Prolonged exposure to microbial products such as lipopolysaccharide can induce a form of innate immune memory that blunts subsequent responses to unrelated pathogens, known as lipopolysaccharide tolerance. Sepsis is a dysregulated systemic immune response to disseminated infection that has a high mortality rate. In some patients, sepsis results in a period of immunosuppression (known as ‘immunoparalysis’)1 characterized by reduced inflammatory cytokine output2, increased secondary infection3 and an increased risk of organ failure and mortality4. Lipopolysaccharide tolerance recapitulates several key features of sepsis-associated immunosuppression5. Although various epigenetic changes have previously been observed in tolerized macrophages6,7,8, the molecular basis of tolerance, immunoparalysis and other forms of innate immune memory has remained unclear. Here we perform a screen for tolerance-associated microRNAs and identify miR-221 and miR-222 as regulators of the functional reprogramming of macrophages during lipopolysaccharide tolerization. Prolonged stimulation with lipopolysaccharide in mice leads to increased expression of miR-221 and mir-222, both of which regulate brahma-related gene 1 (Brg1, also known as Smarca4). This increased expression causes the transcriptional silencing of a subset of inflammatory genes that depend on chromatin remodelling mediated by SWI/SNF (switch/sucrose non-fermentable) and STAT (signal transducer and activator of transcription), which in turn promotes tolerance. In patients with sepsis, increased expression of miR-221 and miR-222 correlates with immunoparalysis and increased organ damage. Our results show that specific microRNAs can regulate macrophage tolerization and may serve as biomarkers of immunoparalysis and poor prognosis in patients with sepsis.
Lipopolysaccharide (LPS) tolerance is an immunosuppressive form of innate immune memory that can be modelled in vitro by prolonged treatment of bone-marrow-derived macrophages (BMDMs) with LPS (Extended Data Fig. 1a). As a result of this functional reprogramming of macrophages, a majority of LPS-induced genes are transcriptionally silenced (that is, tolerized) and are not expressed upon re-stimulation7,9 (Extended Data Fig. 1b). Using this in vitro model (Extended Data Fig. 1c–e) we identified microRNAs (miRNAs) with expression patterns that correlate with tolerance (Fig. 1a). We validated these findings using qPCR (Extended Data Fig. 1f, g) and found that several miRNAs are differentially expressed during tolerance but not during an acute LPS response. Levels of miR-222, in particular, increased late during the LPS response (Extended Data Fig. 1g) and correlated with tolerance induction (Fig. 1b). miR-222 was also upregulated to a lesser extent with prolonged stimulation with tumour necrosis factor (TNF) or interleukin-1β (IL-1β) (Extended Data Fig. 1h), which has been shown to weakly induce innate immune tolerance10,11. Pre-treatment of BMDMs with interferon gamma (IFNγ), which inhibits LPS tolerance8, prevented LPS-induced upregulation of miR-222 (Extended Data Fig. 1i). Although miR-221 is processed from the same primary transcript as miR-22212, mature levels of miR-221 and of miR-222 do not always correlate (Extended Data Fig. 2a–c). Given that miR-221 is not responsive to LPS (Extended Data Fig. 2a) or IFNγ (Extended Data Fig. 2d) in BMDMs, we focused on miR-222 in BMDM experiments.
BMDMs were transfected with an miR-222 mimic and stimulated with LPS to determine whether miR-222 induced reprogramming independently of other tolerogenic factors (Extended Data Fig. 2e). Overexpression of miR-222 inhibited expression of several inflammatory mediators at the protein (Fig. 1c), mRNA (Extended Data Fig. 2f), and primary transcript level (Extended Data Fig. 2g). Conversely, antagonization of miR-222 resulted in increased inflammatory gene expression, even during a naive LPS response. This effect was relatively mild early after stimulation (data not shown), probably owing to low basal levels of miR-222 expression, but increased in magnitude at later time points (Fig. 1d). To test the effect of miR-222 on tolerance, BMDMs were transduced with an miR-222 antagonist and tolerized in vitro. Antagonization of miR-222 reduced the duration and magnitude of suppression of LPS-response genes (Fig. 1e). In some cases, tolerized cells with antagonized miR-222 produced as much IL-6 or IL-12p40 in response to LPS as did non-tolerized cells (Fig. 1f).
In contrast to other genes, Tnf was suppressed at the mRNA level but not at the primary transcript level (Extended Data Fig. 2f, g), which suggests that miR-222 regulates Tnf through a mechanism that is distinct from that of other tolerized genes. Indeed, the untranslated region (UTR) of Tnf has a predicted binding site for miR-222 (Extended Data Fig. 3a). Luciferase reporter assays (Extended Data Fig. 3b) and CRISPR deletions of the predicted binding site (Extended Data Fig. 3c–g) confirmed that Tnf is a target of miR-222. However, the post-transcriptional effects of miR-222 on TNF expression do not contribute to the effects of miR-222 on other genes, as shown by the fact that TNF neutralization did not recapitulate the effects of miR-222 overexpression (Extended Data Fig. 3h, i).
