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Dec 6, 2017 - Wherry EJ, Erkeland SJ and. Katsikis PD (2017) The Transcription. Factor T-Bet Is Regulated by. MicroRNA-155 in Murine Anti-Viral. CD8+ T ...
Original Research published: 06 December 2017 doi: 10.3389/fimmu.2017.01696

The Transcription Factor T-Bet is regulated by Microrna-155 in Murine anti-Viral cD8+ T cells via shiP-1 Jennifer L. Hope1,2, Christopher J. Stairiker1,2, Panagiota I. Spantidea1, Donald T. Gracias2, Alison J. Carey2,3, Adam J. Fike2, Marjan van Meurs1, Inge Brouwers-Haspels1, Laurine C. Rijsbergen1, Joseph A. Fraietta4, Yvonne M. Mueller1, Rosemarieke C. Klop1, Erietta Stelekati5, E. John Wherry5, Stefan J. Erkeland1 and Peter D. Katsikis1*

Edited by: Bernard Malissen, UMR7280 Centre d’immunologie de Marseille-Luminy (CIML), France Reviewed by: Abdelhadi Saoudi, Institut National de la Santé et de la Recherche Médicale, France Toshinori Nakayama, Chiba University, Japan Dietmar Zehn, Swiss Vaccine Research Institute, Switzerland *Correspondence: Peter D. Katsikis [email protected] Specialty section: This article was submitted to T Cell Biology, a section of the journal Frontiers in Immunology Received: 27 June 2017 Accepted: 16 November 2017 Published: 06 December 2017 Citation: Hope JL, Stairiker CJ, Spantidea PI, Gracias DT, Carey AJ, Fike AJ, van Meurs M, Brouwers-Haspels I, Rijsbergen LC, Fraietta JA, Mueller YM, Klop RC, Stelekati E, Wherry EJ, Erkeland SJ and Katsikis PD (2017) The Transcription Factor T-Bet Is Regulated by MicroRNA-155 in Murine Anti-Viral CD8+ T Cells via SHIP-1. Front. Immunol. 8:1696. doi: 10.3389/fimmu.2017.01696

1  Department of Immunology, Erasmus MC University Medical Center, Rotterdam, Netherlands, 2 Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, United States, 3 Department of Pediatrics, Drexel University College of Medicine, Philadelphia, PA, United States, 4 Center for Cellular Immunotherapies and Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, United States, 5 Institute for Immunology, University of Pennsylvania, Philadelphia, PA, United States

We report here that the expression of the transcription factor T-bet, which is known to be required for effector cytotoxic CD8+ T lymphocytes (CTL) generation and effector memory cell formation, is regulated in CTL by microRNA-155 (miR-155). Importantly, we show that the proliferative effect of miR-155 on CD8+ T cells is mediated by T-bet. T-bet levels in CTL were controlled in  vivo by miR-155 via SH2 (Src homology 2)containing inositol phosphatase-1 (SHIP-1), a known direct target of miR-155, and SHIP-1 directly downregulated T-bet. Our studies reveal an important and unexpected signaling axis between miR-155, T-bet, and SHIP-1 in in vivo CTL responses and suggest an important signaling module that regulates effector CTL immunity. Keywords: MicroRNAs, CD8+ T cells, T-bet, influenza, Src homology 2-containing inositol phosphatase-1, miR-155

INTRODUCTION Cytotoxic CD8+ T  cells (CTLs) are an essential component of the adaptive immune system, responsible for the clearance or control of cells infected with viruses or intracellular bacteria and tumors (1, 2). Identifying the mechanisms that control the formation of effector and memory CTL is essential for rational vaccine design and immunotherapeutic approaches. Transcription factors have been recognized as crucial elements that control and direct the fate of CTL toward effector or memory differentiation states (3, 4). In particular, the transcription factors T-bet, Eomes, Blimp-1, and Bcl-6 have been shown to regulate the differentiation and function of CTL (5). T-bet- and Eomes-deficient mice have shown that the “graded expression” of these transcription factors regulate the early effector and memory phenotype of CTL in response to infection with lymphocytic choriomeningitis virus (LCMV) (3). Specifically, T-bet has been linked to the generation of effector and effector memory CD8+ T cells (6–8) and the production of the pro-inflammatory cytokine IFNγ by CTL (6, 7). Therefore, understanding how T-bet expression is regulated in CTL is critical for our understanding of the pathways that control effector and memory CTL formation. MicroRNAs (miRNAs) are ~22 nucleotides small, single-stranded non-coding RNAs that are important post-transcriptional regulators of gene expression and play essential roles in

