
==== Front
iScience
iScience
iScience
2589-0042
Elsevier

S2589-0042(24)01925-4
10.1016/j.isci.2024.110700
110700
Article
Legionella pneumophila modulates macrophage functions through epigenetic reprogramming via the C-type lectin receptor Mincle
Stegmann Felix 12
Diersing Christina 12
Lepenies Bernd bernd.lepenies@tiho-hannover.de
123∗
1 Institute for Immunology, University of Veterinary Medicine Hannover, 30559 Hanover, Lower Saxony, Germany
2 Research Center for Emerging Infections and Zoonoses, University of Veterinary Medicine Hannover, 30559 Hanover, Lower Saxony, Germany
∗ Corresponding author bernd.lepenies@tiho-hannover.de
3 Lead contact

08 8 2024
20 9 2024
08 8 2024
27 9 11070020 10 2023
12 12 2023
6 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

Legionella pneumophila is a pathogen which can lead to a severe form of pneumonia in humans known as Legionnaires disease after replication in alveolar macrophages. Viable L. pneumophila actively secrete effector molecules to modulate the host’s immune response. Here, we report that L. pneumophila-derived factors reprogram macrophages into a tolerogenic state, a process to which the C-type lectin receptor Mincle (CLEC4E) markedly contributes. The underlying epigenetic state is characterized by increases of the closing mark H3K9me3 and decreases of the opening mark H3K4me3, subsequently leading to the reduced secretion of the cytokines TNF, IL-6, IL-12, the production of reactive oxygen species, and cell-surface expression of MHC-II and CD80 upon re-stimulation. In summary, these findings provide important implications for our understanding of Legionellosis and the contribution of Mincle to reprogramming of macrophages by L. pneumophila.

Graphical abstract

Highlights

• L. pneumophila-derived factors reprogram macrophages into a tolerogenic state

• Their epigenetic state is characterized by increased H3K9me3 and decreased H3K4me3

• The C-type lectin receptor Mincle is crucial for mediating tolerance induction

Cell biology; Immune response; Immunology; Molecular biology

Subject areas

Cell biology
Immune response
Immunology
Molecular biology
Published: August 8, 2024
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pmcIntroduction

Legionella pneumophila is a naturally occurring facultative intracellular pathogen in amebae which can lead to a severe form of pneumonia in humans known as Legionnaires disease1 with a mortality rate of approximately 10%.2 Upon inhalation of contaminated aerosols from water systems,3 L. pneumophila infect alveolar macrophages within the pulmonary environment.

Recognition of L. pneumophila by macrophages is versatile, mostly orchestrated by a range of pattern recognition receptors (PRRs) detecting various bacterial components.4 Several Toll-like receptors (TLRs), including TLR2, TLR5, and TLR9 play different roles in this context. For instance, TLR2 is involved in recognizing bacterial lipopeptides and L. pneumophila’s atypical lipopolysaccharide (LPS), characterized by a highly hydrophobic lipid A with long-chain fatty acid acylation and an O-antigen-specific chain composed of homopolymeric legionaminic acid.5,6,7 TLR5 and TLR9 contribute to L. pneumophila recognition by detecting bacterial flagellin and enhancing neutrophil recruitment to the L. pneumophila-infected lung, particularly during the early stages of infection.8,9 NOD1 and NOD2 recognize cytosolic peptidoglycan and initiate signaling through the activation of receptor-interacting protein kinase 2 (RIP2), ultimately leading to nuclear factor κB (NF-κB) activation.10 Retinoic acid-inducible gene-I (RIG-I)-like helicases (RLHs), including melanoma differentiation-associated gene-5 (MDA5) and RIG-I, also contribute by recognition of cytosolic nucleic acids.10 The NAIP5/NLRC4 inflammasome is responsible for detecting bacterial flagellin and plays a pivotal role in restricting replication of L. pneumophila in macrophages.11,12

C-type lectin receptors (CLRs) are PRRs that often recognize pathogen-derived glycoconjugate structures.13 The macrophage inducible Ca2+-dependent lectin receptor (Mincle; CLEC4E) was shown to recognize Mycobacteria,14 Klebsiella,15 Streptococci,16 Lactobacilli17 as well as Helicobacter18 among others. Currently, little is known about the involvement of CLRs in L. pneumophila recognition and L. pneumophila-induced innate responses.

L. pneumophila regulates effector functions of infected innate immune cells19 that are otherwise initiated to combat the pathogen. Tumor necrosis factor (TNF) enhances the phagocytosis of Legionella by macrophages and promotes the production of other pro-inflammatory cytokines and chemokines to recruit additional immune cells to the site of infection.20 Likewise, interleukin (IL)-12 drives the production of interferons (IFNs) in various immune cells.21 Type I IFN signaling restricts L. pneumophila replication in macrophages and lung epithelial cells, while IFN-γ-activated macrophages produce nitric oxide (NO) and inhibit bacterial replication in multiple host-cell types.22,23,24 In contrast, IL-10 was shown to reverse IFN-γ-mediated inhibition of L. pneumophila replication in human monocytes and murine bone-marrow-derived macrophages (BMMs).25,26 However, L. pneumophila secreted effectors such as Lgt1-3, SidL, LegK4, and RavX were shown to affect the host's protein synthesis, thereby strongly influencing cytokine production during infection. Infected macrophages are unable to produce sufficient amounts of TNF, IL-6, and IL-12.27 In such cases, the host heavily relies on the production of said effector molecules by bystander cells to limit the infection.28

L. pneumophila secreted nucleomodulins are effector proteins that access the host-cell nucleus and alter the host’s epigenetic landscape.29 These effector molecules are transferred into the host cell via the type IV secretion system (T4SS) and finally lead to the modification of host proteins through ubiquitination, phosphorylation, lipidation, glycosylation, (de-)AMPylation, phosphocholination, and dephosphocholination.30 They also inhibit host protein translation by targeting eukaryotic elongation factors or translation initiation.31,32 In lung epithelial cells infected with L. pneumophila, histone modifications, including acetylation and phosphorylation, occur globally at the promoters of relevant genes.33 These changes in histone modifications influence pro-inflammatory gene expression in infected cells.34 Just recently, two newly discovered Legionella-derived enzymes working closely together were identified to manipulate host gene expression called LphD and RomA.35

Still, currently little is known about how Legionella-derived factors impact the host’s innate immune memory, how persistent the epigenetic changes are, and which host cell receptors are involved. To this end, we subjected macrophages to a primary and secondary in vitro stimulus with killed L. pneumophila and assessed the influence of the primary stimulus on the chromatin landscape, transcriptome, and macrophage effector functions. In cells primed with killed L. pneumophila, we identified a substantial reduction in the production of cytokines, reactive oxygen species (ROS), and surface markers both on the gene as well as on the protein levels, reminiscent of tolerance effects. Additionally, we observed increasingly condensed chromatin at respective transcriptional regions. We show that the CLR Mincle significantly contributes to the observed tolerance effects. In this study, we provide new insights into how L. pneumophila-derived factors have an impact on the hosts epigenetics, transcription, and protein synthesis in a Mincle-dependent manner and over a time span of several days.

