Thymocytes trigger self-antigen-controlling pathways in immature medullary thymic epithelial stages

  1. Noella Lopes
  2. Nicolas Boucherit
  3. Jérémy C Santamaria
  4. Nathan Provin
  5. Jonathan Charaix
  6. Pierre Ferrier
  7. Matthieu Giraud
  8. Magali Irla  Is a corresponding author
  1. Aix-Marseille University, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, France
  2. Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, France

Abstract

Interactions of developing T cells with Aire+ medullary thymic epithelial cells expressing high levels of MHCII molecules (mTEChi) are critical for the induction of central tolerance in the thymus. In turn, thymocytes regulate the cellularity of Aire+ mTEChi. However, it remains unknown whether thymocytes control the precursors of Aire+ mTEChi that are contained in mTEClo cells or other mTEClo subsets that have recently been delineated by single-cell transcriptomic analyses. Here, using three distinct transgenic mouse models, in which antigen presentation between mTECs and CD4+ thymocytes is perturbed, we show by high-throughput RNA-seq that self-reactive CD4+ thymocytes induce key transcriptional regulators in mTEClo and control the composition of mTEClo subsets, including Aire+ mTEChi precursors, post-Aire and tuft-like mTECs. Furthermore, these interactions upregulate the expression of tissue-restricted self-antigens, cytokines, chemokines, and adhesion molecules important for T-cell development. This gene activation program induced in mTEClo is combined with a global increase of the active H3K4me3 histone mark. Finally, we demonstrate that these self-reactive interactions between CD4+ thymocytes and mTECs critically prevent multiorgan autoimmunity. Our genome-wide study thus reveals that self-reactive CD4+ thymocytes control multiple unsuspected facets from immature stages of mTECs, which determines their heterogeneity.

Editor's evaluation

This manuscript is of interest to readers in the field of immunology and especially in the induction of immune tolerance in the thymus. The work uses several mouse models to substantially broaden the current understanding of MHCII/TCR-mediated cell-cell crosstalk in the thymus and suggests a novel mechanism that contributes to the generation of functional and self-tolerant T-cells.

https://doi.org/10.7554/eLife.69982.sa0

Introduction

The thymic medulla ensures the generation of a self-tolerant T-cell repertoire (Klein et al., 2014; Lopes et al., 2015). By their unique ability to express tissue-restricted self-antigens (TRAs) (Derbinski et al., 2001; Sansom et al., 2014), medullary thymic epithelial cells (mTECs) promote the development of Foxp3+ regulatory T cells and the deletion by apoptosis of self-reactive thymocytes capable of inducing autoimmunity (Klein et al., 2019). The expression of TRAs that mirrors body’s self-antigens is controlled by Aire (Autoimmune regulator) and Fezf2 (Fez family zinc finger 2) transcription factors (Anderson et al., 2002; Takaba et al., 2015). Aire-dependent TRAs are generally characterized by a repressive chromatin state enriched in the trimethylation of lysine-27 of histone H3 (H3K27me3) histone mark (Handel et al., 2018; Org et al., 2009; Sansom et al., 2014). In accordance with their essential role in regulating the expression of TRAs, Aire-/- and Fezf2-/- mice show defective clonal deletion of autoreactive thymocytes and develop signs of autoimmunity in several peripheral tissues (Anderson et al., 2002; Takaba et al., 2015).

Based on the level of the co-expressed MHC class II and CD80 molecules, mTECs were initially subdivided into mTEClo (MHCIIloCD80lo) and mTEChi (MHCIIhiCD80hi) (Gray et al., 2006). The relationship between these two subsets has been established with reaggregate thymus organ cultures in which mTEClo give rise to mature Aire+ mTEChi (Gäbler et al., 2007; Gray et al., 2007). Although mTEChi express a highly diverse array of TRAs under Aire’s action that releases stalled RNA polymerase and modulates chromatin accessibility, mTEClo already express a substantial amount of TRAs (Derbinski et al., 2005; Giraud et al., 2012; Koh et al., 2018; Kyewski and Klein, 2006; Sansom et al., 2014). Recent single-cell transcriptomic analyses indicate that the heterogeneity of mTECs, especially in the mTEClo compartment, is more complex than previously thought (Irla, 2020; Kadouri et al., 2020). mTEClo with low or no expression of CD80 have been shown to be divided into three main subsets: CCL21+ mTECs, implicated in the attraction of positively selected thymocytes in the medulla (Lkhagvasuren et al., 2013), involucrin+TPAhi post-Aire mTECs corresponding to the ultimate mTEC differentiation stage (Metzger et al., 2013; Michel et al., 2017; Nishikawa et al., 2010), and the newly reported tuft-like mTECs that show properties of gut chemosensory epithelial tuft cells expressing the doublecortin-like kinase 1 (DCLK1) marker (Bornstein et al., 2018; Miller et al., 2018). Based on single-cell transcriptomic analyses, mTECs were then classified into four major groups encompassing mTEC I:CCL21+ mTECs, mTEC II:Aire+ mTECs, mTEC III:post-Aire mTECs, and mTEC IV:tuft-like mTECs (Bornstein et al., 2018). Furthermore, mTEClo with intermediate levels of CD80 and MHCII lie into mTEC single-cell clusters that are defined as proliferating and maturational, expressing Fezf2 and preceding the Aire+ mTEChi stage (Baran-Gale et al., 2020; Dhalla et al., 2020). These transit-amplifying cells were recently referred as to as TAC-TECs (Wells et al., 2020).

In the postnatal thymus, while mTECs control the selection of thymocytes, conversely CD4+ thymocytes control the cellularity of Aire+ mTEChi by activating RANK and CD40-induced NF-κb signaling pathways (Akiyama et al., 2008; Hikosaka et al., 2008; Irla, 2020; Irla et al., 2008). These bidirectional interactions between mTECs and thymocytes are commonly referred to as thymic crosstalk (Lopes et al., 2015; van Ewijk et al., 1994). However, it remains unknown whether CD4+ thymocytes act exclusively on mature Aire+ mTEChi or upstream on their TAC-TEC precursors contained in mTEClo and whether the development of the newly identified Fezf2+, post-Aire, and tuft-like subsets is regulated or not by CD4+ thymocytes.

In this study, using high-throughput RNA-sequencing (RNA-seq), we show that self-reactive CD4+ thymocytes induce in mTEClo pivotal transcriptional regulators for their differentiation and function. Accordingly, self-reactive CD4+ thymocytes control the composition of the mTEClo compartment, that is the precursors of Aire+ mTEChi, post-Aire cells, and tuft-like mTECs. Our data also reveal that self-reactive CD4+ thymocytes upregulate in mTEClo the expression of TRAs, chemokines, cytokines, and adhesion molecules involved in T-cell development. This gene activation program correlates with increased levels of the active trimethylation of lysine-4 of histone 3 (H3K4me3) mark, including the loci of Fezf2-dependent and Aire/Fezf2-independent TRAs, indicative of an epigenetic regulation for their expression. Finally, we demonstrate that disrupted MHCII/TCR interactions between mTECs and CD4+ thymocytes lead to the generation of mature T cells containing self-specificities capable of inducing multiorgan autoimmunity. Altogether, our genome-wide study reveals that self-reactive CD4+ thymocytes control the developmental transcriptional programs of mTEClo, which conditions their differentiation and function as inducers of T-cell tolerance.

Results

CD4+ thymocytes induce key transcriptional programs in mTEClo cells

Several NF-κb members are involved in Aire+ mTEChi development (Burkly et al., 1995; Lomada et al., 2007; Riemann et al., 2017; Shen et al., 2019; Zhang et al., 2006). However, it remains unclear whether the NF-κb or other signaling pathways are activated by CD4+ thymocytes specifically in mTEClo cells. To investigate the effects of CD4+ thymocytes in mTEClo, we used mice deficient in CD4+ thymocytes (ΔCD4 mice) because they lack the promoter IV of the class II transactivator (Ciita) gene that controls MHCII expression in cortical TECs (cTECs) (Waldburger et al., 2003). We first analyzed by flow cytometry the total and phosphorylated forms of IKKα, p65, and RelB NF-κb members and p38 and Erk1/2 MAPK proteins in mTEClo from ΔCD4 mice according to the gating strategy shown in Figure 1—figure supplement 1A. Interestingly, the phosphorylation level of IKKα and p38 MAPK was substantially reduced in ΔCD4 mice (Figure 1A and B, Figure 1—figure supplement 2), indicating that CD4+ thymocytes may have an impact in mTEClo by activating the IKKα intermediate of the nonclassical NF-κB pathway and the p38 MAPK pathway.

Figure 1 with 4 supplements see all
The transcriptional profile and IKKα and p38 MAPK signaling pathways are impaired in mTEClo of ΔCD4 mice.

(A, B) Total IKKα, p38 MAPK, phospho-IKKα(Ser180)/IKKβ(Ser181), and p38 MAPK (Thr180/Tyr182) (A) and the ratio of phospho/total proteins (B) analyzed by flow cytometry in mTEClo from WT and ΔCD4 mice. Data are representative of two independent experiments (n = 3–4 mice per group and experiment). (C) Scatter plot of gene expression levels (fragments per kilobase of transcript per million mapped reads [FPKM]) of mTEClo from WT versus ΔCD4 mice. Genes with fold difference ≥2 and p-adj<0.05 were considered as upregulated or downregulated genes (red and blue dots, respectively). RNA-seq was performed on two independent biological replicates with mTEClo derived from 3 to 5 mice. (D) Numbers of tissue-restricted self-antigens (TRAs) and non-TRAs in genes up- and downregulated (left panel) and the proportion of upregulated TRAs compared to those in the all genome (right panel). ND, not determined. (E) Numbers of induced Aire-dependent, Fezf2-dependent, Aire/Fezf2-dependent, and Aire/Fezf2-independent TRAs. (F) The expression of Aire-dependent (Meig1, Nov), Fezf2-dependent (Fcer2a, Kcnj5), Aire/Fezf2-dependent (Krt1, Reig1), and Aire/Fezf2-independent (Crp, Rsph1) TRAs measured by qPCR in WT (n = 3–4) and ΔCD4 (n = 3–4) mTEClo. (G) Expression fold change in HDAC3-induced transcriptional regulators and other transcription factors significantly upregulated in WT versus ΔCD4 mTEClo. The color code represents gene expression level. (H) Heatmaps of genes encoding for cell adhesion molecules and cytokines that were significantly downregulated in mTEClo from ΔCD4 mice. (I) Hierarchical clustering and heatmap of mean expression of these cell adhesion molecules and cytokines in mTEC subsets identified by scRNA-seq. Error bars show mean ± SEM, *p<0.05, **p<0.01 using two-tailed Mann–Whitney test for (A), (B) and (F) and chi-squared test for (D).

To gain insights into the effects of CD4+ thymocytes in mTEClo, we analyzed by high-throughput RNA-seq the gene expression profiles of mTEClo purified from WT and ΔCD4 mice (Figure 1—figure supplement 1B). We found that CD4+ thymocytes upregulated 989 genes (fold change [FC] >2) reaching significance for 248 of them (Cuffdiff p<0.05) (Figure 1C). 957 genes were also downregulated (FC < 0.5) with 178 genes reaching significance (Cuffdiff p<0.05). We analyzed whether the genes significantly up- or downregulated by CD4+ thymocytes corresponded to TRAs, as defined by an expression restricted to 1–5 of peripheral tissues (Sansom et al., 2014). Interestingly, the genes upregulated by CD4+ thymocytes exhibited approximately fourfold more of TRAs over non-TRAs (Figure 1D, left panel). The comparison of the proportion of TRAs among the upregulated genes with those of the genome revealed a strong statistical TRA overrepresentation (p=5.2 × 10–10) (Figure 1D, right panel). Most of the TRAs upregulated by CD4+ thymocytes were sensitive to the action of Aire (Aire-dependent TRAs) or controlled by Aire and Fezf2-independent mechanisms (Aire/Fezf2-independent TRAs) (Figure 1E, Supplementary file 1). The upregulation of some of these TRAs by CD4+ thymocytes was confirmed by qPCR in mTEClo purified from ΔCD4 mice (Figure 1F). The same results were observed with mTEClo purified from MHCII-/- mice, also lacking CD4+ thymocytes, excluding any potential indirect effect of CIITA in the phenotype observed in ΔCD4 mice (Figure 1—figure supplement 3A).

