The transcription factor EHF promotes the maturation and immunosuppression of conventional dendritic cells

Domain: chemistry
Published: 2026-02-23
Editorial outcome: minor_revision

Abstract
The transcriptional program that regulates immunosuppression in CCR7+ conventional dendritic cells (cDCs) is currently unknown. Here, we identify ETS homologous factor (EHF) as a transcription factor that regulates cDC maturation and immunosuppression after TLR7/8/9 stimulation. Mice with conditional deletion of EHF in DCs exhibit increased resistance to autoimmune, infection or tumor challenge. EHF-deficient DCs promotes Th1- and Th17-biased CD4+ helper T cell response in vivo and in vitro. EHF-deficient cDC1s and cDC2s exhibit decreased expression of CCR7, CD200 and PD-L1, increased expression of DC-lineage transcriptional factor IRF4, and decreased expression of inhibitory NFκB family member Rel. EHF overexpression in DCs results in the opposite phenotype. CUT&TAG analysis suggests that EHF directly regulate Ccr7, Cd200, Cd274, Irf4 and Rel expression. Additionally, single-cell RNA-sequencing demonstrates that Ehf expression is highly enriched in CCR7hi DCs in mice and humans. Our study thus reveals a conserved transcriptional program that regulates cDC maturation and immunosuppression.

Introduction
Dendritic cells (DCs) are indispensable for both innate and adaptive immunity. DCs could be generally categorized as type 1 (cDC1s), type 2 (cDC2As and cDC2Bs), type 3 (DC3s), plasmacytoid DCs (pDCs), transitional DCs (tDCs) and monocyte-derived DCs (moDCs)1–9. These subsets were differentially regulated by lineage-specific transcription factors such as BATF3, IRF4, IRF8, E2-2 and T-bet10–21. In addition to their importance in antigen presentation and general inflammation, DCs are also crucial for shaping immunological tolerance in many autoimmune diseases, such as cardiac and neurological autoimmunity, psoriasis, type I diabetes, and systemic lupus erythematosus (SLE)22. Upon maturation, CCR7+ DCs undergo a rapid transition from a pro-inflammatory state to an anti-inflammatory state. In the thymus, tolerogenic DCs are involved in the negative selection of T cells23. During the steady state, migratory CCR7+ DCs in the lymph nodes are found to be more immunosuppressive than resident DCs, as indicated by their superior ability to prime Foxp3+ CD4+ regulatory T cells (Tregs)24. Subsequently, a highly tolerogenic cDC subset (mregDCs) is identified in tumor-associated tissue13. mregDCs share many characteristic markers, such as CD200, FSCN1 and CCR7, with mature migratory DCs25–27. A similar regulatory CD103int DC subset is also found in the intestinal epithelium and lungs of neonatal mice28–30. However, it is unclear which transcription factor specifically regulates the tolerogenic function of mregDCs.

The E26 transformation-specific (ETS) transcription factor family plays a central role in DC development. ETS family members possess a conserved winged helix-turn-helix DNA binding domain that recognizes the core DNA sequence GGA(A/T)31. In myeloid development, mutual repression between the ETS family member PU.1 (Spi1) and GATA1 regulates the divergence of megakaryocyte/erythrocyte progenitors from common myeloid progenitors32–35. In MoDCs, PU.1 binds to ETS motifs on its −50kb enhancer downstream of the Irf8 transcriptional start site. In pDCs, the ETS family member Spi-B plays a critical role in their late development and function36.

ETS homologous factor (EHF, also known as ESE-3) is first identified in its role on the differentiation of epithelium cells37. EHF is also reported to be overexpressed in human cancer cells and may promote tumor growth38. In the immune system, EHF is first implicated in human MoDCs in vitro39. Knockdown of EHF by RNA interference impairs surface marker expression, such as CD1a, in human moDCs generated in vitro39. EHF expression is upregulated upon stimulation of MoDCs by various ligands such as TLR ligands, cytokines, and peroxisome proliferator-activated receptor gamma agonist40,41. Besides DCs, Ehf overexpression by lentiviruses impairs the IgE-related function of murine bone-marrow-cultured mast cells42. Due to the lack of genetic animal tools, the immune function of EHF, especially in DCs, remains poorly defined.

Here, we report that the ETS family transcription factor EHF orchestrates an immunosuppressive program in cDC1s and cDC2s after detection of self-nucleic acids via TLR7/8/9. EHF deletion limits while its overexpression promotes cDCs immunosuppression function both in vitro and in vivo. This finding could be potential of use for treating autoimmune diseases, cancer and pathogenic infections.

Results
EHF maintains tolerance to colitis, and suppresses immunity to bacterial infections and cancer
To study the function of EHF in DCs in vivo, we generated transgenic mice with conditional genetic deletion of the Ehf allele (Ehffl/fl) (Supplementary Fig. 1a). Since the Zbtb46 gene shares the same chromosome 2 as the Ehf gene, it was difficult to cross Zbtb46-Cre with Ehffl/fl mice. Thus, we chose to utilize Itgax-Cre, which is constitutively expressed in all DCs but also in some macrophages. In the Ehffl/fl;Itgax-Cre+ mice (hereafter referred to as Ehf ΔCD11C), qPCR results demonstrated that Ehf was deleted in cDCs from Ehf ΔCD11C mice (Supplementary Fig. 1b). In general, the Ehf ΔCD11C mice were born at Mendelian ratios, had normal immune development, including T cells and DCs, and appeared healthy for at least 9 months (Supplementary Fig. 1c–h). Immature EHF-deficient cDC1s and cDC2s also had normal surface marker expression during steady-state (Supplementary Fig. 1i). In addition, the in vitro proinflammatory cytokine response by activated cDCs, pDCs and macrophages from Ehf ΔCD11C mice was normal (Supplementary Fig. 1j).

To determine the role in EHF in autoimmunity, we fed Ehffl/fl and Ehf ΔCD11C mice dextran sodium sulfate (DSS)-containing water, which can damage the epithelial monolayer of the large intestine, as a model for colitis. After a typically nonlethal 2.5% dose of DSS, none of the Ehf ΔCD11C mice survived beyond 16 days, while 85% of the Ehffl/fl mice survived (Fig. 1a). Correspondingly, the body weight of the Ehf ΔCD11C mice decreased more rapidly than that of the Ehffl/fl mice in the first week (Fig. 1b). The disease activity index (DAI), which measures clinical colitis symptoms, was substantially greater in Ehf ΔCD11C mice than in Ehffl/fl mice (Fig. 1c). Analysis of the inflamed colons revealed that, compared with Ehffl/fl mice, Ehf ΔCD11C mice had shorter colon lengths, more infiltrated immune cells and higher histological H&E scores in colon sections (Fig. 1d–f). While DSS induces colitis in a T-cell independent manner, studies had shown that T-cell responses contribute to the inflammatory response after colitis initiation43–47. Correspondingly, we found that overall CD4+ and CD8+ T cells exhibited higher levels of activation marker CD44 in the DSS-treated Ehf ΔCD11C mice (Supplementary Fig. 1k). PD-1 expression of CD4+ T cells in the DSS-treated Ehf ΔCD11C mice were also lower (Supplementary Fig. 1l). The IFNγ- and IL-17A-producing capacities of activated CD4+ T cells were also increased in DSS-treated Ehf ΔCD11C mice (Supplementary Fig. 1m). Here, we demonstrated that EHF suppressed autoimmunity in a DSS-induced colitis model.

Next, we examined the role of EHF in bacterial, viral infections and cancer in vivo (Fig. 1g). First, we intravenously injected mice with a lethal dose of the intracellular bacteria Listeria monocytogenes. Ehf fl/fl mice reached ~55% mortality on Day 6 post-infection, while all the Ehf ΔCD11C mice survived (Fig. 1h). Correspondingly, we found that all CD4+ T cells, but not CD8+ T cells, exhibited higher levels of activation marker CD44 in the infected Ehf ΔCD11C mice (Supplementary Fig. 1n). The IL-2, IFNγ and TNF producing capacities of CD44+ CD4+ T cells were also increased in the Ehf ΔCD11C mice after L. monocytogenes infection (Supplementary Fig. 1o). Second, we infected mice with vesicular stomach virus (VSV). There was no difference in viral titers between Ehf fl/fl and Ehf ΔCD11C mice (Fig. 1i). However, CD4+ and CD8+ T cells exhibited higher levels of activation marker CD44 in the infected Ehf ΔCD11C mice (Supplementary Fig. 1p). The IL-2, IFNγ and TNF producing capacities of CD44+ CD4+ T cells were also increased in the Ehf ΔCD11C mice after VSV infection (Supplementary Fig. 1q). Third, after transplantation with highly lethal B16 melanoma cells with recombinant ovalbumin (B16-OVA), Ehf ΔCD11C mice had a greater overall survival rate than Ehf fl/fl mice (Fig. 1j). The CTL infiltration in the tumor increased ~2-fold (Fig. 1k). Following injection of OVA-specific TCR-transgenic CD8+ OT-I cells 11 days after B16 transplantation, the OT-I cells in the draining lymph nodes from the Ehf ΔCD11C mice expressed lower levels of PD-1 after 3 days (Fig. 1l). Here we showed that EHF repressed immune response to bacterial infections and melanoma xenograft in mice.

EHF suppresses Th1- and Th17-biased CD4+ T-cell response
Since CD4+ and CD8+ T cells in our disease models all exhibited a more activated phenotype in the EHF-deficient mice, we specifically examined the in vivo CD4+ or CD8+ T-cell priming ability of EHF-deficient DCs (Fig. 2a and Supplementary Fig. S2a). Using CpG-B as a Th1-biased adjuvant, we found that the percentage and number of OVA-specific OT-II cells in the spleen and draining lymph nodes were increased by approximately 2-fold in Ehf ΔCD11C mice (Fig. 2b). Correspondingly, we found that the IL-2, TNF, IFNγ and T-bet expression by OT-II cells were increased in Ehf ΔCD11C mice (Fig. 2c). To determine the role of EHF in the Th2-biased response, we utilized papain and IFA as adjuvants, and found that the overall OT-II ratio, number, IL-4 production and GATA-3 expression remained unchanged between Ehf fl/fl and Ehf ΔCD11C mice (Fig. 2d and Supplementary Fig. S2b). To determine the role of EHF in follicular helper T cells (Tfh), we utilized sheep red blood cells associated with OVA (SRBC-OVA). We found that EHF was dispensable for the expansion of OT-II cells, Tfh and germinal center B cells (Fig. 2e and Supplementary Fig. S2c). Here, we demonstrate that EHF specifically suppresses Th1 response in vivo.

To determine the role of EHF in cross-presentation, we adoptively transferred OT-I cells before injecting mice with cell-associated OVA48–50. We found that EHF is dispensable for CTL cross priming (Fig. 2f and Supplementary Fig. S2d). To determine the role of EHF in the overall CD8+ T-cell priming, we adoptively transferred OT-I cells before challenging mice with a sublethal dose of Listeria monocytogenes with recombinant ovalbumin (LM-OVA). We found that EHF is dispensable for CTL expansion and cytokine production (Supplementary Fig. 2e, f). Our results showed that EHF in DCs is dispensable for the antigen-specific CD8+ T-cell response in vivo.

In the DSS colitis model, we found that IL-17A production by CD44+ CD4+ T cells were increased in EHF-deficient mice (Supplementary Fig. 1m). To further examine Th17 response in vivo, we infected mice with Citrobacter rodentium, an extracellular bacteria that infects the large intestine and causes colon inflammation51. Compared with Ehf fl/fl mice, Ehf ΔCD11C mice had longer colon lengths and decreased immune cell numbers in the mesenteric lymph nodes, suggesting a less severe colon inflammation in Ehf ΔCD11C mice (Supplementary Fig. 2g). IL-17A and IL-22 production by CD44+ CD4+ T cells from mesenteric lymph nodes and colons also increased in Ehf ΔCD11C mice, suggesting a stronger type III immune response (Supplementary Fig. 2h). The IL-2, IFNγ, and TNF-producing capacities of CD44+ CD4+ T cells were increased in the Ehf ΔCD11C mice (Supplementary Fig. 2i). Here we found that Th17 response were enhanced in the Ehf ΔCD11C mice in vivo.

Lastly, we FACS-sorted cDC1s or cDC2s, pulsed them with TCR-specific OVA peptides, and incubated them with FACS-sorted OT-II or OT-I cells with different polarization cytokines in vitro. We found that EHF-deficient cDC1s and cDC2s primed increased IFNγ-producing Th1 and IL-17A-producing Th17 cells (Fig. 2g). In comparison, EHF in cDC1s or cDC2s was dispensable for IL-4-producing Th2 cells or inducible regulatory T cells (Fig. 2g). Natural regulatory T cells were also normal in EHF-deficient mice (Supplementary Fig. 1h). Consistently, EHF in cDC1s or cDC2s was dispensable for CTL priming in vitro (Fig. 2g). Here, we found that EHF in cDC1s or cDC2s suppresses Th1 and Th17 priming in vitro.

EHF is upregulated by TLR7/8/9 and suppressed by TLR3/GM-CSF/IFNγ
Under steady state in C57BL/6J (B6 or WT) mice, Ehf expression was moderately enriched in cDCs, but undetectable in FACS-sorted pDCs, B cells, CD4+/CD8+ T cells, monocytes, neutrophils and macrophages (Supplementary Fig. 3a). We FACS-sorted cDCs and stimulated them in vitro with various ligands. We found that cDC1s and cDC2s upregulated Ehf expression by ~10-fold after TLR9 (CpG-B) stimulation, and by ~5-fold after TLR7/8 (R848) stimulation (Fig. 3a). In contrast, the TLR3 agonist poly(I:C) downregulated Ehf expression by ~3-fold (Fig. 3a). Neither TLR2 nor TLR4 agonizts altered Ehf expression in cDCs (Fig. 3a).

To determine if any immune cells besides cDCs express EHF after TLR9 stimulation, we FACS-sorted cDC1s/cDC2s, pDCs/tDCs, monocytes, macrophages, neutrophils, B and T cells from PBS or CpG-B injected B6 mice, and then performed single-cell RNA-sequencing (scRNA-seq) with the pooled populations (Fig. 3b and Supplementary Fig. S3b). Consistently, we found that Ehf expression was only present in CCR7hi DCs (Fig. 3b). In CpG-B injected WT mice, we FACS-sorted cDC1s, cDC2As, cDC2Bs, DC3s, and moDCs, and examined Ehf expression after CpG-B stimulation in vivo10,12,52. All cDC1s and cDC2 subsets upregulated Ehf expression (Fig. 3c). However, DC3s and moDCs did not appear to express Ehf (Fig. 3c).

