Embed Size (px)
Citation preview
ORIGINAL ARTICLE
Deciphering the host-pathogen protein interface in chikungunyavirus-mediated sickness
Jyoti Rana • R. Sreejith • Sahil Gulati •
Isha Bharti • Surangna Jain • Sanjay Gupta
Received: 27 September 2012 / Accepted: 2 December 2012 / Published online: 20 January 2013
� Springer-Verlag Wien 2013
Abstract Successful infection with chikungunya virus
(CHIKV) depends largely on the ability of this virus to
manipulate cellular processes in its favour through specific
interactions with several host factors. The knowledge of
virus-host interactions is of particular value for under-
standing the interface through which therapeutic strategies
could be applied. In the current study, the authors have
employed a computational method to study the protein
interactions between CHIKV and both its human host and
its mosquito vector. In this structure-based study, 2028
human and 86 mosquito proteins were predicted to interact
with those of CHIKV through 3918 and 112 unique inter-
actions, respectively. This approach could predict 40 % of
the experimentally confirmed CHIKV-host interactions
along with several novel interactions, suggesting the
involvement of CHIKV in intracellular cell signaling,
programmed cell death, and transcriptional and transla-
tional regulation. The data corresponded to those obtained
in earlier studies for HIV and dengue viruses using the
same methodology. This study provides a conservative set
of potential interactions that can be employed for future
experimental studies with a view to understanding CHIKV
biology.
Introduction
Chikungunya virus (CHIKV), a member of the genus
Alphavirus, family Togaviridae, is a virulent re-emerging
human pathogen of major public-health concern and one of
the leading causes of mosquito-borne arthralgia in tropical
countries [1]. Over the past five years, this Old World
alphavirus has caused recurrent outbreaks of epidemic
proportions in European countries by expanding its vector
range [2–4]. The recent La Reunion outbreak has shown
that this virus is no longer just arthritogenic but also causes
neurological complications such as encephalopathy, espe-
cially in neonates [5], which was responsible for the high
mortality rate [6]. As an arbovirus, CHIKV is transmitted
to its human host by mosquito vectors of the genus Aedes.
In mosquitoes, CHIKV establishes a persistent and non-
pathogenic state of infection [7], while in humans, CHIKV
evades the immune system and induces autophagic and
apoptotic pathways for its replication and propagation.
During the course of infection, CHIKV has to manipulate
the cellular machineries of both its hosts at the molecular
level through specific protein-protein interactions for its
survival, replication, transmission and infection. An
understanding of these mechanisms will shed light on viral
dissemination and pathogenesis.
The use of computational approaches for predicting
virus-host protein interactions can provide the basis for
further experimental work to understand viral biology and
identification of credible therapeutic candidates. In the past
decade, various studies have used computational approa-
ches for predicting pathogen-host interactions based on
information available from protein structures [8–10]. For
understanding molecular mechanisms involved in malaria,
different statistical methods had been used to predict
interactions between P. falciparum and human proteins
Electronic supplementary material The online version of thisarticle (doi:10.1007/s00705-013-1602-1) contains supplementarymaterial, which is available to authorized users.
J. Rana � R. Sreejith � S. Gulati � I. Bharti � S. Jain �S. Gupta (&)
Department of Biotechnology, Center for Emerging Diseases,
Jaypee Institute of Information Technology, A-10, Sector 62,
Noida 201 307, Uttar Pradesh, India
e-mail: [email protected]; [email protected]
123
Arch Virol (2013) 158:1159–1172
DOI 10.1007/s00705-013-1602-1
based on domains involved in intraspecies interactions and
orthologous protein pair interactions from eukaryotes [11,
12]. Several different computational methods have also
been employed for predicting the interactions of HIV with
its human host [13–15]. In addition to viral infections, a
few studies have been done for non-viral pathogens, also
based on structural similarities between pathogen and host
proteins [9, 16]. Some of the recent studies have involved
both sequence and structural homology to identify exoge-
nous and endogenous protein interactions between the
pathogen and its host [17, 18].
This study involves the implementation of a computa-
tional approach for predicting the interactions between
CHIKV and its host (human and mosquito) proteins. The
approach is based on protein structure information for
prediction of interactions where proteins with defined
structure and known interactions are mapped to proteins
with similar structures. A number of viral proteins are
known to mimic host protein motifs that are involved in
similar types of interactions to exploit the cellular infra-
structure [19]. This approach has been used earlier for
prediction of HIV-human and dengue virus (DENV)-
human interactions [15, 20] as well as for non-viral
pathogens causing tropical diseases [16]. The present study
generates a network of interactions among CHIKV proteins
and its vertebrate and invertebrate hosts. The functional
processes involving target proteins are broadly categorized
among structural (involved in intracellular signaling; JAK-
STAT pathway and enzyme linked receptor mediated sig-
naling pathways) and nonstructural proteins (involved in
translation-transcription regulation, programmed cell death
and stress responses) of CHIKV. The predicted interactions
are highlighted on the basis of their functional relevance
during CHIKV infection and prioritized based on the
interaction data of other related alphaviruses. Together
with the knowledge of intraviral protein interactions of
CHIKV previously reported by the authors [21], the current
study puts forth a platform for the better understanding of
CHIKV pathogenesis.
Materials and methods
Data sources
The crystal structures of envelope proteins [E1 (PDB ID:
3N42-F), E2 (PDB ID: 3N42-B) and E3 (PDB ID: 3N42-
A)] were obtained from RCSB PDB. The structures of the
capsid and 6K proteins and all four nonstructural proteins
(nsPs) of CHIKV are unknown and hence were modeled
using I-TASSER [22, 23]. The translations of CHIKV gene
sequences previously submitted by the authors [24] were
used for generation of I-TASSER computational models,
GenBank accession no. JF272473 (nsP1), JF272474
(nsP2), JF272475 (nsP3), JF272476 (nsP4), JF272477
(capsid), JF272481 (6K), 2011]. Each of the CHIKV pro-
teins (known or predicted) was compared with proteins of
known structure for structural similarities using the Dali-
Lite v. 3 webserver [25, 26]. The known protein-protein
interactions among human proteins were obtained from
HPRD Release 7 [27] and BIOGRID [28], while those
among Drosophila melanogaster were obtained from
DroID v5.0 [29]. The Ae. aegypti orthologs of D. mela-
nogaster were obtained from FlyBase v.FB2009 [30, 31].
The interaction networks were made using Cytoscape [32].
Identification of structurally similar proteins
among CHIKV and its hosts
The Dali web server was used to determine the structural
similarities among CHIKV and human proteins. This ser-
ver measures structural similarities by comparing the
intramolecular distances between amino acids using the
sum-of-pairs method. The DALI or distance alignment
matrix method divides the input structures into hexapeptide
fragments and calculates a distance matrix by evaluating
the contact patterns between successive fragments [25].
While determining the similarity between two proteins, the
3D structural coordinates of these proteins are compared by
alignment of alpha carbon distance matrices. When dis-
tance matrices of two proteins share the same or similar
features in approximately the same positions, they can be
said to have similar folds with similar-length loops con-
necting their secondary structure elements [22, 25, 26]. In
the present study, each CHIKV protein with known or
predicted structure was submitted to the Dali web server.
