susceptible to bacterial infection primarily caused by a failure to recruit neutrophils [42]. Thus,
IFN induces numerous genes that enhance recruitment of
immune effector cells to the site of production.
IFNs also facilitate the activation immune effector cells [1,
43]. IFN induced the expression of eight genes involved in
antigen processing and six genes involved in antigen presentation, and there was no redundancy between these two categories (see Table 2). The antigen-processing group contained
the IFN-induced peptide transporter protein ABCB2 and also
proteosome subunits involved in antigen peptide production
such as PSMB8, PSMD8, PSMA2, PSME1, and PSMB10.
These proteins form a chain that produces antigen peptides in
the proteosome and then transports them to the major-histocompatibility-class (MHC) molecules for presentation on the
cell surface [44]. IFN also induces both MHC class I (human
leukocyte antigen subtyes) and MHC class II molecules, such
as CD74 and the coactivator of the immune complex, ␤-2macroglobulin (␤2M). These proteins coordinate the activation
of immune effector cells and are important in a robust, lasting
immune response against the infectious agent [45, 46]. Thus
IFN induces the expression of proteins involved in recruitment
of both immune effector cells such as chemokines and adhesion molecules, and IFN potentiates their activation by enhancing the presentation and repertoire of MHC-associated
antigen [1].
Signaling
IFN induced 53 genes involved in modulating other signaling
pathways, the largest single category of genes induced by IFN.
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Journal of Leukocyte Biology Volume 69, June 2001
Fig. 2. Confirmation of five previously unknown ISGs identified using the
Affymetrix murine 6500 array by Northern blotting. Murine L929 cells were
treated with the indicated reagents, and RNA was harvested and transferred to
nylon membrane. The blots were hybridized with radiolabeled DNA corresponding to the coding region of each of the genes indicated. All probes were
verified by DNA sequencing. The fold induction of each gene relative to the
control untreated sample (lane 1) is indicated below each lane and was
determined by standardizing to the GAPDH signal.
The signaling category contained several genes involved in
inflammatory cytokine signaling such as MYD88 [47– 49],
JUN, RELA, MAP2K1, MAP3K8, and TRADD. The MYD88
protein is an important link in toll-like receptor signaling
pathways [49, 50], as well as in the proinflammatory cytokine
IL-1 signaling pathway. The TRADD protein is an adapter in
the TNF-␣ and IL-1 signaling pathways [51]. Both the MYD88
and TRADD genes are important in activation of NF-␬B by
foreign pathogens and proinflammatory cytokines. Induction of
these signaling proteins by IFN could potentially lead to an
increased response to ligand binding, which may have implications for the clinical side effects of IFN, including high fever
http://www.jleukbio.org
[52]. IFNs may alter the tissue specificity of some ligand
responses through the induction of a crucial signaling protein
in cell types where it is not normally expressed.
IFN also induced expression of an anti-inflammatory protein, LGALS3B [cyclophilin-c-associated protein (CYCAP) in
the mouse; see Fig. 2]. Mice with a targeted deletion in the
CyCAP gene are very susceptible to LPS killing [53] and
express elevated levels of IFN-␥, IL-12, and TNF-␣. Thus
CYCAP may function to curb the proinflammatory effects of
IFN. The interplay between the various ISGs that mediate
inflammation may be important in reducing the toxic side
effects of high-dose IFN therapy, but this requires further
study.
Another class of signaling proteins was the G proteins.
These included RAN, RANBP, NET1A, and GEM. G proteins
mediate many intracellular functions, including cytoskeletal
remodeling, vesicle transport, and growth [these functions are
reviewed in ref. 54 –56]. Potential modifiers of the Ras G
protein also were induced by IFN; RAN, ras homologue
ARHC, ras-related rab-8 MEL, and ras GTPase-activating
protein IQGAP1 were all identified as ISGs. The ras signaling
pathway leads to activation of cell growth, and overstimulation
of the ras pathway is a common defect in many cancers [57,
58]. Activation of signaling pathways stimulated by mitogenic
growth factors is also a common property of many cancers. IFN
induced 15 genes that are involved with growth factors and
growth factor signaling. These included VEGF, FGF, VRP,
PDGFRL, ECGF1, EREG, and CTGF. Most of these growth
factors are mitogenic and have been implicated in the control
of angiogenesis and cancer [59 – 62], raising the interesting
possibility that IFNs may influence angiogenesis. IFN is a
potent antitumor agent in a limited number of cancer types
[63]; however, if the induction of growth factors is confirmed in
vivo, IFN treatment of cancers that rely on growth factors for
survival might be detrimental.