Results

Priming with h.i. L. pneumophila reduces critical effector functions in macrophages upon re-stimulation

Recognition of L. pneumophila triggers anti-bacterial defense mechanisms in macrophages, including the production of pro-inflammatory cytokines36,37 and ROS.36 While live (viable) L. pneumophila is known to modulate these functions in its host cell via secreted effectors,4 little is known about how Legionella-derived factors impact the host. To investigate the influence of L. pneumophila-derived factors on host’s innate responses, we primed BMMs with heat-inactivated (h.i.) L. pneumophila of different strains and subjected them to a secondary stimulus with the wild-type (WT) JR32 strain (Figure 1A). Upon initial priming of BMMs with different strains of h.i. L. pneumophila serotype 1 (WT JR32 and Corby as well as the LPS-mutant TF 3/1), we observed an increasing expression of the cytokines TNF, IL-6 and IL-12, ROS, and co-stimulatory molecules with increasing MOI, except for IL-10 (Figures S1A–S1G). After a defined resting period, re-seeding the cells (Figure 1A) ensured the viability of all cells (Figure S1H) as well as a consistency in cell counts prior to re-stimulation. When macrophages were re-stimulated with the same stimulus (h.i. WT JR32), we observed a marked reduction in the secretion of the pro-inflammatory cytokines TNF (Figure 1B), IL-6 (Figure 1C), and IL-12 (Figure 1D) with a lower secretion in case of previously higher MOI of L. pneumophila during priming. On the other hand, the secretion of the anti-inflammatory cytokine IL-10 was not affected by the previous priming (Figure 1E). A reduction was also observed for the production of ROS (Figure 1F) and the surface expression of major histocompatibility complex class II (MHC-II; Figure 1G) and CD80 (Figure 1H). Since all cells returned to the state of homeostasis (Figure S2), this finding excludes a prolonged response and clearly indicates a memory effect. Interestingly, the effects induced by the WT strains JR32 and Corby did not differ substantially from the LPS-mutant TF 3/1 strain, which is characterized by lower O-acetyl group substitution in its polysaccharide chain and thereby unable to generate a high-molecular-weight LPS.38 This suggests that tolerance induction is independent of the regular LPS structure. Similar effects were also observed for L. longbeachae, suggesting that the macrophage modulation is not specific for L. pneumophila but is also mediated by other species of the Legionella genus (Figure S3). Collectively, these observations suggest that Legionella-derived factors of different strains are capable of modulating several effector functions in BMMs over a time span of at least 4 days.Figure 1 Prior exposure to h.i. L. pneumophila diminishes essential effector functions in macrophages upon subsequent re-stimulation

(A–H) (A) Schematic overview of the experimental setup. BMMs were differentiated from bone-marrow cells (BMCs) and primed with different strains of h.i. L. pneumophila for 24 h. Afterward, the stimulus was washed away and the cells let to rest for 4 days. Subsequently, the cells were re-seeded and re-stimulated with the h.i. JR32 strain to assess the production of selected cytokines (B–E), ROS production (F) and surface expression of MHC-II (G), and CD80 (H). Data are presented as mean +SD and are representative of three independent experiments (n = 3) in triplicates. ns = not significant, ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

See also Figures S1–S3.

RNA-sequencing of primed macrophages confirms the downregulation of several effector functions

To elucidate transcriptional changes leading to the observed inhibitory effects, we performed RNA-sequencing of BMMs both 4 h after priming as well as 4 h after re-stimulation (Figure 2A) to investigate the effects of priming and the influence of priming on re-stimulation. Principal-component analysis (PCA) of the transcriptome obtained from unprimed (−), primed (+), unprimed and unstimulated (−/−), unprimed and re-stimulated (−/+), and primed and re-stimulated (+/+) cells revealed distinct clusters (Figure S2A). Comparing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in previously primed (+/+) and previously unprimed (−/+) cells (both re-stimulated), we observed that previously primed (+/+) cells displayed several cytokine- and innate immune receptor-associated pathways among the 20 topmost downregulated pathways (selected are shown in Figure 2B). The same sample of cells (+/+) also showed a strong downregulation of genes responsible for pro-inflammatory responses and chemo attraction relevant in Legionellosis including Tnf, Il6, and Il12 (Figure 2C). Along those lines, several pro-inflammatory cytokines as well as chemokines were downregulated in previously primed cells (+/+). Notably, ll10 was not differentially expressed (Figure 2D), thus confirming our observations at the protein level. This finding indicates that initial priming did not simply lead to a global reduction in transcription of cytokine genes but regulation is more nuanced, as IL-10 production is non-tolerizable by L. pneumophila-derived factors.Figure 2 RNA-sequencing of previously primed macrophages shows the downregulation of numerous effector functions

(A) Time points of RNA isolation and designation of the individual samples. RNA was isolated both 4 h after priming (to compare unprimed (−) vs. primed (+)) as well as 4 h after subsequent re-stimulation (to compare previously unprimed (−/+) vs. previously primed (+/+)).

(B–H) Dot-blot of selected KEGG pathways from the TOP 20 downregulated pathways in previously primed (+/+) vs. previously unprimed (−/+) macrophages. KEGG pathway overviews of (C) Legionellosis, (D) cytokine-cytokine receptor interactions, and (G) C-type lectin receptor signaling pathway comparing previously primed (+/+) vs. previously unprimed (−/+) macrophages with several relevant genes being downregulated. Heatmaps of DEGs in the context of (E) ROS, (F) surface activation markers, and (H) C-type lectin receptors.

See also Figure S4.

Since no KEGG-pathways or Gene Ontology (GO)-terms specifically for ROS and surface activation marker expression were available at the time of this investigation, we employed the Reactome database39 for defined gene-sets to identify differentially expressed genes (DEGs) in our data. For the ROS and reactive nitrogen species (RNS; Reactome: R-MMU-1222556.1), several genes related to their production were differentially regulated after priming and Nos3 remained as significantly downregulated after re-stimulation (Figure 2E). For surface activation markers, we used an extended list of the MHC class II antigen presentation pathway (Reactome: R-MMU-2132295.1), revealing the differential regulation of Cd80 after priming and H2-Ab1 (murine MHC-II) both after priming and re-stimulation (Figure 2F). Thereby, the RNA-sequencing data validated most of our protein-level observations. Notably, cytokine transcripts, including those studied at the protein level, were downregulated. Moreover, some candidates related to ROS and surface markers remained downregulated upon re-stimulation.