Intact Tnf transcription suggested miR-222 does not alter Toll-like receptor 4 (TLR4) signalling. Indeed, miR-222 overexpression did not affect LPS-induced IκBα degradation (Extended Data Fig. 4a–c). We therefore filtered computational predictions for miR-222 targets that were expressed in macrophages, did not affect TLR4 signalling and decreased in expression late in the LPS response (between 8 and 24 h of LPS stimulation; Extended Data Table 1). This approach identified Brg1 as the target most likely to mediate the transcriptional effects of miR-222 during LPS tolerance. BRG1, a catalytic subunit of the SWI/SNF (BAF) complex, evicts polycomb repressive complexes in an ATP-dependent manner, promoting chromatin accessibility and enabling transcription factor recruitment to specific binding sites13. Notably, BRG1 is recruited to the promoters of late LPS-response genes, which require SWI/SNF activity for their transcription14.
The predicted miR-222–Brg1 binding site is evolutionarily conserved (Extended Data Fig. 4d), and RNA levels of Brg1 and miR-222 during the LPS response were inversely correlated (Extended Data Fig. 4e). Artificial modulation of miR-222 caused an inverse effect on Brg1 mRNA and protein levels (Extended Data Fig. 4f–h). To confirm that this was due to direct targeting, we cloned the Brg1 UTR into a luciferase reporter. miR-222 suppressed luciferase activity resulting from co-transfection in a dose-dependent manner only if the miR-222-binding site in the Brg1 UTR was intact (Extended Data Fig. 4i). We compared the effects of miR-222 overexpression on genes previously identified as being SWI/SNF-dependent in macrophages15. Overexpression of miR-222 preferentially suppressed expression of SWI/SNF-dependent genes (Fig. 2a and Extended Data Fig. 4j). Furthermore, BRG1 recruitment to inflammatory gene promoters was reduced after miR-222 overexpression (Fig. 2b). Histone H3 acetylation, which occurs downstream14 of BRG1 activity, was also reduced (Extended Data Fig. 4k). By contrast, histone H4 acetylation at these promoters, which occurs before BRG1 recruitment16,17, was unaffected (Extended Data Fig. 4l). Finally, CRISPR–Cas9 disruption of the miR-222-binding site in the Brg1 UTR in RAW cells (Extended Data Fig. 4m) prevented miR-222-mediated suppression of some SWI/SNF-dependent genes (Extended Data Fig. 4n).
To characterize the biological role of miR-222, we generated a mouse knockout model. However, miR-221 and miR-222 are encoded in the same transcript, are induced by LPS in certain cell types (Extended Data Fig. 2b, c), have similar seed sequences (Extended Data Fig. 5a), have substantial overlap in predicted mRNA targets (Extended Data Fig. 5b) and are both predicted to bind to the same target site in the Brg1 UTR (Extended Data Fig. 5c). Furthermore, as with miR-222, overexpression of miR-221 downregulates levels of Brg1 (Extended Data Fig. 5d) and has downstream effects on inflammatory gene expression (Extended Data Fig. 5e). Therefore, we targeted both miRNAs for deletion18 (Extended Data Fig. 5f–h). We then used qPCR and RNA sequencing to characterize the LPS response in mir-221 mir-222 knockout macrophages (Fig. 2c). Although the increase in Brg1 expression in peritoneal macrophages from knockout mice was modest compared to in vitro experiments, mir-221 mir-222 knockout cells expressed higher levels of many Brg1-dependent genes, as well as Tnf (Extended Data Fig. 5i, j). Some Brg1-dependent genes were more affected by mir-221 mir-222 knockout than others (compare Il6 and Nos2 in Extended Data Fig. 5j), which suggests differential sensitivity to changes in levels of BRG1.
To better understand the mechanisms of altered gene expression in cells that lack miR-221 and miR-222 (Extended Data Fig. 5k), we analysed the promoters of affected genes to identify common regulatory features. Although we obtained similar results in multiple analyses of subsets of affected genes (Extended Data Fig. 6a–f), we limited our main analysis to LPS genes that are most suppressed in tolerized wild-type cells (358 genes out of 1,036 genes in total that are responsive to LPS; Fig. 2d). Roughly half of these 358 genes were expressed at higher levels in tolerized knockout cells compared to tolerized wild-type cells (‘de-repressed’ genes, Fig. 2e), and roughly half were unaffected (‘unaffected’ genes, Fig. 2f). The promoters of de-repressed genes were enriched for the binding motifs of interferon regulatory factors (IRFs) as well as STAT1 and STAT2 (Fig. 2e), whereas those of unaffected genes were enriched for E2F and EGR family motifs (Fig. 2f). An analysis of predicted downstream functions of the de-repressed gene subset found an enrichment for IFN-response genes (Fig. 2e), and LPS-induced expression of many of these genes is reduced in Ifnar1 knockout cells19. This implies that many of these genes are a part of the late LPS response, transcribed as a result of STAT activation by autocrine and/or paracrine signalling by IFN generated from the initial LPS stimulation.
