Type I IFN induces IL-10 production in an IL-27-independent manner and blocks responsiveness to IFN-gamma for production of IL-12 and bacterial killing in Mycobacterium tuberculosis-infected macrophages

Tuberculosis, caused by the intracellular bacterium Mycobacterium tuberculosis, currently causes ~1.4 million deaths per year, and it therefore remains a leading global health problem. The immune response during tuberculosis remains incompletely understood, particularly regarding immune factors that are harmful rather than protective to the host. Overproduction of the type I IFN family of cytokines is associated with exacerbated tuberculosis in both mouse models and in humans, although the mechanisms by which type I IFN promotes disease are not well understood. We have investigated the effect of type I IFN on M. tuberculosis-infected macrophages and found that production of host-protective cytokines such as TNF-a, IL-12, and IL-1ß is inhibited by exogenous type I IFN, whereas production of immunosuppressive IL-10 is promoted in an IL-27-independent manner. Furthermore, much of the ability of type I IFN to inhibit cytokine production was mediated by IL-10. Additionally, type I IFN compromised macrophage activation by the lymphoid immune response through severely disrupting responsiveness to IFN-?, including M. tuberculosis killing. These findings describe important mechanisms by which type I IFN inhibits the immune response during tuberculosis.This work was funded by Medical Research Council, U.K. Grant U117565642 and European Research Council Grant 294682-TB-PATH. M.S. and L.M.-T. were funded by the Fundacao para a Ciencia e Tecnologia, Portugal. M.S. is a Fundacao para a Ciencia e Tecnologia, Portugal investigator. L.M.T. was supported by Fundacao para a Ciencia e Tecnologia, Portugal Grant SFRH/BPD/77399/2011

Differential post-transcriptional regulation of IL-10 by TLR2 and TLR4-activated macrophages

The activation of TLRs by microbial molecules triggers intracellular-signaling cascades and the expression of cytokines such as IL-10. Il10 expression is tightly controlled to ensure effective immune responses, while preventing pathology. Maximal TLR-induction of Il10 transcription in macrophages requires signaling through the MAPKs, ERK, and p38. Signals via p38 downstream of TLR4 activation also regulate IL-10 at the post-transcriptional level, but whether this mechanism operates downstream of other TLRs is not clear. We compared the regulation of IL-10 production in TLR2 and TLR4-stimulated BM-derived macrophages and found different stability profiles for the Il10 mRNA. TLR2 signals promoted a rapid induction and degradation of Il10 mRNA, whereas TLR4 signals protected Il10 mRNA from rapid degradation, due to the activation of Toll/IL-1 receptor domain-containing adaptor inducing IFN-ß (TRIF) and enhanced p38 signaling. This differential post-transcriptional mechanism contributes to a stronger induction of IL-10 secretion via TLR4. Our study provides a molecular mechanism for the differential IL-10 production by TLR2- or TLR4-stimulated BMMs, showing that p38-induced stability is not common to all TLR-signaling pathways. This mechanism is also observed upon bacterial activation of TLR2 or TLR4 in BMMs, contributing to IL-10 modulation in these cells in an infection setting.Fundação para a Ciência e a Tecnologia (FCT

Differential production of type I IFN determines the reciprocal levels of IL-10 and proinflammatory cytokines produced by C57BL/6 and BALB/c macrophages