All of the H. sapiens and D. melanogaster proteins with a
known structure listed in the Dali database with Z-score
C 2 were considered similar to the corresponding CHIKV
protein. These proteins were referred to as hCHIKV and
dCHIKV proteins, respectively.
Prediction of CHIKV-host protein interactions
In order to identify the putative human-host interactors of
CHIKV (exogenous interactions), the cellular protein
partners of hCHIKV (endogenous interactions) were
obtained from HPRD (Human Protein Reference Database)
and BIOGRID (Biological General Repository for Inter-
action Datasets). These datasets are a source of all litera-
ture-curated interactions among human proteins established
through in vitro and/or in vivo methods [27, 28]. It is
plausible that these cellular proteins, which are known to
interact with hCHIKV (human proteins structurally similar
to CHIKV proteins), might also interact with CHIKV
proteins owing to their structural similarity. In an
1160 J. Rana et al.
123
analogous fashion, the possible interactions among CHIKV
and Aedes proteins (exogenous interactions) were identified
by determining the interactions of dCHIKV (drosophila
proteins structurally similar to CHIKV proteins) and other
D. melanogaster proteins (endogenous interactions) using
DroID with a cutoff confidence value of 0.4 [29, 33]. The
Ae. aegypti (aCHIKV) orthologs of D. melanogaster pro-
teins were then obtained from FlyBase (target proteins)
[31]. These target proteins were predicted to interact with
the CHIKV proteome because of the similarity in the
structures of aCHIKV and the target proteins.
GO term enrichment
Gene ontology (GO) is a system to describe and annotate
genes and gene products on the basis of their functionality
and compartmentalization [34]. For this study, DAVID
(Database for Annotation, Visualization and Integrated
Discovery) functional annotation charts were used to
obtain the list of terms enriched among a set of proteins
[35, 36]. The charts were organized as a tree structure, and
as the distance from the root increased, the terms were
considered to be more specific. To make our study more
specific and informative, GO level 4 terms were used. The
Bonferroni procedure was used to obtain the corrected
p-values and transformed into -log10 terms for graphical
representation of data.
Cellular compartmentalization
The interaction dataset obtained was shortlisted on the
basis of protein localization, since two proteins theoreti-
cally must share at least one cellular compartment for
direct interaction among them. The cellular compartment
of viral proteins could not be obtained from DAVID
because DAVID provides the same GO term to each viral
protein. The GO terms for CHIKV proteins were thus
obtained from the GOanna webserver, provided by AgBase
v. 2.00, which provides GO annotations based on sequence
homology [37]. The output obtained from GOanna was
converted to an annotation summary file using the GOan-
na2ga tool. This summary file was then interpreted by GO
slim viewer. The GO terms were assigned on the basis of
previously annotated data similar to the input data identi-
fied by BLAST. A further selection of cellular compart-
ments was based on the reported literature suggesting the
localization of viral proteins in the host cell.
Interaction validation
In the Dali database, a single protein might be represented
by multiple PDB structures, but all of them will generate
the same interaction dataset, since they correspond to the
structure of the same protein. In order to avoid redundancy
in data, each protein was represented as a single PDB
structure. In the case of multiple hCHIKV (human proteins
structurally similar to CHIKV proteins) proteins having
common cellular partners, only unique pairs of interactions
between human uniprot accession and CHIKV proteins
were considered. The predicted interactions were validated
by comparing them with the data obtained from experi-
mental studies carried out for CHIKV. Since there is not
much literature support available for CHIKV-host inter-
actions, a dataset from related alphaviruses, Sindbis virus
(SINV) and Semliki Forest virus (SFV), was also used to
validate our predictions [38–43].
Direct interactions of CHIKV proteins with Aedes pro-
teins are still to be resolved. To date, there has been only
one report on differential expression of proteins in Aedes
during CHIKV infection [44]. These data may not provide
direct evidence for interactions among viral and host pro-
teins, but they certainly support the possibility of interac-
tion among these proteins.
Orthologous target determination
InParanoid 7.0 Refworks:161 provides the genome-wide
list of orthologs between human and Ae. aegypti proteins.
The list of Ensembl protein IDs were mapped to Uniprot
accession by Ensemble 57 using Biomart Refworks:192,
and the orthologous human and Ae. aegypti proteins that
interact with the same viral protein were identified.
Results and discussion
Identification of host proteins similar to CHIKV
proteins
Although two different structures (trypsin and furin
cleavage) were available for the CHIKV envelope proteins
in PDB, furin-cleaved structures were selected, since
immature structural polyprotein (pE2-6K-E1) is cleaved by
host furin during the alphaviral life cycle [45]. Each of the
protein structures (known and predicted) were submitted to
the Dali web server and found to have at least one similar
human protein (Online Resource 1). However, not all viral
proteins had structurally similar counterparts in D. mela-
nogaster. Together, 282 human proteins (hCHIKV) and 14
D. melanogaster proteins (dCHIKV) were found to be
similar in structure to all nine proteins of CHIKV.
Prediction of protein interactions
The list of similar proteins obtained from the DaliLite
server was used to extract the hCHIKV-interacting human
Host proteins potentially interacting with chikungunya virus 1161
123
proteins from HPRD, which contains about 37,000 docu-
mented human protein-protein interactions [27]. Besides
this, another database, BIOGRID, was also used to obtain
interacting partners for some of the hCHIKV proteins
whose interactors were not available in HPRD. BIOGRID
provides a comprehensive resource of protein-protein and
genetic interactions for all major model organisms,
involving more than half a million interactions. These
proteins were considered putative interacting partners of
CHIKV proteins based on the concept of involvement of
structurally similar proteins in the same set of interactions.
A total of 5592 unique interactions with the human host
were predicted for all nine proteins of CHIKV, involving
2931 proteins (Online Resource 1).
Similarly, the interacting D. melanogaster protein part-
ners of dCHIKV were obtained from the IntAct and DroID
databases [29, 33]. The Ae. aegypti proteins that interact
with CHIKV proteins were identified by determining the
orthologs of D. melanogaster proteins obtained from
IntAct and DroID. We have obtained a list of 474 Ae.
aegypti proteins involved in 617 unique interactions with
CHIKV proteins (Online Resource 2).
GO term enrichment
Due to the dearth of sufficient literature support for
direct interactions among CHIKV and host proteins we
inferred the accuracy of our data on the basis of pro-
cesses and functions performed by these predicted host
proteins. The most significant GO terms enriched among
these proteins obtained from DAVID, such as apoptosis,
regulation of transcription and translation are known to
be associated with CHIKV infection [46, 47]. The similar
human structural proteins were enriched with the terms
of antigen processing and presentation, immune respon-
ses and cell communication (Fig. 1), and the similar
human CHIKV nonstructural proteins were mainly
involved in the regulation of molecular functions and cell
death (Fig. 2). Following GO, the structural proteins
were found to be primarily involved in activation of
signaling cascades and positive regulation of immune
responses (Fig. 1), while the nonstructural proteins were
observed to be associated with RNA processing, trans-
lation, macromolecular complex assembly and regulation
of stress responses (Fig. 2). For Ae. aegypti, the most
enriched biological processes included protein modifica-
tion, localization and transport, which can be attributed
to enriched molecular functions like nucleotide-ribonu-
cleotide binding and protein transporter activity (Fig. 3).