Remarkably, among the non-TRAs upregulated by CD4+ thymocytes in mTEClo, 37 corresponded to 50 mTEC-specific transcription factors that are induced by the histone deacetylase 3 (HDAC3) (Goldfarb et al., 2016; Figure 1G). Some of them, such as the interferon regulatory factor 4 (Irf4), Irf7, and the Ets transcription factor member, Spib, are known to regulate mTEC differentiation and function (Akiyama et al., 2014; Haljasorg et al., 2017; Otero et al., 2013). We also identified other transcription factors such as Nfkb2, Trp53, and Relb implicated in mTEC differentiation (Riemann et al., 2017; Rodrigues et al., 2017; Zhang et al., 2006). Finally, we found that CD4+ thymocytes upregulate in mTEClo the expression of some cytokines and cell adhesion molecules such as integrins and cadherins (Figure 1H, Figure 1—figure supplement 3B). Given that mTEClo are heterogeneous (Irla, 2020; Kadouri et al., 2020), we then analyzed whether the cytokines and adhesion molecules, which are upregulated by CD4+ thymocytes, are specific to a particular subset of mTEClo. To this end, we reanalyzed single-cell RNA-seq data performed on total CD45-EpCAM+ TECs (Wells et al., 2020). Single cells were projected into a UMAP reduced-dimensional space and, using the 15 first principal components, six clusters were obtained, as in Wells et al., 2020 (Figure 1—figure supplement 4A). Well-established markers were used to distinguish the different TEC subsets such as Psmb11 and Prss16 for cTECs, Ccl21a and Krt5 for CCL21+ mTECs (also called mTEC I), Stmn1, Ska1, Fezf2 and Aire for TAC-TECs, Aire and Fezf2 for Aire+ mTECs (also called mTEC II), Pigr and Cldn3 for post-Aire mTECs (also called mTEC III), and Avil and Pou2f3 for tuft-like mTECs (also called mTEC IV) (Figure 1—figure supplement 4B). In contrast to CCL21+ mTECs, some genes upregulated by CD4+ thymocytes were expressed by tuft-like mTECs (Figure 1I). Interestingly, many genes encoding for cytokines and cell adhesion molecules were associated with Aire+ mTECs and post-Aire cells with some of them already expressed in TAC-TECs, suggesting that CD4+ thymocytes may act upstream of Aire+ mTEChi. These results thus provide the first evidence that CD4+ thymocytes are able to induce in mTEClo essential transcriptional regulators for mTEC differentiation and function as well as TRAs, adhesion molecules, and cytokines.

CD4+ thymocytes regulate maturational programs in mTEClo through MHCII/TCR interactions

We next investigated by which mechanism CD4+ thymocytes regulate the transcriptional programs of mTEClo. Given that MHCII/TCR interactions with mTECs are critical for CD4+ T-cell selection (Klein et al., 2019), we hypothesized that these interactions could play an important role in initiating transcriptional programs that govern the functional and developmental properties of mTEClo. To this end, we used a unique transgenic mouse model in which MHCII expression is selectively abrogated in mTECs (mTECΔMHCII mice) (Irla et al., 2008). In contrast to their WT counterparts, we found that OVA323-339-loaded mTECs from mTECΔMHCII mice were ineffective at activating OTII-specific CD4+ T cells, demonstrating that the capacity of antigen presentation of mTECs to CD4+ T cells is impaired in these mice (Figure 2A).

Figure 2 with 1 supplement see all
The transcriptional and functional properties of mTEClo are impaired in mTECΔMHCII mice.

(A) Percentages of CD69+ OTII CD4+ T cells cultured or not with variable numbers of OVA323-339-loaded WT or mTECΔMHCII mTECs derived from two independent experiments (n = 2–3 mice per group and experiment). (B) Scatter plot of gene expression levels (fragments per kilobase of transcript per million mapped reads [FPKM]) of mTEClo from WT versus mTECΔMHCII mice. Genes with fold difference ≥2 and p-adj<0.05 were considered as upregulated or downregulated genes (red and blue dots, respectively). RNA-seq was performed on two independent biological replicates with mTEClo derived from 3 to 5 mice. (C) Numbers of tissue-restricted self-antigens (TRAs) and non-TRAs in genes up- and downregulated in mTEClo from WT versus mTECΔMHCII mice. ND, not determined. (D) Numbers of induced TRAs regulated or not by Aire and/or Fezf2. (E) Aire-dependent (Crabp1), Fezf2-dependent (Coch, Sult1c2), Aire/Fezf2-dependent (Fabp9), and Aire/Fezf2-independent (Spon2, Upk3b) TRAs were measured by qPCR in mTEClo from WT (n = 4) and mTECΔMHCII (n = 4) mice. (F) Scatter plot of gene expression variation in mTEClo from WT versus mTECΔMHCII mice and in mTEChi from WT versus Aire-/- mice. The loess fitted curve is shown in blue and the induced Aire-dependent genes (fold change [FC] > 5) in red. (G) Heatmap of significantly downregulated activation factors in mTEClo from mTECΔMHCII mice. (H) Aire and Fezf2 mRNAs were measured by qPCR in mTEClo from WT (n = 3–4) and mTECΔMHCII (n = 4) mice. (I) FC in the expression of HDAC3-induced transcriptional regulators and other transcription factors significantly upregulated in WT versus mTECΔMHCII mice. The color code represents gene expression level. (J) Heatmap of significantly downregulated cytokines, chemokines, and cell adhesion molecules in mTEClo from mTECΔMHCII mice. (K) Hierarchical clustering and heatmap of mean expression of these activation factors, cell adhesion molecules, chemokines, and cytokines in mTEC subsets identified by scRNA-seq. Error bars show mean ± SEM, *p<0.05, **p<0.01, ***p<0.001 using two-tailed Mann–Whitney test for (A), (E) and (H) and chi-squared test for (C) and (F).

The comparison of the gene expression profiles of mTEClo purified from WT and mTECΔMHCII mice (Figure 1—figure supplement 1B) revealed that MHCII/TCR interactions with CD4+ thymocytes resulted in the upregulation of 1300 genes (FC > 2), 449 of them reaching statistical significance (Cuffdiff p<0.05). 846 genes were also downregulated (FC < 0.5) with 340 reaching significance (Cuffdiff p<0.05) (Figure 2B). Similarly to the comparison of WT versus ΔCD4 mice (Figure 1D), the genes significantly upregulated by MHCII/TCR interactions in mTEClo corresponded preferentially to TRAs (p=4.5 × 10–13) that are mainly Aire-dependent and Aire/Fezf2-independent (Figure 2C–E, Supplementary file 2). In line with the recent discovery of Aire expression in mTECs expressing intermediate levels of CD80 identified in the proliferating and maturational stage mTEC single-cell clusters (Dhalla et al., 2020), we found a strong correlation (p=2 × 10–16) between gene upregulation induced by MHCII/TCR interactions and the responsiveness of genes to Aire’s action obtained from the comparison between WT and Aire-/- mTEChi (Figure 2F). These data are in agreement with the identification of a list of activation factors including Aire among the non-TRA genes induced by MHCII/TCR interactions with CD4+ thymocytes in mTEClo (Figure 2G). mTEClo from mTECΔMHCII mice expressed ~4.5-fold less Aire than WT mTEClo, with substantial levels of 15.8 and 73.7 fragments per kilobase of transcript per million mapped reads (FPKM), respectively. For comparison, Aire expression level in WT mTEChi was 448.9 FPKM. mTEClo from mTECΔMHCII mice also expressed ~1.5-fold less Fezf2 than WT mTEClo (90.2 versus 134.5 FPKM, respectively). This reduction in Aire and Fezf2 expression in mTECΔMHCII mice was also confirmed by qPCR (Figure 2H). These results highlight the importance of MHCII/TCR interactions with CD4+ thymocytes in upregulating Aire and Fezf2 mRNAs and some of their associated TRAs in mTEClo. Interestingly, 17 HDAC3-regulated transcription factors as well as Nfkb2, Trp53, and Relb transcription factors were induced by MHCII/TCR interactions with CD4+ thymocytes (Figure 2I). Moreover, the expression of several cytokines, chemokines, and cell adhesion molecules was also upregulated (Figure 2J, Figure 2—figure supplement 1A). Using single-cell RNA-seq data (Figure 1—figure supplement 4), we found that these genes were poorly associated with CCL21+ and tuft-like mTEClo (Figure 2K). Consistently with the altered cellularity of Aire+ mTECs in mTECΔMHCII mice (Irla et al., 2008), some of these genes were associated with post-Aire cells. Strikingly, many genes upregulated by CD4+ thymocytes in mTEClo were highly expressed by Aire+ mTECs. Interestingly, several of these genes were already expressed by TAC-TECs, including Aire and Fezf2, strongly suggesting that an enhanced transcriptional activity promoted by MHCII/TCR interactions with CD4+ thymocytes accompanies the transition from TAC-TECs to Aire+ mTECs. Altogether, these data show that CD4+ thymocytes, through MHCII/TCR interactions, control the functional properties of mTEClo and activate key transcriptional programs governing their differentiation and function.

TCR/MHCII interactions with CD4+ thymocytes regulate the development of Fezf2+ pre-Aire, post-Aire, and tuft-like mTEC subsets

Since key transcription factors implicated in mTEC differentiation were upregulated in mTEClo by MHCII/TCR-mediated interactions with CD4+ thymocytes (Figures 1G and 2I), we next analyzed the composition for the newly identified mTEC subsets in ΔCD4 and mTECΔMHCII mice. In agreement with our previous study (Irla et al., 2008), we first observed a substantial reduction in the frequencies and numbers of mTEChi in both mice (Figure 3A). Furthermore, numbers of mTEClo were also substantially reduced. Consequently, ΔCD4 and mTECΔMHCII mice have a globally reduced cellularity in total mTECs. An Aire/Fezf2 co-staining both by histology and flow cytometry then revealed a substantial reduction in Aire-Fezf2+ and Aire+Fezf2+ cells (Figure 3B and C). We further analyzed by flow cytometry Aire and Fezf2 expression specifically in mTEClo and mTEChi, according to the gating strategy shown in Figure 1—figure supplement 1A. In agreement with the detection of Aire in the proliferating and maturational single-cell clusters in mTEClo (Baran-Gale et al., 2020; Dhalla et al., 2020; Wells et al., 2020), we found that Aire protein was expressed in a small fraction of mTEClo compared to mTEChi in WT, ΔCD4, and mTECΔMHCII mice (Figure 3C). Aire-Fezf2+ and Aire+Fezf2+ mTECs were reduced in mTEClo of ΔCD4 and mTECΔMHCII mice with a more marked effect in mTEChi. This decrease was not due to impaired proliferation since normal frequencies of Ki-67+ proliferating cells were observed in ΔCD4 and mTECΔMHCII mice (Figure 3—figure supplement 1). Furthermore, numbers of involucrin+TPA+Aire- post-Aire cells were reduced in the medulla of ΔCD4 and mTECΔMHCII mice (Figure 3—figure supplement 2A), consistently with the decrease of Aire+ mTEChi (Figure 3B and C). In contrast, the frequencies of CCL21+ cells among mTEClo were not altered in ΔCD4 and mTECΔMHCII mice (Figure 3D). This is in line with the observation that few genes upregulated by TCR/MHCII interactions with CD4+ thymocytes were associated with CCL21+ mTECs (Figures 1I and 2K). We also analyzed tuft-like mTECs since the expression of the transcription factor Pou2f3, known to control the development of this cell type (Bornstein et al., 2018; Miller et al., 2018), was decreased in mTEClo of ΔCD4 and mTECΔMHCII mice (1,2Figures 1G and 2I). We found that numbers of tuft-like mTECs identified by flow cytometry using the DCLK1 marker were reduced in both mice (Figure 3E, Figure 3—figure supplement 2B), indicating that their development is promoted by MHCII/TCR interactions with CD4+ thymocytes. Importantly, Aire-Fezf2+ and Aire+Fezf2+ mTEClo and mTEChi as well as CCL21+ and DCLK1+ tuft-like mTEClo were similarly reduced in MHCII-/- mice, further confirming that CD4+ thymocytes control the cellularity of these novel mTEC subsets (Figure 3—figure supplement 3). Altogether, these data reveal that MHCII/TCR-mediated interactions with CD4+ thymocytes have a broad impact on mTEC composition by controlling the cellularity of not only Aire+Fezf2+ mTECs but also Fezf2+ pre-Aire+ mTECs, post-Aire, and tuft-like cells.

Figure 3 with 3 supplements see all
The composition in medullary thymic epithelial cell (mTEC) subsets is altered in ΔCD4 and mTECΔMHCII mice.

(A) Flow cytometry profiles, frequencies, and numbers of mTEClo and mTEChi in WT, ΔCD4, and mTECΔMHCII mice. Data are representative of 2–3 independent experiments (n = 2–5 mice per group and experiment). (B) Confocal images of thymic sections from WT, ΔCD4, and mTECΔMHCII mice stained for Aire (green) and Fezf2 (red). 12 and 20 sections derived from two WT, two ΔCD4, and two mTECΔMHCII mice were quantified. Scale bar, 50 μm. Unfilled, dashed and solid arrowheads indicate Aire+Fezf2-, Aire-Fezf2+, and Aire+Fezf2+ cells, respectively. The histogram shows the density of Aire+Fezf2-, Aire-Fezf2+, and Aire+Fezf2+ cells. (C–E) Flow cytometry profiles, frequencies, and numbers of Aire-Fezf2-, Aire-Fezf2+, and Aire+Fezf2+ cells in total mTECs, mTEClo, and mTEChi (C), of CCL21+ cells in mTEClo (D) and of DCKL1+ cells in Aire- mTEClo (E) from WT, ΔCD4, and mTECΔMHCII mice. II Abs: secondary antibodies. Data are representative of 2–3 independent experiments (n = 2–5 mice per group and experiment). Error bars show mean ± SEM, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 using unpaired Student’s t-test for (B) and two-tailed Mann–Whitney test for (A) and (C-E).

