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. Author manuscript; available in PMC: 2022 Jun 15.
Published in final edited form as: J Immunol. 2021 May 24;206(12):2949–2965. doi: 10.4049/jimmunol.2001235

ILF3 is a negative transcriptional regulator of innate immune responses and myeloid dendritic cell maturation

Rodolfo Nazitto †,††, Lynn M Amon , Fred D Mast ††, John D Aitchison ††, Alan Aderem †,††, Jarrod S Johnson ‡,¶,*, Alan H Diercks †,*
PMCID: PMC8611108  NIHMSID: NIHMS1693239  PMID: 34031149

Abstract

Antigen presenting cells such as myeloid dendritic cells (DCs) are key sentinels of the innate immune system. In response to pathogen recognition and innate immune stimulation, DCs transition from an immature to a mature state that is characterized by widespread changes in host gene expression, which include the upregulation of cytokines, chemokines, and costimulatory factors to protect against infection. Several transcription factors are known to drive these gene expression changes, but the mechanisms that negatively regulate DC maturation are less well understood. Here, we identify the transcription factor Interleukin Enhancer Binding Factor 3 (ILF3) as a negative regulator of innate immune responses and DC maturation. Depletion of ILF3 in primary human monocyte-derived DCs (MDDCs) led to increased expression of maturation markers and potentiated innate responses during stimulation with viral mimetics or classic innate agonists. Conversely, overexpression of short or long ILF3 isoforms (NF90 and NF110) suppressed DC maturation and innate immune responses. Through mutagenesis experiments, we found that a nuclear localization sequence in ILF3, and not its dual double-stranded RNA-binding domains (dsRBDs), was required for this function. Mutation of the domain associated with zinc finger (DZF) motif of ILF3’s NF110 isoform blocked its ability to suppress DC maturation. Moreover, RNA-seq analysis indicated that ILF3 regulates genes associated with cholesterol homeostasis in addition to genes associated with DC maturation. Together, our data establish ILF3 as a transcriptional regulator that restrains DC maturation and limits innate immune responses through a mechanism that may intersect with lipid metabolism.

1. NF90 and NF110 isoforms of ILF3 restrain monocyte-derived DC maturation.

2. ILF3 dampens innate sensing of HIV-1 and other PAMPs stimulating TLR7/8 and cGAS.

3. NF110-ILF3 regulates genes associated with DC maturation and cholesterol homeostasis.

Keywords: Dendritic Cells, Viral Infection, Transcription Factors, Cell Activation, Inflammation

Introduction

Engagement of the innate immune system in response to an invading pathogen is a central element of host defense. Viral, bacterial, and fungal pathogens can be detected by a wide variety of innate immune cells. Myeloid DCs play key roles in this response by initiating local innate responses and programing subsequent adaptive immune responses (1). Immature DCs are poised to respond to pathogen components and inflammatory cytokines, which trigger morphological and functional changes that facilitate antigen presentation, cytokine/chemokine secretion, and expression of costimulatory molecules. These functional changes are characteristic of mature DCs, which have the capacity to prime naive T cells and program adaptive immunity (2). Many aspects of DC biology are governed by transcription factors that regulate gene expression signatures and influence cell behavior, including DC maturation. While efficient DC maturation may be beneficial for controlling an active infection, sustained or excessive innate responses following maturation can have deleterious pathologic effects (3). Thus, it is important to decipher the complex molecular mechanisms that regulate innate immune responses and DC maturation and understand their function in health and disease.

DCs express an array of pattern recognition receptors (PRRs) that serve as “sensors” positioned to detect extracellular pathogens (e.g. TLR3, TLR4) and intracellular pathogens (e.g. cGAS, IFI16, and RIG-I). Viral nucleic acids within infected cells are detected by DNA and RNA sensors such as cGAS and RIG-I, respectively. These sensors activate signaling cascades that lead to phosphorylation of transcription factors such as IRF3 and NF-κB that drive induction of inflammatory cytokines, including a potent class of antiviral signaling molecules known as type I interferons (IFNs). Autocrine and paracrine signaling of IFNs through the IFN receptor and the JAK-STAT pathway induce a battery of IFN-stimulated genes (ISGs) that can act to directly restrict viral replication or modify cellular processes to establish an antiviral state. During chronic infection with viruses such as HIV-1, sustained production of type I IFNs and expression of ISGs can exacerbate non-specific inflammation, increase target cell susceptibility to the virus, and contribute to pathogenesis (4, 5). Given the pivotal role of DCs in linking innate and adaptive immunity, a deeper understanding of the mechanisms that restrain IFN responses or negatively regulate DC maturation during infection could lead to treatments that engage protective antiviral immune responses during viral transmission and may guide the development of therapies for late-stage disease.

Numerous transcription factors essential for DC differentiation and maturation have been identified, yet the factors that restrain and fine-tune this response remain poorly understood (6). In this report, we have identified ILF3 as one such factor. ILF3 was originally discovered as a positive regulator of IL2 transcription as part of the NFAT-AP1-NF-κB enhanceosome and a positive regulator of IL2 mRNA stabilization via 3’UTR binding (711). Subsequent studies have uncovered diverse functions for ILF3 that include a role in host RNA decay as a component of a messenger ribonucleoprotein (mRNP) complex, and a role as a transcriptional modulator for interleukins in addition to IL-2, such as IL-13 (12), and the oncogene uPA (13). Additionally, ILF3 can interact directly with viral nucleic acids to modulate replication of dengue virus (14), hepatitis C virus (15), bovine viral diarrhea virus (16), human rhinovirus, and Zaire ebolavirus (17). Despite these known roles in regulating transcription, mRNA stabilization, interaction with viral nucleic acids, and regulation of pathogen fitness, ILF3 has not previously been described to regulate the function of myeloid immune cells.

Here, we demonstrate that both major isoforms of ILF3, NF90 and NF110, restrain myeloid DC maturation and type I IFN responses. Analysis of deletion mutants reveals that the nuclear localization sequences of both NF90 and NF110 are required for this function. Interestingly, mutation of the domain associated with zinc finger (DZF) domain ablated the ability of NF110 to suppress DC maturation, but not NF90. Furthermore, through RNA-seq analysis of DCs expressing mutant or wild type ILF3, we demonstrate that ILF3-dependent genes are strongly enriched for genes associated with cholesterol homeostasis. These data establish ILF3 as a regulator of DC responses to innate immune stimuli and therefore a potential target for host-directed therapies to fine-tune inflammation and innate immunity.

Materials and Methods

Primary Human Cells and Cell Lines

We generated immature MDDCs as previously described (18). Briefly, leukocytes from anonymized healthy human donors were acquired under the Bloodworks Donor Products for Research and Test Development/Standardization – External Investigators Protocol (Western Institutional Review Board - WIRB protocol 20150119) and informed consent was obtained from all subjects. CD14+ monocytes from PBMC buffy coats were isolated with anti-human CD14 magnetic beads (Miltenyi Cat:130-050-201) and cultured in RPMI (Thermo Fisher) containing 10% heat-inactivated fetal bovine serum (FBS, Peak Serum, Inc), 50 U/mL penicillin, 50 μg/mL streptomycin (pen/strep, Thermo Fisher), 10 mM HEPES (Sigma), 2-mercaptoethanol (Thermo Fisher), and 2 mM L-glutamine (Thermo Fisher), in the presence of recombinant human GM-CSF at 10 ng/mL and IL-4 at 50 ng/mL (Peprotech). The day following isolation and transduction with lentiviral vectors, fresh media and cytokines were added to cells (50% by volume.) On the fourth day post-isolation, cells were resuspended in fresh media and cytokines for subsequent experiments. 293FT cells (Life Technologies Cat# R70007, RRID:CVCL_6911) were cultured in Dulbecco’s modified Eagle’s medium (DMEM, Thermo Fisher) supplemented with 10% FBS, pen/strep, 10 mM HEPES, and with 0.1 mM MEM non-essential amino acids (Thermo Fisher), 6 mM glutamine, and 1 mM sodium pyruvate (Thermo Fisher). HL116 cells were cultured similar to 293FT cells, except with the addition of HAT supplement (Thermo Fisher Cat: 21060-017). THP-1 cells (ATCC Cat# TIB-202, RRID:CVCL_0006) were cultured in RPMI with 10% heat-inactivated fetal bovine serum, pen/strep, 10 mM HEPES, 2-mercaptoethanol, and 2 mM glutamine and kept at a density between 250,000 and 1,000,000 cells per mL. All cells were maintained at 37°C and 5% CO2 and used at early passage numbers (< 20 for 293FT, or < 8 weeks for HL116 and THP-1 cells. Mycoplasma contaminant checks were performed every 6 months. THP-1 experiments were performed on biological replicates from independent cultures and MDDC experiments were performed using individual donors as biological replicates.

