Venezuelan Equine Encephalitis Virus Induces ... - Journal of Virology

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Jan 20, 2016 - The PERK arm of the UPR was activated as both ATF4 and CHOP (DDIT3), critical. 35 regulators of the pathway, were altered after infection.
JVI Accepted Manuscript Posted Online 20 January 2016 J. Virol. doi:10.1128/JVI.02827-15 Copyright © 2016 Baer et al This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

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Venezuelan Equine Encephalitis Virus Induces Apoptosis through the Unfolded Protein

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Response Activation of EGR1

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Alan Baer1, Lindsay Lundberg1, Danielle Swales2, Nicole Waybright2, Chelsea Pinkham1,

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Jonathan D. Dinman3, Jonathan L. Jacobs2#, Kylene Kehn-Hall1#

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National Center for Biodefense and Infectious Diseases, School of Systems Biology, George

Mason University, Manassas, Virginia, USA 2

MRIGlobal, Global Health Security, Rockville, Maryland, USA

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University of Maryland, Department of Cell Biology and Molecular Genetics, College Park,

Maryland, USA

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#Address correspondence to Kylene Kehn-Hall, [email protected] and Jonathan Jacobs,

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[email protected]

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Running Head: Host-Pathogen Interactions in VEEV Infection

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Abstract word count: 230

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Text word count: 7,677

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Abstract

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Venezuelan equine encephalitis virus (VEEV) is a previously weaponized arthropod-borne virus

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responsible for causing acute and fatal encephalitis in animal and human hosts. The increased

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circulation and spread in the Americas of VEEV, and other encephalitic arboviruses such as

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Eastern equine encephalitis virus and West Nile virus, underscores the need for research aimed at

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characterizing the pathogenesis of viral encephalomyelitis for the development of novel medical

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countermeasures. The host-pathogen dynamics of VEEV-TrD infected human astrocytoma

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U87MG cells were determined by carrying out RNA sequencing (RNA-Seq) of poly(A) +

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mRNAs. To identify critical alterations in the host transcriptome that take place following

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VEEV infection, samples were collected at 4, 8, and 16 hours post-infection and RNA-Seq data

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acquired using the Ion Torrent PGM platform. Differential expression of interferon responsive

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genes, stress response factors, and components of the unfolded protein response (UPR) were

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observed. The PERK arm of the UPR was activated as both ATF4 and CHOP (DDIT3), critical

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regulators of the pathway, were altered after infection. The transcription factor Early Growth

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Response 1 (EGR1) was induced in a PERK dependent manner. EGR1 -/- MEFs demonstrated

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lower susceptibility to VEEV induced cell death as compared to isogenic wild type MEFs,

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indicating that EGR1 modulates pro-apoptotic pathways following VEEV infection. The

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influence of EGR1 is of great importance as neuronal damage can lead to long-term sequelae in

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individuals who have survived VEEV infection.

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Importance

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Alphaviruses represent a group of clinically relevant viruses transmitted by mosquitoes to

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humans. In severe cases, viral spread targets neuronal tissue, resulting in significant and life

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threatening inflammation dependent on a combination of viral-host interactions. Currently there

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are no therapeutics for encephalitic alphaviruses due to an incomplete understanding of their

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molecular pathogenesis. Venezuelan equine encephalitis virus (VEEV) is an alphaviruses

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prevalent in the Americas capable of infecting horses and humans. Here we utilized next

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generation RNA sequencing to identify differential alterations in VEEV infected astrocytes. Our

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results indicated that transcripts associated with interferon and the unfolded protein response

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pathways were altered following infection, and demonstrated that Early Growth Response 1

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(EGR1) contributed to VEEV induced cell death.

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Introduction

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Venezuelan equine encephalitis virus (VEEV) is a New World alphavirus in the family

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Togaviridae that is endemic to the Americas. VEEV is a positive strand RNA virus, transmitted

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by mosquitoes and is naturally present in rodent reservoirs (1). There are six subtypes that are

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categorized by their geographic range and pathology in equines and humans. The two epizootic

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strains, IA/B and IC, arose from mutations among the enzootic strains (2). The IA/B and IC

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strains are of particular concern due to increased rates of morbidity, mortality, and the risks

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associated with viral amplification and potential species spillover (2). In humans VEEV causes a

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febrile illness typified by fever, malaise, and vomiting. In some cases, infection progresses to the

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central nervous system (CNS) and neurological symptoms such as confusion, ataxia, and seizures

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manifest. Neurological cases have a mortality rate as high as 35% in children and 10% in adults, 3

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with long-term neurological deficits often seen in survivors (2). In 1995, an outbreak of VEEV in

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Colombia and Venezuela resulted in over 100,000 human cases (3). In addition to natural

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outbreaks, VEEV is also a concern from a bioterrorism perspective as it can be grown to high

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titers, requires a low infectious dose, and contains multiple serotypes. Both the former Soviet

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Union and the United States previously weaponized the virus, producing large quantities for their

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now defunct offensive bioweapons programs (4). Currently the vaccine strain TC83 is used in

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horses and for high risk personnel, however due to its low rate of seroconversion (5) and a

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reliance on two single attenuating mutations (6), it is considered unfit for mass distribution (7).

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To date there are no FDA approved therapeutics for VEEV and further studies are required for

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clarification of the mechanisms associated with its underlying pathogenesis.

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Viral and host transcriptomic studies can provide a wealth of information on the underlying

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pathogenic mechanisms and interactions following the course of an infection. The use of high-

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throughput next generation sequencing has led to the discovery of previously uncharacterized

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viruses and the establishment of numerous novel experimental systems redefining viral-host

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interactions. To date there has been a number of studies examining alterations in the host

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transcriptome following VEEV infection. A comparative microarray analysis between cells

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persistently infected with VEEV and cells able to clear VEEV resulted in the identification of

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PARP12L as an antiviral factor (8). A molecular comparison utilizing microarrays among host

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based responses to the TC83 strain was able to identify biomarkers differentiating between

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vaccine responder and nonresponder groups, as well as the involvement of interferon, interferon-

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induced pathways, toll like receptor (TLR), and interleukin 12-related pathways (9). A study

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examining the role of adhesion and inflammatory factors in VEEV infected CD-1 mice found

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viral modulation of extracellular matrix and adhesion genes such as integrins (ItgαX, Itg2, 3, and 4