The induction of signaling proteins by IFN was mirrored by
the induction of a large number of transcription factors, 37 in
total. Of the 37 transcription factors 30 activated transcription
whereas only 8 were confirmed to repress transcription. Some
of the genes categorized under transcription factors both activated and repressed transcription, and the direct role of others
was unknown. The array experiments identified six members of
the IFN response factor (IRF) family of proteins: IRF1, IRF2,
IRF3, IRF4, IRF5, and IRF7. The IRF proteins are a family of
secondary effectors that mediate immune modulation, IFN
production after viral infection, and IFN signaling [64 – 67].
This family of proteins, which is crucial to the biological
response to IFNs, was the most represented family of transcription factors identified by the array experiments.
Other interesting transcription factors induced by IFN were
the hypoxia-inducible factor (HIF1A) gene (see Fig. 2) and the
Myc promoter-binding protein (MPB1). The HIF1␣ protein
stimulates transcription of genes that mediate the intracellular
response to anoxia and induces angiogenesis [68, 69]. This
adds further support for a role of IFN in stimulating angiogenesis. HIF1␣ is overexpressed in some cancer types, where it is
thought to be important for protecting the tumor cells from
anoxia and stimulation of angiogenesis into the tumor [70]. The
MPB1 protein binds the Myc promoter and represses transcrip-
tion [71], which might be one of the mechanisms IFN uses to
down-regulate myc expression and reduce proliferation in certain cell types [1]. This protein may play a role in the antitumor
activities of the IFNs. Thus it appears that IFN induces proteins involved in both growth activation and attenuation. It will
be interesting to see whether the induction of these genes is
cell type specific and correlates with the effects of IFN on the
cell type.
IFN also induced many proteins involved in the posttranscriptional regulation of gene expression. These include genes
involved in translation, such as those encoding elongation and
initiation factors: EIF2A, EIF2B, EIF2S2, EIF3S10, and
EIF3S6 and the genes encoding the translational inhibitors
IFN-inducible 56K (IFI56) [72, 73] and PRKR [10]. It is
unclear whether induction of the elongation and initiation
factors increases translation of proteins, because IFN has never
been shown to enhance translation. The role of induction of
translation elongation and initiation factors in the IFN response
remains to be studied. The dsRNA- and IFN-induced IFI56
protein appears to inhibit translation after IFN stimulation of
cells, through sequestration of the translation initiation factor
eIF-3 [73]. The PRKR protein kinase is activated by dsRNA
and inhibits translation by phosphorylation of eIF2␣.
Fig. 3. In-laboratory ISG cDNA array. Shown is the result from hybridizing
the ISG array created in our laboratory with cDNA from IFN-treated human
RCC1 cells. cDNA prepared from RNA from control untreated RCC1 cells was
labeled with the Cy5 dye (red), and cDNA from IFN-treated RCC1 cells was
labeled with Cy4 (green). A sample portion of the array after hybridization is
shown with known ISGs highlighted, and the relative intensities and fold
induction values for known ISGs are indicated.
de Veer et al. Mediators of interferon action
917
IFN also regulated many genes involved in protein degradation with seven proteases and five ubiquitin related genes.
Three of the proteases are catalytic components of the proteosomal subunits which, coupled with the ubiquitination pathway, are responsible for the targeted degradation of many
proteins. As our understanding of the posttranslational modification of proteins expands, the roles it plays are becoming
increasingly important [25, 44, 74]. IFN also regulated several
RNA-interacting proteins, consisting of two helicases (DDX3
and DDX21) and four genes shown to play a role in RNA
splicing (SFPQ, SFRF2, SF3A3, and SF3A1). Thus, IFN may
modulate the production of functional proteins at several levels, transcriptional induction of mRNA, the splicing and processing of this RNA, translation, and finally degradation of the
protein.