Plasmids and Mutagenesis

HIV-1-GFP is env- vpu- vpr- vif- nef-, with the GFP open reading frame in place of nef (19). Vpx-containing virus like particles were generated from the plasmid pSIV3+ (20). Overexpression vectors for wild-type isoforms of ILF3, dual dsRNA-binding domain deletion mutants, Domain associated with zinc finger deletion mutants, and nuclear localization signal deletion mutants were generated in-house using overlap extension mutagenesis to modify NF90 cDNA (GE Dharmacon) or directly synthesized (Thermo Fischer GeneArt) (NF90b mutants and NF110b forms) or GE Dharmacon (NF90b cDNA) in a pLKO.1 vector backbone. lentiCRISPR sgRNAs (single guide RNAs) were designed using the E-CRISP algorithm (http://www.e-crisp.org/E-CRISP/), selecting for the highest scoring guides in target specificity and efficiency for ILF3. Oligos spanning the sgRNA sequence were annealed and ligated into the lentiCRISPRv2 backbone (21) (Addgene plasmid #52961, Gift from Feng Zhang). (ILF3 Target Sequence #1: GCTGGAGGCAGTCCAGAACA. ILF3 Target Sequence #2: GCCTCCAGCTCCTCTTGTGT). The parental control vector (LCV2) was created from lentiCRISPRv2 digested with BsmBI and re-ligated, removing the 2 kb stuffer. All lentiviral constructs were transformed into Stbl3 bacteria (ThermoFisher Cat: C737303) for propagation of plasmid DNA. All plasmids were prepared using a Nucleobond Xtra Maxi Kit (Takara Cat:740414.100). Coding sequences of overexpression constructs, shRNA hairpins, and sgRNAs were confirmed by automated sequencing (Genewiz).

Virus and Virus-Like Particle Production

As described (18), lentivirus stocks were produced by PEI-mediated transfection into 293FT cells. For lentiviral vectors, plasmid amounts were 3.4 μg CMV-VSV-G (Addgene plasmid #8454), 9 μg psPax2 (Addgene plasmid #12260), and 10.1 μg transgene (LKO.1 control (Addgene Cat:10878), LKO ILF3 overexpression vector, shRNA control (Sigma Cat:SHC002), ILF3 shRNA, or lentiCRISPRv2 constructs). For HIV-1-GFP, plasmid amounts were 3.4 μg CMV-VSV-G and 19.1 μg HIV-1-GFP cassette. Virus-like particles containing Vpx were produced using 3.4 μg CMV-VSV-G and 19.1 μg pSIV3+. Media was washed and refreshed the morning after transfection, and virus supernatants were harvested after 32 h later. Sufficient p24 levels were verified using Lenti-Go Stix Plus (Takara Cat:631280). Supernatants were passed through 0.45 μm syringe filters (Corning), transferred to thinwall Conical tubes (Beckman), and concentrated by ultracentrifugation at 24k rpm for 2 hr at 4 °C in a SW28 swing-bucket rotor (Beckman). Pellets were resuspended in RPMI-DC media without cytokines and insoluble material was clarified by centrifuging at 700 rcf for 4 min. 50X concentrated viral stocks were frozen at −80 °C and titered on 293FT and THP-1 cells.

Perturbation of DCs and Cell Lines

MDDCs were modified by lentiviral shRNA and overexpression constructs similar to previously described protocols (Johnson et al., 2018). Isolated CD14+ monocytes were resuspended in media with cytokines and polybrene (Sigma, 1 μg/mL) and aliquotted to 96-well U-Bottom plates with 200,000 cells in 150 μL per well. Supernatant containing virus-like particles packaging Vpx was added to overcome the block to reverse transcription ~30 min prior to adding lentiviral vectors. 10 μL of concentrated lentiviral stocks were used to transduce 200,000 CD14+ cells. shRNA clones for targeting ILF3 were used independently (Sigma: ILF3 sh1: TRCN0000329787, ILF3 sh2: TRCN0000329786). THP-1 monocytic cells were transduced with shRNA or lentiCRISPR constructs in 6-well cluster plates using 1,000,000 cells per well in 2 mL media with polybrene (2 μg/mL) and concentrated viral stocks (150 μL of shRNA or 250 μL of lentiCRISPR per well). Cells were placed under selection with puromycin (1 μg/mL, Invivogen) two days after transduction for 1 week and puromycin-resistant populations were allowed to expand. lentiCRISPR-transduced cells were used at day 8 for infection. shRNA-transduced cells were used for experiments beginning 2 weeks after selection. Independent transductions were performed for biological replicates, unless otherwise indicated. Perturbation of ILF3 expression was confirmed by qPCR or immunoblot.

Infections and Stimulations

MDDCs were infected with HIV-1-GFP for 48 hours beginning on day 4 after differentiation. DCs were spun down on day 4 and resuspended in fresh medium with GM-CSF, IL-4, and polybrene (1 μg/mL). For most assays, DCs were plated in round bottom 96-well plates in 75 μL. Infections and stimulations were performed by diluting virus in MDDC media (without cytokines or polybrene) to a final volume normalized to control (150 μL per well). Antiretroviral drugs (NIH AIDS Reagent Program or Selleck Chemicals Cat:S2005) were added prior to virus infection at the following concentrations: efavirenz (EFV) (20 nM); raltegravir (RAL) (25 μM). Innate and inflammatory stimuli – Immunostimulatory DNA or control DNA complexed with LyoVec (InvivoGen Cat: tlrl-isdc/tlrl-isdcc), 2’3’-cGAMP (InvivoGen Cat: tlrl-cga23-s), and R848 (InvivoGen Cat: tlrl-r848) – were used as indicated in Fig. 3. THP-1 monocytic cells were infected in the absence of Vpx at a density of 70,000 cell per 150 μL in complete RPMI medium with polybrene (2 μg/mL). B18R (R&D Systems Cat: 8185-BR-025) for IFN neutralization was used for flow cytometry analysis of CD86 by adding B18R at a concentration of 100 ng/mL after the media refresh on day 4 and once again 24 h later.

Flow Cytometry

Infected or stimulated MDDCs were washed with phosphate buffered saline (PBS, Corning), cell pellets were incubated for 15 minutes at 4 °C with 2μL of Fc Block (BD Biosciences Cat: 564219), and then exposed to LIVE/DEAD violet (ThermoFisher Cat: L34955) in PBS for 15 min at 4 °C in the dark. Cells were either simultaneously stained for surface markers (CD40 Thermo Fisher Cat: CD4004, CD80 eBioscience Cat:15-0809-42, CD86 eBioscience Cat: 15-0869-42, HLA-DR Biolegend Cat: 307607/Biolegend Cat:307619, CD1c eBioscience Cat:331505, CD83 eBioscience Cat: 305307) or were then washed with PBS and fixed with 0.4% paraformaldehyde (Electron Microscopy Sciences) diluted in PBS. Cells were analyzed on an LSR II flow cytometer (BD Biosciences). For intracellular staining using anti-human ISG15 (R&D Systems Cat: IC8044P) in MDDCs and THP-1 monocytic cells, cells were first exposed to LIVE/DEAD violet and surface markers as described above, washed in PBS, then fixed and permeabilized using a cytofix/cytoperm kit (BD Biosciences Cat: 554714), blocked with 2μL Fc block for 10 min at room temperature, and stained according to the manufacturer’s instructions. Cells were washed and resuspended in PBS with 1% BSA and data were acquired on an LSR II flow cytometer (BD Biosciences) and analyzed using FlowJo software (FlowJo LLC). ArC Amine Reactive Compensation Bead Kit (Thermo Fisher Cat:A10346), UltraComp eBeads Plus Compensation Beads (Thermo Fisher Cat: 01-3333-41), and GFP BrightComp eBeads (Thermo Fisher Cat: A10514) were used for compensation.

