Thymocytes Trigger Self-Antigen-Controlling Pathways In

bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Thymocytes trigger self-antigen-controlling pathways in

immature medullary thymic epithelial stages

Noëlla Lopes1, Nicolas Boucherit1, Jonathan Charaix1, Pierre Ferrier1, Matthieu

Giraud2 and Magali Irla1,*

1Centre d’Immunologie de Marseille-Luminy, INSERM U1104, CNRS UMR7280, Aix-

Marseille Université UM2, Marseille, 13288 cedex 09 France

2Inserm, Université de Nantes, Centre de Recherche en Transplantation et

Immunologie, UMR 1064, ITUN, F-44000 Nantes, France

*Correspondence: [email protected]

1 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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 turn, thymocytes regulate the cellularity of Aire+ mTEChi.

However, it remains unknown whether thymocytes control Aire+ mTEChi-precursors

that are contained in mTEClo cells or other mTEClo subsets that have recently been

delineated or identified 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 in mTEClo the expression of tissue-restricted self-

antigens, cytokines, chemokines and adhesion molecules important for T-cell

development. This gene activation program is combined with a global increase of the

active H3K4me3 histone mark. Finally, we show that these interactions induce key

mTEC transcriptional regulators and govern mTEClo subset composition, including

Aire+ mTEChi-precursors, post-Aire and tuft-like mTECs. Our genome-wide study

thus reveals that self-reactive CD4+ thymocytes control multiple unsuspected facets

from immature stages of mTECs, which determines their heterogeneity.

Keywords: autoimmune regulator; central tolerance; medullary thymic epithelial

cells; thymic crosstalk; tissue-restricted self-antigens

2 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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 to induce 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 (Gabler 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 & Klein,

2006; Sansom et al, 2014). Recent single-cell advances indicate that the

heterogeneity of mTECs, especially in the mTEClo compartment, is more complex

3 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

than previously thought (Kadouri et al, 2019). mTEClo with intermediate levels of

CD80 (CD80int) lie into mTEC clusters that were defined as proliferating and

maturational, expressing Fezf2 and preceding Aire+ mTEChi (Baran-Gale et al, 2020;

Dhalla et al, 2020). On the other hand, mTEClo with low or no expression of CD80

(CD80neg/lo) have been shown to be divided into three main subsets: CCL21+ mTECs

(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).

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 signaling pathways (Akiyama et al, 2008; Hikosaka et al, 2008; 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 Aire+ mature mTEChi

or upstream on their 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 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, particularly in the loci of Fezf2-dependent

and Aire/Fezf2-independent TRAs, indicative of an epigenetic regulation for their

4 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

expression. Our data also reveal that self-reactive CD4+ thymocytes induce in

mTEClo key transcriptional regulators pivotal for mTEC differentiation and function.

Accordingly, self-reactive CD4+ thymocytes control the composition of the mTEClo

compartment, i.e.: Aire+ mTEChi-precursors, post-Aire cells and tuft-like mTECs.

Finally, we demonstrate that disrupted MHCII/TCR interactions between mTECs and

CD4+ thymocytes leads to an altered TCRVβ repertoire in mature T cells containing

self-specificities capable of inducing multi-organ autoimmunity. Altogether, our

genome-wide study reveals that self-reactive CD4+ thymocytes control the

developmental transcriptional programs in mTEClo, which condition their

differentiation and function as inducers of T-cell tolerance.

5 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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 pathways 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 S1A. Interestingly, the phosphorylation level of IKKα and p38 MAPK was

substantially reduced in ΔCD4 mice (Figure 1A,B; Figure S2), indicating that CD4+

thymocytes activate in mTEClo the IKKα intermediate of non-classical NF-κB pathway

and the p38 MAPK pathway.

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 S1B). 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 one to five of peripheral tissues (Sansom et al, 2014).

Interestingly, the genes upregulated by CD4+ thymocytes exhibited ~4-fold more of 6 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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=5.2x10-10) (Figure 1D, right panel). Most of the

TRAs upregulated by CD4+ thymocytes are sensitive to the action of Aire (Aire-

dependent TRAs) or controlled by Aire and Fezf2-independent mechanisms

(Aire/Fezf2-independent TRAs) (Figure 1E and Table S1). The upregulation of some

of these TRAs by CD4+ thymocytes was confirmed by qPCR in mTEClo purified from

ΔCD4 and MHCII-/- mice, the latter also lacking CD4+ thymocytes (Figure 1F and

Figure S3A).

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, have been described 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 control in mTEClo the expression of some

cytokines and cell adhesion molecules such as integrins and cadherins (Figure 1H,I).

Altogether, these results provide the first evidence that CD4+ thymocytes are able to

induce in mTEClo essential transcriptional regulators for mTEC differentiation and

function but also the expression of TRAs, adhesion molecules and cytokines

implicated in thymocyte selection.

CD4+ thymocytes regulate maturational programs in mTEClo through

MHCII/TCR interactions

7 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

We next investigated by which mechanism CD4+ thymocytes regulate the

transcriptional programs in mTEClo. Given that MHCII/TCR interactions with mTECs

are critical for CD4+ T-cell selection (Klein et al, 2019), we hypothesized that they

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

Δ mTECs (mTEC MHCII mice) (Irla et al, 2008). In contrast to their WT counterparts, we

ΔMHCII found that OVA323-339-loaded mTECs from mTEC mice were ineffective at

activating OTII-specific CD4+ T cells, demonstrating that antigen-presentation ability

of mTECs to CD4+ T cells is impaired in these mice (Figure 2A).

The comparison of the gene expression profiles of mTEClo purified from WT and

mTECΔMHCII mice (Figure S1B) 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=4.5x10-13) that are mainly Aire-dependent and Aire/Fezf2-independent

(Figures 2C-E and Table S2). In line with the recent discovery of Aire expression in

the fraction of mTEClo that express intermediate levels of CD80 and which are found

in the proliferating and maturational stage mTEC single-cell clusters (Dhalla et al,

2020), we found a strong correlation (p=2x10-16) 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 of

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the non-TRA genes induced by MHCII/TCR interactions with CD4+ thymocytes in

mTEClo (Figure 2G). WT mTEClo expressed ~4.5-fold more Aire compared to

mTEClo from mTECΔMHCII mice with substantial levels of 73.7 and 15.8 FPKM,

respectively. For comparison Aire expression level in WT mTEChi was 448.9 FPKM.

WT mTEClo also expressed ~1.5-fold more Fezf2 compared to mTEClo from

mTECΔMHCII mice. This upregulation in Aire and Fezf2 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,

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,K).

Altogether, these data show that CD4+ thymocytes control, through MHCII/TCR

interactions, the functional properties of the fraction of mTEClo that express

intermediate levels of MHCII and activate key transcriptional programs governing

mTEC differentiation and function.

MHCII/TCR interactions with CD4+ thymocytes regulate in mTEClo 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 (Figure 1G and

2I), we next analyzed the mTEC composition of ΔCD4 and mTECΔMHCII mice. An

Aire/Fezf2 co-staining both by histology and flow cytometry revealed a substantial

reduction in Aire-Fezf2+ and Aire+Fezf2+ cells (Figure 3A,B). We further analyzed by

flow cytometry Aire and Fezf2 expression in mTEClo and mTEChi according to the

gating strategy shown in Figure S1A. In agreement with the detection of Aire in the

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proliferating and maturational single-cell clusters in mTEClo (Baran-Gale et al, 2020;

Dhalla 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 3B). 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 of these cells were

Δ observed in ΔCD4 and mTEC MHCII mice (Figure S4). Furthermore, numbers of

involucrin+TPA+Aire- post-Aire cells were reduced in the medulla of ΔCD4 and

mTECΔMHCII mice (Figure S5), consistently with the decrease of their Aire+ mTEChi-

precursors (Figure 3B-C).

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 (Figure

1G, 2I). We found that numbers of tuft-like mTECs identified by flow cytometry using

the DCLK1 marker were also reduced in both mice (Figure 3C), indicating that their

development is controlled by MHCII/TCR interactions with CD4+ thymocytes.

Importantly, Aire-Fezf2+ and Aire+Fezf2+ mTEClo and mTEChi as well as DCLK1+ tuft-

like mTECs were similarly reduced in MHCII-/- mice, confirming that CD4+ thymocytes

control the cellularity of these novel mTEC subsets (Figure S3B,C). Altogether, these

data reveal that MHCII/TCR-mediated interactions with CD4+ thymocytes have a

broad impact on mTEClo composition by controlling the cellularity of Fezf2+ pre-Aire

mTECs, post-Aire and tuft-like mTECs.

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 10 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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 mTEClo, we expect

that the Aire-dependent mOVA is expressed in a fraction of cells that express

intermediate levels of CD80 and MHCII, since these cells have been shown to start

expressing Aire. In contrast to total Erk1/2 MAPK, p38 MAPK, IKKα and p65, the

non-classical NF-kB subunit RelB was increased in mTEClo at mRNA and protein

levels in RipmOVAxOTII-Rag2-/- compared to OTII-Rag2-/- mice (Figure 4A,B and

Figure S6). The level of RelB phosphorylation was also higher (Figure 4B),

indicating that self-reactive CD4+ thymocytes activate the non-classical NF-κB

pathway in mTEClo.

