The IFN Response in Bats Displays Distinctive IFN-Stimulated Gene Expression Kinetics with Atypical RNASEL Induction

This information is current as Pamela C. De La Cruz-Rivera, Mohammed Kanchwala, of September 25, 2021. Hanquan Liang, Ashwani Kumar, Lin-Fa Wang, Chao Xing and John W. Schoggins J Immunol published online 27 November 2017 http://www.jimmunol.org/content/early/2017/11/23/jimmun

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The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2017 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. Published November 27, 2017, doi:10.4049/jimmunol.1701214 The Journal of Immunology

The IFN Response in Bats Displays Distinctive IFN-Stimulated Gene Expression Kinetics with Atypical RNASEL Induction

Pamela C. De La Cruz-Rivera,* Mohammed Kanchwala,† Hanquan Liang,† Ashwani Kumar,† Lin-Fa Wang,‡ Chao Xing,†,x,{ and John W. Schoggins*

Bats host a large number of zoonotic , including several viruses that are highly pathogenic to other mammals. The mechanisms underlying this rich viral diversity are unknown, but they may be linked to unique immunological features that allow bats to act as asymptomatic viral reservoirs. Vertebrates respond to viral infection by inducing IFNs, which trigger antiviral defenses through IFN- stimulated gene (ISG) expression. Although the IFN system of several bats is characterized at the genomic level, less is known about bat

IFN-mediated transcriptional responses. In this article, we show that IFN signaling in bat cells from the black flying fox (Pteropus Downloaded from alecto) consists of conserved and unique ISG expression profiles. In IFN-stimulated cells, bat ISGs comprise two unique temporal subclusters with similar early induction kinetics but distinct late-phase declines. In contrast, human ISGs lack this decline phase and remained elevated for longer periods. Notably, in unstimulated cells, bat ISGs were expressed more highly than their human counterparts. We also found that the antiviral effector 2-5A–dependent endoribonuclease, which is not an ISG in humans, is highly IFN inducible in black flying fox cells and contributes to cell-intrinsic control of viral infection. These studies reveal distinctive innate

immune features that may underlie a unique –host relationship in bats. The Journal of Immunology, 2018, 200: 000–000. http://www.jimmunol.org/

ats are recognized as important viral reservoirs, and viruses than all other mammalian orders tested, with the highest viral many highly pathogenic viruses, including richness found in flaviviruses, bunyaviruses, and rhabdoviruses (10). B (1), (2), (3), severe acute Although the mechanisms underlying disease resistance are not known, respiratory syndrome–like coronaviruses (4), and Ebola virus (5), it has been speculated that bats possess unique immune features that have been detected in various bat species. In experimental infec- confer innate antiviral protection (9, 11). In vertebrates, one of the first tion studies, certain bats can be productively infected with path- lines of defense against viral pathogens is the IFN response. Upon viral

ogenic viruses without obvious disease symptoms (6–9). A recent infection, pattern-recognition receptors sense viral components and by guest on September 25, 2021 study identified bats as hosts for a greater proportion of zoonotic initiate a signaling cascade that results in the secretion of IFNs. These IFNs bind their cognate receptors to activate the JAK–STAT signaling *Department of Microbiology, University of Texas Southwestern Medical Center, pathway, leading to the transcriptional induction of hundreds of IFN- Dallas, TX 75390; †McDermott Center Bioinformatics Core, University of Texas stimulated genes (ISGs), many of which have antiviral activity (12). ‡ Southwestern Medical Center, Dallas, TX 75390; Programme in Emerging Infec- The black flying fox (Pteropus alecto) is an asymptomatic natural tious Diseases, Duke-National University of Singapore Medical School, Singapore 169857, Singapore; xDepartment of Bioinformatics, University of Texas Southwest- reservoir for the highly lethal Hendra virus (2, 13, 14). Studies of the ern Medical Center, Dallas, TX 75390; and {Department of Clinical Sciences, Uni- recently sequenced black flying fox genome revealed that genes for versity of Texas Southwestern Medical Center, Dallas, TX 75390 key components of antiviral immunity are conserved in bats, such as ORCIDs: 0000-0002-8806-8688 (P.C.D.L.C.-R.); 0000-0001-6035-2970 (M.K.); pathogen sensors, including TLRs (15), RIG-I–like helicases, and 0000-0003-2752-0535 (L.-F.W.); 0000-0002-1838-0502 (C.X.); 0000-0002-7944- 6800 (J.W.S.). NOD-like receptors, IFNs and their receptors, and ISGs (11, 16). Received for publication August 24, 2017. Accepted for publication October 25, Previous efforts to study bat–virus interactions have mainly focused 2017. on the host response to viral infection (17–20), and global tran- This work was supported in part by National Institutes of Health Grant AI117922 (to J.W.S.), scriptional responses to type I IFN remain uncharacterized. the University of Texas Southwestern Endowed Scholars Program (to J.W.S.), the Univer- Because the black flying fox harbors pathogenic human viruses and sity of Texas Southwestern High Impact/High Risk Grant Program (to J.W.S.), the William F. and Grace H. Kirkpatrick Award (to P.C.D.L.C.-R.), and National Research Foundation- has an annotated genome, we sought to characterize the IFN-induced Competitive Research Programme Grant NRF2012NRF-CRP001–056 (to L.-F.W.). C.X. was transcriptional response in this species. Gene-expression analyses partially supported by National Institutes of Health Grant UL1TR001105. revealed that bat cells induce a pool of common ISGs. However, they The sequences presented in this article have been submitted to the Gene Expression also induced a small number of apparent novel ISGs, including 2-5A– Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE102296. dependent endoribonuclease (RNASEL). Kinetic analyses revealed Address correspondence and reprint requests to John W. Schoggins, University of that bat ISGs can be categorized into distinct groups, depending on Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390-9048. E-mail address: [email protected] their temporal gene-expression patterns. Additionally, maintenance The online version of this article contains supplemental material. of ISG expression over time differed between bat and human cells, Abbreviations used in this article: CRISPR, clustered regularly interspaced short suggesting distinct mechanisms of gene regulation. palindromic repeats; FC, fold change; FDR, false discovery rate; ISG, IFN- stimulated gene; KO, knockout; NEAA, nonessential amino acids; OAS, oligoade- nylate synthase; poly(I:C), polyinosinic-polycytidylic acid; RNASEL, 2-5A– Materials and Methods dependent endoribonuclease; RNA-Seq, RNA sequencing; SC, subcluster; VSV, ve- Cell lines sicular stomatitis virus; WT, wild-type; YFV, virus. Black flying fox–derived PaBr (brain), PaLu (lung), and PaKi (kidney) im- Copyright Ó 2017 by The American Association of Immunologists, Inc. 0022-1767/17/$35.00 mortalized cell lines (21) were grown at 37˚C and 5% CO2 and passaged in