This structure-based study predicts 2931 human proteins
and 474 mosquito proteins that interact with those of
CHIKV through 5592 and 617 unique interactions,
respectively. Our results are in concordance with those
from earlier studies of genes that are expressed in
response to CHIKV infection [44, 47].
Furthermore, the interaction dataset was filtered on the
basis of the subcellular localization of interacting proteins.
This filtering is based on the theory that physical interac-
tion is possible only when two proteins are present in close
proximity to each other, and the interaction dataset was
therefore refined using the GO cellular compartment (CC)
annotation, reducing the number of unique proteins to 2028
for human and 86 for Aedes, which were responsible for
3918 and 112 interactions among human-CHIKV and
Aedes-CHIKV proteins, respectively (Online Resources 3
and 4).
Literature filtering
CHIKV is among the less-studied alphavirus, particularly
with regard to its host interactions. So far, to the best of our
knowledge, there have been only a few reports available in
which the cellular partners of CHIKV have been identified.
Through a comprehensive search of the literature, we were
able to find 30 documented interactions for CHIKV
(Table 1) [48, 49], 11 of which was predicted in the current
study. Given the large number of predictions, together with
the lack of sufficient literature support for the CHIKV-host
interface, the interaction data obtained in the current study
were also compared with those of related alphavirus,
including SINV and SFV (Online Resource 5). To date,
four receptors have been identified for alphaviruses, which
include CD209, laminin receptor, CD41 and 60-kDa neural
cell adhesion molecules [50, 51]. We were able to predict
the interaction of three of these proteins (CD209, laminin
receptor and 60-kDa neural cell adhesion molecules) with
CHIKV E2, thus suggesting their role as putative host-cell
receptors of CHIKV. More than 40 % of the host interac-
tors known for SINV nonstructural proteins (nsPs) matched
the predicted cellular partners of CHIKV nsPs. SINV nsP2
has been shown to interact with multifunctional hetero-
geneous ribonucleoproteins (hnRNPs) [39], which are
involved not only in the processing of heterogeneous
nuclear RNAs (hnRNAs) but also in the regulation of host
gene expression [52]. The interference of host transcrip-
tional and translational machinery by CHIKV nsP2 toge-
ther with the prediction of its interaction with hnRNPs
suggest that host-cell mRNA processing, which is inter-
fered during CHIKV infection, might occur because of the
direct crosstalk among these proteins. Members of 14-3-3
protein family are yet another group of proteins known to
associate with functionally diverse signaling molecules
involved in a multitude of cellular responses. We were able
to identify five proteins of this family that interact with
CHIKV nsP4. We were also able to predict 15 cellular
partners of CHIKV nsP3 that were supported from the
1162 J. Rana et al.
123
SINV nsP3 interaction dataset. Our study correlates with
most of the processes that are altered or manipulated during
CHIKV infection. Numerous interactors, both known and
having the possibility to interact with CHIKV proteins,
have also been identified.
Differential protein expression studies following
CHIKV infection in midguts of Aedes mosquitoes have
shown upregulation and downregulation of proteins that are
mainly involved in oxidative stress, cell detoxification,
cellular metabolism and the cell cytoskeleton [44]. Some of
these proteins, such as enolase, aldoketoreductase, perox-
iredoxin-1, annexin, actin, cofilin, WD-repeat-containing
protein, and transgelin have been found to be associated
with virion of influenza virus A [53]. These proteins also
may have the potential to be incorporated into the CHIK
virion. Moreover, modulation of the host immune response
by mosquito-injected CHIKV infection suggests that
CHIKV could have evolved to facilitate upregulation or
downregulation of secretion of salivary proteins or factors
in mosquitoes that could favour virus replication, trans-
mission and/or persistence in the host [54]. However, in the
absence of literature regarding interactions in the pathogen-
vector interactome, this study can be of great help by
putting forth a platform for bench studies to understand
viral transmission from vector to human host.
Autophagy and apoptosis in CHIKV pathogenesis
Autophagy and apoptosis are different mechanisms of
immune response involving cellular destruction during
Fig. 1 GO term enrichment of
human host proteins interacting
with CHIKV structural proteins.
a Enriched GO biological
process terms. b Enriched GO
molecular function terms. Dark
blue bars represent terms
enriched among target human
proteins, and light blue bars
represent terms enriched among
hCHIKV similar proteins.
Bonferroni corrected p-values
were transformed to -log10
values (colour figure online)
Host proteins potentially interacting with chikungunya virus 1163
123
pathogen infection [55]. Recent studies on autophagic and
apoptotic responses during CHIKV infection have suggested
that the virus interferes with both of these pathways for
survival in its vertebrate host. Autophagy is induced at the
early stages of infection to increase the viral titer, while
caspase-dependent apoptosis is induced during the later
phase, aiding virus dissemination [56]. Programmed cell
death and apoptosis are the most enriched terms among both
structural and nonstructural proteins in the present study.
Autophagic host responses include type I IFN
enhancement, antigen processing and presentation to MHC
I or II and T cell priming [57–60]. Some RNA viruses have
evolved mechanisms to interfere with, escape or exploit
this machinery to facilitate its own replication [61–64]. In
the case of CHIKV, the autophagosomes have been shown
to be the intracellular membranes (spherules) associated
with viral replication [65]. Viral infection induces
autophagy by independent induction of endoplasmic
reticulum (ER) and oxidative stress pathways, which
engage Bcl-2 and Beclin 1 (complex called Atg6) [66–68].
The ER detects changes in cell homeostasis and triggers the
unfolded protein response (UPR) due to the accumulation
Fig. 2 GO term enrichment of human host proteins interacting with
CHIKV nonstructural proteins. a Enriched GO biological process
terms. b Enriched GO molecular function terms. Light brown bars
represent terms enriched among target human proteins, and dark
brown bars represent terms enriched among hCHIKV similar proteins.
Bonferroni corrected p-values were transformed to -log10 values
(colour figure online)
1164 J. Rana et al.
123
of viral proteins [69, 70]. ER stress activates three different
signaling pathways, initiating with eIF2a, IRE1a and
ATF6, respectively [70]. CHIKV infection activates
IRE1a, which triggers the activation of XBP1 and MAPKs
by phosphorylation [70]. IRE1a-induced phosphorylation
of MAPKs regulates cell stress and apoptosis [71]. We
suggest that the interactions of nonstructural proteins (nsP2
and nsP4) with MAPK may be responsible for inhibition of
its phosphorylation and subsequent activation, thereby
delaying apoptosis.