Interestingly, CLRs were among the highly differentially expressed pathways (Figure 2B). So far little is known about their ability to recognize L. pneumophila. By inquiring the KEGG CLR signaling pathway, two major candidates (LSP-1 and Mincle) could be identified (Figure 2G). For an extended overview, we also included the C-type lectin receptors reactome dataset (Reactome: R-MMU-5621481.1). The candidates which were affected by both priming and re-stimulation were Pkd1/2, Cd69, Clec4e (Mincle), Vcan, and Sele (Figure 2H). Notably, polycystin 1/2 (Pkd1/2), versican (Vcan), and E-selectin (Sele) are all adhesion molecules on immune cells or closely related to adhesion functionality.40,41,42 Among those, Mincle not only ranked highest in significance values from RNA-sequencing but is also known to play significant roles in recognition of bacteria especially based on interactions with (glyco-)lipids.43

Taken together, the transcriptional profiles of BMMs indicate that L. pneumophila-derived factors downregulate the capacity of primed macrophages to produce transcripts of crucial importance in innate immunity during Legionellosis, including cytokines such as TNF, IL-6, and IL-12 and possibly hint at CLRs such as Mincle to be of relevance in this process.

In primed macrophages, several histone-modifying enzymes are differentially regulated

To investigate whether the observed reduced macrophage effector functions upon re-stimulation were caused by epigenetic modifications, we wanted to investigate the effect of the initial priming with killed L. pneumophila on histone modifying enzymes (HMEs).

From the same RNA-sequencing dataset (Figure 2A), we observed a large number of DEGs in functional GO-terms related to DNA replication and altered structure/condensation of the chromatin (Figure 3A). Upon further investigation, gene set enrichment analysis (GSEA) displayed a strong negative correlation of unprimed (−) cells with histone H3K9 modification (Figure 3B) and a strong positive correlation of previously primed and re-stimulated (+/+) macrophages with heterochromatin (Figure 3C). This suggests that the priming induces a substantial amount of H3K9 modifications which potentially leads to a persistent heterochromatin organization, correlated with decreased gene transcription. When comparing the expression levels of genes encoding for HMEs (Reactome ID: R-MMU-3247509.1), we observed several DEGs. Notably, those HMEs upregulated in primed (+) cells contained several de-methylases, many of which have a specificity to remove H3K4me3 (Kdm5c, Kdm2b, Kdm7a, Kdm5b, and Kdm5a; Figure 3D) while HMEs which have a specificity to remove H3K9me3 (Suv39h1 and Ehmt1) were downregulated in primed (+) cells. An upregulation of H3K4me3 removing enzymes accompanied by a decrease in H3K9me3 removing enzymes might therefore hint at a general decrease in transcription activity, as large parts of the chromatin will be inaccessible for transcription factors.Figure 3 Within primed macrophages, there are distinct alterations in the regulation of several histone-modifying enzymes

(A–C) (A) Dot-blot of selected GO-terms from the TOP 20 DEG-sets in primed (+) vs. unprimed (−) macrophages. Selected GSEA-plots of (B) Histone H3 K9 modification in primed (+) vs. unprimed (−) macrophages and (C) Heterochromatin in previously primed (+/+) vs. previously unprimed (−/+) macrophages.

(D) Heatmap of significantly DEGs associated with HMEs comparing primed (+) vs. unprimed (−) macrophages. From this analysis, several de-methylating enzymes could be distinguished upregulated in primed (+) cells (red, upper right cluster + enlarged panel).

Chromatin-immunoprecipitation reveals a decrease of H3K4me3 and increase of H3K9me3 in primed macrophages

To validate and extend our findings regarding the deposited epigenetic marks, we performed chromatin-immunoprecipitation (ChIP)-studies after the resting period/immediately before re-stimulation (Figure 4A). This allowed us to assess parts of the epigenetic landscape and determine whether the decrease in both transcription and/or production initially observed for TNF, IL-6, IL-12, IL-10, ROS, MHC-II, and CD80 is due to inaccessibility to their encoding genes. We observed that priming primarily increased the relative amounts of H3K9me3 (closing the chromatin) at the promoters of Tnf, Il-6, Il-12b, Nox, Nos, H2-Ab1, and Cd80. On top of that, the H3K4me3 mark was significantly reduced at promoters of the aforementioned genes, while a similar (although limited) effect could be seen for H3K27ac (Figure 4B).Figure 4 Chromatin-immunoprecipitation unveils a reduction in H3K4me3 and an elevation in H3K9me3 within primed macrophages

(A) Time point of chromatin isolation and subsequent ChIP assay. ChIP was performed immediately before the time point of the secondary stimulus to investigate parts of the underlying epigenetic landscape.

(B) Relative amounts of the epigenetic modifications H3K9me3 (repressive, left column), H3K4me3 (opening; middle column), H3K27ac (opening; right column) at the promoters of Tnf, Il6, Il12b, Il10, Nox, Nos, H2-Ab1, and Cd80 (from top to bottom). Data are presented as mean +SD and are representative of three independent experiments (n = 3) in triplicates. ns = not significant, ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

Since both H3K4me3 and H3K27ac are considered to be opening marks and therefore associated with increased gene transcription, their decrease in previously primed (+) cells suggests that large regions of the chromatin in previously primed (+) cells are closed. Notably, no statistically significant differences in either H3K4me3, H3K27ac, or H3K9me3 could be observed for Il10, which was already unchanged on the protein- and transcriptome layer. This further suggests that the differential regulation of the observed effector functions stems from epigenetic marks changing the accessibility of the chromatin.

Taken together, the increase of H3K9me3, coupled with the decrease in H3K4me3 and H3K27ac, provide a mechanistic explanation for the decreased capacity of primed macrophages to adequately respond to secondary stimulation.