Since “regulatory DCs” are reported to be triggered by the uptake of apoptotic cells, we incubated FACS-sorted WT cDCs with apoptotic WT splenic cells irradiated with a lethal dose of UV light28,29. We found that Ehf expression was upregulated ~ 4-fold by apoptotic cells (Fig. 3d). The EHF upregulation by CpG-B or apoptotic cells was blocked if antigen uptake was inhibited, suggested that it is dependent on antigen uptake (Fig. 3e)53. In MyD88-deficient mice, Ehf upregulation by TLR7/8/9 or apoptotic cells in cDCs was completely abolished, suggesting that the upregulation of EHF was mediated directly via the canonical TLR signaling pathway (Fig. 3f). After CpG-B treatment, there were significant differences in CCR7, CD200 and PD-L1 expression in DCs from MyD88-deficient mice (Supplementary Fig. 3c, d).

The in vivo upregulation of Ehf in cDCs peaked approximately 12–48 hrs after CpG-B injection before Ehf was downregulated (Fig. 3g). To identify factors that may downregulate EHF expression, we screened 23 different cytokines in CpG-B-activated cDCs. We found that only the addition of GM-CSF and IFNγ repressed Ehf expression in cDCs (Fig. 3h). When GM-CSF and IFNγ neutralizing antibodies were used in vivo, Ehf expression in CpG-B-activated cDCs showed further upregulation (Fig. 3i). Consistently, Ehf expression in GM-CSF-cultured BMDCs was not detected by qPCR, even after CpG-B activation (Supplementary Fig. 3e). In comparison, cDCFL-Notch cells, which were cultured from bone marrow cells with FLT3L and OP9 stromal cells expressing the Notch ligand Delta-like 1 (OP9-DL1), had a robust Ehf expression before or after CpG-B expression (Supplementary Fig. 3e)54,55. Here, we showed that GM-CSF and IFNγ could block EHF upregulation in cDCs.

EHF promotes CCR7, CD200 and PD-L1 expression in cDC1s and cDC2s
Since EHF is upregulated the most by TLR9 activation, we investigated the role of EHF in DC maturation by injecting CpG-B into Ehf fl/fl or Ehf ΔCD11C mice. We found that ~25% less MHCII+ CD11c+ DCs in the spleen of Ehf ΔCD11C mice at 12 h post-injection (Fig. 4a). This deficiency was primarily from the compartment of CCR7+ CD200+ DCs (Fig. 4b). Since DC development and cell numbers were normal in EHF-deficient mice, this deficiency in cell numbers could be due to a defect in trafficking (Supplementary Fig. 1c, d). In a transwell-based migration assay, we found that EHF-deficient cDC1s and cDC2s had a defect in trafficking to CCR7 ligands CCL19/CCL21 in a dose-dependent manner (Supplementary Fig. 4a).

Besides traditional cDC1s and cDC2s, we performed a more detailed analysis of DC subsets. The cell number defect is consistent in cDC1s, cDC2As and cDC2Bs (Fig. 4c). CCR7, CD200 and PD-L1 expression in cDC1s, cDC2As, cDC2Bs and DC3s were significantly downregulated in Ehf ΔCD11C mice after CpG-B stimulation (Fig. 4d). This PD-L1 downregulation is consistent with the lower expression of PD-1 on OT-I cells in the tumor model and CD4+ T cells in the DSS model (Fig. 1l and Supplementary Fig. S11). Other surface markers in TLR9-stimulated EHF-deficient cDCs, macrophages and pDCs were normal (Supplementary Fig. 4b and S4c). In the mesenteric lymph nodes of DSS-treated mice, we also found that EHF-deficient migratory cDC1s and cDC2s downregulated CCR7, CD200 and PD-L1 expression (Fig. 4e and Supplementary Fig. S4d).

Next, we measured DC-lineage transcriptional factor IRF4 and IRF8 expression in CpG-B-stimulated Ehf fl/fl and Ehf ΔCD11C cDCs via intracellular staining5. In Ehf ΔCD11C mice, IRF4 expression was upregulated in cDC1s and cDC2s (Fig. 4f). In comparison, IRF8 expression remained unchanged in cDC1s or cDC2s regardless of EHF-deletion (Supplementary Fig. 4e). The NFκB transcriptional family members Rel and RelB are known to dampen the proinflammatory NFκB response in DCs56–58. We found that only Rel (detected by its subunit c-Rel) expression were downregulated in CpG-B-activated Ehf ΔCD11C cDC1s and cDC2s (Fig. 4f and Supplementary Fig. S4e). In addition, overexpressing EHF in cDCFL-Notch via retroviruses upregulated CCR7, CD200 and PD-L1 expression, downregulated IRF4 expression, while upregulating Rel level (Fig. 4g). These experiments demonstrated that EHF regulated the expression of CCR7, CD200 and PD-L1 in cDC1s and cDC2s.

CUT&TAG analysis suggests that EHF may directly regulate gene transcription in DCs
To determine the genetic targets of EHF via chromatin-based sequencing analysis, we generated anti-EHF monoclonal antibodies by vaccinating mice with a partial peptide of EHF that contains its DNA-binding domain (Supplementary Fig. 5a). After several rounds of vaccination, we selected one clone (clone number: XL-32) from multiple positive hybridomas. Based on Western blotting, this clone was able to distinguish EHF expression in different organs and EHF overexpression in 293T cells (Supplementary Fig. 5b, c). Most importantly, this clone detected significantly fewer anti-EHF signals from CpG-B-treated Ehf ΔCD11C DCs, thus demonstrating the specificity of this clone for EHF (Supplementary Fig. 5d).

To determine whether EHF directly or indirectly regulates inhibitory and maturation markers in cDCs, we performed CUT&TAG analysis on CpG-B-stimulated FACS-sorted cDCFL-Notch cells. Isotype control IgG on Ehf fl/fl cDCFL-Notch and anti-EHF antibodies on Ehf ΔCD11C cDCFL-Notch were utilized as two different controls. Genome-wide comparison of CUT&TAG signals at -3kb to 3kb from transcription start site (TSS) indicated that the samples with anti-EHF antibodies exhibited a strong peak compared to the lack of peaks in the IgG sample (Fig. 5a). A weaker signal was still detected in Ehf ΔCD11C cDCs, as Itgax-Cre deletion could not reach 100% in DCs (Fig. 5a)59. Motif enrichment analysis by HOMER showed that DNA fragments pulled down by our in-house anti-EHF monoclonal antibody clone XL-32 were exclusively enriched in ETS domains (Fig. 5b). HOMER analysis also revealed that the de novo motif for EHF contained the conserved ETS family motif GGAA (Fig. 5c). Gene ontology (GO) enrichment analysis of enriched peaks demonstrated that EHF regulated the transcription of multiple pathways such as leukocyte migration, mononuclear differentiation, bacterial response, immune activation, immune inhibition, inflammatory response, T-cell priming, PRR signaling, transcriptional factor activity, and antigen presentation (Fig. 5d and Supplementary Fig. S5e).

We previously demonstrated that CCR7, CD200 and PD-L1 (encoded by Cd274), were downregulated in EHF-deficient cDC1s and cDC2s (Fig. 4d, e). DNA-binding sites generated from CUT&TAG showed that there were multiple strong peaks in the vicinity of the Ccr7, Cd200 and Cd274 loci, while the Ehf ΔCD11C sample had correspondingly reduced signals (Fig. 5e). Besides cDCFL-Notch, we performed CUT&TAG on cDC1s and cDC2s sorted from spleens of CpG-B-treated WT mice, and found similar peaks on the same Ccr7, Cd200 and Cd274 loci (Supplementary Fig. 5f). CUT&TAG analysis also suggested that EHF could bind to ETS domains in a 3’ proximal enhancer located at +20kb from the Irf4 TSS (Fig. 5e and Supplementary Fig. S5f). Similarly, we detected strong peaks in the 5’ promoters (~1kb) region of Rel (Fig. 5e and Supplementary Fig. S5f). Within the denoted boxed peaks, we designed qPCR primers around randomly chosen ETS motifs, and found that the enrichment measured by qPCR were consistent with the CUT&TAG sequencing data (Fig. 5f). After identifying ETS binding motifs within the enriched peaks, we performed motif mutagenesis on these motifs in luciferase reporter assays. We found that these regions with ETS motifs were critical for EHF’s transcriptional activity (Fig. 5g). The combined data from genetic knockout mice, CUT&TAG analysis and in vitro studies demonstrated that EHF may directly regulate Ccr7, Cd200, Cd274, Irf4, and Rel transcription at the chromatin level.

EHF is expressed by human and murine CCR7hi DCs
To get more clarity on which DC subset expresses EHF, we injected WT mice with the TLR9 agonist CpG-B, and then FACS-sorted MHCII+ CD11c+ DCs from the spleen, lymph nodes and peritoneal cavity for single-cell RNA-sequencing (Fig. 6a and Supplementary Fig. S6a). When all six samples were subjected to integrated transcriptional analysis, CCR7+ DCs were separated into 3 clusters (Fig. 6b). Cluster 1 expressed intermediate levels of CCR7, while Clusters 2 and 3 expressed higher levels of CCR7 (Supplementary Fig. 6b). Among the CCR7hi DCs, the marker genes from the spleen, lymph nodes and peritoneal cavity all included Ehf (Supplementary Fig. 6c). In addition to exhibiting high Ehf expression, CCR7hi DCs (Clusters 2 and 3) also exhibited increased expression of inhibitory molecules and maturation markers such as Cd200, Socs2, Il2ra, Cd274 (encodes PD-L1) and Cd83, as well as the anti-inflammatory NFκB family transcription factors Rel and RelB (Fig. 6c). Ehf transcripts were enriched only in CCR7hi DCs (Clusters 2 or 3) from the spleen, lymph nodes and peritoneal cavity after CpG-B injection (Fig. 6d). In summary, we found that Ehf expression was specifically enriched in CCR7hi DCs across different tissues after CpG-B stimulation in vivo.

To validate the EHF expression in humans, we collected PBMCs from healthy volunteers, treated the cells with PBS or CpG-B in vitro for 16 hrs, FACS-sorted CD11C+ HLA-DR+ DCs, and pooled the individual samples for single-cell RNA-seq (Supplementary Fig. 6e). A previously published single-cell RNA-seq database of fresh human DCs from PBMCs was used for the untreated control data18. In the fresh untreated DCs, EHF expression was not specifically enriched in any DC subset (Fig. 6e). In the CpG-B-overnight-treated DCs, EHF expression was highly enriched in CCR7hi DCs (Clusters 2 and 3) but not in CCR7int DCs (Cluster 1) or other DCs (Fig. 6e and Supplementary Fig. S6f, g). In the mock-treated DCs incubated overnight, EHF was upregulated in CCR7hi DCs, perhaps due to TLR7/8/9 autoactivation by apoptotic cells (Fig. 6e). Compared to CCR7int DCs, CCR7hi DCs (Clusters 2 and 3) showed greater enrichment in inhibitory, migratory and maturation-related molecules such as CD25, CD200, SOCS2, CD274, CCR7, FSCN1, CXCL16, CD83, REL and RELB (Fig. 6f). Similar to murine data, human EHF expression was upregulated by TLR7/8/9 stimulation, but downregulated by TLR3 activation (Fig. 6g). In both mice and humans, RNA velocity analysis from single-cell RNA-seq indicated that Ehf expression progressed from CCR7- cDC2s to CCR7int DCs (Cluster 1) and then to CCR7hi DCs (Cluster 2 and 3) (Fig. 6h). In humans, we showed that EHF expression emerged in CCR7hi DCs after TLR7/8/9 stimulation.

Methods
Transgenic mice
All mice were bred and maintained under specific pathogen-free conditions at Shanghai Model Organisms Center Inc., and BSL3 facilities at Sun Yat-sen University, according to the institutional guidelines and protocols approved by the Animal Ethics Committee of Sun Yat-sen University, Guangzhou, Guangdong, China. C57BL/6J (JAX: 000664, B6 or WT), B6.Cg-Tg(TcraTcrb)425Cbn/J(JAX: 004194, OT-II), B6.SJL-PtprcaPepcb/BoyJ (JAX: 002014, CD45.1+), B6.Cg6-Tg (TcraTcrb) 1100Mjb/J(JAX: 003831, OT-I), and B6.Cg-Tg(Itgax-Cre)1-1Reiz/J(JAX: 008068, Itgax-Cre) mice were purchased from the Jackson Laboratory. C57BL/6Smoc-Myd88 em1Smoc (#NM-KO-190192, Myd88 -/-) mice were purchased from Shanghai Model Organisms Center Inc. Age- and sex-matched mice aged 6–16 weeks were used in this study. Sex was not considered in the study design and analysis due to the limited numbers of available mice.

Ehffl/fl mice on the B6 background were generated by Shanghai Model Organisms Center Inc. The genetic modification strategy and subsequent validation are described in Supplementary Fig. 2. The mice were genotyped via PCR (Ehf-fl-Fwd: TTGTCTCCTTGTCCGCATCC; Ehf-flRvs: CTGAATACCCACGGTGTGCT). The insertion of loxP sites was confirmed by the expected 402 bp PCR band, as the wild-type band was 338bp in length. Cell-specific deletion of the Ehf allele was achieved after Cre-mediated recombination by crossing with Itgax-Cre.

In vivo infection models
For lethal L. monocytogenes infection, LM-OVA was grown in TSB (HKM, #024051) medium to an OD of approximately 0.25, diluted in PBS and intravenously injected (2×104 CFU) in a volume of 0.2 ml per mouse. Survival was monitored daily. For antigen-specific T-cell response, mice were additionally injected intravenously with 1×105 OT-I prior to infection of LM-OVA (8×103 CFU). After 5 days, the mice were analyzed.

For VSV infection, 1×106 PFU were diluted in PBS and injected intravenously. The spleens was collected to evaluate viral titers via qPCR.

For C. rodentium infection, Citrobacter rodentium strain DBS100 (ATCC, #51459) was grown in LB medium, diluted in PBS and orally inoculated with 2×109 CFU in a total volume of 200 μL per mouse.

For all pathogenic infections, we adhered to the early endpoint by institutional ethical board: the experiments will be terminated and animals sacrificed by cervical dislocation if body weight loss reached ≥20% from baseline, the clinical severity score reached its maximum, or moribund condition/severe clinical signs (e.g., persistent anorexia/ad libitum water refusal, severe dehydration, dyspnea, neurological symptoms) occurred.

For tumor models, the melanoma cell line B16 (ATCC, CRL-6475) was stably transfected with OVA-expressing plasmids in house and cultured at 37°C in RP10. Next, 5×105 tumor cells were injected subcutaneously (s.c.) in 100 μL of PBS in each flank. On Day 11, the mice were intravenously injected with 1×105 OT-I cells. Three days after OT-I injection, the mice were analyzed. We adhered to the early endpoint by institutional ethical board: experiments will be terminated and animals sacrificed by cervical dislocation if tumor size exceeded 15mm in diameter or 1.8cm³ in volume (the institutional ethics maximum tumor size), or if the animals presented with a body condition score <2.0, reduced activity, failure to respond to gentle stimulation, lethargy, piloerection, persistent hunched posture, or tumor ulceration. Tumor size was determined by the formula: length×width×width×0.5 by a digital caliper.