The apoptosis category contained 19 ISGs. Apoptosis is the
result of a proteolytic cascade of cysteine proteases (caspases)
which leads to cleavage of important substrates and subsequent
cell death [75]. IFN induced mostly proapoptotic proteins
including CASP4 (see Fig. 2), CASP8, trail (TNFSF10), BAK1,
and Fas or CD95 (TNFRSF6). All these proteins are involved
in the activation of apoptosis by numerous inducers [76 –79].
CASP4 and CASP8 are caspases that actively cleave other
caspases and transduce the proteolytic cascade. The TNFRSF6
protein is a death receptor protein involved in signaling T-cell
killing [76], while the TNFSF10 protein is a soluble TNF-like
molecule involved in the induction of apoptosis when it binds
its receptor [78]. Also, the phospholipid scramblase (PLSCR1)
protein, a new ISG, was induced by IFN and is implicated in
moving phosphatidyl serine to the outside of the plasma membrane in apoptotic cells [79]. This is a critical step in facilitating the recognition and destruction of the apoptotic cell by
immune effector cells and is also involved in blood clotting
[79]. The IFNs have been reported to be proapoptotic cytokines; they induce apoptosis in cells infected with certain
viruses [80] and can also cause apoptosis in some transformed
cell lines [81, 82].
To aid in analysis of the IFN-response, we are arraying as
many ISGs as we can obtain onto a single ISG-chip. A prototype of this array has been screened using cDNA from a renal
cancer cell line, RCC1, that was either treated with 100 IU of
IFN␣2b for 16 h or left untreated. Total RNA was isolated
using the Trizol method (Life Technologies). A total of 1 ␮g of
each RNA was amplified using two rounds of ds-cDNA synthesis followed by T7-driven in vitro transcription [83]. The
untreated RNA was labeled with the Cy5 (red) fluorophore, and
the RNA from IFN-treated cells was labeled with the Cy3
(green) fluorophore (Amersham Pharmacia Biotech, Little
Chalfont, Buckinghamshire, England) [83]. The hybridized and
scanned array and a selection of the results that were obtained
are shown in Figure 3. The genes induced by IFN show
increased green fluorescence while those suppressed by IFN
show increased red fluorescence. Those that remained unchanged during this experiment are yellow.
When the ISG array is completed, it will be used to confirm
the induction patterns of the novel ISGs we identified using the
different microarray formats (outlined in Table 1). The ISG
chip will also allow rapid screening of a large number of ISGs
Fig. 4. Schematic diagram indicating the numbers and various functions of ISGs identified by the microarray analysis.
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http://www.jleukbio.org
to identify differences in the their induction pattern after many
stimuli. We intend to monitor the expression of ISGs in cancer
patients treated with IFN, which might identify ISGs that are
important in the clinical responsiveness of cancers to IFN once
the data are correlated with patient responses to IFN therapy.
Figure 4 summarizes the numbers and various functions of
ISGs identified by microarray analysis. The analysis of microarray data has offered new insights into IFN biology through
the identification of genes such as CyCAP, MYD88, and CD47
and the gene encoding phospholipid scramblase. These genes
and others have opened up potential new areas of IFN biology
and have provided insights into some of the proteins that are
important in the IFN response. The further analysis of genes
induced by IFN will lead to a greater understanding of the
multitude of effects these cytokines exert on cells. It may aid in
the identification of novel therapeutic uses for IFN and should
identify new candidates that may prove useful to monitor
clinical responsiveness to IFN therapy. It is important that the
array experiments, although reliable, are still subject to error;
some of the ISGs mentioned may not be confirmed in further
studies or may be induced only by IFN in a small number of
cell lines. The compilation of the ISGs we have presented in
the ISG database provides a new resource that will facilitate
further research on IFN-mediated cell responses. It is our aim
to maintain and update the database and incorporate newly
recognized ISGs as these become known. We will be pleased to
add novel ISGs identified by other laboratories and invite
investigators to contact us with new information and/or comments.
ACKNOWLEDGMENTS
This investigation was supported by U.S. Public Health Service
grants from the Department of Health and Human Services,
National Institute of Allergy and Infectious Diseases (AI34039
to B.R.G.W.) and National Cancer Institute (CA 44059 to
R.H.S.) and by a grant from Ares-Serono (to R.H.S.).
We thank Mathias Frevel for constructive comments on the
manuscript.
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