To determine whether the predicted binding motifs were used during the LPS response, we analysed transcription factor occupancy using previously published chromatin immunoprecipitation followed by sequencing (ChIP–seq) data20,21,22,23. IRF1 and IRF8 were found to be selectively pre-associated with promoters of de-repressed genes (Fig. 2g and Extended Data Fig. 6g). However, STAT1 and STAT2 were recruited specifically to the promoters of de-repressed genes only after LPS stimulation (Fig. 2g). Other transcription factors, such as NF-κB, were not differentially recruited (Extended Data Fig. 6h). Furthermore, in cells with a deletion or mutation of Irf1 or Irf8, respectively24, cytokine-induced H3K27 (histone H3, lysine 27) acetylation—a marker of active transcription—was diminished at the promoters of de-repressed genes, whereas deletion of Stat125 almost completely abolished cytokine-induced H3K27 acetylation at these genes (Fig. 2h). Consistent with this analysis, STAT2 recruitment was significantly higher at the promoters of de-repressed genes in tolerized mir-221 mir-222 knockout cells after re-stimulation, compared to wild-type cells (Fig. 2i). Furthermore, levels of Stat1 mRNA are higher in mir-221 mir-222 knockout cells and in cells in which Brg1 is overexpressed (Extended Data Fig. 7i, j) than they are in wild-type cells. Therefore, miR-221 and miR-222 perturb SWI/SNF promoter recruitment, which leads to the repression of STAT activity at inflammatory gene promoters. As BRG1 and STAT transcription factors work cooperatively only at certain gene promoters to enable IFN- and cytokine-induced gene transcription26,27, miR-221 and miR-222 may limit expression of specific genes (Fig. 2i).
Next, we examined miR-221 and miR-222 activity using a model of sterile inflammatory shock induced by a high-dose LPS injection. In this system, changes that decrease inflammation increase survival: therefore, we used this model mainly to determine whether the anti-inflammatory effects of miR-221 and miR-222 we observe in vitro also occur in vivo. After LPS injection, levels of miR-221 and miR-222 in circulating immune cells were elevated (Fig. 3a). To determine whether this is physiologically relevant, LPS tolerance was induced in wild-type and mir-221 mir-222 knockout littermates by administering two sublethal doses of LPS before a lethal LPS dose: this regimen induces sufficient tolerance to prevent lethality in wild-type mice (Extended Data Fig. 7a, b). Although mir-221 mir-222 knockout mice were also protected from lethality, the mir-221 mir-222 knockout mice exhibited more symptoms of septic shock (Extended Data Fig. 7c), which indicates decreased anti-inflammatory effects in the knockouts. To test whether miR-221 and miR-222 contribute to survival under more extreme conditions, we used a model of septic shock in which tolerance is only partially protective against lethality (Extended Data Fig. 7d, e). In this model, absence of miR-221 and miR-222 decreased the median survival time (from 36.5 h to 20.5 h) as well as the likelihood of septic shock survival over a 72-h period (Fig. 3b).
Although LPS-induced septic shock is used to study acute inflammation in vivo, this model does not recapitulate sepsis in patients or necessarily predict the effect of inflammatory regulators on patient outcome. Therefore, to study the role of miR-221 and miR-222 in a model that better reflects the systemic innate response to pathogen challenge, we used a Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) infection model. First, we performed in vitro assays using green fluorescent protein (GFP)-expressing S. Typhimurium to infect BMDMs. BMDMs from mir-221 mir-222 knockout mice exhibited increased GFP per cell soon after infection (Extended Data Fig. 7f–h). At later time points, this difference was not observed (Extended Data Fig. 7h), which suggests that—despite increased phagocytosis—mir-221 mir-222 knockout cells are more efficient at suppressing intracellular replication and/or survival. We confirmed this finding by lysing BMDMs and comparing bacterial colony-forming unit (CFU) recovery at early and late time points after infection (Extended Data Fig. 7i). To test miR-221 and miR-222 effects in vivo, wild-type and knockout mice were injected intraperitoneally with the same strain of S. Typhimurium. Two days after infection, fewer bacterial CFUs were recovered from the liver and spleen of mir-221 mir-222 knockout mice (Fig. 3c). In addition, mir-221 mir-222 knockout mice exhibited increased survival time (Fig. 3d), suggesting that the loss of miR-221 and miR-222 confers resistance to bacterial replication and/or dissemination. These findings suggest that miR-221 and miR-222 broadly suppress inflammation and innate immune function. During the early stages of sepsis expression of miR-221 and miR-222 may be protective, by limiting the excessive inflammatory cytokine production that contributes to septic shock. Conversely, miR-221 and miR-222 appear to contribute to immunoparalysis, and increased miR-221 and miR-222 expression may enhance lethality during the later stages of sepsis (Fig. 3e).
