ORIGINAL RESEARCH published: 17 November 2021 doi: 10.3389/fimmu.2021.734652 JAK2-STAT Epigenetically Regulates Tolerized Genes in Monocytes in the Edited by: Stefano Ugel, First Encounter With Gram-Negative University of Verona, Italy Reviewed by: Bacterial Endotoxins in Sepsis Pierre Bost, University of Zurich, Switzerland Octavio Morante-Palacios 1†, Clara Lorente-Sorolla 1†, Laura Ciudad 1, Nicola Tamassia, Josep Calafell-Segura 1, Antonio Garcia-Gomez 1, Francesc Català-Moll 1, University of Verona, Italy Adolfo Ruiz-Sanmartı́n 2, Mónica Martı́nez-Gallo 3, Ricard Ferrer 2, Sara Polletti, Juan Carlos Ruiz-Rodriguez 2, Damiana Álvarez-Errico 1 and Esteban Ballestar 1* European Institute of Oncology (IEO), 1 Epigenetics and Immune Disease Group, Josep Carreras Research Institute (IJC), Barcelona, Spain, 2 Intensive Care Italy Department, Vall d’Hebron University Hospital, Shock, Organ Dysfunction and Resuscitation (SODIR) Research Group, *Correspondence: Vall d’ Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain, 3 Immunology Division, Vall Esteban Ballestar d’Hebron University Hospital and Diagnostic Immunology Research Group, Vall d’Hebron Research Institute (VHIR), eballestar@carrerasresearch.org Barcelona, Spain † These authors have contributed equally to this work Microbial challenges, such as widespread bacterial infection in sepsis, induce endotoxin Specialty section: tolerance, a state of hyporesponsiveness to subsequent infections. The participation of This article was submitted to DNA methylation in this process is poorly known. In this study, we perform integrated Immunological Tolerance and Regulation, analysis of DNA methylation and transcriptional changes following in vitro exposure to a section of the journal gram-negative bacterial lipopolysaccharide, together with analysis of ex vivo monocytes Frontiers in Immunology from septic patients. We identify TET2-mediated demethylation and transcriptional Received: 01 July 2021 Accepted: 21 October 2021 activation of inflammation-related genes that is specific to toll-like receptor stimulation. Published: 17 November 2021 Changes also involve phosphorylation of STAT1, STAT3 and STAT5, elements of the Citation: JAK2 pathway. JAK2 pathway inhibition impairs the activation of tolerized genes on the Morante-Palacios O, first encounter with lipopolysaccharide. We then confirm the implication of the JAK2-STAT Lorente-Sorolla C, Ciudad L, Calafell-Segura J, Garcia-Gomez A, pathway in the aberrant DNA methylome of patients with sepsis caused by gram-negative Català-Moll F, Ruiz-Sanmartı´n A, bacteria. Finally, JAK2 inhibition in monocytes partially recapitulates the expression Martı´nez-Gallo M, Ferrer R, changes produced in the immunosuppressive cellular state acquired by monocytes Ruiz-Rodriguez JC, Álvarez-Errico D and Ballestar E (2021) JAK2-STAT from gram-negative sepsis, as described by single cell-RNA-sequencing. Our study Epigenetically Regulates Tolerized evidences both the crucial role the JAK2-STAT pathway in epigenetic regulation and Genes in Monocytes in the First Encounter With Gram-Negative initial response of the tolerized genes to gram-negative bacterial endotoxins and provides Bacterial Endotoxins in Sepsis. a pharmacological target to prevent exacerbated responses. Front. Immunol. 12:734652. doi: 10.3389/fimmu.2021.734652 Keywords: monocytes, endotoxin tolerance, sepsis, DNA methylation, tolerized genes, STATs, JAK2, TET2 Frontiers in Immunology | www.frontiersin.org 1 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis INTRODUCTION genome-wide analyses in human macrophages showed specific epigenetic signatures in H3K4me3, H3K27Ac and H3K4me1 Organisms are steadily exposed to threats from other species. histone marks for LPS tolerant cells (16). Studies in human Innate immune cells are the first line of host defense against monocytic THP-1 cells also revealed that H3K9 dimethylation invading pathogens. They activate the adaptive immune system of the promoters of the TNFa and IL-1b genes is responsible for to restore homeostasis and remove the infection (1). In innate their silencing during endotoxin tolerance (17). In fact, the histone immune cells, recognition of pathogen-associated molecular H3K9 methyltransferase G9a, combined with HP1 and DNA patterns (PAMPs) activates toll-like receptors (TLRs), methylation machinery, regulates TNFa gene expression (18). triggering robust inflammatory responses (2). Different Understanding the molecular and cellular mechanisms by mammalian TLRs recognize distinct microbial ligands. For which TLR stimulation with bacterial endotoxins results in the instance, Gram-negative bacterial lipopolysaccharide (LPS) acquisition of tolerance could have clinical applications in new specifically activates TLR4, while Pam3Cys-Ser-Lys4 (P3C), a biomarkers or promising therapies. DNA methylation is synthetic analog of the triacylated N-terminal part of bacterial potentially relevant following TLR stimulation, given its lipoproteins, binds TLR2. importance in monocyte/macrophage biology (19, 20) and its In TLR2/4 signaling, inflammatory-related transcription relative stability compared with other epigenetic marks. Recently, factors such as nuclear factor kappa-light-chain-enhancer of specific DNA methylation changes were found in monocytes activated B cells (NF-kB), activator protein 1 (AP-1) and from patients with sepsis associated with several clinical factors interferon regulatory factors (IRFs) are activated (3, 4). and functional features, supporting their relevance to the Moreover, as a consequence of the autocrine or paracrine monocyte response to bacterial molecules and the course of the activation of cytokine receptors such as interferon receptors, disease (21). Janus kinases (JAKs) are, in turn, activated, which leads to the In this study, we integrate DNA methylation and phosphorylation and recruitment of members of the signal transcriptome changes following in vitro exposure to bacterial transducer and activator of transcription (STAT) protein LPS, which leads to endotoxin tolerance. We report a negative family. Phosphorylated STAT dimers are then translocated to correlation between the two processes and a temporal sequence the nucleus, where they bind to specific DNA sequences and in relation to accessibility and active histone marks gains. initiate gene transcription (5). JAK/STAT activation is tightly Moreover, we show that JAK2-mediated pathways have a regulated by members of the suppressors of cytokine signaling critical role in establishing the LPS-driven transcriptome and (SOCS) protein family, protein inhibitors of activated STATs methylome remodeling, most probably due to engagement of the (PIAS) and protein tyrosine phosphatases (PTPs) (6). IFNgR upon autocrine/paracrine IFNg release, secondary to LPS Monocytes and macrophages are highly plastic innate immune activation, establishing a potential link with the regulation of cells involved in phagocytosis and cytokine release. After genes that become tolerized. Our data on monocytes isolated stimulation, monocytes, being key mediators of the initial from patients with sepsis indicate that JAK2-STAT pathway- immune response, rapidly migrate to tissues (7). Monocytes and associated methylation and expression alterations are relevant to macrophages develop endotoxin tolerance, a state of patients infected with gram-negative bacteria, and that the hyporesponsiveness following a first challenge with microbial pharmacological activation of this pathway could improve the ligands (8). Tolerant cells show an altered immune response, regulated expression of genes that become tolerized, providing a characterized by lower production of pro-inflammatory potential target to modulate their inflammatory response. cytokines (TNFa, IL-6, IL-1b, etc) and higher levels of anti- inflammatory cytokines (IL-10, TGFb, etc.) (9). Downregulation of major histocompatibility complex (MHC) class II molecules MATERIALS AND METHODS under endotoxin tolerance has also been observed (10). This immunosuppressive state can be achieved under pathological Human Samples conditions, such as the state following a sepsis episode. Sepsis is We selected and diagnosed patients with sepsis based on the characterized by a dysregulated inflammatory response driven by criteria of the Third International Consensus Definitions for an infection. It is a potentially lethal disease, and, in particular, a Sepsis and Septic Shock (Sepsis-3) (22). For each patient, we major cause of death in intensive care units (11). Peripheral blood calculated the Sequential [Sepsis-related] Organ Failure mononuclear cells (PBMCs) from patients with sepsis exposed ex Assessment (SOFA) score. The study included 5 patients (1 vivo to LPS, express lower levels of TNFa, IL-6 and IL-1b male and 4 females) with bacterial infections with SOFA scores compared with controls (12) and upregulated levels of IL-10 (13). ranging from 2 to 8 and 6 healthy donors (2 male and 4 females). Exposure to LPS and other bacterial antigens results in Both patients and healthy donors were obtained at the Intensive epigenetic remodeling. For instance, LPS-treated murine Care Unit and Immunology Service of Vall d’Hebron University monocytes display a group of tolerized genes with reduced gene Hospital. Blood samples from patients were collected within 12 h expression and active histone marks after a second stimulus in of sepsis diagnosis, which was confirmed using clinical and comparison with untreated monocytes (14). Moreover, LPS- analytical data. The Committee for Human Subjects of Vall treated human monocytes show reduced levels of histone d’Hebron University Hospital (PR (ATR)122/2019) approved H3K27ac and H3K4me1 at promoters and enhancers of the study, which was conducted in accordance with the ethical phagocytic and lipid metabolism genes (15). In addition, guidelines of the 1975 Declaration of Helsinki. All samples were Frontiers in Immunology | www.frontiersin.org 2 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis collected and processed in compliance with the guidelines IFN-a2, IFN-g, IL-1b, IL-6, IL-8, IL-10, IL-12p70, IL-17A, IL- approved by the local ethics committee. All participants 18, IL-23, IL-33, and TNFa) related to inflammation in the same (patients with sepsis and healthy controls) received oral and samples, in accordance with the manufacturer’s instructions. written information about the possibility that their blood would be used for research purposes before they gave their signed DNA Methylation Profiling informed consent. and Pyrosequencing Infinium MethylationEPIC BeadChip (Illumina, Inc., San Diego, CD14+ Monocytes Purification and Culture CA, USA) arrays were used to analyze DNA methylation. This For in vitro experiments, we obtained buffy coats from platform allows over 850,000 methylation sites per sample to be anonymous donors via the Catalan Blood and Tissue Bank interrogated at single-nucleotide resolution, and covers 99% of (CBTB). The CBTB follows the principles of the World reference sequence (RefSeq) genes. The samples were bisulfite- Medical Association (WMA) Declaration of Helsinki. Before converted using EZ DNA Methylation-Gold ™ Kit (Zymo providing blood samples, all donors received detailed oral and Research, CA, USA) and were hybridized in the array written information and signed a consent form at the CBTB. following the manufacturer’s instructions. PBMCs were isolated by density-gradient centrifugation using Each methylation data point was obtained from a combination lymphocyte-isolation solution (Rafer, Zaragoza, Spain). Pure of the Cy3 and Cy5 fluorescent intensities from the M (methylated) monocytes (MO) were then isolated from PBMCs by positive and U (unmethylated) alleles. For representation and further selection with magnetic CD14 MicroBeads (Miltenyi Biotec, analysis, we used beta (B) and M values. The B value is the ratio Bergisch Gladbach, Germany). The purity was verified by flow of the methylated probe intensity to the overall intensity (the sum of cytometry, obtaining more than 96% of CD14⁺ cells. Purified the methylated and unmethylated probe intensities). The M value is monocytes were resuspended in Roswell Park Memorial Institute calculated as the log2 ratio of the intensities of the methylated versus (RPMI) Medium 1640 + GlutaMAX™ (Gibco, Thermofisher) unmethylated probe. B values were used to derive heatmaps and to containing 10% human pooled serum (One Lambda, compare DNA methylation percentages from bisulfite- ThermoFisher Scientific Brand, West Hills CA, USA), 100 pyrosequencing experiments. For statistical purposes, the use of units/mL penicillin, and 100 mg/mL streptomycin. Monocytes M values is more appropriate, because their degree of were untreated (control), or treated with lipopolysaccharide homoscedasticity fits better with linear model assumptions. (LPS) (10 ng/ml from E. coli O111:B4, Sigma-Aldrich, Bisulfite pyrosequencing was used to validate CpG Darmstadt, Germany), and 10 µg/ml Pam3Cys (P3C) methylation changes. DNA was isolated using a Maxwell® RSC (InvivoGen San Diego, CA, USA). After 24 hours, monocytes Cultured Cells DNA Kit (Promega). Bisulfite modification of were washed and left to rest for 3 days in medium supplemented genomic DNA isolated from monocytes was performed using an with human pooled serum. Cells were then re-stimulated with EZ DNA Methylation-Gold™ Kit (Zymo Research), following LPS (10 ng/ml) and after 1 day, pelleted cells and supernatants the manufacturer’s protocol. Primers for PCR amplification and were collected and stored until use. sequencing were designed with PyroMark® Assay Design 2.0 For JAK2 inhibition of LPS monocytes (LPS+iJAK2 software (QIAGEN, Hilden, Germany). PCRs were performed monocytes), cells were grown under the same conditions as with the IMMOLASE™ DNA polymerase PCR kit (Bioline), and mentioned above, and in the presence of fedratinib 500 nM the success of amplification was assessed by agarose gel (formerly known as TG101348, Santa Cruz Biotechnology), electrophoresis. PCR products were pyrosequenced with the unless a different concentration is indicated. Pyromark Q24 system (QIAGEN). Transfection of Primary Human Monocytes Quality Control, Data Normalization, We used ON-TARGETplus siRNAs (GE Healthcare Dharmacon) and Detection of Differentially against TET2 to perform knockdown experiments in monocytes. Methylated CpGs We also used ON-TARGETplus Non-targeting Control Pool as a Methylation array data were processed with the statistical negative control. CD14+ monocytes were transfected with siRNAs language R using methods from the Bioconductor packages (100nM) using Lipofectamine 3000 Reagent. siRNAs were added minfi, and limma. Data quality was assessed using the standard 24h before the first stimulus and then, we performed stimulation pipeline from shinyepico (23) and minfi packages (24). The data experiments as mentioned above. Finally, the levels of the target were Illumina-normalized, and beta and M values were then genes were examined by qRT-PCR at day 4. calculated. We excluded CpGs with overlapping SNPs. M values were used to build a linear model using the limma package in R, Cytokine Measurements including the donor as a covariable. The cytokines levels were measured from the cell culture In this study, we considered a CpG to be differentially supernatants using an enzyme-linked immunosorbent assay methylated when the FDR is lower than 0.05. For representation (ELISA), according to the manufacturer’s instructions purp oses, beta-valu es are batch-corrected with the (BioLegend, San Diego, CA, USA). In addition, the Pre-defined removeBatchEffect function of the limma package, using the same Human Inflammatory Panel LegendPlex™ (BioLegend) was covariables of the linear model, when it is indicated in the used for the simultaneous analysis of 13 cytokines (CCL2, figure legend. Frontiers in Immunology | www.frontiersin.org 3 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis RNA Purification and RNA-Seq Analysis occupancy in the differentially methylated CpG genomic Total RNA of uncultured monocytes, LPS monocytes, LPS+iJAK2 positions were calculated. Moreover, data of whole-genome monocytes, and untreated monocytes were isolated using a bisulfite sequencing (WGBS) of the same reference were Maxwell® RSC simplyRNA kit (Promega, Wisconsin, USA). also utilized. RNA-seq libraries were generated, selecting only mRNA with Graphs of the ATAC-seq and WGBS data were created with oligo-dT capture beads, and sequenced in the Genomics Unit of the deeptools toolkit (32). the Centre for Genomic Regulation (CRG) (Barcelona, Spain), For ChIP-seq data of H3K27ac and H3K4me1, bed files were with an Illumina HiSeq 2500 sequencer, in 50-bp paired-end in downloaded from the BLUEPRINT portal (http://dcc.blueprint- 50-bp paired-end. Around 100 million reads were obtained for epigenome.eu/). A file for each histone mark, cell type, and time each sample. point was used. Enrichment of these histone marks around the Fastq files were aligned to the hg38 transcriptome using CpG positions (-3kb, 3kb) was studied applying a Fisher’s Exact HISAT2 (25) with standard options. Reads mapped in proper Test to compare them with the background (EPIC array CpGs), pair, being