Nucleic Acid Isolation and Quantitative PCR

~200,000 DCs were lysed in TRIzol reagent (Thermo Fisher Cat: 15596026) and RNA was isolated according to the manufacturer’s instructions with the following modifications: two sequential chloroform extractions were performed and Glycoblue (Thermo Fisher Cat: AM9516) was added as a carrier prior to precipitation. cDNA was converted using Superscript IV VILO with ezDNase treatment (ThermoFisher Cat: 11766050). Quantitative PCR reactions were carried out using TaqMan primer probes (Thermo Fisher) and TaqMan Fast Universal PCR Master Mix (ThermoFisher) in a CFX96 thermocycler (BioRad) or QuantStudio3 (Thermo Fisher) in a volume of 10 μL according to the following cycling conditions: 50 °C for 2 min, 95 °C for 2 min, then 50 cycles each of 95 °C for 3 sec, to 60 °C for 30 sec, followed by 95 °C for 5 sec. For total ILF3 Sybr green qPCR, PowerUp Sybr Green Master Mix (Thermo Fisher Cat: A25742) was used in a total volume of 10uL with the following cycling protocol: 50 °C for 2 min, 95 °C for 2 min, 95 °C for 15 sec, 58 °C for 15 sec, and 72°C for 15 sec, repeated for 40 cycles. Data were plotted as 2-(ΔCt)•1000 relative to GAPDH. For experiments with larger number of samples, Direct-Zol 96-well RNA isolation kits (Zymo Cat: R2056) were used as per manufacturer’s instructions, utilizing the optional DNase step and forgoing the ezDNase step with the SSIV VILO master mix.

Microarrays

Monocyte-derived dendritic cells from three unique donors were transduced with either a control shRNA, ILF3 sh1, or ILF3 sh2 in the presence of Vpx. RNA was extracted using TRIzol on day 5. Purified RNA was labeled and hybridized to SurePrint G3 8x60K Microarrays (Agilent) and data were acquired at the Institutes for Systems Biology. Probe sequences were mapped against the Ensembl transcript database (ensembl.org, GRCh37.74) and sequences that mapped to more than one gene or had more than five mismatches from the database sequence were removed for a total of 37,623 unique probes. Probe-specific logarithmically transformed expression was quantile-normalized. Duplicate probe sequences were averaged. Gene-specific expression was computed by using the probe that showed the highest average expression across all samples in cases in which multiple probes mapped to a single gene for a total of 26,319 gene-specific probes. Statistical significance of the coefficients were computed with the LIMMA R package (https://bioconductor.org/packages/release/bioc/html/limma.html). p values were adjusted for multiple hypothesis testing with the Benjamini-Hochberg method for controlling the false discovery rate. These data have been deposited in NCBI's Gene Expression Omnibus (Nazitto et al., 2020) and are accessible through GEO Series accession number GSE159458 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159458). Data from our previous publications (18, 22) that were re-analyzed as indicated in Fig. S1 can be accessed through accession number GSE100374 (microarray) and GSE125918 (ATAC-seq).

Gene Set Enrichment Analysis (GSEA)

For ILF3 shRNA knockdown microarray, 37,913 gene features for each construct were ranked by the t-test metric of GSEA using the mean values computed for ILF3 sh1 and sh2 compared to the control shRNA. The analysis was performed using the weighted enrichment statistic, ranking genes using the t-test metric, against the C2 Curated gene sets containing x number of genes (with 15 > x > 200) within the Molecular Signature Database. The Normalized Enrichment Score (NES) was calculated using 1000 gene set permutations. For NF110 overexpression RNA-Seq, a pre-ranked list of fold changes of NF110 wt compared to LKO control was ranked by t-test metric containing 13,125 gene features. The analysis was performed using the standard weighted enrichment statistic against the C1 Hallmark gene sets with 15>x>200 gene membership within the Molecular Signature Database. The Normalized Enrichment Score (NES) was calculated using 1000 permutations.

RNA-Seq

RNA isolated from TRIzol lysates as described above and converted to cDNA libraries using the Illumina TruSeq stranded mRNA Kit per the manufacturer’s instructions. Libraries were amplified and then sequenced on an Illumina NovaSeq (2 x 150, paired-end). Reads with more that 67% identical bases were discarded prior to alignment. The remaining read pairs were aligned to the human genome (hg19, GRCh37 Genome Reference Consortium Human Reference 37 (GCA_000001405.1)) using the gsnap aligner (v. 2016-08-24) allowing for novel splicing. Concordantly mapping read pairs (average 17 million per sample) that aligned uniquely were assigned to exons using the subRead program and gene definitions from GRCh38.87. Genes with low expression were filtered using the filterByExpr function in the edgeR package from Bioconductor.org resulting in a total of 13,125 genes in the final dataset. Differential expression was calculated using the edgeR package. The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE159143 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159143). (Nazitto et al., 2020)

Immunoblotting

Samples were prepared as previously described (18). Blots were incubated with primary ILF3 antibody ILF3 (Abcam Cat:92355) (1:3000). After incubating blots overnight at 4 °C, or for at least 1 hr at room temp, they were washed with TBS/tween, then incubated with the corresponding HRP-conjugated anti-rabbit secondary antibody (1:10,000) for 1 hr at room temperature. To confirm equal protein loading, blots were incubated with an anti-actin-HRP antibody (directly conjugated) (Abcam Cat:AB20272) (1:100,000) for 30 min at room temperature. Alternatively, blots were incubated with primary GAPDH antibody (Cell Signaling Cat:5174T) (1:1000) to confirm equal protein loading in samples taken over the course of differentiation from monocytes to MDDCs, as actin expression is not constant during differentiation. After incubation with primary antibodies, blots were washed with TBS/tween, then incubated with the corresponding HRP-conjugated anti-rabbit secondary antibody (1:10,000) for 1 hr at room temperature. Blots were then washed in TBS/tween, reacted with Wesfemto ECL kit (Thermo Fisher Cat: 34095), and developed using a FluorChem E Imager (Protein Simple).

Immunofluorescence

On day 5 after transduction, 75 μl of MDDCs were plated into a chamber of an 8-chamber mounting slide (MatTek Life Sciences). Cells were collected onto the coverglass by centrifugation for 7 min at 1500 RPM at room temperature. The supernatant was aspirated and cells were fixed with 2% paraformaldehyde for 30 min at room temperature. After washing the cells with PBS the cells were then permeabilized and blocked with a solution of PBS + 0.1% Triton X-100 + 5% FBS (Perm/Block, 0.2 μm filtered) for 1 h at room temperature. The Perm/Block solution was then aspirated and anti-ILF3 antibody was added at a concentration of 1:1000 in Perm/Block solution. Cells were washed 3 for 5 min each with Perm/Block and then incubated with Alexa Fluor 594 goat anti rabbit secondary antibody (Thermo Fisher Cat:A-11012) at 1:750 in Perm/Block for 1 h at room temperature. Cells were washed twice for 5 min each at room temperature in the dark with Perm/Block. 1.25 μl of phalloidin (Thermo Fisher Cat: R415) was added to 200 μl of PBS per chamber and incubated for 20 min at room temperature in the dark, except for where indicated. Cells were washed twice with PBS and then mounted with ProLong anti-fade diamond with DAPI (Thermo Fisher Cat: P36962) and left to cure for 24 h before imaging on a DeltaVision Elite (Cytiva) widefield microscope. Images were collected with a 100 1.4 NA objective (Olympus) on a CoolSnapHQ2 CCD camera (Photometrics). The sides of each pixel are 6.45 μm. Images were deconvolved using algorithms provided by Huygens Software (Scientific Volume Imaging BV, The Netherlands). For deconvolution, three-dimensional data sets were processed to remove noise and reassign blur by an iterative Classic Maximum Likelihood Estimation widefield algorithm using an experimentally derived point spread function. Image processing was performed using Imaris (Bitplane). Cells were segmented using the “Cells” command, which identifies cells and their nuclei. For each cell, the ratio between nuclear and cytoplasmic ILF3 fluorescence signal intensity was determined.

ELISAs

IFNβ in DC supernatants was determined by ELISA (R&D Systems Cat: DIFNB0) according to the manufacturer’s instructions. Supernatants (50 μL) from MDDCs infected with HIV-1-GFP for 32 h were mixed with 50μL assay diluent reagent per well and measured in duplicate compared to a standard curve of IFNβ ranging from 7.81pg/mL to 500pg/mL. IFNβ was also measured in supernatants of ILF3 knockdown MDDC supernatants using high-sensitivity ELISA IFNβ (PBL Cat: 41435) on mock, 2’3’-cGAMP, and B18R-treated MDDCs (R&D Systems Cat: 8185-BR-025) (Added at 100ng/mL 1 hour before 2’3’-cGAMP treatment) 7 hours post treatment, measured in duplicate compared to a standard curve of IFNβ ranging from 150 pg/mL to 2.34 pg/mL.CXCL10 and IL-6 were measured in the supernatants of mock or ILF3 knockdown MDDC supernatants using ELISAs for CXCL10 (Abcam Cat: 173194) and IL-6 (Abcam Cat:178013) on mock, 2’3’-cGAMP, and B18R-treated MDDCs for 7 hours, measured in duplicate compared to a standard curve ranging from 800 pg/mL to 12.5 pg/mL for CXCL10 and 500 pg/mL to 7.8 pg/mL for IL-6. CCL23 was measured in the supernatants of either ILF3 knockdown or NF90/NF110 overexpressing MDDCs compared to relevant controls 48 hours post media refresh on Day 4 using a CCL23 ELISA assay (Abcam Cat: 216169), measured in duplicate compared to a standard curve of CCL23 ranging from 450 pg/mL to 7.03 pg/mL. All duplicates were averaged per donor, represented as the mean.