Because it is unclear which models most accurately resemble conditions in patients, we next examined miR-221 and miR-222 expression in human disease. Consistent with results from mouse cells, miR-221 and miR-222 are both upregulated in response to prolonged LPS stimulation of a human monocyte-like cell line, whereas only miR-222 is upregulated by LPS in this cell line after phorbol 12-myristate 13-acetate (PMA)-induced differentiation to a macrophage-like cell type (Extended Data Fig. 8a, b). Next we analysed miR-221 and miR-222 expression in three patient cohorts. In the first cohort (Extended Data Fig. 8c), we quantified miR-221 and miR-222 levels in peripheral blood mononuclear cells from ten sequential patients from an intensive care unit, who met sepsis criteria28 within 4 h of admission to the intensive care unit. Compared to peripheral blood mononuclear cells from healthy donors, miR-221 and miR-222—but not several other inflammation-associated miRNAs—were significantly higher in samples from patients in the intensive care unit (Fig. 4a). Levels of miR-221 and miR-222 expression were then examined in a second cohort of patients, all of whom had acute decompensated liver disease and clinical suspicion of infection (Extended Data Fig. 8d). Patients with organ failure, as defined by the chronic liver failure–sequential organ failure assessment, had significantly higher levels of miR-222 than patients without such failure (Fig. 4b). Levels of miR-221 correlated with those of miR-222 (Extended Data Fig. 8f), but were not increased to statistically significant levels (Fig. 4c). Levels of miR-222 in this cohort inversely correlated with levels of BRG1 expression (Fig. 4d). In a set of matched peripheral blood mononuclear cell and serum samples, levels of miR-222 and TNF were also inversely correlated (Fig. 4e). Finally, the inverse correlation between miR-222 and BRG1 was also observed in CD14+ monocytes sorted from the peripheral blood mononuclear cell population of a third clinical cohort (Fig. 4f and Extended Data Fig. 8e), confirming changes in levels of miR-222 and BRG1 in myeloid cells.
Unlike generalized inflammatory markers, miR-222 elevation correlates specifically with severe sepsis. miR-222 levels do not correlate with inflammatory markers such as C-reactive protein or white blood cell count, but showed a significant correlation with organ-damage markers, including creatinine and the model for end-stage liver disease score (Extended Data Fig. 8g–j). miR-222 expression may be a useful biomarker for detecting patients who are undergoing septicaemia-induced immunoparalysis and are, therefore, predisposed to organ failure and mortality.
In summary, our data establish a model in which miR-221 and miR-222 restrict chromatin remodelling and silence transcription to enforce innate immune tolerance. After prolonged innate immune signalling, increased expression of miR-221 and miR-222 reduces expression of BRG1, which leads to changes in SWI/SNF complex levels or composition that result in the selective expression of only those LPS-response genes with the most favourable chromatin states. The fact that substantial changes in gene expression result from modest miR-221- and miR-222-dependent changes in BRG1 expression is consistent with previous reports that the mutation or deletion of a single allele of the SWI/SNF subunit is sufficient to confer strong phenotypic effects29,30. Hence, by fine-tuning the levels of BRG1, miR-221 and miR-222 can prevent prolonged expression of STAT-dependent inflammatory genes in macrophages, thereby leading to tolerance or innate immunoparalysis (Extended Data Fig. 9). By contrast, robust activation of STAT1—for example, by co-stimulation with IFNγ—can block8 or even reverse31,32 LPS tolerance and innate immunoparalysis. Consistent with such a role for STAT1, treatment with IFNγ has been shown to improve outcomes in sepsis33.
Although LPS tolerance promotes survival in mouse models of sterile shock, patients with sepsis probably succumb to primary or secondary1 infections owing to immunosuppression as a result of functional reprogramming of myeloid cells. Thus, the same innate immunoparalysis that is protective in the mouse LPS-shock model would be responsible for organ damage and mortality in human patients with sepsis. We identify miR-221 and miR-222 as mediators of tolerance and show that miR-221 and miR-222 expression may distinguish patients with organ failure who are at high risk of mortality from those patients with infection alone. Thus, the monitoring of miR-221 and miR-222, or related bio-markers, may help clinicians to stratify patients with sepsis into groups on the basis of whether they would benefit from pro-inflammatory immunotherapies or classical anti-inflammatory treatments.