Persistent infection may lead to activation of the IRE-
JNK-BCL2 pathway. Our prediction suggests the potential
interaction of E2, 6K and nsP2 with Bcl-2, the key com-
ponent of this pathway. Moreover, we propose that nsP2,
nsP3 and nsP4 interact with other members of the BCL
family, such as BCL2L1 (inhibitor of cell death),
BCL2L10 (suppressor of apoptosis induced by BAX) and
BCL2L11 (facilitator of apoptosis) during the course of
infection. Also, the envelope proteins (E1 and E2), nsP1
and nsP3 are predicted to interact with the ER chaperones
calnexin (CANX) and calreticulin (CALR), which are
negative regulators of the UPR, suggesting that multiple
targets are used to manipulate IRE-JNK-BCL2 pathway.
Oxidative stress is a feature of the host response to viral
infections [72], inducing autophagy and cell death in cases
of prolonged infection [73–75]. The reactive oxygen spe-
cies (ROS) is linked to the mTOR pathway in a TSC2-
dependent manner [76, 77]. The kinase mTOR is a critical
regulator of induction of autophagy, with activated mTOR
(Akt and MAPK signaling) suppressing autophagy and
negative regulation of mTOR (AMPK and p53 signaling)
promoting it. The phosphorylated mTOR can integrate into
two complexes, called mTORC1 and mTORC2 [78], and
only mTORC1 is involved in autophagy [79]. We predicted
the interaction of the nsP4 protein with mTOR, which may
inhibit its phosphorylation and induce autophagy.
In addition to autophagy, CHIKV also hijacks the
apoptotic machinery of the host for its propagation. The
apoptotic blebs produced during the later phase of infection
have been shown to contain progeny virions, which may
represent a mechanism of cell-to-cell spread [46]. Both
Fig. 3 GO term enrichment of Ae. aegypti host proteins interacting
with CHIKV proteins. a Enriched GO biological process terms.
b Enriched GO molecular function terms. Bonferroni corrected
p-values were transformed to -log10 values (colour figure online)
Table 1 Known CHIKV-human interactions experimentally deter-
mined direct interactions between CHIKV and human proteins
Human protein CHIKV protein Reference
PDK2 nsP2 [45]
ZBTB43 nsP2 [45]
GRN nsP2 [45]
CALCOCO2 nsP2 [45]
ASCC2 nsP2 [45]
EFEMP1 nsP2 [45]
EFEMP2 nsP2 [45]
RBM12B nsP2 [45]
TTC7B nsP2 [45]
KLC4 nsP2 [45]
HNTNPK nsP2 [45]
UBQLN4 nsP2 [45]
FBLN5 nsP2 [45]
RCHY1 nsP2 [45]
SRSF3 nsP2 [45]
EWSR1 nsP2 [45]
WWP1 nsP2 [45]
TRIM27 nsP2 [45]
VWF nsP2 [45]
MRFAP1L1 nsP2 [45]
IKZF1 nsP2 [45]
TACC3 nsP2 [45]
TPR nsP2 [45]
CEP55 nsP2 [45]
SLIT2 nsP2 [45]
VIM nsP2 [45]
LCP1 nsP4 [45]
RBPMS nsP4 [45]
ATXN1 E3 [45]
G3BP nsP3 [46]
The interactions highlighted in bold were also predicted in our study
Host proteins potentially interacting with chikungunya virus 1165
123
extrinsic and intrinsic pathways of apoptosis are regulated
by a preformed cascade of proteases called caspases [80,
81] by activation of caspase-8 [82, 83] and caspase-9 [84],
respectively (Fig. 4). Both of these pathways activate
downstream caspases (e.g., caspase-3, caspase-6 and cas-
pase-7), causing morphological and biochemical changes
in the cell that finally lead to apoptosis [81]. In our study,
we found that 6K, nsP2, nsP3 and nsP4 interact with these
caspase proteins, which may be responsible for altering the
apoptotic pathway (Fig. 5). Experimental studies on HeLa
human fibroblast cells and neuroblastoma SH-SY5Y cells
have suggested that CHIKV infection triggers apoptosis
and that inhibition of apoptosis affects infectivity and
dissemination of the virus to neighbouring cells [46, 85].
Transcriptional and translational shutoff of host
machinery
Alphavirus infection inhibits transcription and translation
in host cells by separate processes, all eventually leading to
cell death [86]. These viruses cause major changes in
cellular macromolecular synthesis by downregulating the
transcription and translation of host mRNAs [85]. Among
the viral proteins that cause cytopathic effects, nsP2 has
been reported to alter macromolecule synthesis in host cells
[87, 88]. Mutational studies of SFV nsP2 have suggested
that this protein is neuropathogenic, resulting in slower
viral spread and reduced cell death in infected adult mice
[89]. The GO terms enriched among nonstructural proteins
in the present study included mRNA processes, regulation
of cellular metabolic processes, and macromolecular
complex assembly, all of which are concurrent with the
known pathogenesis of CHIKV.
Although not reported for alphaviruses, studies on
influenza virus and herpes simplex virus have explained the
inhibition of cellular translation by virus-mediated degra-
dation of host mRNAs [90, 91]. Surprisingly, the putative
interacting partners for nonstructural proteins (nsP2 in
particular) were mainly components associated with
spliceosomes involved in different pathways of mRNA
degradation (Fig. 6). The predicted interaction of nsP2
Fig. 4 Apoptotic response to
CHIKV infection. The pathway
represents the extrinsic and
intrinsic activation of host cell
apoptosis during CHIKV
infection. Bold arrows represent
the intrusion sites, and
associated viral proteins are
represented in red (colour figure
online)
Fig. 5 Network of putative protein interactions involved in host
apoptotic response to CHIKV infection. Blue diamonds represent the
viral proteins, green circles represent the interacting host protein, and
lines indicate the proposed interaction (colour figure online)
1166 J. Rana et al.
123
with decapping enzymes Dcp1 and Dcp2 suggests the
possibility of virus-mediated degradation of host mRNA
during CHIKV infection, with the involvement of exonu-
clease XRN1.
During virus replication, the double-stranded RNA
intermediates that are produced are known to activate host
protein kinase R (PKR) [92]. Activated PKR phosphorylates
the a subunit of eukaryotic translation initiation factor 2
(eIF2a), which in turn resists the activity of guanine nucle-
otide exchange factor eIF2B, leading to the inhibition of host
translation. The alphaviruses are known to circumvent the
components of the translation initiation complex in an
attempt to counteract the host cell’s defensive translational
shutdown [93]. Studies on SFV and SINV have shown that
translation of 26S subgenomic RNA even in the presence of
phosphorylated form of eIF2a [93, 94]. The interaction of
nsP2 and nsP4 with eIF2a as obtained in our study might
explain the possible evasion strategy adopted by CHIKV to
overcome the defensive translational shutdown by the host.
Inflammatory response during CHIKV infection
The inflammatory response during CHIKV infection plays
a critical role in the host defense mechanism. Both human
and mosquito hosts of CHIKV use several signaling path-
ways of the innate immune response to clear the virus from
the body. CHIKV infection rapidly activates the production
of type I interferons (IFNs), mainly IFN-a by typical leu-
kocytes and IFN-b by fibroblasts [95–99], and pro-
inflammatory cytokines [100, 101]. Our study includes
several signaling pathways of the immune response as the
most enriched GO terms. A few specific proteins have been
identified, which can be thought of as the targets of viral
proteins that modify the host IFN response.