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December 2017 | Volume 8 | Article 1696

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miRNA-155 Regulating T-Bet in CTL

C57BL6/J background. C57BL/6J mice, miR-155-deficient OT-I mice, and T-bet+/‒ OT-I mice (on the C57BL/6J background) were kept in a barrier facility (certified by the Association for the Assessment and Accreditation of Laboratory Animal Care) at Drexel University College of Medicine, or in a barrier facility at Erasmus University Medical Center. This study was carried out in accordance with the recommendations of the Institutional Animal Care and Use Committee (IACUC) or the Instantie voor Dierenwelzijn (IvD). The protocols were approved by the IACUC or IvD. Female mice 8–10 weeks old were anesthetized with 2.5% isoflurane gas and were infected intranasally with influenza virus strain A/WSN/33-expressing OVA(257–264) (WSN-OVA, a gift from DJ Topham, University of Rochester Medical Center).

modulating key cellular processes (9–11). miRNAs have been shown to control CD8+ T cell formation, differentiation, and function. In the absence of miRNA’s, CD8+ T  cells fail to develop normally (12) or aberrantly respond in vivo to Listeria monocytogenes infection (13). However, the regulatory role of specific miRNAs in generating effective CTL responses to viral infection and tumors is only now being elucidated. Indeed, the regulatory effect of miRNAs is not only cell-type specific, but also context dependent and influenced by the activation state of the cell and in  vivo inflammatory environment (14). Thus, miRNA expression is expected to change depending on the differentiation and activation state of the CTL and this could also be influenced by the milieu and location in which the CTL response is elicited. Previous in  vitro studies have indicated that miRNA expression in CTL changes with differentiation (15) and we have shown that in vivo miR-155 expression levels dynamically change during differentiation from naïve to effector to memory CTL (16). We, and others, have demonstrated that, in the absence of miR-155, effector CTL responses against acute infections with influenza A virus or L. monocytogenes, and tumors are severely diminished (16–19). Furthermore, in the absence of miR-155, the generation of memory CTL is decreased (16). This suggested that overexpression of miR-155 could enhance CTL responses to infections and cancer; indeed, we (16) and others (20) have found that overexpressing miR-155 in CD8+ T  cells causes significant expansion. Multiple mechanisms and targets by which miR-155-deficiency can affect CTL responses have been proposed including type-I IFN and STAT1/2 signaling (16), γc chain cytokine signaling (20), and SOCS-1 (14, 17), which suggests that miR-155 regulation of CTL is context dependent (14). This also raises the question of whether the overexpression of a specific miRNA targets the same mRNA as steady-state endogenous miRNA. Because miRNA and target mRNA availability is a dynamic process (21, 22), it is likely that supranormal levels of a miRNA may allow the targeting of additional mRNA which are normally outcompeted by other targets. Increasing miR-155 expression in CTL augmented anti-viral effector CTL and skewed memory CD8+ T cells toward an effector memory phenotype. miR-155 overexpression induced enhanced T-bet expression and downregulated the inhibitory phosphatase SH2 (Src homology 2)-containing inositol phosphatase-1 (SHIP-1). T-bet upregulation was necessary for effector CTL augmentation by miR-155. Importantly, we show that SHIP-1 regulated T-bet expression and promoted the effector responses in miR-155-overexpressing CTL. Thus, T-bet expression can be controlled by miR-155 via SHIP-1 signaling and we have revealed a novel regulatory pathway for T-bet expression as well as effector and memory CTL generation.

Adoptive Transfer Experiments

Equal numbers of sorted Thy1.1+ or GFP+ OT-I CD45.1+ cells (1 × 104) were intravenously transferred into CD45.2+ C57BL/6J wild-type recipient mice. Three h later, the recipient mice were anesthetized using 2.5% isoflurane gas and infected intranasally with influenza virus WSN-OVA.