For the DSS-based colitis model, 7 to 8-week-old sex-matched cohoused littermates were administered 2.5% DSS (36–50 kDa; MP Biomedicals, #160110) in their drinking water for consecutive days. Survival and clinical parameters, such as weight loss, rectal bleeding and diarrhea, were monitored daily. During the duration of the experiment, the disease activity index (DAI) score was determined to evaluate the clinical progression of colitis. The DAI is the combined score of weight loss compared to initial weight, stool consistency, and bleeding. Scores were defined as follows: weight loss: 0 (no loss), 1 (1–5%), 2 (5–10%), 3 (10–20%), and 4 (>20%); stool consistency: 0 (normal), 2 (loose stool), and 4 (diarrhea); and bleeding: 0 (no blood), 1 (hemoccult positive), 2 (hemoccult positive and visual pellet bleeding), and 4 (gross bleeding, blood around anus). The DAI was determined daily during DSS treatment. For assessment of intestinal pathology, an expert blinded to the genotypes scored H&E-stained intestinal sections based on three parameters of tissue damage and four parameters of inflammation, and multiplied these scores by a factor accounting for the extent of the tissue being affected, as described previously. Formalin-fixed and paraffin-embedded intestinal tissue was sectioned and stained with hematoxylin and eosin. A semi-quantitative composite scoring system, computed as the sum of five histological subscores, multiplied by a factor based on the extent of inflammation, was used for the assessment of spontaneous intestinal inflammation. Histological subscores were as follows (for each parameter: 0, absent; 1, mild; 2, moderate; 3, severe): mononuclear cell infiltrate (0–3), crypt hyperplasia (0–3), epithelial injury/erosion (0–3), polymorphonuclear cell infiltrates (0–3) and transmural inflammation (0, absent; 1, submucosal; 2, one focus extending into muscularis and serosa; 3 up to five foci extending into muscularis and serosa; 4, diffuse). The extent factor was derived according to the fraction of bowel length involved in inflammation: 1, <10%; 2, 10–25%; 3, 25–50%; and 4, >50%. No spontaneous colonic inflammation was detected in any of the reported genotypes. We adhered to the early end point by institutional ethical board: experiments will be terminated and animals sacrificed by cervical dislocation if body weight loss reached ≥20% from baseline, severe systemic signs or complications (e.g., persistent anorexia, hypothermia, dyspnea, anemia, moribund state), maximum DAI score as defined in the protocol, or other critical conditions (e.g., suspected intestinal perforation, severe rectal prolapse, severe perianal infection) were observed.

Flow cytometry
Cell suspensions of bone marrow and splenocytes were subjected to red blood cell lysis. The cells were subsequently washed and resuspended in FACS buffer (1% FCS and 0.1% sodium azide in PBS). Human PBMCs were isolated via Ficoll (Solarbio, #P4350) gradient centrifugation. The cells were stained with the indicated cell surface markers with an Fc blocker (BD, #553141) to exclude nonspecific staining. For intracellular staining, the cells were surface stained, subsequently fixed and permeabilized using an intracellular staining kit (eBioscience, #88-8824-00). For transcription factor staining, the cells were surface stained, subsequently fixed and permeabilized using a transcription factor intracellular staining kit (eBioscience, #00-5523-00) before staining. Flow cytometry was performed on a Beckman Colter CytoFLEX and analyzed using FlowJo software (TreeStar, version X). The MFI was calculated according to the genomic mean by the FlowJo software.

To measure cytokine production, cDCs, pDCs, macrophages were plated in complete RPMI-1640 (RPMI-1640 with 10% FCS, 1% L-glutamate, 1% penicillin/streptomycin, and 55μM 2-mercaptoethanol), and stimulated with 1µg/mL Pam3CSK4 (InvivoGen, #tlrl-pms), 100ng/mL LPS (Sigma-Aldrich, #L2654-1MG), 500ng/mL poly(I:C) (InvivoGen, #tlrl-picw), 1µg/mL R848 (InvivoGen, #tlrl-r848), 100nM CpG-B (InvivoGen, #tlrl-1668) for 2h (TNF, IL-6, IL-12p40) or 4h (IFNα), then added brefeldin A (eBioscience, #00-4506-51) for 10h (TNF, IL-6, IL-12p40) or 5 hrs (IFNα) before intracellular staining. Anti-mouse TNF (Invitrogen, #25-7321-82), IL-6 (Invitrogen, #48-7061-82) and IL-12p40 (Biolegend, #505206) were used at dilutions of 1:400, anti-mouse IFNα (Pbl assay science, clone: RMMA-1, #22100-3) were used at dilutions of 1:100. The antibody information for flow cytometry is shown in Supplementary Table 1.

In vivo and in vitro T-cell priming
For the Th1 response, all mice were injected intravenously with 1×105 OT-II cells before immunization. Mice were immunized subcutaneously (s.c.) in the rear footpads with 50µg of OVA (Sigma, #A5503) and 20µg of CpG-B in 40µL of PBS. Popliteal lymph nodes and spleens were collected at the indicated hours for FACS analysis.

For the Th2 response, all mice were injected intravenously with 1×105 OT-II cells before immunization. Mice were immunized subcutaneously (s.c.) in the rear footpads with 50µg of OVA (Sigma, #A5503), 50µg of papain (Sigma, #76216) and 20µL IFA (Sigma, #F5506) in 20µL of PBS. Popliteal lymph nodes were collected at the indicated hours for FACS analysis.

For the Tfh response, all mice were injected intravenously with 1×105 OT-II cells before immunization. Mice were immunized intraperitoneally with 5×108 SRBCs (Hqbio, #HQ80073-004) conjugated with OVA (Sigma, #A5503) in 100µL of PBS. For conjugation of OVA to SRBCs, SRBCs were washed with PBS three times, incubated with 4mL of 30mg/mL ice-cold OVA in PBS and crosslinked with 1mL of 100mg/mL EDCI (Sigma, #E7750) for 1 hr in ice with occasional mixing, followed by washing four times in PBS to remove the free OVA. Spleens were collected at the indicated time for FACS analysis.

For the antigen cross-presentation assay with cell-associated OVA, all mice were injected intravenously with 1×106 OT-I cells before immunization. BALB/c splenocytes were loaded with OVA by osmotic shock. Cells were incubated in hypertonic medium (0.5 M sucrose, 10% polyethylene glycol, and 10mM Hepes in RPMI 1640, pH 7.2) containing 10mg/ml OVA for 10min at 37°C, and then prewarmed hypotonic medium (40% H2O and 60% RPMI 1640) was added for an additional 2min at 37°C. After washing and irradiation (1350rad), 2×106 OVA-loaded cells were injected intravenously into mice. Spleens were collected at the indicated time for FACS analysis.

For in vitro T cell stimulation, naive CD44lo CD62Lhi OT-II or CD44lo CD62Lhi OT-I cells were sorted with a BD FACS Aria III flow cytometer. DC subsets were FACS-purified after enrichment with streptavidin microbeads. DCs and T cells were co-cultured at a ratio of 2×104 DCs to 1×105 T cells in the presence of 100nM CpG-B (InvivoGen, #tlrl-1668) and 1µg/mL OVA (MCE, #HY-P0286) or 200nM 323-339 SIINFEKL peptide (GenScript, #RP20398), under Th0 conditions (PBS) or with the addition of polarizing cytokines (Th1: 50ng/ml IL-12p70 (PeproTech, #210-12); Th2: 50ng/ml IL-4 (PeproTech, #214-14); Th17: 20ng/ml TGF-β (Novoprotein, #C16W), 20ng/ml IL-6 (PeproTech, #216-16), 10ng/ml IL-1β (PeproTech, #211-11B); iTreg: 2ng/ml TGF-β1 (Novoprotein, #C16W), 50ng/ml IL-2 (PeproTech, #212-12). 5ng/ml IL-2 (PeproTech, #212-12) was added to Th0, Th1 and iTreg on Day 3. Cytokine production was assessed after 5 days of culturing.

To measure cytokine production, T cells were plated in complete RPMI-1640, and stimulated with cell stimulation cocktail (Invitrogen, #00-4970-03), 1µg/mL OVA peptide (MCE, #HY-P0286) or 200nM 323-339 SIINFEKL peptide (GenScript, #RP20398) for 6 hrs before intracellular cytokine staining.

To measure cytokine production, cDCs, pDCs, macrophages were plated in complete RPMI-1640 (RPMI-1640 with 10% FCS, 1% L-glutamate, 1% penicillin/streptomycin, and 55μM 2-mercaptoethanol), and stimulated with 1µg/mL Pam3CSK4 (InvivoGen, #tlrl-pms), 100ng/mL LPS (Sigma-Aldrich, #L2654-1MG), 500ng/mL poly(I:C) (InvivoGen, #tlrl-picw), 1µg/mL R848 (InvivoGen, #tlrl-r848), 100nM CpG-B (InvivoGen, #tlrl-1668) for 2h (TNF, IL-6, IL-12p40) or 4h (IFNα), then added brefeldin A (eBioscience, #00-4506-51) for 10h (TNF, IL-6, IL-12p40) or 5 hrs (IFNα) before intracellular staining. Anti-mouse TNF (Invitrogen, #25-7321-82), IL-6 (Invitrogen, #48-7061-82) and IL-12p40 (Biolegend, #505206) were used at dilutions of 1:400, anti-mouse IFNα (Pbl assay science, clone: RMMA-1, #22100-3) were used at dilutions of 1:100. The antibody information for flow cytometry is shown in Supplementary Table 1.

Cell isolation
For harvesting immune cells from organs, spleens, lymph nodes and tumors were digested in collagenase I (Sigma-Aldrich, #C0130) and DNase I (Roche, #10104159001) for 1 hr at 37°C with stirring in RPMI, then organs were minced, ground, and passed through a 70μm nylon mesh. For the colon, colons were cut longitudinally and then laterally into small pieces. The tissues were incubated with epithelial removal buffer (20mM EDTA, 1mM sodium pyruvate, 10% FCS and 10mM HEPES in PBS) for 30 min at 37°C, then washed with cold PBS, after which the epithelial cells were removed with scissors. The remaining tissue were digested with collagenase D (Roche, #COLLD-RO) and DNase I (Roche, #10104159001) for 1 hr at 37°C with stirring in RPMI. After enzymatic treatment, colon tissues were further dissociated through a 70μm nylon cell strainer. For the isolation of lymphocytes from the tumor and colon, single-cell suspensions were then separated using a 30%/70% Percoll (Biosharp, #BS012) density gradient. Red blood cells were lysed with cold ACK buffer (150mM ammonium chloride, 10mM potassium bicarbonate, and 0.1mM EDTA in ddH2O). The cells were counted using a Beckman Colter CytoFLEX flow cytometer. Before cell sorting, cDCs and pDCs were stained with biotinylated anti-CD11c (eBioscience, #13-0114-82) at a 1:500 dilution for 20 min at 4°C. Then, the cells were washed 3 times with MACS buffer (10mM EDTA, 10% FCS and 10mM HEPES in PBS) and labeled with streptavidin microbeads (Miltenyi Biotech, #130-048-101) for 15 min at 4°C. Target cells were then purified by positive selection using an LS column in a QuadroMACS Separator. Then, the bead-purified cDCs were sorted as MHCII+ CD11c+ cells, and the bead-purified pDCs were sorted as B220+ SiglecH+ BST-2+ cells (purity >95%). Cell-sorting experiments were conducted on a BD FACS Aria III flow cytometer.

In vivo and in vitro DC assays
For CpG-B stimulated cDCs in the spleen, mice were injected intraperitoneally (i.p.) with or without 60µg of CpG-B in 100µL of PBS. Spleens were harvested at the indicated time for FACS analysis and qPCR.

For CpG-B stimulated cDCs in the lymph nodes, mice were injected subcutaneously in the rear footpads with or without 10µg of CpG-B in 40µL of PBS. Popliteal lymph nodes were collected at the indicated time for FACS analysis and qPCR.

For GM-CSF and IFNγ blocking in the spleen, mice were injected intraperitoneally (i.p.) with or without 100µg anti-GM-CSF (Invitrogen, #MA5-23799), 100µg anti-IFNγ (Invitrogen, #16-7311-85) or 60µg CpG-B in 100µL of PBS. Spleens were harvested at 14h for qPCR.

For in vitro stimulation, FACS-sorted DC subsets were plated in complete RPMI-1640 (RPMI-1640 with 10% FCS, 1% L-glutamate, 1% penicillin/streptomycin, and 55μM 2-mercaptoethanol), and stimulated with 1µg/mL Pam3CSK4 (InvivoGen, #tlrl-pms), 100ng/mL LPS (Sigma-Aldrich, #L2654-1MG), 500ng/mL poly(I:C) (InvivoGen, #tlrl-picw), 1µg/mL R848 (InvivoGen, #tlrl-r848), 100nM CpG-B (InvivoGen, #tlrl-1668), 1µg/mL mAb GM-CSF (Invitrogen, #MA5-23799) and the indicated cytokines for 9–12h (FACS analysis) or 14 hrs (qPCR).

For cytochalasin D treated cDCs, FACS-sorted cDCs were resuspended in complete RPMI-1640 (RPMI-1640 with 10% FCS, 1% L-glutamate, 1% penicillin/streptomycin, and 55μM 2-mercaptoethanol), and treated with 10µg/mL cytochalasin D (Sigma, #C2618) for 30 min at 37°C, washed three times with medium, and then cDCs were plated in complete RPMI-1640 medium and stimulated with 100nM CpG-B (InvivoGen, #tlrl-1668) for 14h. The cytokine information for DC assays is shown in Supplementary Table 2.

Migration assay
The migratory capacity of CCR7+ cDCs in a CCR7-dependent manner was measured by a chemotaxis assay as previously described69,70. Briefly, splenic cDCs (~10%) were purified with CD11c-biotin and MACS selection (5×105, 100μL), then seeded in the top chambers with 24-well plate with 8μm pore size (Corning Costar, #3422). Lower chamber wells contained 500μL of medium supplied with different dose (0.5, 5.0, 20ng/mL) of CCL19 (PeproTech, #250-27B) and CCL21 (PeproTech, #250-13) or 20ng/mL CCL2. The plates were incubated for 12h at 37°C at 5% CO2. Then, the top chambers were removed and the migrated cells in the bottom chamber were collected and acquired on a Beckman Colter CytoFLEX for cell count.

Primary cell culture
cDCFL-Notch were cultured as previously described54. In brief, 4×106 bone marrow cells per well were cultured in tissue culture-treated 24-well plates in 2mL of complete RPMI-1640 (RPMI-1640 supplemented with 10% FCS, 1% L-glutamate, 1% sodium pyruvate, 1% MEM with nonessential amino acids, 1% penicillin/streptomycin, and 55μM 2-mercaptoethanol), supplemented with 100ng/mL Flt3L ((PeproTech, #250-31L). On Day 3 of differentiation, half of the volume of cells in DC medium from each well was transferred to a single well containing a monolayer of mitomycin-treated OP9-Notch2 cells in 24-well plates. On Day 6, the cells were stimulated with 100nM CpG-B (InvivoGen, #tlrl-1668) for 12–14h for FACS analysis. cDCFL-Notch were gated as live+ B220- MHCII+ CD11c+ cells.