Bioassays for Type I IFN

IFN activity in HL116 cells was measured as previously described (18). Briefly, 20,000 HL116 cells were incubated with supernatants from MDDC cultures for 7 hours before passively lysing the cells and scoring firefly luciferase activity in the presence of luciferin.

Quantification and Statistical Analysis

Statistical tests were performed as indicated in the figure legends or otherwise using Prism 8.0.1 (GraphPad) to calculate a mixed model two-tailed t-test using paired samples and setting an alpha value of 0.05. In this study, n is defined in the figure legends and represents the number of biological replicates performed of unique donors for MDDC experiments or the number of independent, non-technical replicates for THP-1 experiments, unless otherwise indicated.

Illustrations

All graphical illustrations were done using BioRender.com

Results

Temporal promoter and transcriptome analysis predict ILF3 as a regulator of DC maturation in response to HIV-1-GFP infection

To probe the DC response to innate immune stimuli, we employed HIV-1-GFP, a VSV-pseudotyped, single-cycle, HIV-derived reporter virus that lacks all accessory proteins and expresses GFP in place of Nef. Normally, HIV-1 infection is severely limited in DCs due to expression of the restriction factor SAMHD1, which prevents reverse transcription of viral RNA (23, 24). Providing the SIV accessory protein Vpx, in trans, leads to SAMHD1 degradation, allows for reverse transcription to proceed, and enables efficient, productive infection (19, 20). This system is a well-established model for examining DC maturation and type I IFN responses (18, 19, 25). Infection of MDDCs with HIV-1-GFP robustly induces type I IFN through the DNA sensing pathway cGAS-STING, a key initiator of DC maturation via the transcription factor IRF3 and antiviral immunity through the induction of ISGs.

To identify molecules that restrain maturation and IFN responses in myeloid DCs, we reanalyzed existing datasets that were generated from an Assay for Transposase-Accessible Chromatin using sequencing (ATAC-Seq), together with microarray datasets from MDDCs infected with HIV-1-GFP-infected MDDCs (18, 22). We compared changes in genome-wide chromatin accessibility and the transcriptome over time to search for genes that had increased chromatin accessibility near their transcription start sites and decreased gene expression, profiles that could suggest negative transcriptional regulation (Fig. S1A). As expected, chromatin accessibility was increased at the promoters of numerous genes with established roles in DC maturation (CD40, CIITA, CD80, CD86) and IFN responses (IFNB1, ISG15, OASL, IFIT1) at 24 hours post infection (Fig. S1B) (22) and these increases were associated with altered expression of these genes at later timepoints (Fig S1C). We defined a set of 85 genes that had both large increases in chromatin accessibility (fold-change (FC) > 10, p < 0.01) and significantly reduced expression (FC < 1, FDR < 0.05) at 24 hours following infection with HIV-1-GFP. Of those 85 genes, only five were transcription factors, as defined by the Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) list of human transcription factors: CDK2AP2, ERCC2, HSF1, ILF3, and KLF2. We were particularly intrigued by the transcription factor ILF3, as it has been reported to negatively regulate IFN production during influenza infection in primary human bronchial epithelial cells (26). Additionally, under different experimental conditions in HeLa and A549 cells, ILF3 has been shown to promote the IFN response upon dsRNA stimulation (27, 28). The gene encoding ILF3 produces two major isoforms, NF90 and NF110 (collectively named ILF3 for the purposes of this study), and both of which are known to act as transcriptional regulators (1113, 2931). We found that human CD14+ monocytes isolated from whole blood express low levels of ILF3 isoforms, but these are dramatically upregulated over the course of differentiation into immature MDDC in the presence of IL-4 and GM-CSF (Fig. S1D). Since our data in MDDCs suggested that accessibility of the ILF3 promoter is altered during innate immune stimulation, and the transition from immature to mature MDDC coincides with a decrease in ILF3 expression (Fig. S1E,F), we pursued ILF3 as a candidate regulator of innate immune function in human DCs to better clarify its role.

ILF3 restrains monocyte-derived dendritic cell (MDDC) maturation

To determine the set of genes regulated by ILF3 in unstimulated myeloid cells, we transduced MDDCs with two independent shRNAs targeting all isoforms of ILF3 and analyzed their transcriptomes by microarray (Fig. 1A). Knockdown of ILF3 in resting MDDCs (Fig. 1B) altered the expression of 106 genes (FDR < 0.05; |FC > 1.5|). Gene Set Enrichment Analysis (GSEA) revealed that this set of genes was enriched for members of curated gene sets associated with DC maturation in response to inflammatory stimuli. Up-regulated genes were significantly enriched in three of the four “Lindstedt Dendritic Cell Maturation” gene sets (A-C) (32) (Fig. 1C,S2A). Genes down-regulated during DC maturation showed enrichment in the remaining set (set D), demonstrating the concordance of the effects. (Fig. 1D). The set of 106 genes whose expression was affected by ILF3 knockdown also contained additional genes that are not in the Lindstedt gene sets but have established associations with innate immune activation and myeloid DC maturation (CHI3L1, PPARG, TLR3, WFDC21P (Lnc-DC), CCR2, ST6GAL1, VENTX, and CCL23) (Fig. 1D) (3340). Together, these transcriptomic analyses suggested that ILF3 functions as a negative regulator of MDDC maturation and innate immune responses. To determine whether phenotypic changes to DC maturation accompanied these transcriptional changes, we measured surface expression of the costimulatory factors CD86, CD40, and the MHC II cell surface receptor HLA-DR by flow cytometry following shRNA-mediated knockdown of ILF3. In agreement with the transcriptomic data, knockdown of ILF3 led to significantly elevated surface expression of CD86, HLA-DR, and CD40 (Figs. 1E, S2B), while having a minimal effect on cell viability (Fig. S2C). Upregulation of CD86 correlated with expression HLA-DR in a subset of cells and was associated with reduced expression of CD1c, which are characteristic expression patterns of mature, heterogeneous MDDC cultures (Fig. 1F) (41, 42). In agreement with these findings, we also observed upregulation of CD83 (an additional marker of MDDC maturation) following ILF3 knockdown, although the sh2 condition did not reach statistical significance (Fig. S2D). Nearly all cells in these cultures expressed high levels of CD1c, suggesting that ILF3 knockdown does not alter differentiation of monocytes into MDDCs but rather affects MDDC maturation (Fig. S2E,F).

Figure 1. ILF3 restrains monocyte-derived dendritic cell (MDDC) maturation.

Figure 1.

(A) Schematic illustration for transcriptomic analyses of MDDCs transduced with shRNAs targeting ILF3. Cells from three unique donors were transduced with a control shRNA or one of two shRNAs targeting ILF3. After 4 days, mRNA was isolated and analyzed by microarray. (B) Representative Western blot of MDDC whole cell lysates prepared as in (A), 4 days after transduction with the indicated shRNAs. (C) GSEA plots for the Lindstedt DC maturation gene sets, in each case comparing the mean expression values from ILF3 sh1 and sh2 conditions to the control sh condition. Gene sets A-C (light purple) contain genes up-regulated during DC maturation and gene set D (dark purple) contains genes down-regulated during DC maturation (D) Scatter plot of expression changes for genes significantly differentially expressed following transduction with at least one of two shRNAs targeting ILF3 plotted on a log2 scale (|FC| > 1.5, FDR < 0.05). (E) Flow cytometry analysis of MDDCs after ILF3 knockdown. Plots show a representative histogram of CD86, HLA-DR, and CD40 expression from one donor and corresponding graphs showing pooled data from 13 donors across 4 individual experiments for CD86, 4 donors from 1 experiment for HLA-DR, and 4 donors from 1 experiment for CD40, gated on FSC vs. SSC, singlets, and live cells. Statistics were calculated by matching each donor in a mixed-effects model using Dunnet’s test for multiple comparisons. Mean and SEM for each marker in a given lentiviral treatment across all represented donors indicated on each histogram used throughout the paper. Significance of *P < 0.05, **P < 0.01, ***P < 0.001 used for the entirety of the paper. (F) Flow cytometry plots of CD86 vs CD1c or HLA-DR of one representative donor, gated as in E.

