METHOD FOR TREATING T-HELPER TYPE 2 MEDIATED DISEASE

Abstract

The present invention relates to the treatment of T-helper type 2 (Th2)-mediated disease. Here, the inventors set out to investigate at the genome level the effects of SETDB1-dependent H3K9me3 deposition on CD4 T cell activation, differentiation and commitment. By using conditional Setdb1−/− mice, they show that SETDB1 restricts Th1 cell priming and ensures Th2 cell integrity. Unlike their wild-type counterparts, SETDB1-deficient Th2 cells readily express the entire Th1 gene network when exposed to the Th1-instructing cytokine IL-12. More, SETDB1 methylates H3K9 at a subset of ERVs that flank and repress Th1 enhancers or behave themselves as cis-regulatory elements of a large network of Th1 genes, including Ifng, Stat4, Runx3 and Tbx21. Therefore, H3K9me3 deposition by SETDB1 locks the Th1 gene expression program and thus ensures T cell lineage integrity by repressing a repertoire of ERVs that have been co-opted to behave as Th1 lineage-specific cis-regulatory modules. Thus, the invention relates to a SETDB1 inhibitor for use in a method for increasing the Th1/Th2 ratio of an immune response in a subject in need thereof.

Claims

1. (canceled)

2. The method according to claim 11, wherein the step of administering results in an increase in a Th1 response of an immune response.

3. (canceled)

4. The method according to claim 5, wherein the T-helper type 2 (Th2)-mediated disease is cancer or an infectious disease.

5. A method of treating a T-helper type 2 (Th2)-mediated disease in a subject in need thereof, comprising administering to the subject a therapeutically effective amount of a SETDB1 inhibitor.

6. The method according to claim 5 wherein the T-helper type 2 (Th2)-mediated disease is an allergic disorder or asthma.

7. The method according to claim 5, wherein the SETDB1 inhibitor is administered in combination with an immune adjuvant inducting and/or promoting Th1 cell differentiation.

8. The method according to claim 7, wherein the immune adjuvant is IL-12, LPS, Complete Freund's adjuvant, or an Aluminium salt.

9. The method according to claim 5, wherein the SETDB1 inhibitor is selected from the group consisting of mithramycin, mithramycin analogs, 3-Deazaneplanocin A and paclitaxel.

10. (canceled)

11. A method of increasing the Th1/Th2 ratio of an immune response in a subject in need thereof, comprising administering to the subject a therapeutically effective amount of a SETDB1 inhibitor, wherein the therapeutically effective amount is sufficient to increase the Th1/Th2 ratio of the immune response of the subject.

12. The method according to claim 6 wherein the asthma is allergic asthma.

13. The method of claim 8, wherein the aluminium salt is Alum, CpG or squalene.

Description

FIGURES:

[0077] FIG. 1. Enhanced Th1 priming in the absence of SETDB1.

[0078] (A) Average expression (Geometric mean) of T-bet by Setdb1+/+ and Setdb1−/− CD4 T cells after 6 days of culture in Th1 medium containing increasing concentrations of IL-12. (B, C) Percentage of CD4 T cells-producing cytokine (left) and average cytokine production (Geometric mean) per cell (right) after 6 days of culture in Th1 medium containing increasing concentrations of IL-12. (D) Production of cytokines by Setdb1+/+ and Setdb1−/− CD4 T cells following 6 days of culture in Th1-inducing conditions and overnight restimulation with anti-CD3ε and anti-CD28 antibodies. Data are represented as mean±SD of three (D) independent experiments or of three biological replicates from one representative experiment out of three performed (A, B, C). *p<0.05, **p<0.01, ***p<0.001 (unpaired Student's t-test).

[0079] FIG. 2. SETDB1 is required for stable Th2 cell commitment.

[0080] Setdb1.sup.+/+ and Setdb1.sup.−/− naïve CD4 T cells were cultured for six days in Th2-polarizing conditions, extensively washed in complete medium, and then cultured in Th1-polarizing conditions for two more days. Percentages of cells producing IL-13 and/or IFN-γ as determined by intracellular immunostaining and flow cytometry. Data are representative as mean±SEM of eight independent experiments.

[0081] FIG. 3. Non-specific SETDB1 inhibitors alter Th2 cell commitment.

[0082] Naïve CD4 T cells were isolated from C57BL/6 mice and were cultured for 6 days in Th2-polarizing conditions in the presence (Mithramycine A, DZNep) or absence (DMSO) of SETDB1 inhibitors. Cells were exposed to 100 nM Mithramycine A and to 1 nM DZNep for the first 4 and 24 hours, respectively. At the end of the culture, cytokine production was assessed by flow cytometry following T cell restimulation with PMA/Ionomycine in the presence of Monensin and intracellular staining. (A, C) Representative flow cytometry profiles and (B, D) frequency of IFNg-producing cells in each experiment (n=2) are shown.