www.jimmunol.org/cgi/doi/10.4049/jimmunol.1701214 2 IFN RESPONSE OF A BAT VIRAL RESERVOIR

DMEM/F-12 (catalog number 11320033; Life Technologies) supplemented Data were deposited in the Gene Expression Omnibus database (https:// with 10% FBS. Human A549 lung adenocarcinoma and HEK293 cells were www.ncbi.nlm.nih.gov/geo/) under accession number GSE102296. grown at 37˚C and 5% CO2 and passaged in DMEM (catalog number 11995065) supplemented with 10% FBS and 13 nonessential amino acids Heat maps (NEAA) (catalog number 11140076; both from Life Technologies). Heat maps were constructed using Morpheus (https://software.broadinstitute. Viruses org/morpheus/). Yellow fever virus (YFV)-Venus (derived from YF17D-5C25Venus2AUbi) NanoString analysis stocks were generated by electroporation of in vitro–transcribed RNA into 2 2 A customized panel targeting black flying fox and human genes (Schog- STAT1 / fibroblasts, as previously described (12). Vesicular stomatitis virus gins_1_C4066; NanoString Technologies, Seattle, WA) was used to mea- (VSV) encoding GFP, Indiana serotype (provided by J. Rose) was generated sure the expression of 50 genes per species. Probes were designed to target by passage in BHK-J cells. For both viruses, virus-containing supernatant 2 as many known transcript variants as possible (see Supplemental Table II was centrifuged to remove cellular debris and stored at 80˚C until use. for accession numbers of targeted genes). Total RNA was isolated from Viral infection IFN-treated samples, as described above. One hundred nanograms of total RNA in 5 ml was used as input in each reaction for NanoString assay. Cells were seeded into 24-well plates at a density of 1 3 105 cells per well. RNA samples were processed according to the NanoString nCounter Viral stocks were diluted into DMEM/F-12 media supplemented with 1% XT CodeSet Gene Expression Assay manufacturer’s protocol. Follow- FBS to make infection media. Media were aspirated and replaced with 200 ing hybridization, transcripts were quantitated using an nCounter Digital ml of infection media. Infections were performed at 37˚C for 1 h and then Analyzer. 800 ml of DMEM/F-12 media supplemented with 10% FBS was added back to each well. After approximately one viral life cycle (5 h for VSV, Quantitative real-time PCR 25 h for YFV), cells were harvested for flow cytometry. Total RNA was prepared as described above. Reactions were prepared with a Downloaded from Flow cytometry QuantiFast SYBR Green RT-PCR kit (catalog number 204154; QIAGEN), as recommended by the manufacturer, using 50 ng of total RNA per reaction. Cells were detached using Accumax, fixed in 1% PFA for 10 min at room Samples were run on an Applied Biosystems 7500 Fast Real-Time PCR temperature, and pelleted by centrifugation at 800 3 g. Fixed cell pellets System using 7500 Software v2.0.6. The program consisted of a reverse- were resuspended in 200 ml of FACS buffer (13 PBS supplemented with transcription step at 50˚C for 10 min, a polymerase-activation step at 95˚C 3% FBS). Samples were run on a Stratedigm S1000 instrument using for 5 min, and cycling steps alternating between 95˚C for 10 s and 60˚C for