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 S1B) 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 a ~4-fold more of TRA over

non-TRA genes (p=4.7x10-23), which corresponded mainly to Aire-dependent and

Aire/Fezf2-independent TRAs (Figure 4D-F and Table S3). Similarly to the WT

versus mTECΔMHCII comparison, we found a strong correlation (p=6.2x10-7) 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

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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 of the non-TRA genes (Figure 4H,I). Similarly to the comparisons

of the WT versus ΔCD4 or mTECΔMHCII, numerous HDAC3-induced regulators as well

as Sirt1, Nfkb2, Relb and Trp53 transcription factors were upregulated in mTEClo of

RipmOVAxOTII-Rag2-/- mice (Figure 4J). Interestingly, the Foxn1 transcription factor,

implicated in TEC differentiation and growth, 21 out of its top 30 targets (Zuklys et al,

2016), cytokines, chemokines and cell adhesion molecules were also upregulated

(Figure 4K-L). Altogether, these data reveal that highly self-reactive CD4+

thymocytes control in the mTEClo fraction that express intermediate levels of MHCII

not only key transcription factors driven their differentiation but also crucial 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, 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,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.

Interestingly, numbers of α6-integrinhiSca-1hi thymic epithelial progenitor (TEPC)-

enriched cells in the TEClo subset were also increased (Figure 5D), indicating that

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self-reactive CD4+ thymocytes control TEC development from a precursor stage. Of

Δ note, this strategy of TEC identification was not possible in ΔCD4 and mTEC MHCII

mice since MHCII expression is abrogated in TECs of these mice (Irla et al, 2008).

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 was observed

(Figure S7). In agreement with increased Aire+ mTECs, involucrin+TPA+Aire- post-

Aire cells were enhanced (Figure S8). Altogether, these results demonstrate that

highly self-reactive CD4+ thymocytes regulate mTECs from an early to a late

developmental stage and thus the heterogeneity of mTECs in the thymus.

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 state of mTEC chromatin (Ucar & 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 reduced H3K27me3 levels and a significant enrichment 13 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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 distinct epigenetic regulation.

We next assessed whether 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

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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 a self-tolerant T-cell repertoire by taking advantage that mTECΔMHCII

mice, in which MHCII/TCR interactions between mTECs and CD4+ thymocytes are

abrogated, develop CD4+ and CD8+ T cells. Interestingly, since TRAs induced by

MHCII/TCR interactions showed a diverse peripheral tissue distribution in mTEClo

(Figure 7A and Table S4), we analyzed by flow cytometry whether the TCRVβ

repertoire is altered in mTECΔMHCII mice. The TCRVβ usage was more altered in

CD69- mature CD4+ thymocytes than in CD69- mature CD8+ thymocytes (Figure 7B).

Some TCRVβ specificities were also altered in splenic CD4+ and CD8+ T cells. To

determine whether these T cells contain self-reactive specificities, we adoptively

Δ transferred splenocytes from mTEC MHCII or WT mice into lymphopenic Rag2-/-

Δ recipients (Figure 7C). Mice that received splenocytes derived from mTEC MHCII 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,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 (Figure

7K). Altogether, these data show that in absence of MHCII/TCR interactions between

15 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

mTECs and CD4+ thymocytes, T cells contain self-reactive specificities and thus that

these interactions are critical to the establishment of T-cell tolerance.

Discussion

Since mTECs play a crucial role in immunological tolerance by expressing TRAs, it is

essential to deepen our knowledge of the mechanisms that sustain their

development. Here using three distinct transgenic models, we found that self-reactive

CD4+ thymocytes control the developmental transcriptional programs from the

mTEClo stage. CD4+ thymocytes induce 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 induce RelB expression and

increase its phosphorylation level. Interestingly, this non-classical NF-κB subunit is

crucial for mTEC development and Aire-dependent and independent TRA expression

(Riemann et al, 2017). These data thus show that CD4+ thymocyte development and

antigen-specific interactions play non-redundant roles in activating intracellular

pathways from the mTEClo stage. Analysis of the mTEClo transcriptional landscape by

high throughput RNA-seq revealed that self-reactive CD4+ thymocytes also induce

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, reduced Aire+

mTEC numbers and Aire-dependent TRA representation. Our results are in

agreement with the fact that RANK signaling is activated by membrane-bound

16 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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 induce 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 that limits mTEC differentiation

(Akiyama et al, 2014) were also induced. Self-reactive CD4+ thymocytes thus induce

key transcription factors that not only positively but also 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 that self-

reactive CD4+ thymocytes control the cellularity of these cells. Our data thus identify

that CD4+ thymocytes control in mTEClo the expression of master transcriptional

regulators of mTEC differentiation and function.

In line with these results, we found that self-reactive CD4+ thymocytes control the

differentiation of distinct mTEC subsets. Interestingly, self-reactive CD4+ thymocytes

regulate TEC development from a precursor stage since they increase numbers of

TEPC-enriched cells that express non-negligible MHCII levels. We provide the first

evidence that they control Fezf2+ pre-Aire mTECs. Accordingly, the expression of

Fezf2 and its respective TRAs was enhanced by CD4+ thymocytes. Moreover, self-

reactive CD4+ thymocytes regulate tuft-like and post-Aire cell numbers in mTEClo.

These results thus reveal that antigen-specific interactions with CD4+ thymocytes

17 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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 mTECs. Among them,

CCL19 (CCR7 ligand) is implicated in the medulla localization of thymocytes and the

emigration of newly generated T cells (Ueno et al, 2004); and CCL22 (CCR4 ligand)

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; Cowan et al, 2018; Lopes et al, 2018).

mTEC-CD4+ thymocyte interactions thus induce key chemokines that regulate the

trafficking of thymocytes and thymic entry of peripheral 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, these results

show that self-reactive CD4+ thymocytes regulate functional properties of mTEClo by

inducing chemokines, cytokines and adhesion molecules that are critical for T-cell

development.

The expression of TRAs is regulated by Aire and at 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), we

detected Aire by flow cytometry, qPCR and RNA-seq in a small subset (~1.5%) of

18 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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, Gad67, 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 TCRVβ repertoire of mature T cells was altered when MHCII/TCR

interactions were abrogated between mTECs and CD4+ thymocytes. Importantly,

mTEC-CD4+ thymocyte interactions are critical for the generation of a self-tolerant T-

cell repertoire since the adoptive transfer of splenocytes from mTECΔMHCII mice into

Rag2-/- recipients led to signs of autoimmunity.

In summary, our genome-wide scale study reveals that self-reactive CD4+

thymocytes activate in mTEClo transcriptional programs that sustain the

differentiation into Aire+ mTEChi-precursors, Aire+Fezf2+, post-Aire and tuft-like

mTECs. These interactions also induce TRAs, cytokines, chemokines and adhesion

molecules that are all implicated in mTEC function. Thus, CD4+ thymocytes control

19 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

several unsuspected aspects of mTEClo required for the establishment of T-cell

tolerance.

Acknowledgments

We gratefully thank Pr. Walter Reith for CiitaIII+IV-/- and K14xCiitaIII+IV-/- mice and Dr.

Bruno Lucas for MHCII-/- mice. We also thank Lionel Chasson for help with paraffin-

embedded tissues. We acknowledge the flow cytometry and animal facility platforms

at CIML for excellent technical support. This project was funded by the Marie Curie

Actions (Career Integration Grants, CIG_SIGnEPI4Tol_618541 to M.I.), the Agence

Nationale de la Recherche (2011-CHEX-001-R12004KK to M.G), the ‘Fondation

Princesse Grace de la Principauté de Monaco (to P.F) and institutional grants from

Institut National de la Santé et de la Recherche Médicale, Centre National de la

Recherche Scientifique and Aix-Marseille Université. We acknowledge financial

support from n°ANR-10-INBS-04-01 France Bio Imaging and the France Génomique

national infrastructure, funded as part of the "Investissements d'Avenir" program

managed by the Agence Nationale de la Recherche (ANR-10-INBS-0009). N.L. and

J.C. are supported by a PhD fellowship from Aix-Marseille University and the

Ministère de l’Enseignement Supérieur et de la Recherche, respectively.

Author contributions

N.L., N.B., J.C. and M.I. performed experiments and analyzed the data. M.G.

performed all the bioinformatics analysis and interpreted the data. P.F. participated to

the conceptualization of ChIP-seq experiments. N.L and M.I. wrote the manuscript.

M.I. initiated, supervised and conceived the study.

Declaration of interests

20 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

The authors declare no competing interests.

21 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure legends

Figure 1. 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 2

independent experiments (n=3-4 mice per group and experiment).

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 2 independent biological replicates with mTEClo derived from 3-5 mice.

(D) Numbers of 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 in WT versus ΔCD4 mTEClo. The color code represents gene

expression level.

(H) Heatmaps of selected genes encoding for cell adhesion molecules and cytokines.

22 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(I) Itgb6, Cdh2, Il21 and Il5 mRNAs were measured by qPCR in WT (n=4) and ΔCD4

(n=4) mTEClo.

Error bars show mean±SEM, *p<0.05, **p<0.01, ***p<0.001 using two-tailed

Mann-Whitney test for A,B,F,I and Chi-squared test for D.

Figure 2. 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

ΔMHCII OVA323-339-loaded WT or mTEC mTECs derived of 2 independent experiments

(n=2-3 mice per group and experiment).

(B) Scatter-plot of gene expression levels (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 2 independent biological replicates with mTEClo derived from 3-5 mice.

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

Δ measured by qPCR in mTEClo from WT (n=4) and mTEC MHCII (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 (FC>5) in red.

(G) Heatmap of selected activation factors.

23 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(H) Aire and Fezf2 mRNAs were measured by qPCR in mTEClo from WT (n=3-4) and

Δ mTEC MHCII (n=4) mice.

(I) Fold change in the expression of HDAC3-induced transcriptional regulators and

other transcription factors in WT versus mTECΔMHCII mice. The color code represents

gene expression level.