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7), cadherin 1 and 2, vascular cell adhesion molecule-1, and intracellular adhesion molecule-1

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(ICAM-1) in the brains of VEEV infected mice (10). Follow-up experiments utilizing ICAM-1

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knockout mice demonstrated reduced inflammation in the brain and a subsequent delay in the

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onset of neurological sequale (10). A study by Sharma et al. utilizied microarrays to analyze

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gene expresion changes in the brain tissue of VEEV infected mice over the course of an

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infection, discovering numerous immune pathways involved in antigen presentation,

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inflammation, apoptosis and the traditional antiviral response (Cxcl10, CxCl11, Ccl5, Ifr7, Ifi27,

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Oas1b, Fcerg1,Mif, Clusterin and MHC class II) (11). A second study by the same group

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identified regulation of miRNAs in the brains of VEEV infected mice which enabled the

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correlation of the miRNA changes with earlier mRNA expression data (11, 12). These analyses

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suggest that VEEV may be utilzling cellular miRNAs in order to regulate downstream mRNA,

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which may correspond with VEEV induced histological changes to the nervous system (11, 12).

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In the current study, next generation RNA sequencing (RNA-Seq) was used to identify clinically

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relevant alterations in the mRNA transcriptome of human astrocytes infected with wild type

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VEEV- Trinidad Donkey (TrD). The analysis of host mRNA’s by RNA-Seq provides novel

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insight into how a host responds to a viral infection through the identification of a wide and

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dynamic range of transcripts in an unbiased manner. Selectively sequencing mRNA’s,

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specifically polyadenylated (poly-A) transcripts, which are responsible for encoding the genome

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and account for ~1% of the entire transcriptome, enhances the detection of the most relevant and

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low abundant transcripts (13). As VEEV has been shown to productively infect astrocytes both

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in vitro and in vivo (14, 15) we chose astrocytes as our model of interest. Astrocytes are the most

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abundant cell in the brain, outnumbering neurons by at least fivefold (16), providing an abundant

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resource for viral replication within the brain. In addition to their well described structural role in 5

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neuronal tissue, astrocytes play critical roles in other processes including regulation of blood

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flow and of the blood brain barrier, synapse transmission, and response to infection (16). VEEV

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infected astrocytes have been shown to produce multiple cytokines including IL-8, IL-17, IFN-

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gamma and IP-10, all of which were associated with viral attenuation (14).

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In order to obtain a dynamic view of the viral-host interactome, RNA-Seq was used to monitor

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changes in gene expression in VEEV-TrD infected astrocytes at 4, 8 and 16 hours post infection.

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By viewing alterations among multiple early time points using triplicate biological replicates, a

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robust and dynamic range of information is generated, providing an increase in both the power

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and accuracy of detection for differentially expressed transcripts in a highly relevant clinical

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model (17). Among VEEV infected cells, an increase in interferon regulated genes including

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IFIT1, IFIT2, IFIT3, and OASL was observed. Increased expression of genes involved in the

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stress induced unfolded protein response (UPR) pathway was also noted. Interestingly, VEEV

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infection resulted in an increase in early growth response protein 1 (EGR1), which may serve as

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a link between the two pathways. The identification of altered host mRNAs, specifically EGR-1

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and its up and downstream interactors following VEEV replication, may provide novel host

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based therapeutic targets critical for VEEV replication, while providing a greater understanding

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of the underlying mechanisms underpinning alphavirus replication.

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Materials and Methods

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Viral infections and plaque assays. VEEV TrD was obtained from BEI Resources. All

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experiments with VEEV TrD were performed under BSL-3 conditions. All work involving select

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agents is registered with the Centers for Disease Control and Prevention and conducted at 6

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George Mason University’s Biomedical Research Laboratory, which is registered in accordance

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with Federal select agent regulations. For infections, VEEV was added to supplemented DMEM

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to achieve an MOI of 0.05, 0.5 or 5. Cells were infected for one hour at 37°C and rotated every

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fifteen minutes to ensure adequate coverage. Cells were then washed with PBS and complete

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growth media added back to the cells. Viral supernatants and cells were collected at various

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times post infection for further analysis. Plaque assays were performed as previously described

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(18).

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mRNA isolation and Poly A library preparation. RNA from U87MG cells were purified from

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both VEEV-TrD (biosafety level 3) infected and mock infected U87MG cells at 4, 8, and 16

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h.p.i. utilizing the mirVana isolation kit (Life Technologies). Quality control of purified RNA

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was then performed using an Agilent 2100 Bioanalyzer with a RNA integrity number (RIN)

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cutoff of 8 utilized for all samples. An ERCC RNA Spike-In Control mix was then added to the

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total RNA inputs (10ug RNA) before poly(A) selection using Life Technologies Dynabeads

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mRNA DIRECT Kit. Whole transcriptome RNA library preparation from purified mRNA was

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then performed using the Ion Total RNA-Seq Kit v2 (Life Technologies). Quality control of

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cDNA libraries was then performed using the Agilent 2100 Bioanalyzer along with sterility

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testing for removal of libraries for sequencing from a BSL-3 to BSL-2 laboratory.

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RNA Sequencing. Library template preparation was performed on the One Touch 2 platform

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(Life Technologies). Next generation RNA sequencing was performed on the Ion Torrent PGM

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platform and carried out for each sample to assess differential gene expression of infected vs.

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uninfected cells over time.

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Data Filtering & RNA-Seq Analysis Pipeline. A total of ~119M sequencing reads were used as

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input into our analysis pipeline, an average of 6.6M reads per sample. Unless otherwise noted,

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downstream RNA-Seq analysis was carried out using CLC bio Genomics Workbench v7. Raw

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RNA-Seq reads were trimmed to remove any residual sequencing adapter fragments that

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remained on the 5’ or 3’ ends after sequencing. In addition, end trimming of reads was done

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using the modified Mott algorithm with a Q20 quality score, and any reads below 15 bp were

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discarded. Following read trimming, reads were mapped to Human Genome hg19 with the

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following RNA-Seq parameters: a 10 hit limit for multiple mapped positions; similarity fraction

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0.8; length fraction 0.8; mismatch cost 2; and indel cost 3. The expression level of individual

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genes and transcripts was calculated using the RPKM (Reads Per Kilobase of exon model per

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Million mapped reads) method of Mortazavi et al. (19). In addition, unmapped reads were also

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mapped to the ERCC92 synthetic RNA sequence set (20), as well as to the VEEV reference

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genome (GeneBank Accession #L01442). In all samples, the R2 correlation coefficient between

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expected and mapped number of reads for ERCC92 spike in controls was above 0.90. A

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summary of the overall sequencing results is shown in Table 1. The raw sequencing data for all

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RNA-seq runs included in this work are publically available at the NCBI BioProject

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PRJNA300864 (http://www.ncbi.nlm.nih.gov/bioproject/PRJNA300864).