For BMDCs, 10×106 bone marrow cells per well were cultured in tissue-culture-treated 6-well plates in 4mL of RPMI-1640 supplemented with 10% FCS, 1% L-glutamate, 1% sodium pyruvate, 1% MEM with nonessential amino acids, 1% penicillin/streptomycin, and 55μM 2-mercaptoethanol, and 20ng/mL GM-CSF (PeproTech, #315-03). Half of the medium was removed on Day 2, and new medium supplemented with 40ng/mL GM-CSF and warmed to 37°C was added. The culture medium was entirely discarded on Day 3 and replaced with fresh warm medium containing 20ng/mL GM-CSF. On Day 5, the cells were stimulated with 100nM CpG-B (InvivoGen, #tlrl-1668) for 12–14h for FACS analysis. BMDCs were gated as live+ MHCII+ CD11c+ cells.

For retroviral transduction of primary cells, Phoenix packaging cells (ATCC, #CRL-3214) were transfected with 20µg of the plasmid pCL-Eco (Addgene, #25099) and 16µg of pMXs-EHF, or the empty vector pMXs, with 90µL of the transfecting reagent polyethyleneimine (Polysciences, #23966-1) at 1mg/mL in a 10cm culture dish. After 48h, the supernatant containing retrovirus was harvested and filtered. On Days 1 and 2, 2mL of retroviral supernatant was added, and the cells were spin-infected at 2500 rpm for 90 min at room temperature. On Day 3, fresh medium was added to the cells, and they were cultured for additional days. All retroviral transduction rates were measured by GFP via FACS.

RNA extraction and quantitative PCR
TRIzol reagent (GenStar, P118-05) and chloroform were added to homogenize single cells, after which RNA precipitation, washing and resuspension were performed following the manufacturer’s protocol. The extracted RNA was reverse transcribed according to the manufacturer’s protocol (HiScript II IRT SuperMix for qPCR, Vazyme, R323-01). Quantitative RT-PCR analysis was performed with SYBR Select Master Mix (GenStar, A301-10) using StepOnePlus (LifeSciences). All the data were normalized to β-actin expression. The primers used are shown in Supplementary Table 3.

Generation of an anti-EHF monoclonal antibody
Mice were immunized with a partial recombinant EHF antigen (1–127 AA) and adjuvants for 3 rounds. After immunization, antibody-producing cells were harvested from the mice and fused with tumor cells to become hybridomas. The hybridomas were screened for positive monoclonal antibodies via Western blotting. One clone (XL-32) was chosen for subsequent experiments. The anti-EHF monoclonal antibody was generated with assistance from AtaGenix Laboratories Co., Ltd. (Wuhan), China. This antibody is generated in-house and would be available to the public with any reasonable request.

Western blotting
For Western blotting, protein samples were subjected to SDS-PAGE on a 4–12% gradient gel and then transferred to a membrane. The membranes were blocked with 5% dry milk and incubated with primary antibodies overnight. After washes with PBS containing Tween 20, the membranes were incubated with secondary antibodies for 1.5h. The bands were visualized by chemical composition using ChemiDoc Touch (Bio-Rad).

The following antibodies were used for Western blotting at 1:5000 dilution:
Tubulin (Abcam, host: mouse, #ab78078)
β-actin (Abcam, host: mouse, #ab6276)
Anti-mouse IgG H&L (HRP) (Abcam, host: goat, #ab6789).

CUT&TAG
For cDCFL-Notch samples, cDCFL-Notch were cultured for 6 days and stimulated with 100nM CpG-B (InvivoGen, #tlrl-1668). Live B220- MHCII+ CD11c+ cells were subjected to FACS sorting after 18 hrs of CpG-B stimulation for CUT&TAG library preparation.

For spleen samples, mice were immunized intraperitoneally (i.p.) with or without 60µg CpG-B in 100µL of PBS. Splenic cells were collected 18h after CpG-B injection for cDC1 (live MHCII+ CD11c+ XCR1+ CD8α+ CD11b-) and cDC2 (live MHCII+ CD11c+ CD11b+ XCR1- CD8α-) FACS-sorting for CUT&TAG library preparation.

cDCFL-Notch were cultured for 6 days and stimulated with 100nM CpG-B (InvivoGen, #tlrl-1668). Live B220- MHCII+ CD11c+ cells were subjected to FACS sorting after 18 hrs of CpG-B stimulation. For CUT&TAG library preparation, the CUT&TAG assay was performed following the manufacturer’s instructions (Vazyme, #TD903). In brief, the cells were harvested, counted and centrifuged for 5 min at 600×g at room temperature. A total of 500,000 cells per sample were washed twice in 1.5mL of wash buffer by gentle pipetting. Ten microliters of activated conA beads were added to each sample (100µL aliquots of cells) and incubated at RT for 10 min. The unbound supernatant was removed, and the bead-bound cells were resuspended in 50µL of antibody buffer containing a 1:50 dilution of the appropriate primary antibody. The primary antibody incubation was performed overnight at 4°C. The primary antibody was removed by placing the tube on the magnet stand to clear and removing all of the liquid. An appropriate secondary antibody (goat anti-mouse IgG antibody for a mouse primary antibody) was diluted 1:100 in 50µL of Dig-Wash buffer, and the cells were incubated at RT for 45 min. The cells were washed using the magnet stand 3 times for 5 min in 0.2mL Dig-Wash buffer to remove unbound antibodies. A 1:200 dilution of the pA-Tn5 adapter complex (~0.04µM) was prepared in Dig-300 Buffer. After the liquid was removed on the magnet stand, 100µL was added to the cells with gentle vortexing, followed by incubation with pA-Tn5 at RT for 1 hr. The cells were subsequently washed 3 times for 5 min in 0.2mL of Dig-300 Buffer to remove unbound pA-Tn5 protein. Next, the cells were resuspended in 50µL of fragmentation buffer (40µL of Dig-300 Buffer and 10µL of 5× TTBL) and incubated at 37°C for 1 hr. For the extraction of the DNA, 5µL of proteinase K, 100µL of buffer L/B and 20µL of DNA extract beads was added to 100µL of sample, which was incubated for 10 min at 55°C. The tubes were placed on a magnet stand to clear, then the liquid was carefully removed. Without disturbing the beads, the beads were washed twice in 1mL of 80% ethanol. After the samples were allowed to dry for ~5 min, 22µL of ddH2O was added, and the tubes were vortexed, quickly spun and allowed to sit for 5 min. The tubes were placed on a magnet stand, and the liquid was withdrawn to a fresh tube.

For the amplification of the libraries, 15µL of DNA was mixed with 5µL of a universal i5 and a uniquely barcoded i7 primer (Vazyme, #TD202), using a different barcode for each sample. A volume of 25µL of 2× CAM was added, and the solution was mixed. The sample was placed in a thermocycler with a heated lid using the following cycling conditions: 72°C for 3 min (gap filling); 95°C for 3 min; 15 cycles of 98°C for 10s and 60°C for 5s; final extension at 72°C for 1 min and a hold at 4°C. Post-PCR clean-up was performed by adding a 1.1× volume of Ampure XP beads (Vazyme, #N411), and the libraries were incubated with the beads for 5 min at RT, washed twice gently in 80% ethanol, and eluted in 20µL of ddH2O. The size distribution of the libraries was determined by LabChip Touch, and the libraries were mixed to achieve equal representation as desired, aiming for a final concentration as recommended by the manufacturer. Paired-end Illumina sequencing (PE150) using Illumins HiSeq 3000 (Annoroad Gene Technology, Guangzhou, China) was performed on the barcoded libraries following the manufacturer’s instructions. The raw FASTQ data were first processed through fastp version 0.23.2 with the options: -q 20 -u 50 -n 15 -l 50, and all the downstream analyses were based on high-quality clean data. An index of the reference genome was built, and paired-end clean reads were aligned to the reference genome (mm10) using Bowtie2 version 2.2.5 with default parameters.

For peak calling, the parameters used were macs2 callpeak --shift 0 --extsize 200 --nomodel -B --SPMR -g mm -p 0.05. Peak assignment was performed on the intersected bed file using deepTools: computeMatrix reference-point -p 15 --referencePoint TSS -b 3000 -a 3000 -R ucsc_refseq.bed -S Ehf fl/fl.bw Ehf ΔCD11C.bw IgG.bw --skipZeros -out ./wt_cko.TSS.gz --outFileSortedRegions ./wt_cko.genes.bed. Then, a heatmap was constructed with plotHeatmap. For identification of motifs enriched in peak regions over the background, HOMER’s motif analysis (findMotifsGenome.pl), which included known default motifs and de novo motifs, was used with default parameters. The UCSC genome browser was used to visualize the CUT&TAG tracks.

Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the clusterProfiler R package (v4.2.2). GO terms with corrected P-value <0.05 were considered significantly enriched by differential expressed genes. The ClusterProfiler R package was used to test the statistical enrichment of differential expression genes in GO pathways.

Luciferase reporter assays
For luciferase analyses, 293T cells were first transfected with pMXs-EHF or control vector (pMXs-vector), then they were transfected with the following vectors: pGL3-Ccr7-original motif, pGL3-Ccr7-mutated motif, pGL3-Cd200-original motif, pGL3-Cd200-mutated motif, pGL3-Cd274-original motif, pGL3-Cd274-mutated motif, pGL3-Irf4-original motif, pGL3-Irf4-mutated motif, pGL3-Rel-original motif, pGL3-Rel-mutated motif and pGL3-empty vector (pGL3-Promoter). Forty-eight hours later, cells were subjected to dual luciferase analyses with the Dual Luciferase Reporter Assay Kit (Vazyme, #DL101-01). The results were expressed as fold induction relative to the cells transfected with the control vector after normalization to renilla activity. ETS binding sites were mutated to GGGGGTTTTT. The ETS-motif-based primers used to validate CUT&TAG samples by real-time quantitative PCR are shown in Supplementary Table 3.

Single-cell RNA-sequencing
For spleen samples, mice were immunized intraperitoneally (i.p.) with or without 60µg CpG-B in 100µL of PBS. Splenic cells were collected 18 hrs after CpG-B injection for cDC (live MHCII+ CD11c+) FACS-sorting for scRNA-seq library preparation.

For pLN samples, mice were immunized subcutaneously (s.c.) in the rear footpads with or without 10µg of CpG-B in 40µL PBS. Popliteal lymph node cells were collected 48h after CpG-B injection for cDC (live MHCII+ CD11c+) FACS-sorting for scRNA-seq library preparation.

For PLF samples, mice were immunized intraperitoneally (i.p.) with or without 10µg of CpG-B in 100µL of PBS, and peritoneal cavity cells were obtained by lavage of the peritoneal cavity twice with 5mL of ice-cold PBS. The cells were collected 6h after CpG-B injection for cDC (live MHCII+ CD11c+) FACS-sorting for scRNA-seq library preparation.

For pooled samples, mice were immunized intraperitoneally (i.p.) with or without 60µg CpG-B in 100µL of PBS. Splenic cells were collected 12h after CpG-B injection. cDCs (live MHCII+ CD11c+), pDCs (live SiglecH+ BST-2+), macrophages (live F4/80+ CD11b+), monocytes (live CD11b+ Ly6Chi Ly6Gint), neutrophils (live CD11b+ Ly6Cint Ly6Ghi), T cells (live CD3ε+ TCR-β+), B cells (live CD19+ B220+) were FACS-sorted at roughly equal cell numbers and pooled for scRNA-seq library preparation.

For scRNA-seq library preparation, single cells were encapsulated in emulsion droplets using a Chromium Controller (10× Genomics). The scRNA-seq libraries were constructed using the Chromium Single Cell 3ʹ Library and Gel Bead & Multiplex Kit (10× Genomics, V3.1) following the manufacturer’s instructions. In brief, the sample volume was decreased, and the cells were examined with a light microscope and counted with a hemocytometer. Cells were loaded in each channel with a target output of ~5000 cells. The cells were partitioned into Gel Beads in Emulsion in the Chromium Controller instrument, where cell lysis and barcoded reverse transcription of RNA were performed. cDNA was generated, amplified, and quality-assessed using an Agilent 4200 system. Individual libraries were pooled for sequencing on the Illumina NovaSeq 6000 platform with 150bp paired-end reads with assistance from AccuraMed Technology Limited (Guangzhou, China).

For scRNA-seq cell-clustering and annotation, Cellranger (10 × Genomics, v6.1.2) was used for demultiplexing and generation of barcodes and count matrices. Both mouse and human PBMC matrix data were analyzed using Seurat (v.4.1.1) in R 4.1.2. The single-cell sequencing data were imported into Rstudio to create seurat object according to the minimum number of cells greater than 3 and the minimum number of genes greater than 200.

For spleen samples, the cells were selected for a number of genes over 1000, less than 4000, a number of total counts less than 25,000 (Spl CpG-B) and 20000 (Spl PBS) and the percentage of mitochondrial genes was less than 10%. The number of starting cells in the Spl PBS sample was 8018, and the number of cells after QC was 7135. The number of starting cells in the Spl CpG-B sample was 7031, and the number of Spl CpG-B cells after QC was 6114.

For pLN samples, the cells were selected for a number of genes over 1000, less than 6000, a number of total counts less than 60,000 and a percentage of mitochondrial genes less than 10%. The number of starting cells in the pLN PBS sample was 3167, and the number of cells after QC was 2093. The number of starting cells in the pLN CpG-B sample was 7767, and the number of pLN CpG-B cells was 6963.

For PLF samples, the cells were selected for a number of genes over 1000, less than 6000, a number of total counts less than 60,000 and a percentage of mitochondrial genes less than 10% (PLF PBS) and 5% (PLF CpG-B). The number of starting cells in the PLF PBS sample was 3805, and the number of cells after QC was 3590. The number of starting cells in the PLF CpG-B sample was 5003, and the number of PLF CpG-B cells after QC was 3565.

For pooled samples, the cells were selected for a number of genes over 1000, less than 3000, a number of total counts less than 30,000 (Spl CpG-B), and a percentage of mitochondrial genes was less than 5%. The number of starting cells in the pooled PBS sample was 6408, and the number of cells after QC was 6056. The number of starting cells in the pooled CpG-B sample was 9411, and the number of Spl CpG-B cells after QC was 8478.

For human samples, the cells were selected for a number of genes over 1000, less than 4000, a number of total counts less than 30,000 and a percentage of mitochondrial genes less than 10%. The number of starting cells was 4743, and the number of cells after QC was 3689 (PBS). The number of starting cells was 4058, and the number of cells after QC was 3299 (CPG-B).