EXAMPLE

Material & Methods

Mice

[0083] Suv39h1-deficient mice were kindly provided by T. Jenuwein (Peters et al., 2001). The Setdb1 mutant mouse strain (common strain name EPD0028_1_B07; international strain designation B6Dnk;B6N-Setdb1tm1a(EUCOMM)Wtsi) was established as part of the International Mouse Phenotyping Consortium (EMMA ID: EM:04052) at the German Research Center for Environmental Health (Helmholtz Zentrum, Muenchen). The targeting vector was composed of the promoterless L1L2_gt1 cassette inserted in the L3L4_pZero_kan plasmid backbone. The construct was microinjected into C57BL/6 ES cells (JM8.N4 parental cell line) and the L1L2_gt1 cassette was inserted at position 95350414 of chromosome 3, upstream of Setdb1 exon 4. The cassette was composed of a lacZ-neomycin sequence flanked by Flp Recombinase Target (FRT) sites and followed by a loxP sequence. An additional loxP site was inserted downstream of Setdb1 exon 4 at position 95349598. Additional information on the Setdb1 mutant mouse strain can be found at https://www.infrafrontier.eu/search?keyword=EM:04052. Mice with a conditional ready Setdb1 allele (Setdb1f1) were generated by intercrossing Setdb1 mutant mice with mice expressing the Flipper recombinase under the control of the ubiquitous Rosa26 promoter. Conditional knockout mice (Setdb1−/−) were obtained by intercrossing Setdb1f1/f1 and CD4-CRE mutant mice. All the mice were bred and housed at the Regional Centre of Functional Exploration and Experimental Resources (CREFRE, UMS006/INSERM). Sex-matched 6- to 12-week-old mice were used and compared in all experiments. All experiments involving animals were conducted according to animal study protocols approved by the local ethics committee (#16-U1043-JVM-496 and 16-U1043-JVM-20).

Naïve CD4+ T Cell Isolation

[0084] Spleen and lymph nodes (mesenteric, inguinal, axillary, brachial and cervical) were collected and digested with Liberase TM and DNAse I (Sigma). Single-cell suspensions were then pooled and depleted of erythrocytes by osmotic shock (Red Blood Cell Lysis buffer, Sigma). CD4 T cells were enriched by negative selection by using antibodies specific for CD16/32 (2.4G2), I-A/I-E (M5/114.15.2), CD8a (H59) and B220 (RA3-6B2), and Dynabeads sheep anti-rat IgG (Thermo Fischer Scientific). Naïve CD4 T cells, defined as CD4+CD25-CD62LhighCD44low, were labeled with fluorochrome-conjugated monoclonal antibodies specific for CD4 (GK1.5, BD Biosciences), CD25 (PC61, BD Biosciences), CD62L (MEL14, Thermo Fischer Scientific) and CD44 (IM7, BD Biosciences), and purified from the enriched fraction of CD4 T cells by fluorescence-activated cell sorting (FACS Aria, BD Biosciences).

T helper Cell cultures

[0085] Naïve CD4 T cells were cultured for three days in 96-well flat bottom plates coated with 10 μg/mL anti-CDR antibody (145-2C11, InVivoMab™, BioXcell) in RPMI 1640 Glutamax™ supplemented with 1 mM sodium pyruvate, non-essential amino acids, 10 mM HEPES, 100 units/mL penicillin, 100μg/mL streptomycin, 50 mM 2β-mercaptoethanol, 10% fetal calf serum (all from Thermo Fischer Scientific) and 1 μg/mL anti-CD28 antibody (37.51, InVivoMab™, BioXcell). Unless stated otherwise, Th1 medium also contained 10 ng/mL recombinant mouse IL-12 (R&D Systems) and 10 μg/mL anti-IL-4 neutralizing antibody (11B11, InVivoMab™, BioXcell). Th2 medium contained 50 ng/mL recombinant mouse IL-4 (R&D systems) and 10 μg/mL anti-IFN-γ neutralizing antibody (XMG1.2, InVivoMab™, BioXcell). At day 3, the cells were re-plated in the same conditioning medium but without the anti-CD3ε and anti-CD28 antibodies and with 30 IU/mL recombinant IL-2 (Proleukin). To test for Th2 cell lineage commitment, cells were harvested at day 6, extensively washed in complete medium, and re-plated in Th1-polarizing conditions as indicated above. To assess the role of the IFN-γ pathway in Th2 cell plasticity, Th1 medium was supplemented with 10 μg/mL anti-IFN-γ. In co-culture experiments, Setdb1+/+ and Setdb1−/− Th2 cells were differentiated separately, mixed at a 1:3 ratio, and then plated in Th1 culture conditions.

T Cell Proliferation and Differentiation Analysis by Flow Cytometry

[0086] To analyze intracellular transcription factor expression upon T helper cell differentiation, cells were collected at the requires time points, stained with the fixable viability dye eFluor 506 (Thermo Fischer Scientific), and labeled with fluorochrome-conjugated antibodies specific for T-bet (ebio4B10, Thermo Fischer Scientific) and GATA-3 (TWAJ, Thermo Fischer Scientific) by means of the Transcription Factor Staining Buffer Set (Thermo Fischer Scientific). For intracellular cytokine staining, cells were first stimulated at 37° C. with 20 ng/mL phorbol 12-myristate 13-acetate (Millipore) and 1 μg/mL ionomycin (Millipore) for 5 hours in the presence of GolgiStop™ (BD Biosciences). Cells were then labelled with the fixable viability dye eFluor 506 and stained with fluorochrome-coupled antibodies specific for IL-13 (ebiol3A, Thermo Fischer Scientific), IFN-γ (XMG1.2, Thermo Fischer Scientific), GM-CSF (MP1-22E9, BD Biosciences) or TNF (MP6-XT22, Thermo Fischer Scientific) by using the Cytofix/Cytoperm™ Fixation/Permeabilization Kit (BD Biosciences). When indicated, naïve CD4 T cells were labeled prior to culture with 0.5 μM CellTrace Violet (Thermo Fischer Scientific). Flow cytometry was performed by using a LSRII Fortessa cytometer (BD Biosciences) or MACSQuant analyzer 10 (Myltenyi) and the data were analyzed by using FlowJo software (Tree Star).