Highly self-reactive CD4+ thymocytes activate maturational programs in mTEClo

We next assessed the impact of highly self-reactive interactions with CD4+ thymocytes in mTEClo using OTII-Rag2-/- and RipmOVAxOTII-Rag2-/- transgenic mice. Both models possess CD4+ thymocytes expressing an MHCII-restricted TCR specific for the chicken ovalbumin (OVA). The Rip-mOVA line expresses a membrane-bound OVA form specifically in mTECs, and consequently high-affinity interactions between OVA-expressing mTECs and OTII CD4+ thymocytes are only possible in RipmOVAxOTII-Rag2-/- mice (Kurts et al., 1996). In contrast to total Erk1/2 MAPK, p38 MAPK, IKKα, and p65, the nonclassical NF-kB subunit RelB was increased in mTEClo at mRNA and protein levels in RipmOVAxOTII-Rag2-/- compared to OTII-Rag2-/- mice (Figure 4A and B, Figure 4—figure supplement 1). By reanalyzing single-cell RNA-seq data on mTEClo subsets, we found that in contrast to CCL21+ and tuft-like mTECs Relb is highly expressed by TAC-TECs and post-Aire cells, arguing again in favor that self-reactive CD4+ thymocytes act from the TAC-TEC stage to induce their differentiation into Aire+ cells and then into post-Aire cells (Figure 4—figure supplement 2A). The level of RelB phosphorylation was also higher in RipmOVAxOTII-Rag2-/- than OTII-Rag2-/- mice (Figure 4B), suggesting that self-reactive CD4+ thymocytes may activate the nonclassical NF-κB pathway in mTEClo.

Figure 4 with 2 supplements see all
Highly self-reactive CD4+ thymocytes control the transcriptional and functional properties of mTEClo.

(A) Relb mRNA was measured by qPCR in mTEClo from RipmOVAxOTII-Rag2-/- (n = 4) and OTII-Rag2-/- (n = 5) mice. (B) Total and phospho-RelB (Ser552) were analyzed by flow cytometry in mTEClo from RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice. Data are representative of two independent experiments (n = 3–4 mice per group and experiment). (C) Scatter plot of gene expression levels (fragments per kilobase of transcript per million mapped reads [FPKM]) in mTEClo from RipmOVAxOTII-Rag2-/- versus OTII-Rag2-/- mice. Genes with fold difference ≥2 and p-adj<0.05 were considered as upregulated or downregulated genes (red and blue dots, respectively). RNA-seq was performed on two independent biological replicates with mTEClo derived from 5 to 8 mice. (D) Numbers of tissue-restricted self-antigens (TRAs) and non-TRAs in genes up- and downregulated in mTEClo from RipmOVAxOTII-Rag2-/- versus OTII-Rag2-/- mice. ND, not determined. (E) Numbers of induced Aire-dependent, Fezf2-dependent, Aire/Fezf2-dependent, and Aire/Fezf2-independent TRAs. (F) Aire-dependent (Fam183b, Nts), Fezf2-dependent (Resp18, Grap), Aire/Fezf2-dependent (Fabp9), Aire/Fezf2-independent (Csn2, Crp) TRAs, Aire and Fezf2 mRNAs were measured by qPCR in mTEClo from RipmOVAxOTII-Rag2-/- (n = 4) and OTII-Rag2-/- (n = 4) mice. (G) Scatter plot of gene expression variation in mTEClo from RipmOVAxOTII-Rag2-/- versus OTII-Rag2-/- mice and in mTEChi from WT versus Aire-/- mice. The loess fitted curve is shown in blue and induced Aire-dependent genes (fold change [FC] > 5) in red. (H) Heatmap of significantly upregulated activation factors in mTEClo from RipmOVAxOTII-Rag2-/- compared to OTII-Rag2-/- mice. (I, J) Expression FC in HDAC3-induced transcriptional regulators and other transcription factors (I) and in Foxn1 targets (J) in mTEClo from RipmOVAxOTII-Rag2-/- versus OTII-Rag2-/- mice. The color code represents gene expression level. (K) Heatmap of significantly upregulated cytokines, chemokines, and cell adhesion molecules in mTEClo from RipmOVAxOTII-Rag2-/- mice. (L) Hierarchical clustering and heatmap of mean expression of these activation factors, cell adhesion molecules, chemokines, and cytokines in mTEC subsets identified by scRNA-seq. Error bars show mean ± SEM, *p<0.05, **p<0.01, ***p<0.001 using two-tailed Mann–Whitney test for (A), (B) and (F) and chi-squared test for (D) and (G).

To define the genome-wide effects of highly self-reactive CD4+ thymocytes in mTEClo, we compared the gene expression profiles of mTEClo from RipmOVAxOTII-Rag2-/- versus OTII-Rag2-/- mice (Figure 1—figure supplement 1B) and found an upregulation of 1438 genes (FC > 2) reaching statistical significance for 522 of them (Cuffdiff p<0.05). 620 genes were also downregulated (FC < 0.5) with 136 reaching significance (Cuffdiff p<0.05) (Figure 4C). The genes upregulated exhibited an approximately fourfold more of TRA over non-TRA genes (p=4.7 × 10–23), which corresponded mainly to Aire-dependent and Aire/Fezf2-independent TRAs (Figure 4D–F, Supplementary file 3). Similarly to the WT versus mTECΔMHCII comparison, we found a strong correlation (p=6.2 × 10–7) between the genes upregulated by self-reactive CD4+ thymocytes and the responsiveness of genes to Aire’s action obtained from the comparison between WT and Aire-/- mTEChi (Figure 4G). These results support an impact of antigen-specific interactions in the expression of TRAs in mTEClo, notably on Aire-dependent TRAs. Importantly, these results are in agreement with the induction of a list of activation factors including Aire and Fezf2 among the non-TRA genes (Figure 4H). Similarly to the comparisons of the WT versus ΔCD4 or mTECΔMHCII mice, numerous HDAC3-induced regulators as well as Sirt1, Nfkb2, Relb, and Trp53 transcription factors were upregulated in mTEClo of RipmOVAxOTII-Rag2-/- mice compared to OTII-Rag2-/- mice (Figure 4I). Interestingly, 21 out of 30 top targets of the Foxn1 transcription factor, implicated in TEC differentiation and growth (Žuklys et al., 2016), as well as cytokines, chemokines, and cell adhesion molecules, were also upregulated (Figure 4J, K Figure 4—figure supplement 2B). We found that few of these genes were associated with CCL21+ and tuft-like mTECs (Figure 4L). In contrast, many genes encoding for activation factors, cytokines, chemokines, and cell adhesion molecules were associated with Aire+ and post-Aire mTECs, consistently with the fact that antigen-specific interactions with CD4+ thymocytes control the cellularity of Aire+ mTECs. Moreover, most of these genes, including Aire and Fezf2, were already expressed by TAC-TECs, further highlighting that CD4+ thymocytes act upstream of Aire+ mTEChi. Altogether, these data reveal that highly self-reactive CD4+ thymocytes control in mTEClo not only key transcription factors driven by their differentiation but also key molecules for T-cell development and selection such as TRAs, cytokines, chemokines, and adhesion molecules.

Highly self-reactive CD4+ thymocytes control mTEC subset composition from a progenitor stage

Given that highly self-reactive CD4+ thymocytes induce key transcription factors in mTEClo (Figure 4H and I), we examined their respective impact on mTEC subset development. Strikingly, numbers of total TECs and mTECs were higher in RipmOVAxOTII-Rag2-/- than in OTII-Rag2-/- mice (Figure 5A and B). We analyzed four TEC subsets based on MHCII and UEA-1 levels, as previously described (Lopes et al., 2017; Wong et al., 2014; Figure 5C). In contrast to cTEChi (MHCIIhiUEA-1lo), numbers of TEClo (MHCIIloUEA-1lo), mTEClo (MHCIIloUEA-1+), and mTEChi (MHCIIhiUEA-1+) were higher in RipmOVAxOTII-Rag2-/- than in OTII-Rag2-/- mice. Consistently, numbers of mTEClo and mTEChi identified based on the level of CD80 expression were also higher in RipmOVAxOTII-Rag2-/- mice (Figure 5—figure supplement 1). Interestingly, numbers of α6-integrinhiSca-1hi thymic epithelial progenitor (TEPC)-enriched cells in the TEClo subset were also increased (Figure 5D), indicating that self-reactive CD4+ thymocytes control TEC development from a progenitor stage. Of note, this strategy of TEC identification was not possible in ΔCD4 and mTECΔMHCII mice since MHCII expression is abrogated in TECs of these mice (Irla et al., 2008).

Figure 5 with 3 supplements see all
Highly self-reactive CD4+ thymocytes control medullary thymic epithelial cell (mTEC) development from an early progenitor stage.

(A–D) Flow cytometry profiles and numbers of total thymic epithelial cells (TECs) (EpCAM+) (A), cortical thymic epithelial cell (cTECs) (UEA-1-Ly51hi), mTECs (UEA-1+Ly51lo) (B), TEClo (MHCIIloUEA-1lo), cTEChi (MHCIIhiUEA-1lo), mTEClo (MHCIIloUEA-1hi), and mTEChi (MHCIIhiUEA-1hi) (C), α6-integrinhiSca-1hi TEPC-enriched cells in TEClo (D) in CD45neg-enriched cells from RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice. Data are representative of four experiments (n = 3 mice per group and experiment). (E) Confocal images of thymic sections from RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice stained for Aire (green) and Fezf2 (red). 11 and 22 sections derived from two RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice were quantified, respectively. Scale bar, 50 μm. Unfilled, dashed and solid arrowheads indicate Aire+Fezf2lo, Aire-Fezf2+, and Aire+Fezf2+ cells, respectively. The histogram shows the density of Aire+Fezf2lo, Aire-Fezf2+, and Aire+Fezf2+ cells. (F–H) Flow cytometry profiles, frequencies, and numbers of Aire-Fezf2-, Aire-Fezf2+, and Aire+Fezf2+ cells in total mTECs, mTEClo, and mTEChi (F), of CCL21+ cells in mTEClo (G) and of DCKL1+ cells in Aire- mTECs (H) from RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice. II Abs: secondary antibodies. Data are representative of two independent experiments (n = 3–4 mice per group and experiment). Error bars show mean ± SEM, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 using unpaired Student’s t-test for (A–E) and two-tailed Mann–Whitney test for (F–H).

A higher density of Aire+Fezf2-, Aire-Fezf2+, and Aire+Fezf2+ cells was observed in medullary regions of RipmOVAxOTII-Rag2-/- mice by immunohistochemistry (Figure 5E). Furthermore, numbers of Aire-Fezf2- mTEClo analyzed by flow cytometry were also higher in RipmOVAxOTII-Rag2-/- mice, confirming that self-reactive CD4+ thymocytes control mTEC differentiation from an early stage (Figure 5F). Numbers of Aire-Fezf2+ and Aire+Fezf2+ mTEClo and mTEChi were also markedly increased in these mice, although similar frequencies of proliferating Ki-67+ cells were observed (Figure 5—figure supplement 2). In agreement with increased Aire+ mTECs, involucrin+TPA+Aire- post-Aire cells were enhanced (Figure 5—figure supplement 3). Furthermore, numbers of CCL21+ and DCLK1+ tuft-like cells in mTEClo were also increased in RipmOVAxOTII-Rag2-/- mice compared to OTII-Rag2-/- mice (Figure 5G and H). These observations are consistent with our previous findings that antigen-specific interactions between mTECs and CD4+ thymocytes induce medulla development (Irla et al., 2012). Altogether, these results demonstrate that highly self-reactive CD4+ thymocytes regulate mTECs from an early to a late developmental stage and thus mTEC composition.

Self-reactive CD4+ thymocytes enhance the level of active H3K4me3 mark in mTEClo

Since histone modifications constitute important regulatory mechanisms that control the open and closed states of mTEC chromatin (Ucar and Rattay, 2015), we investigated whether self-reactive CD4+ thymocytes induce histone modifications in mTECs. We first analyzed in WT mTEClo the repressive H3K27me3 and the active H3K4me3 marks using chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq). As expected, metagene analyses showed that Aire-dependent TRAs had higher levels of H3K27me3 in their genes than in all genes of the genome, confirming that they are in a repressive state (Figure 6A). In contrast, Fezf2-dependent TRAs had a significant enrichment of H3K4me3 in their transcriptional start site (TSS) (Figure 6B). Similarly, Aire/Fezf2-independent TRAs were associated with low levels of H3K27me3 in their genes and high levels of H3K4me3 in their TSS. Thus, in contrast to Aire-dependent TRAs that are associated with the repressive H3K27me3 histone mark, Fezf2-dependent and Aire/Fezf2-independent TRAs are associated with the active H3K4me3 mark, indicating that these distinct TRAs are subjected to a specific epigenetic regulation.

H3K27me3 and H3K4me3 landscape in tissue-restricted self-antigen (TRA) genes of mTEClo from WT, RipmOVAxOTII-Rag2-/-, and OTII-Rag2-/- mice.

(A, B) Metagene profiles of the average normalized enrichment of H3K27me3 (A) and H3K4me3 (B) against input for Aire-dependent, Fezf2-dependent, and Aire/Fezf2-independent TRAs as well as for all genes of WT mTEClo. Boxplots represent the median enrichment, the 95% CI of the median (notches), and the 75th and 25th percentiles of H3K27me3 and H3K4me3. (C) H3K27me3 and H3K4me3 levels were analyzed by flow cytometry in mTEClo from RipmOVAxOTII-Rag2-/- (n = 4) and OTII-Rag2-/- (n = 5) mice. Histograms show the MFI. (D) Boxplots represent the median enrichment of H3K27me3 and H3K4me3 of Aire-dependent, Fezf2-dependent, and Aire/Fezf2-independent TRAs and in all genes of mTEClo from RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice. (E) Expression (RNA-seq) and H3K27me3 and H3K4me3 chromatin state (ChIP-seq) of the Aire/Fezf2-independent TRA, E2f2, in mTEClo from RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice. *p<0.05, **p<10–3, ***p<10–7, using the Mann–Whitney test for (A) and (B) and unpaired Student’s t-test for (C).