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Post-mapping filtering was carried out next for all RNA-Seq data to include only genes with at

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least one uniquely mapped read (26,230 genes remained across all datasets) and only those with

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a non-zero interquartile range across the entire experiment. Principal component analysis of the

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resulting “filtered dataset” (13,906 genes in total) was carried out using raw counts of uniquely

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mapped reads (Fig. 2A). The remaining RPKM expression values for each gene included in the

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filtered dataset were subjected to quantile normalization with a 5% cutoff. A box-plot of log2

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transformed RPKM values for each sample before normalization is shown in Fig. 2B. Pairwise

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sample to sample variation R2 value within each biological replicate set was observed to range

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from 0.89 to 0.99, indicating that our biological replicates were consistent and showed no strong

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bias (data not shown).

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Differential Gene Expression Analysis. Differentially expressed genes were identified using

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two approaches. First, the empirical analysis of differential gene expression algorithm, part of the

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edgeR Bioconductor package (21), was applied to the integrated dataset of all 18 experiments

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using the default parameters and a false-discovery rate corrected p-value. At each time point,

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infected vs. mock sample comparisons were made and genes whose expression differed by more

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than 2-fold with a significance value of p ≤ 0.05 were provisionally considered differentially

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expressed.

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In addition to the method above, an orthogonal statistical test of differential expression was

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applied to the data using a statistical test developed by Baggerly et al. (22) for counting the

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number of expressed sequence tags associated with individual genes, a common feature of both

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serial analysis of gene expression (SAGE) data and RNA-Seq data. Comparing infected vs. mock

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samples, individual genes were provisionally considered differentially expressed with a greater

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than 2-fold change and a significance of p ≤ 0.05. Differentially expressed genes found to be in

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the intersection of the sets of genes identified these both of the methods outlined above were

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considered high-quality candidates and used as the starting point for further investigation.

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Clustering & Gene Set Enrichment Analysis. Filtered, normalized expression data was

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subjected to k-means clustering using a Euclidian distance metric where genes were grouped by

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means of normalized gene expression (RPKM) values for each experimental condition.

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Clustering was fitted to 20 distinct clustering groups, and individual gene expression profile

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clustered were further tested for enrichment of gene ontology (GO) terms associated with

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individual genes. Gene annotations were obtained from Reactome a database of biological

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pathway and gene functional annotations (23). Enrichment analysis was performed using two

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approaches. First, a hypergeometric test on GO annotations was carried out using an

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implementation of GOStats on each of the individual clusters obtained from k-means clustering

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(24). In addition, gene set enrichment analysis (GSEA) was carried out on the entire filtered

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dataset using 100,000 permutations, while removing duplicates and applying an ANOVA test. A

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total of 1,419 categories passed a minimum feature size of 10, and were used for further

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investigation.

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Pathway Analysis. Cohorts of genes with shared patterns of expression over time were

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identified by k-means clustering. Those found to be enriched for differentially expressed genes

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were subsequently subjected to pathway analysis using the GeneMania system (25). Using an ad

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hoc manual approach, relevant pathways and the connections between them were identified

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based on existing data in the literature coupled with the temporal gene expression data obtained

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from this study.

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qRT-PCR analysis. Purified mRNA was converted to cDNA using the High Capacity RNA-to-

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cDNA kit (Life Technologies) according to the manufacturer’s instructions. Analysis of the viral

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copy numbers was performed by qRT-PCR as previously described (26). Host gene expression

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was assayed with the following Taqman assays: ATF3 (Hs00231069_m1), ATF4

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(Hs00909569_g1), CEBPB (Hs00270923_s1), CEBPD (Hs00270931_s1), DDIT3

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(Hs00358796_g1), FOS (Hs04194186_s1), JUN (Hs01103582_s1), EGR1 (Hs00152928_m1),

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IFI6 (Hs00242571_m1), IFIT1 (Hs01911452_s1), IFIT2 (Hs01922738_s1), IFIT3

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(Hs01922738_s1), ISG15 (Hs01921425_s1), ISG20 (Hs00158122_m1), OASL

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(Hs00984387_m1), BIRC5 (Mm00599749_m1) and XIAP (Mm01311594_mH). 18S assays

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(Hs99999901_s1 or Mm04277571_s1) were used for normalization. Assays were performed

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according to the manufacturer’s instructions using an ABI StepOne Plus instrument.

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Treatment with PERK inhibitor and collection for western blots. U87MG cells were pre-

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treated for two hours with 10 µM of the PERK inhibitor (PERKi) GSK2606414 (EMD Millipore,

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516535) or DMSO in DMEM prior to infection with VEEV TrD (MOI 5). After one hour, the

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viral inoculum was removed and cells were washed with sterile PBS (1X). Media containing the

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inhibitor or DMSO was replaced. Sixteen hours post-infection media was removed, cells washed

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with PBS, and then collected for western blot analysis.

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Knockdown of EGR1 with siRNA. U87MG seeded at 6.7 x 104 cells per well in a 12-well plate

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were transfected with 50 nM of siGENOME SMARTpool EGR1 (Dharmacon, M-006526-01) or

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AllStar Negative Control siRNA (Qiagen, 1027280), using 1.33 µl of Dharmacon DharmaFECT

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1 Transfection Reagent (T-2001-02). Twenty-four hours post-transfection, cells were infected

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with VEEV TrD (MOI 5) for one hour. Fresh media was replaced after infection. Twenty-four

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hours after infection supernatants or cells were collected for analysis.

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Protein lysate preparation and western blot analysis. Protein lysates and western blot analysis

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were performed as previously described (27). The primary antibodies were used: EGR1 (44D5)

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(Cat#4154, Cell Signaling), Polyclonal Anti-Venezuelan Equine Encephalitis Virus, TC83

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(Subtype IA/B) Capsid Protein (BEI Resources), CHOP (L63F7) (Cat#2895, Cell Signaling), p-

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eIF2a (Ser51) (D9G8) (Cat#3398, Cell Signaling), ATF4 (D4B8) (Cat#11815, Cell Signaling),

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Activated Caspase 3 (Asp175) (Cat#9661, Cell Signaling), and HRP conjugated β-actin (Cat#

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ab49900-100, Abcam).