During viral infection, the interferon response is acti-
vated by host pattern recognition receptors (PRRs) that
recognize the pathogen-associated molecular patterns
(PAMPs) of the virus [102, 103]. The most common among
the PRRs are the Toll-like receptors (TLRs) that recognize
both ssRNA (TLR7 and TLR8) and dsRNA (TLR3) [104,
105], but in the case of CHIKV, TLRs are not directly
involved in IFN production, which is dependent on the
adaptor molecule CARDIF (CARD adaptor inducing
interferon b) [106] acting downstream of melanoma dif-
ferentiation associated gene 5 (MDA5) and retinoic acid
inducible gene 1 (RIG1), suggesting that fibroblasts are the
primary cell types for CHIKV infection and interferon
production [107].
IFNs from infected cells stimulate IFN-a/b receptors
(IFNAR) and activate signal transducers and activators of
transcription (STATs) through janus kinases (JAKs)
(Fig. 7). During CHIKV infection, STATs associate with
IFN regulatory factor 3 (IRF3) [108] and bind to IFN-
stimulated response elements (ISREs) upstream of IFN-
stimulated genes (ISGs) that encode antiviral proteins
[109]. However, in the later phase of infection, CHIKV
blocks the transcription of IRF3-dependent antiviral genes,
thereby inhibiting further IFN production [108]. The results
of our study suggest possible interactions of nsP2, nsP4 and
structural proteins with various components of the JAK-
STAT pathway, such as IPS-1, PIAS2, STAT1, 2 and 3, to
name a few, suggesting evasion mechanisms of CHIKV
against the antiviral response mediated by IFN (Fig. 8).
Interaction of nsP2 with IPS-1 (interferon promoter stim-
ulator 1) might inhibit the activation of IRF3, and inter-
action with PIAS2 might block the binding of the STAT1/
STAT2 heterodimer to ISER. On the other hand, interac-
tion of nsP4 with STAT1 might inhibit IFN signaling by
Fig. 6 Predicted viral-host
protein interactions involved in
host mRNA degradation. Blue
diamonds represent the viral
proteins, green circles represent
the interacting host protein, and
lines indicate the proposed
interaction (colour figure
online)
Host proteins potentially interacting with chikungunya virus 1167
123
reducing the level of phosphorylated STAT1. Furthermore,
only CHIKV nsP2 has been shown to have the potential to
inhibit the IFN-induced JAK-STAT pathway [110].
Knockout studies have demonstrated increased severity of
CHIKV infection in mice lacking IFNAR [111] and
STAT1 [98] proteins.
The elicitation of an innate immune response is followed
by an adaptive immune response through activation and
proliferation of CD4? and CD8? T cells, activating Th1
and Th2 cytokine responses. During the initial phase of
infection, the Th1 cytokine response is induced, while in
later stages (acute phase) Th2 cytokines are predominant
[112]. Th2 cytokines are involved in B cell maturation and
might be responsible for the persistence of CHIKV-specific
IgGs and IgMs among infected individuals for prolonged
periods [113, 114]. The results obtained in our study pre-
dict the interaction of envelope proteins with CD proteins,
which can be presumed to activate T cells, NK cells and
dendritic cells through direct interaction or through anti-
gen-presenting cells.
Human and Aedes orthologs
CHIKV is maintained in nature mainly through cycling
between its human and mosquito hosts. Despite being two
very different hosts, certain proteins and essential pro-
cesses are conserved between them, and hence it is possible
that some of the proteins that are manipulated by CHIKV
in one host might have orthologous counterparts in the
other [20]. Comparison of interacting proteins from both
hosts identified 123 orthologs between them. Out of these
123 proteins, 60 interact with the same CHIKV protein
Fig. 7 Activation of the JAK-
STAT pathway in response to
CHIKV infection. The pathway
represents activation of
interferon signaling by the host
cell during CHIKV infection.
Bold arrows represent the
intrusion sites, and associated
viral proteins are represented in
red (colour figure online)
Fig. 8 Predicted viral-host protein interactions involved in the JAK-
STAT pathway. Blue diamonds represent the viral proteins, green
circles represent the interacting host protein, and lines indicate the
proposed interaction (colour figure online)
1168 J. Rana et al.
123
(Fig. 9 and Online Resource 6). The functional roles of
these proteins were predicted on the basis of biological
processes and molecular functions enriched among them
using the GO term enrichment tool. The biological pro-
cesses involving intracellular and nucleocytoplasmic
transport were enriched among human-interacting proteins,
and protein and macromolecule localization among Aedes
proteins. In addition to these processes, protein targeting
and cell morphogenesis involved in differentiation were
enriched among the orthologs, but none of the molecular
functions had significant p-values (\0.05) and hence were
not considered.
Conclusion
During the course of pathogenesis, the establishment of a
successful infection depends on the ability of the virus to
manipulate the biological pathways of its host towards its
own ends while evading the immune system. As with other
pathogens, CHIKV is dependent on host-cell machineries
to perform the bulk of the functions that are necessary for
its survival and replication. The protein interface between
CHIKV and its host provides one means by which the virus
manipulates the host machinery during its life cycle. The
present study creates a network of putative protein-protein
interactions between CHIKV and its hosts, Homo sapiens
and Aedes aegypti, by employing a statistical method
previously used to study the viral host interactions of HIV
and dengue virus. The principle underlining this compu-
tational methodology is based on the assumption that
proteins with similar structures will share similar interac-
tion partners. In light of this approach, we predict that
CHIKV proteins invade host cellular pathways at points
that were previously occupied by structurally similar host
proteins. The viral protein might mimic the structurally
similar host protein and invade the pathway, or it might
interact with a host protein involved in the pathway. Either
way, the invasion results in manipulation of the host by
(i) circumventing the components of the cellular machinery
or at times hijacking the machinery as a whole to aid in
processes that favour successful viral replication and/or (ii)
altering gene expression levels. In the current study, using
filtering based on Gene Ontology cellular component
annotation, we predict the interactions of 2028 human
proteins and 86 mosquito proteins with those of CHIKV,
through 3918 and 112 unique interactions, respectively.
We also propose the potential interactions between CHIKV
Fig. 9 Orthologous pairs of H. sapiens and Ae. Aegypti targets predicted to interact with CHIKV proteins. Human proteins are represented by
gene symbol, Aedes proteins by gene IDs in purple circles, and red diamonds represent viral proteins (colour figure online)
Host proteins potentially interacting with chikungunya virus 1169
123
proteins and the key components of human host translation,
apoptotic, autophagic and interferon pathways during viral
pathogenesis. Deciphering the crosstalk between CHIKV
and its hosts will shed light on the viral life cycle and the
ways in which this virus is able to manipulate its hosts. The
network of putative associations presented here puts forth a
set of potential interactions that are amenable to further
experimental investigation as well as potential targets for
therapeutic intervention.
Acknowledgment This work was funded by a research grant from
the Department of Biotechnology, Government of India (Grant no.
BT/PR11162/MED/29/97/2008).