RAW 264.7 cells (ATCC TIB-7) were cultured in DMEM supplemented with 10% fetal bovine serum. 293FT cells (Invitrogen R7007) and L-929 cells (ATCC CCL-1) were cultured in DMEM supplemented with 10% fetal bovine serum. Cells were purchased from vendor and tested for mycoplasma contamination before use (no further authentication of line identity was performed). L-cell conditioned medium (LCM) was generated by filter-sterilizing the supernatant of L-929 cells that were allowed to grow for one week in culture. Primary BMDMs were generated by isolation and culture of mouse bone marrow in complete RPMI supplemented with 20% LCM for up to 12 days. Immortalization of BMDMs was performed as previously described35 by inoculation with the J2 retrovirus. For cell stimulations, 10 ng/ml LPS (Sigma L8274), 10 ng/ml recombinant human TNF (R&D Systems 210-TA), 100 ng/ml recombinant mouse IL-1β (R&D Systems 401-ML-005), 100 ng/ml recombinant mouse IFNγ (BD Pharmingen 554587), 10 pg/ml recombinant mouse IL-10 (eBioScience 88-7104-ST), 10 μM dexamethasone (Sigma D402) and 0.01 μM oestrogen (Sigma E2758) were used unless otherwise indicated. For tolerization experiments, BMDMs were stimulated with 10 ng/ml LPS for 15 h (or as indicated), washed 5 times with 1× PBS, then allowed to rest for 2 h in LPS-free complete medium supplemented with 20% LCM. BMDMs were then stimulated with 1 μg/ml LPS for 4 h (for qPCR) or 12 h (for enzyme-linked immunosorbent assay (ELISA)), or as indicated.
Samples were treated as described, rinsed with 1× PBS, lysed in TRIzol and sent to a commercial microRNA array profiling service (Exiqon). As part of the service, samples were labelled using the miRCURY Hy3/Hy5 Power labelling kit and hybridized on the miRCURY LNA array (v.11.0 hsa, mmu and rno). All capture probes for the control spike-in oligonucleotides produced signals in the expected range. The quantified background-corrected signals were normalized using the global Lowess (locally weighted scatterplot smoothing) regression algorithm, and a list of differentially expressed miRNAs was returned.
miRNA mimic and antagonist oligonucleotides
Pre-miR miRNA precursors (Ambion AM17100) and anti-miR miRNA inhibitors (Ambion AM17000) were transfected into BMDMs to modulate miRNA function in short-term experiments. Part numbers for oligonucleotides are as follows: for overexpression experiments, pre-miR negative control #1 (Invitrogen AM17110), miR-222-3p (PM11376), miR-221-3p (PM10337); for antagonization experiments, anti-miR miRNA negative control #1 (Ambion AM17010), miR-222-3p (AM11376), miR-221-3p (AM10337). To optimize transfection conditions, the FAM dye-labelled pre-miR negative control #1 (Invitrogen AM17121) oligonucleotide was used. Transfection of 50,000 BMDMs per well of a 12-well plate with 6 μl lipofectamine and 0.1 nmol oligonucleotide diluted in 200 μl of Opti-MEM (total) was found to provide transfection of >80% of cells (as measured by flow cytometry), and these conditions were used for all further experiments in BMDMs. Medium was replaced with complete RPMI containing 20% LCM after 4 h to minimize cytotoxicity. Cells were allowed to recover for 24–48 h before stimulation.
Production of virus and BMDM transduction
Plasmids for miRNA overexpression (GeneCopoeia CmiR0001-MR01, MmiR3289-MR01, or MmiR3434-MR01) or antagonization (GeneCopoeia CmiR-AN0001-a.m.03 or HmiR-AN0399-a.m.03) were transfected into 293FT cells with the Lenti-Pac HIV Expression Packaging Kit (GeneCopoeia HPK-LVTR-20) or Lenti-Pac FIV Expression Packaging Kit (GeneCopoeia FPK-LVTR-20) to generate viral particles. BMDMs were inoculated by spin infection in 6-well plates in the presence of 6 μg/ml polybrene (Sigma H9268). Following spin inoculation, viral supernatant was immediately replaced with complete RPMI supplemented with 20% LCM. Cells were allowed to recover overnight. For primary BMDMs, plating for inoculation was generally performed on day five of differentiation. The first spin infection was performed on day six, second spin infection (if necessary) was performed on day seven, and plating for experiments was performed on day eight.
BMDMs were plated at 50,000 cells per well, and cytokine concentrations in cell supernatants were measured using the BD OptEIA Mouse IL-6 ELISA Set (BD 555240), BD OptEIA Mouse IL-12 (p40) ELISA Set (BD 555165), or BD OptEIA Mouse TNF (Mono/Mono) ELISA Set (BD 555268) according to the manufacturer’s instructions.
RNA extraction, reverse transcription and qPCR
Total RNA was extracted from samples using TRIzol reagent (Invitrogen 15596018). For reverse transcription and detection of miRNAs, the Universal cDNA Synthesis Kit (Exiqon 203301) and locked nucleic acid primers (Exiqon) were used. For other genes, approximately 1 μg of RNA was reverse transcribed with SuperScript III (Invitrogen 18080085). qPCR was then performed with VeriQuest Fast SYBR (Affymetrix 75675). The amplified transcripts were quantified using the comparative Ct method.
Computational prediction of miRNA-binding sites
miR-222-binding sites were predicted using the PITA algorithm36 (http://genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html) or MicroCosm Targets program (which utilizes the miRanda algorithm) as indicated in the text. MicroCosm Targets Version 5 was used to search for targets for mmu-miR-22237. UTRs and miRNA sequences were manually input to the PITA algorithm, and default search settings were used. All predictions were re-verified with their respective programs on 5 December 2013.