CellCapTure software and gated based on GFP fluorescence. Data analysis 30 s (40 cycles). Melt curves were generated for all experiments by ramping http://www.jimmunol.org/ was done using FlowJo software (v9.7.6). up the temperature 1˚C/min from 60 to 95˚C. Only a single melt curve peak was observed for all primer sets. Primers used to amplify RNASEL IFN treatment and RNA isolation (XM_006907762.2) are 59-CCACCCTGGGGAAAATGTGA-39 and 59- 3 5 GGAGGATCCTGCTTGCTTGT-39. Primers used to amplify reference gene Cells were seeded into six-well plates at 3 10 cells per well. The RPS11 (XM_006905029) are 59-ATCCGCCGAGACTATCTCCA-39 and 59- following day, cells were treated with 2 ml of DMEM/F-12 media sup- GGACATCTCTGAAGCAGGGT-39. Relative expression in IFN-treated plemented with 10% FBS and 50 U/ml Universal Type I IFN-a (catalog samples versus untreated samples was calculated using the comparative CT number 11200; PBL Assay Science). The cells were harvested by aspi- method using RPS11 as the reference gene. rating the media, washing twice with 2 ml of sterile 13 PBS, and lysing with 350 ml of Buffer RLT from the RNeasy Mini Kit (QIAGEN). The cell DNA constructs and plasmid propagation 2 lysate was stored at 80˚C until RNA isolation was completed using an by guest on September 25, 2021 RNeasy kit, following the manufacturer’s protocol. lentiCRISPR v2 was a gift from F. Zhang (plasmid number 52961; Addgene). RNASEL targeting guides were generated by cloning annealed RNA sequencing complementary 20-bp oligonucleotides with Esp3I-compatible overhangs (59-caccgAGACCCACACCCTCCAGCAG-39 and 59-aaacCTGCTGGAG- Total RNA samples for each time point were prepared in three independent GGTGTGGGTCTc-39) targeting the black flying fox RNASEL gene into experiments, as described above. An Agilent 2100 Bioanalyzer was used to . the lentiCRISPR v2 backbone, as described (29). Clustered regularly determine RNA quality; all samples had an RNA Integrity Number 9. A interspaced short palindromic repeats (CRISPR) guide oligonucleotides Qubit fluorometer was used to determine RNA concentration. Libraries were were designed using CRISPRdirect (30). Proper assembly was confirmed prepared using the TruSeq Stranded mRNA LT Sample Prep Kit (Illumina), using Sanger sequencing. following the manufacturer’s instructions, summarized as follows. Four micrograms of DNase-treated total RNA was used as input. Poly-A RNA was Lentiviral pseudoparticles purified and fragmented before strand-specific cDNA synthesis. cDNA was then A-tailed and ligated with indexed adapters. Samples were PCR amplified Lentiviral pseudoparticles were made as described (31), with some mod- using the following parameters: 5 ml of PCR Primer Cocktail was added to ifications. Briefly, 2 3 106 HEK293T cells were seeded on a poly-lysine– 20 ml of adapter ligated library and then 25 ml of PCR Master Mix was added coated 10-cm plate. The following day, media were changed to 7.5 ml of to each sample. Samples were mixed by pipette, and the plate was sealed and DMEM with 3% FBS and 13 NEAA. Cells were cotransfected with 5 mg cycled on a thermal cycler with a 100˚C heated lid under the following of lentiCRIPSR v2, 2.5 mg of pCMV-VSVg, and 3.5 mg of pGag-pol. Four conditions: initial denaturing at 98˚C for 30 s; 15 cycles of 98˚C for 10 s, hours posttransfection, media were changed to 7.5 ml of fresh DMEM with 60˚C for 30 s, and 72˚C for 30 s; and final extension at 72˚C for 5 min, hold 3% FBS and 13 NEAA. Supernatant was collected 48 h posttransfection, at 4˚C. Samples were then purified with AMPure XP beads and revalidated passed through a 0.45-mm filter to remove debris, and stored at 280˚C on an Agilent 2100 Bioanalyzer and Qubit. Normalized and pooled samples until use. were run on an Illumina HiSEquation 2500 using SBS v3 reagents. Paired-end 100-bp read length FASTQ files were checked for quality RNASEL-knockout bulk PaKi cell lines using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) A total of 3 3 105 PaKi cells was seeded on six-well plates. The following and FastQ Screen (http://www.bioinformatics.babraham.ac.uk/projects/ day, media were changed to DMEM/F-12 supplemented with 3% FBS, fastq_screen/) and were quality trimmed using fastq-mcf (https://github. 4 mg/ml polybrene, and 20 mM HEPES. lentiCRISPR v2 lentiviral pseu- com/ExpressionAnalysis/ea-utils/blob/wiki/FastqMcf.md). Trimmed fastq doparticles were added, and cells were spinoculated at 800 3 g for 45 min files were mapped to black flying fox assembly ASM32557v1 (https://ftp. at 37˚C. Media were changed to DMEM/F-12 with 10% FBS immediately ncbi.nih.gov/genomes/Pteropus_alecto) using TopHat (22). Duplicates following spinoculation. Forty-eight hours after transduction, cells were were marked using Picard tools (https://broadinstitute.github.io/picard/). pooled into a 10-cm dish and selected in DMEM/F-12 with 10% FBS and Reference annotation–based transcript assembly was done using cufflinks 5 mg/ml puromycin. (23), and read counts were generated using featureCounts (24). Pairwise differential expression analysis was performed using edgeR (25), and Genomic characterization of PaKi RNASEL-knockout bulk cell longitudinal analysis was performed using time course (26) after data lines transformation by voom (27). Differentially expressed unannotated genes were manually annotated using a nucleotide Basic Local Alignment Search Genomic DNA was isolated from wild-type (WT) or RNASEL-knockout Tool (28) search for homologous genes. (KO) bulk populations using a DNeasy Blood and Tissue Kit (QIAGEN). The Journal of Immunology 3