As various interferons and pro-inflammatory cytokines can influence DC maturation, we examined whether ILF3’s effect on maturation is solely cell intrinsic or mediated by secreted factors. We prepared immature DCs transduced with either a control shRNA or ILF3-targeting shRNAs and after four days we replaced the culture media. 24 hours later, the supernatants of the cultures with control or ILF3-targeting shRNAs were exchanged and incubated for another 24 h before measuring CD86 levels by flow cytometry (Fig S2G). Supernatants from cultures transduced with ILF3-targeting shRNAs induced baseline maturation in control DCs to levels similar to those in DCs transduced with ILF3 shRNAs (Fig. S2H). Supernatants from control cells applied to ILF3 knockdown cells had no additional effect on CD86 expression (Fig. S2H). Together, these data indicate that loss of ILF3 promotes steady-state maturation of MDDCs and that secreted factors contribute to this effect.

To further decipher the molecular mechanisms involved in ILF3-mediated regulation of myeloid cell biology, we perturbed ILF3 expression using two complementary approaches in THP-1 cells, a myeloid leukemia-derived monocytic cell line. Knockdown of ILF3 with shRNAs led to elevated expression of the maturation marker CD80, the differentiation markers CD209 and SAMHD1, as well as the IFN-related markers CXCL10 and ISG15 (Fig. S2I). Additionally, we generated ILF3 knockout THP-1 cells using CRISPR-Cas9 (Fig. S2J). We found that expression of the maturation marker CD86 and the differentiation markers CD209, SAMHD1, and CD14 were all significantly elevated after ILF3 was knocked out using either of two independent guide RNA sequences nine days post-transduction (Fig. S2K). These data further support the role of ILF3 as a negative regulator of IFN-related genes and myeloid cell maturation. Given that THP-1 monocytic cell lines do not fully recapitulate primary cell behavior, we chose to focus on primary MDDCs for further studies.

ILF3 restrains DC Maturation and antiviral IFN responses to HIV-1-GFP infection

Our transcriptomic analysis of unstimulated MDDCs indicated a role for ILF3 in regulating myeloid cell maturation, so we also examined the impact of ILF3 knockdown or overexpression on MDDC responses to infection with HIV-1-GFP. Knockdown of ILF3 resulted in significantly elevated expression of CD86 (Fig. 2A), CD80, and HLA-DR (Fig. S3A,B) in HIV-1-GFP-infected cells compared to controls. In contrast to markers of mature MDDCs, expression of CCL23, a chemokine marker of immature MDDCs, was suppressed by knockdown of ILF3, as measured at the levels of mRNA and protein (Fig. 2B). Similarly, expression of CIITA, which typically decreases during DC maturation (43), was further decreased by ILF3 knockdown during HIV-1-GFP infection (Fig. 2C). In addition, knockdown of ILF3 increased expression and secretion of type I IFN (Fig. S3C,D) and expression of the hallmark IFN-stimulated gene ISG15 in response to HIV-1-GFP infection (Fig. 2D).

Figure 2. ILF3 restrains MDDC maturation and IFN signaling in response to HIV-1-GFP viral challenge.

Figure 2.

(A) Flow cytometry analysis of CD86 expression in ILF3 knockdown MDDCs that were either mock treated or infected with HIV-1-GFP at the indicated MOIs, gated on FSC vs. SSC, singlets, and live cells. n = 6 donors quantified over 2 individual experiments. (B) qPCR of CCL23 and CCL23 ELISA data of mock-treated MDDCs. n=4 donors. (C) qPCR of CIITA expression in MDDCs infected with HIV-1-GFP for 32 hours. n = 3 donors. (D) MFI of ISG15 expression in MDDCs infected with HIV-1-GFP for 48 h. n = 4 donors over 2 experiments. (E) Major protein isoforms of ILF3 and their domains: Nuclear export signal (NES), Domain associated with Zinc Finger (DZF), double-stranded RNA-binding domain (dsRBD), RGG-repeat motif (RGG=RGG-repeat), GQSY-repeat motif (GQSY-repeat), Nuclear localization signal (NLS), and NVKQ motif (NVKQ). Western blot of NF90 and NF110 expression in MDDC whole cell lysates after transduction with LKO, NF90, or NF110 overexpression vectors. (F) Flow cytometry analysis of CD86 surface expression in MDDCs overexpressing NF90 or NF110 that were infected with HIV-1-GFP for 48 hours. n = 8 donors from 2 individual experiments. (G) qPCR of CCL23 and CCL23 ELISA data of mock-treated MDDCs. n=4 donors. (H) Expression of CIITA, CCL22, and IFNB1 in MDDCs infected with HIV-1-GFP (MOI=1 for CIITA, MOI=.5 for CCL22 and IFNB1) for 32 h. n=8 donors over 2 experiments for CIITA and n = 4 donors for CCL22 and IFNB1. (I) MFI of ISG15 expression under the conditions shown in (E). For (A, B, C, D, F, G, H & I), statistics were calculated by matching each donor in a mixed-effects model using Dunnet’s test for multiple comparisons.

To confirm that the negative regulation of DC maturation and IFN responses was specific to modulation of ILF3, we overexpressed each of its two major isoforms, NF90 and NF110 (Fig. 2E), and tested responses to HIV-1-GFP infection. We observed inverse effects compared to knockdown, as overexpression of either isoform suppressed surface expression of CD86 in resting MDDCs relative to controls (Fig. 2F). In HIV-1-GFP-infected MDDCs, both isoforms suppressed surface expression of CD86 (Fig. 2F). Similarly, overexpression of either ILF3 isoform potentiated CCL23 mRNA and secreted protein (Fig. 2G), enhanced expression of CIITA in response to HIV-1-GFP infection, and suppressed expression of CCL22, an additional chemokine marker of mature DCs (Fig. 2H). We also found that overexpression of NF90 or NF110 suppressed IFNB1 in HIV-1-GFP-infected MDDCs and trended toward suppression of ISG15, with only the NF110 isoform reaching statistical significance (Fig. 2H,I). Taken together, these experiments demonstrate that ILF3 negatively regulates myeloid DC maturation and innate immune responses driven by HIV-1-GFP.

ILF3 dampens innate sensing of HIV-1-GFP and of other innate stimuli

Under permissive conditions, HIV-1 is detected in myeloid cells primarily through the cGAS-STING pathway (25, 44). These responses depend on reverse transcription of the incoming HIV-1 RNA, which in MDDCs is allowed to proceed by providing Vpx in trans to disable SAMHD1 (19). They are also facilitated by capsid destabilization (45), are limited by the exonuclease TREX1 (46), and can occur both before and after integration (18). Detection of HIV-1 components has also been reported to occur through TLR7/TLR8 (47, 48), through the MAVS pathway (49, 50), through NONO (51), and through other cellular sensors (52). To determine whether ILF3 impacts sensing of HIV-1-GFP at different stages of the virus life cycle, we inhibited reverse transcription using EFV, or blocked integration using RAL, and tested innate responses to HIV-1-GFP in ILF3 knockdown cells (Fig. 3A). RAL treatment did not affect ILF3’s ability to restrain MDDC maturation upon HIV-1-GFP infection as measured by the percentage of live, CD86+ cells (Fig. 3B) or CD86 MFI (Fig. S3E). We noted that blocking reverse transcription inhibited responses to HIV-1-GFP as expected (18, 19) and did not affect baseline increases in maturation in ILF3 knockdown conditions as measured by CD86 (Fig. S3F,G). These data suggested that loss of ILF3 potentiates baseline maturation and innate immune signaling in MDDCs and that these responses persist during stimulation with HIV-1 when reverse transcription is allowed to proceed.

Figure 3. ILF3 dampens innate sensing of HIV-1 and other PAMPs.

Figure 3.

