INTRODUCTION
Flaviviruses are a group of positive-sense single- stranded RNA viruses belonging to Flaviviridae family that includes medically important species such as dengue, Japanese encephalitis (JE), and West Nile viruses. Among these single stranded RNA viruses, Japanese encephalitis virus (JEV) causes infection of the central nervous system in humans. It has been estimated that the mortality rate of JE is very high ranging from 20 to 50%; especially in regions where JEV is widely distributed like in East and Southeast Asia. Among the 50,000 annually reported hu- man cases of JE in Asian countries, 10,000–15,000 results in fatality
1-2. A high proportion (nearly 50%) of survivors, especially young children and those > 65 yr of age, exhibit permanent neurological and psychiatric sequelae.
The genome of JEV (approximately 11 kb in size), upon translation, forms a single polyprotein, which is cleaved by host and viral proteases into three structural proteins, namely capsid (C), pre-membrane (prM), and envelope (E) proteins; and seven non-structural proteins, viz. NS1, NS2a, NS2b, NS3, NS4a, NS4b and NS5. The
virus has a zoonotic transmission cycle between birds and mosquitoes, with swine serving as intermediate amplifier hosts, and the virus can spread from swine to humans through mosquito bites
3. The severity of JEV pathogen- esis is governed by several factors. The inability of the host to produce antibodies against the virus is associated with an increased likelihood of the disease to turn lethal
4. Crossing the blood-brain barrier is an important factor in the increased pathogenesis and clinical outcome of the neurotropic viral infection. Through a mosquito bite, the virus enters the body and reaches the central nervous sys- tem (CNS) via leukocytes (presumably T-lymphocytes), where JEV virions subsequently binds to the endothelial surface of the CNS and are embodied by endocytosis;
however, it is still unclear whether macrophages and B lymphocytes can also nurture JEV. The symptoms of JE generally develops in hosts after an incubation period of 5–15 days. It is possible that during this time, the virus dwells and proliferates within host leukocytes, which act as carriers to the CNS. T-lymphocytes and IgM play sig- nificant role in the recovery and clearance of the virus af- ter infection. A feasible therapy of clearing the virus load
Research Articles
Identification and characterization of differentially expressed genes from human microglial cell samples infected with Japanese encephalitis virus
Manoj Kumar Gupta, Santosh Kumar Behera, Budheswar Dehury & Namita Mahapatra
Biomedical Informatics Centre, ICMR-Regional Medical Research Centre, Chandrasekharpur, Odisha, India
ABSTRACT
Background & objectives: Limited studies have been reported on Japanese encephalitis (JE) with reference to microarray data analysis. The present study involved an in silico approach for identification and characterization of differentially expressed genes in human microglial cell (CHME3) samples, infected with P20778 strain of Japanese encephalitis virus (JEV).
Methods: Gene expression data (GSE57330) belonging to mRNA expression profile of CHME3 cells infected with JEV, was downloaded from the gene expression omnibus (GEO) database, processed and normalized by robust multichip averaging (RMA) method using affy packages of R. The Bayes method was used to correct multiple testing. The log fold change (logFC > 1) and p< 0.05 were used as cut-off to identify differentially expressed genes (DEGs). The newly identified hub genes were set at the centre for construction of protein-protein interaction network using search tool for the retrieval of interacting genes/proteins (STRING) database considering human genome as reference. Gene ontology and pathway enrichment analysis of the hub gene and its associated genes were performed using STRING and DAVID tool.
Results: Microarray data analysis revealed that STAT1 gene was down-regulated during JEV infection. STAT1 gene was found to interact with tyrosine protein kinase family members, and showed strong interaction with JAK1 and JAK2 genes.
Interpretation & conclusion: The identified transcription factors and the binding sites in the promoter region of STAT1 gene might act as potential drug targets in near future.
Key words Differentially expressed genes; drug design; Japanese encephalitis; protein kinases; STAT1 gene; transcription factors
while in its incubation period in peripheral lymphatic tis- sues and spleen may actually impede JEV pathogenesis.
In addition to neuronal cells, astrocytes have also been found to be infected by JEV
5.