Mouse Phenotyping

[0087] To determine the frequency and phenotype of immune cells in primary and secondary lymphoid organs, thymus, spleen and lymph nodes were collected from Setdb1−/− and Setdb1+/+ mice and single-cell suspensions were obtained by mechanical disruption. Cells were then incubated on ice in FACS buffer (PBS, 3 mM EDTA, 3% fetal calf serum) containing 10 μg/mL anti-CD16/32 antibody for 20 minutes. Fluorochrome-conjugated antibodies were added at saturating concentrations, and cell suspensions were incubated on ice and protected from light for a further 20 minutes. For intracellular staining, cells were fixed and permeabilized by using the Transcription Factor Staining Buffer Set (Thermo Fischer Scientific) according to the manufacturer's instructions. The following antibodies were used for phenotyping: anti-TCR-β (H57-597), anti-CD4 (GK1.5), anti-NKp46 (29A1.4), anti-CD11b (M1/70), anti-CD19 (1D3), anti-CD25 (PC61), anti-CD69 (H1.2F3), anti-Siglec-F (E50-2440), anti-H-2Kb (AF6-88.5) and anti-Ki67 (B56) all from BD Biosciences; anti-CD8β (ebioH35-17.2), anti-CD8ϵ (53-6.7), anti-PDCA1 (ebio927), anti-I-A/I-E (M5/114), anti-CD11c (N418), anti-Gr1 (RB6-8C5), anti-B220 (RA3-6B2), anti-CD44 (IM7) and anti-CD62L (MEL-14), all from Thermo Fischer Scientific. Dendritic cells (DC) were gated based on I-Ab and CD11c expression (CD19-TCR-β-CD11c+I-Ab+) and CD8α, CD11b and PDCA-1 were used as markers to identify the conventional type 1 (cDC1, CD8α+CD11b−), conventional type 2 (cDC2, CD8α-CD11b+) and plasmacytoid (pDC, PDCA-1+) sub-populations. Monocytes/macrophages (Macro) and B cells (B cell) were defined as lin-CD11c-CD11b+SSC-AlowGr-1low/- and TCR-β-CD19+B220+, respectively. Neutrophils (Neutro), natural killer cells (NK) and eosinophils (Eosino) were identified as lin-CD11c-CD11b+SSC-AhighGr-1+, TCR-β-NKP46+ and lin-CD11c-CD11b+SSC-AhighGr-1-, respectively. Flow cytometry was performed by using a LSRII Fortessa cytometer (BD Biosciences) and the data were analyzed by using FlowJo software (Tree Star).

Ex Vivo Measurement of Apoptosis

[0088] Single-cell suspensions of spleen, mesenteric lymph nodes and thymus were obtained as described above. Spleen and lymph node cells were labeled with antibodies specific for TCR-β (H57-597, Thermo Fischer Scientific) and CD4 (GK1.5, BD Biosciences), whereas thymocytes were stained with antibodies specific for CD4 and CD8β (ebioH35-17.2, ThermoFischer Scientific). Apoptotic cells were then labeled by using the Cell Event™ Caspase-3/7 Green Detection Reagent (Thermo Fischer Scientific) according to the manufacturer's instructions. Flow cytometry was performed by using a LSRII Fortessa cytometer (BD Biosciences) and the data were analyzed by using FlowJo software (Tree Star).

Measurement of Cytokines in Cell Culture Supernatants

[0089] Following 6 days of culture in Th1 polarizing conditions, T cells were collected and extensively washed in complete medium. The differentiated cells (7.5×104 per well) were cultured overnight in 96-well flat bottom plates coated with anti-CDR antibody in complete culture medium containing anti-CD28 antibody. The concentrations of cytokines in the cell culture supernatants were then measured by flow cytometry using the FlowCytomix Kit (a bead-based multiple cytokine detection system) according to manufacturer's instructions (FlowCytomix, eBiosciences). Flow cytometry was performed by using a MACSquant Q10 flow cytometer (Miltenyi).

Western Blotting

[0090] The different subpopulations of thymocytes were sorted on a FACS Aria (BD Biosciences) based on their expression of CD4 and CD8. Naïve CD4 T cells were purified from the spleen as described above. Cells were lysed in 1× NuPAGE LDS sample buffer and 1× NuPAGE sample reducing agent (Thermo Fischer Scientific). Whole cell lysates were then sonicated briefly and proteins were separated by SDS-PAGE on 4-12% Bis-Tris gels (Thermo Fischer Scientific), transferred onto nitrocellulose membranes (BA-S 83 Optitran, GE Healthcare Life Sciences), and probed with antibodies specific for SETDB1 (ab107225, Abcam), total H3 (ab1791, Abcam), H3K9me3 (D4W1U, Cell Signaling Technology) or beta ACTIN (ab8227, Abcam). The bands were detected by using Amersham ECL western blotting detection reagent (GE Healthcare Life Sciences) and the ChemiDoc XRS+ imaging system (Bio-Rad) after staining with secondary antibodies coupled to horseradish peroxidase. Images were analyzed by using Image Lab software (Bio-Rad).