Retroviral Production

The miR-155-encoding region from the MigR1-miR-155-GFP vector (16) was cloned into the MSCV-IRES-Thy1.1 vector (provided by P. Marrack, University of Colorado). A scrambled control insert producing no functional miRNA was similarly cloned into the MSCV-IRES-Thy1.1 vector. The PINCO-empty vector-GFP, PINCO-SHIP-1-overexpressing-GFP, and PINCOD675A-SHIP-1 (SHIP-1 dominant negative) retroviral vectors were a kind gift from Dr. M. Caligiuri (Ohio State University) (23). Retroviruses were produced in the Platinum-E cell line (Cell Biolabs, San Diego, CA, USA).

CD8+ T-Cell Isolation and Retroviral Transduction

Retroviral transduction of primary OT-I CD8+ T  cells was completed as previously described (16). Splenic CD8+ T cells were isolated by negative selection with magnetic beads (EasySep; Stemcell Technologies) from uninfected OT-I CD45.1+ female mice 8–10  weeks old. The purity of CD8+ T  cells was >90% as determined by flow cytometry. Isolated CD8+ T cells were activated for 48 h using solid-phase α-CD3 (0.25 µg/mL, clone: 17A2; eBioscience, San Diego, CA, USA) and α-CD28 antibodies (5  µg/mL, clone: 37.51; eBioscience) in 10% RPMI medium with 20  U/mL recombinant human IL-2 (Roche, Switzerland), 5  ng/mL of recombinant murine IL-7 and 5  ng/mL recombinant murine IL-15 (both from PeproTech, Rocky Hill, NJ, USA). Cells were collected and plated at a density of 3 × 106 cells per 2 mL in poly-d-lysine plates (ThermoFisher, Waltham, MA, USA) coated with 20 µg/mL of Retronectin (Takara, Japan) and pre-loaded with retroviral supernatants. Cells were incubated for an additional 48  h. Transduction efficiency was determined by expression of GFP or Thy1.1. Transduced cells were sorted with an FACS Aria III sorter (BD Biosciences, San Jose, CA, USA). Overexpression of miR-155 was confirmed by TaqMan miRNA

MATERIALS AND METHODS Animals and Infections

C57BL/6 Tg(TcraTcrb)1100Mjb/J (OT-I) were backcrossed with B6.SJL-Ptprca Pepcb/BoyJ (CD45.1+) mice (both from the Jackson Laboratory) to generate OT-I CD45.1+ mice on the Frontiers in Immunology  |  www.frontiersin.org

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gene expression quantitative real-time polymerase chain reaction (qRT-PCR) (ThermoFisher) and was determined to be ~5-fold increased over control-transduced cells (Figure S1G in Supplementary Material).

samples were collected with an LSR-Fortessa (BD Biosciences) and analyzed with FlowJo software (Treestar, Ashland, OR, USA).

Quantitative Real-time PCR

Flow Cytometry

For ex vivo measurement of miR-155, SHIP-1 (Inpp5d), T-bet (Tbx21), Eomes (Eomes), Blimp-1 (Prdm1), Bcl-6, (Bcl6) Granzyme B (gzmb) and PD-1 (Pdcd1), donor OT-I CD8+ T cells were sorted from the lungs or spleens of influenza virus-infected animals at the indicated time points. Total RNA, including miRNA, was extracted using the miRNeasy mini kit (Qiagen, Germantown, MD, USA) as per manufacturer’s instructions. cDNA was synthesized from 100-ng RNA with the high-capacity cDNA Reverse Transcription Assay (ThermoFisher). The expression of miR-155 was measured by qRT-PCR with the TaqMan mmu-miR-155-5p miRNA Assay (ThermoFisher). The expression of snoRNA-429 served as endogenous control. Primers for Inpp5d, Tbx21, Eomes, Prdm1, Bcl6, gzmb, and Pdcd1 (TaqMan gene expression assays all from ThermoFisher) were used to detect expression of these genes by qRT-PCR. Expression was normalized to GAPDH expression. All assays were run using a 7900 HT RT-PCR System. Expression was evaluated by the comparative cycling threshold (ΔΔCT) method.