We next assessed whether highly self-reactive CD4+ thymocytes control the H3K27me3 and H3K4me3 chromatin landscape in mTEClo. In contrast to H3K27me3, we found an increased global level of H3K4me3 in RipmOVAxOTII-Rag2-/- compared to OTII-Rag2-/- mice by flow cytometry (Figure 6C). We further analyzed by nano-ChIP-seq whether self-reactive CD4+ thymocytes regulate in mTEClo the level of these two histone marks in Aire-dependent, Fezf2-dependent, and Aire/Fezf2-independent TRA genes. H3K27me3 levels in Aire-dependent TRA genes were comparable in mTEClo from RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice (Figure 6D, left panel). Although lower, H3K27me3 levels in Fezf2-dependent and Aire/Fezf2-independent TRAs as well as in all genes were similar in both mice, indicating that the interactions with self-reactive CD4+ thymocytes do not regulate this repressive mark in TRA genes (Figure 6D, left panel). In contrast, H3K4me3 global level was increased in the TSS of all TRAs in RipmOVAxOTII-Rag2-/- compared to OTII-Rag2-/- mice as well as in all genes (Figure 6D, right panel). For representation, whereas the Aire/Fezf2-independent TRA, E2F transcription factor 2 (E2f2) induced by these interactions, was barely devoid of H3K27me3 in both mice, it was marked by H3K4me3 in its TSS specifically in RipmOVAxOTII-Rag2-/- mice (Figure 6E). These results thus show that self-reactive CD4+ thymocytes enhance the global level of the active H3K4me3 histone mark in mTEClo and in particular in the TSS of Fezf2-dependent and Aire/Fezf2-independent TRAs, indicative of an epigenetic regulation for their expression.

MHCII/TCR interactions between mTECs and CD4+ thymocytes prevent the development of autoimmunity

We next evaluated the impact of mTEC-CD4+ thymocyte interactions on the generation of self-tolerant T cells by taking advantage that CD4+ and CD8+ T cells develop in mTECΔMHCII mice, in which MHCII/TCR interactions between mTECs and CD4+ thymocytes are abrogated. Interestingly, since TRAs induced by MHCII/TCR interactions showed a diverse peripheral tissue distribution in mTEClo (Figure 7A, Supplementary file 4), we analyzed the TCRVβ usage in mTECΔMHCII mice by flow cytometry. TCRVβ usage was more altered in CD69- mature CD4+ thymocytes than in CD8+ thymocytes (Figure 7B). Some TCRVβ were also altered in splenic CD4+ and CD8+ T cells. To determine whether these T cells contained self-reactive specificities, we adoptively transferred splenocytes from mTECΔMHCII or WT mice into lymphopenic Rag2-/- recipients (Figure 7C). Mice that received splenocytes derived from mTECΔMHCII mice lost significantly more weight than mice transferred with WT splenocytes (Figure 7D). They also exhibited splenomegaly with increased follicle areas and T-cell numbers showing a CD62LloCD44hi effector and CD62LhiCD44hi central memory phenotype (Figure 7E–G). Immune infiltrates in lungs and salivary glands were observed by histology and flow cytometry in 75 and 41% of mice, respectively (Figure 7H and I). These two tissues contained increased numbers of central memory as well as CD44+CD69+ and CD44+CD69- activated CD4+ and CD8+ T cells (Figure 7J). T-cell infiltrates were also observed in other tissues such as kidney, liver, and colon in agreement with the defective TRA expression associated with these tissues (Figure 7A and K). Altogether, these data show that in the absence of MHCII/TCR interactions between mTECs and CD4+ thymocytes, T cells contained self-reactive specificities and thus that these interactions are critical to the establishment of T-cell tolerance.

Figure 7 with 1 supplement see all
The adoptive transfer of T cells from mTECΔMHCII mice into Rag2-/- recipients induces autoimmunity.

(A) Tissue-restricted self-antigens (TRAs) underexpressed in mTEClo from mTECΔMHCII mice were assigned to their peripheral expression. (B) TCRVβ usage by CD69- mature CD4+ and CD8+ thymocytes (left panel) and CD4+Foxp3- and CD8+ splenic T cells (right panel) from WT and mTECΔMHCII mice. (C) Body weight of Rag2-/- recipients transferred with splenic T cells from WT or mTECΔMHCII was monitored during 6 weeks, and tissue infiltration was examined. (D) Weight loss relative to the initial weight. (E, F) Representative spleen pictures and their weights (E) and hematoxylin/eosin counterstained splenic sections (F). Scale bar, 1 mm. The histogram shows follicle areas. (G) Numbers of splenic CD3+, CD4+, and CD8+ T cells and of naive (CD44loCD62Lhi), effector memory (EM; CD44hiCD62Llo) and central memory (CM; CD44hiCD62Lhi) phenotype. (H) Lung and salivary gland (SG) immune infiltrates detected by hematoxylin/eosin counterstaining. Scale bar, 1 mm. (I, J) Numbers of T cells (I) and of naive, effector and central memory phenotype as well as CD44+CD69+ and CD44+CD69- T cells (J) in lungs and SG. (K) Schematic of T-cell infiltrates in mice transferred with mTECΔMHCII T cells relative to those transferred with WT T cells. Each circle and black triangles represent an individual mouse and T-cell infiltration in a specific tissue, respectively. Data are representative of two independent experiments (n = 5–7 mice per group and experiment). Error bars show mean ± SEM, ****p<0.0001 using two-way ANOVA for (D) and unpaired Student’s t-test for (B) and (E-J). *p<0.05, **p<0.01, ***p<0.001.

Discussion

Since mTECs play a crucial role in immunological tolerance by their exclusive expression of TRAs, it is essential to deepen our knowledge of the mechanisms that sustain their differentiation. Here using three distinct transgenic models, we found that self-reactive CD4+ thymocytes control the developmental transcriptional programs from the mTEClo stage, including TAC-TECs that precede Aire+ mTECs. CD4+ thymocytes increase in mTEClo the phosphorylation of p38 MAPK and IKKα, the latter implicated in mTEC development (Lomada et al., 2007; Shen et al., 2019). Moreover, self-reactive CD4+ thymocytes increase RelB phosphorylation level. Interestingly, this nonclassical NF-κB subunit is crucial for mTEC differentiation and Aire-dependent and -independent TRA expression (Riemann et al., 2017). These data thus suggest that CD4+ thymocytes activate intracellular pathways from the mTEClo stage, although alterations in mTEClo subset composition could also contribute to the differences observed. Nevertheless, the substantial and homogeneous reduction in the levels of phospho-IKKα, -p38, and -RelB argues instead for impaired activation of IKKα, p38, and RelB signaling in the absence of self-reactive CD4+ thymocytes. Analysis of the mTEClo transcriptional landscape by high-throughput RNA-seq revealed that self-reactive CD4+ thymocytes upregulate Nfkb2 (p52), known to form an heterocomplex with RelB in the nucleus upon activation (Irla et al., 2010). p52 is important for mTEC development, Aire, and TRA expression (Zhang et al., 2006; Zhu et al., 2006). Consequently, ΔCD4, mTECΔMHCII, and OTII-Rag2-/- mice in which MHCII/TCR interactions between mTECs and CD4+ thymocytes are disrupted have altered Relb and Nfkb2 expression, and reduced Aire+ mTEC numbers and Aire-dependent TRA representation. Our results are in agreement with the fact that RANK-induced NF-κB signaling is activated by membrane-bound RANKL and not soluble RANKL and thereby in the context of physical interactions between mTECs and CD4+ thymocytes (Asano et al., 2019).

These interactions also upregulate Trp53 (p53) that controls the mTEC niche (Rodrigues et al., 2017) and Irf4 and Irf7 transcription factors that regulate key chemokines implicated in thymocyte medullary localization and mTEC differentiation (Haljasorg et al., 2017; Otero et al., 2013). Furthermore, the deacetylase Sirtuin-1 (Sirt1), which regulates Aire activity (Chuprin et al., 2015), and Spib, which limits mTEC differentiation (Akiyama et al., 2014), were also upregulated. Self-reactive CD4+ thymocytes thus induce key transcription factors that both positively and negatively control mTEC differentiation. Remarkably, our three different transgenic models revealed that CD4+ thymocytes induce HDAC3-dependent mTEC-specific transcription factors (Goldfarb et al., 2016). Among them, Pou2f3 is involved in tuft-like mTEC development (Bornstein et al., 2018; Miller et al., 2018), which is consistent with our results showing that self-reactive CD4+ thymocytes control the cellularity of these cells. Our data thus identify that CD4+ thymocytes control the expression of master transcriptional regulators of mTEC differentiation and function.

In line with these data, we found that self-reactive CD4+ thymocytes regulate TEC development from a progenitor stage since they increase numbers of TEPC-enriched cells that express non-negligible MHCII levels. Interestingly, we provide the first evidence that self-reactive CD4+ thymocytes control the cellularity of Fezf2+ mTECs. Accordingly, the expression of Fezf2 and its respective TRAs was enhanced by CD4+ thymocytes. Moreover, self-reactive CD4+ thymocytes regulate the cellularity of CCL21+, post-Aire, and tuft-like cells in mTEClo. These results are in full agreement with our previous findings that self-reactive thymocytes drive medulla expansion and increase the overall cellularity of the mTEC compartment (Irla et al., 2012). Because the heterogeneous composition in mTEClo could influence the expression of the upregulated genes by self-reactive CD4+ thymocytes, we reanalyzed single-cell RNA-seq data in order to define their respective expression pattern in mTEC subsets. In accordance with the moderately altered frequencies of CCL21+ cells among mTEClo observed by flow cytometry, few genes upregulated by self-reactive CD4+ thymocytes were associated with this mTEC subset. In contrast, we found that antigen-specific interactions with CD4+ thymocytes strongly upregulate genes associated with TAC-TECs, Aire+ mTECs, and post-Aire cells. These findings indicate that self-reactive CD4+ thymocytes act from the precursors of Aire+ mTEChi (i.e., in TAC-TECs) to the post-Aire stage. It is interesting to note that although strongly altered the development of mTEChi is not completely abrogated in the absence of CD4+ thymocytes or MHCII/TCR-mediated interactions with CD4+ thymocytes. This could be explained by the fact that invariant NKT have been proposed to participate in mTEC differentiation by expressing RANKL (White et al., 2014). Overall, our results thus reveal that antigen-specific interactions with CD4+ thymocytes have an unsuspected broad impact on mTEC composition by driving their development from an early progenitor to a late post-Aire stage.

Interestingly, high-throughput RNA-seq showed that MHCII/TCR interactions with CD4+ thymocytes upregulate the expression of chemokines in mTEClo. Among them, CCL19 (CCR7 ligand) is implicated in the medullary localization of thymocytes and the emigration of newly generated T cells (Ueno et al., 2004); and CCL22 (CCR4 ligand) implicated in medullary entry and thymocyte/dendritic cell interactions (Hu et al., 2015). Self-reactive CD4+ thymocytes also enhance CCL2 (CCR2 ligand) and CCL20 (CCR6 ligand) that promote the entry of peripheral dendritic cells and Foxp3+ regulatory T cells into the thymus (Baba et al., 2009; Cédile et al., 2014; Cowan et al., 2018; Lopes et al., 2018; Borelli and Irla, 2021). mTEC-CD4+ thymocyte interactions thus induce key chemokines that regulate the trafficking of thymocytes and dendritic cells that participate in tolerance induction. Moreover, cytokines such as Il15 and Fgf21 implicated in invariant NKT development and TEC protection against senescence, as well as adhesion molecules involved in mTEC-thymocyte interactions, were also induced (Pezzi et al., 2016; White et al., 2014; Youm et al., 2016). Altogether, our data show that self-reactive CD4+ thymocytes regulate functional properties of mTECs by inducing chemokines, cytokines, and adhesion molecules that are critical for T-cell development.