RNA-Seq Sample Preparation and Analysis

[0091] Total RNA was extracted by using the RNeasy Micro Kit (Qiagen) and its quality was assessed on a 2100 Bioanalyzer (Agilent Technologies). Only high-quality RNA (i.e. RNA of integrity number >7) was subsequently used to prepare the libraries according to the ScriptSeq RNA-seq protocol (Illumina). Quality controls of the libraries were performed by using standard methods, including quantification by Qubit (Thermo Fisher Scientific) and assessment of size distribution by using the 2100 Bioanalyzer. Samples were indexed and sequenced on an Illumina HiSeq 2500 or 3000 (paired-end reads of 100 or 150 bp, respectively). After trimming of adaptor sequences (Cutadapt 1.3) and removal of low-quality bases (−q value, <15), high-quality reads were aligned to the mouse reference genome mm10 (Genome Reference Consortium) by using TopHat version 2.0.5 (Trapnell et al., 2009). Count of the reads mapping to each gene was performed using Htseq-count. Differential expression analysis was performed by using the DESeq package (Bioconductor software) (Anders and Huber, 2010). An adjusted P value of <0.1 (P value adjusted for multiple testing with the Benjamini-Hochberg procedure) was used as cutoff to select the genes differentially expressed.

ChIP, Semi-Quantitative PCR and Library Preparation and Sequencing

[0092] ChIP was performed as previously described (Lee et al., 2006). Briefly, following cell lysis, the chromatin was sonicated with a Bioruptor Pico (Diagenode) to obtain fragments of 100-300 bp. In each assay, we used 5-50 million cells and 2-10 μg of antibody specific for H3K9me3 (ab8898, Abcam) or H3K4me1 (ab8895, Abcam). Immunoprecipitation was performed by using Dynabead® Protein G (Thermo Fisher Scientific). Library preparation was carried out by using the TruSeq ChIP Sample Preparation Kit (Illumina). Library quality was assessed by using the 2100 Bioanalyzer and sequencing was performed on an Illumina HiSeq 3000 (paired-end reads of 150 bp). When indicated, semi-quantitative PCR was performed on a Light Cycler® 480 (Roche) using LightCycler 480 SYBR Green I Master (Roche). Primers specific for Ifng CNS17-20 (forward: tccctagactctgccactct; and reverse: gctcaccatcaataggcgtg) and for the glyceraldehyde-3-phosphate dehydrogenase (Gapdh) promoter (forward: gctccttgcccttccagatt and reverse: cccttcccaccctgttcatc) were used. The results were expressed as the percentage of input DNA normalized to the signal from the Gapdh promoter.

ChIP-Seq Data Processing

[0093] Reads were filtered as described for RNA-seq and aligned to the mm10 reference genome by using BWA v.0.7.10 (Li and Durbin, 2009). H3K4me1 peaks were detected by using the ‘broad’ option of MACS2 v.2.1.0 (Zhang, 2008). To detect H3K9me3 peaks, we used the R Bioconductor package CSAW v.1.4.1 (Lun and Smyth, n.d.). The minimum mapping score was set to 50 and a window size of 300 was used. Differential binding windows were clustered in regions with the ‘mergeWindows’ function and the Benjamini-Hochberg method was applied to control the False Discovery Rate (FDR) across all detected clusters (‘combineTests’ function).

Bioinformatics Analyses

[0094] R (https://www.R-project.org), SAMtools (Li et al., 2009) and the BEDtools suite v2.22.1 (Quinlan and Hall, 2010) were used to analyze high-throughput sequencing files. To determine the genome-wide distribution of H3K9me3 peaks, we defined the different genomic regions as follows: gene body coordinates were extracted from assembly GRCm38; promoters were defined as transcription start sites +1 kb/−2 kb; enhancers were identified as H3K4me1 peaks with no overlap with promoters; ERV coordinates were rebuilt from the RepeatMasker database as described below. As a control, we randomly distributed H3K9me3 peaks through the genome using the shuffle sub-command of the BEDtools suite. R package ‘Genomation’ was used to visualize genomic intervals (Akalin et al., 2015). Biological functions analysis of H3K9me3 ChIP-seq peak coordinates was performed by using the Genomic Regions Enrichment of Annotations Tool (GREAT) (McLean et al., 2010) with default settings and using the ‘single nearest gene’ option (each gene is assigned a regulatory domain that extends in both directions (within 100 kb) to the midpoint between the gene's TSS and the nearest gene's TSS but no more than the maximum extension in one direction). For Gene Ontology analysis the “enrichment analysis” tool from the Gene Ontology Consortium was used (http://geneontology.org). For analysis of motif enrichment, we used AME software of the MEME Suite version 4.11.3 with defaults options (0.05≥adjusted p-value) (McLeay and Bailey, 2010); the HOCOMOCOv10 MOUSE was used as the input transcription factor motif database. We also used the gene set enrichment analysis (GSEA) software (http://software.broadinstitute.org/gsea/index.jsp) with default settings except for the ‘Collapse dataset to gene symbols’ and ‘the permutation type’ which were set as ‘false’ and ‘gene set’, respectively. Heat maps were generated by using matrix2png version 1.2.1 (http://www.chibi.ubc.ca/matrix2png).

ERV Reconstruction

[0095] Annotations of ERV elements were downloaded from the UCSC Genome Browser (assembly GRCm38, release of RepeatMasker: 2012 Feb. 7). We used the four major subfamilies (ERV1, ERVK, ERVL and ERVL-MaLR) of LTRs and excluded elements for which the curator was unsure of the classification. We merged ERV fragments from the same family (identical ‘repName’) into a single ERV when located within 20 bp, as previously described (Goke et al., 2015). Count of the reads mapping to each ERV was performed using Htseq-count (Anders et al., 2015) and normalization was performed with DESeq. ERVs with an expression score ≥1 were considered as expressed.