Furthermore, we removed potential doublets using the DoubletFinder package (version 2.0.2) of R. Data were normalized using the ‘LogNormalize’ method (scale factor 10,000) of Seurat’s Normalize-Data function. The top 2000 highly variable genes (HVGs) from the normalized expression matrix were identified, centered, and scaled before we performed principal component analysis (PCA). The batch effects were removed by the Harmony package (version 1.0) of R. PCA was performed in Seurat, and the top 10 dimensions and resolution set at 0.5 were used for UMAP. The clustering analysis was performed based on the integrated joint embedding produced by Harmony with the Louvain algorithm. For annotation of the cell clusters, differentiating markers for each cluster were identified using the Wilcoxon rank sum test implemented in Seurat’s FindAllMarkers function (min.pct=0.25, logfc.threshold=0.25). The cell clusters were annotated based on the DEGs and well-known cellular markers from the literature.

For RNA-velocity, analysis of the cellular trajectory by RNA velocity was performed using the package scVelo (v0.2.4) via dynamical modeling. For estimation of the RNA velocities in DCs subtypes, velocyto was used to distinguish unspliced and spliced messenger RNAs in each sample. The Python package scVelo was then used to recover the directed dynamic information by leveraging RNA-splicing information. Specifically, the data were first normalized using the filter_and_normalize function with the following parameter settings: min_shared_counts 30, n_top_genes 2000. The first and second order moments were computed for velocity estimation using the moments function with the following parameter settings: n_pcs 30 and n_neighbors 30. The velocity vectors were obtained using the velocity function of dynamical modeling. Finally, the velocities were visualized in the UMAP embedding via the UMAP function with default parameters.

For regulatory network inference, a single-cell regulatory network for each subcluster was constructed with the SCENIC Python workflow. Specifically, GRNBoost2 (https://github.com/tmoerman/arboreto) in pySCENIC (v0.11.2) was applied to infer gene regulatory networks from the raw count data. Then, potential direct-binding targets (regulons) were selected based on DNA motif analysis. Finally, gene regulatory network activity for individual cells was identified. For the identification of the different regulators for each subcluster in the groups, the regulon activity was averaged. A regulon-group heatmap was generated with the R pheatmap package. Moreover, the specific regulators of each subcluster were the union of the predicted regulator lists and marker gene lists and were used to generate a specific regulator heatmap with the ComplexHeatmap R package (v2.10.0).

Human samples
Informed consent was obtained from all subjects. There were no exclusion criteria for healthy volunteers. Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood by using Ficoll (Solarbio, #P8900) gradient centrifugation. PBMC DCs were sorted as Lin- (CD3/CD19/CD14) CD123lo CD11C+ HLA-DR+ from freshly isolated PBMCs (purity >93%).

For single-cell analysis, PBMCs from four healthy male volunteers were plated individually in complete RPMI-1640 (RPMI-1640 supplemented with 10% FCS, 1% L-glutamate, 1% penicillin/streptomycin, and 55μM 2-mercaptoethanol), and treated with PBS or 100nM CpG-B (InvivoGen, #tlrl-2006) for 16h. Then, DCs (Lin- CD123lo CD11C+ HLA-DR+) were subjected to FACS sorting and pooled for scRNA-seq library preparation.

For qPCR analysis, PBMC DCs were stimulated for 14h with 1µg/mL Pam3CSK4 (InvivoGen, #tlrl-pms), 100ng/mL LPS (Sigma-Aldrich, #L2654-1MG), 500ng/mL Poly(I:C) (InvivoGen, #tlrl-picw), 1µg/mL R848 (InvivoGen, #tlrl-r848), or 100nM CpG-B (InvivoGen, #tlrl-2006).

Statistical analysis
Statistical analysis was performed with Prism 10 (GraphPad) software unless otherwise indicated. Survival rate statistics were analyzed according to the Log-rank Mantel–Cox test. Weight change statistics were analyzed according to two-way ANOVA with Sidak’s multiple comparisons test. Comparisons between two groups were performed by the two-tailed Student’s t test. Comparisons between more than two groups were performed by one-way ANOVA with Dunnett’s multiple comparisons test. P-value of 0.05 or less was considered significant. ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001 (two-tailed test).