The expression of TRAs is regulated by Aire and to a lesser extent by Fezf2 (Anderson et al., 2002; Takaba et al., 2015). In agreement with other studies (Gray et al., 2007; Takaba et al., 2015), we found Fezf2 in both mTEClo and mTEChi, whereas Aire protein is mainly expressed in mTEChi. Nevertheless and in line with recent single-cell transcriptomic analyses (Baran-Gale et al., 2020; Dhalla et al., 2020; Wells et al., 2020), we detected Aire by flow cytometry, qPCR, and RNA-seq in a small subset (~1.5%) of mTEClo. CD4+ thymocyte interactions upregulate Aire and Fezf2 and some of their respective TRAs in these cells. Interestingly, in contrast to Aire-dependent TRAs that are characterized by high levels of H3K27me3 (Handel et al., 2018; Sansom et al., 2014), we found that Fezf2-dependent TRAs show high levels of H3K4me3. This highlights that Aire and Fezf2 use distinct epigenetic modes in regulating TRA expression. Remarkably, these interactions also induce in mTEClo numerous Aire/Fezf2-independent TRAs, whose regulation remains unknown. Similarly to Fezf2-dependent TRAs, they had high levels of H3K4me3 in their TSS, suggesting that Aire/Fezf2-independent TRAs are not subjected to the same regulatory transcriptional mechanisms than Aire-dependent TRAs. Our results are consistent with a previous study indicating that the Aire-independent TRA, Gad1, shows active epigenetic marks (Tykocinski et al., 2010). Remarkably, self-reactive CD4+ thymocytes increase H3K4me3 level in the TSS of all TRA categories, thus providing a novel epigenetic mechanistic insight into how they regulate the mTEC gene expression profile. In line with TRA regulation and the development of distinct mTEC subsets, the repertoire of mature T cells contains autoreactive cells when MHCII/TCR interactions were abrogated between mTECs and CD4+ thymocytes. Accordingly, the adoptive transfer of splenocytes from mTECΔMHCII mice is capable of inducing signs of autoimmunity, illustrating the fact that mTEC-CD4+ thymocyte interactions are critical for the generation of a self-tolerant T-cell repertoire. Future investigations based on TCR sequencing analysis are expected to define to which extent the TCR repertoire is altered in mTECΔMHCII mice.

In summary, our genome-wide scale study reveals that self-reactive CD4+ thymocytes activate transcriptional programs from the TAC-TEC stage that sustains the differentiation into Aire+Fezf2+ and post-Aire mTECs (Figure 7—figure supplement 1). These interactions also upregulate the expression of TRAs, cytokines, chemokines, and adhesion molecules that are all implicated in mTEC function. Thus, CD4+ thymocytes control several unsuspected aspects of mTEClo required for the establishment of T-cell tolerance.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Genetic reagent (Mus musculus)C57BL/6J backgroundCharles RiverRRID:IMSR_JAX:000664
Genetic reagent (M. musculus)Ciitatm2Wrth/Ciitatm2WrthLeibundGut-Landmann et al., 2004RRID:MGI:3052466C57BL/6 background, ΔCD4 mice
Genetic reagent (M. musculus)H2dlAb1-Ea/H2dlAb1-EaMadsen et al., 1999RRID:MGI:4436873C57BL/6 background, MHCII-/- mice
Genetic reagent (M. musculus)K14xCiitaIII+IV-/-Irla et al., 2008C57BL/6 background, mTECΔMHCII mice
Genetic reagent (M. musculus)Tg(TcraTcrb)425CbnBarnden et al., 1998RRID:MGI:3762632C57BL/6 background, OTII mice
Genetic reagent (M. musculus)Tg(Ins2-TFRC/OVA)296WehiKurts et al., 1996RRID:MGI:3623748C57BL/6 background, Rip-mOVA mice
Genetic reagent (M. musculus)Rag2tm1Fwa/Rag2tm1FwaShinkai et al., 1992RRID:MGI:2174910C57BL/6 background, Rag2-/- mice
AntibodyAnti-IKKα (rabbit polyclonal)Cell Signaling TechnologyCat# 2682; RRID:AB_331626FACS (1:500)
AntibodyAnti-phospho IKKα (Ser180)/IKKβ(Ser181) (rabbit polyclonal)Cell Signaling TechnologyCat# 2681S; RRID:AB_331624FACS (1:500)
AntibodyAnti-p38 MAPK (rabbit polyclonal)Cell Signaling TechnologyCat# 9212; RRID:AB_330713FACS (1:500)
AntibodyAnti-phospho p38 MAPK (Thr180/Tyr182) (rabbit polyclonal)Cell Signaling TechnologyCat# 9211S; RRID:AB_331641FACS (1:500)
AntibodyAnti-Erk1/2(rabbit polyclonal)Cell Signaling TechnologyCat# 9102; RRID:AB_330744FACS (1:500)
AntibodyAnti-phospho Erk1/2 (Thr202/Tyr204) (rabbit polyclonal)Cell Signaling TechnologyCat# 9101S;RRID:AB_331646FACS (1:500)
AntibodyAnti-NF-κB p65 (clone D14E12, rabbit monoclonal)Cell Signaling TechnologyCat# 8242S; RRID:AB_10859369FACS (1:500)
AntibodyPhospho-NF-κB p65 (Ser536) (clone 93H1, rabbit monoclonal)Cell Signaling TechnologyCat# 3033S; RRID:AB_331284FACS (1:3000)
AntibodyAnti-RelB (clone C-19, rabbit polyclonal)Santa Cruz BiotechnologyCat# sc-226; RRID:AB_632341FACS (1:200)
AntibodyAnti-phospho RelB (ser552) (clone D41B9, rabbit monoclonal)Cell Signaling TechnologyCat# 5025S; RRID:AB_10622001FACS (1:1000)
AntibodyAnti-DCLK1 (clone D2U3L, rabbit monoclonal)Cell Signaling TechnologyCat# 62257; RRID:AB_2799622FACS (1:200)
AntibodyAnti-H3K4me3 (rabbit polyclonal)AbcamCat# ab8580; RRID:AB_306649FACS(1:1000)ChIP-seq (2 µg:25 µg chromatin)
AntibodyAnti-H3K27me3 (clone C36B11, rabbit monoclonal)Cell Signaling TechnologyCat# 9733; RRID:AB_2616029FACS(1:1000)ChIP-seq(1:50)
AntibodyPE-Cy7 anti-CD326 (EpCAM) (clone G8.8, rat monoclonal)eBioscienceCat# 25-5791-80; RRID:AB_1724047FACS(1:3000)
AntibodyAlexa Fluor 488 anti-Aire (clone 5H12, rat monoclonal)eBioscienceCat# 53-5934-82; RRID:AB_10854132FACS, IF(1:200)
AntibodyPE anti-Ly51 (clone BP-1, mouse monoclonal)BD BiosciencesCat# 553735; RRID:AB_395018FACS(1:3000)
AntibodyPerCP-Cy5.5 anti-CD80 (clone 16-10A1, Armenian hamster monoclonal)BioLegendCat# 104722; RRID:AB_2291392FACS(1:200)
AntibodyeFluor 450 anti-Ki-67 (clone SolA15, rat monoclonal)eBioscienceCat# 48-5698-82; RRID:AB_11149124FACS(1:200)
AntibodyAnti-Fezf2 (clone F441, rabbit polyclonal)IBL TecanCat# JP18997; RRID:AB_2341444FACS, IF(1:200)
AntibodyAnti-Involucrin (clone Poly19244, rabbit polyclonal)BioLegendCat# 924401; RRID:AB_2565452IF(1:100)
AntibodyPE anti-Ly-6A/E (Sca-1) (clone D7, rat monoclonal)BD BiosciencesCat# 553108; RRID:AB_394629FACS(1:600)
AntibodyBiotin anti-CD49f (α6-integrin) (clone GoH3, rat monoclonal)BioLegendCat# 313604; RRID:AB_345298FACS(1:200)
AntibodyAlexa Fluor 647 anti-I-Ab (MHCII) (clone AF6-120.1, mouse monoclonal)BioLegendCat# 116412; RRID:AB_493141FACS(1:200)
AntibodyBrilliant Violet 421 anti-CD4 (clone RM4-5, rat monoclonal)BioLegendCat# 100544; RRID:AB_11219790FACS(1:200)
AntibodyPerCP-Cy5.5 anti-CD4 (clone RM4-5, rat monoclonal)BD BiosciencesCat# 550954; RRID:AB_393977FACS(1:200)
AntibodyPacific Blue anti-CD8α (clone 53-6.7, rat monoclonal)BD BiosciencesCat# 558106; RRID:AB_397029FACS(1:200)
AntibodyPE/Cy7 anti-CD8α (clone 53-6.7, rat monoclonal)BioLegendCat# 100722; RRID:AB_312761FACS(1:600)
AntibodyAlexa Fluor 488 anti-CD44 (clone IM7, rat monoclonal)BioLegendCat# 103016; RRID:AB_493679FACS(1:200)
AntibodyPE anti-CD69 (clone H1.2F3, rat monoclonal)BioLegendCat# 104508; RRID:AB_313111FACS(1:400)
AntibodyPE anti-CD62L (clone MEL-14, rat monoclonal)BD BiosciencesCat# 553151; RRID:AB_394666FACS(1:300)
AntibodyPerCP-Cy5.5 anti-CD3ε (clone 17A2, rat monoclonal)BD BiosciencesCat# 560527;RRID:AB_1727463FACS(1:200)
AntibodyAlexa Fluor 405 anti-CCL21 (clone 59106, rat monoclonal)R&D SystemsCat# IC457VFACS(1:100)
AntibodyCD45 MicroBeads, mouse (clone 30F11.1, rat monoclonal)MiltenyiCat# 130052301; RRID:AB_2877061
AntibodyCy5 anti-rabbit IgG (goat polyclonal)InvitrogenCat# A10523; RRID:AB_2534032FACS(1:500)
AntibodyCyanine 3 anti-rabbit IgG (goat polyclonal)InvitrogenCat# A10520; RRID:AB_2534029IF(1:500)
AntibodyFITC anti-TCR Vβ2 (clone B20.6, rat monoclonal)BD BiosciencesCat# 557004; RRID:AB_647180FACS(20 µl per 106 cells)
AntibodyFITC anti-TCR Vβ3 (clone KJ25, Armenian hamster monoclonal)BD BiosciencesCat# 557004; RRID:AB_647180FACS(20 µl per 106 cells)
AntibodyFITC anti-TCR Vβ4 (clone KT4, rat monoclonal)BD BiosciencesCat# 557004; RRID:AB_647180FACS(20 µl per 106 cells)
AntibodyFITC anti-TCR Vβ5.1, 5.2 (clone MR9-4, mouse monoclonal)BD BiosciencesCat# 553189; RRID:AB_394697FACS(1:100)
AntibodyFITC anti-TCR Vβ6 (clone RR4-7, rat monoclonal)BD BiosciencesCat# 557004; RRID:AB_647180FACS(20 µl per 106 cells)
AntibodyPE anti-TCR Vβ8.1, 8.2 (clone MR5-2, mouse monoclonal)BioLegendCat# 140103; RRID:AB_10641144FACS(1:300)
AntibodyFITC anti-TCR Vβ9 (clone MR10-2, mouse monoclonal)BD BiosciencesCat# 557004; RRID:AB_647180FACS(20 µl per 106 cells)
AntibodyPE anti-TCR Vβ10b (clone B21.5, rat monoclonal)BD BiosciencesCat# 553285; RRID:AB_394757FACS(1:300)
AntibodyBiotin anti-TCR Vβ11 (clone RR3-15, rat monoclonal)BD BiosciencesCat# 553196; RRID:AB_394702FACS(1:300)
AntibodyFITC anti-TCR Vβ12 (clone MR11-1, mouse monoclonal)BD BiosciencesCat# 557004; RRID:AB_647180FACS(20 µl per 106 cells)
AntibodyFITC anti-TCR Vβ13 (clone MR12-3, mouse monoclonal)BD BiosciencesCat# 557004; RRID:AB_647180FACS(20 µl per 106 cells)
AntibodyFITC anti-TCR Vβ14 (clone 14-2, rat monoclonal)BD BiosciencesCat# 557004; RRID:AB_647180FACS(20 µl per 106 cells)
AntibodyFITC anti-TCR Vβ17a (clone KJ23, rat monoclonal)BD BiosciencesCat# 557004; RRID:AB_647180FACS(20 µl per 106 cells)
AntibodyCD4+ T cell isolation kit, mouseMiltenyi BiotecCat# 130-104-454
Peptide, recombinant proteinPerCP-Cy5.5 StreptavidinBioLegendCat# 405214; RRID:AB_2716577FACS(1:400)
Peptide, recombinant proteinAlexa Fluor 488 StreptavidinInvitrogenCat# S11223IF(1:1000)
Peptide, recombinant proteinOvalbumin (323–339)PolyPeptideCat# SC13035 μM
Chemical compound, drugLiberase TMRocheCat# 0540112700150 μg/ml
Chemical compound, drugDNase IRocheCat# 10104159001100 μg/ml
Chemical compound, drugTRIzolThermo Fisher ScientificCat# 15596018
Software, algorithmGraphPad PrismGraphPad SoftwareRRID:SCR_002798
Software, algorithmFlowJoFlowJohttps://www.flowjo.com/RRID:SCR_008520
Software, algorithmFiji/ImageJ softwareFiji-ImageJhttps://imagej.nih.gov/ij/RRID:SCR_003070
Software, algorithm7500 Real-Time PCR SoftwareThermo Fisherhttps://www.thermofisher.com/us/en/home/technical-resources/software-downloads/applied-biosystems-7500-real-time-pcr-system.htmlRRID:SCR_014596
Software, algorithmPheatmap 0.2https://github.com/raivokolde/pheatmap (Kolde, 2018)RRID:SCR_016418
Software, algorithmSeuratHao et al., 2021RRID:SCR_016341
Sequence-based reagentActin-FWSigma-AldrichPCR primersCAGAAGGAGATTACTGCTCTGGCT
Sequence-based reagentActin-RVSigma-AldrichPCR primersGGAGCCACCGATCCACACA
Sequence-based reagentAire-FWSigma-AldrichPCR primersGCATAGCATCCTGGACGGCTTCC
Sequence-based reagentAire-RVSigma-AldrichPCR primersCTGGGCTGGAGACGCTCTTTGAG
Sequence-based reagentCcl19-FWSigma-AldrichPCR primersGCTAATGATGCGGAAGACTG
Sequence-based reagentCcl19-RVSigma-AldrichPCR primersACTCACATCGACTCTCTAGG
Sequence-based reagentCcl2-FWSigma-AldrichPCR primersTGGAGCATCCACGTGTTG
Sequence-based reagentCcl2-RVSigma-AldrichPCR primersACTCATTGGGATCATCTTGCT
Sequence-based reagentCcl22-FWSigma-AldrichPCR primersCTGATGCAGGTCCCTATGGT
Sequence-based reagentCcl22-RVSigma-AldrichPCR primersGGAGTAGCTTCTTCACCCAG
Sequence-based reagentCcl25-FWSigma-AldrichPCR primersGCCTGGTTGCCTGTTTTGTT
Sequence-based reagentCcl25-RVSigma-AldrichPCR primersACCCAGGCAGCAGTCTTCAA
Sequence-based reagentCdh2-FWSigma-AldrichPCR primersAGCGCAGTCTTACCGAAGG
Sequence-based reagentCdh2-RVSigma-AldrichPCR primersTCGCTGCTTTCATACTGAACTTT
Sequence-based reagentCoch-FWSigma-AldrichPCR primersGTGCAGCAAAACCTGCTACAA
Sequence-based reagentCoch -RVSigma-AldrichPCR primersAGCTAGGACGTTCTCTTTGGT
Sequence-based reagentCrabp1-FWSigma-AldrichPCR primersCAGCAGCGAGAATTTCGACGA
Sequence-based reagentCrabp1-RVSigma-AldrichPCR primersCGCACAGTAGTGGATGTCTTGA
Sequence-based reagentCrp-FWSigma-AldrichPCR primersCATAGCCATGGAGAAGCTAC
Sequence-based reagentCrp-RVSigma-AldrichPCR primersCAGTGGCTTCTTTGACTCTG
Sequence-based reagentCsn2-FWSigma-AldrichPCR primersCTCCACTAAAGGACTTGACAG
Sequence-based reagentCsn2-RVSigma-AldrichPCR primersACCTTCTGAAGTTTCTGCTC
Sequence-based reagentFabp9-FWSigma-AldrichPCR primersCACTGCAGACAACCGAAAAG
Sequence-based reagentFabp9-RVSigma-AldrichPCR primersTCTGTTTGCCAAGCCATTTT
Sequence-based reagentFam183b-FWSigma-AldrichPCR primersCGTGTGGGGCAGATGAAGAAT
Sequence-based reagentFam183b-RVSigma-AldrichPCR primersGGTGAATGAGGTTCAGGAACTTG
Sequence-based reagentFcer2a-FWSigma-AldrichPCR primersCCAGGAGGATCTAAGGAACGC
Sequence-based reagentFcer2a-RVSigma-AldrichPCR primersTCGTCTTGGAGTCTGTTCAGG
Sequence-based reagentFezf2-FWSigma-AldrichPCR primersCAGCACTCTCTGCAGACACAA
Sequence-based reagentFezf2-RVSigma-AldrichPCR primersTGCCGCACTGGTTACACTTA
Sequence-based reagentGrap-FWSigma-AldrichPCR primersGATCAGGGAGAGTGAGAGTTCC
Sequence-based reagentGrap-RVSigma-AldrichPCR primersCAGCTCGTTGAGGGAGTTGA
Sequence-based reagentIcam2-FWSigma-AldrichPCR primersATCAACTGCAGCACCAACTG
Sequence-based reagentIcam2-RVSigma-AldrichPCR primersACTTGAGCTGGAGGCTGGTA
Sequence-based reagentIl15-FWSigma-AldrichPCR primersAGCAGATAACCAGCCTACAGGA
Sequence-based reagentIl15-RVSigma-AldrichPCR primersTGTTGAAGATGAGCTGGCTATGG
Sequence-based reagentIl21-FWSigma-AldrichPCR primersCGCCTCCTGATTAGACTTCG
Sequence-based reagentIl21-RVSigma-AldrichPCR primersTGGAGCTGATAGAAGTTCAGGA
Sequence-based reagentIl5-FWSigma-AldrichPCR primersCCGCCAAAAAGAGAAGTGTGGCGA
Sequence-based reagentIl5-RVSigma-AldrichPCR primersGCCTCAGCCTTCCATTGCCCA
Sequence-based reagentIl7-FWSigma-AldrichPCR primersGGGTCCTGGGAGTGATTATGG
Sequence-based reagentIl7-RVSigma-AldrichPCR primersCGGGAGGTGGGTGTAGTCAT
Sequence-based reagentItgad-FWSigma-AldrichPCR primersCGAAAGGGTTCAGACTTTGC
Sequence-based reagentItgad-RVSigma-AldrichPCR primersACACCTCCACGGATAGAAGTC
Sequence-based reagentItgb6-FWSigma-AldrichPCR primersGCTGGTCTGCCTGTTTCTGC
Sequence-based reagentItgb6-RVSigma-AldrichPCR primersTGAGCAGCTTTCTGCACCAC
Sequence-based reagentKcnj5-FWSigma-AldrichPCR primersAAAACCTTAGCGGCTTTGTATCT
Sequence-based reagentKcnj5-RVSigma-AldrichPCR primersAAGGCATTAACAATCGAGCCC
Sequence-based reagentKrt1-FWSigma-AldrichPCR primersTGGGAGATTTTCAGGAGGAGG
Sequence-based reagentKrt1-RVSigma-AldrichPCR primersGCCACACTCTTGGAGATGCTC
Sequence-based reagentMeig1-FWSigma-AldrichPCR primersCTTCAGCGGAGGGACAATAC
Sequence-based reagentMeig1-RVSigma-AldrichPCR primersCAAGGTTTCAAGGTGGGTGT
Sequence-based reagentNov-FWSigma-AldrichPCR primersAGACCCCAACAACCAGACTG
Sequence-based reagentNov-RVSigma-AldrichPCR primersCGGTAAATGACCCCATCGAAC
Sequence-based reagentNts-FWSigma-AldrichPCR primersGCAAGTCCTCCGTCTTGGAAA
Sequence-based reagentNts-RVSigma-AldrichPCR primersTGCCAACAAGGTCGTCATCAT
Sequence-based reagentReig1-FWSigma-AldrichPCR primersATGGCTAGGAACGCCTACTTC
Sequence-based reagentReig1-RVSigma-AldrichPCR primersCCCAAGTTAAACGGTCTTCAGT
Sequence-based reagentResp18-FWSigma-AldrichPCR primersCCAGCCAAGATGCAGAGTTCGTTAAAG
Sequence-based reagentResp18-RVSigma-AldrichPCR primersTCAGTCAGCAACAAGGTTGAGGCCCAC
Sequence-based reagentRsph1-FWSigma-AldrichPCR primersACGGGGACACATATGAAGGA
Sequence-based reagentRsph1-RVSigma-AldrichPCR primersGGCCGTGCTTTTTATTTTTG
Sequence-based reagentSpon2-FWSigma-AldrichPCR primersATGGAAAACGTGAGTCTTGCC
Sequence-based reagentSpon2-RVSigma-AldrichPCR primersTGATGCTGTATCTAGCCAGAGG
Sequence-based reagentSult1c2-FWSigma-AldrichPCR primersATGGCCTTGACCCCAGAAC
Sequence-based reagentSult1c2-RVSigma-AldrichPCR primersTCGAAGGTCTGAATCTGCCTC
Sequence-based reagentUpk3b-FWSigma-AldrichPCR primersCATCTGGCTAGTGGTGGCTTT
Sequence-based reagentUpk3b-RVSigma-AldrichPCR primersGGTAATGTCATATAGTGGCCGTC
OtherBiotinylated Lotus Tetragonolobus Lectin (LTL)Vector LaboratoriesCat# B-1325; RRID:AB_2336558IF(1:500)
OtherFITC Ulex Europaeus Agglutinin I (UEA I)Vector LaboratoriesCat# FL-1061; RRID:AB_2336767FACS(1:600)
OtherSuperScript II Reverse TranscriptaseThermo FisherCat# 18064022
OtherSYBR Premix Ex Taq master mixTakaraCat# RR390A
OthermiRNeasy Micro KitQIAGENCat# 217084
OtherTruSeq ChIP Library Preparation KitIlluminaCat# IP-202-2012