The region flanking the CRISPR-targeted sequence was amplified via PCR met a minimal read count threshold (mean log2CPM $ 0). Dif- using primers 59-ATGGAGACCAAGAGCCACAACA-39 and 59- ferential gene expression analysis revealed that IFN induced 93 CGTCCTCGTCCTGGAAATTGA-39. PCR products were gel purified genes at 4 h, 104 genes at 8 h, 103 genes at 12 h, and 103 genes at using a QIAquick Gel Extraction Kit (QIAGEN) and subsequently used in TOPO cloning reactions with a TOPO TA Cloning Kit (Thermo Fisher). 24 h (fold change [FC] $1.5, false discovery rate [FDR] # 0.05) Several colonies were selected for each cell background, and colony PCR (Fig. 2B). There were no downregulated genes at 4 h, 2 down- was used to amplify the CRISPR-targeted region. Samples were analyzed regulated genes at 8 h, 105 downregulated genes at 12 h, and 279 using Sanger sequencing. downregulated genes at 24 h. However, statistical significance of rRNA degradation assay upregulated genes was more robust than the statistical significance of the downregulated genes. Overall, IFN treatment of PaKi cells Atotalof33 105 PaKi cells was seeded on six-well plates. The following day, universal IFN (100 U/ml) was added to the media. After 24 h, cells were produced a positive gene-induction signature. transfected with 100 ng/ml polyinosinic-polycytidylic acid [poly(I:C)] in Next, heat maps were generated to assess individual gene in- Opti-MEM using Lipofectamine 3000. After 4 h, RNA was harvested using duction, using FDR # 0.05 and FC $ 4 for at least two time points an RNeasy Mini Kit (QIAGEN), and RNA integrity was measured on a Total (Fig. 2C). Many genes in this list have known roles in innate RNA Nano Chip using an Agilent 2100 Bioanalyzer. immunity, including well-known ISGs (IFIT1, MX1, OAS2, RSAD2/viperin, USP18), members of the JAK–STAT signaling Results cascade (STAT1, STAT2, IRF9), pattern recognition receptors Black flying fox–derived cell lines respond to exogenous type I (DDX58/RIG-I, IFIH1/MDA5, ZBP1), and transcription factors IFN (ETV7, IRF7, SP110) (35). Notably, we detected induction of

Previous studies have shown that exogenous IFN-a and IFN-g GVIN1 (IFN-induced very large GTPase), which is predicted to Downloaded from treatment of cells from the black flying fox can suppress Pter- encode a protein in bats but is annotated as a pseudogene in hu- opine orthoreovirus Pulau virus (32) and Hendra virus (33), re- mans. This list also included transcripts predicted to encode an spectively. We treated immortalized black flying fox kidney- endogenous (ERVK25) and several transcripts predicted derived (PaKi) cells for 24 h with universal IFN-a,whichis to be long noncoding RNAs. designed for activity across multiple species. We then infected Differentially expressed genes were cross-referenced to the the cells with two model GFP-expressing reporter viruses: a INTERFEROME v2.0 database (36) to determine whether they http://www.jimmunol.org/ negative-sense RNA rhabdovirus (VSV) and a positive-sense had previously been reported as IFN-induced genes. At the 4- and RNA flavivirus (YFV). We observed a dose-dependent inhibi- 8-h time points, .80% of the genes in our data set overlapped tion of both viruses with IFN treatment (Fig. 1A, 1B). VSV in- with INTERFEROME v2.0. Because the INTEFEROME database fection was maximally inhibited by only 50%, whereas YFV consists predominantly of human and mouse data sets, this result infection was suppressed completely at the highest IFN dose. In suggests that antiviral responses in bat cells include a conserved addition, we confirmed IFN-mediated dose-dependent and time- repertoire of IFN-inducible genes commonly found in other dependent induction of the canonical ISG MX1 (Fig. 1C, 1D) mammalian species. (34). These data confirm that PaKi cells are capable of mounting