Over the last decade, various studies have been un- dertaken to identify and characterize the differentially ex- pressed genes (DEGs) in wide spectrum of tissue samples using microarray data from gene expression omnibus (GEO) repository
6. In 2013, Wu et al
7reported a similar kind of study on DEG in osteoporosis using microarray data. The objective of the present study was to find sets of possible marker genes, associated with JE by using computational methods and analysis. More specifically, the study was aimed to analyse and identify DEGs us- ing microarray data of human microglial cells (CHME3) infected with JEV (P20778 strain) obtained from GEO (Accession No. GSE57330)
8. Genes found to be differen- tially expressed during JE infection and the transcription factors associated with these DEGs might serve as prob- able drug targets for JE.
MATERIAL & METHODS Collection of microarray data
Gene expression data (GSE57330) belonging to mRNA expression profile of microglial cells infected with JE was downloaded from the GEO database, which included samples of 12 human microglial cells (CHME3);
six uninfected and six infected with JEV (P20778) strain.
Chip data containing mRNA expression profile of human microglial cells were acquired by utilising the GPL15207 Affymetrix human gene expression array platform.
Data processing and identification of differentially ex- pressed genes
Raw data were processed and normalized by the robust multichip averaging (RMA) method
9using affy packages of R (v.3.1.3)
10. The linear regression model package Limma
11was used to classify chips into groups.
The Bayes method
12was used to correct multiple test- ing inorder to adjust the statistical confidence measures between samples investigated. The log fold change, i.e.
|logFC| >1 and p< 0.05 were used as cut-off to identify DEGs in diseased condition
13.
Screening of hub genes
Search tool for the retrieval of interacting genes/
proteins (STRING) database
14was used to identify in- teractions between DEGs. Known protein interaction networks are mostly scale-free
15, i.e. a lot of connections shared by few hubs (hub nodes), while few connections
usually shared by the key hub of the network
16. The inter- action pairs bearing scores > 0.9 were considered as hub gene candidates
14.
Construction of the interaction network for the hub gene The newly identified hub genes were set at the centre to construct protein-protein interaction network keeping human genome as reference using STRING database. Us- ing the edge length among the interacting genes with the hub gene, protein-protein interaction network was con- structed using Gephi tool
17.
Gene ontology
Gene ontology of the hub and its associated genes was performed using STRING and database for annotation vi- sualization and integrated discovery (DAVID) tool
18. Pathway-enrichment analysis
DAVID tool
18was used to predict interaction network of all genes. Enrichment of pathways was per- formed using hyper-geometric distribution algorithm [false-discovery rate (FDR) <0.05 and count >2].
Hub gene-related transcription factors
The DNA-binding domain in transcription factors (TFs) binds to cis-acting elements in DNA sequences
19, which lead to either inhibition or enhancement of gene expression. The text mining-based utility of the free on- line tool EpiTect ChIP qPCR Primers
20was used for ex- tracting TFs of the hub gene.
RESULTS Microarray data processing
Raw data were normalised to fix the measured intensities among control and JE infected samples, and screening out genes that were significantly differentially expressed. The distribution of data pre- and post-normal- ization was depicted by box plot and histogram (Fig. 1).
Screening of differentially expressed genes
According to the pre-set criterion |logFC| >1 and p <0.05 as a threshold value, only one DEG, i.e. signal transducer and activator of transcription 1 (STAT1) was obtained between the infected and uninfected samples.
The STAT1 is a TF which in humans is encoded by the STAT1 gene. It is a member of the STAT protein family.
Construction of the interaction network for the hub gene
The newly identified hub gene STAT1 was placed at
the centre to construct an interaction network. The analy-
sis from STRING database depicted that STAT1 interacts with 10 different genes, viz. STAT2, JAK1, JAK2, TYK2, IFNGR1, EGFR, IRF1, EP300, CREBBP and PIAS1 (Fig.
2). Subsequently, using the edge length between different genes and inbuilt force-field parameters, i.e. ForceAtlas2 and Fruchterman-Reingold force-field of Gephi, protein- protein interaction network was constructed (Fig. 3). The STAT1 showed strong interaction with JAK1 and JAK2, and least interaction with IFNGR1.