Mice

C57BL/6 WT mice were purchased from Charles River. CiitaIII+IV-/- (ΔCD4) (LeibundGut-Landmann et al., 2004), MHCII-/- (Madsen et al., 1999), K14xCiitaIII -/- (mTECΔMHCII) (Irla et al., 2008), OTII (Barnden et al., 1998), RipmOVAxOTII (Kurts et al., 1996), and Rag2-/- (Shinkai et al., 1992) mice were on C57BL/6J background. OTII and RipmOVAxOTII were backcrossed on Rag2-/- background. All mice were maintained under specific pathogen-free conditions at an ambient temperature of 22°C at the animal facilities of the CIML (Marseille, France). Standard food and water were given ad libitum. Males and females were used at the age of 5–6 weeks. All experiments were done in accordance with national and European laws for laboratory animal welfare (EEC Council Directive 2010/63/UE), and were approved by the Marseille Ethical Committee for Animal Experimentation (Comité National de Réflexion Ethique sur l’Expérimentation Animale no. 14).

mTEC purification

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mTECs were isolated by enzymatic digestion with 50 μg/ml of Liberase TM (Roche) and 100 μg/ml of DNase I (Roche) in HBSS medium, as previously described (Lopes et al., 2017). CD45+ hematopoietic cells were depleted using anti-CD45 magnetic beads by autoMACS with the depleteS program (Miltenyi Biotec). Total mTECs (EpCAM+UEA-1+Ly51lo), mTEClo (EpCAM+UEA-1+Ly51loCD80lo/int), and mTEChi (EpCAM+UEA-1+Ly51loCD80hi) were sorted with a FACSAriaIII cell sorter (BD). The purity of sorted mTEClo was >98%. Flow cytometry gating strategies are shown in Figure 1—figure supplement 1.

mTEC antigen presentation assays

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Variable numbers of mTECs from WT and mTECΔMHCII mice loaded or not with OVA323-339 (5 µM, PolyPeptide group) were co-cultured with 105 OTII CD4+ T cells (purified with a CD4+ T cell isolation kit, Miltenyi Biotec) in RPMI medium (Thermo Fisher) supplemented with 10% FCS (Sigma-Aldrich), L-glutamine (2 mM, Thermo Fisher), sodium pyruvate (1 mM, Thermo Fisher), 2-mercaptoethanol (2 × 10−5 M, Thermo Fisher), penicillin (100 IU/ml, Thermo Fisher), and streptomycin (100 μg/ml, Thermo Fisher). The activation of OTII CD4+ T cells was assessed 18 hr later by flow cytometry based on the upregulation of the CD69 marker.

Flow cytometry

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TECs, thymocytes, and splenic T cells were analyzed by flow cytometry (FACSCanto II, BD) with standard procedures. Cells were incubated for 15 min at 4°C with Fc-block (anti-CD16/CD32, 2.4G2, BD Biosciences) before staining. Antibodies are listed in the Key resources table. For intracellular staining with anti-Foxp3, anti-Ki-67, anti-p38 MAPK, anti-phospho p38 MAPK (Thr180/Tyr182), anti-IKKα, anti-phospho IKKα(Ser180)/IKKβ(Ser181), anti-Erk1/2 MAPK, anti-phospho Erk1/2 MAPK (Thr202/Tyr204), anti-p65, anti-phospho p65(ser536), anti-RelB, anti-phospho RelB(ser552), and anti-DCLK1 antibodies, cells were fixed, permeabilized, and stained with the Foxp3 staining kit according to the manufacturer’s instructions (eBioscience). Intracellular staining with anti-Aire, anti-Fezf2, anti-CCL21, anti-H3K4me3, and anti-H3K27me3 antibodies was performed with Fixation/Permeabilization Solution Kit (BD). Secondary antibodies (II Abs) were used to set positive staining gates. Flow cytometry analysis was performed with a FACSCanto II (BD), and data were analyzed using FlowJo software (BD).

Quantitative RT-PCR

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Total RNA was prepared with TRIzol (Invitrogen). cDNAs was synthesized with oligo(dT) using Superscript II reverse transcriptase (Invitrogen). qPCR was performed with the ABI 7500 fast real-time PCR system (Applied Biosystems) and SYBR Premix Ex Taq master mix (Takara). Primers are listed in the Key resources table.

In vivo transfer of splenocytes into Rag2-/- recipients

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3.106 splenocytes purified from the spleen of WT and mTECΔMHCII mice of 8 weeks of age were intravenously injected into Rag2-/- female recipients. CD3+, CD4+, and CD8+ T-cell infiltrates were analyzed 6 weeks after transfer by histology and flow cytometry in different peripheral tissues.