(A) Illustration of the first half of the HIV-1 lifecycle and the point of action of inhibitors that target reverse transcription (efavirenz (EFV)) and integration (raltegravir (RAL)). (B) Flow cytometry analysis of CD86 expression on MDDCs infected with HIV-1-GFP for 48 h that were treated or untreated with RAL. n = 6 donors from 2 individual experiments. (C) Illustration of ILF3 knockdown in MDDCs and stratification of stimulation with either immunostimulatory DNA (ISD, 1.5μg/mL), 2’3’-cGAMP (1μg/mL), or R848 (3μg/mL). (D) Illustration of signaling pathways downstream of cGAS and TLR7/8. (E) qPCR analysis of the indicated targets in MDDCs that were mock treated or stimulated with the agonists depicted in (C). n = 4 donors. (F) Flow cytometry analysis of HLA-DR expression on MDDCs that were mock-treated or stimulated with 2’3’-cGAMP (1μg/mL), for 24 hours. n=4 donors. (G) Flow cytometry analysis of CD86 expression on MDDCs that were mock-treated or stimulated with R848 (3μg/mL). n = 6 donors from 2 individual experiments. For (B, E, F, & G), statistics were calculated by matching each donor in a mixed-effects model using Dunnet’s test for multiple comparisons.

We next sought to determine whether loss of ILF3 potentiated responses to stimulation specifically through the cGAS-STING and TLR-MyD88 pathways, which are known to act through several shared downstream kinases and transcription factors (Fig 3C). cGAS can be directly activated by transfection of immunostimulatory DNA (ISD). Alternatively, robust cGAS activation can by phenocopied by exogenous delivery of its enzymatic product, 2′3′-cGAMP, a secondary messenger that binds to and activates the adapter protein STING. We also tested the ssRNA mimetic R848, which is detected through TLR8 and relays innate immune activation of inflammatory genes downstream of the adapter protein MyD88 (Fig. 3D). Stimulation with 2′3′-cGAMP in ILF3 knockdown MDDCs led to increased expression IFNB1, the maturation factor WFDC21P, the ISGs CXCL10, and ISG15 and the pro-inflammatory cytokine IL6, and decreased expression of CIITA compared to controls (Fig. 3E). Stimulation with ISD similarly potentiated expression of IFNB1, CXCL10, ISG15, and IL6. This result supports a role for ILF3 as a negative regulator of DC responses to cGAS-STING stimuli. Following stimulation with R848, knockdown of ILF3 increased expression of IL1B and IL6, pro-inflammatory cytokines downstream of MyD88 and NF-κB, as well as ISG15, and WFDC21P, while also decreasing CIITA (Fig. 3E). Knockdown of ILF3 also significantly increased surface expression of HLA-DR and CD86 in response to 2’3’-cGAMP and R848 stimulation, respectively, compared to controls (Fig. 3F,G). In THP-1 cells lacking ILF3, expression of CD80, IFNB1, CXCL10, and ISG15 were also significantly increased at baseline and when stimulated with 2′3′-cGAMP (Fig. S3H). Knockdown of ILF3 in MDDCs also led to elevated expression of CCL22 and a reciprocal suppression of CCL23, both at baseline and following 2’3’-cGAMP stimulation (Fig. S3I). These data indicate that ILF3 dampens responses through both STING and MyD88 pathways, suggesting that ILF3 might function broadly as a negative regulator of innate responses.

To verify that these changes in expression of IFN and maturation-related genes in ILF3 knockdown MDDCs correspond to increases in protein expression, we measured secretion of IFNβ, CXCL10, and IL-6 under mock or 2’3’-cGAMP conditions (Fig. S3J). Knockdown of ILF3 elaborated secretion of all three cytokines following stimulation with 2’3’-cGAMP and a trend toward increased secretion at baseline. Because we observed significant potentiation of IFNβ upon ILF3 knockdown, we sought to neutralize IFN produced in culture using the vaccina virus IFN receptor decoy protein B18R to evaluate IFN’s contribution to ILF3-dependent gene expression. Interestingly, treatment with B18R only partially suppressed CD86 induction in unstimulated ILF3 knockdown MDDCs, which suggested that IFNs might contribute to, but are not solely responsible for MDDC maturation under these conditions (Fig. S3K). Neutralization of IFN was effective, as B18R treatment efficiently blocked induction of ISG15, MX1, and STAT1 and reduced expression of CXCL10 and OASL in cells stimulated with 2’3’-cGAMP (Fig. S3L). In ILF3 knockdown conditions, B18R treatment reduced expression of these ISGs, and yet, similar to observations for CD86, differences between control and ILF3 sh conditions persisted, particularly for STAT1, CXCL10, and OASL. Since B18R did not completely block elevated expression of ISGs and CD86 in ILF3 knockdown conditions, we can interpret these results in one of two ways: 1) B18R neutralization was insufficient to block all IFN-driven signaling, or 2) IFN-dependent and IFN-independent signaling pathways contribute to the ILF3 phenotype. The latter interpretation should be strongly considered, given that induction of ISGs and MDDC maturation can occur through IFN-independent signals (22, 53). This would suggest that ILF3 negatively regulates innate immune responses and MDDC maturation through IFN-dependent and -independent mechanisms.

Nuclear localization of NF90 and N110 are required for suppression of innate responses

Having established a role for NF90 and NF110 in restraining MDDC maturation and innate responses to various stimuli, we sought to determine the specific domains of each protein required for their function. Both NF90 and NF110 forms of ILF3 have been reported to exist predominantly in the nucleus (54, 55). To test whether nuclear localization is required for suppression of DC maturation and type I IFN responses, we overexpressed versions of NF90 and NF110 lacking the bipartite nuclear localization sequence (a.a. 370-394: KRPMEEDGEEKSPSKKKKKIQKKE) (NF90/NF110ΔNLS) in MDDCs (Fig. 4A) and confirmed via immunofluorescence microscopy that NF90/NF110ΔNLS mutants were localized to the cytosol (Fig. 4B), as indicated by the shift in the ratio of total nuclear to cytosolic ILF3 (Fig. 4C). We also confirmed overexpression of wild type and ΔNLS forms of NF90 and NF110 by qPCR (Fig. 4D). In MDDCs challenged with HIV-1-GFP, full-length NF90 and NF110 were able to suppress CD86, WFDC21P, and ISG15 induction, and deletion of the NLS from either isoform prevented suppression of these markers (Fig. 4D). By flow cytometry, we saw concordant effects on protein expression of CD86 and ISG15 (Fig. 4E,F). NF90 and NF110 have been previously found to translocate to the cytosol during influenza infection of epithelial cells (56). To determine whether HIV infection could influence ILF3 localization, we compared the nuclear vs cytosolic ratios of endogenous NF90 and NF110 in uninfected or HIV-1-GFP+ MDDCs, and we found no significant shift in their localization (Fig 4G). These experiments suggest that nuclear localization is essential to the function of both of ILF3’s isoforms in regulating myeloid maturation and IFN responses, and in this context, infection with HIV-1-GFP does not impact ILF3 localization.

Figure 4. Nuclear localization of NF90 and N110 are required for suppression of innate responses to HIV-1-GFP.

Figure 4.

(A) Illustration of the bipartite nuclear localization sequence in NF90 and NF110. (B) Immunofluorescence of mock-treated MDDCs that were transduced with the indicated constructs and stained for actin (magenta), ILF3 (yellow, staining endogenous and overexpression constructs), and DNA (cyan, DAPI). (C) Quantification of three-dimensional immunofluorescence microscopy of total ILF3 antibody staining represented as the intensity of signal over voxel space, presented as a ratio between nuclear and cytosolic compartment. Data is from one representative donor using 50 cells per condition. Statistics were calculated using a one-way ANOVA with Sidak’s test for multiple comparisons. (D) qPCR quantification of NF90 and NF110 expression using SybrGreen probes to detect endogenous and overexpressed isoforms of ILF3. (E) Flow cytometry analysis of CD86 expression in MDDCs that were transduced with the indicated overexpression constructs and either mock treated or infected with HIV-1-GFP for 48 h. n = 4 donors. (F) MFI quantification of ISG15 expression. n = 4 donors. Donor #2 was excluded from the HIV-1-GFP condition for aberrant activation. For (D, E, & F), statistics were calculated using a paired mixed-effects model with Dunnet’s test for multiple comparisons. (G) Representative three-dimensional immunofluorescence microscopy quantification of total NF90 and NF110 from one donor, demonstrating the intensity of signal over voxel space as a ratio of nuclear versus cytosolic space. 75 cells were quantified from each condition: mock infected or HIV-1-GFP+ infected MDDCs at 27 hours post-infection (selecting GFP+ cells). Image channels depict total ILF3 (yellow), DNA (cyan, DAPI), and GFP. An unpaired, two-tailed t-test was used for statistical analysis.