Study approval
The human study was approved by the Ethics Committee of the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China (Approval No. 2022-436). Written informed consent was received prior to participation. The animal studies was approved by approved by the Animal Ethics Committee of Sun Yat-sen University, Guangzhou, Guangdong, China.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary: In this study, the authors investigated the role of EHF, an ETS-family transcription factor, in dendritic cell (DC) function using mouse disease models, CUT&Tag analysis, and single-cell RNA sequencing. Using Ehf^ΔCD11c mice, they found that cDCs developed normally under physiological conditions. However, in a DSS-induced colitis model, these mice exhibited exacerbated disease with significantly increased IL-17A–producing CD4⁺ T cells. In a CpG-induced Th1 model, the number of OT-II cells and their IFN-γ production were also elevated, whereas no differences were observed in IL-4 production in a papain-induced Th2 model. These findings were recapitulated in an in vitro co-culture system of cDC1/cDC2 with OT-II T cells.\nStimulation with CpG and R848 upregulated Ehf expression in cDC1 and cDC2, while poly(I:C) suppressed it. scRNA-seq analysis revealed that Ehf expression is restricted to CCR7⁺ cDCs. In vitro, GM-CSF and IFN-γ downregulated Ehf in cDCs. In vivo, cDC1 and cDC2 from CpG-treated Ehf^ΔCD11c mice showed reduced expression of Ccr7, Cd200, and Pd-l1, while overexpression of Ehf enhanced their expression. CUT&Tag experiments suggested that EHF directly binds to and regulates these genes. Human scRNA-seq data further confirmed that EHF is preferentially expressed in CCR7⁺ cDCs, which also express maturation and inhibitory markers.\nCollectively, the authors conclude that EHF promotes the maturation of cDC1 and cDC2, while concurrently restraining their capacity to activate Th1 and Th17 cells.\nThe experiments are generally well designed and the results are mostly convincing. However, additional functional validation would be necessary to firmly support the conclusion that EHF directly regulates its target genes.\nMajor comments\n1. The authors provide a broad overview of dendritic cell (DC) classification and the transcriptional regulators involved in DC development. While this background is informative, some of the content is somewhat redundant and could be streamlined. Additionally, the introduction offers only minimal context regarding EHF and its potential role in DC function. To strengthen the narrative and clarify the rationale of the study, I suggest reorganizing the introduction to focus more concisely on key aspects of DC biology relevant to the study’s aim, and expanding on prior knowledge of EHF’s role in immune cells, particularly in DCs if available.\n2. The authors present CUT&Tag and qPCR validation to suggest that EHF directly regulates genes such as Ccr7 and Cd274. While the data are highly suggestive, further experiments — such as functional assays (e.g., luciferase reporter assays or motif mutagenesis) — would be required to definitively establish direct transcriptional control.”. \nSpecific comments\n3. The authors describe the DSS-induced colitis model as an autoimmune disease model. However, DSS-induced colitis is typically considered a chemically induced epithelial injury model that triggers innate immune activation, rather than a classical T cell–mediated autoimmune model. If the authors regard this model as autoimmune, it would be helpful to clarify how T cell–mediated autoimmune responses contribute to disease development in this context, and to cite relevant literature supporting this classification.\n4. In the B16-OVA melanoma model, CTL responses were enhanced in Ehf^ΔCD11c mice (Figure 1), suggesting that EHF in DCs plays a critical role in regulating CTL function in vivo. However, in the in vitro co-culture experiments, DCs lacking EHF did not affect the development or activation of CTLs. This apparent discrepancy between in vivo and in vitro findings is somewhat confusing. Could the authors clarify how they interpret this inconsistency? For example, are additional in vivo factors or microenvironmental cues (e.g., cytokines or TME signals) necessary for EHF-mediated modulation of CTL responses?\n5. In Figure 2, the data suggest that Ehf^ΔCD11c DCs promote enhanced Th1 and Th17 responses, while Th2 responses appear unaffected. This indicates a selective inhibitory role for EHF in Th1/Th17 development. It would strengthen the manuscript if the authors could discuss potential mechanisms underlying this selectivity. For example, are there differences in how EHF influences the expression of co-stimulatory molecules or cytokines (e.g., IL-12, IL-6, IL-23) that preferentially affect Th1/Th17 polarization but not Th2?\n6. The authors screened various cytokines and found that GM-CSF and IFN-γ downregulate Ehf expression in DCs in vitro. However, the rationale for focusing on cytokines as potential negative regulators of Ehf is not clearly explained. Could the authors elaborate on why cytokines were prioritized in this analysis over other potential regulatory factors (e.g., PRRs, transcriptional repressors)? Additionally, are GM-CSF and IFN-γ expressed and functionally involved in the downregulation of Ehf in the CpG injection model in vivo? Addressing these points would help clarify the relevance of the in vitro findings to the in vivo system.\n7. In Figure 4A, the number of splenic cDCs appears to be reduced in Ehf^ΔCD11c mice. However, this finding is not mentioned or discussed in the text. Please consider including this result in the main text and providing a possible interpretation. For example, is this reduction due to impaired survival, reduced development, or altered migration of cDCs in the absence of EHF?\n8. In Figure 4B, the authors show reduced expression of CCR7, CD200, PD-L1, IRF4, and c-Rel in cDC1 and cDC2 from Ehf^ΔCD11c mice after CpG stimulation. To better understand the regulatory role of EHF, it would be helpful to also present the expression levels of these molecules under basal (unstimulated) conditions. This would clarify whether EHF controls their expression constitutively or primarily in response to CpG stimulation.\n9. In Figure 4D, the expression levels of CCR7 and CD200 are assessed in BMDCs, whereas the surrounding experiments primarily utilize the cDC-FL-Notch in vitro system. Could the authors clarify why BMDCs were used in this particular experiment instead of cDC-FL-Notch-derived cells? This inconsistency may raise concerns about comparability, especially given the differences in differentiation and maturation status between BMDCs and FL-derived cDCs.\n10. The authors show that EHF expression in human cDCs is enriched in CCR7⁺ populations, which also express maturation and inhibitory markers. However, the functional relevance of these human CCR7⁺EHF⁺ cDCs in shaping Th1, Th2, or Th17 responses remains unclear. Incorporating functional assays — such as T cell co-culture experiments — would help validate whether EHF plays a comparable immunomodulatory role in human cDCs and strengthen the translational relevance of the findings.\n11. The term “regulate” in the title is somewhat ambiguous, as it may refer to either positive or negative regulation. I recommend using a more specific verb that clearly reflects the functional role of EHF in cDCs, such as “promotes,” “restrains,” or “modulates,” depending on the authors’ intended emphasis. Additionally, the term “dendritic cells” is broad, while this study focuses specifically on conventional dendritic cells (cDC1 and cDC2). For clarity and precision, I suggest explicitly referencing cDCs in the title.\n12. The graphical abstract effectively captures the core message of the study but is somewhat simplified. Enhancing the figure by including key mechanistic elements — such as the role of EHF in regulating CCR7, PD-L1, or its impact on Th1/Th17 responses — would improve its clarity and impact. Please consider refining the graphical abstract to better reflect the depth of the findings.", "reviewer_2": "The manuscript by Liu et al. aims at characterizing the role of the transcription factor EHT in dendritic cells. EHT is a member of the ETS family, which has been implicated in DC development. They report that EHT is expressed in a subset of DCs, which are characterized by the expression of CCR7, CD200 and FSCN1, or mRegDCs. Deletion of EHT in DCs led to worsening of colitis, and better control of B16 tumour growth and Listeria infection. They mainly attributed these phenotypes to changes in CD4 T cells, although changes are often mild.\nUnderstanding of CCR7+ CD200+ DCs are regulated is important, given their emerging central role in controlling T-cell responses. The data generated is interesting in places, but deleting EHF in DCs has only a subtle impact on DC phenotype and subsequent T-cell responses, reducing the enthusiasm for this manuscript. In addition, while experiments are well designed, the conclusions are not always fully supported by the data.\nSome specific comments that might help improve the manuscript:\n1- In Figure 1, the authors described an increase in CD8 T cells (CTLs or OTI) following B16-OVA in Ehf depletion in DCs. But in figure 2, they do not find any difference in CD8 T cell expansion/activation when activated by WT or EhfKO DCs. How do the authors explain this?\n2- The acquisition of the maturation programme from DC1/DC2 to CCR7+ CD200+ DCs is dependent on the uptake of tumour antigens (for example, Maier et al. Nature 2020), as the authors also suggest by incubating DCs with dead cells in Figure 3. How does EHT expression/function fit into this? Is there a synergy between antigen uptake and TLR signalling for EHF expression? Is it the actual uptake that’s important? What happens if antigen uptake is inhibited?\n3- In multiple parts of the manuscript, but I will take Fig.S1 as an example, I disagree with the way protein expression is quantified from flow cytometry data. MFI should be reported if there is just one peak; mean can be used if the distribution is normal, but this is often not the case in flow data, so median (or g-mean) should rather be used. The authors seem to have reported the mean throughout the manuscript. In fig.S1K or S1N, there are clear positive and negative populations. Therefore, quantifying the percentage of positive cells is more relevant, or the MFI of the positive cells only. In addition, the difference in MFI presented, albeit statistically significant, is often very small. What is the functional/biological relevance of expressing slightly more CD44 per cell, for example? There is not direct link between slight differences in CD44 expression in a given cell and its function. Finally, in Fig.S1N, the CD44 negative fraction has a higher MFI in the KO than WT condition, making comparing MFI problematic. Why is that the case? Was the same number of cells stained?\n4- In Fig.S2G, the percentage of IL17+ and IL22+ CD4 T cells is very small. Where the cells restimulated in vitro? What is their percentage in the colon?\n5- In Figure 3, the authors demonstrate that EHF expression is induced by TLR9 activation and dependent in Myd88. Is there a difference in mRegDC generation in Myd88KO mice at steady state and in disease (or at least after CpG treatment)?\n6- In Figure 3C, in the CpG treated condition, it is unclear which cluster corresponds to which cell type, especially in the myeloid area. For example, where are the DC1?\n7- In Figure 3G, what is the effect of IL-17 on Ehf expression? As the authors show that EHF inhibits the expression of Th1 and Th17 responses and IFNg decreases EHF expression, I wonder whether that’s also the case for IL-17. It seems to be that EHF is part of a negative feedback loop, rather than controlling mRegDC maturation per se.\n8- Similarly as point 3, in Figure 4B, the differences in CCR7 expression, especially in DSS treated conditions, is subtle and it is unclear if that’s biologically relevant. In Fig.S4A, functional migration assay also shows only mild differences and the migratory defect is also present without cytokines, it is therefore unclear how much of the difference in migration is due to differences in CCR7 expression. It will be helpful to use different does of CCL19/21, and characterizing the migration to another chemokine for which the receptor is not affected by EHF deletion.\n9- In Figure 5, what are the genes driving the GO enrichment? A table would be useful.\n10- In Fig.6, only peritoneal cavity is tested as a non-lymphoid organ, I would be careful in generalising. Quantifying the expression of EHF within DC subsets in the colon or tumor in disease settings as in Fig.1 would help with this. Similarly, quantifying the frequency/activation potential of mRegDCs vs other DCs in the same disease models in mice with DC-specific depletion of EHF would strengthen the role of EHF in mRegDCs.\n11- EHF is expressed in different CCR7+ clusters (cluster 2 vs cluster 3) in the spleen vs LN (Fig6D). Do the authors know why this is the case? Does it correlate with differences in CCR7/CD200/CD274 expression? The authors present the overall integrated data in Sup figS6, but given the difference in EHF expression in LN vs spleen, it is important to look at those other markers per site too. This dataset could also be helpful in confirming other points. For example, do the authors see a correlation between TLR or IFN pathways and EHF expression within specific DC subsets?\n11- In Fig.6, stats on vlnplots are done based on n being a cell. Since the authors have 6 samples, it should rather be done on a sample basis, to be sure that the phenotype is not driven by one sample.\n12- It should be noted that, while the authors focus on the potential inhibitory role of mReg DCs, whether they have a positive or negative effect on disease outcomes remains unclear, and enrichment of a CCR7+ DC signature is associated with improved survival in multiple cancers (Lee et al, Nat Comms, 2024). It would be interesting for the authors to discuss how their data integrate with this.\n13- In some places, the number of independent experiments performed is not stated.", "reviewer_3": "The study by Liu et al. identifies ETS homologous factor (EHF) as a key transcriptional regulator controlling maturation and immunosuppressive functions of conventional dendritic cells (cDCs) following TLR7 and TLR9 activation. Using conditional knockout mice, the authors demonstrate that EHF deficiency in DCs enhances resistance against DSS colitis, bacterial infections, and melanoma growth, primarily by promoting stronger Th1- and Th17-biased CD4+ T-cell responses. Mechanistically, the authors claim that EHF directly regulates the transcription of genes involved in dendritic cell maturation and immunosuppression, including Ccr7, Cd200, Cd274 (PD-L1), Irf4, and Rel, thus highlighting its pivotal role in suppressing inflammatory responses.\nThis study represents the first comprehensive characterization of EHF’s in vivo role in DC biology, expanding our understanding beyond its previously described functions in epithelial and tumor cell biology. The authors performed an extensive amount of experimental work, including the generation and analysis of new conditional knockout mouse models across several in vivo disease settings. Additionally, single-cell RNA sequencing analyses further reinforced their findings, providing high-resolution insights into EHF expression patterns across DC subsets in mice and humans at steady state and stimulatory conditions. A notable technical advance is the development of a novel monoclonal antibody recognizing EHF, which enabled CUT&TAG analyses to precisely identify putative EHF DNA binding sites.\nWhile this manuscript provides novel insights into the role of EHF in DC biology and is supported by a big and diverse set of experiments, certain aspects of the study feel inconsistent, with some datasets conflicting or not fully aligning with the authors’ claims. Some conclusions are stated too strongly relative to the presented data and would benefit from either additional experimental validation or a more nuanced interpretation.\nMajor points:\nFigure 1:\nMy main concern is the limited analysis of DC2 subsets (cDC2a, cDC2b and DC3): although the data indicate that resident cDC2s express EHF at steady state, this EHF-expressing population was not examined or characterized in detail. Furthermore, DC2 subsets were not analyzed in sufficient depth in the in vivo models, either by flow cytometry or single-cell RNA sequencing. A more detailed investigation of DC2 heterogeneity in WT versus KO mice, both at steady state and in disease models, would greatly strengthen the manuscript’s conclusions.\nFigure 1L: The authors challenge Ehf CD11c-Cre mice with B16-OVA and show that Ehf-deficient mice have increased survival compared to WT controls. They also demonstrate that, upon OT-I transfer, OT-I cells exhibit decreased PD-1 expression in Ehf-deficient mice, but they do not provide an explanation for this observation. Normally, PD-1 can act as both an activation and exhaustion marker, yet it remains unclear why OT-I cells show reduced PD-1 expression in the absence of EHF and what mechanisms drive this phenotype. Furthermore, the authors should include data on the total numbers of engrafted OT-I cells and perform tetramer staining to assess antigen-specific OT-I populations in WT versus Ehf-deficient mice.\nFigure S1J: How did the authors evaluate the expression of IFNα by pDCs using intracellular staining? The use of intracellular antibodies against IFNα is not described in the Materials and Methods section. This information is important, as intracellular staining for IFNα is technically challenging and has only been described by a few groups.\nFigure S1O and S1Q: The authors need to include non-treated/non-infected mice as control groups.\nFigure 2:\nFigure 2A-F: The study would benefit from an analysis that goes beyond cytokine production of OT-II engrafted cells by also assessing their true T helper cell identity through intracellular staining of GATA3, T-bet, Foxp3, and RORγt. Furthermore, non-infected/non-treated mice should also be included as controls in this figure in the revised manuscript.\nFigure S2F-G: The authors show that Ehf-deficient mice have reduced inflammation when challenged with C. rodentium. This finding is unexpected and contrasts with the data shown in Figure 1/S1, where DSS-treated Ehf-deficient mice exhibited stronger inflammation compared to WT mice. Is the phenotype different if the authors analyze C. rodentium-infected mice at an earlier time point post-infection (e.g., day 6)? Could day 11 post-C. rodentium infection already be too late to observe the increased pro-inflammatory responses that might be expected in Ehf-deficient mice?\nFigure S2E: I would recommend moving Figure S2E into Figure S1, as it directly relates to the DSS experiment described in Figure 1 and would be better placed there for clarity and consistency.\nFigure 3:\nFigure 3A: The authors performed qPCR on cDC1 and cDC2 stimulated in vitro with distinct TLR agonists to measure Ehf expression. They present the data as relative expression compared to the control sample. However, a more appropriate approach would be to normalize Ehf expression to a housekeeping gene. This would not only allow for comparison between the different stimulations but also provide an indication of the Ehf expression levels between cDC1 and cDC2.\nFigure 3B: The authors performed scRNA sequencing on splenocytes derived from B6 mice that were either treated with CpG-B or left untreated. In the CpG-B–treated panel, the cDC1 population does not appear to be annotated. Is this because cDC1 cells were absent in CpG-B–treated mice, or is this an annotation error?\nFigure 4:\nFigure 4A: The authors demonstrate that cDCs derived from Ehf-deficient mice are reduced upon stimulation with CpG-B. It is important to elucidate which specific cDC subset is affected by the loss of EHF in this context. In particular, the authors should assess whether there is a decrease in cDC1, cDC2a, cDC2b, and/or DC3 populations.\nFigure 4B-D: The authors demonstrate that cDC1 and cDC2 derived from Ehf-deficient mice show reduced expression of activation markers such as CCR7, CD200, and PD-L1, along with an increase in the transcription factor IRF4 and a decrease in c-Rel upon CpG-B stimulation. While these results are intriguing, they appear somewhat ambiguous, and it is unclear how the authors arrived at these findings. Performing bulk RNA sequencing on cDC1 and cDC2 from WT and Ehf-deficient mice treated with either CpG-B or PBS would help clarify these results and better capture the transcriptional changes associated with the absence of EHF.\nFigure 5:\nFigure 5A-F: The authors performed a Cut&TAG experiments targeting EHF on BM DC cultures and used a broad population of cDCs, that contains both, cDC1 and cDC2 subsets to target EHF. Eventhough it gives already a good preliminary idea about the DNA binding regions of EHF, it still overlaps the peaks present in cDC1 and cDC2. A clearer and better approach would be to target ex vivo isolated cDC1 and cDC2 from the spleen that were stimulated with CpG-B or PBS. This would reveal the EHF binding sites in both, steady state as well as activation state and give a more and proufnd idea about how EHF regulates the transcriptional landscape within each cDC subset.