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Immunofluorescence analysis. U87MG cells were grown on coverslips in a 6-well plate,

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infected with VEEV TrD as described above, washed with PBS (without Ca and Mg) and then

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fixed with 4% formaldehyde. Cells were permeabilized with 0.5% Triton X-100 in PBS for 20

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minutes then washed twice with PBS. The cells were blocked for 10 minutes at room

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temperature in 3% BSA in PBS. Primary antibodies, VEEV-capsid (BEI Resources, NR-9403)

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diluted 1:600, and EGR1 (44D5) (Cat#4154, Cell Signaling) diluted 1:400 were incubated in

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fresh blocking buffer at 37°C for 1 hour and washed 3 times for 3 minutes in 300 mM NaCl with

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0.1% Triton X-100. Alexa Fluor 568 donkey anti-goat secondary (Invitrogen, A11057) and

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Alexa Fluor 488 donkey anti-mouse secondary (Invitrogen, A21202) dilution 1:400 were used as

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secondary antibodies and treated in the same manner as the primary antibody. DAPI, dilution

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1:1000, was used to visualize nuclei. Cover slips were mounted to glass slides using 10 µL of

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Fluoromount G (Southern Biotech, 0100-01). A Nikon Eclipse TE 2000-U was used for

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fluorescence microscopy. Images were viewed using an oil-immersion 60X objective lens. Each

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sample was subjected to at least five images, with a representative image shown. All images

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were subjected to four line averaging. The images were processed through Nikon NIS-Elements

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AR Analysis 3.2 software. 12

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Cell TiterGlo and Caspase 3/7 Glo assays. Wild-type and EGR1 -/- MEFs were infected with

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TrD at various MOIs for an hour then washed with PBS and media replaced. Cell viability was

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measured at 24 hours post-infection using Promega’s CellTiter Luminescent Cell Viability Assay

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(G7571) according to the manufacturer’s protocol. Luminescence was read using Beckman

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Coulter’s DTX 880 Multimode Detector with an integration time of 100 ms per well. Similarly,

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caspase activation at 24 hours post-infection in infected wild-type and EGR1 -/- MEFs was

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measured using Promega’s Caspase 3/7 Glo assay (G8090) according to the manufacturer’s

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protocol. Luminescence was read using the DTX 880 with an integration time of 100 ms per

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well.

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Results

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VEEV replication kinetics in U87MG astrocytes. VEEV replicates in vivo in monocytes,

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macrophages, neurons, and astrocytes (14). Common cell lines used to study VEEV infection

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include Vero and BHK cells; in this study U87MG astrocytes were chosen as an in vitro model

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due to their physiological relevance and greater clinical significance. Initial experiments were

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performed to characterize viral replication in U87MG cells. VEEV replication kinetics in

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U87MG cells were measured using plaque assays, and by monitoring viral protein and RNA

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expression levels, and cytopathic effect (CPE) on the infected cells (Fig. 1). Viral release was

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observed as early as 4 hours post-infection (h.p.i.), with ~4 logs of virus observed, followed by a

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consistent increase in replication at 8 and 16 h.p.i. (Fig. 1A). Viral replication peaked at sixteen

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h.p.i. with no additional increase in viral titers observed at 24 h.p.i. Viral capsid expression

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followed a similar pattern, with protein being detected at 8 h.p.i. and plateauing at 16 h.p.i (Fig.

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1B). Among infected U87MG cells, significant CPE was observed by microscopy at 24 h.p.i.,

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with little to no CPE detected at 16 h.p.i. (data not shown). Consistent with these observations,

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increased caspase 3/7 activity was only observed at 24 h.p.i. (Fig. 1C). Based on these data, 4, 8,

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and 16 h.p.i. were selected for RNA-Seq analysis, reflecting early, mid, and late stages of the

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viral life cycle, in order to provide a dynamic view of the host-pathogen transcriptome profile.

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RNA sequencing analysis of VEEV infected astrocytes. mRNA from triplicate sets of mock

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and VEEV infected U87MG cultures was isolated and purified at 4, 8, and 16 h.p.i. and used to

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prepare cDNA libraries for downstream RNA-Seq (see Materials & Methods). A high level

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summary of the RNA-Seq results is shown in Table 1. VEEV RNA samples were assayed by

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quantitative RT-PCR at each time point as a control to demonstrate the increasing viral RNA

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load over time (Fig. 1D), consistent with increasing number of RNA-Seq reads mapped to the

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VEEV genome at later time points (Table 1).

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For RNA-Seq analysis, individual genes were expressed as reads per kilobase per million

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mapped reads (RPKM) (19). Log2 normalized RPKM expression values for each experimental

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sample are shown in Fig. 2A and can be found in Dataset S1. Minimal sample-to-sample

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variation expression values within biological replicates was consistently low (R2 > 0.89 for all

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replicates; data not shown). In addition, intersample variation was also found to be minimal

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when tested pairwise across the entire experiment by using RPKM values for ERCC97 synthetic

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spike-in control RNAs (R2 > 0.90 for all comparisons; data not shown).

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As anticipated, two-component principal component analysis of RNA-Seq data for mock vs.

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VEEV infected cells showed a clear separation of 16 h.p.i. samples compared to earlier time

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points (Fig. 2B). However, the clustering of VEEV infected samples with mock samples at

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earlier time points suggested that the response to viral infection was limited to a narrow subset of

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early response genes, thus placing a higher burden of proof on identifying differentially

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expressed genes (DEGs) during the first few hours of infection. Along these lines two orthogonal

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methods were used to identify DEGs suitable for further characterization: the edgeR method

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(21), and that developed by Baggerly et al. (22). Genes identified by one test were provisionally

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considered as DEGs, and those identified by both were candidate DEGs to be confirmed by qRT-

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PCR. In addition to comparing individual gene expression values for mock vs. VEEV infected

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cells at each time point, gene expression values were also compared serially within each time

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series of VEEV infected cells for genes that did not show any statistically significant changes in

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mock infected cells. A schematic of the comparative analysis is shown in Fig. 2C. The number of

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statistically significant DEGs identified by each of these comparisons is shown in Fig. 2D.