primary alignments were selected with samtools (26). dividing the studied region in tiles of 10 bp. The calculated odds Reads were assigned to genes with FeatureCounts (27). ratio of each tile is represented. Differentially expressed genes were detected with DESeq2 (28). The donor was used as a covariable in the model. The Methylation and Expression Association Ashr shrinkage algorithm was applied and only protein-coding Hypomethylated CpGs were associated with the nearest TSS genes with abs(logFC) > 1 and FDR < 0.05 were selected as using the annotatePeaks.pl command in the HOMER suite (31). differentially expressed. After removing duplicate genes, expression of that gene set was For representation purposes, Variance Stabilizing studied using a public dataset of an RNA-seq time course (15) Transformation (VST) values and normalized counts provided and our RNA-seq data. by DESeq2 were used. Quantitative Reverse-Transcription Gene Ontology Over-Representation Polymerase Chain Reaction (qRT-PCR) Total RNA was isolated with a Maxwell® RSC simplyRNA kit Analysis, Gene Set Enrichment (Promega, Wisconsin, USA) and reverse-transcribed using a Analysis and Transcription Factor Transcriptor First Strand cDNA Synthesis Kit (Roche, Basel, Enrichment Analysis Switzerland), in accordance with the manufacturer’s instructions. Gene ontology (GO) over-representation of differentially qRT-PCR was performed in triplicate using LightCycler® 480 SYBR methylated CpGs was analyzed using the Genomic Regions Green Mix (Roche). The standard double delta Ct method was used Enrichment of Annotations Tool (GREAT, version 4.0.4) to determine the relative quantification of target genes, and values (http://great.stanford.edu/public/html/), adopting the standard were normalized against the expression of endogenous control options (29) and using EPIC array CpGs as background. genes such as RPL38. Enrichment is measured as the –log10 binomial FDR. Gene Set Enrichment Analysis (GSEA) was performed from the Western Blotting LPS versus untreated, and LPS versus LPS-iJAK2 comparisons. Cytoplasmic and nuclear protein fractions were obtained using Genes were ranked using this formula: -log10(FDR) * sign [log hypotonic lysis buffer (Buffer A; 10 mM Tris pH 7.9, 1.5 mM (FC)]. As genesets collection, hallmarks (H) from the Molecular MgCl2, 10 mM KCl supplemented with protease inhibitor Signatures Database (MSigDB) were selected, adding the specified cocktail (Complete, Roche) and phosphatase inhibitor cocktail custom genesets. GSEA analysis and graphs were created with the (PhosSTOP, Roche) to lyse the plasma membrane. Protein ClusterProfiler (30) and enrichplot Bioconductor packages. Gene pellets were resuspended in Laemmli 1X loading buffer. Ontology over-representation of upregulated and downregulated Proteins were separated by SDS-PAGE electrophoresis. protein-coding genes was performed with ClusterProfiler, using all Immunoblotting was performed on polyvinylidene difluoride detected protein-coding genes as background. (PVDF) membranes following standard procedures. We used the findMotifsGenome.pl command in the Membranes were blocked with 5% bovine serum albumin Hypergeometric Optimization of Motif EnRichment (HOMER) (BSA) and blotted with primary antibodies. After overnight suite to look for motifs that are enriched in the target set relative to incubation, membranes were washed three times for 10 the background set (software v4.11) (31). It was used to identify minutes with TBS-T (50 mM Tris, 150 mM NaCl, 0.1% enrichment of TF binding motifs in the 250bp-window upstream Tween-20) and incubated for 1 hour with HPR-conjugated and downstream of the differentially methylated CpG sites. mouse or rabbit secondary antibody solutions (Thermo Fisher) Annotated CpGs in the EPIC array were used as background. diluted in 5% milk (diluted 1/10000). Finally, proteins were detected by chemiluminescence using WesternBright™ ECL Accessibility and Histone-Mark Profiling of (Advansta). The following antibodies were used: Phospho- Differentially Methylated CpGs STAT3 (Tyr705) (D3A7), rabbit mAb (Cell Signaling Using public data sets of ATAC-seq and H3K4me1 and Technology, ref: #9145; Phospho-STAT5b (Tyr694) (C11C5), H3K27ac ChIP-seqs of untreated and LPS-treated monocytes rabbit mAb) (Cell Signaling Technology, ref: #9359), Phospho- at 1, 4 and 24 hours (15), the accessibility and histone mark STAT1 (Tyr701) (58D6), rabbit mAb (Cell Signaling Frontiers in Immunology | www.frontiersin.org 4 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis Technology, ref: #9167); Phospho-STAT6 (Tyr641) Antibody through TLR4 and TLR2, respectively, and lead to a tolerized (Cell Signaling Technology, ref: #9361); STAT3 (79D7), rabbit state. After the first stimulation, cells were washed and left to rest mAb (Cell Signaling Technology, ref: #4904); STAT5B for 3 days in the presence of human pooled serum. Cells were (Millipore, ref: #06-969), STAT1(42H3), rabbit mAb (Cell then stimulated again with LPS for another 24 hours Signaling Technology, ref: #9175; LaminB1 (Abcam, (Figure 1A). Monocytes maintained with only RPMI medium ref: #ab65986). and serum for 1 + 3 days (∅ ‘untreated’) were used as negative controls. Under these conditions, we confirmed that cells pre- pSTAT1 Flow Cytometry exposed to LPS or P3C produced much lower levels of TNFa Peripheral blood mononuclear cells (PBMCs) were purified from after a second exposure to LPS (Figure 1B). blood samples of septic patients and healthy donors by density We then obtained the DNA methylation profiles using gradient centrifugation using lymphocytes isolation solution Infinium MethylationEPIC bead arrays of bisulfite-treated (Rafer, Zaragoza, Spain). PBMCs were counted and 5 million biological triplicates of monocytes, exposed for 24 hours to per condition were cultured in T25 flasks in Roswell Park LPS or P3C, and after the 3-day resting period, with the Memorial Institute (RPMI) Medium 1640 + GlutaMAX ™ matching untreated controls (4-days cultured monocytes; ∅ (Gibco, Life Technologies, CA, USA) containing 2% Fetal ‘untreated’) and ‘uncultured’ monocytes at day 0 (MO). Bovine Serum (FBS), 100 units/mL penicillin, and 100 mg/mL Statistical analysis of the data revealed specific DNA methylation streptomycin, for 4 hours, with or without LPS (10 ng/ml from changes between LPS-/P3C-treated, untreated monocytes and E. coli O111:B4, Sigma-Aldrich, Darmstadt, Germany). Cells uncultured monocytes (Figure S1A). Methylation changes were collected and stained with CD14 (APC) (Miltenyi Biotec, produced with LPS and P3C are comparable (Figure S1B), ref: #130-091-243), CD15 (FITC), and pSTAT1 (mouse Anti- suggesting that downstream events following engagement of pStat1 BV421 (pY701), BD Biosciences; ref: #562985), using TLR4 and TLR2 respectively (Figure S1C) converge in the mouse IgG2a, k Isotype BV421 (BD Biosciences; ref: #563464) acquisition of similar DNA methylome changes. as a control according to the BD Fixation/Permeabilization Since LPS has a more intense effect than P3C (Figure S1D), as Solution Kit (#554714) and the antibodies manual. reflected in the greater number of differentially methylated sites, but a very similar pattern (Figure S1E), we focused on the analysis of Mann–Whitney U Test and Student’s changes between LPS-treated and untreated monocytes. We found Paired T Test 331 hypomethylated and 29 hypermethylated CpGs, with an FDR < Data were analyzed with Prism version 6.0 (GraphPad). 0.05 and a DB > 0.2. (Figure 1C and Supplementary Table 1). LPS- Statistical analyses consisted of non-parametric Mann– hypomethylated CpGs were located mostly in intergenic and Whitney U tests, to determine differences between pairs of intronic regions and were outside CpG islands (Figure 1D). separate groups, and Student’s paired t test, to compare the We then compared our DNA methylation data in relation to a means of matched pairs of groups, except where indicated time series (1, 4, 24 hours) of chromatin accessibility and DNA otherwise. The levels of significance were: *, p < 0.05; **, p < methylation datasets obtained under similar conditions (15). 