Quantification and Statistical Analysis

[0096] Statistical parameters including the exact value and significance of n and precision measures (Mean+/−SEM or SD) as well as statistical significance are reported in the figures and figure legends. Unless stated otherwise, asterisks denote statistical significance as calculated by Student's t test in GraphPad PRISM 6 (*, p<0.05; **, p<0.01; ***, p<0.001). When large sets of unpaired data were compared, Pearson's chi-squared test was calculated in R to determine whether the observed difference between the sets of data arose by chance.

Data Resources

[0097] Raw and processed data files from ChIP-seq and RNA-seq experiments have been deposited in the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE101546.

Results

Enhanced Th1 Priming in the Absence of SETDB1

[0098] To analyze the role of SETDB1 in

[0099] CD4 T cell differentiation and plasticity, we generated mice homozygous for a LoxP-flanked Setdb1 allele and expressing (Setdb1−/−), or not (Setdb1+/+), the CRE recombinase under the control of the Cd4 promoter. This strategy resulted in the almost complete absence of SETDB1 protein from CD4 single-positive (SP) thymocytes (data not shown). As SETDB1-deficiency was not compensated by overexpression of the other methyltransferases targeting H3K9 (data not shown), we also observed a marked loss of trimethylated H3K9 from naïve Setdb1−/− CD4 T cells (data not shown). The use of the Cd4-Cre transgene, which induces SETDB1 deletion relatively late in ontogeny, allowed for normal intrathymic T cell development. The total number of cells in the thymus, the relative proportions of the four main populations of thymocytes, and the proportion of semi-mature and mature CD4 SP cells were similar in SETDB1-deficient mice and control littermates (data not shown). In previous studies, by contrast, deletion of Setdb1 at the very early CD4/CD8 double-negative (DN) stage severely hampered T cell development in mice (Martin et al., 2015; Takikita et al., 2016).

[0100] In peripheral lymphoid tissues, we detected no consequences of T cell-specific SETDB1-deficiency on other populations of immune cells (data not shown). SETDB1 was previously implicated in the survival in various cell types (Collins et al., 2015; Karimi et al., 2014; Sarraf and Stancheva, 2004). For example, conditional deletion of the enzyme in mice expressing the CRE recombinase under the control of the Mb1 promoter abolished the B cell lineage (Collins et al., 2015). The impact of Setdb1 deletion on T cell survival was less pronounced: despite substantially increased activity of caspase-3/7, we only observed a partial loss of the T cell pool (data not shown), and the proportions of naïve and memory CD4 T cells were similar in Setdb1−/− and Setdb1+/+ mice (data not shown).

[0101] To obtain a more global view of the changes in gene expression induced by Setdb1 deletion, we performed directional RNA sequencing (RNA-seq) on FACS-sorted naïve Setdb1−/− and Setdb1+/+ CD4 T cells. Most of the differentially expressed genes were upregulated in Setdb1−/− cells (data not shown), consistent with a globally repressive effect of H3K9me3 on gene transcription. Among the upregulated genes, those involved in cell division were particularly enriched (data not shown). The proportion of CD4 T cells expressing the nuclear antigen Ki-67 (a marker of proliferating cells) being higher in Setdb1−/− mice than in control Setdb1+/+ littermates (data not shown), this upregulation of cell division-related genes most likely resulted from the observed lymphopenia rather than from a direct impact of Setdbl deletion on the regulation of these genes. Moreover, our ChIP-seq analysis found no particular enrichment for H3K9me3 peaks at cell cycle-related genes in naïve CD4 T cells (data not shown).

[0102] To assess if Setdb1 deletion could affect T cell function, we next analyzed the differential transcription of a gene set related to T helper cell activation and differentiation in our RNA-seq dataset. We found no major differences in the expression of these T helper-related genes between Setdb1−/− and Setdb1+/+ cells (data not shown), despite the presence of H3K9me3 peaks close to loci involved in lymphocyte-mediated immunity (data not shown). The vast majority of the genes were equally expressed in Setdb1−/− and Setdb1+/+ cells, most of the differentially expressed loci were transcribed at very low levels, and no lineage-specific transcriptomic signature appeared when focusing on deregulated genes. This lack of effect of SETDB1 deficiency on naïve CD4 T cell programming was further confirmed when we analyzed the production of lineage-specific mediators. Upon acute stimulation in neutral conditions, neither Setdb1−/− nor Setdb1+/+ cells produced Th1- or Th2-related cytokines spontaneously (data not shown). Together, these observations show that gene expression is very similar in Setdb1−/− and Setdb1+/+ naïve CD4 T cells, and that SETDB1-deficient cells are not a priori biased toward a specific T helper lineage.