Inhibition of (de-)methylases prior to priming mitigates the L. pneumophila induced tolerance effect

To better understand the role of HMEs in a mechanistic way, we investigated the effect of added pan-inhibitors of methyltransferases (methylthioadhenosine; MTA), de-methylases (pargyline), acetyltransferases (epigallocatechin gallate; EGCG), and de-acetylases (Na-butyrate) on the induction of tolerance (Figure 5A).Figure 5 Pre-treatment with (de-)methylase inhibitors before priming partially alleviates the tolerance effect induced by L. pneumophila

(A–H) Time points and duration of pan-inhibitor treatment against methyltransferases (MTA; M), de-methylases (pargyline; P), acetyltransferases (EGCG; E), and de-acetylases (Na-butyrate; N) or all combined (A). The respective inhibitors were added prior to the priming and renewed during the resting time to investigate their impact on the readout of cytokine release (B–E), ROS production (F) and surface marker expression (G and H) after re-stimulation. Data are presented as mean +SD and are representative of three independent experiments (n = 3) in triplicates. ns = not significant, ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

See also Figure S5.

Out of the four inhibitors, pargyline (inhibiting de-methylases) displayed the strongest effect in mitigating the tolerance effect in expression of the cytokines TNF, IL-6, and IL-12 as well as ROS, MHC-II, and CD80 otherwise induced by the initial priming. A similar effect, albeit to a slightly lesser extent, was observed for the inhibitor MTA (inhibiting methyltransferases). Interestingly, the acetyltransferase and de-acetylase inhibitors EGCG and Na-butyrate, respectively, had significant effects on the production of TNF, IL-6, IL-12, and ROS, while H3K27ac decrease at the promoters of their respective genes was not significant in previous ChIP analysis (Figure 4B). This observation suggests that acetylations other than H3K27ac could also play a role. By combining all inhibitors, the levels of TNF, IL-6, IL-12, ROS, MHC-II, and CD80 could be restored to a level that closely resembled the previously unprimed status (Figures 5B–5H).

The inhibitor studies indicate that macrophage effector functions are regulated by epigenetic modifications by the responsible HMEs upon the initial priming. Notably, we observed a rather consistent pattern that inhibition of de-methylases (via pargyline) and methyltransferases (via MTA) had the strongest effect on modulating effector functions for most of the selected readouts, while the effects of (de-)acetylase inhibitors were weaker, most likely due to the fact that acetyl marks are generally considered less durable and may already have been degraded after four days.

The CLR Mincle markedly contributes to the L. pneumophila induced tolerance effect

As the CLR Mincle belonged to the strongest DEGs (Figures 2B, 2G, and 2H), we aimed at identifying the role of Mincle by subjecting WT as well as Mincle−/− BMMs to the same priming and re-stimulation conditions to compare their reaction regarding cytokines, ROS, and surface markers. Strikingly, Mincle−/− BMMs displayed a significantly less pronounced tolerance effect as determined by the secretion of the pro-inflammatory cytokines TNF (Figure 6A), IL-6 (Figure 6B), and IL-12 (Figure 6C). Additionally, the expression of MHC-II (Figure 6F) was also significantly different, while no significant differences between WT and Mincle−/− BMMs were observed for the production of ROS (Figure 6E) and the expression of CD80 (Figure 6G) on the surface of primed and re-stimulated macrophages when compared to their WT counterparts. To analyze whether the Mincle-mediated tolerance induction was specific for the Legionella genus, we tested two additional gram-negative bacteria, namely Escherichia coli and Pasteurella multocida. While we did see significant tolerance induction regarding TNF, IL-6, and IL-12 by h.i. E. coli, the observed tolerance effect was fully independent of the CLR Mincle. Moreover, the markedly less pronounced tolerance effects upon macrophage stimulation with additional unrelated bacteria such as h.i. P. multocida were neither dependent on Mincle (Figure S6). Therefore, neither E. coli LPS nor LPS from other gram-negative bacterial species demonstrated the previously observed Mincle-dependent effect.Figure 6 BMMs deficient in the CLR Mincle exhibit a milder tolerance effect induced by L. pneumophila

(A–G) BMMs (WT and Mincle−/−) were primed and re-stimulated with h.i. L. pneumophila to determine cytokine secretion (A–D), ROS production (E) as well as surface marker expression (F and G). In most cases, primed Mincle−/− BMMs showed higher effector functions upon re-stimulation compared to their WT counterparts. Data are presented as mean +SD and are representative of three independent experiments (n = 3) in triplicates. ns = not significant, ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

See also Figures S6–S8.

Finally, to assess the role of Mincle in the observed reprogramming effects, we performed ChIP studies comparing WT and Mincle−/− BMMs focusing on the most prominent prior targets being Tnf, Il6, and Il12b. Indeed, we observed that lack of Mincle led to a less condensed chromatin at the promoters of Tnf (Figure 7A), Il6 (Figure 7B), and Il12b (Figure 7C) as indicated by significantly higher levels of H3K4me3 and lower levels of H3K9me3. These results indicate that the CLR Mincle significantly contributes to the observed tolerance effects induced by killed L. pneumophila, thereby indicating a crucial role for Mincle in Legionella-induced modulation of macrophage functions.Figure 7 Chromatin-immunoprecipitation in Mincle−/− BMMs reveals a less condensed chromatin state at the promoters of Tnf, Il6 and Il12b

BMMs (WT and Mincle−/−) were primed with h.i. L. pneumophila and ChIP was performed prior to re-stimulation (Figure S4A). Mincle deficient BMMs displayed significantly lower amounts of H3K9me3 and higher amounts of H3K4me3 at the promoters of Tnf, Il6, and Il12b. Data are presented as mean +SD and are representative of three independent experiments (n = 3) in triplicates. ns = not significant, ∗p < 0.05, ∗∗p < 0.01.

Discussion

Immunomodulation by L. pneumophila in macrophages is a well-documented phenomenon in the context of Legionellosis.4,32 L. pneumophila secretes effector proteins to hamper host protein synthesis, thereby reducing cytokine production. T2SS secreted effector proteins are known to reduce TNF, IL-6, IL-10, IL-8, and IL-1β,44 while T4SS secreted proteins lowered TNF, IL-6, and IL-12.28,45 However, TSS secretion is only functional in viable L. pneumophila. Here, we show that factors derived from killed Legionella additionally induce modulation of macrophages, thereby reducing secretion of the cytokines TNF, IL-6, IL-12, the production of ROS, and presentation of MHC-II and CD80.