Cells were stained as previously described (24). In all stains, cells were pretreated with anti-CD16/32 (Fc Block; 2.4G2; BioLegend, San Diego, CA, USA) for 15  min before continuing with surface staining. For surface stains, cells were stained for 20  min on ice. Cells were stained with the following fluorochrome conjugated monoclonal antibodies: CD8a (clone 53-6.7), CD45.1 (clone A20), CD45.2 (clone 104), Thy1.1 (clone HIS51) (all from eBioscience), CD25 (clone PC61), CD69 (clone H1.2F3), CD44 (clone 1M7), CD62L (clone MEL-14) (all from BD Bioscience, San Jose, CA, USA), KLRG1 (clone 2F1/KLRG1), IL-7R/CD127 (clone A7R34), and PD-1 (clone 29F-IAI2) (all from BioLegend). Cells were also stained with Cy5.5-labeled Annexin V (BD Biosciences) and APC-labeled tetramers of H-2b major histocompatibility complex class I loaded with OVA(257–264). After staining, cells were washed two times with HBSS containing 3% FBS and 0.02% sodium azide and fixed with 1% PFA. For Annexin V staining, all buffers contained 2.5 mM CaCl2. For staining of intracellular cytokines, cells were stimulated with SIINFEKL peptide for 6  h at 37°C, 5% CO2 in the presence of GolgiPlug (BD Biosciences) and monoclonal antibody against CD107a (clone ID4B) or isotype control. Cells were surface stained as above including fluorochrome conjugated monoclonal antibody against CD107a (clone ID4B) or the appropriate isotype control (both BioLegend), then fixed overnight at 4°C with IC Fixation Buffer, washed using Perm/Wash buffer (eBioscience) and stained for intracellular cytokines for 45  min at 4°C. Fluorochrome-conjugated anti-IFNγ monoclonal antibody (clone XMG1.2), anti-TNF-α monoclonal antibody (clone MP6-XT22), or the appropriate isotype controls (all from eBioscience) were used for intracellular stains. After staining, cells were washed twice with Perm/ Wash buffer (eBioscience) and fixed with 1% PFA. For staining of transcription factors, cells were surface stained as above then fixed for 1 h at 4°C with FoxP3 Fixation Buffer, washed using Perm/Wash buffer (eBioscience), and stained for transcription factors for 1  h at 4°C. The following antibodies were used in combination with intracellular flow cytometry: anti-T-bet antibody (clone 4B10, BioLegend), anti-Eomes antibody (clone DAN11MAG, eBioscience), or the appropriate isotype controls. After staining, cells were washed twice with Perm/Wash buffer (eBioscience) and fixed with 1% PFA. Anti-T-bet antibody staining was titrated using wild-type (T-bet+/+) and T-bet+/‒ splenocytes to achieve clear distinction between heterozygote and homozygote T-bet expression (Figure S3 in Supplementary Material). For staining of SHIP-1, cells were surface stained as above, then fixed using Fixation Buffer (BioLegend) for 20 min at RT, washed with Perm/Wash Buffer (BioLegend), and stained for 20 min at RT. The following antibodies were used in combination with intracellular flow cytometry: anti-SHIP-1 antibody (clone P1C1-A5). After staining, cells were washed twice with Perm/Wash buffer (BioLegend) and fixed with 1% PFA. All

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Western Blot

Donor OT-I CD8+ T cells were sorted from the lungs of wildtype recipient mice 9 days post-influenza virus infection, washed two times with 1X phosphate-buffered saline (PBS) and cell pellets were stored at −20°C in RIPA buffer (150-mM sodium chloride, 1.0% NP-40, 0.5% sodium deoxycholate, 0.1% sodium dodecyl sulfate, 50 mM Tris, pH 8.0). Samples were freezethawed three times and then heated to 99°C on a heat block for 10  min. Samples were loaded into a 4–12% Mini-PROTEAN TGX Gel (Bio-Rad, Hercules, CA, USA), then transferred at 4°C to PVDF membrane (EMD Millipore, Darmstadt, Germany). Membranes were blocked at room temperature for 1  h in 5% BSA in Tris-buffered saline with tween (TBST), then incubated overnight at 4°C with primary antibodies in TBST as follows: Mouse IgG1 SHIP-1 (clone: P1C1) (Santa Cruz, Dallas, TX, USA); Rabbit Granzyme B (cell signaling, Danvers, MA, USA); and Mouse β-Actin (8H10D10) (cell signaling). Membranes were washed, then incubated with LI-COR IRDye 800CW Donkey Anti-Rabbit IgG (H + L) and LI-COR IRDye 680RD Donkey Anti-Mouse IgG (H  +  L) in 1% milk/TBST for 1  h at room temperature, and imaged using an Odyssey imaging system.

Statistics

For flow cytometry and qRT-PCR data analysis, the normality of the population distribution was assessed using the D’AgostinoPearson (n = ~8 mice per group) or Shapiro–Wilk (n =