(J) Heatmap of selected cytokines, chemokines and cell adhesion molecules.

(K) Il21, Il5, Il15, Ccl22 and Icam2 mRNAs were measured by qPCR in mTEClo from

WT (n=4) and mTECΔMHCII (n=3-4) mice.

Error bars show mean±SEM, *p<0.05, **p<0.01, ***p<0.001 using two-tailed

Mann-Whitney test for A,E,H,K and Chi-squared test for C,F.

Figure 3. mTEClo subset composition is altered in ΔCD4 and mTECΔMHCII mice.

Δ (A) Confocal images of thymic sections from WT, ΔCD4 and mTEC MHCII mice

stained for Aire (green) and Fezf2 (red). 12 and 20 sections derived from 2 WT, 2

ΔCD4 and 2 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.

(B,C) Flow cytometry profiles and numbers of Aire-Fezf2-, Aire-Fezf2+ and Aire+Fezf2+

cells in total mTECs, mTEClo and mTEChi (B) and of DCKL1+ cells in Aire- mTECs

representative of 2-3 independent experiments (n=3-4 mice per group and

experiment).

24 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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 and two-tailed Mann-Whitney test for B, C.

Figure 4. 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 2

independent experiments (n=3-4 mice per group and experiment).

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 2 independent biological replicates with mTEClo derived

from 5-8 mice.

(D) Numbers of 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.

25 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(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 (FC>5) in red.

(H) Heatmap of selected activation factors.

(I,J) Expression fold change 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 selected cytokines, chemokines and cell adhesion molecules.

(L) Il5, Il15, Il7, Ccl19, Ccl2, Ccl25, Cdh2 and Itgad mRNAs were measured by qPCR

in mTEClo from RipmOVAxOTII-Rag2-/- (n=3-4) and OTII-Rag2-/- (n=3-4) mice.

Error bars show mean±SEM, *p<0.05, **p<0.01, ***p<0.001 using two-tailed

Mann-Whitney test for A, B, F, L and Chi-squared test for D, G.

Figure 5. Highly self-reactive CD4+ thymocytes control mTEC development

from an early progenitor stage.

(A-D) Flow cytometry profiles and numbers of total TECs (EpCAM+) (A), 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 4

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 2

RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice were quantified, respectively. Scale

26 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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,G) Flow cytometry profiles and numbers of Aire-Fezf2-, Aire-Fezf2+ and Aire+Fezf2+

cells in total mTECs, mTEClo and mTEChi (F) and of DCKL1+ cells in Aire- mTECs (G)

from RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- mice. II Abs: secondary antibodies.

Data are representative of 2 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,G.

Figure 6. H3K27me3 and H3K4me3 landscape in 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.

RipmOVAxOTII-Rag2-/- and OTII-Rag2-/- 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.

27 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(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.

Figure 7. The adoptive transfer of splenocytes from mTECΔMHCII mice in Rag2-/-

recipients induces autoimmunity.

(A) 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.

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.

28 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(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 splenocytes

relative to those transferred with WT splenocytes. Each circle and black triangles

represent an individual mouse and T-cell infiltration in a specific tissue, respectively.

Data are representative of 2 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.

29 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Materials and Methods

Mice

C57BL/6 WT mice were purchased from Charles River. CiitaIII+IV-/- (ΔCD4)

(LeibundGut-Landmann et al, 2004), MHCII-/- (Madsen et al, 1999), K14xCiitaIII+IV-/-

Δ (mTEC MHCII) (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.

Experiments were performed in accordance with the animal care guidelines of the

European Union and French laws. 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

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

30 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

purity of sorted mTEClo was >98%. Flow cytometry gating strategies are shown in

Figure S1.

mTEC antigen presentation assays

Variable numbers of mTECs from WT and mTECΔMHCII mice loaded or not with

5 + OVA323-339 (5µM, PolyPeptide group) were co-cultured with 10 OTII CD4 T cells

(purified with a CD4+ T cell isolation kit, Miltenyi Biotec) in RPMI medium

(ThermoFisher) supplemented with 10% FCS (Sigma Aldrich), L-glutamine (2mM,

ThermoFisher), sodium pyruvate (1mM, ThermoFisher), 2-mercaptoethanol

(2×10−5 M, ThermoFisher), penicillin (100IUper ml, ThermoFisher) and

streptomycin (100μgper ml, ThermoFisher). The activation of OTII CD4+ T cells

was assessed 18h later by flow cytometry based on the upregulation of the CD69

marker.

Flow Cytometry

TECs, thymocytes and splenic T cells were analyzed by flow cytometry (FACS Canto

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 Table S5. 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-H3K4me3 and anti-H3K27me3

antibodies was performed with Fixation/Permeabilization Solution Kit (BD).

31 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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

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 Table S6.

In vivo transfer of splenocytes into Rag2-/- recipients

3.106 splenocytes from 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

Tissues were fixed in 10% buffered formalin (Sigma) and embedded in paraffin

blocks. 4μm thick sections were stained with hematoxylin-eosin (Thermofisher) and

analyzed by light microscopy (Nikon Statif eclipse Ci-L). For immunofluorescence

experiments, frozen thymic sections were stained as previously described(Serge 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

32 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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

Total RNA purified from mTEClo (Figure S1) 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 manufacturers’

instructions. Libraries were sequenced with the Illumina NextSeq 500 machine to

generate datasets of single-end 75bp reads. Two independent biological replicates

were used per each condition. RNA-seq analysis is detailed in the Supplemental

Methods.

Nano-ChIP-seq experiments

Nano-ChIP-seq was performed as previously described (Adli & Bernstein, 2011) on

5.104 purified mTEClo (Figure S1). ChIP-seq libraries were prepared with TruSeq

ChIP Sample Preparation Kit (Illumina) and 2x75-bp paired-end reads were

sequenced on an Illumina HiSeq. ChIP-seq analysis is detailed in the Supplemental

Methods.

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; **,

33 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

p<0.01; *, p<0.05. Normal distribution of the data was assessed using d’Agostino-

Pearson omnibus normality test.

Data availability

RNA-seq and ChIP-seq data reported in this paper were deposited with Gene

Expression Omnibus under accession numbers: GSE144650 and GSE144680.

34 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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A B TotalIK K to IKKt b is α Total p38 IKKα 100 p 3 8 to t fin a l im m a tu re b is ra tio IK K a to t/IK K a p m T E C lo b is

II Ab 252 5 0 0 8 0 0 6 ** 80 8 6 )

) *** 2 0 0 0 2 WT 2 20

60 /total) ΔCD4 44 151 5 0 0 4 0 0 % of Max 40 4 1 0 0 0

10 Ratio 2 20 2 MFI (10 MFI MFI (10 MFI 550 0 Events(%max) Events(%max) 0 0 2 3 4 5 0 Phospho 0 0 0 10 10 10 10 0 ( 0 T A T A T A W T : p38 tot W T W T II II II IKKα-APC C P38-APC C C

Phospho-IKKα Phospho-p38 p38 100 p 3 8 ra tio m T E C lo b is 100 IK K p m T E C lo b is p 3 8 P fin a l im m a tu re b is 3 8 0 0 0 ** 1 0 0 0 ** * 80 8 80 10 3 )

II Ab )

2 WT 2 6 0 0 0 60 60 6 2 WT /total) v v 2 ΔCD4 % of Max 40 ΔCD4 4 040 0 % of Max 40 550 0

1 Ratio 20 2 0 0 0 20 1 MFI (10 MFI

MFI (10 MFI 2 Events(%max) Events(%max)

0 0 0 2 3 4 5 0 0 0 102 103 104 105 0 0 10 10 10 10 0 Phospho 0

T A ( : IKKP T A T W IT : p38 phospho A I W IT W IT C I I Phospho IKKα-APC WT Phospho p38-APC C C ΔCD4

Upregulated Downregulated Activation Factors C D Chemokines -10 E +4 O W T -CP<0.05IIT A F IpNOrAoLp o rtio n o f g e nP=5.2x10e s a ll g e n e d s g e n o m e T eRt AuTRA pu p C IIT A 989 1 5 0 1 0 0 Activation Factors 8 0 Chemokines RipmOva-OTII vs OTII +3 WT vs K14 WT vs CIITAKO 150 RipmOva-OTII vs OTII 100WT vs K14 WT vs CIITAKO80 73 O O FC 248 FC FC 120 FC FC lo 97 RipmOva-OTII vs OTII WT6600 vs K14 WT vs CIITAKO RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO Sox21 17 +2 CIIta(p<0.05) 18 Noto 11000 0 Ccl20 9 Ccl1 7 FC FC FC FC FC 73 40 Myog Alx3 CIIta 9 Cxcl13 59 Ccl22505 0 Sox21 17 CIIta4400 18 Noto Ccl20 9 Ccl1 7 16 Cdx1 +1 Spic Foxf2 5 0 Ccl1946 Ccl17 mTEC Myog Alx3 CIIta 9 Cxcl13 Ccl22