We show here that this modulation is mediated by L. pneumophila-induced epigenetic reprogramming. This conclusion is supported by the following observations: First, by RNA-sequencing, we analyzed participating pathways and saw major downregulation of transcripts encoding for crucial effector functions such as cytokines in Legionellosis like TNF, IL-6, and IL-12 and for enzymes that in return increase heterochromatin. Second, by ChIP, we observed marked increases of the closing mark H3K9me3 and decreases of the opening mark H3K4me3. Third, the use of inhibitors targeting HMEs validates the role of these enzymes in the observed immune modulation. In addition, we show that the CLR Mincle markedly contributes to the tolerogenic reaction of primed macrophages upon re-stimulation. These findings might have important implications for our understanding of Legionellosis. During infection, a large percentage of monocytes become associated with bacteria-derived material,46 potentially interfering with monocyte development. Moreover, considering the ability of alveolar macrophages to efficiently transport other bacteria to lung-draining lymph nodes,47 a similar phenomenon might occur with L. pneumophila. While this could be advantageous for initiating adaptive immune responses, it might also facilitate the dissemination of L. pneumophila-derived factors, contributing to immunomodulation.

Mechanistically, we have identified a central role of epigenetic reprogramming in macrophages. Here, we observed increased H3K4me3 and decreased H3K9me3 removing enzymes in RNA-sequencing (Figure 3D), while ChIP assays revealed increases in H3K9me3 and decreases in H3K4me3 (Figure 4B) which were less pronounced in the absence of Mincle (Figure 7). In inhibitor experiments, the most significant effects occurred with methyltransferase and demethylase inhibitors (Figure 5B). Tying together, these results highlight the central role of methylations for the observed effects. Earlier studies identified L. pneumophila-expressed flagellin to induce global, genome-wide histone modifications as well as histone H4 acetylation and H3 phosphoacetylation at the Il8 promoter34 and the promoter of the transcription factor iκbζ.48 In this case, however, flagellin-mediated effects may be excluded due to the heat treatment and so are most of the other Legionella-derived proteins. This also renders the contribution of effectors such as RomA to the observed epigenetic reprogramming highly unlikely, which was recently described to influence the host epigenetics on a H3K14me3 basis.35 While host-derived factors upon priming cannot be formally excluded, this leaves mainly lipids, carbohydrates, or glycolipids derived from Legionella to be considered. The distinct Legionella-derived factor(s) responsible for the epigenetic reprogramming in macrophages remain to be identified. While prior studies report similar gene-specific control of inflammation by LPS,49 it is rather unlikely that the tolerance effects we observed are mediated by L. pneumophila LPS due to the following reasons: First, the structure of L. pneumophila LPS is different from E. coli LPS, marked by an extremely hydrophobic lipid A, extensively acylated with long-chain fatty acids and featuring an O-antigen-specific chain constructed from homopolymeric legionaminic acid.7 Along those lines, we observed that tolerance induction in macrophages by h.i. E. coli was in fact independent of the CLR Mincle. Similarly, the less pronounced effects induced by h.i. P. multocida were Mincle-independent as well (Figure S6). This observation provides further evidence of the specificity of the Mincle-mediated tolerance induction by Legionella-derived factors. Second, both TLR4 (the classical LPS-receptor) and TLR2 (the Legionella LPS-receptor) were not significantly differentially expressed on the mRNA level (data not shown). Third and most importantly, we show that the TF 3/1 mutant of L. pneumophila displaying a truncated LPS structure still exhibits comparable effects to the WT strains JR32 and Corby. Taken together, Legionella-derived factors other than LPS are likely to be the responsible mediators.

In recent years, numerous studies have identified CLRs as key participants in the recognition of various pathogens and host antigenic determinants.50 However, their specific role in L. pneumophila infection has not yet been elucidated. Recently, the myeloid inhibitory C-type lectin receptor (MICL; CLEC12A) was found to recognize L. pneumophila but had a limited role in the host’s response against L. pneumophila.51 Mincle has gained considerable attention as a crucial mediator in a wide array of immune interactions.52 In recent years, the number of pathogens recognized by Mincle has significantly expanded.53 Bacterial ligands, such as trehalose dimycolate (TDM),14 glycerol monomycolate,54 or β-gentiobiosyl diacylglyceride55 from Mycobacteria spp., as well as monoglucosyldiacylglycerol from Streptococcus pyogenes16 or the S-layer protein from Lactobacillus brevis17 contribute to the recognition of the respective bacteria by Mincle. Nonetheless, the characteristics of many other ligands remain unknown and the potential interactions involving Mincle, especially with ligands from gram-negative bacteria, is not fully understood.56 While we observed significant binding of Mincle-hFc fusion proteins to L. pneumophila in ELISA-based binding studies (Figure S7), a prior publication reported no significant binding to live L. pneumophila in a flow cytometry-based binding study.51,57,58 This suggests that the recognized factor may not be freely accessible at the bacterial surface but is rather released upon lysis and/or heat inactivation of Legionella.

CLR engagement and downstream signaling is a fine-tuned balance between immune activation and repression.13 Mincle can both stimulate and resolve inflammation and its engagement leads to the activation of the Syk/CARD9 axis.59,60 Indeed, we saw downregulation of Mincle (Figures 2G and S8), MALT1 as part of the CARD9 complex (Figure 2G) as well as the reduction of IL-12 on several levels.

A good example for the ambivalence in Mincle signaling is demonstrated by Mincle/TDM interactions. While TDM is known to induce inflammatory reactions in macrophages upon Mincle-engagement, several reports document anti-inflammatory reactions as well.43 For example, the Mincle/TDM interaction was reported to induce IL-10 production18,61 or the recruitment of Src homology region 2 domain-containing phosphatase-1 (SHP-1)53 to induce anti-inflammatory conditions.43 The recruitment of SHP-1 by Mincle was also observed to be exploited by ligands released in Leishmania major infection. In turn, this impeded the activation of antigen-presenting cells and consequently lowered initiation of adaptive immune responses.62 Regarding IL-10 production, we did not observe any Mincle influence.

Furthermore, Mincle was reported to potentially intervene in other PRR-signaling events.45,61,63,64 Co-stimulation of macrophages with Pam3CSK4 (a TLR2 ligand) and beads coated with TDM reduced IL-12p40 secretion.61 Additionally, the ongoing activation of TLRs and Mincle during prolonged exposure to mycobacterial components inhibits the general translational machinery through 4EBP-1 dephosphorylation and inhibition of Nod-like receptor protein 3 (NLRP3) to reduce IL-1β production.63 Interestingly, reduction of IL-1β was also observed in our data at least on the transcriptional level (Figures 2C and 2D) which is required to produce TNF or IL-12.45 However, whether interference of other receptors by crosstalk plays a role in these effects remains speculative. Therefore, the exact signaling events initiated upon detection of Legionella-derived factors by Mincle should be unraveled in future studies.