Gene ontology analysis
The sub-ontology analysis of 11 genes was carried out using STRING database and DAVID gene ontology tool.
The genes with FDR < 0.05 were considered to be signifi- cant (Tables 1 and 2). The important biological process
Fig. 1: Normalization of microarray data from GEO (Accession No.
GSE57330). Box plots of the raw chip data pre- (a) and post- normalization (b); the blue and red colour of the box repre- sents data from uninfected and infected samples respectively.
The plots consist of boxes with a central line and two tails;
the central line represents the median of the data, whereas, the tails represent the upper and lower quartile. Histogram of raw data pre-normalization (c); and post-normalization (d).
Fig. 2: The protein-protein interaction network of STAT1 obtained from STRING database. The predicted network summa- rizes the network of predicted associations with other pro- teins. The network nodes are proteins and the edges signify the predicted functional associations. The coloured lines of the edges represent the existence of different types of evi- dence used in predicting the associations (Green: Neighbour- hood evidence; Blue: Co-occurrence evidence; Purple: Ex- perimental evidence; Light blue: Database evidence; and Black: Co-expression evidence). Further, the thickness of the edges specifies the degree of confidence prediction of the interaction.
IFNGR1 STAT1 EGFR
EP300
IRF1
STAT2 TYK2 CREBBP
JAK1
JAK2
PIAS1
Fig. 3: Protein-protein interaction networks of STAT1 employing Force Atlas2 (a and b); and Fruchterman-Reingold force (c and d) field using Gephi tool. The strength of the protein-protein interaction are depicted by different shades of blue colour (fad- ing colour indicates decreasing strength). From this protein net- work, it can be hypothesized that STAT1 possess strong interac- tion with JAK1 and JAK2 and least interaction with IFNGR1.
Table 1. List of gene ontology (GO) terms and their corresponding biological processes (BPs) obtained using DAVID database Go: Term Description Count (No.
of genes) Genes p-value
Hyper geometric
distribution Bonferroni Benjamini FDR GO: 0007243~ Protein kinase
cascade 6 EGFR TYK2
JAK1 JAK2 STAT1 STAT2
1.71E–06 7.34E–04 7.34E–04 0.002416
GO: 0018108~ Peptidyl-tyrosine
Phosphorylation 4 TYK2 JAK1
JAK2 STAT1 3.05E–06 0 6.52E–04 0.004292
GO: 0018212~ Peptidyl-tyrosine
Modification 4 TYK2 JAK1
JAK2 STAT1 3.47E–06 0 4.95E–04 0.004887
Table 2. List of GO terms and their corresponding molecular functions (MFs) obtained using STRING database
GO term Pathway description Observed
gene count False discovery
rate Matching proteins in the network (Labels)
GO:0045088 Regulation of innate immune response 9 1.25E–11 CREBBP, EP300, IFNGR1, IRF1, JAK1, JAK2, STAT1, STAT2, TYK2
GO:0050776 Regulation of immune response 10 2.67E–11 CREBBP, EGFR, EP300, IFNGR1, IRF1, JAK1, JAK2, STAT1, STAT2, TYK2
GO:0045087 Innate immune response 10 7.90E–11 CREBBP, EGFR, EP300, IFNGR1, IRF1,
JAK1, JAK2, STAT1, STAT2, TYK2