0.01; ***, p < 0.001. Interestingly, chromatin accessibility of the genomic positions bearing LPS-specific hypomethylated CpGs in LPS-treated monocytes started to increase very quickly (1 hour) and RNA-Seq Public Data Reanalysis increased progressively over time (Figure 1E). In contrast, Counts matrix from a RNA-seq experiment (GSE133822) was demethylation at such sites was only observed after 24 hours, downloaded from the Gene Expression Omnibus database. Only indicating that accessibility precedes DNA methylation loss septic patients and healthy donors were used. (Figure 1F). Similar to the changes in chromatin accessibility, Differentially expressed genes were detected with DESeq2 histone marks characteristic of active enhancers (H3K4me1 and [43]. The donor was used as a covariable in the model. The H3K27ac) also increased faster than DNA methylation changes Ashr shrinkage algorithm was applied and only protein-coding (Figure 1G). No significant enrichment in other histone marks genes with abs(logFC) > 1 and FDR < 0.05 were selected as was found (Figure S1F). In summary, the genomic composition differentially expressed. and dynamics of LPS-demethylated sites indicate that a high proportion of methylation changes occur in regulatory regions, potentially controlling gene expression of phenotype-relevant RESULTS genes. Moreover, these results suggest that LPS/P3C-driven demethylation requires a pioneer factor that can access closed TLR2/4 Stimulation of Monocytes Yields chromatin following TLR4/TLR2 stimulation to enable, directly Specific DNA Demethylation or indirectly, such specific demethylation. To investigate the mechanisms underlying DNA methylation Gene ontology (GO) over-representation analysis of changes associated with the exposure to bacterial endotoxins that hypomethylated CpGs in LPS-treated monocytes revealed the lead to tolerance, human CD14+ monocytes isolated from enrichment of functional categories associated with monocyte/ healthy donor blood were pre-incubated for 24 hours with macrophage cell biology and inflammation including cell lipopolysaccharide (LPS) or Pam3Cys (P3C), which signal response to cytokine stimulus, cell response to IL-1, cell Frontiers in Immunology | www.frontiersin.org 5 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis A B C D E F G H I J FIGURE 1 | Continued Frontiers in Immunology | www.frontiersin.org 6 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis FIGURE 1 | DNA methylation profile of LPS-treated human CD14+ monocytes. (A) Schematic diagram depicting in vitro experiments for endotoxin tolerance models. (B) Release of TNFa from CD14+ monocytes isolated from healthy donor blood samples treated with LPS (10 ng/ml, 24 hours) or P3C (10 mg/ml, 24 hours), then washed and rested (3 days) and treated again for 24 hours with LPS (10 ng/ml). These are all compared with untreated monocytes. Graphs show the mean ± SEM of five healthy donors. (C) DNA methylation heatmap of differentially methylated CpGs comparing untreated monocytes (Ø) with LPS monocytes (Db- ≥ 0.2, adjusted p (FDR) < 0.05). Scaled b-values are shown, ranging from -2 (lower DNA methylation levels, blue) to +2 (higher methylation levels, red). On the right, violin plots of hypomethylated and hypermethylated clusters depicting normalized DNA methylation data. (D) Barplot presenting the percentages of different genomic features of hypomethylated CpGs in comparison with background (Bg) CpGs (left panel), and barplot of CpG island contexts (right panel). FDRs of the Fisher’s exact tests to estimate the enrichment or depletion in categories of hypomethylated CpGs in comparison with Bg are also shown ( ***p < 0.001; ****p < 0.0001) (E) Accessibility (ATAC-seq) data of hypomethylated CpGs after 1, 4 and 24 hours of monocyte culture with LPS (in red) or without treatment (Ø) (in blue). Public ATAC- seq data were used (15) (F) Methylation of hypomethylated CpGs after 1, 4 and 24 hours of monocyte culture with LPS (in red) or without treatment (Ø) (in blue). Public WGBS data were used (15). (G) ChIP-seq data of H3K27ac and H3K4me1 of LPS-treated and untreated monocytes were downloaded from the Blueprint database. Odds ratios were calculated for bins of 10 bp up to ±3000 bp around hypomethylated CpGs. CpGs annotated in the EPIC array were used as background. (H) GO (Gene Ontology) over-represented categories in the hypomethylated CpGs. The fold change relative to the background (EPIC array CpGs) and -log10(FDR) is shown. (I) Bubble scatterplot of TF binding motif enrichment for hypomethylated CpGs. The x-axis shows the percentage of windows containing the motif; the y-axis shows the magnitude of enrichment of the motif. Bubbles are colored according to TF family. FDR is indicated by bubble size (selected TF with FDR ≤ 1e-05). (J) DNA methylation measured by bisulfite pyrosequencing after TET2 silencing with siRNA (siTET2) in comparison with a control siRNA (siScramble). One- tailed t-test p values are depicted ( *p < 0.05). response to bacterial molecules and inflammatory response LPS-Driven Gene Expression Changes Are (Figure 1H). In contrast, GO analysis of demethylated sites Correlated With DNA Methylation and Are specific to untreated monocytes included regulatory categories Concomitant With STAT1, STAT3 and such as regulation of IL-1 production, negative regulation of LPS STAT5 Activation signaling, and negative regulation of IL-12 production We performed RNA-seq with LPS-stimulated monocytes under (Figure S1G). the same conditions as before (3-day cultured monocytes To detect potential transcription factors involved in the following 24h stimulation), including ‘untreated’ and demethylation process, we performed motif enrichment ‘uncultured’ monocytes. LPS exposure induced upregulation of analysis. Hypomethylated CpGs of LPS-treated monocytes 1142 genes and downregulation of 1025 genes (logFC > 1, FDR < were enriched in transcription factor binding motifs relevant to 0.05) in relation to’ untreated’ monocytes (Figure 2A and inflammation such as NF-kB, the AP-1 family, and some Supplementary Table 2). Principal component analysis (PCA) members of the STAT family (STAT1, STAT5 and STAT3) showed that LPS-treated and ‘untreated’ monocytes were (Figure 1I). In contrast, demethylated sites specific to separated along the axis of PC2, and these two along the PC1 ‘untreated’ monocytes (4-days culture without exposure to axis with respect to ‘uncultured’ monocytes (Figure S2A). LPS) also contained motifs for NF-kB and AP-1, but not In the Gene Set Enrichment Analysis (GSEA), LPS-treated STAT family members (Figure S1H), suggesting a specific role monocytes were enriched in significant inflammatory pathways for this transcription factor family in the LPS-driven such as TNFa signaling via NF-kB, inflammatory response, demethylation process most probably due to a second wave of interferon gamma/alpha response, and IL6/JAK/STAT3 chromatin changes triggered by IFN release a consequence of signaling (Figure 2B), indicating that the downstream targets LPS-mediated monocyte response and the subsequent activation of these signaling pathways had been transcriptionally activated. of the IFNR/JAK2/STAT axis that can in turn activate TET2 (33). On the other hand, pathways depleted in LPS-treated monocytes To validate the methylation array results, we performed included oxidative phosphorylation, MYC targets and fatty acid bisulfite pyrosequencing on a selection immune relevant genes, metabolism, as has also been described by others (Figure S2B) such as CCL20, ETS1, HDAC9, IL24, IL2RA, IL36G and IRAK2, (35, 36). from our methylation screening (Figure S1I). Similarly, GO over-represented categories in LPS-upregulated Under these conditions, MOs do not proliferate. Therefore, any genes included terms such as cytokine secretion, response to loss of DNA methylation is replication-independent or, in other interferon-gamma, response to LPS, and the JAK/STAT cascade words, resulting from an active demethylation mechanism. Ten- (Figure 2C), with coincident categories with hypomethylated eleven translocation methylcytosine dioxygenases (TET) mediate CpG GO categories (Figure 1H), whereas LPS-downregulated active demethylation. TET2 has been implicated in catalyzing GO categories were related to metabolism (Figure S2C). active demethylation in other monocyte-related differentiation To identify possible transcription factors leading to gene processes (19, 34). We then downregulated TET2 to determine upregulation, we performed Discriminant Regulon Expression whether this enzyme was involved in the demethylation processes Analysis (DoRothEA) (37), with which we identified several observed under our conditions. Using specific siRNAs, we candidates coincident with those identified in our previous achieved around 50% TET2 downregulation, in both mRNA HOMER analysis of the LPS-specific DNA demethylation set: and protein levels after 3 days of treatment (Figures S1J, K). STAT (STAT1, STAT2, STAT3, STAT5B), NF-kB (RELA, Under these conditions, demethylation of LPS-specific NFKB1) and AP-1 (FOS) (Figure 2D). Additionally, in the set hypomethylated CpG