Taken together, we provide evidence for the reprogramming of macrophage effector functions by and in response to L. pneumophila-derived factors. The CLR Mincle was shown to be a key component in mediating this process. Future studies should therefore elucidate the respective ligand(s) responsible for the observed effects, in particular those interacting with the CLR Mincle while also unraveling the accompanying downstream signaling.

Limitations of the study

Our study demonstrates that factors originating from L. pneumophila induce a shift in macrophages toward a tolerogenic state, with a significant involvement of the C-type lectin receptor Mincle (CLEC4E) in this process. While we reason that the observed responses are independent of LPS-mediated effects, the putative ligand remains elusive, which is a significant limitation of this study. Another limitation is the focus on the L. pneumophila-mediated modulation of effector functions in bone-marrow-derived macrophages. Future studies should investigate these effects in alveolar macrophages as they represent the primary reservoir for L. pneumophila.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER	
Antibodies	
	
anti-H3K27ac	Diagenode	CAT#C15210016; RRID:AB_2904604	
anti-H3K4me3 antibody	Diagenode	CAT#C15410003; RRID: AB_2924768	
anti-H3K9me3 antibody	Diagenode	CAT#C15210014; RRID:AB_3068324	
anti-Histone H3 antibody	Abcam	CAT#ab1791; RRID:AB_302613	
anti-mouse CD11b APC	eBiosciences	CAT#17-0112-81; RRID:AB_469343	
anti-mouse CD16/32	Biolegend	CAT#101302; RRID:AB_312801	
anti-mouse CD80 PE (clone 16-10A1	Biolegend	CAT#104707; RRID:AB_313128	
anti-mouse MHC-2 FITC (clone AF6-120.1,	BD Biosciences	CAT#553551; RRID:AB_394918	
rabbit IgG antibody	Diagenode	CAT#C15410206; RRID:AB_2722554	
	
Chemicals, peptides, and recombinant proteins	
	
7-AAD viability staining solution	ThermoFisher	CAT#00-6993-50	
ACES buffer	Carl Roth	CAT#9138.2	
Activated charcoal	Carl Roth	CAT#X865.1	
Agar	Carl Roth	CAT#2266.3	
Chelex-100 (Bio-Rad	Bio-Rad	CAT#1421253	
C-type lectin receptor Fc fusion proteins	Maglinao M. (2014)57
Klatt A. B. (2023)51
Mayer S. (2018)58	N/A	
Dihydrorhodamine (DHR)-123	Sigma-Aldrich	CAT#D1054-2MG	
Dynabeads protein A	Invitrogen	CAT#10002D	
Epigallocatechin gallate (EGCG)	Sigma-Aldrich	CAT#324880	
Ferric nitrate	Carl Roth	CAT#CN84.1	
L-Cysteine	Carl Roth	CAT#3467.2	
Lipopolysaccharide	Sigma-Aldrich	CAT#L4391-1MG	
Methylthioadhenosine (MTA)	Sigma-Aldrich	CAT#D5011	
Na-butyrate	Roth	CAT#1441.1	
Pargyline	Sigma-Aldrich	CAT#P8013	
Phenylmethansulfonylfluorid (PMSF)	Sigma-Aldrich	CAT#P7626	
Phorbol-12-myristate-13-acetate (PMA)	AppliChem	CAT#A0903,0001	
Protease inhibitor mix	Sigma-Aldrich	CAT#535140	
Proteinase K	ThermoFisher	CAT#EO0491	
QIAzol lysis reagent	QIAgen	CAT#79306	
Yeast extract	Carl Roth	CAT#2363.3	
	
Critical commercial assays	
	
Murine IL-10 Standard ABTS ELISA Development Kit	Peprotech	CAT#900-K53	
Murine IL-12 Standard ABTS ELISA Development Kit	Peprotech	CAT#900-K97	
Murine IL-6 Standard TMB ELISA Development Kit	R&D Systems	CAT#DY-406-05	
Murine TNF Standard TMB ELISA Development Kit	R&D Systems	CAT#DY-410-05	
Qubit broad range assay kit	FischerSci	CAT#Q10210	
RNeasy mini kit	QIAgen	CAT#74104	
SYBR Green based Luna qPCR Master mix	NEB	CAT#M3003X	
	
Experimental models: Organisms/strains	
	
L. pneumophila serotype 1 strain Corby	Lück P. C. (2001)65	N/A	
L. pneumophila serotype 1 strain JR32	Lück P. C. (2001)65	N/A	
L. pneumophila serotype 1 strain TF 3/1	Lück P. C. (2001)65	N/A	
L. longbeachae	Gift from the Charité Berlin	N/A	
E. coli	Gift from the TiHo Institute for Microbiology	N/A	
P. multocida	National Collection of Type Cultures (NCTC)	CAT#10322	
Mincle-/- mice	Kostarnoy A.V. (2017)66	N/A	
	
Oligonucleotides	
	
Mincle primers	ThermoFisher Scientific	CAT#Mm01183703_m1	
18S primers	ThermoFisher Scientific	CAT#Mm03928990_g1	
For ChIP-qPCR primers, please refer to Table S1			
	
Deposited data	
	
RNA-sequencing dataset	This study	SRA: PRJNA1030160	
	
Software and algorithms	
	
BD Accuri C6 Plus Software	BD Biosciences	N/A	
FlowJo Version 10	FlowJo LLC	N/A	
GraphPad Prism Version 7	GraphPad Software	N/A	

Resource availability

Lead contact

Further information, data and request for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Bernd Lepenies (Bernd.Lepenies@tiho-hannover.de).

Materials availability

This study did not generate new unique reagents.

Data and code availability

• RNA sequencing data have been deposited at SRA and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.

• This paper does not report original code.

• Any additional information is available from the lead contact upon request.

Experimental model and study participant details

Animals

All mice were housed in the animal facility of the University of Veterinary Medicine (Hannover, Germany) in individually ventilated cages under controlled conditions (12 h light/12 h dark cycle, 22-24°C, humidity 50-60 %) and specific pathogen-free conditions with permanent access to water and standard rodent feed. The source of the Mincle−/− mice (generated by the Consortium for Functional Glycomics) was described previously.66 Sacrificing of mice for scientific purposes was approved by the Animal Welfare Officers of the University of Veterinary Medicine Hannover (AZ 02.05.2016, TiHo-T-2019-13, TiHo-T-2024-6). Bone marrow cells were isolated from female WT or Mincle-/- mice (C57BL/6 background, aged 8 to 16 weeks).