GO:0034097 Response to cytokine 9 4.24E–10 CREBBP, EP300, IFNGR1, IRF1, JAK1,
JAK2, STAT1, STAT2, TYK2
GO:0080134 Regulation of response to stress 10 1.53E–09 CREBBP, EGFR, EP300, IFNGR1, IRF1, JAK1, JAK2, STAT1, STAT2, TYK2 GO:0001959 Regulation of cytokine-mediated signaling
pathway 6 5.99E–09 IFNGR1, JAK1, JAK2, STAT1, STAT2,
GO:0019221 Cytokine-mediated signaling pathway 7 6.22E–08 TYK2IFNGR1, IRF1, JAK1, JAK2, STAT1, STAT2, TYK2
GO:0060333 Interferon-gamma-mediated signaling
pathway 5 6.22E–08 IFNGR1, IRF1, JAK1, JAK2, STAT1
GO:0060337 Type I interferon signaling pathway 5 6.85E–08 IRF1, JAK1, STAT1, STAT2, TYK2 GO:0071357 Cellular response to type I interferon 5 6.85E–08 IRF1, JAK1, STAT1, STAT2, TYK2 GO:0009966 Regulation of signal transduction 10 1.44E–07 CREBBP, EGFR, EP300, IFNGR1, IRF1,
JAK1, JAK2, STAT1, STAT2, TYK2 GO:0060334 Regulation of interferon-gamma-mediated
signaling pathway 4 2.33E–07 IFNGR1, JAK1, JAK2, STAT1
GO:0071346 Cellular response to interferon-gamma 5 2.36E–07 IFNGR1, IRF1, JAK1, JAK2, STAT1 GO:0071345 Cellular response to cytokine stimulus 7 3.35E–07 IFNGR1, IRF1, JAK1, JAK2, STAT1,
STAT2, TYK2 GO:0060338 Regulation of type I interferon-mediated
signaling pathway 4 1.44E–06 JAK1, STAT1, STAT2, TYK2
GO:0002376 Immune system process 9 1.55E–06 CREBBP, EGFR, EP300, IFNGR1, JAK1,
JAK2, STAT1, STAT2, TYK2
GO:0007166 Cell surface receptor signaling pathway 9 2.05E–06 CREBBP, EGFR, EP300, IFNGR1, IRF1, JAK1, JAK2, STAT2, TYK2
GO:0070887 Cellular response to chemical stimulus 8 0.000196 CREBBP, IFNGR1, IRF1, JAK1, JAK2, STAT1, STAT2, TYK2
GO:0018108 Peptidyl-tyrosine phosphorylation 4 0.000266 EGFR, JAK1, JAK2, TYK2 GO:0018076 N-terminal peptidyl-lysine acetylation 2 0.000278 CREBBP, EP300
GO:0010033 Response to organic substance 8 0.000305 CREBBP, IFNGR1, IRF1, JAK1, JAK2, STAT1, STAT2, TYK2
GO:0038083 Peptidyl-tyrosine autophosphorylation 3 0.000309 JAK1, JAK2, TYK2
GO:0046677 Response to antibiotic 3 0.000498 EP300, JAK1, JAK2
GO:0046777 Protein autophosphorylation 4 0.00051 EGFR, JAK1, JAK2, TYK2
GO:0007259 JAK-STAT cascade 3 0.000725 JAK2, STAT1, STAT2
GO:0009615 Response to virus 4 0.000919 IFNGR1, IRF1, STAT1, STAT2
GO:0030154 Cell differentiation 8 0.00145 CREBBP, EGFR, EP300, IRF1, JAK1,
JAK2, STAT1, TYK2
GO:0051707 Response to other organism 5 0.00194 IFNGR1, IRF1, JAK2, STAT1, STAT2
GO:0032481 Positive regulation of type I interferon
production 3 0.00212 CREBBP, EP300, IRF1
GO:0007169 Transmembrane receptor protein tyrosine
kinase signaling pathway 5 0.00214 EGFR, JAK1, JAK2, STAT1, TYK2
GO:0033160 Positive regulation of protein import into
nucleus, translocation 2 0.00227 EP300, JAK2
GO:0051704 Multiorganism process 7 0.0023 CREBBP, EP300, IFNGR1, IRF1, JAK2,
STAT1, STAT2
GO:0097306 Cellular response to alcohol 3 0.00237 EGFR, EP300, JAK2
GO:0006950 Response to stress 8 0.00247 CREBBP, EGFR, IFNGR1, IRF1, JAK1,
STAT1, STAT2, TYK2 GO:0042307 Positive regulation of protein import into
nucleus 3 0.0027 EGFR, EP300, JAK2
GO:0035458 Cellular response to interferon-beta 2 0.00301 IRF1, STAT1
contd...