sites was partially impaired (Figure 1J), of downregulated genes, we found enrichment of transcription demonstrating the involvement of TET2 in this process. factors such as FOXP1, MYC and SREBF2 (Figure S2D). Frontiers in Immunology | www.frontiersin.org 7 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis A B C D E F G H I J FIGURE 2 | Continued Frontiers in Immunology | www.frontiersin.org 8 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis FIGURE 2 | Gene expression, association with methylation and activation of JAK2-dependent STATs (A) Gene expression heatmap of differentially expressed genes, comparing LPS monocytes with untreated (Ø) monocytes (LogFC > 1, FDR < 0.05). Scaled variance stabilizing transformation (VST) values are shown, ranging from -2 (lower gene expression level, green) to +2 (higher gene expression level, orange). (B) Gene set enrichment analysis (GSEA) of LPS versus untreated (Ø) monocytes, using MSigDB hallmarks (H) as gene sets. The running enrichment score and the normalized enrichment score (NES) are shown above the graph (FDR < 0.001). (C) Gene ontology (GO) over-representation of GO Biological Process categories. Fold change of LPS-upregulated genes relative to background and -log10(FDR) of the Fisher’s exact tests are shown. (D) Discriminant regulon expression analysis (DoRothEA) of LPS versus untreated (Ø) monocytes. NES and -log10 (FDR) of transcription factors (TFs) enriched on the LPS side are shown (20 TF with the highest NES and FDR < 0.001). (E) Hypomethylated CpGs were associated with the nearest gene. The resulting gene set (Hypo-CpG-associated genes) was used in the GSEA of LPS versus untreated (Ø) monocytes. The running enrichment score and the normalized enrichment score (NES) are shown above the graph (FDR < 0.001). (F) DNA methylation of differentially methylated CpGs was correlated with gene expression of differentially expressed genes in the LPS versus untreated (Ø) monocyte comparison. Expression is represented on the y-axis as the LogFC, where higher values indicate higher levels of expression in LPS monocytes, and lower values indicate higher levels of expression in untreated monocytes. DNA methylation is depicted on the x-axis as Db, where lower numbers indicate lower levels of methylation in LPS monocytes, and higher numbers indicate lower levels of methylation in untreated monocytes. Points are colored according to their genomic context. A significant negative Pearson’s correlation between methylation and expression was observed (R = -0.38, p < 6.2e-05). (G) Hypo-CpG-associated gene expression was analyzed in time-course RNA-seq data from LPS-treated and untreated monocytes (15). Principal components 1 and 2 from a principal component analysis (PCA) of the expression data are shown. (H) Heatmap of Hypo-CpG- associated gene expression in a time-course RNA-seq, from LPS monocytes (left panel). The dendrogram can be considered to separate the sample into three clusters (C1, C2 and C3), depicting three distinct behaviors. Boxplot of normalized gene expression of genes in C1, C2 and C3 clusters, in LPS-treated (pink) and untreated (blue) monocytes. (I) Significant transcription factors of hypomethylated CpGs (Figure 1) were analyzed separately in the three clusters. Transcription factors are clustered by their position weight matrix differences. Scaled fold-change (FC) is represented as a color scale (a redder color indicates a greater change in relative to the background). The border of significant transcription factors (FDR < 1e-5) are colored black; non-significant borders are shown in grey. (J) Western blot of protein phosphorylated STAT1, total STAT1, phosphorylated STAT3, total STAT3, phosphorylated STAT5 and total STAT5 (left panel) and phosphorylated-STAT6 (right panel) in LPS-treated relative to untreated (Ø) monocytes. LaminB was used as loading control. Signal of western blot bands was quantified for each replicate and pSTAT/LaminB signal ratio is shown (n = 3-6). One-tailed t-test p values are depicted (*p < 0.05; **p < 0.01). The similarities of methylation and expression GO categories C1-associated CpGs (with concomitant increase of expression and transcription factors potentially involved in the and demethylation) revealed specific enrichment of STAT1, demethylation and upregulation reinforce the notion of their STAT3 and STAT5 binding motifs, which were not present in mutual relationship. In fact, LPS-upregulated genes are enriched genes in C2 and C3 (Figure 2I), which points at the necessity of in hypo-CpG-associated genes, as revealed by the GSEA analysis the activation of a second pathway such as IFNgR/JAK2/STAT in (Figure 2E). We also found a significant inverse correlation response to LPS triggered events in order to promote DNA between DNA methylation and expression (Figure 2F). methylation changes. Furthermore, using the expression data from the previously Since individual and combined DNA methylation and gene mentioned time-course study (1, 4 and 24 hours) in a similar expression analysis suggested a specific role for the STAT model to ours (15), we monitored the expression of the LPS- transcription factor family, we studied the protein levels and hypomethylated CpGs-associated genes. Principal component phosphorylation of STAT1, STAT3 and STAT5 (JAK2 targets) in analysis (PCA) revealed a divergent trajectory of LPS-treated LPS-treated and untreated monocytes. We observed an increase and untreated monocytes (Figure 2G). The exposure of in the phosphorylation of these three transcription factors monocytes to LPS was sufficient to promote gene expression (Figure 2J) in LPS-treated samples. Moreover, P3C treatment changes antagonistic to those in untreated monocytes, cultured also induces phosphorylation of JAK2 targets (Figure S2H). In without cytokines during the same period. Time-course contrast, phosphorylated-STAT6 (JAK3 target), which did not expression analysis of LPS hypo-CpG-associated genes also appear in our HOMER analysis, was not detected in LPS revealed three main temporal clusters based on their dynamics monocytes, providing further evidence of the potential specific (Figure 2H and Supplementary Table 3). The largest cluster (C1), involvement of JAK2-related pathways. showed an increase of gene expression at 24 hours, concomitant with DNA demethylation (Figures 1F and 2H). In contrast, Inhibition of JAK2 Partially Prevents the cluster 2 (C2) presented an increase of gene expression prior to Activation of LPS-Upregulated Genes and DNA demethylation, but coincident with the accessibility and Tolerance-Related Genes enhancer-associated histone mark gains. Finally, cluster 3 (C3) To investigate the potential role of JAK2 pathway in modulating showed a reduction in gene expression over time, in untreated and tolerance-specific DNA methylation changes, we treated CD14+ LPS monocytes, although the effect was less pronounced in the monocytes with LPS (10 ng/ml) for 24 hours in the presence or latter. These data imply that DNA methylation has different absence of TG101348, a selective JAK2 inhibitor (iJAK2) (500 nM). relationships with gene expression changes, possibly depending Monocytes were washed and left to rest for 3 days (with or without on the genomic context, and that most DNA methylation changes iJAK2) and after that, cells were stimulated again with LPS (24 require previous chromatin remodeling, consistent with the timing hours). Control experiments without LPS were performed in observed for those changes to appear. Whereas gene upregulation parallel. We confirmed by western blot and observed that JAK2- is concomitant with DNA demethylation in C1, DNA associated STAT phosphorylation was blocked by JAK2 inhibition, demethylation occurs after gene upregulation in C2 and C3. at 4 hours (left) and 96 hours (right) (Figure S3A). C1-, C2-, and C3-associated CpGs also had distinctive To examine the effects of JAK2 inhibition on monocyte genomic features (Figures S2E–G). HOMER analysis of response, we measured the production of TNFa and IL-10 Frontiers in Immunology | www.frontiersin.org 9 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis A B C D E F G H I FIGURE 3 | Continued Frontiers in Immunology | www.frontiersin.org 10 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis FIGURE 3 | Role of JAK2/STAT in LPS monocytes gene expression and phenotype. (A) Gene expression heatmap of differentially expressed genes, comparing LPS with LPS+iJAK2 monocytes (LogFC > 1, FDR < 0.05). Scaled variance stabilizing transformation (VST) values are shown, ranging from -2 (lower gene expression level, green) to +2 (higher gene expression