NK Cells Demonstrate IFN-Induced, but STAT1-Independent, Transcriptional Changes during Viral Infection

To determine if all genes induced during type I IFN exposure were dependent on STAT1 signaling for their expression in vivo, we performed RNA-seq on wild-type (WT), Ifnar1^−/−, and Stat1^−/− Ly49H⁺ NK cells from MCMV-infected and uninfected mixed bone marrow chimeric mice (mBMCs) (Figure 2A). We observed 872 differentially expressed genes between WT and Ifnar1^−/− NK cells (Figure 2B) and 838 differentially expressed genes between WT and Stat1^−/− NK cells (Figure 2C) at day 1.5 post-infection (PI). Many of the most significant changes were canonical ISGs (e.g., Irf7, Rtp4, Mx1) that were induced ex vivo and common between both knockouts. Interestingly, the majority of differentially expressed genes did not overlap with our list of genes induced by IFN-α ex vivo. Importantly, we found that after MCMV infection, expression of all three ISGF3 components was upregulated in WT Ly49H⁺ NK cells, but this increase was almost completely abolished in the absence of STAT1 (Figure 2D) or the type I IFN receptor (Figure S1A). These findings are consistent with the auto-regulatory mechanism proposed above and also suggest that ISGF3 components operate in a feedforward manner to promote their own transcription.

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Figure 2.

During MCMV Infection, a Subset of IFNARI-Dependent Transcriptional Changes in NK Cells Are STAT1 Independent

(A) Schematic of experimental design. Briefly, mBMC mice harboring both WT and Ifnarl or WT and Statl NK cells were infected with MCMV. Splenic Ly49H⁺ NK cells of each genotype were sorted for RNA-seq at 0 and 1.5 days PI (n = 1–3 per genotype pertime point).

(B) Volcano plot of RNA-seq data comparing Ifnar1^−/− and WTNK cells at day 1.5PI. IFNa-induciblegenes (i.e., differentially expressed genes in Figure 1) that are also differentially expressed in Ifnar1^−/− versus WT NK cells are shown in blue, with select genes highlighted. Number of blue genes of total differentially expressed between Ifnar1^−/− and WT NK cells at day 1.5 PI genes indicated at upper right.

(C) As in (B) but showing Stat1^−/− versus WT.

(D) Normalized counts of Stat1, Stat2,and Irf9 in WT and Stat1^−/− NK cells from RNA-seq. Symbols represent biological replicates, and error bars show SEM (*p<0.05).

(E) Overlap of differentially expressed genes between WT and Ifnar1^−/− versus WT and Stat1^−/− NK cells at day 1.5 PI.

Although the transcriptional profile of IFNAR1- and STAT1- deficient NK cells appeared similar at the global level, direct comparison of genes revealed that fewer than 60% of the differentially expressed transcripts were common to both knockouts relative to WT NK cells (Figures ​(Figures2E2E and S1B-S1E). The dysregulated genes unique to Stat1^−/− NK cells could be due to downstream influences of other cytokine signaling pathways that have been proposed to induce phosphorylation of STAT1 (e.g., IFN-γ or IL-21); however, the transcriptional changes unique to Ifnar1^−/− NK cells suggest that non-canonical type I IFN signaling pathways may be active in NK cells during viral infection.

Individual ISGF3 Components Promote Clonal Expansion of Antiviral NK Cells in a Cell-Autonomous and Non-redundant Manner

This noncongruent transcriptomic profile of Ifnar1^−/− versus Stat1^−/− NK cells led us to hypothesize that IRF9, and potentially STAT2, may also play important, non-redundant roles in the response of NK cells to MCMV infection. Because both Ifnar1^−/− and Stat1−^/− mice are highly susceptible to MCMV (Gil et al., 2001), we determined whether IRF9 is also required for host control of MCMV infection. We challenged WT and Irf9^−/− mice with MCMV and found that Irf9^−/− mice were unable to control the infection, resulting in higher viral titers in the blood (Figure S2A) and early death (Figure S2B).