Construction of reporter vectors and luciferase reporter assays
The Brg1 UTR was amplified from IMAGE clone 30533489 (Open Biosystems MMM1013-9498346) and cloned into the pMIR-Report (Ambion AM5795) multiple cloning site using HindIII and SpeI restriction sites. The Tnf UTR was amplified from cDNA generated from BMDMs stimulated with LPS for 1 h, and inserted into the pMIR-Report vector as performed for the Brg1 UTR. Reporter plasmids were transfected into 293FT cells along with a Renilla luciferase reporter (used to normalize for transfection efficiency). After 24 h, Firefly and Renilla luciferase activity was quantified using the Dual-Luciferase Reporter Assay (Promega E1980).
The CRISPR design tool (http://crispr.mit.edu) was used to design guide RNAs for cloning into the PX458 (Addgene 48138) and PX459 (Addgene 48139) dual Cas9 and single-guide RNA expression plasmids38 to generate plasmids to target identified miR-222-binding sites for deletion. Cells were transiently transfected with empty vector or targeting vectors. After 24 h, transfected cells were selected using 48 h of puromycin treatment (PX459) or by sorting for GFP positive (PX458) cells. Limiting dilution was performed to isolate clonal cell lines. Clones were screened for appropriate deletion by PCR. Deletion of targeted regions was confirmed by sequencing when necessary. Gene expression was compared between lines with successful deletion, unsuccessful deletion and lines generated by transfection with expression plasmids that lacked a Cas9-targeting sequence.
For deletion of the miR-222-binding site in the Tnf UTR, the following guide sequences were used: combination 1, TCAGCGTTATTAAGACAATT GGG and ATTACAGTCACGGCTCCCGT GGG; combination 2, TTGTCTTAATAACGCTGATT TGG and ATTTCTCTCAATGACCCGTA GGG. For deletion of the miR-222-binding site in the Brg1 UTR, the following guide sequences were used: GGAGTAGCCCTTAGCAGTGA TGG and ACCAGATGTAGTTTCGAACT TGG.
Intracellular staining for flow cytometry
Cells were rinsed and fixed for 15–30 min at room temperature in 4% paraformaldehyde. Cells were rinsed and permeabilized by resuspension in 5% saponin for 10–20 min at room temperature. Either anti-IκBα (L35A5, Cell Signaling 4814), anti-Brg1 (H88, Santa Cruz sc-10768), or rabbit monoclonal antibody IgG isotype control (Cell Signaling 3900) was added, and cells were incubated for an additional 20 min at room temperature. Cells were rinsed and re-suspended in saponin with 1:300 dilution of fluorochrome-conjugated secondary antibody (Alexa Fluor 488 donkey anti-rabbit IgG, Invitrogen A21206; Alexa Fluor 546 goat anti-rabbit IgG, Invitrogen A11010; or Alexa Fluor 546 donkey anti-mouse IgG, Invitrogen A10036). After incubation at room temperature for 20 min, cells were rinsed, re-suspended in PBS and analysed on a BD LSRII flow cytometer.
Cells from a 15-cm plate were fixed by incubation in 1% formaldehyde for 5 min, rinsed and lysed by incubation for 5 min on ice in buffer L1 (50 mM Tris at pH 9, 2 mM EDTA, 0.1% NP-40, 10% glycerol, with protease inhibitors). Nuclei were spun down and re-suspended in 500 μl buffer L2 (50 mM Tris at pH 8, 0.1% sodium dodecyl sulphate and 5 mM EDTA). Sonication was performed in a Bioruptor, using 10 cycles of 30 s each. Immunoprecipitation was performed using 20 μl magnetic protein A beads and 5 μg anti-acetyl-histone H4 (Lys5; Millipore 07-327), 2 μg anti-BRG1 (H-88; Santa Cruz sc-10768), or 5 μg anti-acetyl-histone H3 (Millipore 06-599) per 50 μl of chromatin in a 500 μl volume. After overnight rotation at 4 °C, supernatant was isolated. DNA was recovered from the supernatant by adding 20 μl of 5 M NaCl, 50 μl of 10% SDS and 5 μl of proteinase K, shaking for 2 h at 60 °C (unbound fraction). Beads were washed 3× in high salt buffer (20 mM Tris at pH 8.0, 0.1% SDS, 1% NP-40, 2 mM EDTA, and 0.5 M NaCl), and 3× in Tris–EDTA. DNA was eluted from beads by re-suspending beads in 100 μl elution buffer and shaking for 2 h at 60 °C (bound fraction). Bound and unbound fractions were heated to 95 °C for 10 min. DNA was purified from fractions using the Qiagen PCR Purification Kit (28104). To check for promoter binding, qPCR was performed using DNA from the bound and unbound fractions. Bound:unbound ratios were normalized to alpha-crystallin ratios, as this should represent a silent gene.