level, orange). (B) Gene ontology (GO) over-representation of GO Biological Process categories. Fold change of iJAK2-downregulated and LPS-upregulated genes relative to background and -log10(FDR) of the Fisher’s exact tests are shown. (C) Gene set enrichment analysis (GSEA) of LPS-treated versus LPS+iJAK2-treated monocytes, using MSigDB hallmarks as gene sets. The running enrichment score and the normalized enrichment score (NES) are shown above the graph (FDR < 0.001). (D) Discriminant regulon expression analysis (DoRothEA) of LPS-treated versus LPS+iJAK2-treated monocytes. NES and -log10(FDR) of transcription factors (TF) enriched on the LPS side are shown (20 TF with the highest NES and FDR < 0.001). (E) GSEA of LPS-treated versus untreated (Ø) monocytes using the iJAK2-downregulated genes as the gene set. The running enrichment score and the normalized enrichment score (NES) are shown above the graph (FDR < 0.001). (F) Gene Set Enrichment Analysis (GSEA) of LPS vs LPS+iJAK2 monocytes using the top100 tolerized genes (blue) or the top100 non-tolerized genes (pink) as gene sets. Running enrichment score is represented and the normalized enrichment score (NES) and FDR are shown above. (G) Selected examples of iJAK2-downregulated genes (FDR < 0.05, logFC of LPS vs LPS+iJAK2 > 1). Bar plots show the mean ± SEM (standard error of the mean) of normalized counts. (H) GSEA of LPS-treated versus LPS+iJAK2-treated monocytes using the Hypo-CpG-associated genes as the gene set. The running enrichment score and the normalized enrichment score (NES) are shown above the graph (FDR < 0.05). (I) DNA methylation percentage obtained from pyrosequencing of three selected CpGs from the hypo-CpG group in LPS monocytes, before and after a second LPS stimulus, and with or without iJAK2 treatment. (Figure S3B). JAK2 inhibition resulted in decreased production a ‘score’ of tolerization as (Untreatedreexposure – Untreated) – of TNFa and increased IL-10 production after a second LPS (LPSreexposure – LPS), where a positive score means that the stimulus (Figure S3B). This further reduction in TNFa gene is tolerized and a negative score that is non-tolerized. We production is consistent with the role of JAK2 in LPS- then compiled a top100-tolerized gene-set comprising the 100 mediated inflammatory response (38). genes with the highest scores and a top100-non-tolerized gene- We then performed RNA-seq of biological triplicates under the set comprising the 100 genes with the lowest scores. We previously described cell conditions (LPS-treated monocytes, LPS performed a GSEA analysis of both gene-sets with the +iJAK2-treated monocytes, untreated monocytes, and uncultured comparison LPS/LPS+iJAK2 and found specific significant monocytes). JAK2 inhibition resulted in downregulation of 124 enrichment of the tolerized genes but not of the non-tolerized genes (iJAK2-downregulated genes), whereas only 6 genes were genes among the iJAK2-downregulated genes (Figure 3F and upregulated (LogFC > 1, FDR < 0.05) (iJAK2-upregulated genes) Supplementary Table 5). Some examples of tolerized-gene (Figure 3A and Supplementary Table 4). expression are shown in Figure 3G, where the direct inhibition The over-represented GO categories among the genes of their transcriptional activation by JAK2 inhibition is depicted. downregulated upon JAK2 inhibition (Figure 3B) were very Notably, these genes are also involved in the IFNa or similar to those found in LPS-upregulated genes (Figure 2C), IFNg response. including categories related to response to interferon-alpha, Given the correlation between our DNA methylation and interferon-gamma, defense response to other organisms, etc. expression datasets (Figure 2F), the enrichment of STAT1/3/5 in However, we found a generally higher magnitude change and a the individual and combined data, and the effects of JAK2-STAT lower FDR in the iJAK2-downregulated genes than in the LPS- inhibition in the regulation of some LPS-upregulated genes upregulated genes, suggesting specific and stronger enrichment (Figure 3F), we studied the effects of the inhibition of the in interferon-related genes. In this respect, the GSEA analysis JAK2-STAT pathway on hypomethylated CpG-associated revealed four hallmarks downregulated in LPS+iJAK2 cells: genes. The GSEA of LPS versus LPS+iJAK2 monocytes inflammatory response, IL6/JAK/STAT3 signaling, interferon revealed a small but significant enrichment in the LPS side, gamma response and interferon alpha response (Figure 3C). suggesting that genes related to CpGs hypomethylated with LPS These categories were previously associated with LPS-treated are, at least partially, under the transcriptional control of the monocytes (Figure 2B). JAK2-STAT pathway (Figure 3H). DoRothEA analysis showed several putatively related To determine whether the JAK2-STAT pathway is related to transcription factors depleted in LPS+iJAK2 monocytes, the LPS-specific DNA demethylation process we carried out including STAT1, STAT2, STAT3, STAT5A and STAT5B pyrosequencing of three selected CpGs, with and without a (Figure 3D). NF-kB was not identified by either the DoRothEA second LPS stimulus (Figure 3I). A general trend of or the GSEA analysis, in contrast to the LPS-associated hallmarks demethylation blockage was found, although only one CpG and transcription factors (Figures 2B, D). (cg09909990) had a p value < 0.05. The fact that iJAK2 did not A relationship, as suggested by previous data, was found fully inhibit the phosphorylation of STATs, and the possible between iJAK2-downregulated genes and LPS-upregulated redundancy of other JAK/STAT pathways could explain the genes. In a GSEA, iJAK2-downregulated genes were strongly heterogeneity in the triplicate. enriched in LPS-upregulated genes side in comparison with Untreated(Ø) monocytes (Figure 3E). Relevance of the JAK2/STAT Pathway in We also found a link between iJAK2-downregulated genes Monocytes From Septic Patients Infected and tolerized genes. Using a public dataset (15) of gene With Gram-Negative Bacteria expression (log 2 RPKM) of LPS-treated and untreated Given the relevance of JAK2/STAT pathways secondary to LPS monocytes before and after a second LPS stimulus, we defined response in monocytes, and to the transcriptional regulation of Frontiers in Immunology | www.frontiersin.org 11 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis A B C D E F FIGURE 4 | STAT1 phosphorylation in monocytes from healthy donors and septic patients with or without an LPS stimulus. (A) Violin plot of normalized bvalues of the LPS Hypomethylated CpGs associated with STAT1/STAT3/STAT5 motifs in CD14+ monocytes from peripheral blood of healthy donors, gram-negative sepsis, other sepsis and SIRS patients (91 CpGs) (ns p > 0.05; ****p < 0.0001 ). (B) Example of the strategy adopted to gate only monocytes. CD14+CD15- (Q1) are mostly monocytes, whereas some CD15+ cells (neutrophils) can also express CD14 (Q2). (C) Median fluorescence intensity (pSTAT1) of monocytes (Q1) from septic patients (pink) and healthy donors (blue), with and without an LPS stimulus. Two-tailed Wilcoxon test p values are depicted (ns p > 0.05, * p < 0.05). (D) Example of a pSTAT1 signal histogram from a healthy donor (blue) and a septic patient (pink), with and without an LPS stimulus. (E) GSEA of LPS-treated versus LPS+iJAK2- treated monocytes using Sepsis upregulated genes (logFC >1, FDR < 0.05). The running enrichment score and the normalized enrichment score (NES) are shown above the graph. (F) GSEA of LPS-treated versus LPS+iJAK2-treated monocytes using MS1 LPS-downregulated genes (40) as gene set. The running enrichment score and the normalized enrichment score (NES) are shown above the graph. Frontiers in Immunology | www.frontiersin.org 12 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis tolerized genes, we compared the effect in DNA methylation of expression dataset revealing a downregulation of these genes, patients with sepsis caused by gram-negative bacteria in in our model, with iJAK2 treatment (Figure 4F). comparison with other patients with sepsis and patients with This result is consistent with the decrease of STAT1 activation systemic inflammatory response syndrome (SIRS) following in monocytes from septic patients after an LPS stimulus cardiac surgery, from our previous study (13). The comparison (Figure 4B) and shows that the inhibition of JAK2 in of healthy donors with gram-negative sepsis yielded 339 monocytes can partially recapitulate the expression changes differentially methylated positions (DMPs) (FDR < 0.05), produced in septic monocytes after a second immune challenge. whereas only 1 DMP between