To more directly interrogate the importance of IRF9 and STAT2 in NK cells, we investigated whether any effector functions of NK cells were abrogated in the absence of these factors. During MCMV infection, activated Ly49H⁺ NK cells proliferate to expand the pool of effector cells (Dokun et al., 2001; Sun and Lanier, 2011). To test the role of IRF9 in the expansion of MCMV-specific NK cells, we co-transferred equal numbers of WT (CD45.1) and Irf9^−/− (CD45.2) Ly49H⁺KLRG1^lo NK cells into Ly49H-deficient recipients, and following MCMV infection, we tracked the expansion of the transferred Ly49H⁺ NK cells (Figure 3A). In contrast to WT NK cells, which underwent robust expansion, IRF9-deficient NK cells expanded modestly and represented a smaller proportion of the effector and memory NK cell pools (Figures 3B and 3C). STAT2-deficient NK cells showed a similar defect in expansion and contribution to the memory cell pool after MCMV infection (Figures 3D and 3E). Together, these findings demonstrate a non-redundant requirement for all ISGF3 components in promoting NK cell expansion following viral infection.

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Figure 3.

Non-redundant and Cell-Intrinsic Requirement for ISGF3 Components for the Optimal Expansion of Antiviral NK Cells

(A) Experimental schematic. Equal numbers of splenic Ly49H⁺ KLRG1^lo NK cells from WT (CD45.1) and Irf9^−/− or Stat2^−/− mice (CD45.2) were co-transferred into Ly49H-deficient recipients prior to infection with MCMV.

(B) Absolute percentages of adoptively transferred WT versus Írf9^−/− Ly49H⁺ NK cells measured in peripheral blood at the indicated times following MCMV infection or inferred from transfer (day 0).

(C) Relative percentage of transferred WT and ίrf9^−/− Ly49H⁺ NK cells in indicated organs at day 28 PI.

(D) As in (B), except that NK cells were transferred from WT and Stat2^−/− mice.

(E) As in (C), except that NK cells were transferred from WT and Stat2^−/− mice.

(F) Expression of intracellular granzyme B in splenic WT and Irf9^−/− Ly49H⁺ NK cells from mBMC animals on day 2 PI with MCMV.

(G) As in (F), except showing IFN-g.

(H and I) KLRG1 (H) and CD27 and CD11b (I) expression were assessed in the blood 7 days PI with MCMV following co-adoptive transfer ofWT and irf9^−/− cells, as in (B).

Data are representative of two (D-G) or three (B, C, H, and I) independent experiments with n = 3–5 mice per group. Symbols represent mean (B and D) or individual mice (C and E-G), and error bars show SEM (*p < 0.05).

As has been previously reported for Ifnar1−^/− mice (Guan et al., 2014; Mizutani et al., 2012), we observed minor defects in NK cell development in the global absence of IRF9 and STAT2 (Figures S3A-S3C; data not shown). To confirm that the expansion defects we observed were due to cell-intrinsic roles for IRF9 and STAT2 during MCMV infection, we generated mBMC by reconstituting irradiated mice with bone marrow from Irf9^−/− or Stat2^−/− and WT mice. In this setting, both IRF9- and STAT2-deficient NK cells repopulated the mice with similar efficiency to WT NK cells (Figure S3D; data not shown), and the knockout cells that arose were phenotypically indistinguishable from WT NK cells (Figure S3E; data not shown), suggesting that the developmental defects observed in NK cells directly isolated from the Irf9^−/− or Stat2^−/− mice were cell extrinsic. When WT and IRF9- or STAT2-deficient cells were adoptively transferred from mBMC into Ly49H-deficient mice, we observed a similar expansion defect in knockout NK cells following MCMV infection (data not shown).