50 Numbers Pou2af1 Twist1 Prrx1 31 Cxcl14 Ccl2 2200 178 genes%of Cdx1 Spic Foxf2 Ccl19 Ccl17 0 5 2 Barx1 Cux2 Sdpr Genenumbers Ccl9 Ccl5 WT WT Pou2af12 Twist1 Prrx1 Cxcl14 Ccl2 (p<0.05) 0 00 0 Zbtb39 Aire Stat4 é é lé Cxcl9lé lé lé A Barx1A A A Cux2 p 2 Sdprs Ccl9 Ccl5 l l p f e 2 u u u u u u R R R R e e z r 8 g g g g g g T T T T d d e t - - -1 é é é é é é o RPKM o e F u Aire Tcf7 Lmo2 r r r Cxcl10r r r Zbtb39 Aire r f2 - A Stat4 Cxcl9 p Upregulatedp p n Downregulatedn n N UpregulatedN Whole i z e u A r u u w w w Airee -dependenti u o o o F A RPKM A A d d d 0.9 42 Dner Sdpr 957Satb2 R R ig Ccl8 Aire Tcf7 Lmo2 Cxcl10 T T b A A u 2 genome g -2 n m R R i log2 scale 0.9 42 o a T T b Dner Sdpr Fezf2-dependentSatb2 Ccl8 Insm1 Runx3 Trps1 n n m Non-TRA TRA ND 2 o a n log2 scale Tbx21 -2E2F7 -1 0 +1 +2 +3Klf12 +4 4 Insm1 Runx3 Aire/Fezf2-dependentTrps1 Foxj1 Brca1 Foxj1 Ccl2 13 Tbx21 E2F7 Klf12 ΔCD4 mTEClo Aire/Fezf2-independent 13 Dll4 Uhrf1 Sox21 Ccl25 2 Foxj1 Brca1 Foxj1 Ccl2 Ccl25 2 Hes6 Zmyd10 Sox7 2 Dll4 Uhrf1 Sox21 Hes6 Zmyd10 Sox7 2 Cbfa2t3 Tbx21 2 RPKM F Aire-dependent Aire-independent Aire/Fezf2- Aire/Fezf2-Cbfa2t3 Tbx21 RPKM Ascl1 Hmgb2 0.6 128 Fezf2-independent Fezf2-dependentRPKM dependent independentAscl1 Hmgb2 0.6 128 Arid3b Klf2 2 log2 scale RPKM 0.2 42 Arid3b Klf2 log2 scale Fezf2 2 0.2 42 Meig1 Nov Fcer2alog2 scale Kcnj5 Krt1 Reig1 Crp Fezf2Rsph1 M e ig 1 C IIT A b is N o v b is F c e r2 a b is K c1n j5 b is K rt1 b is R e ig 1 b is C R P C IIT A b is R s p h 1 C IIT A b is log2 scale Ikzf1 Ikzf1 4 1 .5 2 * * 2 Spib 1.51 .5 Fezf2 4 1.5 1.5 * 22 2 1.01 .0 22 2 * * Spib* Fezf2 1.5

* Irf7 Irf4 Irf7 Ehf Tes

Pou2f3 3 Utf1 WT Spib * St18 Pou2f3 Klf12 Stat4 Arnt2 Ascl2 Foxj1 Mxd1 Ascl1 3 1 .0 Fezf2 1 .0 Satb2 Satb1 Asb11 Nfatc2 Stat5a Bach1 1.0 RPKM 1.0 Pdlim3 Bach1 RPKM Cdkn2b

Bhlhe41 ΔCD4 2 1 0.5 1 0 .5 1 1 Egr2 0.1 2 137 1 1 0.5 1 Ivns1abp 1 Egr2 0.1 137 0 .5

0 .5 Nfkb2(p52) Zbtb43 0.5 log2 scale1 0.5 FoldchangeCIITA vs WT Zbtb43 log2 scale Irf5 2 1 Irf5 2 0 .0 00 0 .0 00 00 0 .0 00 00 T a T T T T T T a t A A 0.25 A A A t T ta W 2 W T W T T W T W IT 2 Relativeexpression I II W I II I W W 2 RPKM C I I C RPKMC C C C C C 0.1 400 0.1 400 log2 scale Regulated by HDAC3 Others log2 scale Cytokines G 4 0.125 Cytokines H Cell adhesion O Cell adhesion O O O Cytokines O Cell adhesion O O O Mean expression RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII WT vs K14 WTFC vs CIITAKO FC FC FC FC FC 1 FC 331 FC FC FC Δ CD4 FC FC 0.0625 3 13 3 5 3 13 3 5 Il12a 8 Itgb6 Il22 PdgfrbPdgfrb 3 Syt1 Il12a 8 Il22 Pdgfrb Syt1 3 9 vs. 2 Fgf21 Il12b Il10 2 Mmp9 Fgf21 Il12b Il10 Mmp9 9 FC 2 Tnfrsf11b Col1a1Retnla Il10 2 Cdh2 2 Tnfrsf11b Retnla Cdh2 FC Il23a 2 Itga1 Flt3 22 Il21Il21 Itgb2 2 Il23a 2 Flt3 Il21 Itgb2 2 Il33 Il5 2 Ncam1 2 7.7 Il33 Il5 2 Ncam1 Cdhr3 8 FC WT FC WT 2 Il12a 7.7 Il5 4.8 Il5 18 2 18 1 Il12a 18 Cdh15Fgf21 4.8 3 Itgad 2.5 3.9 Il5 18 Il15 1 RPKM Fgf21 3 Itgad 2.5 Itgae 3.9 RPKM 2.4 2.5 Irf7 Irf7 Irf4 Irf4 Ehf

Il15 Vdr 1 RPKM Tes Il7 1 0.50.5 1818

Spi1 Spib 2.5 1.8 Relb St18 2.4 2.4 Klf12

Stat4 Itga2b Arnt2 Myog Ascl1 Il7 Ascl2 Foxj1 Mxd1 Zfhx2 Fezf1 Fezf2 Lmo7

1 Trp53 Nfkb2 Satb2 log2 scale Satb1 Hnf4g 0.5 18 Hnf1a Il21 32 Asb11 Asb11 Foxa1

Stat5a log2 scale Cited2 Nfatc2 1.8 RPKM 2.1 1.7

Pdlim3 2.4 Tfap2b Kdm4a Pou2f3 Tfcp2l1 Il21 32 log2 scale Cdkn2b L1cam Il5 FC4 Bhlhe41 RPKM 2.1 1.7 0.5 49 2.1 1.6 Ivns1abp RPKM Il15 2 Il5 4 0.5 49 2.1 Cdh2 1.6 log2 scale 2.1 1.3 RPKM Il15 2 0.2 158 1.8 1.3 Cell adhesion Cytokines log2 scale 2.1 Cdhr1 RPKM1.3 0.2 158I log2 scale 1.7 1.1 1.8 Cdhr50.2 1.3 169 log2 scale RPKMItgb6 Cdh2 Il21 Il5 1.6 1.7 log2 1.1scale 0.2 Itg b 6 n o rm169 1 b isc d h 2 W T v s C 2 ta n o rm 1 b is IL 2 1 C IIT A b is Madcam1 * * IL 5 C IIT A b is 1.6 1.6 log21 .0 scale 2 1 .5 2 Pecam1 1 1.5 RPKM 1.0 2 * 1.5 2 WT 1.6 * 1.4 0.1 52 ΔCD4 1.5 RPKM 1 .0 RPKM 1.3 log2 scale 1.0 1.4 0.10.1 52 0.50 .5 11 11 1.1 1.3 log2log2 scalescale 0.50 .5 Relative 1.1 expression RPKM 0 .0 00 00.0 00 T a T a T a t t T a t W 2 W 2 t W 2 0.03 52 C C W 2 C C RPKM log2 scale 0.03 52 log2 scale bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 2

Upregulated A B Downregulated C D P<0.05 W T v s K 1 4 fin a l +4 W T -K 1 4 F IN A L T RTRAA u p K 1 4 Activation Factors Chemokines 1300 1 5 0 O 8800 O 2 5 0 150

+3 250 OTII alone W449T 231 RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII *** WT vslo K14 WT vs CIITAKO 115 +2 (p<0.05)K 1 4 2002 0 0 FC FC FC 6600 FC FC 99 Aire-dep + WT T a lo n e 1001 0 0 Sox21 17 CIIta 18 Noto ΔMHCIICcl20 ***9 Ccl1 +1 7 1501 5 0 131 128 Fezf2-dep

mTEC mTEC 4400 119 Aire/Fezf2-dep Myog Alx3 CIIta 9 Cxcl13 ** Ccl22 1001 0 0 93 CD69 87 ** 0 340 16 505 0 Numbers Aire/Fezf2-indep

Cdx1 Spic Foxf2 Ccl19 Ccl17 WT 2200 * (p<0.05) 5 0 Pou2af1 Twist1 Prrx1 cells (%) OTII Cxcl14 Ccl2 -1 50 Genenumbers 7 10 Barx1 Cux2 Sdpr Ccl9 Ccl5 0 0 0 -2 2 846 0 0 0 0 0 0 0 0 é é é é é lé p 2 s 8 l l l l l p f e Zbtb39 Aire Stat4 0 6.255 12.5Cxcl90 250 500 1000 u u u u u u e e z r 2 5 0 0 0 g d t g g g g g - -d e 6 2 5 0 0 é Upregulatedé é éDownregulatedé é e F u 1 2 5 0 RPKM-2 -1 0 +1 +2 +3 +4 r r r r r r r f2 - A 1 n p p n n n i z e Aire Tcf7 Lmo2 Cxcl10 3 u r p u w w w A e i mTEC numbers (x10 ) u u o o o F A ΔMHCII lo A d d d 0.9 mTEC42 mTEC A R ig Dner Sdpr Satb2 Ccl8 R T b Non-TRAA A TRAu ND g 2 T m R R i log2 scale n a T T b o n m Insm1 Runx3 Trps1 4 n o a n Tbx21 E2F7 Klf12 -16 E 13 F P=2x10 G Foxj1 Brca1 Foxj1 Ccl2 Aire-dep Aire-indep Aire/Fezf2 Aire/Fezf2- 1010 Activation factors