GO term Pathway description Observed
gene count False discovery
rate Matching proteins in the network (Labels)
GO:1902680 Positive regulation of RNA biosynthetic
process 6 0.00301 CREBBP, EGFR, EP300, IRF1, JAK2,
STAT1
GO:0071396 Cellular response to lipid 4 0.00373 EGFR, EP300, JAK2, STAT1
GO:0018193 Peptidyl-amino acid modification 5 0.00437 CREBBP, EGFR, JAK1, JAK2, TYK2 GO:0034612 Response to tumor necrosis factor 3 0.00437 EP300, JAK2, STAT1
GO:0009411 Response to UV 3 0.00442 CREBBP, EGFR, EP300
GO:0006357 Regulation of transcription from RNA
polymerase II promoter 6 0.00561 CREBBP, EGFR, IRF1, JAK2, STAT1,
STAT2
GO:0010628 Positive regulation of gene expression 6 0.00561 CREBBP, EGFR, EP300, IRF1, JAK2, STAT1
GO:0071383 Cellular response to steroid hormone
stimulus 3 0.00642 EGFR, EP300, JAK2
GO:0045944 Positive regulation of transcription from
RNA polymerase II promoter 5 0.0075 CREBBP, EGFR, IRF1, JAK2, STAT1
GO:0035556 Intracellular signal transduction 6 0.00852 EGFR, JAK1, JAK2, STAT1, STAT2,
GO:0051607 Defense response to virus 3 0.00852 TYK2IRF1, STAT1, STAT2
GO:0071417 Cellular response to organonitrogen
compound 4 0.00868 EGFR, EP300, JAK2, STAT1
GO:0071549 Cellular response to dexamethasone stimulus 2 0.00897 EP300, JAK2
GO:0009605 Response to external stimulus 6 0.00926 EGFR, IFNGR1, IRF1, JAK2, STAT1, STAT2
GO:0060397 JAK-STAT cascade involved in growth
hormone signaling pathway 2 0.00933 JAK2, STAT1
GO:0060396 Growth hormone receptor signaling pathway 2 0.0113 JAK2, STAT1
GO:0032870 Cellular response to hormone stimulus 4 0.0116 EGFR, EP300, JAK2, STAT1 GO:0071378 Cellular response to growth hormone
stimulus 2 0.0118 JAK2, STAT1
GO:0007165 Signal transduction 8 0.0132 CREBBP, EGFR, IFNGR1, IRF1, JAK1,
JAK2, STAT2, TYK2 GO:0033209 Tumor necrosis factor-mediated signaling
pathway 2 0.016 JAK2, STAT1
GO:0061418 Regulation of transcription from RNA polymerase II promoter in response to hypoxia
2 0.0169 CREBBP, EP300
GO:0043388 Positive regulation of DNA binding 2 0.0184 EP300, JAK2
GO:0045429 Positive regulation of nitric oxide
biosynthetic process 2 0.0184 EGFR, JAK2
GO:0044700 Single organism signaling 8 0.0194 CREBBP, EGFR, IFNGR1, IRF1, JAK1,
JAK2, STAT2, TYK2
GO:0007154 Cell communication 8 0.0224 CREBBP, EGFR, IFNGR1, IRF1, JAK1,
JAK2, STAT2, TYK2
GO:0016032 Viral process 4 0.026 CREBBP, EP300, STAT1, STAT2
GO:0071214 Cellular response to abiotic stimulus 3 0.026 CREBBP, EP300, IRF1 GO:0080135 Regulation of cellular response to stress 4 0.026 CREBBP, EGFR, EP300,
STAT2
GO:0042127 Regulation of cell proliferation 5 0.0285 IRF1, JAK1, JAK2,
STAT1, TYK2
GO:0045595 Regulation of cell differentiation 5 0.0285 CREBBP, EP300, IRF1, JAK2, STAT1
GO:0016477 Cell migration 4 0.0287 EGFR, JAK1, JAK2, TYK2
GO:0034644 Cellular response to UV 2 0.0369 CREBBP, EP300
GO:0051674 Localization of cell 4 0.0373 EGFR, JAK1, JAK2, TYK2
GO:0010557 Positive regulation of macromolecule
biosynthetic process 5 0.0383 CREBBP, EGFR, EP300,
JAK2, STAT1
GO:0000186 Activation of MAPKK activity 2 0.0432 EGFR, JAK2
GO:0001819 Positive regulation of cytokine production 3 0.0491 CREBBP, EP300, JAK2 GO:0031328 Positive regulation of cellular biosynthetic
process 5 0.0491 CREBBP, EGFR, EP300,
JAK2, STAT1 GO:1900034 Regulation of cellular response to heat 2 0.0495 CREBBP, EP300 GO:0031325 Positive regulation of cellular metabolic
process 6 0.0496 CREBBP, EGFR, EP300,
JAK2, STAT1, STAT2 Table 2. (Contd.)