\nFigure 5E: The authors should tone down their statement (“…we found that EHF may directly promote the transcription of inhibitory molecules such as CCR7, CD200, and PD-L1.”, Line 491–492) regarding EHF specifically regulating the expression of these genes. Most of the depicted EHF DNA-binding sites are located within introns or in upstream/downstream enhancer regions, with only Cd274 showing a peak ~3 kb downstream of the promoter region. Since none of these enhancer regions were functionally evaluated, it cannot be conclusively claimed that EHF directly regulates these target genes."}, "author_response": "We would like to thank all reviewers for their constructive criticism of our manuscript. They have raised important concerns and we have addressed them in the revised manuscript with new experiments. The revisions are described below and also highlighted in the manuscript.\nReviewer #1 (Remarks to the Author):\nSummary: In this study, the authors investigated the role of EHF, an ETS-family transcription factor, in dendritic cell (DC) function using mouse disease models, CUT&Tag analysis, and single-cell RNA sequencing. Using Ehf^ΔCD11c mice, they found that cDCs developed normally under physiological conditions. However, in a DSS-induced colitis model, these mice exhibited exacerbated disease with significantly increased IL-17A–producing CD4⁺ T cells. In a CpG-induced Th1 model, the number of OT-II cells and their IFN-γ production were also elevated, whereas no differences were observed in IL-4 production in a papain-induced Th2 model. These findings were recapitulated in an in vitro co-culture system of cDC1/cDC2 with OT-II T cells. Stimulation with CpG and R848 upregulated Ehf expression in cDC1 and cDC2, while poly(I:C) suppressed it. scRNA-seq analysis revealed that Ehf expression is restricted to CCR7⁺ cDCs. In vitro, GM-CSF and IFN-γ downregulated Ehf in cDCs. In vivo, cDC1 and cDC2 from CpG-treated Ehf^ΔCD11c mice showed reduced expression of Ccr7, Cd200, and Pd-l1, while overexpression of Ehf enhanced their expression. CUT&Tag experiments suggested that EHF directly binds to and regulates these genes. Human scRNA-seq data further confirmed that EHF is preferentially expressed in CCR7⁺ cDCs, which also express maturation and inhibitory markers.\nCollectively, the authors conclude that EHF promotes the maturation of cDC1 and cDC2, while concurrently restraining their capacity to activate Th1 and Th17 cells.\nThe experiments are generally well designed and the results are mostly convincing. However, additional functional validation would be necessary to firmly support the conclusion that EHF directly regulates its target genes.\nMajor comments\n1. The authors provide a broad overview of dendritic cell (DC) classification and the transcriptional regulators involved in DC development. While this background is informative, some of the content is somewhat redundant and could be streamlined. Additionally, the introduction offers only minimal context regarding EHF and its potential role in DC function. To strengthen the narrative and clarify the rationale of the study, I suggest reorganizing the introduction to focus more concisely on key aspects of DC biology relevant to the study’s aim, and expanding on prior knowledge of EHF’s role in immune cells, particularly in DCs if available.\nThank you very much for the suggestion. We streamlined the first three paragraphs into just one. We focused more on mregDCs, ETS family’s role in DCs and expanded more on EHF. Unfortunately, there isn’t much prior knowledge of EHF’s role in DCs except for the human MoDC studies from the same group.\n2. The authors present CUT&Tag and qPCR validation to suggest that EHF directly regulates genes such as Ccr7 and Cd274. While the data are highly suggestive, further experiments — such as functional assays (e.g., luciferase reporter assays or motif mutagenesis) — would be required to definitively establish direct transcriptional control.”. \nThank you for this suggestion. We performed the motif mutagenesis in luciferase reporter assays on all putative ETS-binding sites. The results were confirmatory. These data were added in Fig. 5g (shown below). We hope this functional assay support our claim of direct transcriptional control.\nSpecific comments\n3. The authors describe the DSS-induced colitis model as an autoimmune disease model. However, DSS-induced colitis is typically considered a chemically induced epithelial injury model that triggers innate immune activation, rather than a classical T cell–mediated autoimmune model. If the authors regard this model as autoimmune, it would be helpful to clarify how T cell–mediated autoimmune responses contribute to disease development in this context, and to cite relevant literature supporting this classification.\nWe agree that the DSS-induced colitis model is initiated by innate immunity, rather than T cells. While DSS induces acute colitis in a T-cell independent (RAG knockout) manner, many studies in DSS-induced colitis had shown a steady upregulation of Th1/Th2/Th17 cytokines, aggravation of the inflammatory response by T-cells in wild-type mice with intact innate and adaptive immunity, and Treg’s involvement. T-cell related therapies such as anti-TL1A and α4β7, IL-23R antibodies had shown good efficacy in DSS-colitis models and associated effects on T cells. Thus, we still regard this model as autoimmune but not necessarily entirely T-cell-driven. We have refined statements and citations (Ref: 43-47) reflective of this distinction in the text (highlighted). We regret that we cannot cite more literature due to citation limit.\n4. In the B16-OVA melanoma model, CTL responses were enhanced in Ehf^ΔCD11c mice (Figure 1), suggesting that EHF in DCs plays a critical role in regulating CTL function in vivo. However, in the in vitro co-culture experiments, DCs lacking EHF did not affect the development or activation of CTLs. This apparent discrepancy between in vivo and in vitro findings is somewhat confusing. Could the authors clarify how they interpret this inconsistency? For example, are additional in vivo factors or microenvironmental cues (e.g., cytokines or TME signals) necessary for EHF-mediated modulation of CTL responses?\nThank you for pointing it out. We think there might be two reasons. First, we found that EHF in DCs regulates Th1 response (Fig. 2c, shown below), which directly controls CTL response. However, this CTL-Th1 interplay was difficult to capture in an CTL assay in vitro with just CD8+ T cells.\nSecond, we found that EHF is downregulated by IFNү (new Fig. 3h and 3i, shown below). It is well known that the IFNү production by CTLs dwarfs that of Th1s. The microenvironmental IFNү concentration may accumulate more in a cell culture well than what happens in vivo. Thus, EHF in DCs may downregulate more quickly in a well with CTLs compared to a well with Th1s. If there is little or no EHF upregulation, there might be not any phenotypical difference between EHF-wildtype and -knockout. This IFNү-driven negative regulation could also explain the very limited CTL phenotype in infection models, in which IFNү is massively produced compared to “not-so-hot” tumor model.\nSince the in vitro and in vivo results are inconsistent, we err on the side of caution in concluding that EHF is dispensable for CTL response. We have included the explanation for this discrepancy in the discussion.\n5. In Figure 2, the data suggest that Ehf^ΔCD11c DCs promote enhanced Th1 and Th17 responses, while Th2 responses appear unaffected. This indicates a selective inhibitory role for EHF in Th1/Th17 development. It would strengthen the manuscript if the authors could discuss potential mechanisms underlying this selectivity. For example, are there differences in how EHF influences the expression of co-stimulatory molecules or cytokines (e.g., IL-12, IL-6, IL-23) that preferentially affect Th1/Th17 polarization but not Th2?\nThank you for this key observation. We had examined cytokine production by FACS and RNA-seq. However, we found no specific pattern for Th1/Th2 related molecules (Supplementary Fig. 1j, shown below). EHF did not seem to regulate cytokine production.\nWe think that a possible mechanism could be the sustained up-regulation of EHF expression in type I and type III immune response. We found that TLR7/8/9 activation via apoptotic cells is a main trigger of EHF upregulation. TLR7/8/9 signaling are well-known to induce Th1/Th17 response, and the increased apoptosis in type I and type III immune response to intracellular pathogens (compared to Type II humoral response to extracellular pathogens) may reinforce EHF expression. Therefore, the differential EHF regulation of Th1/Th17 response could be due to a longer activation of TLR7/8/9 and subsequent sustained EHF expression in DCs. This is now included in the discussion.\n6. The authors screened various cytokines and found that GM-CSF and IFN-γ downregulate Ehf expression in DCs in vitro. However, the rationale for focusing on cytokines as potential negative regulators of Ehf is not clearly explained. Could the authors elaborate on why cytokines were prioritized in this analysis over other potential regulatory factors (e.g., PRRs, transcriptional repressors)?\nThank you for these questions. First, we have examined PRRs first (Fig. 3a, shown below).\nNext, we looked at MyD88 as a transcriptional regulator (Fig. 3f, shown below).\nThen at last, we turned to cytokines (new Fig. 3h, see below).\nAdditionally, are GM-CSF and IFN-γ expressed and functionally involved in the downregulation of Ehf in the CpG injection model in vivo? Addressing these points would help clarify the relevance of the in vitro findings to the in vivo system.\nIn the original Fig. 3i, the anti-GM-CSF antibodies blocking experiments were actually performed in vivo. In this revision, we added the anti-IFNү antibodies blocking in vivo experiments (new Figure 3i, shown below). Both in vivo and in vitro findings matched.\n7. In Figure 4A, the number of splenic cDCs appears to be reduced in Ehf^ΔCD11c mice. However, this finding is not mentioned or discussed in the text. Please consider including this result in the main text and providing a possible interpretation. For example, is this reduction due to impaired survival, reduced development, or altered migration of cDCs in the absence of EHF?\nThank you for pointing this out. Although we mention this phenotype in the original text, we did a poor job explaining it. Since DC development and cell numbers were normal in EHF-deficient mice (Supplementary Fig. 1d, shown below), we reasoned it could be altered migration.\nThus, we performed a DC trafficking experiment to confirm that (new Supplementary Fig. 4a, see below).\nThis was also consistent with a reduced CCR7 expression by EHF-deficient DCs (new Fig. 4d, see below).\nWe have now revised the description accordingly in the text (highlighted).\n8. In Figure 4B, the authors show reduced expression of CCR7, CD200, PD-L1, IRF4, and c-Rel in cDC1 and cDC2 from Ehf^ΔCD11c mice after CpG stimulation. To better understand the regulatory role of EHF, it would be helpful to also present the expression levels of these molecules under basal (unstimulated) conditions. This would clarify whether EHF controls their expression constitutively or primarily in response to CpG stimulation.\nWe have now added the expression levels of these molecules under basal condition in the new Fig. 4d and 4f (shown below). They showed that EHF’s role was not constitutive and primarily in response to TLR7/8/9 stimulation.\n9. In Figure 4D, the expression levels of CCR7 and CD200 are assessed in BMDCs, whereas the surrounding experiments primarily utilize the cDC-FL-Notch in vitro system. Could the authors clarify why BMDCs were used in this particular experiment instead of cDC-FL-Notch-derived cells? This inconsistency may raise concerns about comparability, especially given the differences in differentiation and maturation status between BMDCs and FL-derived cDCs.\nWe have performed the overexpression in both BMDCs and cDCFL-Notch. The results are similar (see below). We chose the data from BMDCs because cDCFL-Notch did not seem to express much CD200 and PD-L1 at the first place. To be consistent, we have now replaced the BMDC data in the new Fig. 4d with data from cDCFL-Notch.\n10. The authors show that EHF expression in human cDCs is enriched in CCR7⁺ populations, which also express maturation and inhibitory markers. However, the functional relevance of these human CCR7⁺EHF⁺ cDCs in shaping Th1, Th2, or Th17 responses remains unclear. Incorporating functional assays — such as T cell co-culture experiments — would help validate whether EHF plays a comparable immunomodulatory role in human cDCs and strengthen the translational relevance of the findings.\nYes, the human T cell experiment would strengthen the translational relevance of the findings. We collected more human samples and looked at the CCR7 expression in PMBCs (see below).\nConsistent with our previous observation, there was only ~6% of CCR7+ DCs (gated from all DCs). We are only allowed by our ethnic committee to extract a maximum of 100cc of blood from each volunteer. Thus, in theory that is about 40,000 cells per sample (before sorting). However, after MACS purification and FACS sorting, we only collected a few thousand CCR7+ DCs. These CCR7+ DC were not so viable and they failed to activate T cells.\nWe simply do not have enough DCs cells to perform the Th1/2/17 experiments, given the limitation on blood volume. Given human T cells are allogenic, we cannot combine individual samples. We have also tried different titration of CCR7 antibodies, and the gating looked similar.\nPerhaps PBMCs are not like murine spleens, they are always CCR7+ or cannot get activated as much. In light of this, we have taken care in the text to avoid any claims of translational relevance.\n11. The term “regulate” in the title is somewhat ambiguous, as it may refer to either positive or negative regulation. I recommend using a more specific verb that clearly reflects the functional role of EHF in cDCs, such as “promotes,” “restrains,” or “modulates,” depending on the authors’ intended emphasis. Additionally, the term “dendritic cells” is broad, while this study focuses specifically on conventional dendritic cells (cDC1 and cDC2). For clarity and precision, I suggest explicitly referencing cDCs in the title.\nThank you for the suggestion. We have changed the title to “The transcription factor EHF promotes the maturation and immunosuppression of conventional dendritic cells”.\n12. The graphical abstract effectively captures the core message of the study but is somewhat simplified. Enhancing the figure by including key mechanistic elements — such as the role of EHF in regulating CCR7, PD-L1, or its impact on Th1/Th17 responses — would improve its clarity and impact. Please consider refining the graphical abstract to better reflect the depth of the findings.\nWe have refined the graphical abstract (see below) with more details accordingly.\nReviewer #2 (Remarks to the Author):\nThe manuscript by Liu et al. aims at characterizing the role of the transcription factor EHT in dendritic cells. EHT is a member of the ETS family, which has been implicated in DC development. They report that EHT is expressed in a subset of DCs, which are characterized by the expression of CCR7, CD200 and FSCN1, or mRegDCs. Deletion of EHT in DCs led to worsening of colitis, and better control of B16 tumour growth and Listeria infection. They mainly attributed these phenotypes to changes in CD4 T cells, although changes are often mild.\nUnderstanding of CCR7+ CD200+ DCs are regulated is important, given their emerging central role in controlling T-cell responses. The data generated is interesting in places, but deleting EHF in DCs has only a subtle impact on DC phenotype and subsequent T-cell responses, reducing the enthusiasm for this manuscript. In addition, while experiments are well designed, the conclusions are not always fully supported by the data.\nSome specific comments that might help improve the manuscript:\n1- In Figure 1, the authors described an increase in CD8 T cells (CTLs or OTI) following B16-OVA in Ehf depletion in DCs. But in figure 2, they do not find any difference in CD8 T cell expansion/activation when activated by WT or EhfKO DCs. How do the authors explain this?\nThank you for pointing it out. We think there might be two reasons. First, we found that EHF in DCs regulates Th1 response (Fig. 2c, shown below), which directly controls CTL response. However, this CTL-Th1 interplay was difficult to capture in an CTL assay in vitro with just CD8+ T cells.\nSecond, we found that EHF is downregulated by IFNү (new Fig. 3h and 3i, shown below). It is well known that the IFNү production by CTLs dwarfs that of Th1s. The microenvironmental IFNү concentration may accumulate more in a cell culture well than what happens in vivo. Thus, EHF in DCs may downregulate more quickly in a well with CTLs compared to a well with Th1s. If there is little or no EHF upregulation, there might be not any phenotypical difference between EHF-wildtype and -knockout. This IFNү-driven negative regulation could also explain the very limited CTL phenotype in infection models, in which IFNү is massively produced compared to “not-so-hot” tumor model.\nSince the in vitro and in vivo results are inconsistent, we err on the side of caution in concluding that EHF is dispensable for CTL response. We have included the explanation for this discrepancy in the discussion.\n2- The acquisition of the maturation programme from DC1/DC2 to CCR7+ CD200+ DCs is dependent on the uptake of tumour antigens (for example, Maier et al. Nature 2020), as the authors also suggest by incubating DCs with dead cells in Figure 3. How does EHT expression/function fit into this? Is there a synergy between antigen uptake and TLR signalling for EHF expression? Is it the actual uptake that’s important? What happens if antigen uptake is inhibited?\nThank you for suggesting this. We performed an antigen uptake inhibition experiment (new Fig. 3e, see below). EHF expression was regulated by antigen uptake.\n3- In multiple parts of the manuscript, but I will take Fig.S1 as an example, I disagree with the way protein expression is quantified from flow cytometry data. MFI should be reported if there is just one peak; mean can be used if the distribution is normal, but this is often not the case in flow data, so median (or g-mean) should rather be used. The authors seem to have reported the mean throughout the manuscript.\nWe are sorry for the misunderstanding. We always used genomic mean for all analysis throughout the manuscript. It was stated in our original methods: “The MFI was calculated according to the genomic mean by FlowJo software.”\nIn fig.S1K or S1N, there are clear positive and negative populations. Therefore, quantifying the percentage of positive cells is more relevant, or the MFI of the positive cells only. In addition, the difference in MFI presented, albeit statistically significant, is often very small. What is the functional/biological relevance of expressing slightly more CD44 per cell, for example? There is not direct link between slight differences in CD44 expression in a given cell and its function. Finally, in Fig.S1N, the CD44 negative fraction has a higher MFI in the KO than WT condition, making comparing MFI problematic. Why is that the case? Was the same number of cells stained?\nThank you for pointing it out. We have reanalyzed the data based on positive and negative gating, and the findings were similar (new Supplementary Fig. 1m, n, p).\n4- In Fig.S2G, the percentage of IL17+ and IL22+ CD4 T cells is very small. Where the cells restimulated in vitro? What is their percentage in the colon?\nYes, they were restimulated in vitro. We have now added the percentages in the colon (new Supplementary Fig. 2h, see below). The percentages are increased 10-fold in the colon compared to the mLN. Thank you for directing us to look at the colon!\n5- In Figure 3, the authors demonstrate that EHF expression is induced by TLR9 activation and dependent in Myd88. Is there a difference in mRegDC generation in Myd88KO mice at steady state and in disease (or at least after CpG treatment)?\nThere was no difference in CCR7+ DCs in MyD88-deficient mice during steady state. However, after CpG-B treatment, there was a significant difference in CCR7, CD200 and PD-L1 expression (new Supplementary Fig. 3c-d).\n6- In Figure 3C, in the CpG treated condition, it is unclear which cluster corresponds to which cell type, especially in the myeloid area. For example, where are the DC1?\nThank you for noticing this important point. After CpG-B treatment, scRNA-seq cannot differentiate CCR7+ DC1 and DC2, as all mature cDCs become just one cluster of CCR7+ DCs. Cluster analysis suggested that transcriptional programming of CCR7+ DC1 and DC2 are very similar. In FACS, it is still possible to differentiate them using traditional surface markers. However, there is a marked decrease in XCR1 expression by cDC1s (Supplementary Information, Gating strategy, see below).