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Furthermore, k-means clustering was employed (against normalized RPKM values) to identify

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gross changes in gene expression over time for cohorts of genes potentially sharing the same

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pathway or regulatory triggers (Fig. 3 and Dataset S2). Gene set enrichment analysis (GSEA, see

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Material & Methods and Dataset S3) was carried out on each k-means cluster. In particular,

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Cluster_20 (Table 2) was significantly enriched for genes involved in translational control, type I

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interferon-mediated signaling pathway, and the unfolded protein response (UPR) pathway

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(GSEA p-value < 0.01). Although there is a well-established connection between translational

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control and UPR, a novel connection between UPR and the type-I mediated interferon response

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in response to viral replication was suggested by pathway analysis (see Materials & Methods)

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implicating EGR1 as a potential bridge between these two pathways (Fig. 4). EGR1 belongs to

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Cluster 20, and is strongly induced during VEEV infection; and several other genes associated

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with the interferon response belong to the same cluster: IRF1, IFIT1, IFIT2, ISG15, and ILF3.

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EGR1 has been associated with increases in ATF3 (28), a key component of the UPR, and also

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belongs to Cluster 20. This connection represented a potential between the UPR pathway and the

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interferon response pathway, with EGR1 as one of the potential key transcription factors driving

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this connection. Consequently, 15 genes were selected from this analysis for further

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characterization by qRT-PCR (see below): ATF3, ATF4, CEBPB, CEBPD, DDIT3/CHOP,

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EGR1, FOS, IFI6, IFIT1, IFIT2, IFIT3, ISG15, ISG20, JUN, and OASL. The expression values

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of these genes, as measured by RNA-Seq, are shown in Figs. 5A and 5B. Confirmatory qRT-

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PCR analysis indicated concordant gene expression (Figs. 5C and 5D). The induction of

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interferon responsive genes is in agreement with previously published studies (11, 29, 30),

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serving as an internal positive control. Moreover, the link between EGR1 and the interferon

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pathway has been demonstrated; EGR1 is induced by IFN-γ in mouse fibroblasts, and by IFN-α,

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β and γ in human fibroblasts (31, 32). EGR1 and the UPR pathway were selected for further

352

analysis as their role in VEEV infection has not been elucidated.

353

VEEV infection induces the UPR late in infection. The RNA-Seq and pathway analysis data

354

indicated that UPR and stress response genes were induced after VEEV infection. During an

355

infection, host cells respond to cellular stresses resulting from increased viral protein translation

356

and secretion by triggering the onset of the UPR pathway. The UPR pathway is an adaptive

357

cellular response activated by endoplasmic reticulum (ER) stress due to protein misfolding. In

358

order to regulate cellular homeostasis during protein folding and secretion, the UPR pathway has

359

developed three classes of sensors to ensure proper cellular regulation: inositol-requiring enzyme

360

1 (IRE1), protein kinase RNA-like ER kinase (PERK), and activating transcription factor 6

361

(ATF6) (33, 34). During VEEV infection, the PERK arm of the UPR appeared to be altered, as

362

two critical regulators of this pathway were differentially expressed: activating transcription 16

363

factor 4 (ATF4) and CHOP (DDIT3) (35). To determine if DEG’s altered subsequent protein

364

expression, western blot analysis was performed for CHOP, ATF4 and phosphorylated-eIF2α (p-

365

eIF2α). Tunicamycin, a glycosylation inhibitor and inducer of UPR (36), was included as a

366

positive control. A time course analysis of U87MG cells treated with 1 μM of Tunicamycin

367

indicated that 8 hours of treatment provided the most robust induction of UPR proteins (data not

368

shown). VEEV infected, but not mock or UV-inactivated VEEV (UV-VEEV) infected cells

369

displayed a dramatic increase in p-eIF2α and a modest but consistent increase in CHOP and

370

ATF4 at 16 h.p.i. (Fig. 6A). No change in protein expression was observed at 4 h.p.i. (data not

371

shown). Confocal microscopy confirmed CHOP and ATF4 upregulation, demonstrating a more

372

robust and nuclear staining pattern in VEEV infected cells as compared to mock infected cells

373

(Fig. 6C-E). While ATF4 protein expression levels increased, ATF4 mRNA abundances

374

decreased following VEEV infection (Fig. 5B and 5D). These results are consistent with the

375

observation that ATF4 expression is regulated at the translational level upon UPR induction (37).

376

As eIF2α can be phosphorylated by multiple kinases [PERK, PKR (protein kinase double-

377

stranded RNA-dependent), GCN2 (general control non-derepressible-2), and HRI (heme-

378

regulated inhibitor)] (38), the PERK inhibitor GSK2606414 (PERKi) was used to determine if

379

the observed phosphorylation was PERK dependent. Treatment of VEEV infected cells with

380

PERKi resulted in a marked decrease in eIF2α phosphorylation (Fig. 6B). These results indicate

381

that PERK contributes to eIF2α phosphorylation, but that there is likely an additional kinase

382

contributing to the phosphorylation event. Collectively, these findings indicate that the PERK

383

arm of the UPR pathway is induced at later time points following VEEV infection.

384

EGR1 is upregulated in infected cells and localizes to the nucleus. Early Growth Response 1

385

(EGR1) is a transcription factor that can be induced by numerous signals including oxidative 17

386

stress, hypoxemia, and growth factors (39, 40). It can also be activated upon infection by both

387

DNA and RNA viruses, including Epstein - Barr virus, Mouse Hepatitis Virus, Murine

388

Coronavirus and Japanese Encephalitis Virus (41-43). Treatment of MEFs with the UPR

389

activator, thapsigargin, has been shown to induce EGR1 expression in a PERK dependent

390

manner (44). Given the link between EGR1 and UPR, and the robust induction of EGR1 mRNA

391

expression following VEEV infection (Figs. 4 and 5), EGR1 was chosen for further study. EGR1

392

protein expression was analyzed after VEEV infection by western blot analysis. As previous

393

studies have indicated that EGR1 can be activated by Mouse Hepatitis Virus independent of

394

virus replication (likely due to cellular membrane disruption following entry) (41), a UV-

395

inactivated virus control was included (UV-VEEV). EGR1 protein levels were increased

396

following VEEV infection as compared to mock infected cells and UV-VEEV infected cells

397

(Fig. 7A, compare lanes 3, 6, and 9). The most dramatic upregulation of EGR1 occurred at 16

398

h.p.i.; this correlates with the highest levels of VEEV capsid production (see Fig. 1B). Following

399

induction, EGR1 has been shown to translocate to the nucleus to induce gene expression through

400

binding to the Egr binding sequence (EBS) [(GCG(G/T)GGCG] (40, 45). Confocal microcopy

401

revealed high levels of EGR1 in the nuclei of infected cells, whereas only low levels of both

402

nuclear and cytoplasmic EGR1 were detected in mock cells (Fig. 7B). PERKi treatment of

403

VEEV infected cells resulted in a complete loss of EGR1 induction (Fig. 7C), indicating that

404

EGR1 was induced in a PERK dependent fashion. These results demonstrate that EGR1 protein

405

levels and nuclear localization are increased following VEEV infection and that the induction of

406

EGR1 is dependent on PERK.