References
1. Higgs S (2006) The 2005–2006 chikungunya epidemic in the
Indian Ocean. Vector Borne Zoonotic Dis 6:115–116
2. Rezza G, Nicoletti L, Angelini R et al (2007) Infection with
chikungunya virus in Italy: an outbreak in a temperate region.
Lancet 370(9602):1840–1846
3. Wang E, Volkova E, Adams AP et al (2008) Chimeric alpha-
virus vaccine candidates for chikungunya. Vaccine 26:5030–
5039
4. Tsetsarkin K, Higgs S, McGee C et al (2006) Infectious clones
of chikungunya virus (La Reunion isolate) for vector compe-
tence studies. Vector Borne Zoonotic Dis 6:325–337
5. Arpino C, Curatolo P, Rezza G (2009) Chikungunya and the
nervous system: what we do and do not know. Rev Med Virol
19:121–129
6. Chandak R, Kashyap S, Kabra D, Karandikar P, Saha SS, Morey
SH, Purohit J, Girdhar I, Taori M, Daginawala HF (2009)
Neurological complications of chikungunya virus infection.
Neurol India 57(2):177–180
7. Hardy JL, Houk EJ, Kramer LD, Reeves WC (1983) Intrinsic
factors affecting vector competence of mosquitoes for arbovi-
ruses. Annu Rev Entomol 28:229–262
8. Aloy P, Russell RB (2003) InterPreTS: protein interaction pre-
diction through tertiary structure. Bioinformatics 19:161
9. Lu L, Lu H, Skolnick J (2002) MULTIPROSPECTOR: an
algorithm for the prediction of protein-protein interactions by
multimeric threading. Proteins 49:350–364
10. Davis FP, Braberg H, Shen MY et al (2006) Protein complex
compositions predicted by structural similarity. Nucleic Acids
Res 34:2943–2952
11. Dyer MD, Murali TM, Sobral BW (2007) Computational pre-
diction of hostpathogen protein protein interactions. Bioinfor-
matics 23:I159–I166
12. Lee SA, Chan C, Tsai CH et al (2008) Ortholog-based protein-
protein interaction prediction and its application to inter-species
interactions. BMC Bioinformatics 9:S11
13. Tastan O, Qi Y, Carbonell JG, Klein-Seetharaman J (2009)
Prediction of interactions between HIV-1 and human proteins by
information integration. Pac Symp Biocomput 14:516–527
14. Evans P, Dampier W, Ungar L, Tozeren A (2009) Prediction of
HIV-1 virus host protein interactions using virus and host
sequence motifs. BMC Med Genomics 2:27
15. Doolittle JM, Gomez SM (2010) Structural similarity-based
predictions of protein interactions between HIV-1 and Homo
sapiens. Virol J 7:82
16. Davis FP, Barkan DT, Eswar N, McKerrow JH, Sali A (2007)
Host pathogen protein interactions predicted by comparative
modeling. Protein Sci 16:2585
17. Franzosa EA, Xia Y (2011) Structural principles within the
human-virus protein-protein interaction network. PNAS
108(26):10538–10543
18. Zhang QC, Petrey D, Deng L et al (2012) Structure based pre-
diction of protein-protein interactions on a genome wide scale.
Nature 490:556–561
19. Davey NE, Trave G, Gibson TJ (2011) How viruses hijack cell
regulation. Trends Biochem Sci 36(3):159–169
20. Doolittle JM, Gomez SM (2011) Mapping protein interactions
between dengue virus and its human and insect hosts. PLoS Neg
Trop Dis 5(2):e954
21. Sreejith R, Rana J, Dudha N, Kumar K, Gabrani R, Sharma SK,
Gupta A, Vrati S, Chaudhary VK, Gupta S (2012) Mapping of
interactions among chikungunya virus nonstructural proteins.
Virus Res 169(1):231–236
22. Berman HM, Westbrook J, Feng Z et al (2000) The protein data
bank. Nucleic Acids Res 28:235–242
23. Yang Z (2008) I-TASSER server for protein 3D structure pre-
diction. BMC Bioinformatics 9
24. Dudha N, Appaiahgari MB, Bharati K, Gupta D, Gupta Y,
Kumar K, Gabrani R, Sharma SK, Gupta A, Chaudhary VK,
Vrati S, Gupta S (2012) Molecular cloning and characterization
of chikungunya virus genes from Indian isolate of 2006 out-
break. J Pharm Res 5(7):3860–3863
25. Holm L, Sander C (1993) Protein structure comparison by
alignment of distance matrices. J Mol Biol 233:123–138
26. Holm L, Kaariainen S, Rosenstrom P, Schenkel A (2008)
Searching protein structure databases with DaliLite v. 3. Bio-
informatics 24:2780
27. Mishra GR, Suresh M, Kumaran K et al (2006) Human protein
reference database—2006 update. Nucleic Acids Res 34:D411–
D414
28. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A,
Tyers M (2006) BioGRID: a general repository for interaction
datasets. Nucleic Acids Res 1(34):535–539
29. Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A
et al (2009) The IntAct molecular interaction database in 2010.
Nucleic Acids Res 38:D525–D531
30. Berglund AC, Sjolund E, Ostlund G, Sonnhammer ELL (2007)
InParanoid 6: eukaryotic ortholog clusters with inparalogs.
Nucleic Acids Res 33:D76–D480
31. Crosby MA, Goodman JL, Strelets VB et al (2006) FlyBase:
genomes by the dozen. Nucleic Acids Res 00:D1–D6
32. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a soft-
ware environment for integrated models of biomolecular inter-
action networks. Genome Res 13:2498–2504
33. Yu J, Pacifico S, Liu G, Finley R (2008) DroID: the Drosophila
interactions database, a comprehensive resource for annotated
gene and protein interactions. BMC Genomics 9:461
34. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology:
tool for the unification of biology. Nat Genet 25:25–29
35. Dennis G, Sherman B, Hosack D et al (2003) DAVID: database
for annotation, visualization, and integrated discovery. Genome
Biol 4:R60
36. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and
integrative analysis of large gene lists using DAVID bioinfor-
matics resources. Nat Protoc 4:44–57
37. McCarthy F, Wang N, Magee GB et al (2006) AgBase: a
functional genomics resource for agriculture. BMC Genomics
7:229
38. Atasheva S, Gorchakov R, English R, Frolov I, Frolova E (2007)
Development of Sindbis viruses encoding nsP2/GFP chimeric
1170 J. Rana et al.
123
proteins and their application for studying nsP2 functioning.
J Virol 81:5046–5057
39. Burnham AJ, Gong L, Hardy RW (2007) Heterogeneous nuclear
ribonuclear protein K interacts with Sindbis virus nonstructural
proteins and viral subgenomic mRNA. Virology 367:212–221
40. Cristea IM, Rozjabek H, Molloy KR et al (2010) Host factors
associated with the Sindbis virus RNA-dependent RNA poly-
merase: role for G3BP1 and G3BP2 in virus replication. J Virol
84:6720–6732
41. Frolova E, Gorchakov R, Garmashova N, Atasheva S, Vergara
LA, Frolov I (2006) Formation of nsP3-specific protein com-
plexes during Sindbis virus replication. J Virol 80:4122–4134
42. Gorchakov R, Garmashova N, Frolova E, Frolov I (2008) Dif-
ferent types of nsP3-containing protein complexes in Sindbis
virus-infected cells. J Virol 82:10088–10101
43. Montgomery SA, Berglund P, Beard CW, Johnston RE (2006)
Ribosomal protein S6 associates with alphavirus nonstructural
protein 2 and mediates expression from alphavirus messages.