The DZF domain of NF110 is required for suppression of MDDC maturation and type I IFN responses

To determine the domains of NF90 and NF110 that are required for regulation of DC responses, we designed constructs that lacked the dual double-stranded RNA-binding domains (dsRBDs) (ΔdsRBD1: a.a. 402-465, ΔdsRBD2: a.a. 531-576) or lacked the DZF domain (ΔDZF: a.a. 89-342) (Fig. 5A). These constructs were robustly overexpressed at the mRNA level in MDDCs (Fig. 5B) and were translated to significantly higher levels than the corresponding native proteins (Fig. 5C). Deletion of the dsRBDs in either NF90 or NF110 had no effect on levels of the maturation transcripts CD86, CD80, or CCL23 or surface expression of CD86 following HIV-1-GFP infection (Fig. 5D,E). Surprisingly, deletion of the DZF domain eliminated the ability of NF110 to suppress MDDC maturation, whereas deletion of the same domain in NF90 had no effect (Fig. 5D,E). We also quantified IFNβ secretion during HIV-1-GFP infection in MDDCs that overexpressed wild type or mutant ILF3 constructs. Although none of these experimental conditions reached statistical significance, all ILF3 constructs except for the NF110 DZF-deletion mutant trended toward IFNβ suppression (Fig. 5F).

Figure 5. The DZF domain of NF110 suppresses MDDC maturation.

Figure 5.

(A) Illustration of wt NF90 and NF110 constructs and the corresponding domain mutants with deletions in either the dsRBD or the DZF domains. (B) Representative qPCR quantification of both endogenous and overexpressed ILF3. n = 4 donors. (C) Representative western blot of MDDC whole cell lysates depicting overexpression of NF90 and NF110 constructs. Red arrowheads indicate overexpressed mutant constructs. (D) qPCR quantification of CD86, CD80, and CCL23 expression in MDDCs that were transduced with the indicated overexpression constructs and either mock treated or infected with HIV-1-GFP (MOI = 0.5) for 32 h. n = 8 donors from 2 independent experiments. (E) Flow cytometry quantification of CD86 expression in MDDCs infected with HIV-1-GFP (MOI = 0.5) for 48 h. n = 8 donors from 2 independent experiments. (F) ELISA of IFNβ in supernatants from MDDCs infected with HIV-1-GFP (MOI = 0.5) for 32 h. n = 4 donors. Shapes represent individual donors. For (B, D, & E), statistics were calculated using a paired mixed-effects model with Dunnet’s test for multiple comparisons.

RNA-seq analysis of NF110 reveals DZF-dependent genes associated with DC maturation and metabolic pathways

In order to more comprehensively define the molecular pathways regulated by ILF3 in DCs, we performed RNA-seq analysis of mock MDDCs transduced with the LKO control vector, a vector expressing full-length NF110, or the NF110 DZF-deletion mutant (ΔDZF). 355 genes were significantly differentially expressed in cells overexpressing wildtype NF110 compared to the LKO control (|log2(NF110/LKO)| > log2(1.5), FDR < 0.05, for genes with an average log2(CPM)>1). For 97 of these genes, more than 50% of the expression change induced by overexpression of NF110 was retained when NF110ΔDZF was overexpressed. We termed these genes “DZF-independent” with respect to the ILF3-mediated effect (Fig. S4A). Many of the most strongly down-regulated, DZF-independent genes were found to be non-coding RNAs (Fig. 6A), consistent with ILF3’s known role as a binder and modulator of lncRNAs (5762). Interestingly, genes that were up-regulated by NF110 overexpression in a DZF-independent manner were generally protein-coding (Fig. S4A). Of note, NF110 overexpression led to upregulation of AQP7, CD163L1, CD14, C3AR1, CD300a, ABCA9, MAFB, FMN1, STEAP4, CD163, IL10, and CFH (Fig.6B), which are all associated with immature or tolerogenic myeloid/DC phenotypes (6373). Conversely, DCSTAMP, CHI3L1, CH25H, ALDH1A2, BHLHE41, and CCL22, which are characteristic genes expressed in mature DCs (33, 7478), were down-regulated in a DZF-dependent manner (Fig. 6B). Together, these data demonstrate that NF110 restrains maturation in uninfected DCs and that this activity is dependent on the DZF domain.

Figure 6. RNA-Seq analysis of NF110 overexpression reveals DZF-dependent genes associated with DC maturation and metabolic pathways.

Figure 6.

(A) Plot of 355 differentially expressed genes on log2 scale, averaged across n=4 donors, with a |FC| > 1.5, average Log2 CPM > 1, and FDR < 0.05 of NF110 and NF110 ΔDZF constructs under unstimulated conditions. Blue points indicate noncoding-RNAs down-regulated by NF110 and NF110 ΔDZF. Red point indicates overexpression of NF110 (ILF3), as expected. (B) Genes represented in (A), |FC| > 2, ordered by magnitude of fold change by group. (C) GSEA plot of top-ranking Cholesterol Homeostasis Hallmark gene set from analysis of MDDCs overexpressing NF110 compared to the LKO control. (D) Log2 fold change of core enrichment genes (FDR < 0.05) from (C), pre-ranked by t-test. (E) Average Log2 fold change for genes from the Lindstedt DC Maturation gene sets (Groups A-C) in MDDCs overexpressing NF110 (closed circles) or NF110 ΔDZF (open circles) compared to the LKO control. Highlighted genes represent genes with a p-value < 0.05.

We also examined the set of genes that were differentially expressed following overexpression of NF110 wt compared to the LKO control by GSEA (79). Of note, the expression of genes belonging to the Cholesterol Homeostasis (32/74) and Oxidative Phosphorylation (111/200) gene sets was suppressed under conditions of NF110 overexpression. These were the only statistically significantly enriched gene sets out of all tested Hallmark Gene Sets in the Molecular Signatures Database (80) containing between 15 and 200 genes (Fig. 6C, S4B). Inspection of individual genes belonging to the Oxidative Phosphorylation gene set revealed they had subtle changes in magnitude compared to the control (Fig. S4C). Genes in the Cholesterol Homeostasis gene set were suppressed to a greater degree by NF110, and included PPARG, ATF3, LGALS3, LPL, and TNFRSF21 (FDR<0.05) (Fig. 6D). The top DZF-dependent gene in the Cholesterol Homeostasis group, PPARG (the gene that encodes PPARγ), was also significantly up-regulated in ILF3 KO THP-1 populations (Fig. S4D).

To determine if our RNA-seq data from NF110 overexpression reflected known biological perturbations, we compared our set of NF110-regulated DZF-dependent genes with public datasets using EnrichR (81). We found significant overlap with genes affected by the PPARγ agonists rosiglitazone and atorvastatin in MDDCs and monocytes, respectively (Fig. S4E). This overlap is noteworthy, considering that PPARγ is known to control oxidation of fatty acids and regulate NF-κB-mediated proinflammatory responses (82). Taken together, these data suggest that ILF3 shapes the transcriptome of myeloid DCs through pathways that may intersect, at least in part, with PPARγ and lipid metabolism.

We also observed that many genes in the Lindstedt DC maturation gene sets were moderately suppressed by NF110 overexpression (Fig. 6E), although these gene sets did not exhibit high GSEA scores when analyzed in full. This is likely due to the fact that we tested MDDCs in an immature state, with expression of these genes already being low, thereby making further gene suppression difficult to detect. Nevertheless, the majority of genes (75%) in gene sets A-C (elevated in mature DCs in Fig.1) were down-regulated by overexpression of NF110, predominantly in a DZF-dependent manner (Fig. 6E). Apart from the significantly down-regulated genes by NF110, the majority of genes down-regulated by the wildtype form of NF110 had consistently lower p-values than those down-regulated by NF110 ΔDZF. These data are in agreement with what we observed in ILF3 knockdown experiments (as expression of these genes associated with DC maturation was increased), and support a specific role for NF110 in negatively regulating myeloid DC maturation.