407

Loss of EGR1 inhibits VEEV induced apoptosis but does not alter VEEV replication

408

kinetics. As EGR1 influences cell survival and apoptosis (46), the impact of EGR1 on VEEV 18

409

induced cell death was assessed. Caspase 3 cleavage was observed in WT MEFs at 24 h.p.i.

410

when infected at an MOI of 0.5, and started as early as 16 h.p.i. when infected at an MOI of 5

411

(Fig. 8A). In contrast, EGR1 -/- cells showed little to no detectable caspase cleavage following

412

infection with VEEV. Two sets of experiments were performed to quantitatively confirm these

413

results: Cell Titer Glo assays to measure total cell viability (ATP production) and Caspase 3/7

414

Glo assays to measure caspase 3/7 activity. Both WT and EGR1 -/- MEFs displayed dose

415

dependent decreases in cell viability following VEEV infection, with EGR1-/- cells having

416

significantly more viable cells at each MOI examined (Fig. 8B). Concordantly, a dose dependent

417

increase in caspase 3/7 activity was observed following VEEV infection, with EGR1 -/- cells

418

demonstrating reduced caspase 3 activity at MOIs of 0.5 and 5 (Fig. 8C). These results were

419

replicated in EGR1 siRNA transfected U87MG cells (Fig. 8D).

420

EGR1 has been shown to negatively regulate the transcription of BIRC5 (Survivin), an inhibitor

421

of apoptosis (IAP) family member (47). RNA-Seq data indicated that BIRC5 gene expression

422

was decreased following VEEV infection, -1.16, -1.18, and -1.50 log2 transformed fold change

423

values of normalized gene expression at 4, 8, and 16 h.p.i. respectively (see Table S1 and NCBI

424

BioProject PRJNA300864). WT and EGR1 -/- MEFs were used to determine if EGR1 was

425

influencing BIRC5 gene expression following VEEV infection. BIRC5 expression was

426

significantly decreased at 16 h.p.i. in VEEV infected WT MEF, but this reduction was not

427

observed in VEEV infected EGR1 -/- MEFs (Fig. 8E). Gene expression of X-linked inhibitor of

428

apoptosis (XIAP), another IAP family member, was not significantly differentially altered after

429

infection (data not shown). Collectively these results demonstrate that EGR1 contributes to

430

VEEV induced apoptosis.

19

431

VEEV replication kinetics were determined for both EGR1 -/- and WT MEFs to determine the

432

relevance of EGR1 in viral replication. Cells were infected at two different MOIs (0.5 and 5),

433

with viral supernatants collected at 4, 8, 16, and 24 h.p.i., and analyzed by plaque assay. The

434

replication kinetics were similar between EGR1 -/- and WT MEFs at both MOI’s with titers

435

peaking at 16 h.p.i. (Fig. 9A). Lack of EGR1 expression was confirmed by western blotting (Fig.

436

9B). These results were replicated in EGR1 siRNA transfected U87MG cells. EGR1 siRNA

437

transfection resulted in a >90% decrease in EGR1 protein expression (Fig. 9D) without any

438

significant effect on viral replication (Fig. 9C). These results suggest that the observed decreased

439

in apoptosis in EGR1 -/- MEFs was not due to altered VEEV replication kinetics.

440 441

Discussion

442

Despite being recognized as an emerging threat, relatively little is known about the virulence

443

mechanisms of alphaviruses, largely due to a knowledge gap in the host-pathogen interactome.

444

VEEV infection often results in fatal encephalitis and is known to inhibit both cellular

445

transcription and translation in order to downregulate the innate immune response (1, 48). In

446

contrast, in the CNS VEEV has been shown to upregulate numerous genes in both the

447

inflammatory response and apoptotic pathway (1, 48). Specifically, numerous pro-inflammatory

448

cytokines including interleukin-1β (IL-1β), IL-6, IL-12, GSK-3β, iNOS and tumor necrosis

449

factor -α (TNF-α) have all been shown to play a role in VEEV pathogenesis (49-53). The use of

450

high throughput next generation sequencing technologies such as RNA-Seq allows for an in

451

depth and unbiased look into the viral-host transcriptome, thus enabling changes in the

452

expression of specific mRNAs to be connected with phenotypic outcomes. To this end,

453

identifying critical differentially expressed transcripts among clinically relevant infected cells 20

454

will help lead to a greater understanding of viral pathogenesis and may prove beneficial for the

455

identification of therapeutic targets.

456 457

In this study, network analysis/ RNA-Seq data and protein expression studies revealed that

458

VEEV infection resulted in activation of the PERK arm of the UPR pathway, including ATF4,

459

CHOP, and eIF2α phosphorylation. Several alphaviruses have previously been reported to

460

hijack key components of the UPR pathway in order to promote viral replication, as the reliance

461

of enveloped viruses on the ER for the synthesis of viral envelope associated glycoproteins and

462

their transport to the plasma membrane often stress the ER due to rapid viral protein production

463

(54, 55). Modulation of the UPR is not unique to alphaviruses; rather it is a shared trait of many

464

positive sense RNA viruses. Dengue virus has been shown to suppress PERK by inhibiting

465

continued eIF2α phosphorylation in order to inhibit immediate apoptosis, increasing viral protein

466

translation, and extending the length of productive viral replication (34). Studies with Hepatitis E

467

virus (HEV), have demonstrated that expression of HEV capsid protein open reading frame 2

468

(ORF2) activates the expression of CHOP and ATF4 (56). In HEV, ORF2 was shown to

469

stimulate CHOP through both ER stressors and amino acid response elements (AARE) through

470

interaction with ATF4 (56).