[0103] To test whether SETDB1 regulates cell lineage commitment in response to environmental signals, we analyzed Setdb1−/− and Setdb 1+/+ CD4 T cell fate in an IL-12-mediated Th1 differentiation assay. As expected from our experiments measuring caspase activity ex vivo (data not shown), SETDB1 deficiency impaired T cell survival at early time points (data not shown). However, a significant proportion of cells remained viable and showed normal activation upon TCR triggering, as measured by the upregulation of CD25 and CD69 (data not shown). As T cell differentiation depends on cell cycle progression, we analyzed the proliferative response of activated Setdb1−/− and Setdb1+/+ CD4 T cells by using a cell-tracing reagent, CellTrace Violet (CTV), to identify different generations of proliferating cells based on dye dilution. There was no difference between control and mutant cells (data not shown), which had similar proliferation indexes and percentages of divided cells (data not shown). Upon exposure to increasing doses of IL-12, Setdb1−/− cells also expressed T-bet, the Th1 master regulator, at a similar level as their Setdb1+/+ counterparts (FIG. 1A). Together, these observations indicate that SETDB1 deficiency does not affect naïve CD4 T cell activation, proliferation and commitment to the Th1 lineage. It does however lead to greater acquisition of lineage-specific functions. At all tested concentrations of IL-12, the percentage of cells producing Th1-related cytokines and the amount of cytokine synthesized per cell were higher in Setdb1−/− than in Setdb1+/+ cells (FIGS. 1B, C and D). This exacerbated production of cytokines was not the result of a global transcriptional derepression as we saw no aberrant secretion of soluble mediators related to alternative lineages (FIG. 1D). Together these results highlight a key role for SETDB1 in regulating the magnitude of the Th1 response.

Normal Acquisition of the Th2 Phenotype by SETDB1-Deficient Cells

[0104] Most of the genes encoding lineage-specific cytokines in naïve CD4 T cells have both permissive and repressive epigenetic marks on their promoters and enhancers. They are thus poised for transcription to guarantee the pluripotency while also preserving the identity of the cells. The enhanced Th1 response observed in Setdb1−/− cells might, therefore, result from a loss of H3K9me3 at these cis-regulatory regions, and this may potentially affect other lineages. To test this hypothesis, we cultured Setdb1−/− and Setdb1+/+ naïve CD4 T cells in Th2 polarizing conditions, with exogenous IL-4 as lineage-instructive signal. The proliferative response and viability at day six of Setdb1−/− and Setdb1+/+ cells were similar (data not shown). Moreover, Setdb1−/− cells committed to the Th2 lineage as completely as did their control counterparts, with almost all cells expressing the master regulator of the Th2 lineage GATA-3 (data not shown) and no aberrant expression of the Th1-specifying transcription factor T-bet (data not shown). Production of IL-13, a Th2-specific cytokine, was also similar in Setdb1−/− and Setdb1+/+ cells (data not shown). Thus, in contrast to what we observed in Th1 polarizing conditions, there was no enhanced production of Th2 lineage-specific mediators by SETDB1-deficient cells grown in the presence of IL-4. Therefore, this lysine methyltransferase seems not to play a key role in establishing the Th2-specific gene expression program.

SETDB1 is Required for Stable Th2 Cell Commitment

[0105] Interestingly, unlike their wild-type counterparts, Setdb1−/− cells grown in Th2-polarizing conditions produced small amounts of the Th1 cytokine IFN-γ (data not shown). This observation correlated with lower expression of the Th2-promoting transcription factor GATA-3 in the Setdb1−/− cells than in the Setdb1+/+ cells (data not shown), suggesting that SETDB1 may have a role in naïve CD4 T cell commitment to the Th2 phenotype. This IFN-γ ‘leak’ may result from defective repression of Th1-related loci in Th2 cells, which could potentially lead to functional and phenotypic instability. To assess if SETDB1 might control Th2 cell plasticity, we cultured Setdb1−/− and Setdb1+/+ cells for six days in Th2-polarizing conditions, washed them extensively, and then cultured them in Th1-polarizing conditions. In agreement with the Th1/Th2 paradigm, the control Setdb1+/+ Th2 cells remained phenotypically and functionally stable after two days of culture in Th1 conditions; they still produced large amounts of the Th2-type cytokine IL-13 and had not switched on IFN-γ production (FIG. 2A). By contrast, a large fraction of the Setdb1−/− cell population secreted IFN-γ; this phenomenon was even more pronounced after four days of culture (FIG. 2A). IFN-γ secretion was accompanied by downregulation of GATA-3 and upregulation of T-bet (data not shown). In fact, SETDB1-deficiency allowed the virtually complete reprogramming of Th2 cells upon exposure to Th1-instructing signals, with extinction of Th2 gene expression and induction of the entire Th1 gene set (data not shown).

[0106] SETDB1 plays a key role in silencing ERVs (Bulut-Karslioglu et al., 2014; Matsui et al., 2010). Ectopic expression of these retrotransposons can lead to activation of nucleic acid-sensing by the innate immune system and, eventually, to production of the type I IFNs such as IFN-γ and IFN-γ (Chiappinelli et al., 2015). Together with IL-12 and IFN-γ, type I IFNs can reprogram Th2 cells into stable cells producing IFN-γ and expressing both GATA-3 and T-bet (Hegazy et al., 2010). Activation of ERVs in SETDB1-deficient Th2 cells might thus account for the trans-differentiation into the Th1 lineage that we observed: ERV-induced secretion of IFN-γ and IFN-γ in combination with exogenous IL-12 and the observed aberrant production of IFN-γ might reprogram the Th2 cells. We found no aberrant levels of type I IFN mRNA in SETDB1-deficient cells, however (data not shown), and neutralization of IFN-γ did not prevent Setdb1−/− Th2 cells from switching to a Th1 phenotype (data not shown).

[0107] To assess more directly if ectopic expression of Th1-instructive mediators by Setdb1−/− Th2 cells might account for their phenotypic instability, we co-cultured Setdb1−/− and Setdb1+/+ Th2 cells in Th1-polarizing conditions. In this setting, the SETDB1-deficient cells still showed substantial plasticity while their control counterparts did not (data not shown). Although the IL-12 added to the culture and the pro-Th1 mediators secreted by Setdb1−/− cells partly antagonized the Th2 program, they failed to switch on the Th1 gene network in Setdb1+/+ cells. Together, these data clearly identify SETDB1 as a key controller of Th2 cell commitment through a cell-intrinsic mechanism that is not limited to direct or indirect type I and/or type II IFN genes silencing.