Pattern recognition receptors detect microbial products and induce cytokines, which shape the immunological response. IL-12, TNF-alpha, and IL-1 beta are proinflammatory cytokines, which are essential for resistance against infection, but when produced at high levels they may contribute to immunopathology. In contrast, IL-10 is an immunosuppressive cytokine, which dampens proinflammatory responses, but it can also lead to defective pathogen clearance. The regulation of these cytokines is therefore central to the generation of an effective but balanced immune response. In this study, we show that macrophages derived from C57BL/6 mice produce low levels of IL-12, TNF-alpha, and IL-1 beta, but high levels of IL-10, in response to TLR4 and TLR2 ligands LPS and Pam3CSK4, as well as Burkholderia pseudomallei, a Gram-negative bacterium that activates TLR2/4. In contrast, macrophages derived from BALB/c mice show a reciprocal pattern of cytokine production. Differential production of IL-10 in B. pseudomallei and LPS-stimulated C57BL/6 and BALB/c macrophages was due to a type I IFN and ERK1/2-dependent, but IL-27-independent, mechanism. Enhanced type I IFN expression in LPS-stimulated C57BL/6 macrophages was accompanied by increased STAT1 and IFN regulatory factor 3 activation. Furthermore, type I IFN contributed to differential IL-1 beta and IL-12 production in B. pseudomallei and LPS-stimulated C57BL/6 and BALB/c macrophages via both IL-10-dependent and -independent mechanisms. These findings highlight key pathways responsible for the regulation of pro- and anti-inflammatory cytokines in macrophages and reveal how they may differ according to the genetic background of the host.his work was supported by The Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001126), the U.K. Medical Research Council (FC001126), and the Wellcome Trust (FC001126) since April 1, 2015 and before that by U.K. Medical Research Council Grant MRC U117565642 and also by European Research Council Grant 294682-TB-PATH (Crick 10127). A.H. was additionally funded by a U.K. Medical Research Council Centenary Award. M.S. was funded by Fundação para a Ciência e Tecnologia, Portugal Grant FCT-ANR/BIM-MEC/ 0007/2013. M.S. is an associate Fundação para a Ciência e Tecnologia, Portugal investigator.info:eu-repo/semantics/publishedVersio

Identification of the Key Differential Transcriptional Responses of Human Whole Blood Following TLR2 or TLR4 Ligation In-Vitro.

The use of human whole blood for transcriptomic analysis has potential advantages over the use of isolated immune cells for studying the transcriptional response to pathogens and their products. Whole blood stimulation can be carried out in a laboratory without the expertise or equipment to isolate immune cells from blood, with the added advantage of being able to undertake experiments using very small volumes of blood. Toll like receptors (TLRs) are a family of pattern recognition receptors which recognise highly conserved microbial products. Using the TLR2 ligand (Pam3CSK4) and the TLR4 ligand (LPS), human whole blood was stimulated for 0, 1, 3, 6, 12 or 24 hours at which times mRNA was isolated and a comparative microarray was undertaken. A common NFκB transcriptional programme was identified following both TLR2 and TLR4 ligation which peaked at between 3 to 6 hours including upregulation of many of the NFκB family members. In contrast an interferon transcriptional response was observed following TLR4 but not TLR2 ligation as early as 1 hour post stimulation and peaking at 6 hours. These results recapitulate the findings observed in previously published studies using isolated murine and human myeloid cells indicating that in vitro stimulated human whole blood can be used to interrogate the early transcriptional kinetic response of innate cells to TLR ligands. Our study demonstrates that a transcriptomic analysis of mRNA isolated from human whole blood can delineate both the temporal response and the key transcriptional differences following TLR2 and TLR4 ligation. PLoS One 2014 May 19; 9(5):e97702

<i>k</i>-means clustering of the significant transcript lists reveals similar clusters.

<p>Mean normalised expression profile of individual clusters (y axis, ±SD, n = 4) at each time point (x axis; 0, 1, 3, 6, 12 and 24 hours) for each cluster. N: number of transcripts within cluster; P: most significant canonical pathway (IPA). Clusters are grouped by similarity in kinetic profile (Pearsons correlation, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097702#pone.0097702.s007" target="_blank">Table S2</a>) and top canonical pathway.</p

Identifying potential cytokines involved in autocrine gene regulation by upstream analysis within IPA.

<p>(<b>A</b>) Predicted activated cytokines from IPA upstream analysis; stimulations analysed independently using Pam3CSK4 1202 and LPS 4777 transcripts lists, at each time point only genes whose expression were 1.8 fold different from the media control at that time point were taken into consideration. Predicted upstream cytokines which met the criteria (<i>p</i> value <0.01 (Fishers Exact Test) and <i>z</i>- activation score >2.5) shown plotted by <i>z</i>-activation score only at the time points where significance criteria met. (<b>B</b>) Cytokine identified from either predicted upstream analysis or canonical pathway analysis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097702#pone-0097702-g003" target="_blank">Fig. 3</a>) mean mRNA expression plotted as log2 fold change (y axis) across time (x axis; 0, 1, 3, 6, 12 and 24 hours), fold change is relative to the media control at each time point, only those predicted cytokines whose mRNA expression is >1.8 fold upregulated relative to media control at one or more one time points are shown.</p

LPS or Pam3CSK4 stimulations results in a differential response in gene expression over time.