Dll4 Uhrf1 Sox21 Ccl25 2 MHCII

Δ 9256 882 Fezf2-indep Fezf2-dep dep indep 2 WT mTECΔMHCII Hes6 Zmyd10 Sox7 2 Activation Factors Chemokines 55 O O Cbfa2t3 Tbx21 S p o n 2 b is Crabp1C r a b p 1 b is CCocho c h b is RPKM Sult1c2S u lt1 c 2 b is FFabp9a b p 9 b is Spon2 Upk3bU p k 3 b b is Alx3 18 (log2)

RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO 2 .5 RipmOva-OTII1 .5 vs OTII WT vs K14 1 .0 WT vs CIITAKO1 .5 Ascl1 Hmgb2 1 .5 * 2 mTEC

0.6 128 * lo Spic FC FC 1.5 FC 2.5 1.5 FC 2 * FC1.0 * 1.5 RPKM * 0 Activation Factors Chemokines Arid3b Klf2 1 0 O O Sox21 2 17 CIIta 18 Noto 2 .0 log2Ccl20 scale 9 Ccl1 7 Twist1 0.2 42 * 2.0 vs. RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO Fezf2 1 .0 1 .0 1 .0 Myog Alx3 CIIta1.0 9 1 .5 Cxcl131.0 Ccl22 1.0 FC Cux2 FC FC FC FC Irf7 Irf4 Irf7 Activation Factors log2 scale Chemokines Ehf 1.5 Tes Utf1 FoldChangeK14vs (log2) WT

0 .5 Spib St18 1 -5-5 Sox21 CIIta Noto Ccl20 9 Ccl1 7 mTEC Cdx1 Spic O Foxf2 OCcl19 Ccl17 Klf12 17 18 Stat4 Arnt2

Ikzf1 1 Ascl2 Foxj1 Mxd1 Ascl1 0.5 Fezf2 Aire Satb2 Satb1 Asb11 Nfatc2 1 .0 Stat5a

Pdlim3 Myog Alx3 FCCIIta Cxcl13 Ccl22 Pou2af1 Twist1 Prrx1 1.0 Cxcl140 .5 Ccl2 0 .5 9 Fezf2 1.5 0 .5 Cdkn2b Spib 0.5 0.5 0.5 0.5 Bhlhe41 8672 454

Ivns1abp Cdx1 Tcf7Spic Foxf2 Ccl19 Ccl17 RipmOva-OTII vs OTII WT vs K14 WT vsActivation CIITAKO Factors Relative RipmOva-OTII vs OTII Chemokines WT vs K14 WT vs CIITAKO Barx1 Cux2 Sdpr 0 .5 O Ccl9 Ccl5O 2

expression 0.5 Pou2af1 Twist1 Prrx1 Cxcl14 Ccl2 FC WT FC WT Pou2f3 FC FC FC FC FC Nfkb2(p52) Sdpr

FoldchangeCIITA vs WT -10 Zbtb39 AireRipmOva-OTII vs OTII Stat4 WT vs K14 WT vs CIITAKO Cxcl9 RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO -10 0 .0 Barx1 Cux2 Sdpr Ccl9 Ccl5 2 FC 0 .0 FC 0 .0 FC 0 .0 00 FCRPKM 0 .0 FC 0 Bach1 AireRPKM Tcf7 Lmo2 T 4 Cxcl10 T 4 T 4 -5 0 5 10 15 Zbtb39 Runx3Aire Stat4 Cxcl9 T 4 T 4 T 4 1 9 1 1 7 1 1 Sox21 17 CIIta 18 Noto W Ccl20 1 Ccl1 W W -5 0 5 +10 +15 K W W K W K RPKM Dner Sdpr Sox21 17Satb2 CIIta 18 Noto K Ccl8 K Ccl20 0.9 9 ΔMHCIICcl142 7 K Aire Tcf7 Lmo2 Cxcl10 Egr2 0.1 137 WT2 mTEC FoldChange WT vs Aire-KO mTEChi (log2)-/- E2F7 0.9 42 Myog Alx3Insm1 Runx3CIIta Myog 9 Trps1 Alx3 CIItaCxcl13 9 Cxcl13Ccl22 log2 scaleCcl22 0.25 FC WT vs. Aire Dner Sdpr Satb2 Ccl8 2 Zbtb43 log2 scale Cdx1 Spic Foxf2 Ccl19 Ccl17 hi Insm1 Brca1Runx3 Trps1 log2 scale Cdx1 SpicTbx21 Foxf2E2F7 Pou2af1 Klf12Twist1 Prrx1Ccl19 Cxcl14Ccl17 Ccl2 mTEC (log2) Tbx21 E2F7 Klf12 Ccl2 13 13 Pou2af1Irf5 2 Twist1 Foxj1 Brca1Prrx1 Barx1 Foxj1Cux2 SdprCxcl14 Ccl9Ccl2 Ccl5 2 Foxj1 Uhrf1Brca1 Foxj1 Ccl2 Dll4 Uhrf1 Zbtb39 H Sox21 Aire TRA regulatorsStat4 Ccl25 2 Cxcl9 I Dll4 Zmyd10Uhrf1 Sox21 Ccl25 2 Barx1 Cux2 Hes6 Zmyd10Sdpr Aire Sox7 Tcf7 Lmo2 Ccl9 Cxcl10Ccl5 2 RPKM4 Regulated by HDAC3 Others Hes6 Zmyd10 Sox7 2 2 0.9 42 0.125 RPKM Dner Sdpr Satb2 Ccl8 2 Cbfa2t3 Tbx21Tbx21 RPKM Zbtb39 AireCbfa2t3 Tbx21Stat4 AireA ire b is Cxcl9Fezf2F e z f2 b is RPKM log2 scale Insm1 Runx3 Trps1 Ascl1 Hmgb2 0.6 128 0.1 400 Ascl1 Hmgb2 RPKM Δ CMHII Mean expression Hmgb2 RPKM Aire Tcf7 Lmo2 Tbx21 E2F71 .5 Klf122Cxcl10 0.6 128 Arid3b Klf2 2 log2 scale 1.5RPKM 2 * 13 0.2 42 log2 scale Arid3b Klf2 Foxj1 2 Brca1 Foxj1 log2 scale Ccl2 Fezf2 0.2 * 42 0.9 42 330 Klf2 2 log2 scale Dner Sdpr Fezf2 Satb2 Dll4 Uhrf1 Sox21Ccl8 2 Ccl25 2 1.8 Ikzf1 log2 scale WT log2 scale Hes6 Zmyd101 .0 Sox7 2 2 0.0625 Spib Fezf2Fezf2 1.5 Insm1 Runx3 Ikzf1 Trps1 1.0 FC 1.5 Cytokines Cbfa2t3O Tbx21 Cell adhesion O ΔMHCII O mTEC O Pou2f3 RPKM Spib Fezf2 1.5 1 mTEC Tbx21 E2F7 Klf12 Ascl1 Hmgb2 1 Bach1 RPKM RPKM 0.6 128 RPKM Pou2f3 Arid3b Klf20 .5 13 log2 scale vs. Egr2 0.1 137 Foxj1 Brca1 Foxj1 0.5 2 0.2 Ccl2 42 0.1 137 RipmOva-OTII vs OTII WTBach1 vs K14 WT vs CIITAKORPKMFezf2 Relative RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO Zbtb43 log2 scale log2 scale log2 scale Dll4 FC Uhrf1 Egr2 FC Sox210.1 Ikzf1 137FC expression Ccl25 FC 2 FC FC Irf5 2 Fezf20 .0 1.5 0

Spib WT Zbtb43 log2 scale 0 0 1 Hes6 Zmyd10 Sox7 T 4 T 4 2 1 1 RPKM 8 13 Pou2f3 3 W W 5 3 Il12a Il22 Pdgfrb K Syt1 K Irf5 2 Bach1 0.1 400

RPKM Irf4

Cbfa2t3 Tbx21 Tes

Fgf21 Il12b Il10 Mmp9 RPKM 9 Utf1 log2 scale 2 Spi1 Egr2 0.1 137 Relb Klf12 Foxj1 Myog Ascl1 Fezf2 Nfkb2 Tpr53 Satb1 Ascl1 Hmgb2 RPKM Zbtb43 log2 scale Hnf4g Hnf1a