SETDB1-Dependent H3K9 Trimethylation at a Subset of ERVs

[0108] To determine how SETDB1 controls Th2 cell commitment and stability, we first performed high-throughput chromatin immunoprecipitation (ChIP) for genome-wide mapping of H3K9me3 in Setdb1+/+ Th2 cells. We observed no more peaks at genes bodies or promoters than would be expected by a random distribution (data not shown). By contrast, we found substantial and statistically significant enrichment of H3K9me3 peaks at enhancers (defined as non-promoter H3K4me1+genomic regions) and at ERVs (data not shown). Unlike what was observed previously in mouse embryonic stem cells (Mikkelsen et al., 2007), in Th2 cells the repertoire of ERVs marked by H3K9me3 was not specifically enriched for ERV1 and ERVK: the representation of the three classes of retroviruses in the mouse genome was similar to that of the 17,556 retrotransposons (1.9% of total) associated with H3K9me3 (data not shown). Interestingly, 73% of the peaks at enhancers also overlapped with ERVs (data not shown), suggesting that the ERVs rather than the enhancers themselves may be the targets for H3K9 trimethylation. To test this hypothesis, we analyzed the distribution of H3K9me3 across the length of individual ERV and enhancer sequences. The H3K9me3 signal clearly peaked at and aligned with the center of the ERVs (data not shown). By contrast, the signal appeared randomly distributed across the enhancer sequences with only a little accumulation on the flanking regions of these cis-regulatory elements (data not shown). We next calculated the distance between the center of these genomic regions and the center of the H3K9me3 peaks in cases where the ERVs and enhancers overlapped and where the two genomic regions were mutually exclusive (data not shown). In both cases, the H3K9me3 peaks were closer to the ERVs than to the enhancers (data not shown). Moreover, we also observed significant H3K9me3 signal enrichment at enhancers that did not overlap with a peak of H3K9me3 but that were located close to an ERV marked by the repressive mark (data not shown). These data indicate that H3K9 trimethylation is directed at ERVs and only marks enhancers when these regions overlap or flank the retrotransposons.

[0109] To determine which lysine methyltransferase is necessary for H3K9me3 deposition at ERVs in Th2 cells, we compared H3K9me3 deposition at ERVs in SETDB1-deficient Th2 cells with that in SUV39H1-deficient Th2 cells by ChIP sequencing. Whereas SUV39H1-deficiency had no significant impact on H3K9me3 deposition at ERVs in Th2 cells, most, if not all of the peaks vanished in the SETDB1-deficient cells (data not shown). Together, these data show that SETDB1 targets H3K9me3 at a subset of ERVs in Th2 cells, and that some of these retrotransposons overlap or flank enhancers.

SETDB1-Deficiency Upregulates ERVs and Their Neighboring Genes

[0110] Recent studies have strengthened the hypothesis that transposable elements have been co-opted for the regulation of host gene networks (Chuong et al., 2017; 2016). The impact of SETDB1 deletion on CD4 T cell fate might, therefore, result from a loss of H3K9me3 at ERVs that behave as cis-regulatory modules of transcription, and/or regulate the activity of enhancers.

[0111] To test this hypothesis, we analyzed the consequences of SETDB1 deletion on ERV activity, on the status of their nearest enhancers, and on the expression of associated genes. We first compared ERV expression levels in Setdb1−/− and Setdb1+/+ Th2 cells. To avoid any bias, we excluded from the analysis transposable elements located on gene bodies and those that overlapped with promoters. In Setdb1−/− Th2 cells, the expression of 22% of the ERVs that lost H3K9me3 was deregulated, 77% of which were overexpressed at low levels in Setdb1−/− cells (data not shown). As we observed that H3K9me3 signal spread from ERV to close enhancers (data not shown), we hypothesized that the cis-regulatory elements could also be de-repressed in Setdb1−/− cells. We thus analyzed bidirectional transcription of enhancers located in the vicinity of ERVs marked by H3K9me3 in Setdb1+/+ cells and upregulated in Setdb1−/− cells. The enhancers overlapping or flanked by ERVs that were activated (i.e. transcribed) following H3K9me3 disappearance were themselves more expressed in Setdb1−/− than in Setdb1+/+ Th2 cells (data not shown). Together these data show that SETDB1 deletion leads to loss of H3K9me3 from ERVs, and to the concomitant activation of these retroelements and their neighboring enhancers. Finally, we tested if this cascade of events resulted in deregulation of gene expression. Our analysis clearly demonstrated that the genes associated with enhancers flanking or overlapping ERVs that showed overexpression in SETDB1-deficient cells were also significantly upregulated (data not shown). We conclude that SETDB1-dependent H3K9me3 deposition at ERVs inactivates neighboring enhancers and concomitantly participate to the silencing of their target genes.