A major function of NK cells during viral infection is to produce pro-inflammatory cytokines such as IFN-γ, in addition to mediating cytotoxicity. In uninfected mice, WT and Irf9^−/− NK cells showed comparable IFN-γ production and degranulation after ex vivo stimulation (Figure S3F; data not shown) and killed m157-expressing target cells similarly (Figure S3G), suggesting that IRF9 does not play a critical role in the acquisition of NK cell effector functions during development. However, during MCMV infection, Irf9^−/− NK cells failed to upregulate granzyme B to the same extent as WT cells (Figure 3F), although they expressed similar levels of IFN-γ (Figure 3G) and activation markers (Figures 3H and 3I). Thus, defects in expansion and expression of cytolytic molecules by Irf9^−/− NK cells likely contribute to the increased susceptibility of Irf9^−/− mice to MCMV infection. However, because Irf9^−/− NK cells can still produce IFN-γ, this may afford the Irf9^−/− mice some degree of protection.

Virus

MCMV (Smith strain) was serially passaged through BALB/c hosts twice, then viral stocks were prepared by dissociating salivary glands 3 weeks after infection with a dounce homogenizer.

In vivo Virus Infection

Adoptive transfer studies were performed by mixing splenocytes from WT (CD45.1) and genetic-deficient (CD45.2) mice to achieve equal numbers of Ly49H⁺KLRG1^lo NK cells, and injecting intravenously into adult Ly49H-deficient mice 1 day prior to MCMV infection. Recipient mice in adoptive transfer studies were infected by i.p. injection of 7.5 × 10² PFU of MCMV. Experimental mixed bone marrow chimera mice were infected by i.p. injection of 7.5 × 10³ PFU of MCMV. For survival studies, mice were infected by i.p. injection of 4 × 10⁴ PFU of MCMV.

Determination of Viral Titers

Viral titers were determined as previously described (Kamimura and Lanier, 2014). DNA was isolated from peripheral blood using a genomic purification kit (QIAGEN). Following isolation, DNA concentration was measured using Nanodrop for each sample, and 3 μL was added into mastermix containing iQ Sybr Green (Bio-Rad) and primers specific to MCMV IE-1 DNA (forward: TCGCCCATCGTTTCGAGA, reverse: TCTCGTAGGTCCACTGACCGA). Copy number was determined by comparing Cq values to a standard curve of known dilutions of an MCMV plasmid and normalizing relative to total DNA content.

Isolation of Lymphocytes

Spleens were dissociated using glass slides and filtered through a 100-mm strainer. To isolate lymphocytes from liver, the tissue was physically dissociated using a glass tissue homogenizer and purified using a discontinuous gradient of 40% over 60% Percoll. To isolate cells from the lung, the tissue was physically dissociated using scissors and incubated for 30 min in digest solution (1 mg/mL type D collagenase in RPMI supplemented with 5% fetal calf serum, 1% L-glutamine, 1% penicillin-streptomycin, and 10 mM HEPES). Resulting dissociated tissue was passed through 100-mm strainers, centrifuged, and lymphocytes were removed from the supernatant. To isolate bone marrow lymphocytes, cleaned femur and tibia bones were ground with mortar and pestle, and the resulting solution filtered through a 100-mm strainer. To isolate the stromal vascular fraction (SVF) of adipose tissue, visceral adipose tissue was physically dissociated using scissors and incubated for 40 min in digest solution (2mg/ml type II collagenase in RPMI supplemented with 5% fetal calf serum, 1% L-glutamine, 1% penicillin-streptomycin, and 10 mM HEPES). Resulting dissociated tissue was passed through 100-mm strainers, centrifuged, and adipocytes were removed from the supernatant. Red blood cells in spleen, liver, lung, adipose and bone marrow were lysed using ACK lysis buffer.

Flow Cytometry and Cell Sorting

Cell surface staining of single-cell suspensions from various organs was performed using fluorophore-conjugated antibodies (BD Biosciences, eBioscience, BioLegend, Tonbo, R&D Systems). Intracellular staining was performed by fixing and permeabilizing with the eBioscience Foxp3/Transcription Factor Staining Set for staining intranuclear proteins and cytokines.