471 472

The results shown here indicate that during VEEV infection, initiation of the UPR pathway and

473

subsequent activation of EGR1 play a role in the outcome of viral induced apoptosis. During the

474

initial detection of ER stress, PERK is able to identify misfolded proteins in the lumen of the ER

475

and phosphorylates eIF2α in order to initiate pro-survival pathways in the UPR through the

476

general inhibition of protein synthesis (33, 34). VEEV appears to be inducing the UPR and

21

477

promoting increased eIF2α phosphorylation, which results in translational inhibition of most

478

mRNAs, while selectively increasing translation of ATF4. ATF4 is responsible for the

479

expression of genes that encode proteins involved in apoptosis, redox processes, amino acid

480

metabolism, and ER chaperone recruitment and is a well-known mediator of the PERK pathway

481

and CHOP (33, 34). CHOP activation facilitates the increased expression of cellular chaperones

482

in order to counteract the buildup of misfolded proteins (57). Failure to suppress protein

483

misfolding in persistently stressed cells, such as during a viral infection, can then result in

484

activation of the pro-apoptotic transcription factor CHOP, leading to suppression of the anti-

485

apoptotic protein B cell lymphoma-2 (Bcl-2). CHOP can also function as a pro-survival

486

transcription factor by dephosphorylating eIF2α through activation of the DNA damage-

487

inducible protein (GADD34) in a self-regulating feedback look (33, 34). However, the data

488

presented here supports a model whereby VEEV infection leads CHOP to function in its pro-

489

apoptotic role, as no change in GADD34 gene expression was detected by RNA-Seq analysis.

490 491

While the UPR was induced following VEEV infection, robust activation was not observed until

492

later time points after infection. This is somewhat surprising as VEEV infection is expected to

493

induce significant ER stress due to the massive production of viral proteins during the course of

494

an acute robust infection. The structural proteins of VEEV are translated from the viral

495

subgenomic RNA into polyproteins on the rough ER. The E1 and pE2 precursor glycoproteins

496

are then assembled as heterodimers in the ER, undergoing conformational changes requiring

497

numerous chaperones (1, 58). It is possible that VEEV has developed mechanisms to subvert the

498

induction of the UPR. In order to counteract the UPR, the non-structural proteins (nsPs) of

499

Chikungunya virus (CHIKV) have been shown to inhibit expression of ATF4 and other known

22

500

UPR target genes including GRP78/BiP, GRP94 and CHOP (59). Through nsPs activity, CHIKV

501

has developed a means of suppressing UPR activity resulting from viral glycoprotein induced ER

502

stress, thus preventing immediate autophagy and apoptotic activation. VEEV capsid is

503

responsible for interfering with nucleocytoplasmic trafficking, inhibiting rRNA and mRNA

504

transcription, and has been implicated in the regulation of type-I IFN signaling and the antiviral

505

response through regulation of both viral RNA and protein production (1, 48, 60). Therefore we

506

hypothesize that the ability of VEEV capsid to inhibit cellular transcription and block

507

nucleocytoplasmic trafficking results in delayed induction of the UPR.

508 509

A detailed network analysis, based on existing data in the literature, coupled with the temporal

510

gene expression profiles obtained from this study, pointed towards EGR1 as an important node

511

in the novel link between VEEV activation of the type-1 interferon response and UPR. EGR1 is

512

known to form a DNA binding complex with C/EBPB, a critical dimerization partner of CHOP

513

(61). Previous studies have demonstrated that nuclear localization of CHOP may act as an

514

inducer of EGR1 and as a transcriptional cofactor for regulation of C/EBPB-EGR1 target genes

515

(61). The western blot and microscopy analysis presented in this study support this model as

516

VEEV infection was found to increase both overall levels and the nuclear distribution of CHOP

517

along with ERGR1. Previous studies demonstrated EGR1 mRNA induction by IFN-γ in mouse

518

fibroblasts, and by TNF-α, TNF-β, IL-1, IFN-α,β and γ in human fibroblasts (32, 62). EGR1,

519

also known as Zif268 and NGF1-A, is a zinc finger protein and mammalian transcription factor.

520

It has been implicated in cellular proliferation and differentiation, but may also have pro-

521

apoptotic functions, depending on the cell type and stimulus (63). Of particular interest, EGR1

522

directly controls proliferation when activated by the MAP kinase/ERK pathway in mitogen

23

523

stimulated astrocytes (64). Virus-induced changes in EGR1 expression have been observed in

524

several in vitro systems. In HIV-1 infected astrocytes, EGR1 upregulation was found to be

525

induced by Tat through transactivation of the EGR1 promoter, leading to cellular dysfunction

526

and Tat-induced neurotoxicity (65). Increased amounts of EGR1 mRNA have also been

527

demonstrated to act in a region specific manner, corresponding temporally with viral RNA

528

production in the brain tissues of rats infected with both rabies and borna disease virus (66).

529 530

In summary, the current study demonstrates a potential link between UPR activation and EGR1.

531

EGR1 -/- MEFs demonstrated lower susceptibility to VEEV induced cell death as compared to

532

wildtype MEFs, indicating that EGR1 modulates pro-apoptotic pathways following infection.

533

Studies are underway to determine if alteration of UPR through small molecule inhibitors or

534

siRNA interference influences VEEV replication and/or cell death. To date the mechanisms

535

underlying VEEV pathogenesis and subsequent neuronal degeneration have only been partially

536

elucidated. Therefore, determining the role of EGR1 and UPR may play a significant role in the

537

development of a novel therapeutic target resulting in decreased neuronal death and the

538

subsequent neuronal sequela resultant from infection.

539 540

Funding Information

541

This work was funded through a Defense Threat Reduction Agency grant, HDTRA1-13-1-0005,

542

to KK and JJ. The content of the information does not necessarily reflect the position or the

543

policy of the federal government, and no official endorsement should be inferred.

544

24

545

Acknowledgments

546

The following reagents were obtained through the NIH Biodefense and Emerging Infections

547

Research Resources Repository, NIAID, NIH: 1) Polyclonal Anti-Venezuelan Equine

548

Encephalitis Virus, TC83 (Subtype IA/B) Capsid Protein (antiserum, Goat), NR-9403 and 2)

549

Venezuelan equine encephalitis virus Trinidad Donkey (subtype IA/B), NR-332. We thank Dr.