associated with these genes were protein kinase cascade, peptidyl-tyrosine phosphorylation and peptidyl-tyrosine modification. The cellular components were identified as chromatin, an extrinsic component of cytoplasmic side of plasma membrane, nuclear chromatin, chromosome and intracellular non-membrane-bounded organelle. The important molecular function associated with these genes was reported as growth hormone receptor binding.
Pathway-enrichment analysis
Pathway-enrichment analysis performed using DAVID tool, uncovered one signifi cantly enriched path- way (FDR <0.05), i.e. JAK-STAT signalling pathway.
As evident from Figs. 2 and 3, most of the nodes were found to interact with STAT1, belonging to the tyrosine protein kinase family. Thus, it can be speculated that the hub gene, STAT1 interacts with tyrosine protein kinase family members and participate in JEV infection through the JAK-STAT pathways.
Hub gene-related transcription factors
A total of nine TFs including STAT1α, STAT1β, p53, ISFG-3, STAT2, STAT3, IRF-1, STAT4 and STAT6 which binds to the promoter of the key gene STAT1 were identi- fied along with their corresponding binding sites (Fig. 4).
DISCUSSION
Most of the microarray experiments were performed to inspect patterns of gene expression by analysing the ex- pression levels of thousands of genes in one platform. The key hypothesis behind each microarray analysis is that, the measured intensities for each arrayed gene symboliz- es its relative expression level
21. Biologically, significant patterns of expression are generally identified by correlat- ing measured expression levels between different states in various biological samples on a gene-by-gene basis.
However, before comparing levels appropriately, a num- ber of transformations must be performed on the raw data to remove ambiguous or low-quality measurements, to fix the measured intensities to simplify comparisons and screening out genes which get differentially expressed
13.
There are commonly three normalization techniques, i.e. median, quantile, and cyclic loess
22. The quantile nor- malization method works excellent in reducing dissimi- larities in miRNA expression values for duplicate tissue samples as compared to other two methods
23. Kumari et al
24have carried out cellular miRNA and mRNA expres- sion analysis at multiple time points during viral infec- tion in human microglial (CHME3) cells using Affyme- trix microarray platform which revealed a phased pattern of miRNAs expression, associated with JEV replication and provided unique signatures of infection through an in silico analysis.
In this study, the raw data was normalised using RMA method which was a form of Quantile normalisation. Re- gardless of the experiment carried out, the final result was intended to identify the DEGs between one or more pairs of samples in the data set.
In an investigation, Yang et al
25performed a system- atic mRNA profiling in spleens and brains of JEV-infected mice to globally identify candidate host genes associated with JEV pathogenesis. The microarray analysis showed that 437 genes in spleen and 1119 genes in brain were dif- ferentially expressed in response to JEV infection, with obviously up-regulated genes like pro-inflammatory chemokines and cytokines, apoptosis-related proteases and IFN inducible transcription factors. Most of DEGs are associated with antiviral response of host, which may provide important information for investigation of JEV pathogenesis and therapeutic method.
In this study, the analysis of the chip data showed that the STAT1 was the only gene which was differentially ex- pressed during JEV infection. Transcription factors are potential targets of new anti-JE drugs that are engaged in the regulation of gene expression and play an impor- tant role in the onset, invasion, and development of JEV.
Screening of TFs led to identification of the nine TFs includ ing STAT1 alpha, STAT1 beta, p53, ISFG-3, STAT2, STAT3, IRF-1, STAT4, and STAT6 along with their bind- ing sites in the STAT1 promoter. Gupta and Rao
26were the first to report a detailed picture of the host transcriptional response in a natural route of exposure and opens up new avenues for potential therapeutic and prophylactic strate- gies against JEV.