Differential temporal regulation of black flying fox ISGs by guest on September 25, 2021 an antiviral response and highlight virus-specific sensitivities to IFN in this cellular background. We next used a clustering algorithm to group genes in the RNA-Seq data set based on induction kinetics, without imposing FC or FDR IFN induces a classical ISG signature in PaKi cells cutoffs (26). This analysis revealed that genes were organized into We next used total RNA sequencing (RNA-Seq) to profile the eight subclusters (SC) based on changes in expression levels global transcriptional response of PaKi cells treated with IFN-a throughout the IFN time course (Fig. 3A, Supplemental Table I). over time (Fig. 2A). Transcriptome assembly analysis across all Genes in SC1 and SC2 increased in expression after 8 h. Genes in experimental conditions returned ∼30,000 genes, of which 11,559 SC3 and SC4 were induced by 4 h and peaked at 4–8 h, with genes

FIGURE 1. Bat-derived cells respond to type I IFN. (A) PaKi cells were treated with increasing doses of IFN-a for 24 h and then infected with VSV encoding GFP at an multiplicity of infection of 1 for 5 h. The percentage of infected cells was quantified via GFP fluorescence using flow cytometry and normalized to a mock-treated control. Data are mean 6 SD for three independent experiments. (B) Same as (A), using YFV17D-Venus for 25 h. (C) PaKi cells were treated with increasing doses of IFN-a, and RNA was har- vested at 8 h. MX1 mRNA levels were quantified using quantitative RT-PCR and normalized to reference gene RPS11. Data are mean 6 SD for three independent experiments. (D) PaKi cells were treated with IFN-a (50 U/ml), and RNA was harvested at several time points. MX1 mRNA levels were measured using qRT- PCR and normalized to reference gene RSP11. Data are mean 6 SD for three independent experiments. 4 IFN RESPONSE OF A BAT VIRAL RESERVOIR Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021

FIGURE 2. PaKi transcriptome response to IFN. (A) Schematic diagram of the experiment. (B) Volcano plots for all time points. Differentially expressed genes are quantified in the upper left corner. Gray bars represent log2FC $ 1.5 (vertical) or FDR # 0.05 (horizontal). Data are a result of three independent experiments. (C) Heat map including genes that meet the cutoffs mean log2CPM $ 0, log2FC $ 2, and FDR # 0.05 for at least two time points. XLOC genes represent unannotated genes. in SC3 returning close to baseline by 12 h and SC4 genes direct detection of mRNA without nucleic acid amplification. A remaining elevated for $24 h. These two SCs were highly customized nCounter CodeSet was designed containing 50 genes enriched for genes found in the INTERFEROME v2.0 database. from several temporal SCs, with a focus on the ISG-rich SC3 and Genes in SC5 increased slightly and peaked at 8 h, returning to SC4 (Supplemental Fig. 1, Supplemental Table II). Temporal baseline levels by 12 h. Levels of SC6 genes either increased or expression profiles using NanoString were generally similar to the decreased by 4 h, followed by decreased levels at 12–24 h. SC7 RNA-Seq data (Fig. 3B). SC1 and SC2 contained genes with in- gene expression levels decreased sharply by 8 h, followed by a creased expression levels at the later time points. SC3 and SC4 partial recovery by 12 h and a further decrease in expression by had genes with peak expression levels at 8 h, with genes in SC4 24 h. Finally, genes in SC8 exhibited a sustained decrease in ex- exhibiting expression levels that remained elevated at 24 h. Genes pression levels starting at 8–12 h. These data demonstrate that IFN in SC8 decreased in expression over time, with the lowest ex- induces distinct subsets of genes that are characterized by differ- pression levels observed at 24 h. ing temporal expression patterns. Human versus bat temporal ISG regulation Orthogonal validation of RNA-Seq data We next used NanoString to compare temporal regulation of ISGs To validate our RNA-Seq results, we used NanoString nCounter between bat and human cell lines. We first compared gene ex- technology, which uses colorimetrically barcoded DNA probes for pression between PaKi and HEK293 cells, because both cell lines The Journal of Immunology 5 Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021

FIGURE 3. Temporal clustering analysis. (A) Temporal SCs. The number of genes in each SC is indicated. Blue lines represent mean log2FC over time for a single gene. Black lines represent median of all genes in the SC. (B) RNA-Seq validation using NanoString. RNA was isolated from PaKi cells, and gene expression over time was compared between RNA-Seq data (left panels) and NanoString data (right panels). Data presented are the mean of three independent experiments. HK, housekeeping genes.