SETDB1-Dependent H3K9 Trimethylation at ERVs Represses Th1-Specific Genes

[0112] In our functional assays, SETDB1 deletion led to enhanced Th1 priming and to Th2 cell instability (FIGS. 1A-D and 2). As discussed above, loss of regulation of the Th1 gene network could explain these observations. Based on our epigenetic and transcriptomic studies, we postulated that SETDB1 might control Th1 gene expression by repressing ERVs acting as cis-regulatory elements of these genes and/or regulating the activity of their enhancers. To test this hypothesis, we first used GREAT (Genomic Regions Enrichment of Annotations Tool) (McLean et al., 2010) to analyse the set of ERVs marked by H3K9me3 in a SETDB1-dependent manner. GREAT assigns biological significance to a set of non-coding genomic regions by analyzing the annotations of their nearby genes. To avoid any bias, we excluded the transposable elements overlapping Th2 gene enhancers. We observed a strong association of the retrotransposons with genes involved in immune processes, including leukocyte activation and cytokine production (data not shown). Interestingly, this distribution was cell-type specific: there was very little overlap between the ERVs marked by H3K9me3 in Th2 cells and those in adipocytes (data not shown), and the ERVs marked by H3K9me3 in white adipose cells were associated with genes that have no direct link with immunity (data not shown). In Th2 cells, thus, SETDB1 is responsible for H3K9 trimethylation at a restricted and cell type-specific set of ERVs that are associated with genes that control T cell functions. Motif enrichment analysis of H3K9me3-marked ERV sequences strengthened this observation; it revealed a strong enrichment for the binding sites of Th1-related transcription factors, including STAT1, FOXO3, IRF1 and STAT4 (data not shown). The two STAT proteins are mobilized upon exposure of cells to IFN-γ and IL-12. Crucially, they control naïve CD4 T cell differentiation into T helper cells by shaping the lineage-specific active enhancer repertoire (Vahedi et al., 2012). The unbiased “Ingenuity Upstream Regulator Analysis” of our RNA-seq data performed in IPA also identified these four transcription factors as very likely to be responsible for the differences in gene expression observed in Setdb1−/− vs Setdb1+/+ Th2 cells upon culture in Th1-polarizing conditions (data not shown). From these data, we conclude that the differences in stability observed between Setdb1−/− and Setdb1+/+ Th2 cells are very likely explained by SETDB1 causing H3K9me3 deposition at, and thus suppression of, ERVs that are located in the vicinity of genes involved in leukocyte function and that are targeted by Th1-specific transcription factors.

[0113] To test more directly if SETDB1-dependent H3K9me3 deposition at ERVs plays a role in the repression of the Th1 program and, as a consequence, in Th2 cell stability, we analyzed the location of the retroelements relative to lineage-specific enhancers, defined as non-promoter genomic regions showing significant enrichment for H3K4me1 in Th1 cells (Vahedi et al., 2012). We found 4397 putative Th1 enhancers associated with H3K9me3+ERVs in Th2 cells (data not shown). Interestingly, they included the previously identified conserved, non-coding sequences located 17-1-20 kb downstream of the Ifng gene (CNS17-20). While they are poised in naïve T cells, with a strong H3K4me1 signal flanked by a large domain of H3K9me3, they lose competence upon Th2 cell commitment, with an accumulation of H3K9me3 and a complete loss of H3K4me1 (data not shown). In SETDB1−/− cells, this enhancer as well as the thousands of others marked or flanked by an ERV, loose H3K9me3 and thus probably become accessible to the Th1-specific transcription factors mobilized downstream of the IFN-γ and IL-12 receptor. Consistent with this hypothesis, we found that the H3K4me1 signal at Ifng CNS17-20 was substantially higher in Setdb1−/− than in Setdb1+/+ Th2 cells (data not shown). It therefore appears that, in the absence of SETDB1, the lack of deposition of H3K9me3 at ERVs overlapping or flanking Th1-related enhancers makes them accessible to transcription factors, including IRF1, FOXO3 and lineage-associated STATs. This lack of repression potentially affects a large number of Th1-associated loci including those encoding IFN-γ and T-bet, the signature cytokine and so-called master regulator of the lineage, respectively. They also include those encoding other critical transcriptional regulators such as STAT4, IRF1, HLX and RUNX3 (data not shown). All of these genes have at least one enhancer associated with an ERV marked by H3K9me3 in Th2 cells and are, in addition, upregulated in SETDB1-deficient Th2 cells upon culture in Th1-inducing conditions. In conclusion, our data reveal for the first time that Th2 cell lineage stability is controlled at the level of the chromatin by the SETDB1-dependent deposition of H3K9me3 at a restricted set of ERVs flanking or behaving as Th1-gene enhancers.

SETDB1 Inhibitors Alter Th2 Cell Commitment

[0114] To test whether the acute inhibition of SETDB1 could impact on Th2 cell differentiation and commitment, we next targeted SETDB1 in differentiating wild-type Th2 cells using non-specific inhibitors (DZNEP and Mithramycin). The use of these pharmacological agents results in the destabilization of the Th2 cells which start to produce pro-Th1 factor, in particular IFN-γ (FIGS. 3A, 3B, 3C and 3D). Interestingly, after 6 days of culture in the presence of mithramycine A, and without exposing the differentiating Th2 cells to a pro-Th1 signal, the Th2 cells start to produce IFN-γ and to decrease their production of IL-13 (pro-Th2 factor) (FIGS. 3A and 3B).

CONCLUSION

[0115] In conclusion, we have shown here that the lysine methyltransferase SETDB1 controls CD4 T cell identity by repressing ERVs that flank or overlap Th1 lineage-specific enhancers. This enzyme is thus a potential target for drugs that might be useful, for example, to promote Th1 cell differentiation in various infectious diseases, or to prevent harmful Th2 responses in allergic disorders.

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