Flow cytometry and cell sorting were performed on the LSR II and Aria II cytometers (BD Biosciences), respectively. For RNA-Seq experiments, cell population were sorted to > 95% purity. Data were analyzed with FlowJo software (Tree Star). Flow cytometry of purified lymphocytes was performed using the following fluorophore-conjugated antibodies: CD3e (17A2), TCRß (H57–597), CD19 (ID3), F4/80 (BM8.1), NK1.1 (PK136), Ly49H (3D10), CD45.1 (A20), CD45.2 (104), CD11b (M1/70), CD27 (LG.3A10), KLRG1 (2F1), Ly49D (4E5), Ly49A (YE1/48.10.6), Ly49C/I (5E6), CD69 (H1.2F3), Granzyme B (GB11), IFN-g (XMG1.2), CD107a (1D4B), and CD49b (DX5).

Apoptosis was evaluated by caspase activity staining using the carboxyfluorescein FLICA poly caspase assay kit (BioRad) or Annexin V (BioLegend). NK cell proliferation was analyzed by labeling cells with 5 μM Cell Trace Violet (CTV, Thermofisher) before transfer, and CTV labeling was performed according to manufacturer protocol.

Ex vivo stimulation of Lymphocytes

Approximately 10⁶ spleen lymphocytes were stimulated for 4 hr in RPMI containing 10% fetal bovine serum with 20 ng/mL recombinant mouse IL-12 (R&D Systems) plus 10 ng/mL IL-18 (MBL) or 50 ng/mL PMA (Sigma) plus 500 ng/mL Ionomycin (Sigma). Cells were cultured in media alone as a negative control.

Ex vivo killing assay

Totest the cytotoxic capacity of Irf9^−/− NK cells, Ba/F3 target cells and Ba/F3-m157 control cells were labeled differentially with Cell Trace Violet (CTV, Invitrogen), according to manufacturer protocol (Ba/F3, CTV^lo; Ba/F3-m157, CTV^hi). 5 × 10³ of each cell line were mixed with 5 × 10⁴ purified NK cells from WT or Irf9^−/− mice, or without NK cells (control condition). Effector and target cells were co-cultured for 6 hours at 37°C in RPMI-1640 containing 10% FBS. After 6 hours, cells were stained with propidium iodide prior to flow cytometry. Percentages of target cell killing were determined using the following formula (adapted from (Viant et al., 2017)): 100-([(% Ba/F3-m157 cells/% Ba/F3 cells)]/[(% Ba/F3-m157 cells/% Ba/F3 cells) control] × 100). For this formula, only CTV⁺ cells within live cells were considered. Percentage of PI⁺ target and control cells was determined from total CTV^hi and CTV^lo cells respectively.

To test the ability of activated WT NK cells to directly kill Irf9^^/^ NK cells, NK cells were first enriched from spleens of pooled WT or Irf9^−/− mice by negative selection over BioMag goat anti-rat IgG beads (QIAGEN) coated with rat anti-mouse CD8a, CD4, CD19, and Ter-119 antibodies (Bio × Cell, clones 2.43, GK1.5, 1D3, and TER-199 respectively). Enriched CD45.1⁺CD45.2⁺ WT NK cells were used as effectors cells against a 1:1 mix of CD45.1⁺ WT and CD45.2⁺ Irf9^−/− NK cell targets. Effector and target cells were co-cultured for 16 hours at 37°C in RPMI-1640 containing 10% FBS and IL-2 (10 ng/mL), IL-12 (100 ng/mL), IL-18 (10 ng/mL), and IFN-α (100U/mL). After 16 hours, cells were stained with Annexin V and Fixable Viability Dye (eBioscience) prior to flow cytometry; double positive cells were considered dead.