are kidney derived, but HEK293 cells responded poorly to type I To identify potential differences in induction kinetics, we cal- IFN (Supplemental Fig. 1). After screening for robust IFN re- culated the percentage of genes that were induced to $50% of sponses in other human cell lines, we chose human A549 cells for maximum expression for each time point. More than 80% of PaKi comparative studies (Fig. 4A). A striking difference was observed genes in both SCs were induced by 4 h (Fig. 4D). A549 genes in between expression profiles of SC1 and SC2 when comparing SC3 behaved similarly, although a small subset of genes was in- PaKi and A549 cells. Of the five selected genes in SC1 and SC2, duced by 2 h. However, ∼40% of A549 genes in SC4 were in- none was significantly upregulated in IFN-treated A549 cells. duced by 2 h, indicating faster induction of this subset of genes. Similarly, the decreased expression levels observed for bat genes Genes in this list include HERC5, IFI6, IFIH1, MX1, NLRC5, in SC8 were not observed in A549 cells. OAS2, OASL, and PARP12. Notably, PaKi cells express higher For further analysis, we chose to focus on previously reported baseline levels of ISGs, such that by full induction at 8 h, they ISGs, particularly those found in SC3 and SC4. When comparing express similar or greater mRNA counts compared with A549 changes in gene expression in SC3, the median FC of all genes in cells, despite faster induction in A549 cells (Fig. 4D, 4G, SC3 followed similar trends between bat and human cell lines Supplemental Fig. 1). (Fig. 4B). However, in SC4, genes from A549 cells exhibited Next, we calculated the percentage of genes that had recovered to higher fold induction and remained elevated at later time points ,50% of maximum expression for each time point. In both SCs, compared with PaKi cells. recovery occurred earlier in PaKi cells, with most genes recov- We next determined the time point at which we observed ering by 16 h (Fig. 4E). In contrast, the expression levels for most maximum gene induction. For SC3 and SC4, ∼80% of IFN- A549 genes remained elevated throughout the time course. To- induced PaKi genes peaked at 8 h (Fig. 4C). However, A549 gether, these data suggest that the regulatory mechanisms gov- genes had a bimodal pattern within both SCs, with most genes erning IFN-mediated gene induction and maintenance of gene peaking at 4–8 h and a smaller subset peaking at $12 h. expression differ in each cell type, particularly with genes in SC4. 6 IFN RESPONSE OF A BAT VIRAL RESERVOIR Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021

FIGURE 4. ISG expression over time in bat and human cells. (A) NanoString gene expression for PaKi cells (left panel) and A549 cells (right panel). PaKi cell data are the same shown in Fig. 3B (right panel), with additional time points. Data presented are the mean of three independent experiments. (B) Expression over time for

PaKi and A549 genes in SC3 and SC4. Each line represents the median log2FC for all genes in a SC. (C)TimeatpeakinductionforPaKiandA549genesinSC3and SC4. (D) Percentage of genes induced to levels $50% of maximum induction at specified time points in SC3 and SC4. (E) Percentage of genes that recovered to levels #50% of maximum induction at specified time points in SC3 and SC4. (F) Baseline mRNA counts for PaKi and A549 genes in SC3 and SC4. Data are mean + SD for three independent experiments. Darker symbols represent mean counts for each SC. (G) Maximum mRNA counts for PaKi and A549 genes in SC3 and SC4. Data are mean + SD for three independent experiments. Darker symbols represent mean counts for each SC. *p , 0.05, student t test. HK, housekeeping genes.

It was recently reported that black flying fox tissues have result of constitutive expression of certain ISGs (32). Indeed, we constitutive and ubiquitous expression of IFN-a, suggesting that observed higher overall ISG mRNA levels in unstimulated PaKi cells from this species may be primed to inhibit viral infection as a cells, particularly for SC4 genes (Fig. 4F). In addition, we found The Journal of Immunology 7 Downloaded from

FIGURE 5. RNASEL is induced by IFN in P. alecto.(A) PaKi cells were treated with increasing doses of IFN-a, and RNA was harvested at 8 h. RNASEL mRNA levels were measured using quantitative RT-PCR and normalized to untreated control using reference gene RNASEL. Data are mean 6 SD for two independent experiments. (B) RNASEL induction in P. alecto brain, lung, and kidney cells. Cells were treated with IFN-a (100 U/ml), and RNA was harvested at 8 h. RNASEL levels were quantified using qRT-PCR and normalized to untreated control using RPS11 as housekeeping control. Data are mean 6 SD for three independent experiments. (C) PaKi cells were treated with IFN-a (100 U/ml) or mock treated for 24 h, followed by transfection with poly(I:

C) (100 ng/ml) or mock transfection for 4 h, and RNA integrity was monitored using a bionalyzer. One hundred nanograms of total RNA was run per lane. http://www.jimmunol.org/ Data are representative of two independent experiments. (D)WTorRNASEL-KO bulk PaKi cells were infected with YFV17D-Venus for 24 h, and viral infection was quantified via GFP fluorescence using flow cytometry. Data are mean 6 SD for three independent experiments. (E)WTorRNASEL-KO bulk PaKi cells were treated with increasing doses of IFN-a for 24 h and then infected with YFV-17D Venus at a multiplicity of infection of 0.5 for 24 h. Viral infection was quantified using flow cytometry. Data are mean 6 SD for three independent experiments. *p , 0.05, student t test. that more than half of SC4 PaKi ISGs were induced to higher rRNA degradation when cells were treated with poly(I:C) but not maximum counts than corresponding A549 ISGs (Fig. 4G). with IFN alone. Treating cells with IFN for 24 h to induce RNASEL expression before poly(I:C) transfection resulted in in- Bat cells express multiple noncanonical ISGs, including an creased RNA degradation and accumulation of smaller products, by guest on September 25, 2021 active RNASEL suggesting increased RNASEL activity. The RNA degradation Our gene-expression profiling revealed several genes not previously observed in WT cells was reduced in the bulk KO cells. Together, reported to be ISGs. These included EMC2, FILIP1, IL17RC, these data indicate that RNASEL is a functional RNase that, un- OTOGL, SLC10A2,andSLC24A1 (Fig. 2A). It is unclear whether like its human ortholog, is IFN inducible. the induction of these genes is cell type or species specific. In ad- To test whether the presence of RNASEL is important in the dition, we observed IFN-mediated induction of RNASEL,which context of viral infection, we infected WT and bulk KO cells with encodes a 29-59-oligoadenylate synthetase–dependent RNase. This YFV17D-Venus and quantified infectivity after one viral life cycle protein exerts its antiviral effect by degrading viral RNA in response (Fig. 5D). RNASEL-KO cells were more permissive to infection at to 29-59-linked oligoadenylates, which are generated by the IFN- all doses used, suggesting that RNASEL is important for suppres- inducible oligoadenylate synthase (OAS) family of enzymes upon sion of initial viral infection. To test whether RNASEL induction stimulation by dsRNA in the cytosol (37). In human cells, RNASEL played a role in the protective effect of the IFN response, we treated is a constitutively expressed latent enzyme and is not IFN inducible PaKi WT and KO bulk cells with increasing doses of IFN-a for 24 (Fig. 4A, Supplemental Fig. 1) (38). Interestingly, we observed a h, followed by infection with YFV17D-Venus (Fig. 5E). Consistent dose-dependent induction of RNASEL in IFN-treated PaKi cells with previous results, KO bulk cells were more permissive to in- (Fig. 5A). Of note, similar mRNA expression levels of RNASEL fection than WT cells. In addition, KO bulk cells were resistant to were observed in unstimulated human and bat cell lines (Fig. 4F, the protective activity of IFN. IFN-a (100 U/ml) pretreatment Supplemental Fig. 1). In addition, we observed IFN-mediated resulted in 80% reduction of infection in WT cells but only 50% RNASEL induction in brain-derived (PaBr) and lung-derived reduction in bulk KO cells. Together, these data suggest RNASEL (PaLu) black flying fox cell lines (Fig. 5B), suggesting that IFN- plays a significant role in the inhibition of viral infection, particu- mediated induction of RNASEL in the black flying fox is not cell larly in the context of the IFN response. type specific. Next, we constructed RNASEL-deficient PaKi cells using Discussion CRISPR (39). Because of the lack of a bat-specific RNASEL Ab, This study aimed to identify IFN-stimulated transcripts in a cell line we were not able to monitor RNASEL protein levels. However, from the black flying fox. Transcriptional analysis revealed .100 we detected modifications to the RNASEL gene in the PaKi-KO genes induced in response to IFN-a. Most of these genes have bulk population compared with the parental WT population been previously described as ISGs, suggesting strong evolutionary (Supplemental Table III). conservation of the ISG pool, as would be predicted by previous To test whether RNASEL protein was functional, we activated genomic studies of immune genes in the black flying fox (11, 16). RNASEL with poly(I:C) transfection after IFN or mock treatment We have provided a framework of black flying fox ISGs, or- and monitored RNA integrity (Fig. 5C) (40, 41). We observed ganized by early, mid, and late responses to type I IFN. Temporal 8 IFN RESPONSE OF A BAT VIRAL RESERVOIR expression profiling delineated two ISG pools based on unique species. Second, in using universal IFN in place of black flying fox temporal induction profiles. Although both SCs are characterized IFN, we made the assumption that the IFN response would be by similar peak mRNA levels and a subsequent decline by 12–24 h, comparable between both types of IFN. Although we observed a SC4 contained some genes that remained elevated. These genes transcriptional profile that is expected for IFN-treated cells, we may offer residual antiviral protection, even when IFN signaling cannot rule out possible differences in downstream signaling ki- has returned to basal levels. In addition, many genes in SC4 have netics or magnitude of the response between universal and bat- higher baseline and higher maximal induction levels in bat PaKi derived IFN. Nonetheless, a recent transcriptomic study done in cells compared with human A549 cells, which could result in PaKi cells using black flying fox IFN showed ISG profiles that were species-specific differences in susceptibility to viral infection. It comparable to those observed in our study (42). remains unclear why only particular ISGs are differentially Overall, this work lays the foundation for future investigation expressed in this context, but it would be interesting to determine into the potential unique features of the bat IFN response. Although whether there were unique features or functions of these ISGs that this study focused on ISG induction, we know little about whether could explain their differing expression patterns. Compared with bat ISG-encoded effectors possess unique antiviral properties. human A549 cells, bat PaKi cells have a more rapid decline in ISG Uncovering mechanisms of bat ISGs will provide insight into the levels, suggesting tightly regulated expression kinetics. The reason innate immune responses of an important viral reservoir and may for this strict transcriptional regulation remains unclear, but such a inform research and development of antiviral therapies. mechanism may exist to prevent excessive inflammation in a highly metabolically active host (11). Acknowledgments We also identified several previously unrecognized or non- Downloaded from We thank Jeanine Baisch and Cynthia Silverman (Genomics Core at Baylor Re- canonical ISGs, including RNASEL. Notably, while our manuscript search Institute) for assistance with NanoString samples and the McDermott Cen- was under review, another transcriptome study of IFN-treated ter Sequencing Core and Bioinformatics Core for sequencing and analysis. black flying fox cells also uncovered RNASEL as an ISG (42). In addition, a transcriptomic study done in Jamaican fruit bats (Artibeus jamaicensis) reported that RNASEL levels are increased Disclosures The authors have no financial conflicts of interest. in the spleen following experimental infection with Tacaribe virus, http://www.jimmunol.org/ indicating that RNASEL induction can be observed in certain bat species in vivo (43). The OAS/RNASEL pathway, through which References OAS proteins use viral dsRNA to create short oligonucleotides 1. Chua, K. B., C. L. Koh, P. S. Hooi, K. F. Wee, J. H. Khong, B. H. Chua, that act as second messengers to activate the constitutively Y. P. Chan, M. E. Lim, and S. K. Lam. 2002. Isolation of Nipah virus from expressed latent enzyme RNASEL, is used to clear viral genetic Malaysian Island flying-foxes. Microbes Infect. 4: 145–151. 2. Halpin, K., P. L. Young, H. E. Field, and J. S. Mackenzie. 2000. Isolation of material from the cell. 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t t t t 10 t HEK-293