Histology

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Tissues were fixed in 10% buffered formalin (Sigma) and embedded in paraffin blocks. 4-μm-thick sections were stained with hematoxylin-eosin (Thermo Fisher) and analyzed by light microscopy (Nikon Statif eclipse Ci-L). For immunofluorescence experiments, frozen thymic sections were stained as previously described (Sergé et al., 2015) with Alexa Fluor 488 or Alexa Fluor 647-conjugated anti-Aire (5H12; eBioscience), biotinylated anti-TPA (LTL; Vector Laboratories), rabbit anti-Fezf2 (F441; IBL Tecan), and rabbit anti-involucrin (BioLegend) antibodies. Rabbit anti-Fezf2 and rabbit anti-involucrin were revealed with Cy3-conjugated anti-rabbit IgG (Invitrogen), and biotinylated anti-TPA was revealed with Alexa Fluor 488-conjugated streptavidin (Invitrogen). Sections were mounted with Mowiol (Calbiochem). Immunofluorescence confocal microscopy was performed with a LSM780 Leica SP5X confocal microscope. Images were analyzed with ImageJ software.

RNA-seq experiments

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Total RNA purified from mTEClo (Figure 1—figure supplement 1) was extracted with miRNeasy Micro Kit (QIAGEN), and RNA quality was assessed on an Agilent 2100 BioAnalyzer (Agilent Technologies). RNA Integrity Number values over 8 were obtained. RNA-seq libraries were generated using the SMART-Seq-v4-Ultra Low Input RNA Kit (Clontech) combined to the Nextera library preparation kit (Illumina) following the manufacturer’s instructions. Libraries were sequenced with the Illumina NextSeq 500 machine to generate datasets of single-end 75 bp reads. Two independent biological replicates were used per each condition. RNA-seq data have been deposited with Gene Expression Omnibus (GEO) under the accession number GSE144650.

RNA-seq analysis

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The sequencing reads were mapped to the Mus musculus (mm10) reference genome using the TopHat 2 (version 2.0.12) aligner (Kim et al., 2013). The reads mapping to the annotated genes (igenome UCSC mm10 GTF: https://support.illumina.com/sequencing/sequencing_software/igenome.html) were counted, normalized, and compared using Cuffdiff2 (version 2.2.1; Trapnell et al., 2013) between two conditions. Cuffdiff2 generated expression levels as FPKM, FCs, and p-values to assess the statistical significance of the FPKM difference of each gene between the tested two conditions. Genes showing a significant variation in gene expression between WT and ΔCD4, or WT and mTECΔMHCII, or RIPmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice (p-value≤0.05, FC difference ≥ 2 or ≤ 0.5) were considered as up- or downregulated. The TRA and non-TRA gene assignments were obtained from Sansom et al., 2014. In this report, the identification of the specificity of expression for each gene in the genome was carried out by analyzing the microarray expression profiles of a large number of different mouse tissues. Aire-dependent, Fezf2-dependent, Aire/Fezf2-dependent, and Aire/Fezf2-independent TRAs were identified using Aire-/- mTEChi RNA-seq datasets and Fezf2-/- total mTEC microarray datasets, obtained from the NCBI GEO database (GSE87133 and GSE69105, respectively).

Correlation between the variation of gene expression in mTEClo from WT versus mTECΔMHCII or RIPmOVAxOTII-Rag2-/- versus OTII-Rag2-/- mice, and of the same genes in mTEChi from WT versus Aire-/- mice was performed doing a locally regression (loess) with the R software (http://www.r-project.org/). Differential gene expression in WT versus Aire-/- mTEChi was obtained by processing using TopHat2 and Cuffdiff2, the sequencing reads corresponding to WT (Chuprin et al., 2015) and Aire-/- (Danan-Gotthold et al., 2016) mTEChi RNA-seq datasets, which were obtained from the NCBI GEO database (GSE68190 and GSE87133, respectively). HDAC3-dependent mTEC-specific transcription factors regulated by mTEC-thymocyte crosstalk were identified by comparing the top 50 transcriptional regulators that are induced by HDAC3 (Goldfarb et al., 2016) with genes upregulated in the different mouse models. TRAs differentially expressed between mTEClo from WT and mTECΔMHCII mice were classified according to their tissue distribution using the mouse ENCODE transcriptome database (Yue, 2014). Only tissues that showed the highest expression were taken into account.

Single-cell RNA-seq analysis

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Single-cell RNA-seq count matrix from Wells et al., 2020 (accession number GSE137699) was reanalyzed with the Seurat package (Hao et al., 2021). QC analysis was performed by filtering out cells with a number of feature counts under 200 or over 4000, and a proportion of mitochondrial counts over 4%. Sample integration was performed as described in the Seurat vignette. After PCA for dimension reduction, 15 first dimensions were conserved. Cells were clustered and visualized with UMAP. Cluster annotation was performed by identifying sets of specific markers to each cluster using a differential expression test (FindMarkers function, test = ‘roc’). Heatmaps were generated using the pheatmap R package.

Nano-ChIP-seq experiments

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Nano-ChIP-seq was performed as previously described (Adli and Bernstein, 2011) on 5.104 purified mTEClo (Figure 1—figure supplement 1). ChIP-seq libraries were prepared with TruSeq ChIP Sample Preparation Kit (Illumina), and 2 × 75 bp paired-end reads were sequenced on an Illumina HiSeq. ChIP-seq data have been deposited with GEO under the accession number GSE144680.

ChIP-seq analysis

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Reads were aligned on the mouse genome (mm10) using Bowtie 2 and default parameters (Langmead and Salzberg, 2012). Properly paired alignments were selected using Samtools view with the 0x2flag (-f option). Nonuniquely mapped reads-pairs were filtered out by removing reads with the ‘XS’ tag set by Bowtie 2. Normalized bedgraphs for ChIP and input samples were generated using MACS2 (Zhang et al., 2008) with the callpeak command in BAMPE mode with the --SPMR option. For the diffused H3K27me3 histone mark, the -broad option was used. ChIP enrichment was calculated parsing the ChIP and input normalized bedgraphs with MACS2 and the bdgcmp command (-m FE option). The obtained bedgraphs were converted to wig using the bedGraphToWig.pl script with the --step 10 parameter. MACS2-generated peak calling files were converted to BED files using the cut -f 1-6 command. The obtained Wig and BED files were parsed by CEAS (Shin et al., 2009) to generate metagene profile plots corresponding to the average enrichment of H3K4me3 in 3 kb TSS windows or H3K27me3 at gene loci. H3K4me3 and H3K27me3 CEAS-dumped files were parsed to compute ratios of ChIP/input in 1 kb TSS windows and gene loci, respectively. Statistical significance between ChIP enrichment data was tested using the nonparametric Mann–Whitney test. Data were visualized using the Integrative Genomics Viewer (IGV) (Robinson et al., 2011).

Statistics

Data are presented as means ± standard error of mean (SEM). Statistical analysis was performed with GraphPad Prism 7.03 software by using ANOVA, chi-square, unpaired Student’s t-test, or Mann–Whitney test. ****p<0.0001, ***p<0.001, **p<0.01, *p<0.05. Normal distribution of the data was assessed using d’Agostino–Pearson omnibus normality test.

Data availability

RNA-seq data have been deposited in GEO under the accession number GSE144650. ChIP-seq data have been deposited in GEO under the accession number GSE144680.

The following data sets were generated
    1. Irla M
    2. Giraud M
    (2020) NCBI Gene Expression Omnibus
    ID GSE144650. mTEClo/int RNAseq profiling in three mouse models of impaired mTEC/Tcell crosstalk.
    1. Irla M
    2. Giraud M
    (2020) NCBI Gene Expression Omnibus
    ID GSE144680. Chromatin state of mTEClo/int in the RipmOVAxOTII mTEC/Tcell crosstalk mouse model.
The following previously published data sets were used
    1. Wells KL
    (2020) NCBI Gene Expression Omnibus
    ID GSE137699. ingle cell sequencing defines a branched progenitor population of stable medullary thymic epithelial cells.
    1. Abramson J
    2. Giraud M
    (2015) NCBI Gene Expression Omnibus
    ID GSE68190. Sirt1 is essential for Aire-mediated induction of central immunological tolerance.

References

Decision letter

  1. Sarah Russell
    Reviewing Editor; Peter MacCallum Cancer Centre, Australia
  2. Tadatsugu Taniguchi
    Senior Editor; Institute of Industrial Science, The University of Tokyo, Japan
  3. Sarah Russell
    Reviewer; Peter MacCallum Cancer Centre, Australia

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Thymocytes trigger self-antigen-controlling pathways in immature medullary thymic epithelial stages" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Sarah Russell as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Tadatsugu Taniguchi as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

With regard to the elucidation of how CD4 cells signal to mTECS to shape their differentiation:

1. According to the recent scRNA sequencing studies (reviewed in Kadouri et al. 2020), the mTEClow mTECs contain at least two distinct subpopulations: the functionally mature CCL21-producing mTEC I and the immature mTECs giving rise to mTEC II and III. In its current form, however, the manuscript largely ignores the presence of mTEC I cells. It would be important to analyze the changes in this population in the knockout models (by sequencing and/or qPCR) and cover this population also in the introduction and discussion.

2. The authors conclude that their RNAseq data in figures 1 and 4 show that genes are upregulated/downregulated. However, it could also be that their differential gene/cytokine expression is due to the presence of different mTEClo subsets, and the authors show this in figure 3. This would change the conclusion to: CD4+ thymocytes alter mTEClo differentiation states, associated with differential gene expression. This is also the case in figure 2. For instance, Lopez et al. state that AIRE expression 4.5-fold higher in mTECΔMHCII cells but then they show that there are different percentages of AIRE+ cells (change in the mTEClo subsets in the ko mice). It is important to make clear that the signalling/expression data are confounded by differences in mTEC cell populations changes might reflect different cell composition rather than different signalling or expression states.

3. In figure 3, the authors show mTEChi cells in dCD4 and mTECΔMHCII mice. It is surprising that mTEChi cells are present since these shouldn't differentiate in the absence of an MHCII-TCR interaction. It is important to address how these cells develop and whether their presence alters interpretation of the results.

With regard to how these interactions shape the TCRVβ repertoire in mature T cells:

4. Differences in the percentage of a few TCRVβ families are not sufficient to conclude that there is an alteration in the TCRVβ repertoire. Sequencing the different TCRs and evaluate constraints on TCR-CDR3 segments will be required for this conclusion.

Reviewer #1 (Recommendations for the authors):

The assumption by the authors that the observed phenotypes are driven by CD4 deficiency is central to the conclusions of the manuscript, but is not tested in the manuscript. This seems an important caveat to address.

The authors should make clear whether the genetic disruptions they use directly or indirectly influence the cell types that they claim to be relevant for each phenotype, and what other alternative explanations might lead to the phenotypes.

Reviewer #2 (Recommendations for the authors):

Occasionally (as in page 12 para 2) the authors interpret a decrease (of transcription factors) in their knockout model as an increase in wild-type mouse. Although hypothetically right, this should be corrected especially in the Results section.

Reviewer #3 (Recommendations for the authors):

1. The authors present data demonstrating that the lack of CD4 on thymocytes changes NFkb signalling in mTEClo cells, with decreased IKKa/p38 signalling and increased pRELB. MFI would likely be better evaluated as a staining index or ratio, to control staining in that mouse type.

2. Staining for DclK+ cells in the AIRE- subset should show all gates.

3. In figure 4, the staining for pRELB relative to total RELB would suggest higher phosphorylation in the OTII-RAG2-/- mice (i.e., total RELB is more decreased than pRELB). Staining for RelB appears very heterogeneous, suggesting that it is expressed at different levels in different subsets. Also, staining controls for both types of mice are not shown. This is also the case in figure 6C.

https://doi.org/10.7554/eLife.69982.sa1

Author response

Essential revisions:

With regard to the elucidation of how CD4 cells signal to mTECS to shape their differentiation:

1. According to the recent scRNA sequencing studies (reviewed in Kadouri et al. 2020), the mTEClow mTECs contain at least two distinct subpopulations: the functionally mature CCL21-producing mTEC I and the immature mTECs giving rise to mTEC II and III. In its current form, however, the manuscript largely ignores the presence of mTEC I cells. It would be important to analyze the changes in this population in the knockout models (by sequencing and/or qPCR) and cover this population also in the introduction and discussion.

We thank the Reviewers for highlighting that mTEClo also contain CCL21-producing cells, called mTEC I. In this revised version, we analyzed CCL21+ mTEClo by flow cytometry in the different mouse models described in this study, i.e., ΔCD4 (absence of CD4+ thymocytes), mTECΔMHCII (absence of MHCII-TCR interactions between mTEC and CD4+ thymocytes), MHCII-/-, OTII and RipmOVA x OTII mice. As shown in the new Figure 3D and Figure 3—figure supplement 3B, the frequencies of CCL21+ cells among mTEClo cells were globally unaffected in ΔCD4, mTECΔMHCII and MHCII-/- mice, although their numbers were reduced, consistently with a smaller medulla compartment in these mice, previously reported by our group (cf. Irla et al. Plos One 2012 and J Immunol. 2013). Numbers of CCL21+ mTEClo were also increased in RipmOVA x OTII mice compared to OTII mice (cf. New Figure 5G), consistently with our previous observation that antigen-specific interactions between mTECs and CD4+ thymocytes induce medulla development (cf. Irla et al. Plos One 2012).

We now cover the CCL21+ mTEClo subset in the introduction and discuss these new results.

2. The authors conclude that their RNAseq data in figures 1 and 4 show that genes are upregulated/downregulated. However, it could also be that their differential gene/cytokine expression is due to the presence of different mTEClo subsets, and the authors show this in figure 3. This would change the conclusion to: CD4+ thymocytes alter mTEClo differentiation states, associated with differential gene expression. This is also the case in figure 2. For instance, Lopez et al. state that AIRE expression 4.5-fold higher in mTECΔMHCII cells but then they show that there are different percentages of AIRE+ cells (change in the mTECmhciisubsets in the ko mice). It is important to make clear that the signalling/expression data are confounded by differences in mTEC cell populations changes might reflect different cell composition rather than different signalling or expression states.