BMDMs were nucleofected with 2 μg of plasmid DNA using the Amaxa Mouse Macrophage Nucleofector Kit (VPA-1009), in conjunction with the Amaxa Nucleofector II Device, according to the manufacturer-optimized protocol.
Salmonella enterica serovar Typhimurium infection
For these experiments, a GFP-expressing Salmonella enterica serovar Typhimurium strain (SL1344) was used. S. Typhimurium cultures were grown in LB medium supplemented with 100 μg/ml carbenicillin and 30 μg/ml streptomycin. Overnight cultures were diluted and allowed to grow for an additional hour before use, to ensure bacteria were in log-growth phase. OD600 nm readings were correlated to previously determined CFU values and used to quantify number of bacteria present in culture. BMDMs were infected by inoculation of DMEM growth medium (containing only streptomycin) with bacteria at a multiplicity of infection of 50. Plates were spun at 800 r.c.f. for 5 min at 4 °C. BMDMs were incubated for 30 min at 37 °C. Cells were washed 3 times, and then incubated in medium containing gentamycin (100 μg/ml for incubations of 2 h or less, 12 μg/ml for longer incubations). BMDMs were subsequently analysed for GFP content by flow cytometry, or lysed in water to enable plating of lysate dilutions on LB agar plates containing carbenicillin to determine bacterial CFU counts.
For BMDM generation, female C57Bl/6J mice of 7–10 weeks of age were used, unless otherwise noted. For tolerance and septic shock experiments, male C57Bl/6J mice of 6–10 weeks of age were used. LPS (Escherichia coli O55:B5; Sigma L2880) and d-(+)-galactosamine hydrochloride (Sigma G0500) were re-suspended in sterile PBS and filter-sterilized before intraperitoneal injection. For in vivo infection experiments, mice were given intraperitoneal injections of 1 × 107 CFUs per kg of a GFP-expressing Salmonella enterica serovar Typhimurium strain (SL1344) suspended in PBS. Mice were maintained under specific-pathogen-free conditions in animal facilities at Columbia University Medical Center. All animal experiments were carried out with the approval of the Columbia University Institutional Animal Care and Use Committee, and in compliance with regulations and guidelines set forth by Columbia University.
Generation of knockout mice
mir-221 mir-222 knockout mice were generated at the Columbia University Transgenic Mouse facility. In brief, KV1 (129B6 hybrid) ES cells were electroporated with the linearized targeting construct discussed in Extended Data Fig. 6. After positive and negative selection, clonal cell lines were screened by PCR for proper integration of the construct. Positive lines were expanded, blastocyst injection was performed and germline transmission was confirmed. mir-221 mir-222 knockout mice were backcrossed to the C57Bl/6 background 5–8 times before experimental use.
Peritoneal macrophage isolation
Five millilitres of cold PBS was injected into the peritoneal cavity of euthanized mice. The peritoneum was gently massaged. Fluid was collected and the process was repeated. Cell suspension was spun down and cells were plated at 500,000 cells per well in 12-well plates. Macrophages were allowed to adhere overnight. Non-adherent cells were rinsed off with PBS washes.
Thioglycollate elicitation of peritoneal macrophages
Three per cent thioglycollate was sterilized and aged for at least two months. One millilitre of thioglycolate preparation was injected into the peritoneal cavity of each mouse five days before the isolation of macrophages (as described in ‘Peritoneal macrophage isolation’).
Bones were isolated from wild-type C57Bl6/J mice. Marrow was retrieved by crushing. Monocytes were purified using the EasySep Mouse Monocyte Isolation Kit.
RNA sequencing was performed by the JP Sulzberger Columbia Genome Center. Poly-A pull-down was used to enrich mRNAs from total RNA samples (200 ng–1 μg per sample, RIN > 8 required). Libraries were prepared using the Illumina TruSeq RNA prep kit. Libraries were then sequenced using Illumina HiSeq2000. Multiplexed and pooled samples were sequenced to a depth of 24–34 × 106 reads per sample as 100-bp single-end reads. RTA (Illumina) was used for base calling, and bcl2fastq (version 1.8.4) was used for converting BCL to fastq format, coupled with adaptor trimming. Reads were mapped to a reference genome (mouse: UCSC/mm9) using Tophat (version 2.1.0) with 4 mismatches (–read-mismatches = 4) and 10 maximum multiple hits (–max-multihits = 10). To tackle the mapping issue of reads that are from exon–exon junctions, Tophat infers novel exon–exon junctions ab initio and combines them with junctions from known mRNA sequences (refgenes) as the reference annotation. The relative abundance (also known as the expression level) of genes and splice isoforms were estimated using cufflinks (version 2.0.2) with default settings.