RNA Sequencing

To determine the transcriptome of NK cells following stimulation with IFN-α, NK cells (TCRβ⁻CD19⁻CD3s⁻F4/80⁻NK1.1⁺CD49b⁺) were sorted from C57BL/6 mice, incubated overnight with 100 U/mL IFN-a or no cytokine (unstimulated), then 5–8 × 10⁴ NK cells were sorted again for purity. To determine the STAT1-, IFNAR1- and IRF9-dependent transcriptional events in NK cells during MCMV infection, 1.5–10 × 10⁴ Ly49H⁺ NK cells (TCRβ⁻CD19⁻CD3s⁻F4/80⁻NK1.1⁺Ly49H⁺) were sorted from spleens of individual mBMC mice harboring WT (CD45.1) and IFN signaling-deficient (CD45.2) cells at d0, d2 or d4 PI with MCMV. RNA was isolated from sorted cell populations using TRIzol (Invitrogen) and total RNA was amplified using the SMART-seq V4 Ultra Low Input RNA kit (Clontech). Subsequently, 10 ng of amplified cDNA was used to prepare Illumina HiSeq libraries with the Kapa DNA library preparation chemistry (Kapa Biosystems) using 8 cycles of PCR. Samples were barcoded and run on Hiseq 2500 1T, in a 50bp/50bp paired-end run, using the TruSeq SBS Kit v3 (Illumina).

ChIP-Seq analysis and peak annotation

For STAT1-ChIP, paired-end reads were trimmed for adaptors and removal of low quality reads using Trimmomatic (v.0.36)(Bolger et al., 2014). Trimmed reads were mapped to the Mus musculus genome (mm10 assembly) using Bowtie2 (v2.2.9)(Langmead and Salzberg, 2012). Concordantly aligned paired mates were used for individual peak calling and pooled replicate peak calling by MACS2 (v2.1.1.20160309)(Zhang et al., 2008) with the arguments “-p 1e-2 -m 2 50 -to-large.” Irreproducible discovery rate (IDR)(Li et al., 2011) calculations using scripts provided by the ENCODE project (https://www.encodeproject.org/software/idr/; v2.0.2 and v2.0.3) were performed on all pairs of individual replicate peak lists against the oracle peak list of pooled replicates. Reproducible peaks were thresholded for those showing an IDR value of 0.05 or less and used for further analysis. Peak assignment to genes and gene features was performed using ChipPeakAnno(Zhu et al., 2010). Promoter peaks were defined as peaks that overlapped a region that was +2kb to −0.5kb from the transcriptional start site (TSS). Intragenic (intronic and exonic) peaks were defined as any peak that overlapped with annotated intronic and exonic regions, respectively, based on the UCSC Known Gene annotation da- tabase(Speir et al., 2016). Intergenic peaks were defined as any non-promoter or non-intragenic peaks, and were assigned to the gene of the nearest TSS based on the distance from the start of the peak. Priority was given to transcripts that were canonical, based on the UCSC Known Canonical database.

For IRF9-ChIP, paired-end reads were mapped to the Mus musculus genome (mm10 assembly) using Bowtie2 (v2.3.2) with parameters -no-unal -X 500 -no-mixed -no-discordant. Concordantly aligned paired mates were used for individual peak calling and pooled replicate peak calling by MACS2 with the arguments “-nomodel -shift 100 -extsize 200 -p 0.2 -B -mfold 3 50 -SPMR -keep-dup ‘auto’.” IDR was calculated using custom bash scripts on all pairs of individual replicate peak lists against the oracle peak list of pooled replicates. Reproducible peaks were thresholded for those showing IDR < 0.01 and used for further analysis.

Gene tracks were generated by converting BAM files to bigWig files using bedtools2 (v.2.26.0)(Quinlan and Hall, 2010) and UCSC’s bedGraphToBigWig (v.4)(Kent et al., 2010) and visualized using the Gviz R package (v.1.18.2)(Hahne and Ivanek, 2016).

We thank members of the Sun and Leslie labs for technical support, experimental assistance, insightful comments, and helpful discussions. N.M.A. and S.V.G. were supported by a Medical Scientist Training Program grant from the NIH National Institute of General Medical Sciences (T32GM007739 to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program). N.M.A. was also supported by an F30 Predoctoral Fellowship from the NIH National Institute of Allergy and Infectious Diseases (F30 AI136239). C.M.L. was supported by a T32 award from the NIH (CA009149). A.R.T. was supported by the Novo Nordisk Foundation. C.S.L. was supported by NIH grant U01 HG007893. J.C.S. was supported by the Ludwig Center for Cancer Immunotherapy, the American Cancer Society, the Burroughs Well-come Fund, and the NIH (AI100874, AI1E7. 30043, and P30CA008748).