Tnfrsf11b Retnla Cdh20.6 128 Stat5a

2 Pdlim3 Tfap2b Cytokines O Cell adhesion O O O 0.1 400 RPKMIrf5 2 Pou2f3 Arid3b Klf2 2 Il21 Cytokines Chemokineslog2 scale Cell adhesion Bhlhe41 Il23a 2 Flt3 log2 scale Itgb2 2 0.2 RPKM 42 RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO Fezf2 Il5 J2 ΔMHCII FC FC FC FC FC FC Il33 0.1log2 scale 400 ΔNcam1MHCII 2 ΔMHCII 7.7 Activation Factors ChemokinesWT mTEC WT mTEC WT mTEC 13 3 5 Ikzf1 Cytokines O log2 scale O O Cell adhesion O O O Il12a 8 Il22 Pdgfrb Syt1 3 Il12a Cdh7 18 4.8 Fgf21 Il12b Il10 Mmp9 9 SpibIl5 RipmOva-OTII18 vs OTII Fezf2 WT vs K141.5 WT vs CIITAKO RipmOva-OTIIIl22 vs13 OTII Ccl1WT vs K14 7 WT vs CIITAKO 19 2 Fgf21RipmOva-OTIIFC vs OTII3FC WTCytokines vs K14FC WT vs CIITAKO FCItgadRipmOva-OTIIO vsFC OTIICell adhesion Icam4O WT vs K14 2.5O WT vs CIITAKOK O 3.9 Cytokines ChemokineTnfrsf11b Cell adhesionRetnla Cdh2 2 Il15 Il12b Ccl22 Il23a 2 Flt3 Il21 Itgb2 2 Pou2f3 Sox211 CIIta FC Noto RPKMFC Ccl20 FC 9 Ccl1 7 FC FC FC 17 18 RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII 2.4WT vs K14 WT vs CIITAKO 2.5 Il33 Il5 2 Ncam1 2 7.7 13 Retnla 3 FC 5 Icam2 3 Bach1Il7 Myog1 Il12aRPKMAlx3 8 CIItaIl22 9 FCPdgfrb Cxcl13 FC Ccl17Ccl22 FCSyt1 FC FC FC Il12a 4.8 0.5 18 Il211.8 Il5 Il15 Ccl22 Il5 18Icam2 18 Cdx1 Fgf21Spic Foxf2Il12b Il12a 8 Il10Flt3Il22Ccl19 2 13 FC Pdgfrb Ccl17 Mmp93 Ceacam18Syt1 5 92.4 3 IL 1 5 b is * C C L 2 2 b is Ic a m 2 n o rmFgf21 1 b is 3 2.5 3.9 log2 scale Ccl2 IL 2 1 b is IL 5 b is Il15 1 Itgad Egr2 Pou2af1 0.1Il21 Twist1 32137 Prrx1 Fgf21 Il12bCxcl14 Il10 Ccl2 2 Mmp9 9 0.057 RPKM 2.5 Tnfrsf11b Retnla RPKMCdh2 Itgae 2.1 2 1.7 4 1 .5 1 .5 2.4 Il33 1.51 .5 * 44 4 1.5 Il7 1.5 1 0.5 18 Zbtb43 Barx1 Il5 Il23aCux2log2 scale 4 2 SdprFlt3Tnfrsf11b Il21Retnla Ccl9 Ccl5Ccl5 Itgb2 22 Cdh2 2 2 * * * WT 2.4 1.8 Zbtb39 Aire Stat4 Il23a 2 Il12aFlt3Cxcl9 0.5Il21 49 Itgb2 Itga4 2.1 2 1.6 Il21 32 log2 scale RPKM 2.1 1.7 Il33 Il5 2 Ncam1RPKM 2 7.7 3 3 Il5 4 ΔMHCII Irf5 RPKM Aire2 Il15 Tcf7 2 Lmo2 Il33Cxcl10 Il5 RPKM2 Ncam1 2 7.7 3 1 .0 0.5 49 1.6 log2 scale 1 .0 1.3 3 1 .0 mTEC 2.1 Il12a 0.90.9 4242 Icam5 2.1 4.81.0 1.0 RPKM1.0 Il15 2 Dner Sdpr Satb2 Fgf21Il12a Ccl8 3 2 18 18 4.8 log2 scale 2.1 1.3 0.2 158 Il5 18 Il5 18 FC 2 Insm1 Runx3 Trps1Fgf21 3 Fgf21 3 log2log2Itgad scalescale ItgadCdh22 2.51.8 2.5 3.9 3.9 1.3 22 2 0.2 158 1.8 1.3 RPKM Il15 1 Il15 1 RPKM log2 scale RPKM RPKM RPKM 2.4 2.5 0 .5 1.1 log2 scale Tbx21 E2F7 Klf12 Il7 1 2.4 2.50.50 .5 0.5 0.50 .5 1.7 Il21 32 0.5 18 1.7 Relative 1.1 0.2 169 Il7 1 Ccl2 13 Itga8 2.4 1.8 1 1 1.6 0.1 Foxj1400 Brca1 Foxj1 0.5 18 1 1 log2 scale 0.2 169 Il21 32 log2 scale 2.4 expression 1.8 Ccl25 2 RPKM 2.1 1.7 1.6 Dll4 Uhrf1 Sox21Il21 32 Il5log2Il5 scale 4 Ceacam9 1.6 log2 scale log2 scale 4 RPKM 2.1 1.7 0 .0 1.6 0 Hes6 Zmyd10 Sox7 2 0.5 49 2.1 0 0 00 0 .0 0 .0 1.5 RPKM Il5 RPKM4 Il15 2 T 4 Il15 log2 scale 1.6 1.6 1.3 1 T 4 T 4 T 4 4 0.5 49Ceacam15 1 T 2 2.1 2.1 W 1 1 1 1.4 K W W 0.1 52 Cbfa2t3 Tbx21 RPKM K W K K W K RPKM Il15 0.2 2 158 1.8 1.3 Ascl1 Hmgb2 RPKM log2 scale Itgb3bp 2.11.5 RPKM1.3 1.3 log2 scale Cytokines 0.2 158 RPKMO log2 scale RPKM0.6 Cell adhesion128 O O 1.7 O 1.1 1.1 Arid3b Klf2 0.20.2 169 1.8 1.3 2 0.2 RPKM 42 log2 scale Cdh11 1.4 1.6 0.1 52 Fezf2 log2 scale log2 scale 1.7 1.1 0.2 log2 scale 169 log2 scale 1.6 RipmOva-OTII vs OTII Ikzf1 WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII Itga1WT vs K14 1.61.3 1.5WT vs CIITAKOlog2 scale Fezf2 1.5 log2 scale RPKM RPKM SpibFC FC FC FC Cdhr1 1.61.1FC 1.4 0.1 52 FC 0.03 52 Pou2f3 1 1.5 1.3 RPKM log2 scale log2 scale Il12a Bach18 Il22 RPKM 13 Pdgfrb 3 Syt1 5 1.1 3 Ceacam11 91.4 Egr2 0.1 137 0.1 52 Fgf21 Il12b Il10 2 Mmp9 Madcam1 1.39 log2 scale Zbtb43 log2 scale FC Tnfrsf11b Irf5 Retnla2 Cdh2 Itga2bRPKM 1.1 RPKM 2 Il21 0.03 52 Il23a 2 RPKM Flt3 Itgb2 0.03Pecam1 2 252log2 scale 0.1 400Il33 Il5 2 Ncam1 2 log2RPKM scale 7.7 log2 scale RPKM Il12a 0.030.03 5252 4.8 Il5 18 18 Cytokines Fgf21 3 O Cell adhesion O O O log2log2 scale scale 2.5 3.9 Il15 1 Itgad RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKORPKM RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO 2.4 2.5 Il7 1 FC FC 0.5 FC 18 FC FC FC Il12a 8 Il22 13 Pdgfrb 3 Syt1 5 3 2.4 1.8 Il21 32 log2 scale Fgf21 Il12b Il10 2 Mmp9 RPKM 9 2.1 1.7 Tnfrsf11b Il5 Retnla 4 Cdh2 2 Il21 0.5 49 2.1 1.6 Il23a 2Il15 Flt3 Itgb2 2 RPKM Il33 2 Il5 2 Ncam1 2 log2 scale 7.7 2.1 1.3 Il12a 4.8 0.2 158Il5 18 18 Fgf21 3 2.5 3.9 1.8 1.3 Il15 1 RPKM Itgad log2 scale RPKM 2.4 2.5 Il7 1 1.7 1.1 0.2 169 0.5 18 2.4 1.8 Il21 32 log2 scale 1.6 log2 scale RPKM 2.1 1.7 Il5 4 0.5 49 2.1 1.6 Il15 1.6 RPKM 2 log2 scale 2.1 1.3 0.2 158 1.8 1.3 1.5 RPKM RPKM log2 scale 1.7 1.1 0.2 169 1.4 0.1 52 1.6 log2 scale 1.6 1.3 log2 scale 1.5 RPKM 1.1 1.4 0.1 52 1.3 log2 scale 1.1 RPKM RPKM 0.03 52 0.03 52 log2 scale log2 scale bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 3

A WT ΔCD4 mTECΔMH C II

A ire -F e z f2 W T -C IIT A -K 1 4 Aire * 8008 0 0 *** WT 2 ** ΔCD4 4004 0 0 * mTECΔMHCII * Fezf2 Cells/mm 00 T A 4 T A 4 T A 4 T +1 1- T +1 W I K W IT W I k AireI AireI K AireI + C + + + C + e 2 C + 2 2 r + e f 2 f f i ir - z + f + z + z + A e 2 z e 2 ir Fezf2A e f Fezf2 fFezf2e F z e F z F A e F + e + e F r F e i + ir A e ir A A

Merge

N E W F IN A L A ire /F e z f2 in to t m T E C W T K 1 4 b is W T C 2 ta K 1 4 ΔMHCII

Control WT ΔCD4 mTEC 4 ) 0 0 0 0 0 ** B 4 40 5 10 105 *** 0.1 10 4 10 ** 4 10 104 *** Gated on 2 0 0200 0 0 3 total mTEC 10 103

: Aire 55 83 0.4 29 61 55 41 : Aire 34

2 2 55 10 Numbers(10 Aire-AF488 10 N E W F IN0A L A ire /F e z f2 in to t m T E C lo b is W T C 2 ta K 1 4 0 0 0 T a 4 T a 4 T a 4 t 1 t 1 t 1 2 3 4 5 W 2 W 2 W 2 0 10 10 10 10 0 102 103 104 105 4 0 0 0 0 0 C K C K C K : Fezf2 : Fezf2 40 * 5 5 ) 105 10 10 105 4 ** WT 0.03 4 1.3 4 0.8 1.2 4 10 10 10 104 ΔCD4 2 0 0 0 0 0 Gated on 3 3 3 10 10 3 20 ΔMHCII lo 10 10 mTEC : Aire : Aire : Aire mTEC 99 0.6 28 71 47 52: Aire 46 52