Method details

Culture of Legionella pneumophila

The L. pneumophila wildtype (WT) strain JR32, belonging to serogroup type I, alongside the WT Corby strain and its isogenic LPS-mutant TF 3/1 were included in the study.65 The isogenic mutant TF 3/1 was generously provided by Dr. Christian Lück from the Technische Universität Dresden and both the JR32 and Corby strain were obtained from Dr. Bastian Opitz from the Charité Berlin. All L. pneumophila strains were cultivated on buffered charcoal yeast extract (BCYE) agar plates for a duration of two days at 37°C. For experimental investigations, bacterial cultures were grown in a medium containing N-(2-acetamido)-2-aminoethanesulfonic acid (ACES)-buffered yeast extract (AYE) and were subsequently subjected to two washes with PBS. L-cysteine and ferric nitrate supplements were added to both the BCYE agar plates and the AYE medium. The heat-inactivation (h.i.) process was carried out at 75°C for one hour and its effectiveness was confirmed by plating the bacteria on BCYE agar.

Generation of bone-marrow-derived macrophages

Bone marrow cells from both WT or Mincle-/- (C57BL/6 background) were isolated from the tibia and femur of female mice aged 8 to 16 weeks. The bones were cleaned with 70 % ethanol followed by flushing using IMDM supplemented with 10 % FCS, 2 mM L-glutamine, and 100 U/mL penicillin/streptomycin. The resulting cell mixture was filtered through a 40 μm cell strainer and then subjected to centrifugation at 300 × g for 5 minutes. To remove red blood cells, a solution of 90 % 160 mM NH4Cl and 10 % 100 mM Tris-HCl (pH 7.5) was used. The bone marrow cells were then washed and preserved at -150°C using a 10 % DMSO solution. To induce the differentiation to BMMs, the bone marrow cells were cultured in a BMM differentiation medium, comprising IMDM, 10 % FCS, 30 % L929 fibroblast supernatant, 4.5 mM L-glutamine, and 100 μg/mL penicillin/streptomycin. On the second and fourth day of cultivation, fresh medium was introduced.

Priming/re-stimulation setup

BMMs for the priming/re-stimulation setup were cultured as described above. On day 5 of culture, 3 x 105 cells/well in 1 ml BMM differentiation medium were seeded into a 6-well flat bottom plate to attach and rest for minimum 1 h. Afterwards, the medium was renewed and BMMs were primed with the indicated amounts of h.i. L. pneumophila. After 24 h, the stimulant was removed and the cells were rinsed twice with pre-warmed PBS and fresh BMM differentiation medium was added to start a resting period of 4 days. At the end of the resting period, BMMs were detached, centrifuged and counted to allow re-seeding of equal amounts (1 x 105 cells/well in 100 μl) in a 96-well plate for re-stimulation experiments. Re-seeded cells were stimulated with the indicated amounts of h.i. L. pneumophila for either 1 h (ROS) or 24 h (activation markers, cytokines) and the cytokine containing supernatant was stored at -80°C (Figure 1A).

Inhibitor studies

For inhibitor studies, culture of BMMs and read-outs were performed as described above with the exception to adding the inhibitors methylthioadhenosine (MTA; 500 μM), pargyline (5 μM), epigallocatechin gallate (EGCG; 50 μM), Na-butyrate (100 μM) or a combination of all during the priming and resting period.

Cytokine ELISA

Culture supernatants acquired subsequent to priming/stimulation of BMMs were analyzed for the presence of various cytokines. TNF and IL-6 were assessed using DuoSet ELISA kits (R&D Systems) while IL-12 and IL-10 was evaluated using the ABTS ELISA Development Kit (PeproTech) according to the respective manufacturer’s instructions. The plates were developed using the substrate 3,3′,5,5′-Tetramethylbenzidine (TMB) or 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS), respectively. To quantify the results, absorbance readings were taken at 450 nm with a wavelength correction at 570 nm or 405 nm with a wavelength correction at 650 nm, respectively, using a TECAN infinite M1000 spectrophotometer (TECAN).

Measurement of reactive-oxygen species

After re-seeding of the BMMs into a 96-well round bottom plate as described above, cells were incubated for a minimum amount of 2 h to attach and rest. Afterwards, the supernatant was removed and a mixture of IMDM, DHR123 and stimulant (h.i. L. pneumophila or 10 mM PMA as positive control) was added. After an incubation of 1 h, the samples were put on ice until the measurement of reactive oxygen species (ROS) by flow cytometry (Accuri flow cytometer, BD Biosciences) and data were analyzed using the BD Accuri™ C6 software (BD Biosciences).

Activation marker and cell vitality staining

After re-seeding of the BMMs into a 96-well round bottom plate as described above, BMMs were blocked using anti-mouse CD16/32 (clone 93, eBioscience; 1:100) for 10 minutes at 4°C. Subsequently, the cells were stained with APC-conjugated anti-mouse CD11b (clone M1/70, eBiosciences; 1:200), PE-conjugated anti-mouse CD80 (clone 16-10A1, Biolegend; 1:200), and FITC-conjugated anti-mouse MHC-2 (clone AF6-120.1, BD Pharmingen; 1:200) for 20 minutes at 4°C. Following this staining, the cells were incubated with 1 % paraformaldehyde (PFA). To determine the cell vitality, unfixed cells were incubated with the cell vitality dye 7AAD (Thermofisher; 1:80) and incubated for 15 min before measurement. Cells treated with intense UV-light for 20 min and a 50:50 mixture of viable and UV-treated cells served as controls. Flow cytometry analysis was performed using the Accuri flow cytometer (BD Biosciences). Data analysis of the obtained results was performed using the BD Accuri™ C6 software (BD Biosciences).

RNA isolation and concentration determination

Total RNA for sequencing was isolated 4 h after priming or re-stimulation, respectively (Figure 2A). Briefly, the cells were centrifuged at 300 xg for 5 min and washed twice with ice-cold PBS. Next, the cells were lysed using QIAzol (QIAgen) and purified using RNeasy kits (QIAgen) according to the manufacturer’s instructions. The concentration and integrity of the isolated RNA was determined using the Qubit broad range assay kit (Invitrogen). Isolated total RNA was sent to Novogene Co, Ltd. for sequencing and subsequent data analysis.

RNA sequencing

RNA sequencing as well as data analysis was performed by Novogene Co, Ltd. The methodological descriptions provided are summarized in the following paragraphs.

Library preparation for transcriptome sequencing

Messenger RNA was purified from total RNA using poly-T oligo-attached magnetic beads. After fragmentation, the first strand cDNA was synthesized using random hexamer primers, followed by the second strand cDNA synthesis using either dUTP for directional library or dTTP for non-directional library. The non-directional library was ready after end repair, A-tailing, adapter ligation, size selection, amplification, and purification. The directional library was ready after end repair, A tailing, adapter ligation, size selection, USER enzyme digestion, amplification, and purification. The library was checked with Qubit and real-time PCR for quantification and bioanalyzer for size distribution detection. Quantified libraries were pooled and sequenced on Illumina platforms, according to effective library concentration and data amount.