healthy donors and other sepsis Overall, the data suggest that the JAK2-STAT1 pathway is was found, and 0 DMPs between healthy donors and SIRS profoundly disturbed in patients with sepsis caused by gram- patients (Figure S4A). However, the heatmap and PCA of negative bacteria, producing reduced levels of pSTAT1 after a DMPs showed also a demethylation trend in other sepsis, second immune challenge with LPS and potentially leading to suggesting that similar upstream pathways are involved in both abnormal STAT dependent gene expression patterns that could sepsis groups, with the highest effect in DNA methylation in drive the dysfunctional monocyte response in septic monocytes. patients with sepsis caused by gram-negative bacteria (Figures Our results may explain the previously reported partial S4B, C). Moreover, the gram-negative hypomethylated CpGs restoration of leukocytic function observed in septic patients were enriched in STAT motifs (Figure S4D) and Gene Ontology after therapy with recombinant interferon-g (41). categories related to inflammatory response and LPS stimulus (Figure S4E), showing high similarities with the motifs and Gene Ontology categories enriched in the LPS hypomethylated CpGs DISCUSSION (Figures 1H, I). Interestingly, when we tested the set of hypomethylated CpGs Our results indicate that TLR4/TLR2 stimulation induces specific in LPS-treated monocytes (Hypo-CpGs) with STAT1/3/5 TET2-dependent demethylation in monocytes, accompanying binding motifs with the DNA methylation data of the sepsis the acquisition of endotoxin tolerance. LPS (TLR4)-specific and healthy donor’s cohort, we found lower methylation levels in changes in DNA methylation are concomitant with gram-negative sepsis patients in comparison with any of the upregulation of inflammatory-related genes, and these involve other groups (Figure 4A), reinforcing the notion of the the JAK2/STAT pathway. Inhibition of the JAK2 pathway in this participation of the JAK2-STAT pathway in the first encounter in vitro model of endotoxin tolerance impairs the upregulation of with gram-negative bacteria under in vivo conditions. genes that become tolerized following a first encounter with To test whether the STAT activation was altered in patients bacterial LPS. The implication of this pathway is proven for infected with gram negative bacteria, we incubated PBMCs from patients with sepsis caused by gram-negative bacteria. patients with gram negative bacterial sepsis and healthy donors Many of the CpG sites that become demethylated after TLR4/ in RPMI (2% FBS) for 4 hours, in the presence of LPS (10 ng/mL) TLR2 stimulation are associated with inflammatory genes. and in its absence. Since activated neutrophils can contaminate Examples include the chemokine CCL20, which has the PBMC section of Ficoll and can express CD14, we adopted a antimicrobial activity (42), IL36G, member of the IL-1 cytokine gating strategy to analyze only the intracellular pSTAT1 signal of family, and the inflammatory cytokine IL-24 (43). Other CD14+ CD15- cells (Q1) (Figure 4B). No differences were found demethylated CpGs are associated with genes encoding the between healthy donors and septic patients in the basal transcription factor ETS1 and the molecule IRAK2, an essential (unstimulated) signal of pSTAT1. However, after the LPS regulator for IL-1R and TLR signaling (44). CpGs undergoing stimulus, pSTAT1 was increased in healthy donors, as LPS-driven demethylation, are preceded by increases in chromatin expected, but decreased in septic patients (Figures 4C, D). accessibility and H3K4me1/H3K27ac gains and correlate with To provide more insight into the JAK2-STAT1 pathway transcriptional activation of the associated genes. This temporal relevance in sepsis, we used expression data from CD14+ uncoupling between chromatin accessibility, transcription and monocytes isolated from patients with sepsis and healthy age- DNA methylation in terminally differentiated myeloid cells, has matched (40). Interestingly, genes upregulated in septic patients also been recently demonstrated by simultaneously assessing are those inhibited by iJAK2 in our model, suggesting that the chromatin conformational changes and DNA methylation in a JAK2-STAT pathway is also involved in the transcriptomic genome-wide manner on the same population of DNA molecules changes after the first encounter with bacterial molecules in (ATAC-Me technique), supporting the idea of DNA methylation septic patients (Figure 4E). as a required event in during enhancer activation that underlies Moreover, we utilized data from a single-cell study of PBMCs cell state transitions (45). Moreover, transcription factors can from septic patients (39), in which a specific monocyte subset directly recruit DNMTs or TET enzymes and influence gene associated with sepsis (MS1) is defined. After isolating that expression (46). Our analysis revealed that binding motifs of population from patients with sepsis caused by gram-negative STAT1, STAT3 and STAT5, which are phosphorylated by JAK2, bacteria and stimulating it with LPS in vitro, some genes (MS1 specifically associate with the observed LPS-specific LPS-downregulated genes) were downregulated, revealing a hypomethylated CpGs and expression changes. tolerized phenotype. We performed a GSEA comparing the JAK/STAT signaling is not directly downstream of TLR4/ MS1 dataset with our LPS versus LPS+iJAK2 monocytes TLR2 receptors, and its activation requires the production of Frontiers in Immunology | www.frontiersin.org 13 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis other molecules, such as IFNg or IL-6, in order to activate the methylation, but also to gene expression remodeling. We also pathways autocrinally or paracrinally through their receptors (6, show that dysfunction in this pathway can be related to the 47). This could explain why STAT factors are specifically phenomenon of endotoxin tolerance, in an in vitro model and in associated with C1-associated CpGs, whose associated genes ex vivo monocytes from septic patients. are ‘late responsive’ in comparison with others, upregulated prior to demethylation (C2- and C3-associated CpGs). These CpGs are enriched in NF-kB and AP-1, directly downstream of DATA AVAILABILITY STATEMENT TLR4/TLR2 signaling. In our analysis, JAK2/STAT inhibition appeared to The datasets presented in this study can be found in online accentuate the tolerant phenotype of monocytes. JAK2 repositories. The names of the repository/repositories and inhibition also reduces the expression of tolerized genes (15) accession number(s) can be found below: https://www.ncbi. following the first encounter with LPS, suggesting that these nlm.nih.gov/, GSE159250. genes are under the transcriptional control of JAK2/STAT. In fact, several of these genes are also involved in the IFNa or IFNg response. In contrast, JAK2 inhibition only partially interferes with LPS-driven DNA demethylation, supporting the existence ETHICS STATEMENT of a complex genomic regulatory network in which JAK2/STAT The studies involving human participants were reviewed and plays a role in LPS-driven demethylation, but also involves the approved by Committee for Human Subjects of Vall d’Hebron participation of additional pathways. University Hospital (PR (ATR)122/2019). The patients/participants We can speculate whether the observed effects of this JAK2 provided their written informed consent to participate in this study. inhibition could be countered by direct activation with, for instance, IFNg, which could reduce the acquired endotoxin tolerance and restore the expression of some tolerized genes. In this respect, demethylation has been previously associated AUTHOR CONTRIBUTIONS with the IFNg pathway, through STAT1 stimulation, binding to OM-P, CL-S, LC and JC-S conducted experiments; OM-P, CL-S, TET2 and recruitment to specific sites in the genome (33). This AG-G and FC-M performed bioinformatics analysis; ARS, suggests that the direct modulation of this pathway may have MM-G, RF-R, JCR-R provided patient samples; OM-P, CL-S, more widespread effects on DNA methylation and expression. In JC-S, AG-G, ARS, MM-G, RF-R, JCR-R, DAE and EB analyzed fact, a fundamental role of type 1 and type 2 interferon signaling and interpreted the results; OM-P, CL-S, DAE and EB designed in modulating response to endotoxin and endotoxin tolerance the experiments and wrote the paper. All authors contributed to has been reported, including a clinical trial using IFNg to the article and approved the submitted version. mitigate immune paralysis in sepsis (41, 48–51). The link between tolerized genes and downregulated genes upon JAK2 inhibition indicates that JAK2-dependent signaling pathway dysfunction may contribute to the acquisition of FUNDING endotoxin tolerance. EB was funded by the Spanish Ministry of Science, Innovation Moreover, LPS-driven demethylation of the in vitro model and Universities (grant numbers SAF2017-88086-R & PID2020- also occur in monocytes from gram-negative septic patients, and 117212RB-I00), and was cofunded by FEDER funds/European vice-versa, providing a clinical significance of the in vitro model, Regional Development Fund (ERDF) - a way to build Europe. and reinforcing the role of the JAK2-STAT pathway in OM-P holds an i-PFIS PhD fellowship (grant number IFI17/ septic monocytes. 