J Virol 80:7729–7739
44. Tchankouo-Nguetcheu S, Khun H, Pincet L, Roux P, Bahut M,
Huerre M, Guette C, Choume V (2010) Differential protein
modulation in midguts of Aedes aegypti infected with chi-
kungunya and dengue 2 viruses. PLoS One 5(10):e13149
45. Zhang X, Fugere M, Day R, Kielian M (2003) Furin processing
and proteolytic activation of Semliki forest virus. J Virol
77:2981–2989
46. Krejbich-Trotot P, Denizotm M, Hourau JJ et al (2011) Chi-
kungunya virus mobilizes the apoptotic machinery to invade
host cell defenses. FASEB J 25(1):314–325
47. Dhanwani R, Khan M, Alam SI, Rao PVL, Parida M (2011)
Differential proteome analysis of chikungunya virus infected
new-born mice tissues reveal implication of stress, inflammatory
and apoptotic pathways in disease pathogenesis. Proteomics
11:1936–1951
48. Bouraı M, Lucas-Hourani M, Gad HH et al (2012) Mapping of
chikungunya virus interactions with host proteins identified nsP2
as a highly connected viral component. J Virol 86(6):3121–3133
49. Foss JJ, Domeradzka NE, Baggen J, Geertsema C, Flipse J, Vlak
JM, Pijlman GP (2012) Chikungunya virus nsP3 blocks stress
granule assembly by recruitment of G3BP into cytoplasmic foci.
J Virol 86(19):10873–10879
50. Salvador B, Zhou Y, Michault A, Muench MO, Simmons G
(2009) Characterization of chikungunya pseudotyped viruses:
identification of refractory cell lines and demonstration of cel-
lular tropism differences mediated by mutations in E1 glyco-
protein. Virology 393:33–41
51. Leung JYS, Ng MML, Chu JJH (2011) Replication of alphavi-
ruses: a review on the entry process of alphaviruses into cells.
Adv Virol 24:9640
52. Dreyfuss G, Matunis MJ, Pinol-Roma S, Burd CG (1993)
hnRNP proteins and the biogenesis of mRNA. Annu Rev Bio-
chem 62:289–321
53. Shaw ML, Stone KL, Colangelo CM, Gulcicek EE, Palese P
(2008) Cellular proteins in influenza virus particles. PLoS Path
4:e1000085
54. Thangamani S, Higgs S, Ziegler S, Vanlandingham D, Tesh R,
Wikel S (2010) Host immune response to mosquito-transmitted
chikungunya virus differs from that elicited by needle inoculated
virus. PLoS One 5(8):e12137
55. Schmid D, Munz C (2007) Innate and adaptive immunity
through autophagy. Immunity 27(1):11–21
56. Joubert PE, Werneke SW, de la Calle C et al (2012) Chi-
kungunya virus-induced autophagy delays caspase dependent
cell death. J Exp Med 209:1029–1047
57. Dengjel J, Schoor O, Fischer R et al (2005) Autophagy promotes
MHC class II presentation of peptides from intracellular source
proteins. Proc Natl Acad Sci USA 102:7922–7927
58. English L, Chemali M, Duron J, Rondeau C, Laplante A,
Gingras D, Alexander D, Leib D, Norbury C, Lippe R, Desjar-
dins M (2009) Autophagy enhances the presentation of endog-
enous viral antigens on MHC class I molecules during HSV-1
infection. Nat Immunol 10:480–487
59. Uhl M, Kepp O, Jusforgues-Saklani H, Vicencio JM, Kroemer
G, Albert ML (2009) Autophagy within the antigen donor cell
facilitates efficient antigen cross-priming of virus-specific
CD8? T cells. Cell Death Differ 16:991–1005
60. Crotzer VL, Blum JS (2010) Autophagy and adaptive immunity.
Immunology 131:9–17
61. Jackson WT, Giddings TH, Taylor MP et al (2005) Subversion
of cellular autophagosomal machinery by RNA viruses. PLoS
Biol 3(5):e156
62. Prentice E, Jerome WG, Yoshimori T et al (2004) Coronavirus
replication complex formation utilizes components of cellular
autophagy. J Biol Chem 279(11):10136–10141
63. Lee YR, Lei HY, Liu MT et al (2008) Autophagic machinery
activated by dengue virus enhances virus replication. Virology
374(2):240–248
64. Wong J, Zhang J, Si X et al (2008) Autophagosome supports
coxsackie virus B3 replication in host cells. J Virol 82(18):
9143–9153
65. Krejbich-Trotot P, Gay B, Li-Pat-Yuen G et al (2011) Chi-
kungunya triggers an autophagic process which promotes viral
replication. Virol J 8:432
66. Thorburn A (2008) Apoptosis and autophagy: regulatory con-
nections between two supposedly different processes. Apoptosis
13:1–9
67. Djavaheri-Mergny M, Maiuri MC, Kroemer G (2010) Cross talk
between apoptosis and autophagy by caspase-mediated cleavage
of Beclin 1. Oncogene 29:1717–1719
68. Kang R, Zeh HJ, Lotze MT, Tang D (2011) The Beclin 1 net-
work regulates autophagy and apoptosis. Cell Death Differ
18:571–580
69. Lee AH, Iwakoshi NN, Glimcher LH (2003) XBP-1 regulates a
subset of endoplasmic reticulum resident chaperone genes in the
unfolded protein response. Mol Cell Biol 23:7448–7459
70. McGuckin MA, Eri RD, Das I, Lourie R, Florin TH (2010) ER
stress and the unfolded protein response in intestinal inflam-
mation. Am J Physiol Gastrointest Liver Physiol 298:G820–
G832
71. Urano F, Wang X, Bertolotti A, Zhang Y, Chung P, Harding HP,
Ron D (2000) Coupling of stress in the ER to activation of JNK
protein kinases by transmembrane protein kinase IRE1. Science
287:664–666
72. Cataldi A (2010) Cell responses to oxidative stressors. Curr
Pharm Des 16:1387–1395
73. Djavaheri-Mergny M, Amelotti M, Mathieu J, Besancon F,
Bauvy C, Codogno P (2007) Regulation of autophagy by
NFkappaB transcription factor and reactives oxygen species.
Autophagy 3:390–392
74. Filomeni G, Desideri E, Cardaci S, Rotilio G, Ciriolo MR (2010)
Under the ROS thiol network is the principal suspect for
autophagy commitment. Autophagy 6:999–1005
75. Guo WJ, Ye SS, Cao N, Huang J, Gao J, Chen QY (2010) ROS
mediated autophagy was involved in cancer cell death induced
by novel copper(II) complex. Exp Toxicol Pathol 62:577–582
76. Alexander A, Cai SL, Kim J et al (2010) ATM signals to TSC2
in the cytoplasm to regulate mTORC1 in response to ROS. Proc
Natl Acad Sci USA 107:4153–4158
Host proteins potentially interacting with chikungunya virus 1171
123
77. Alexander A, Kim J, Walker CL (2010) ATM engages the
TSC2/ mTORC1 signaling node to regulate autophagy.