n n 15 n 60 n n u u u u u 20 PaKi 40 o o o o o

C C 10 C 40 C C 5 10 20 5 20 0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h)

5 CIITA (SC3) 40 CXCL10 (SC3) 100 DHX58 (SC3) 200 ETV7 (SC3) 100 IL17RC (SC3) 4 30 80 150 80 s s s s s t t t t t

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C 40 10 50 1 20 20 0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h) 600 IRF7 (SC3) 500 IRF9 (SC3) 25 ISG20 (SC3) 100 NLRC5 (SC3) 800 PARP12 (SC3)

400 20 80 600 s s s s s

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C C 200 C 10 C 40 C 200 100 5 20 200 0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h)

1500 RNF213 (SC3) 80 RSAD2 (SC3) 80 RTP4 (SC3) 300 TAP1 (SC3) 500 TDRD7 (SC3)

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C C C C C 200 500 100 20 20 100 0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h)

150 TRANK1 (SC3) 250 UBA7 (SC3) 800 UBE2L6 (SC3) 300 ZBP1 (SC3) 10 APOBEC3G (SC4)

200 600 8 s s s s s

t 100 t t t 200 t

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u u u 400 u u o o o o o

C C 100 C C C 4 50 100 50 200 2 0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h)

1500 BST2 (SC4) 25 CD274 (SC4) 300 CMPK2 (SC4) 500 DTX3L (SC4) 250 GBP6 (SC4) 20 400 200 s s s s s t t 1000 t 200 t t n n 15 n n 300 n 150 u u u u u o o o o o C C 10 C C 200 C 100 500 100 5 100 50 0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h)

400 HERC5 (SC4) 2000 IFI6 (SC4) 1000 IFIH1 (SC4) 500 IFIT2 (SC4) 1500 IFIT3 (SC4)

300 1500 800 400 s s s s s

t t t t t 1000

n n n 600 n 300 n

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C C C 400 C 200 C 500 100 500 200 100 0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h)

6000 MX1 (SC4) 250 MX2 (SC4) 400 OAS2 (SC4) 250 OASL (SC4) 1000 PARP9 (SC4)

200 300 200 800 s s s s s t t t 4000 t t n n n 150 n 150 n 600 u u u u 200 u o o o o o C C C 100 C 100 C 400 2000 50 100 50 200 0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h)

8 PARP15 (SC4) 150 RNASEL (SC4) 250 SP100 (SC4) 400 XAF1 (SC4) 250 PPM1F (SC8)

6 200 300 200 s s s s s

t t 100 t t t

n n n 150 n n 150

u 4 u u u 200 u o o o o o

C C C 100 C C 100 50 2 50 100 50 0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h)

150 SPHK1 (SC8) 3000 MAPRE (HK) 15000 RPS11 (HK) 800 TBP (HK) 15000 TUBB (HK)

600 s s s s s

t 100 t 2000 t 10000 t t 10000 n n n n n

u u u u 400 u o o o o o

C 50 C 1000 C 5000 C C 5000 200

0 0 0 0 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Time (h) Time (h) Time (h) Time (h) Time (h) Supplemental Figure 1. Normalized Nanostring mRNA counts over time in bat and human cells. A549 (blue), HEK293 (red), or Paki (green) cells were treated with IFNa (50U/mL) and RNA was harvested at the indicated time points. A customized Nanostring nCounter library was used to detect mRNA counts of 50 genes. Data are represented as mean ± SD for three independent experiments.