00034) from Acción Estraté gica en Salud 2013-2016 ISCIII, Since monocytes from septic patients have been exposed to cofinanced by the Fondo Social Europeo. bacteria before they are extracted, they have a reduced immune response in a second immune challenge with LPS due to the endotoxin tolerance. We have shown that monocytes from septic patients exhibit a lower level of STAT1 phosphorylation after an ACKNOWLEDGMENTS immune challenge with LPS, compared with monocytes from healthy donors. This lower level of pSTAT1 due to a reduced We thank the CERCA Programme/Generalitat de Catalunya and activity of JAK2, could explain such a phenotype. In fact, IFNg, the Josep Carreras Foundation for institutional support. whose receptor signals through JAK2, is believed to partially rescue the endotoxin tolerance phenotype in human monocytes (41), and IFNg is sometimes used in sepsis treatment to improve immune SUPPLEMENTARY MATERIAL host defense (52), perhaps activating JAK2 more intensively. Taken together, our results demonstrate an important role of The Supplementary Material for this article can be found online the JAK2/STAT pathway in the monocyte LPS response, both in at: https://www.frontiersin.org/articles/10.3389/fimmu.2021. vitro and in vivo, which contributes not only to the DNA 734652/full#supplementary-material Frontiers in Immunology | www.frontiersin.org 14 November 2021 | Volume 12 | Article 734652 Morante-Palacios et al. JAK2-STAT Epigenetic Control in Sepsis Supplementary Figure S1 | DNA methylation profile of P3C-treated human treated versus untreated (Ø) monocytes. NES and -log10(FDR) of transcription CD14+ monocytes. (A) DNA methylation heatmap of differentially methylated CpGs factors (TF) enriched on the Ø side are shown (FDR < 0.05). (E) Barplot of genomic obtained with all the possible contrasts between the represented groups (Db- ≥ 0.2, features as percentages of C1-, C2- and C3-associated CpGs (Figure 2H) adjusted p (FDR) < 0.05). Scaled b-values are shown, ranging from -2 (lower DNA compared with total hypo-methylated CpGs. (F) Accessibility (ATAC-seq) data of methylation levels, blue) to +2 (higher methylation levels, red). (B) Principal CpGs associated with C1 (in red), C2 (in blue) and C3 (in green) after 1, 4 and component analysis (PCA) of the CpGs shown in Supplementary Figure 1A. 24 hours of monocyte culture with LPS. Public ATAC-seq data were used [15] Principal components 1 and 2 are shown. (C) Simplified scheme of TLR4 and TLR2 (G) ChIP-seq data of H3K27ac and H3K4me1 of LPS-treated monocytes were signaling pathways. (D) Venn diagram of differentially methylated CpGs of LPS- downloaded from the Blueprint database. Odds ratios were calculated with C1-, treated versus untreated monocytes (LPS) and P3C-treated versus untreated C2- and C3-associated CpGs, using EPIC array CpGs as background. Odds ratio monocytes (P3C). (E) Scatter plots of LPS hypomethylated and P3C and -log10(FDR) obtained from Fisher’s exact tests are shown. (H) Western blot of hypomethylated CpGs showing differential of DNA methylation of LPS-treated protein phosphorylated (p)-STAT1, p-STAT3 and p-STAT5 in P3C-treated relative versus untreated monocytes (x-axis) and P3C-treated versus untreated monocytes to untreated (Ø) monocytes. LaminB was used as loading control. Signal of western (y-axis). Contours in blue represent a 2D kernel density estimation. The R value and blot bands was quantified and the pSTAT/LaminB ratios are shown above each protein. p values were obtained with Person’s correlation. (F) ChIP-seq data of depicted histone marks were downloaded from the Blueprint database. Over-representation Supplementary Figure S3 | Additional characterization of JAK2 inhibition in of CpGs in histone marks were calculated using the Fisher’s test. Data points with LPS-treated monocytes. (A) Western blot of JAK2-dependent STAT proteins in FDR < 0.05 and Odds Ratio > 5 were considered significant. (G) GO (gene LPS-treated and untreated (Ø) monocytes, with and without JAK2 inhibitor (iJAK2) ontology) over-represented categories in M2 cluster CpGs. Fold change relative to after 4 hours (left) or 96 hours (right) of treatment. LaminB was used as the loading background (EPIC array CpGs) and -log10(FDR) are shown. (H) Bubble scatterplot control. (B) TNFa and IL-10 levels measured in cell supernatants to assess the of TF binding motif enrichment for M2 cluster CpGs. The x-axis shows the tolerance state after JAK2 inhibition. percentage of windows containing the motif and the y-axis shows the magnitude of enrichment of the motif. Bubbles are colored according to the TF family. The FDR is Supplementary Figure S4 | DNA methylation alterations in CD14⁺ monocytes indicated by the bubble size (selected TF with FDR ≤ 1e-05). (I) Percentage from Gram-negative sepsis. (A) Differentially methylated positions (DMPs) found in methylation level of some selected LPS-hypomethylated CpGs as obtained by the comparisons of Gram⁻ sepsis (SEP), other sepsis and SIRS patients with bisulfite pyrosequencing in the LPS and P3C samples. (J) qRT-PCR analysis to healthy donors (HD) (FDR < 0.05). (B) DNA methylation heatmap of differentially validate the downregulation of TET2 by siRNA (siScramble used as control). Data methylated CpGs comparing all groups shown pairwise (FDR < 0.05). Scaled and are normalized relative to the RPL38 gene. (**p < 0.01). (K) Western blot depicting batch-corrected b-values are shown, ranging from -4 (lower DNA methylation TET2 protein levels in LPS-treated (LPS) and untreated (Ø) monocytes. levels, blue) to +4 (higher methylation levels, red). On the right, violin plots of Quantification of TET2/LaminB ratio is also shown (* p < 0.05). hypomethylated and hypermethylated clusters depicting normalized DNA methylation data. (C) Principal components 1 and 2 from a principal component Supplementary Figure S2 | Additional features of differentially expressed genes analysis (PCA) of the DMPs from Supplementary Figure 4B are shown. (D) Bubble and associated CpGs. (A) Principal component analysis (PCA) of the 500 most scatterplot of TF binding motif enrichment for Gram-negative hypomethylated variable genes. Principal components 1 and 2 are shown. (B) Gene set enrichment CpGs. The x-axis shows the percentage of windows containing the motif; the y-axis analysis (GSEA) of LPS-treated versus untreated (Ø) monocytes, using MSigDB shows the magnitude of enrichment of the motif. Bubbles are colored according to hallmarks (H) as gene sets. Gene sets enriched in untreated (Ø) monocytes are TF family. FDR is indicated by bubble size (selected TF with FDR ≤ 0.05). (E) GO shown. 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Immune Responses. Cell Metab (2017) 26:142–56. doi: 10.1016/j.cmet.2017.06.001 36. Liu L, Luc Y, Martinez J, Bi Y, Lian G, Wang T, et al. Proinflammatory Signal Copyright © 2021 Morante-Palacios, Lorente-Sorolla, Ciudad, Calafell-Segura, Suppresses Proliferation and Shifts Macrophage Metabolism From Myc- Garcia-Gomez, Català-Moll, Ruiz-Sanmartın, ́ Martınez-Gallo, ́ Ferrer, Ruiz-Rodriguez, Dependent to HIF1a-Dependent. Proc Natl Acad Sci USA (2016) 113:1564– ́ Alvarez-Errico and Ballestar. This is an open-access article distributed under the terms 9. doi: 10.1073/pnas.1518000113 of the Creative Commons Attribution License (CC BY). The use, distribution or 37. Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. reproduction in other forums is permitted, provided the original author(s) and the Benchmark and Integration of Resources for the Estimation of Human copyright owner(s) are credited and that the original publication in this journal is Transcription Factor Activities. Genome Res (2019) 29:1363–75. doi: 10.1101/ cited, in accordance with accepted academic practice. No use, distribution or gr.240663.118 reproduction is permitted which does not comply with these terms. Frontiers in Immunology | www.frontiersin.org 16 November 2021 | Volume 12 | Article 734652