Clustering and sequencing

The clustering of the index-coded samples was performed according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina platform and paired-end reads were generated.

RNA sequencing data analysis

Quality control

Raw data (raw reads) of fastq format were firstly processed through fastp software. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing poly-N and low-quality reads from raw data. At the same time, Q20, Q30 and GC contents of the clean data were calculated. All downstream analyses were based on clean data with high quality.

Reads mapping to the reference genome

Reference genome and gene model annotation files were downloaded from the UCSC genome browser website. The index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. Hisat2 was selected as the mapping tool as Hisat2 can generate a database of splice junctions based on the gene model annotation file and thus generates a better mapping result than other non-splice mapping tools.

Quantification of gene expression level

FeatureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. Subsequently, the FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. FPKM, expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels.

Differential expression analysis

Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.20.0). DESeq2 provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted P-value less than or equal to 0.05 found by DESeq2 were assigned as differentially expressed.

GO and KEGG enrichment analysis of differentially expressed genes

Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the clusterProfiler R package, in which gene length bias was corrected. GO terms with corrected P-values less than 0.05 were considered significantly enriched by differential expressed genes. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies (http://www.genome.jp/kegg/). The clusterProfiler R package was used to test the statistical enrichment of differential expression genes in KEGG pathways.

Gene Set Enrichment Analysis

Gene Set Enrichment Analysis (GSEA) is a computational approach to determine if a pre-defined Gene Set can show a significant consistent difference between two biological states. The genes were ranked according to the degree of differential expression in the two samples, and then the predefined Gene Set were tested to see if they were enriched at the top or bottom of the list. Gene set enrichment analysis can include subtle expression changes. We use the local version of the GSEA analysis tool http://www.broadinstitute.org/gsea/index.jsp, GO, KEGG data set were used for GSEA independently.

Chromatin immunoprecipitation and ChIP-qPCR

Chromatin immunoprecipitation (ChIP) was performed following a previously described method with slight modifications.67 Briefly, a total of 3 x 105 cells per sample at the end of the resting period from previously primed or unprimed BMMs (Figure 4A) were initially harvested and cross-linked using 1 % formaldehyde for 8 min followed by quenching the reaction with glycine for 5 min at room temperature. Subsequently, the cross-linked cells were lysed with 120 μL of lysis buffer composed of 50 mM Tris-HCl (pH 8.0), 10 mM EDTA, 1 % (wt/vol) SDS, a protease inhibitor mix (diluted 1:100; Sigma) with additional 1 mM PMSF, and 20 mM Na-butyrate. The chromatin present within the lysate underwent sonication to generate fragments in the range of 300-500 base pairs using a Sonopuls cuphorn sonicator (Bandelin Sonopuls; 100 % power, 8x 30 sec on/off on ice). Following this, the sonicated chromatin was diluted with 800 μL of RIPA ChIP buffer, which comprised 10 mM Tris·HCl (pH 7.5), 140 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 1% (vol/vol) Triton X-100, 0.1% (wt/vol) SDS, 0.1% (wt/vol) Na-deoxycholate, a protease inhibitor mix (Sigma, diluted 1:1000), 1 mM PMSF, and 20 mM Na-butyrate.

For immunoprecipitation, Dynabeads protein A (10 μL; Invitrogen) were incubated separately with 1 μg of either anti-H3 (Abcam), anti-H3K4me3 (Diagenode), anti-H3K27ac (Diagenode) or anti-H3K9me3 antibody (Diagenode) for 2 hours with 40 rpm rotation. Additionally, a rabbit IgG antibody (Diagenode) was used as a negative control for ChIP-grade antibodies. After this pre-treatment, 100 μL of the sheared chromatin was subjected to immunoprecipitation using the antibody-bound bead complexes. Afterwards, protein-DNA complexes were eluted and purified using 10 % (w/v) chelex-100 (Bio-Rad Laboratories) in Tris-EDTA. In parallel, another 100 μL of the sheared chromatin was utilized for the separate extraction of total input DNA. Both immunoprecipitated and input DNA were purified by incubation with 20 μg proteinase K (10 mg/ml), followed by heating the samples at 100°C for 10 min and quick centrifugation to remove the DNA containing supernatant from the magnetic beads.

For the quantification of the ChIP samples, qPCR was carried out using the SYBR Green based Luna qPCR Master mix (NEB) and specific primers targeting Tnf, Il6, Il12b, Il10, Nox, Nos, H2-Ab and Cd80 (for sequences see Table S1). The amount of precipitated DNA of target regions was determined relative to the input to assess the changes of epigenetic epitopes of target regions between primed and unprimed samples.

Quantification and statistical analysis

Except for the RNA sequencing data (described above), all analyses conducted were processed using GraphPad Prism (version 8, GraphPad Software). Paired, two-tailed Student’s t-tests were employed for the analysis. In all analyses, asterisks indicate significant differences (ns = not significant, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001).

Supplemental information

Document S1. Figures S1–S8 and Table S1

Acknowledgments

B.L. acknowledges support by the 10.13039/501100001659 Deutsche Forschungsgemeinschaft (LE 2498/11-1 , LE 2498/14-1 ). We thank the Hannover Graduate School for Neurosciences, Infection Medicine and Veterinary Sciences (HGNI) for supporting the PhD project of which this manuscript was a part of. We acknowledge financial support by the Open Access Fund of the University of Veterinary Medicine Hannover, Foundation. We would like to thank Bastian Opitz and Christian Lück for providing the L. pneumophila strains JR32, Corby and TF 3/1 as well as L. longbeachae. We would like to thank Hans-Joachim Schuberth and Silke Schöneberg for critical discussions and expert technical assistance, respectively. Parts of the figures use (modified) assets from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/).

Author contributions

Conceptualization, F.S. and B.L.; methodology, F.S. and C.D.; validation, F.S., C.D., and B.L.; formal analysis, F.S.; investigation, F.S. and C.D.; resources, F.S. and C.D.; data curation, F.S.; writing – original draft, F.S. and B.L.; writing, review and editing, F.S., C.D., and B.L.; visualization, F.S.; supervision, B.L.; project administration, F.S. and B.L.; funding acquisition, B.L.

Declaration of interests

The authors declare no competing interest.

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2024.110700.
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