Track data of genes of interest were loaded into Galaxy39 (http://usegalaxy.org) using the UCSC table browser and mouse mm10 genome. Using Galaxy, previously published ChIP–seq data20,21,22,23,24,25 was then aligned to the mouse mm10 genome using the HISAT program (Galaxy version 2.03) with default settings. BamCoverage (Galaxy version 220.127.116.11) was then used to generate a coverage bigwig file, using default settings to scale to the size of the mm9 mouse genome. ComputeMatrix (Galaxy version 18.104.22.168) and plotHeatmap (Galaxy version 22.214.171.124) were then used to compare transcription factor occupancy at gene promoters, using the transcription start site as the reference point.
ChIP–seq data that were analysed were from the European Nucleotide Archive accession ERA319838 (IRF5), and from the following Gene Expression Omnibus accessions: GSE5612320 (IRF1, IRF8, STAT1, STAT2); GSE6734321 (IRF3); GSE3610422 (IRF2, IRF4, NF-κB subunits); GSE6269723 (IRF7); GSE7788624 (IRF mutants); and GSE3837925 (STAT1 knockout).
Patient sample selection and processing
We selected 10 consecutive patients, newly admitted to a medical or surgical intensive care unit (ICU), who had systemic inflammatory response syndrome and a known or suspected infection40. Patients were excluded from the study if they had an ICU admission or bacteraemia within the previous 30 days. After obtaining informed consent from the patient or a surrogate, whole blood was drawn within 4 h of ICU admission. Peripheral blood mononuclear cells were isolated from whole blood of healthy human volunteers or buffy coat isolates from patients from the ICU meeting sepsis criteria, by centrifugation on a Ficoll cushion. RNA was isolated with the miRNeasy micro kit (Qiagen 217084) and reverse transcribed as in ‘RNA extraction, reverse transcription and qPCR’. Experiments were performed with approval of the Institutional Review Board at Columbia University and in accordance with regulations and guidelines set forth by the university. The results of these experiments are shown in Fig. 4a.
Patient sample selection and processing
Additional patient cohorts were obtained from hospitalized patients with acute decompensation of chronic liver disease and suspected bacterial infection. Baseline characteristics and outcome of patients with decompensated liver disease in the absence or presence of multiple organ failure syndrome (according to the EASL CLIF-C criteria for acute-on-chronic liver failure41) are given in Extended Data Fig. 8. Clinical scores—such as model for end-stage liver disease scores, bacterial culture count, protein analysis, blood count and serum levels of C-reactive protein and creatinine—were obtained from routine laboratory analysis. The determination of serum concentration of TNF was performed by ELISA. The results of these experiments are shown in Fig. 4b–f.
The isolation and characterization of human immune cells and the use of clinical data was approved by the internal review board (ethics committee of the Jena University Hospital, no. 3683-02/3). The study conformed to the ethical guidelines of the 1975 Declaration of Helsinki, and patients granted written informed consent before inclusion.
Statistics and sample collection
Student’s t-tests were performed using the T.TEST function in Microsoft Excel. All other statistical tests were performed using Prism software. Unless otherwise stated, two-sided tests were performed. For samples using cell lines and cells isolated from inbred mice, the Student’s t-test was often used. The distributional requirements for the test are assumptions. This means that—for instance—under the assumption of normal-distributed residuals, the t-test is an exact test. However, given a non-normal distribution of cell line data the test is not exact, but is instead approximative. For patient samples, nonparametric tests were used to avoid the assumption of a normal distribution. In all figures, error bars represent s.e.m. unless otherwise indicated. Standard deviations and s.e.m. values were calculated for each group of data and used to estimate variation (s.e.m. values are shown as error bars in most experiments). Variation generally appears similar between groups being compared. All experiments were replicated in the laboratory at least two times. Unless otherwise indicated, in experiments using primary cells n represents the number of experiments performed with separate cell isolations; in experiments using immortalized cells or cell lines, n represents the number of experiments performed using separate cell populations. Systematic randomization and blinding were not performed. Samples were excluded from the analysis if they were identified as outliers using the Grubbs’ test (also known as the extreme studentized deviate method).
For mouse LPS-shock studies, an appropriate sample size was estimated on the basis of an outcome variable of survival time, measured in hours. An estimate was based on using a one-tailed Student’s t-test to determine statistical significance. Control mice were expected to succumb within 62 h. Knockout mice were expected to become moribund 52 h after LPS injection at the latest. Therefore, the minimal effect size was estimated to be 10 h. On the basis of the literature and experiments previously performed by our laboratory, we anticipated a standard deviation of 10 h. Taking into account a power of 80% and alpha of 0.05, we calculated a sample size of 10 mice per genotype.
Further information on experimental design is available in the Nature Research Reporting Summary linked to this paper.
RNA sequencing data that support the findings of this study have been deposited in Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) with the accession code GSE89918. All other data are available from the corresponding author upon reasonable request.
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We thank M. McManus (UCSF) for generously providing us with a targeting construct used in the generation of mir-221 mir-222 knockout mice. This work was supported by grants R21-AI116082 and R37-AI33443 to S.G. from the National Institutes of Health.
Nature thanks G. Crabtree, R.Hotchkiss, M. Netea and the other anonymous reviewer(s) for their contribution to the peer review of this work.