550

Stephen Hann, Vanderbilt University School of Medicine, for the EGR1 -/- cells.

551 552

Figure Legends

553

Fig. 1. VEEV replication kinetics in U87MG cells. U87MG cells were infected with VEEV

554

TrD (MOI 5). A) Viral supernatants were collected at 4, 8, 16, and 24 h.p.i. and viral titers

555

determined by plaque assays. B) Protein lysates were collected at 4, 8, 16, and 24 h.p.i. and

556

western blot analysis performed with anti-capsid and anti-actin antibodies. C) U87MG cells were

557

mock treated or infected as in panel A. Caspase 3/7 activity was analyzed using the Caspase 3/7

558

Glo assay (Promega). * p-value < 0.01 D) RNA was extracted using the miRVana kit and VEEV

559

RNA quantitated by qRT-PCR. Data shown are fold change values (compared to the VEEV 4

560

h.p.i. sample) of normalized gene expression (ΔΔCt method).

561

Fig. 2. RNA-Seq Data Analysis. A) Box-plot of log2 transformed RPKM expression values

562

from each individual biological replicate. B) Principal component analysis of mock vs. VEEV

563

infected U87MG cells at 4, 8, 16 h.p.i. C) Process diagram of comparative analysis approach to

564

identify differentially expressed genes (DEG) at each sampling time point. D) Total number of

565

DEGs identified for each comparison shown in C using two different statistical methods (see

25

566

Materials & Methods). The union of DEGs identified by both methods is shown in orange. V and

567

M are used for brevity for VEEV infected and mock infected cells respectively.

568

Fig. 3. K-means Clustering of Expressed Genes. K-means clustering (20 groups) of genes

569

based Euclidean distance metrics of expression over time for both mock and VEEV infected

570

U87MG cells. Data was normalized based on group means and plotted as relative transformed

571

expression level for each gene. Each cluster has six data points per gene (VEEV 4, 8, 16 h.p.i.

572

followed by mock 4, 8, 16 h.p.i). See Dataset S2 for a complete list of expressed genes within

573

each cluster.

574

Fig. 4. Network of Type I Interferon Response & UPR Related Genes. Differentially

575

expressed genes (large circles), no significant change (small circles), type-I interferon response

576

factors (red circles), regulation of DNA transcription (yellow circles), unfolded protein response

577

genes (blue circles), physical protein-protein interactions (red lines), common pathway (blue

578

lines). This network was seeded with kmeans Clusters 18 and 20 – many ribosomal protein genes

579

were removed.

580

Fig. 5. Next-generation RNA sequencing (RNA-Seq) results and confirmatory qRT-PCR

581

analysis. A) and B) RNA-Seq data for Interferon Responsive Genes (panel A) and UPR &

582

Stress Response Genes (panel B). Data shown in panels A and B are log2 transformed fold

583

change values of normalized gene expression (RPKM). Error bars are ratiometric standard

584

deviation of a two-component log function (VEEV/mock)*. C) and D) RNA was extracted

585

using the miRVana kit and gene expression determined by qRT-PCR using Taqman assays for

586

Interferon Responsive Genes (panel C) or for UPR and Stress Response Genes (panel D). Data

26

587

shown in panels C and D are log2 transformed fold change values of normalized gene expression

588

(ΔΔCt method).

589

Fig. 6. UPR is activated in VEEV infection at later time points. A) U87MG cells were mock,

590

VEEV, or UV-VEEV infected (MOI 5) and protein lysates collected at 4, 8, and 16 h.p.i.

591

Western blot analysis was performed using antibodies against CHOP, ATF4, p-eIF2α and actin.

592

T8=U87MG cells treated with Tunicamycin for 8 hours as a control for UPR induction. Results

593

are representative of three independent experiments. B) U87MG cells were pre-treated with

594

DMSO or PERK inhibitor (PERKi) for two hours prior to infection with VEEV TrD (MOI 5)

595

and replacement of drug media. Western blot analysis was performed using antibodies against p-

596

eIF2α and actin. C) U87MG cells were treated with Tunicamycin for 8 hours (T=8) as a control

597

for UPR induction. D) and E) U87MG cells were mock or VEEV infected (MOI 5). Sixteen

598

hours after infection, cells were fixed and probed with DAPI, anti-VEEV capsid, anti-ATF or

599

anti-CHOP primary antibodies, and Alexa Fluor 488 and 568 labeled secondary antibodies.

600

Slides were imaged on a Nikon Eclipse TE2000-U after immunofluorescent staining. Results are

601

representative of two independent experiments.

602

Fig. 7. EGR1 is upregulated in infected cells and localizes to the nucleus. A) U87MG cells

603

were mock, VEEV, or UV-VEEV infected (MOI 5) and protein lysates collected at 4, 8, and 16

604

h.p.i. Western blot analysis was performed using antibodies against EGR1, capsid, and actin.

605

Results are representative of two independent experiments. B) U87MG cells were mock or

606

VEEV infected (MOI 5). Sixteen hours after infection, cells were fixed and probed with DAPI,

607

anti-VEEV capsid and anti-EGR1 primary antibodies, and Alexa Fluor 488 and 568 labeled

608

secondary antibodies. Slides were imaged on a Nikon Eclipse TE2000-U after

609

immunofluorescent staining. Results are representative of two independent experiments. C) 27

610

U87MG cells were pre-treated with DMSO or PERK inhibitor (PERKi), infected with VEEV

611

TrD (MOI 5), and post-treated with drug media. Protein lysates were collected at 16 h.p.i.

612

Western blot analysis was performed using antibodies against EGR1, capsid, and actin.

613

Fig. 8. Loss of EGR1 reduces VEEV induced apoptosis. A) EGR1 -/- and WT MEFs were

614

infected with VEEV at an MOI of 0.5 or 5. Protein lysates were prepared at 4, 8, 16, and 24 h.p.i.

615

and separated by SDS-PAGE and western blot analysis was performed using antibodies against

616

cleaved caspase 3 and actin. Mock infected cells are included as a control. Results are

617

representative of two independent experiments. B) EGR1 -/- and WT MEFs were infected with

618

VEEV at an MOI of 0.05, 0.5 or 5. Twenty-four h.p.i. cells were analyzed for viability using the

619

Cell Titer Glo assay (Promega). Mock infected cells were included as a control and set to 100%

620

viability. *p-value