In another study, consistent up-regulation of CX3C chemokine receptor 1(CX3CR1) gene was observed on JEV exposed human microglia. CX3CR1 gene is a
Fig. 4: The list of transcription factors and binding sites of the hub gene STAT1 (indicated within a red circle). The red colour arrow indicates the starting site of the transcription, and green bars indicate the binding sites for each transcription factor.
STAT1 STATα STATβ p53 ISGF-3 STAT2 STAT3 IRF-1 STAT4 STAT6
chr2:191,868,976 191,898,976
Transcription factor finding site Transcription starting site of STAT1;
670 bp Legend:
Scale:
receptor for the fractalkine ligand CX3CL1 (chemokine (C-X3-C motif) encoded by the CX3CL1 gene in human.
CX3CR1 and STAT1 are associated with microglial cells.
The differential expression of gene CX3CR1 is found to be proportional to STAT1 in most of the diseases. CX- 3CL1 is secreted essentially by neurons and binds to its receptor CX3CR1 in microglia, thus acting as messen- ger that is involved in neuron-microglia communication.
The CX3CL1/CX3CR1 signaling pathway plays an im- portant role in regulating microglial dynamics and mi- gration, which is central for surveying neuronal function through lifespan
27. Since, CX3CR1 is mainly expressed by microglia and its unique ligand CX3CL1 is primarily expressed by neurons
28-29, the CX3CL1/CX3CR1 signal- ing is central for microglia-neuron interactions regulating neuro-inflammation, neuro-protection as well as chemo- taxis. CX3CL1 inhibits the production of pro-inflamma- tory cytokines by microglia
30which could control neuro- toxicity of JEV-infected microglia derived inflammatory factors in mice
31. Importantly, human microglia support- ed JEV replication; but infectivity was only transmitted to neighbouring cells in a contact-dependent manner which suggests that human microglia may be a source of neuro- nal infection and sustain JEV brain pathogenesis
32.
Ligation of interferon (IFN) and its receptors leads to initiation of the activities of JAK1 and Tyk2 via tyrosine phosphorylation, which in turn excites the phosphoryla- tion of STATs
33. Subsequently, phosphorylated STAT1 and STAT2 dimerize and correlate with IFN response factor 9 (IRF9), which ultimately leads to the formation of inter- feron-stimulated gene factor 3 (ISGF3) complexes. The formation of ISGF3 complexes in the cytoplasm results in nuclear translocation, followed by binding to the IFN- stimulated responsive element and subsequent expres- sion of proteins including the IFN-stimulated antiviral proteins. Numerous cellular proteins have also been rec- ognized as negative regulators of the JAK-STAT signal- ing pathway; these consist of the suppressor of cytokine signaling (SOCS) proteins, the protein inhibitors of ac- tivated STATs (PIAS), and the protein tyrosine phospha- tases (PTPs)
34–37.
CONCLUSION
The findings of the study showed that the STAT1 gene is down-regulated during JEV infection in CHME3 cells.
The interaction of STAT1 gene with tyrosine protein ki- nase family members indicates that they might partici- pate in JEV infection through the JAK-STAT pathways.
Hence, the STAT1 gene and its associated TFs might act as potential drug targets for JE infections. Further,
in silico, in vivo and in vitro analysis of these drug-target interactions needs to be studied for development of novel therapeutics against JEV infection.
ACKNOWLEDGEMENTS
The study was financially supported by the extra- mural research grant of the Indian Council of Medical Research, II phase of Biomedical Informatics Centres of ICMR Grant Number: BIC/12(19)/2013. The authors also acknowledge the Director, ICMR-Regional Medical Research Centre, Bhubaneswar for providing necessary infrastructure facilities.
Conflict of interest
The authors declare that they have no conflict of interest.
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Correspondence to: Dr Namita Mahapatra, Scientist 'F', Biomedical Informatics Centre, ICMR-Regional Medical Research Centre, Chandrasekharpur, Nandankanan Road, Bhubaneswar–751023, Odisha, India.
E-mail: nmrmrc@gmail.com
Received: 7 September 2016 Accepted in revised form: 30 May 2017