We fully agree that mTEClo cells are heterogeneous and that changes in mTEClo subset composition in the transgenic mouse models analyzed could contribute to the differences observed. To clarify this point, we aimed at investigating in this revised version whether the upregulation/downregulation of genes observed by RNAseq in figures 1, 2 and 4 could be due to the impact of the crosstalk at the gene expression level or rather due to the presence of different mTEClo subsets. To this end, we analyzed single cell RNA-seq data on mTEC recently published by Wells KL et al. (eLife. 2020) (cf. new Figure 1—figure supplement 4) to determine whether the genes found to be affected by the thymic crosstalk, are specific to a particular mTEC subset (i.e. CCL21+, TAC-TEC, Aire+, Post-Aire and Tuft-like cells). In accordance with the reduced proportions of Post-Aire and DCLK1+ Tuft-like cells observed respectively by histology and flow cytometry (cf. Figure 3E, Figure 5H, Figure 3—figure supplement 2A and Figure 5—figure supplement 3A), some of these genes were associated with Post-Aire and Tuft-like cells (cf. new Figure 1I, new Figure 2K, new Figure 4L). Importantly, this new analysis revealed that many genes upregulated by CD4+ thymocytes were highly expressed by Aire+ mTECs, strongly suggesting that their increase in mTEClo is due to an enhanced transcriptional activity accompanying the transition to Aire+ mTECs rather than changes in mTEClo subset composition. In line with this hypothesis, we found that several of these genes were already expressed in TAC-TECs that precede Aire+ mTECs.

These new results, suggesting a direct upregulation of a number of mTEChi-specific genes in mTEClo, as well as the possibility that signaling data could be confounded by differences in mTEClo subsets, are now discussed in this revised version.

3. In figure 3, the authors show mTEChi cells in dCD4 and mTECΔMHCII mice. It is surprising that mTEChi cells are present since these shouldn't differentiate in the absence of an MHCII-TCR interaction. It is important to address how these cells develop and whether their presence alters interpretation of the results.

To clarify this point, we now show frequencies and numbers of mTEChi analyzed by flow cytometry in WT, dCD4 and mTECΔMHCII mice (cf. new Figure 3A). The development of these cells is substantially impaired but not totally abrogated in these mice, which is in total agreement with previous observations from our group (Irla et al. Immunity 2008 and Plos One 2012). Since the development of mTEChi, including Aire+ cells, relies on RANK signaling (Khan et al. J Exp Med. 2014, Akiyama et al. Immunity 2008, Hikosaka et al. Immunity 2008, Rossi et al. J Exp Med. 2007, Irla Front Immunol 2021), the residual development of mTEC in dCD4 and mTECΔMHCII mice could be explained by the fact that in addition to CD4+ thymocytes, RANKL is also expressed by invariant NKT cells, which are present in these mice. In agreement with this hypothesis, invariant NKT cells have been shown to participate in Aire+ mTEC differentiation in the post-natal thymus, but to a lesser extent than CD4+ thymocytes (White AJ et al. J Immunol 2014). We clarified this point in the discussion (lines 441-444).

The residual development of mTEChi in these mice does not interfere with the interpretation of the results. Indeed, we took into consideration the impact of the residual development of mTEChi in the composition of mTEClo by analyzing post-Aire cells that derived from Aire+ mTEChi (cf. Figure 3—figure supplement 2A and Figure 5—figure supplement 3A). In agreement with the impaired development of Aire+ mTEChi in dCD4, mTECΔMHCII mice, numbers of Involucrin+TPA+Aire- post-Aire cells were reduced. In this revised version, we also analyzed whether genes found to be differentially expressed by RNA-seq are associated with post-Aire cells using single-cell RNAseq data (cf. new Figures 1I, 2K and 4L). In line with the reduction in post-Aire cells, some differentially expressed genes were associated with these cells.

With regard to how these interactions shape the TCRVβ repertoire in mature T cells:

4. Differences in the percentage of a few TCRVβ families are not sufficient to conclude that there is an alteration in the TCRVβ repertoire. Sequencing the different TCRs and evaluate constraints on TCR-CDR3 segments will be required for this conclusion.

We fully agree that the differences observed by flow cytometry in the percentages of TCRVβ usage are insufficient to conclude that the TCRVβ repertoire is altered. We have modified the text in the Results section (cf. lines 97, 368) and discussed that future experiments based on TCR sequencing are expected to clarify this point (cf. lines 482-489), which is beyond the scope of the current study.

Reviewer #1 (Recommendations for the authors):

The assumption by the authors that the observed phenotypes are driven by CD4 deficiency is central to the conclusions of the manuscript, but is not tested in the manuscript. This seems an important caveat to address.

The authors should make clear whether the genetic disruptions they use directly or indirectly influence the cell types that they claim to be relevant for each phenotype, and what other alternative explanations might lead to the phenotypes.

We understand the point raised by the Reviewer that a genetic mutation in the class II transactivator CIITA in dCD4 and mTECΔMHCII mice could indirectly influence the mTEC subsets analyzed. This is the reason why we have completed our study by analyzing mTEClo from MHCII-/- mice both by qPCR and flow cytometry (cf. Figure 1—figure supplement 3A and Figure 3—figure supplement 3). Similar defects in TRA expression and in the cellularity of mTEC subsets (i.e. Aire-Fezf2+ and Aire+Fezf2+ mTECs, as well as in CCL21+ and DCLK1+ mTEClo) were observed in these mice, ruling out any potential indirect effect of CIITA in the observed results (cf. lines 132-134).

Furthermore, to strengthen our general conclusion that self-reactive CD4+ thymocytes have a direct effect on mTEC and further exclude any potential indirect impact of CIITA or even MHCII molecules in the observed results, we compared the transcriptome of mTEClo from OTII-Rag2-/- and RipmOVA x OTII-Rag2-/- mice by RNA-seq and analyzed their respective composition in mTEC subsets by flow cytometry. Importantly, the only difference in these two transgenic mice relies on the expression (RipmOVA x OTII-Rag2-/- mice) or not (OTII-Rag2-/- mice) of the cognate OVA antigen, which is driven by the rat insulin promoter (Rip) specifically in mTEC. Again, the analysis of these mice led to the same overall phenotype as observed in the WT vs. dCD4 or the WT vs. mTECΔMHCII comparison.

In summary, the comparison of these three distinct transgenic models by the different approaches used strongly supports the notion that self-reactive CD4+ thymocytes directly affect the mTEC subsets analyzed.

Reviewer #2 (Recommendations for the authors):

Occasionally (as in page 12 para 2) the authors interpret a decrease (of transcription factors) in their knockout model as an increase in wild-type mouse. Although hypothetically right, this should be corrected especially in the Results section.

We accordingly modified the text (cf. Lines 189-194).

Reviewer #3 (Recommendations for the authors):

1. The authors present data demonstrating that the lack of CD4 on thymocytes changes NFkb signalling in mTEClo cells, with decreased IKKa/p38 signalling and increased pRELB. MFI would likely be better evaluated as a staining index or ratio, to control staining in that mouse type.

Thank you for this comment. We now represent MFI as a ratio to secondary antibody control staining (cf. new Figures 1A and 4B).

2. Staining for DclK+ cells in the AIRE- subset should show all gates.

We now show the gating strategy concerning the identification of DCLK1+ mTEC (cf. new Figure 3—figure supplement 2B and new Figure 5—figure supplement 3B).

3. In figure 4, the staining for pRELB relative to total RELB would suggest higher phosphorylation in the OTII-RAG2-/- mice (i.e., total RELB is more decreased than pRELB). Staining for RelB appears very heterogeneous, suggesting that it is expressed at different levels in different subsets. Also, staining controls for both types of mice are not shown. This is also the case in figure 6C.

To clarify whether the heterogeneous staining of RelB expression observed by flow cytometry could be due to different expression levels in distinct mTEClo subsets, we analyzed Relb gene expression level in CCL21+, TAC-TEC, post-Aire and Tuft-like mTEC using single-cell RNA-seq data. We found that Relb is highly expressed in TAC-TEC and post-Aire cells (cf. New Figure 4-supplement 2A). These observations are in agreement with the hypothesis that self-reactive CD4+ thymocytes activate NFkB signaling from the TAC-TEC stage that precedes Aire+ mTECs and that this activation persists in post-Aire cells. We modified the text accordingly (cf. lines 259-263).

Unfortunately, it is technically not possible to detect RelB in Tuft-like mTEC because anti-RelB and anti-DCLK1 antibodies are both produced in rabbit and are detected through anti-rabbit secondary antibodies. It is also not possible to evaluate RelB level in post-Aire and TAC-TEC cells because there is no valuable markers to date that allow the identification of these cells by flow cytometry. Therefore, we discuss that we cannot exclude that the heterogeneity in RelB expression could also be due to different representation of mTEClo subsets (cf. lines 396-400).

Moreover, we verified that the secondary antibody control staining gives the same signal in RipmOVA x OTII-Rag2-/- and OTII-Rag2-/- mice (Author response image 1).

Author response image 1
https://doi.org/10.7554/eLife.69982.sa2

Article and author information

Author details

  1. Noella Lopes

    Aix-Marseille University, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
    Contribution
    Formal analysis, Investigation, Methodology, Validation, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6296-8426
  2. Nicolas Boucherit

    Aix-Marseille University, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
    Contribution
    Methodology, Validation
    Contributed equally with
    Jérémy C Santamaria and Nathan Provin
    Competing interests
    No competing interests declared
  3. Jérémy C Santamaria

    Aix-Marseille University, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
    Contribution
    Data curation, Formal analysis, Validation
    Contributed equally with
    Nicolas Boucherit and Nathan Provin
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7613-3668
  4. Nathan Provin

    Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France
    Contribution
    Conceptualization, Data curation, Formal analysis, Visualization
    Contributed equally with
    Nicolas Boucherit and Jérémy C Santamaria
    Competing interests
    No competing interests declared
  5. Jonathan Charaix

    Aix-Marseille University, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
    Contribution
    Data curation, Formal analysis, Methodology, Software, Validation, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Pierre Ferrier

    Aix-Marseille University, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
    Contribution
    Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Matthieu Giraud

    Nantes Université, INSERM, Center for Research in Transplantation and Translational Immunology, UMR 1064, Nantes, France
    Contribution
    Data curation, Formal analysis, Methodology, Software, Validation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1208-9677
  8. Magali Irla

    Aix-Marseille University, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Supervision, Visualization, Writing – original draft, Writing - review and editing
    For correspondence
    Magali.Irla@inserm.fr
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8803-9708

Funding

H2020 Marie Skłodowska-Curie Actions (CIG_SIGnEPI4Tol_618541)

  • Magali Irla

Agence Nationale de la Recherche (2011-CHEX-001-R12004KK)

  • Matthieu Giraud

Agence Nationale de la Recherche (ANR-19-CE18-0021-01 RANKLthym)

  • Magali Irla

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Pr. Arnauld Sergé (LAI, Marseille, France) for critical reading of the manuscript, Pr. Walter Reith (University of Geneva, Switzerland) for providing CiitaIII+IV-/- and K14xCiitaIII -/- mice, and Dr. Bruno Lucas (Institut Cochin, Paris, France) for providing MHCII-/- mice. We also thank Cloé Zamit and Alexia Borelli (CIML, Marseille, France) for help with mouse genotyping and Lionel Chasson (CIML, Marseille, France) for help with paraffin-embedded tissues. We acknowledge the flow cytometry, the imaging core (ImagImm) and animal facility platforms at CIML for excellent technical support. NL and JC were supported by a PhD fellowship from Aix-Marseille University and the Ministère de l’Enseignement Supérieur et de la Recherche, respectively.

Ethics

All mice were housed, bred and manipulated under specific pathogen-free conditions at the animal facilities of the CIML (Marseille, France). All experiments were done in accordance with national and European laws for laboratory animal welfare (EEC Council Directive 2010/63/UE), and were approved by the Marseille Ethical Committee for Animal Experimentation (Comité National de Réflexion Ethique sur l'Expérimentation Animale no. 14; Permit Number: 02373.03).

Senior Editor

  1. Tadatsugu Taniguchi, Institute of Industrial Science, The University of Tokyo, Japan

Reviewing Editor

  1. Sarah Russell, Peter MacCallum Cancer Centre, Australia

Reviewer

  1. Sarah Russell, Peter MacCallum Cancer Centre, Australia

Publication history

  1. Preprint posted: November 29, 2020 (view preprint)
  2. Received: May 3, 2021
  3. Accepted: January 14, 2022
  4. Version of Record published: February 21, 2022 (version 1)
  5. Version of Record updated: February 22, 2022 (version 2)

Copyright

© 2022, Lopes et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Noella Lopes
  2. Nicolas Boucherit
  3. Jérémy C Santamaria
  4. Nathan Provin
  5. Jonathan Charaix
  6. Pierre Ferrier
  7. Matthieu Giraud
  8. Magali Irla
(2022)
Thymocytes trigger self-antigen-controlling pathways in immature medullary thymic epithelial stages
eLife 11:e69982.
https://doi.org/10.7554/eLife.69982

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