Autophagy 6:672–673
78. Zoncu R, Efeyan A, Sabatini DM (2011) mTOR: from growth
signal integration to cancer, diabetes and ageing. Nat Rev Mol
Cell Biol 12:21–35
79. Thomson AW, Turnquist HR, Raimondi G (2009) Immunoreg-
ulatory functions of mTOR inhibition. Nat Rev Immunol
9:324–337
80. Danial NN, Korsmeyer SJ (2004) Cell death: critical control
points. Cell 116:205–219
81. Kurokawa M, Kornbluth S (2009) Caspases and kinases in a
death grip. Cell 138:838–854
82. Wilson NS, Dixit V, Ashkenazi A (2009) Death receptor signal
transducers: nodes of coordination in immune signaling net-
works. Nat Immunol 10:348–355
83. Kurokawa M, Kornbluth S (2010) Stalling in mitosis and
releasing the apoptotic brake. EMBO J 29:2255–2257
84. Kroemer G, Galluzzi L, Brenner C (2007) Mitochondrial
membrane permeabilization in cell death. Physiol Rev 87:
99–163
85. Dhanwani R, Khan M, Bhaskar ASB et al (2012) Character-
ization of chikungunya virus infection in human neuroblastoma
SH-SY5Y cells: role of apoptosis in neuronal cell death. Virus
Res 163:563–572
86. Gorchakov R, Frolova E, Frolo I (2005) Inhibition of tran-
scription and translation in Sindbis virus infected cells. J Virol
79:9397–9409
87. Tamm K, Merits A, Sarand I (2008) Mutations in nuclear
localization signal of nsP2 influence RNA synthesis, protein
expression and cytotoxicity of Semliki forest virus. J Gen Virol
89:676–686
88. Garmashova N, Gorchakov R, Frolova E, Frolov I (2006)
Sindbis virus nonstructural protein nsP2 is cytotoxic and inhibits
cellular transcription. J Virol 80:5686–5696
89. Fazakerley JK, Boyd A, Mikkola ML, Karriainen L (2002) A
single amino acid change in the nuclear localization sequence of
the nsP2 protein affects the neurovirulance of Semliki forest
virus. J Virol 76:392–396
90. Inglis SC (1982) Inhibition of host protein synthesis and deg-
radation of cellular mRNAs during infection by influenza and
herpes simplex virus. Mol Cell Biol 2(12):1644–1648
91. Beloso A, Martinez C, Valcarcel J, Santaren JF, Ortin J (1992)
Degradation of cellular mRNA during influenza virus infection:
its possible role in protein synthesis shutoff. J Gen Virol
73:575–581
92. Lindquist ME, Mainou BA, Dermody TS, Crowe JE Jr (2011)
Activation of protein kinase R is required for induction of stress
granule by respiratory syncytial virus but dispensable for viral
replication. Virology 413:103–110
93. McInerney GM, Kedersha NL, Kaufman RJ, Anderson P, Lil-
jestrom P (2005) Importance of eIF2a phosphorylation and
stress granule assembly in alphavirus translation regulation. Mol
Biol Cell 16:3753–3763
94. Ventoso I, Sanz MA, Molina S, Berlanga JJ, Carrasco L, Este-
ban M (2006) Translational resistance of late alphavirus mRNA
to eIF2a phosphorylation: a strategy to overcome the antiviral
effect of protein kinase PKR. Genes Dev 20:87–100
95. Stetson DB, Medzhitov R (2006) Type I interferons in host
defense. Immunity 25:373–381
96. Sourisseau M, Schilte C, Casartelli N, Trouillet C, Guivel-Be-
nhassine F, Rudnicka D et al (2007) Characterization of
reemerging chikungunya virus. PLoS Path 3:e89
97. Her Z, Malleret B, Chan M et al (2010) Active infection of
human blood monocytes by chikungunya virus triggers an innate
immune response. J Immunol 184:5903–5913
98. Schilte C, Couderc T, Chretien F et al (2010) Type I IFN con-
trols chikungunya virus via its action on nonhematopoietic cells.
J Exp Med 207:429–442
99. Werneke SW, Schilte C, Rohatgi A et al (2011) ISG15 is critical
in the control of chikungunya virus infection independent of
UbE1L mediated conjugation. PLoS Pathog 7:e1002322
100. Ng LFP, Chow A, Sun YJ et al (2009) IL-1beta, IL-6, and
RANTES as biomarkers of chikungunya severity. PLoS ONE
4:e4261
101. Broz P, Monack DM (2011) Molecular mechanisms of inflam-
masome activation during microbial infections. Immunol Rev
243:174–190
102. Thompson MR, Kaminski JJ, Kurt-Jones EA, Fitzgerald KA
(2011) Pattern recognition receptors and the innate immune
response to viral infection. Viruses 3:920–940
103. Kumar H, Kawai T, Akira S (2011) Pathogen recognition by the
innate immune system. Int Rev Immunol 30:16–34
104. Bowie AG, Unterholzner L (2008) Viral evasion and subversion
of pattern-recognition receptor signalling. Nat Rev Immunol
8:911–922
105. Carty M, Bowie AG (2010) Recent insights into the role of toll-
like receptors in viral infection. Clin Exp Immunol 161:397–406
106. Hiscott J, Lin R, Nakhaei P, Paz S (2006) Master-CARD: a
priceless link to innate immunity. Trends Mol Med 12:53–56
107. Loo YM, Gale M (2011) Immune signaling by RIG-Ilike
receptors. Immunity 34:680–692
108. White LK, Sali T, Alvarado D et al (2011) Chikungunya virus
induces IPS-1-dependent innate immune activation and protein
kinase R-independent translational shutoff. J Virol 85:606–620
109. Takaoka A, Yanai H (2006) Interferon signaling network in
innate defense. Cell Microbiol 8(6):907–922
110. Fros JJ, Liu WJ, Prow NA et al (2010) Chikungunya virus
nonstructural protein 2 inhibits type I/II interferon stimulated
JAK-STAT signaling. J Virol 84:10877–10887
111. Couderc T, Chretien F, Schilte C et al (2008) A mouse model for
chikungunya: young age and inefficient type-I interferon sig-
naling are risk factors for severe disease. PLoS Path 4:e29
112. Akahata W, Yang ZY, Andersen H et al (2010) A VLP vaccine
for epidemic chikungunya virus protects nonhuman primatesagainst infection. Nat Med 16(3):334–338
113. AbuBakar S, Sam IC, Wong PF, MatRahim N, Hooi PS, Roslan
N (2007) Reemergence of endemic chikungunya, Malaysia.
Emer Infect Dis 13(1):147–149
114. Antar AA, Konopka JL, Campbell JA et al (2009) Junctional
adhesion molecule-A is required for hematogenous dissemina-
tion of reovirus. Cell Host Microbe 5(1):59–71
1172 J. Rana et al.
123