<p>(<b>A</b>) 1 ml of human whole blood from healthy volunteers (N = 4) was stimulated with either Pam3CSK4 (200 ng/ml), LPS (1 ng/ml) or media control for different lengths of time (0, 1, 3, 6, 12 and 24 hours). Stimulations were analysed independently: media control compared to Pam3CSK4 and media control compared to LPS revealed 1202 and 4777 significantly expressed transcripts respectively. Transcripts were identified by normalising expression values to the median of the 0 hour samples, filtering by detection from background, statistical filtering (2 way ANOVA with Benjamini Hochberg multiple testing correction <i>p</i><0.01) and retaining transcripts whose expression was greater than 1.8 FC different between the media control and stimulation samples at one or more time point. (<b>B</b>) A Venn diagram of both significant transcript lists. Within the Venn for each subset the number of transcripts is given, with unique genes within IPA in brackets. For transcript lists the top 5 canonical pathways (IPA) are shown as well as a heat map of the normalised expression values of these transcripts for both stimulations over time.</p

Predicted transcriptional regulator identification and NFκB, IRF and STAT gene expression following LPS and Pam3CSK4 stimulation.

<p>(<b>A</b>) Predicted upstream transcriptional regulators from IPA; stimulations analysed independently using Pam3CSK4 1202 and LPS 4777 transcripts lists, at each time point only genes whose expression were 1.8 FC different from the media control at that time point were taken into consideration. Predicted upstream transcription regulators which met the criteria (<i>p</i><0.01 (Fishers Exact Test) and <i>z</i> activation score >2.5) shown plotted by <i>z</i>-activation score only at the time points where significance criteria met. (<b>B</b>) Mean mRNA expression of predicted transcription regulators plotted as log2 fold change (y axis) across time (x axis; 0, 1, 3, 6, 12 and 24 hours), fold change is relative to the media control at each time point, only those predicted transcription regulators whose mRNA expression is >1.8 FC relative to media control at one or more one time points are shown. (<b>C</b>) <b>NFkB genes.</b> Mean mRNA expression of the NFkB family genes, All 5 genes were significantly expressed and present in both Pam3CSK4 1202 and LPS 4777 transcript lists. (<b>D</b>) <b>Interferon Regulatory Factors.</b> Mean mRNA expression of the IRF and STAT genes. IRF1, IRF4, IRF7, IRF8, IRF9, STAT1, STAT2, STAT3, STAT4 and STAT5A were present in the LPS 4777 significantly expressed transcript list, none of the IRF or STAT genes (except STAT5B) were present in the 1202 Pam3CSK4 significant transcript list. (<b>E</b>) <b>Temporal Kinetics of NFkB and induced IRF and STAT genes.</b> Plotted for both LPS and Pam3CSK4 are mean fold change relative to media controls of the NFkB genes (NFKB1, NFKB2, REL, RELA, RELB) and mean fold change relative to media controls of selected IRF genes (IRF1, IRF2, IRF4, IRF7, IRF8, IRF9, STAT1, STAT2, STAT3, STAT4 and STAT5A).</p

NFκB and Interferon signalling pathways.

<p>Shown at the peak of their significance, 3 and 6 hours respectively. Significantly expressed genes within the pathway (from Pam3CSK4 1202 and LPS 4777 lists) shaded red if upregulated or blue if down regulated.</p

Transcript lists analysed at each time point.

<p>(<b>A</b>) A graph showing the number of genes from the respective significant transcript lists (4777 LPS and 1202 Pam3CSK4 lists) at each time point which are more than 1.8 FC different compared to the media control at that time point. (<b>B</b>) The significantly expressed transcript lists (1202 Pam3CSK4 transcript list and 4777 LPS transcript list) were analysed in IPA. For each time point only genes whose expression were 1.8 FC different from the media control at that time point were taken into consideration. Shown is a heatmap of pathway significance of the top 25 IPA canonical pathways for each time point where significance criteria met (Fishers Exact test <i>p</i><0.01). The IPA canonical pathways were chosen by identifying from the LPS stimulation analyses the top 25 most significant pathways across the time points (mean –log <i>p</i> value) and then compared to Pam3CSK4. (<b>C</b>) Venn diagrams of cytokine/chemokines and transcriptional regulators identified using IPA gene functional classification from LPS 4777 and Pam3CSK4 1202 transcript lists with mean expression greater than 1.8 FC different to media control at 1 hour. Listed adjacent to the Venn diagrams are the genes from each subset.</p