Discussion

Delineating the mechanisms that regulate DC maturation and innate responses is critical to understanding antiviral immunity and can inform design of new therapeutic agents to modulate inflammation. Here, we have identified that the transcription factor ILF3 acts as a negative regulator of DC maturation and innate immune responses. We discovered that CD14+ monocytes express minimal levels of ILF3 and then increase its expression upon derivation into immature dendritic cells. As DCs are exquisitely sensitive to IFN signaling compared to circulating monocytes (22), it is plausible that DCs change expression levels of ILF3 in order to deploy or withdraw an additional regulator of innate immune responses. Although several previous studies have examined the role of ILF3 in regulating innate immune responses during stimulation, they have not reached a consensus on the role of ILF3, as these functions are likely context dependent. Our findings are consistent with the only other study of ILF3 in primary human cells during virus infection, which demonstrated that knockdown of ILF3 in primary human bronchial epithelial cells led to an increase in IFNβ production in response to infection with influenza virus (26). However, this effect is not observed in all experimental systems, since siRNA knockdown of ILF3 in HeLa and mouse embryonic fibroblast cells did not affect levels of type I IFNs following stimulation with the double-stranded RNA-mimetic polyinosinic:polycytidylic acid (83). Other examples suggest that ILF3 may positively regulate IFN responses under certain conditions. In one case, A549 cells infected with Sendai virus exhibited decreased IFN upon siRNA knockdown of ILF3 (28). A more recent study in HeLa cells found that NF110 enhanced translation of IFNB1 mRNA and a subset of ISGs (27). Given that the mechanisms regulating innate immune sensing and IFN production are known to be different between cell lines of immune and non-immune origin (84), it is not particularly surprising that ILF3’s function is context-dependent. Our data, based on knockdown, knockout, and overexpression studies, demonstrate that ILF3 acts to restrain IFN and downstream ISGs in MDDCs and myeloid cell lines, and is a significant contributing factor in regulating DC maturation state.

More broadly, the role of ILF3 in regulating innate immune responses has been evaluated primarily during RNA virus infection (26, 28). Previous studies have also established that NF90 and NF110 isoforms inhibit replication of RNA viruses by physical association with the ISG PKR via their dsRBDs to block viral translation (85, 86). Until now, it has not been established whether ILF3 also has a role in regulating DNA sensing pathways. Here, we have used HIV-1-GFP to model retrovirus infection and innate immune stimulation through the cGAS-STING pathway and have used antiretroviral drugs to separate stages of the virus life cycle. We found that in ILF3 knockdown conditions, the elevated innate immune responses and DC maturation phenotype persisted during infection with HIV-1-GFP. Responses to HIV-1-GFP were dependent on reverse transcription, as previously described (18, 19, 25, 44). Our observation that the ILF3 phenotype was sustained during stimulation with ISD, 2’3’-cGAMP, or R848 suggest that ILF3 acts broadly to negatively regulate pathways in the innate immune response. Placed in context with earlier publications, our data emphasize the varied roles of ILF3, not only across different cell types but across different pathogen sensing pathways.

ILF3 has been described to affect several aspects of HIV-1 infection. Many of these studies focused on a variant of ILF3, NF90ctv, that contains a two base-pair CT insertion that results in a frame-shift and translation of a highly acidic C-terminus (87). This variant does not appear to be expressed in the human transcriptome (29, 87) but has been implicated in the positive regulation of ISGs (87), interactions with HIV-1 Rev and the Rev-responsive element in HIV-1 RNA (88), and binding to HIV-1 TAR RNA (89). In light of our results demonstrating that loss of ILF3 is associated with heightened innate responses and DC maturation, we speculate that the NF90ctv variant may behave as a dominant negative to influence ILF3-dependent gene transcription. Interestingly, NF90 has been shown to enhance HIV gene expression through cyclin T1 regulation (90). Testing these roles for ILF3 and ILF3 variants is beyond the scope of our study, but future work will likely uncover whether these effects dovetail with our findings that ILF3 modulates myeloid cell activation.

Another unresolved question that has emerged from our studies is why the DZF domain in NF110, but not in NF90, is required for suppression of DC maturation. Of the two major isoforms of ILF3, NF110 has been shown to be more effective than NF90 at stimulating transcription through a proliferating cell nuclear antigen (PCNA) promoter in a transient reporter assay, whereas NF90 appears to have a greater capacity to bind RNA (29, 56). We note that the dsRBDs have been found to be dispensable for transcriptional coregulatory activity in other experimental systems (30). One hypothesis for why NF110 is a more effective transcriptional regulator is that its GQSY-repeat-containing C-terminus sterically interferes with the double-stranded RNA-binding domain (dsRBD), thereby reducing its activity relative to the DZF domain, which is required for interactions with DNA and for protein-protein interactions with binding partners like NF45 or NF90 (56), and increasing the DZF domain’s relative importance in transcriptional regulation. Thus, it is possible that the DZF domain in NF110 plays a key role in regulating transcription in MDDCs, likely through interactions with its binding partners that are not impacted by mutation of the DZF domain in NF90. We speculate that ILF3 transcriptional phenotypes may arise from disruptions in the balance of NF90 and NF110 interactions with other protein partners due to the following observations: 1) overexpression of either full-length NF90 or an NF90 mutant lacking the DZF domain suppressed MDDC maturation, and 2) NF90 and NF110 are known to form large heterodimeric complexes (91, 92), the stoichiometry of which will be impacted by isoform expression and availability. Future studies are required to disentangle the functional role of the DZF domain of each isoform.

The largest effects resulting from manipulation of ILF3 levels in uninfected MDDCs occurred in gene expression pathways related to cholesterol homeostasis. Though the Oxidative Phosphorylation gene set members were affected, dendritic cells have been found to exhibit unique plasticity with respect to their ability to generate ATP from either glycolysis or oxidative phosphorylation during stimulation (93). GSEA analysis of MDDCs overexpressing NF110 identified the Cholesterol Homeostasis pathway as the most significantly enriched Hallmark gene set (FDR < 0.05). PPARG, ATF3, LGALS3, LPL, TNFRSF12A, and other genes in this pathway were suppressed by NF110. Expression of PPARG was also significantly elevated in unstimulated THP-1 ILF3 knockout cells that displayed elevated maturation and IFN responses at baseline. Decreased expression of genes such as PPARG and CH25H would be expected to result in a buildup of cholesterol through suppressed efflux and conversion of lipid products, which could impact IFN signaling, inflammatory responses, and myeloid cell maturation as others have shown (94, 95).

PPARγ is known to heterodimerize with retinoid X receptor family members (RXR) and the resulting transcriptional complex has an important function in regulating energy balance, including roles in triglyceride metabolism, fatty acid processing and storage, and glucose homeostasis (96). RXRs can partner with retinoic acid receptor family members (RARs) as well as PPARs (97). We recently reported that RAR alpha (RARA) functions as negative regulator of DC maturation (22) and we speculate that this phenotype intersects with our observations reported here regarding ILF3. Along these lines, activation of PPARγ has been found to result in retinoid synthesis in dendritic cells (98), and RAR/RXR signaling can be activated through certain retinoids in DCs to suppress maturation (99, 100). Additionally, natural agonists of PPARγ (such as 15d-PGJ2) and synthetic agonists (such as troglitazone and ciglitizone) have been shown to inhibit NF-κB and mitogen-activated protein (MAP) kinase inflammatory pathways, resulting in the decreased surface expression of DC maturation markers (101, 102). PPARγ has been shown in murine DCs to be important for sustained expression of Aldh1a2 which promotes tolerogenic CD103+ DCs, and for suppressing the Th17-skewing cytokines IL-6 and IL-23p19 in all CD11c+ DCs (103). Given that that PPARγ was recently found to interact with both ILF3 and RXRA (RARA’s heterodimeric partner) (104), it is likely that ILF3 isoforms can also impact PPARγ/RXR and RARA/RXR transcriptional control of lipid metabolism by altering these heteromeric transcriptional complexes, and consequently influence innate immune responses and DC maturation. Additional studies are required to determine whether there is a direct link between ILF3, PPARγ, and RARA as a transcriptional coregulatory complex in the context of lipid metabolism, myeloid cell inflammatory responses, and DC maturation.

Importantly, there is evidence that ILF3 could have a critical role in inflammatory pathophysiology in vivo. Small nucleotide polymorphisms within the ILF3 locus are correlated with more frequent cardiac events in individuals with high and low HDL cholesterol profiles (105). These findings are congruent with a role for ILF3 in regulating cholesterol/lipid metabolism and inflammation. A separate small nucleotide polymorphism within the ILF3 locus is associated with increased susceptibility to rheumatoid arthritis (106), further reinforcing the principal findings from our study which indicate that ILF3 perturbations are linked with spontaneous induction of inflammatory gene expression. Taken together, our data identify ILF3 as an important negative regulator of inflammation and myeloid cell maturation, which has broad implications for how innate immune responses are governed during viral infection and inflammatory disease.

Supplementary Material

1

Acknowledgements

We thank Pamela Troisch at the Institute for Systems Biology for processing samples for microarray and thank members of the Aderem lab and Aitchison lab for helpful discussions and for critically reading the manuscript. The data supporting this publication is available at ImmPort (https://www.immport.org) under study accession SDY1722.

Funding:

This project was supported by NIH/NIAID T32 Immunology training grant AI106677 to R.N., NIH/NIAID P50 AI150464 to J.S.J., NIH/NIAID R01 AI032972 and U19 AI100627 to A.A.

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