\nThe sc-RNA-seq and FACS profiling suggests that the blurring of DC1/DC2 programing after CpG-B activation and EHF upregulation. This population of XCR1int DCs may be of similar origin to the previously reported regulatory CD103int DCs found in neonatal mice (Sci. Immul. 2023).\nThis is also consistent with our finding that EHF repressed DC-lineage transcriptional factor such as IRF4, thus blurring the boundary between DC1 and DC2 when EHF is expressed (Fig. 4f, see below). We have now added related discussion in the text.\n7- In Figure 3G, what is the effect of IL-17 on Ehf expression? As the authors show that EHF inhibits the expression of Th1 and Th17 responses and IFNg decreases EHF expression, I wonder whether that’s also the case for IL-17. It seems to be that EHF is part of a negative feedback loop, rather than controlling mRegDC maturation per se.\nWe have now added the IL-17 data. However, it had no effect (Fig. 3h, see below).\nWe found no specific pattern for Th1/Th17 related molecules on EHF regulation. EHF seems to be broadly immunosuppressive in terms of its downstream targets. Thus, we think that a possible mechanism for specific Th1/Th17 inhibition could be the sustained up-regulation of EHF expression in type I and type III immune response. We found that TLR7/8/9 activation via apoptotic cells is a main trigger of EHF upregulation. TLR7/8/9 signaling are well-known to induce Th1/Th17 response, and the increased apoptosis in type I and type III immune response may reinforce EHF expression. Therefore, the differential EHF regulation of Th1/Th17 response could be due to a longer activation of TLR7/8/9 and subsequent sustained EHF expression in DCs. This is now included in the discussion.\n8- Similarly as point 3, in Figure 4B, the differences in CCR7 expression, especially in DSS treated conditions, is subtle and it is unclear if that’s biologically relevant.\nWe agree that CCR7 defect is subtle if we just looked at MFI. However, if we look at the FACS plots with CCR7/CD200 gating, it might be more obvious (new Fig. 4b, see below).\nIn Fig.S4A, functional migration assay also shows only mild differences and the migratory defect is also present without cytokines, it is therefore unclear how much of the difference in migration is due to differences in CCR7 expression. It will be helpful to use different does of CCL19/21, and characterizing the migration to another chemokine for which the receptor is not affected by EHF deletion.\nWe redid the transwell assay with a concentration gradient of CCL19/21 (Supplementary Fig. 4a). The effect was dose-dependent. We also had CCL2 as a control, and it is not affected by EHF deletion.\n9- In Figure 5, what are the genes driving the GO enrichment? A table would be useful.\nWe have added the tables in Supplementary Fig. 5e (see below) for your information.\n10- In Fig.6, only peritoneal cavity is tested as a non-lymphoid organ, I would be careful in generalising.\nWe have removed all wording about non-lymphoid organ in the text and figure 6 title (highlighted).\nQuantifying the expression of EHF within DC subsets in the colon or tumor in disease settings as in Fig.1 would help with this. Similarly, quantifying the frequency/activation potential of mRegDCs vs other DCs in the same disease models in mice with DC-specific depletion of EHF would strengthen the role of EHF in mRegDCs.\nWe have now quantified the frequency and surface markers of CCR7+ DCs vs CCR7- cDCs in DSS-colitis model. We found that EHF only regulates the phenotype of CCR7+ mregDCs but not CCR7- cDCs (new Supplementary Fig. 4d).\n11- EHF is expressed in different CCR7+ clusters (cluster 2 vs cluster 3) in the spleen vs LN (Fig6D). Do the authors know why this is the case?\nYes, there are some differences between cluster 2 and cluster 3 across organs. That is why we defined both cluster 2 and 3 as CCR7hi DCs. Our hypothesis is that lymph nodes uniquely contain both resident and migratory DCs (higher CCR7), which differ in CCR7 expression and function.\nDoes it correlate with differences in CCR7/CD200/CD274 expression?\nYes, EHF expression is correlated to CCR7/CD200/CD274 (Fig. 6c, see below).\nThe authors present the overall integrated data in Sup figS6, but given the difference in EHF expression in LN vs spleen, it is important to look at those other markers per site too. This dataset could also be helpful in confirming other points.\nYes, the organs are different. We provided a more detailed analysis of all six samples (3 organs) in Supplementary Fig. 6c.\nFor example, do the authors see a correlation between TLR or IFN pathways and EHF expression within specific DC subsets?\nWe have added this data in the new Supplementary Fig. 6d (see below). The correlation did not seem to be too strong.\n11- In Fig.6, stats on vlnplots are done based on n being a cell. Since the authors have 6 samples, it should rather be done on a sample basis, to be sure that the phenotype is not driven by one sample.\nYes, the data for all six samples (3 organs) were presented in Fig. 6d (see below).\n12- It should be noted that, while the authors focus on the potential inhibitory role of mReg DCs, whether they have a positive or negative effect on disease outcomes remains unclear, and enrichment of a CCR7+ DC signature is associated with improved survival in multiple cancers (Lee et al, Nat Comms, 2024). It would be interesting for the authors to discuss how their data integrate with this.\nThank you for sharing this information. First, this discrepancy might be the different clusters of CCR7+ DC. EHF is not expressed in CCR7lo (Cluster 1), which is still considered to be CCR7+. This CCR7+ population in cancers might contains more CCR7lo (Cluster 1). Second, EHF expression is downregulated quickly by GM-CSF and IFNү in CCR7+ DCs after maturation. CCR7+ population in the more survivable “hot” cancers may not have sustained expression of EHF due to abundance of such cytokines. We added this in our discussion.\n13- In some places, the number of independent experiments performed is not stated.\nWe have double-checked and made sure to correct any omission. Thank you!\nReviewer #3 (Remarks to the Author):\nThe study by Liu et al. identifies ETS homologous factor (EHF) as a key transcriptional regulator controlling maturation and immunosuppressive functions of conventional dendritic cells (cDCs) following TLR7 and TLR9 activation. Using conditional knockout mice, the authors demonstrate that EHF deficiency in DCs enhances resistance against DSS colitis, bacterial infections, and melanoma growth, primarily by promoting stronger Th1- and Th17-biased CD4+ T-cell responses. Mechanistically, the authors claim that EHF directly regulates the transcription of genes involved in dendritic cell maturation and immunosuppression, including Ccr7, Cd200, Cd274 (PD-L1), Irf4, and Rel, thus highlighting its pivotal role in suppressing inflammatory responses.\nThis study represents the first comprehensive characterization of EHF’s in vivo role in DC biology, expanding our understanding beyond its previously described functions in epithelial and tumor cell biology. The authors performed an extensive amount of experimental work, including the generation and analysis of new conditional knockout mouse models across several in vivo disease settings. Additionally, single-cell RNA sequencing analyses further reinforced their findings, providing high-resolution insights into EHF expression patterns across DC subsets in mice and humans at steady state and stimulatory conditions. A notable technical advance is the development of a novel monoclonal antibody recognizing EHF, which enabled CUT&TAG analyses to precisely identify putative EHF DNA binding sites.\nWhile this manuscript provides novel insights into the role of EHF in DC biology and is supported by a big and diverse set of experiments, certain aspects of the study feel inconsistent, with some datasets conflicting or not fully aligning with the authors’ claims. Some conclusions are stated too strongly relative to the presented data and would benefit from either additional experimental validation or a more nuanced interpretation.\nMajor points:\nFigure 1:\nMy main concern is the limited analysis of DC2 subsets (cDC2a, cDC2b and DC3): although the data indicate that resident cDC2s express EHF at steady state, this EHF-expressing population was not examined or characterized in detail. Furthermore, DC2 subsets were not analyzed in sufficient depth in the in vivo models, either by flow cytometry or single-cell RNA sequencing. A more detailed investigation of DC2 heterogeneity in WT versus KO mice, both at steady state and in disease models, would greatly strengthen the manuscript’s conclusions.\nWe have now conducted a more detailed analysis of DC subsets in steady state, CpG-B activation and colitis model (new Fig. 4d-e, see below). EHF regulated CCR7, CD200 and PD-L1 expression in cDC1s, cDC2As, and cDC2Bs.\nFigure 1L: The authors challenge Ehf CD11c-Cre mice with B16-OVA and show that Ehf-deficient mice have increased survival compared to WT controls. They also demonstrate that, upon OT-I transfer, OT-I cells exhibit decreased PD-1 expression in Ehf-deficient mice, but they do not provide an explanation for this observation.\nThank you for pointing it out. We think that EHF-deficient DCs expressed lower levels of PD-L1 and that might contribute to the lower PD-1 expression on OT-I cells. We have now added this explanation (highlighted in text).\nFurthermore, the authors should include data on the total numbers of engrafted OT-I cells\nWe have now included this data in the new Supplementary Fig. 2d (see below).\nand perform tetramer staining to assess antigen-specific OT-I populations in WT versus Ehf-deficient mice.\nWe are sorry for the misunderstanding. OT-I cells are TCR transgenic that all share the same OVA-specific TCR, and they would be 100% tetramer positive. Thus, we do not think it is necessary to use tetramer staining on OT-I cells.\nFigure S1J: How did the authors evaluate the expression of IFNα by pDCs using intracellular staining? The use of intracellular antibodies against IFNα is not described in the Materials and Methods section. This information is important, as intracellular staining for IFNα is technically challenging and has only been described by a few groups.\nWe have now added the details in the methods. We are proficient in IFNα staining since I was a postdoc with Dr. Boris Reizis, who is an expert on pDCs.\nAs an independent PI, I have published several papers which performed IFNα staining in pDCs:\nThioesterase PPT1 balances viral resistance and efficient T cell crosspriming in dendritic cells. J Exp Med. 2019. Sep 2;216(9):2091-2112\nThe RNase MCPIP3 promotes skin inflammation by orchestrating myeloid cytokine response. Nat Commun. 2021 Jul 2;12(1):4105\nCyclical palmitoylation regulates TLR9 signalling and systemic autoimmunity in mice. Nat Commun. 2024 Jan 2;15(1):1.\nFigure S1O and S1Q: The authors need to include non-treated/non-infected mice as control groups.\nWe now added these control groups in the new Supplementary Fig. 1o and S1q (see below).\nFigure 2:\nFigure 2A-F: The study would benefit from an analysis that goes beyond cytokine production of OT-II engrafted cells by also assessing their true T helper cell identity through intracellular staining of GATA3, T-bet, Foxp3, and RORγt.\nWe have now confirmed the Th1 lineage via T-bet and Th2 lineage via GATA3 in Fig. 2c-d (see below). The results were consistent.\nAs mentioned in our methods, we utilized Foxp3+ staining as a standard definition of Tregs.\nFurthermore, non-infected/non-treated mice should also be included as controls in this figure in the revised manuscript.\nWe have now added these controls in the new Supplementary Fig. 2a (see below).\nFigure S2F-G: The authors show that Ehf-deficient mice have reduced inflammation when challenged with C. rodentium. This finding is unexpected and contrasts with the data shown in Figure 1/S1, where DSS-treated Ehf-deficient mice exhibited stronger inflammation compared to WT mice. Is the phenotype different if the authors analyze C. rodentium-infected mice at an earlier time point post-infection (e.g., day 6)? Could day 11 post-C. rodentium infection already be too late to observe the increased pro-inflammatory responses that might be expected in Ehf-deficient mice?\nThank you for pointing out this difference. We think that the DSS model mimics colitis, which is an autoimmune disease. In comparison, C. Rodentium is a pathogenic infection. Since EHF promotes both maturation and immunosuppression, it should have different outcomes in autoimmunity and infections.\nIn colitis, EHF-deficiency would result in less immunosuppression and mice would die quickly due to too much autoinflammation. In C. Rodentium infection, EHF-deficiency would result in stronger immunity and clear the pathogen faster, resulting in healthier mice with less inflammation.\nFigure S2E: I would recommend moving Figure S2E into Figure S1, as it directly relates to the DSS experiment described in Figure 1 and would be better placed there for clarity and consistency.\nWe now moved it to Figure S1.\nFigure 3:\nFigure 3A: The authors performed qPCR on cDC1 and cDC2 stimulated in vitro with distinct TLR agonists to measure Ehf expression. They present the data as relative expression compared to the control sample. However, a more appropriate approach would be to normalize Ehf expression to a housekeeping gene. This would not only allow for comparison between the different stimulations but also provide an indication of the Ehf expression levels between cDC1 and cDC2.\nThis must be a misunderstanding based on the words we used. We do normalize it to a housekeeping gene first. In our original methods regarding qPCR, we stated: “All the data were normalized to β-actin expression.” We have added the wording regarding housekeeping gene in Figure 3 legend accordingly. We are sorry for the confusion.\nFigure 3B: The authors performed scRNA sequencing on splenocytes derived from B6 mice that were either treated with CpG-B or left untreated. In the CpG-B–treated panel, the cDC1 population does not appear to be annotated. Is this because cDC1 cells were absent in CpG-B–treated mice, or is this an annotation error?\nThank you for noticing this important point. After CpG-B treatment, scRNA-seq cannot differentiate CCR7+ DC1 and DC2, as all mature cDCs become just one cluster of CCR7+ DCs. Cluster analysis suggested that transcriptional programming of CCR7+ DC1 and DC2 are very similar. In FACS, it is still possible to differentiate them using traditional surface markers. However, there is a marked decrease in XCR1 expression by cDC1s (see below, also in Supplementary information, Gating strategy).\nThe sc-RNA-seq and FACS profiling suggests that the blurring of DC1/DC2 programing after CpG-B activation and EHF upregulation. This population of XCR1int DCs may be of similar origin to the previously reported regulatory CD103int DCs found in neonatal mice (Sci. Immul. 2023).\nThis is also consistent with our finding that EHF repressed DC-lineage transcriptional factor such as IRF4, thus blurring the boundary between DC1 and DC2 when EHF is expressed (new Fig. 4f, see below). We have now added related discussion in the text (highlighted).\nFigure 4:\nFigure 4A: The authors demonstrate that cDCs derived from Ehf-deficient mice are reduced upon stimulation with CpG-B. It is important to elucidate which specific cDC subset is affected by the loss of EHF in this context. In particular, the authors should assess whether there is a decrease in cDC1, cDC2a, cDC2b, and/or DC3 populations.\nWe have now conducted a more detailed analysis of DC subsets in steady state and CpG-B activation (Fig. 4c, see below). There is a general decrease in cDC1s, cDC2As, and cDC2Bs.\nFigure 4B-D: The authors demonstrate that cDC1 and cDC2 derived from Ehf-deficient mice show reduced expression of activation markers such as CCR7, CD200, and PD-L1, along with an increase in the transcription factor IRF4 and a decrease in c-Rel upon CpG-B stimulation. While these results are intriguing, they appear somewhat ambiguous, and it is unclear how the authors arrived at these findings. Performing bulk RNA sequencing on cDC1 and cDC2 from WT and Ehf-deficient mice treated with either CpG-B or PBS would help clarify these results and better capture the transcriptional changes associated with the absence of EHF.\nSorry, this must be a misunderstanding due to the order of the figures. In Figure 6, we conducted scRNA-seq analysis, which gave better clarity than bulk RNA-seq.\nFigure 5:\nFigure 5A-F: The authors performed a Cut&TAG experiments targeting EHF on BM DC cultures and used a broad population of cDCs, that contains both, cDC1 and cDC2 subsets to target EHF. Eventhough it gives already a good preliminary idea about the DNA binding regions of EHF; it still overlaps the peaks present in cDC1 and cDC2. A clearer and better approach would be to target ex vivo isolated cDC1 and cDC2 from the spleen that were stimulated with CpG-B or PBS. This would reveal the EHF binding sites in both, steady state as well as activation state and give a more and proufnd idea about how EHF regulates the transcriptional landscape within each cDC subset.\nWe have performed the CUT&TAG experiments on ex vivo isolated cDC1s and cDC2s from PBS or CpG-B treated mice as requested (Supplementary Fig. 5f, see below).\nThe EHF bindings sites were similar in cDC1s and cDC2s.\nHowever, due to the fragile state of ex vivo FACS-sorted cells, the background noise was much greater than that of cDCFL-Notch. Compared to traditional ChIP-seq, CUT&TAG has a more stringent requirement for cell viability. That was why we originally utilized cDCFL-Notch to obtain a higher cell viability for lower background noise.\nFigure 5E: The authors should tone down their statement (“…we found that EHF may directly promote the transcription of inhibitory molecules such as CCR7, CD200, and PD-L1.”, Line 491–492) regarding EHF specifically regulating the expression of these genes. Most of the depicted EHF DNA-binding sites are located within introns or in upstream/downstream enhancer regions, with only Cd274 showing a peak ~3 kb downstream of the promoter region. Since none of these enhancer regions were functionally evaluated, it cannot be conclusively claimed that EHF directly regulates these target genes.\nWe had performed motif mutagenesis in luciferase reporter assays on all putative ETS-binding sites. The results were confirmatory. These data were added in the new Fig. 5g (shown below). We hope this functional assay support our claim of direct transcriptional control.\n(END)"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed all reviewer concerns and questions, and the revision is acceptable.\n############# As the mediator for Reviewer #3 #####################\nOverall, the authors’ responses to the reviewers’ comments are satisfactory, and the major scientific concerns have been adequately addressed. I only note two minor issues where statements in the rebuttal do not appear to be reflected in the revised manuscript text, as detailed below.\n1.The authors state in their response that an explanation linking reduced PD-L1 expression on EHF-deficient DCs to lower PD-1 expression on OT-I cells has been added to the manuscript. However, I was unable to identify an explicit statement to this effect in the revised text. Please indicate where this explanation is described or revise the text to clearly state this interpretation.\n2.The authors state in their response that details of IFN-α staining have been added to the Methods; however, I could not locate any description of IFN-α staining in the revised manuscript or Supplementary Information. Please clarify where this information is provided, or revise the response accordingly.", "reviewer_2": "The authors have responded to prior critiques. I do not have any further comments."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThe authors have adequately addressed all reviewer concerns and questions, and the revision is acceptable.\n############# As the mediator for Reviewer #3 #####################\nOverall, the authors’ responses to the reviewers’ comments are satisfactory, and the major scientific concerns have been adequately addressed. I only note two minor issues where statements in the rebuttal do not appear to be reflected in the revised manuscript text, as detailed below.\n1.The authors state in their response that an explanation linking reduced PD-L1 expression on EHF-deficient DCs to lower PD-1 expression on OT-I cells has been added to the manuscript. However, I was unable to identify an explicit statement to this effect in the revised text. Please indicate where this explanation is described or revise the text to clearly state this interpretation.\nThis statement is highlighted at line 260-262.\n2.The authors state in their response that details of IFN-α staining have been added to the Methods; however, I could not locate any description of IFN-α staining in the revised manuscript or Supplementary Information. Please clarify where this information is provided, or revise the response accordingly.\nThis information is highlighted at line 560-570.\nReviewer #2 (Remarks to the Author):\nThe authors have responded to prior critiques. I do not have any further comments."}]

Decision letter