2 0 0 10 102 Numbers(10 0 0 00 2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5 T a 4 T a 4 T a 4 0 10 10 10 10 0 10 10 10 10 0 10 10 10 10 N E W F IN A L A iret /F e1 z f2 in tot t m1 T E C h i tb is1 W T C 2 ta K 1 4 0 10 10 10 10 W 2 W 2 W 2 : Fezf2 5 : Fezf2 5 : Fezf2 5 : Fezf2 C K C K C K 105 10 10 10 1 0 0 0 0 0 *** ) 10

0.3 56 40 28 4 4 4 4 *** 104 10 10 10 Gated on ** 3 3 103 3 *** hi 10 10 10 12 59 : Aire : Aire

mTEC : Aire : Aire 43 0.7 11 32 20 38 5 0 050 0 2 2 2 102 10 10 10

0 0 0 0

2 3 4 5 2 3 4 5 2 3 4 5 Numbers(10 0 102 103 104 105 0 10 10 10 10 0 10 10 10 10 0 10 10 10 10 : Fezf2 : Fezf2 : Fezf2 : Fezf2 00 II Abs Fezf2-APC T a 4 T a 4 T a 4 t - t - t + 2 1 2 1 2 1 W AireK W AireK W AireK C C C Fezf2- Fezf2+ Fezf2+

C Control WT ΔCD4 mTECΔMHCII N b s D C L K 1DCLK1+ W T C IIT+A K 1 4 F IN A L 3 0 0300 0 0 5 5 * 105 10 105 10 ) 4 WT

4 4 10-PE/Cy7 4 10 104 10 Gated on 2 0 0200 0 0 ΔCD4

- 3 3 ΔMHCII Aire mTEC 103 0.9 10 51 103 40 10 33 mTEC : EpCAM : EpCAM : EpCAM : EpCAM 1 0 0 0 0 0 2 10

EpCAM 2 2 102 10 10 10

0 0 0 0 Numbers(10

2 3 4 5 2 3 4 5 2 3 4 5 0 0 102 103 104 105 0 10 10 10 10 0 10 10 10 10 0 10 10 10 10 0 :II II-abs Abs : DCLK1 : DCLK1 DCLK1-APC: DCLK1 W T C IIT A K 1 4 bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 4

A Relb B Total RelB Phospho-RelB R e lB O T 2 -R ip in v e rs é R ip O T 2 b is M F I R e lB to t m T E C lo b is m T E C lo - p h o s p h o R e lB in v e rs é R ip O T 2 b is 33 * 5 0 050 0 ** 100 2 020 0 *** ) ) 3 4 4 040 0 0 80 22 3 030 0 0 60 RipmOVAxOTII 1 0 0 0 % of Max 1 2 020 0 0 40 11 OTII MFI (10 MFI Relative MFI (10 MFI 1 010 0 0 20 expression Events(%max) 00 Events(%max) 0 0 0 0 2 3 4 5 0 I p 2 A II 0 10 10 10 10 A I i t V T R o V T : RelB-phospho O O O O m m P RelB-APC IP I RipmOVAxOTII R Phospho-RelB-APC R OTII

Upregulated C lo Downregulated D E +4 1438 O T IIP<0.05-R ip fi n a l T R TRAsA u p R ip 2 0 0 +3 522 44000 0 200 170 (p<0.05) +2 33000 0 Non-TRA 11505 0 Aire-dependent

Fezf2-dependent x OTII mTEC OTII x +1 TRA 98 22000 0 11000 0 Aire/Fezf2-dependent ND 0 136 Numbers Aire/Fezf2-independent (p<0.05) 11000 0 5500 15 16 -1 Genenumbers 00 00 é é é é é é s RipmOVA 620 l l l l p 2 l l p f e u u u u u u e e z r -2 g g g g g g d d t é - - e é é é é é r e F u r r r r r r 2 - A p Upregulatedp p n Downregulatedn n i f e u z r u u w w w A e i -2 -1 0 +1 +2 +3 +4 u o o o F A A A d d d Activation Factors Chemokines R R ig T T b A A u g O O lo n m R R i OTII mTEC o a T T bActivation Factors Chemokines n n m o a O O n RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO FC FC FC FC FC F Aire-dependent Aire-independent Aire/Fezf2-RipmOva-OTIIAire/Fezf2- vs OTII WT vs K14 GWT vs CIITAKO P=6.2x10-7 RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO

FC FC FC FC FC Fezf2-independent Fezf2-dependent dependent independent TRA regulators Sox21 10 10 17 CIIta 18 Noto Ccl20 9 Ccl1 7 Sox21 17 CIIta 18 MyogNoto 8828 Alx3518 Ccl20 CIIta 9 9 Ccl1 7 Cxcl13 Ccl22

Fam183b Nts Resp18 Grap Fabp9 Csn2Myog Crp AireAlx3 Fezf2 Cdx1CIIta 5 9 Spic Cxcl13 Foxf2 Ccl22 Ccl19 Ccl17 A IR E in v e rs é rip O T 2 b is 5 F a m 1 8 3 b in v e r s é R ip O T 2 b is R e s p 1 8 b is F a b p 9 b is N ts in v e rs é R ip O T 2 b is G ra p in v e rs é R ip O T 2 b is C s n 2 in v e rCdx1s é R ip O T 2C bRisP in v e rs é R ip O T 2 b is Spic F e z f2 b is Pou2af1Foxf2 Twist1 Ccl19 Prrx1 Ccl17 Cxcl14 Ccl2 * 1 .5 * (log2) 4 2 .0 2 .0 * 8 3 0 4 1 .0 1.5 1 .0 4 8 * 2 * 30 2 * 4 Pou2af1* 1.0 Twist1 1.0 Barx1Prrx1 Cux2 Cxcl14 Sdpr Ccl2 Ccl9 Ccl5 2 * * 0 0 6 1 .5 1 .5 3 Barx1 Cux2 Zbtb39Sdpr Aire Ccl9 Stat4 Ccl5 Cxcl9 6 1 .0 2 2 0 3 1.0 OTII 20 Zbtb39 Aire AireStat4 Tcf7 Cxcl9 Lmo2 Cxcl10 RPKM 2 4 1 .0 1 .0 0 .5 0 .5 RipmOVAxOTII 2 1 1 2 0.5 4 2 0.5 -5 RPKM 0.9 42 Dner vs. -5 Sdpr Satb2 Ccl8 Aire 0 .5 Tcf7 Lmo2 FoldChange(log2) RipmOVAOTII vs Cxcl10 1 0 0.5 2 2 0 .5 10 0 .5 1 log2 scale Relative 0.9 42 2 1 Dner Sdpr Insm1Satb2FC Runx3 Ccl8 Trps1 2

expression 7687 319 log2 scale 0 0 0 .0 0 0 .0 0 .0 0 .0 0 .0 0 0 0 0 0 00 Insm1 0 0 Runx3 0 Tbx21Trps1 -10 -10 E2F7 Klf12 I I p I p 2 I ip + I p I i T i ip I ip 2 p p I ip 2 i T R T T T i + R 2 i T T 13 R O R R R R 2 T R R O O O O O O -5 0 5 10 15 Ccl2 Tbx21T O E2F7 Foxj1Klf12 -5 0 Brca15 +10 +15 Foxj1 RipmOVAxOTII O OTII Activation factors FoldChange WT vs Aire-KO mTEChi (log2) 13 2 Foxj1 Brca1 Dll4Foxj1 FC WT Uhrf1vs. Aire-/- Ccl2Sox21 Ccl25 Ccl25 2 Dll4 Uhrf1 Hes6Sox21 mTECZmyd10hi (log2) Sox7 2 H Hes6 Zmyd10 Cbfa2t3Sox7 2 Tbx21 RPKM I Cbfa2t3 Tbx21 Ascl1 Hmgb2 RPKM 0.6 128 RPKM Ascl1 Hmgb2 Arid3b Klf2 Regulated by HDAC3 RPKM Others 20.6 0.2 128 42 log2 scale Sox21 Hes6 17 16 Arid3b Klf2 Fezf2 2 0.2 42 log2 scalelog2 scale Myog Cbfa2t3 . OTII Fezf2 Ikzf1 Mean expression log2 scale Cdx1 Ascl1 vs Fezf2 1.5 8 Spib 0.3 Ikzf1 272 Pou2af1 Arid3b Spib Fezf2 1.5 Pou2f3 Barx1 Fezf2 4 FC Pou2f3 Bach1 RPKM Zbtb39 Ikzf1 Bach1 RPKM Egr2 0.1 137 Aire Spib 2 Egr2 0.1 137 Zbtb43 log2 scale Dner Pou2f3 Zbtb43 log2 scale Irf5 2

Insm1 Bach1 1 Irf5 2 Tbx21 Egr2 RipmOVAxOTII RPKM Irf7 Irf7 Irf4 Irf4 Tes Vdr Elf3 Utf1 Gan Spi1 Spib Relb St18 Sirt1 Egr3 Egr2

Zbtb43 Klf12 RPKM Hes6 Stat4 0.1 400 Esrrg Foxj1 Arnt2 Ascl1 Foxj1 Ascl2 Myog Mxd1 Zfhx2 Fezf1 Fezf2 Trp53 Trp53 Prox1 Nfkb2 Hnf1a Asb11 Asb11 Tbx15 Cited2 Stat5a Pdlim3 Tfap2b Kdm4a Dll4 Irf5 0.1 400 Pou2f3 log2 scale Cdkn2b 2 Bhlhe41 RPKM FC log2 scale Ivns1abp 0.1 400 Cytokines O Cell adhesion O O O Cytokines O Cell adhesion O O O Activation Factorslog2 scale Chemokines J KO Cytokines ChemokinesO RipmOva-OTIICell adhesion vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO

FC FC FC FC FC RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKORipmOva-OTII vs OTII RipmOva-OTIIWT vs OTIIvs K14 WT vsvs K14 CIITAKO WT vs CIITAKORipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO FC 16 Foxn1FC and its targets FC FC FC FC FC Il12a FC 8FC Il22 13 Pdgfrb FC 3 FC Syt1 5 FC 3 Il12a 8 Il22 13 Fgf21Pdgfrb 3 Il12b Syt1 Il10 5 Mmp9 3 9 . OTII Sox21 17 CIIta 18 Noto Ccl20 9 Ccl1 7 2 8 Mean expression Fgf21 Il12b Tnfrsf11b Il10 Retnla Mmp9 9 Cdh2 vs Myog Alx3 CIIta 9 Cxcl13 Ccl22 2 2 Ccl20 Cdx1 Spic0.3 272 Foxf2 Tnfrsf11bIl12a 8 Ccl19Retnla 9 Ccl17Il23aSyt1 5 2 Flt3 Cdh2 Il21 Itgb2 2 2 Cxcl13 Mmp9 4 Pou2af1 Twist1 Prrx1 Il23aFgf21 FC2 Cxcl14Flt3 Ccl2 Il21 Il33 Itgb2 Il5 2 2Ncam1 2 7.7 FC Barx1 Cux2 Sdpr Tnfrsf11b Ccl19Ccl9 Ccl5 FC Il33 FC Cdh2 Il5 2 2 Il12a Ncam1 2 7.7 18 4.8 Zbtb39 Aire Stat4 Il23a 2 Cxcl14Cxcl9 Il5 18 2 Il12a Itgb2 Fgf21 3 18 Itgad 4.8 2.5 3.9 Aire Tcf7 Lmo2 Il5 18 Cxcl10Ccl9 Il15 RPKM 1 Fgf21 3 Ncam1 2 RPKM 2.5 3.9 2.4 2.5 Dner Sdpr Satb2 Il15 1 Cxcl9Ccl8 0.9 Il7 421 Itgad

2 RPKM 0.5 18 1 Il5 18 log2 scale 2.4 2.5 2.4 1.8 Insm1 Runx3 Trps1 Il7 1 Cxcl10 Itgad0.5 18 Il21 32 log2 scale RipmOVAxOTII RPKM 1.8 1.7 Il15 2.1 Tbx21 E2F7 Klf12 1 Ccl8 2.4 Stc2 2 Zar1 Dtx4 Il21 32 log2 scale Il5 4 Tmie

Cd83 Il7 13 RPKM 2.10.5 49 1.7 2.1 1.6 Ajap1 Ccl25 Tbata Srxn1 Cecr6 Ccl2

Foxj1 Brca1 Foxn1 Foxj1 1 Pds5b Akap2 Apba2 Ptprn2

Prss16 Il5 Il15 2 Ctnna2 Ccl2 4 RPKM Mfsd12 13 RPKM log2 scale Snap91 0.5 49 2.1 1.6 2.1 1.3 Psmb11 Psmb11 Ccl25 2 Dll4 Uhrf1 Sox21 Il15 RPKM Ccl25 2 2 0.20.5 15849 log2 scale 2.1 1.3 1.8 1.3 Hes6 Zmyd10 AA467197 Sox7 2 RPKM 0.20.2 158 log2log2 scalescale 1.8 1.3 1.7 1.1 Cbfa2t3 Tbx21 RPKMRPKM 0.2 169 Ascl1 Hmgb2 log2log2 scalescale 1.7 1.1 1.6 0.60.6 128 log2 scale 5031414D18Rik RPKM 0.2 169 Arid3b Klf2 2 log2 scale 1.6 1.6 0.2 42 log2 log2scale scale Fezf2 1.6 1.5 RPKM L Cytokines Chemokineslog2 scale Cell adhesion Ikzf1 1.5 RPKM 1.4 0.1 52 Spib Fezf2 1.5 1.4 0.1 52 1.3 log2 scale Il5 Il15 Il7 Ccl19 Ccl2 Ccl25C d h 2 O T 2 Cdh2v s R ip n o rm 1 in v e rt bItgadis IL 5 in v e rsPou2f3é R ip O T 2I Lb1is5 in v e rs é R ip O T 2 b is C C L 1 9 in v e rs é R Ip O T 2 b is * IL 7 in v e rs é R ip O T 2 b is C C L 2 in v e rs é R ip O T C2 Cb iLs 2 5 in v e rs é R ip O T 2 b is Itg a d in v e rs é R ip O T 2 b is 1.3 log2 scale 1.1 * * 1 .5 * * 4 1 .0 Bach1 1 .0 1 .0 RPKM 1 .0 1 .0 1 .5 1.0 1.0 1.0 1.5 1.0 1.0 4 1.5 1.1 Egr2 0.1 * 137 * RipmOVAxOTII 33 * Zbtb43 1.0log21 .0 scale 1.01 .0 OTII RPKM 0.50 .5 Irf50.50 .5 20.50 .5 0.50 .5 0.50 .5 22 0 .5 0 .5 RPKM 0.03 52

Relative 0.5 0.5 11 log2 scale

expression RPKM 0.03 52

0 .0 0.1 0 .0 400 0 .0 0 .0 00 .0 00 .0 00 0 .0 log2 scale I p p p I p p i + i + ip i i + ip + p 2 i + R 2 + T 2 i T R 2 R 2 R 2 R R R 2 R T log2 scale T T O T T O T O O O O O O Cytokines O Cell adhesion O O O

RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO RipmOva-OTII vs OTII WT vs K14 WT vs CIITAKO FC FC FC FC FC FC Il12a 8 Il22 13 Pdgfrb 3 Syt1 5 3 Fgf21 Il12b Il10 2 Mmp9 9 Tnfrsf11b Retnla Cdh2 2 Il23a 2 Flt3 Il21 Itgb2 2 Il33 Il5 2 Ncam1 2 7.7 Il12a 4.8 Il5 18 18 Fgf21 3 2.5 3.9 Il15 1 Itgad RPKM 2.4 2.5 Il7 1 0.5 18 2.4 1.8 Il21 32 log2 scale RPKM 2.1 1.7 Il5 4 0.5 49 2.1 1.6 Il15 RPKM 2 log2 scale 2.1 1.3 0.2 158 1.8 1.3 RPKM log2 scale 1.7 1.1 0.2 169 1.6 log2 scale 1.6 1.5 RPKM 1.4 0.1 52 1.3 log2 scale 1.1

RPKM 0.03 52 log2 scale bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 5

A CD45neg- enriched cells B TEC (EpCAM+)

RipmOVAxOTII OTII RipmOVAxOTII OTII cTEC F IN A L n b rmTEC m T E C in v e rs é rip O T 2 F IN A L Totaln b r T E C i nTECv e rs é R i p O T 2 F IN A L n b r c T E C in v e rs é R ip O T 2 ) ) 2 0 0 0 0 0

5 5 0 0 0 0 4 0.052 14 8 3 0 0 030 0 51 18 5 20 **** **** 4 040 0 0 1155 0 0 0 0 RipmOVAxOTII 2 0 0 0 0 0 2 3 030 0 0 1 0 0 0 0 0 OTII SSC 10 2 020 0 0 1 0 0 0 0 0 1 UEA-1-FITC 5 0 0 0 0 1 010 0 0 5

0 0 Numbers(10 0 40 71 Numbers(10 0 0 0 p + ip + i 2 2 p + R R i 2 T T R T O O RipmOVAxOTIIO EpCAM-PE/Cy7 OTII Ly51-PE TEClo TEPC- C D lo lo enriched cells Total EpCAM+ T EC RipmOVAxOTII OTII (MHCII UEA-1 ) RipmOVAxOTII OTII

F IN A L T E C s u b s e t c h id g e y IN V E R S 2 R IP o t2 ) n b r T E P C b is

50 49 4 ) * hi lhii 4 TEPC- cT EC mT EC 1 0 0 0100 0 2 0 0 0 0 0 R IP m O V A 20 *** ** enrichedO T 2 +cells T EClo mT EClo

5 0 0 0 05 6-integrin- 1 0100 0 0 0 α PercPCy5.5

MHCII-APC * 11 7 10 2 Numbers(10

45 30 Numbers(10 0 49 23 0 0 i i p + lo lo h h i 2 C C C C R T E E E E O T T T T UEA-1-FITC m m c Sca-1-PE RipmOVAxOTII OTII E F Aire Fezf2 Merge F IN A L F IN A L - m T E C T O T In v e rs é R ip O T 2 b is Control RipmOVAxOTII OTII

6 5 4 ´1 0 10 ) 40 RipmOVAxOTII 4 ** A ire -F e z f2 O T II-R ip in v e rs é R ip -O T II 4 1 6 61 OTII 10 8 0 0

2 800 Total * 6 103 2 ´201 0 6 0 0

600 mTEC : Aire **

Aire-AF488 89 0.9 23 70 42 56 **** 102 4004 0 0 0.06 RipmOVAxOTII

0 Numbers(10 2 0 0 ** 0

Cells/mm 200 F IN A L F0IN A L - m T E C lo In v e rs é R ip O T 2 b is 2 3 4 5 0 10 10 10 10 A + A + A + : Fezf2 V 2 V 2 V 2 T T T 0 5 5 5 3 0 0 0 0 0 0 O O O 10 10 10 ) O O O 0 I I m m m I 30 p I i p I ip I 4 P P P T + i T - T + I I I R R R Aire O AireO Ai O re R R R + + + ** e + 2 + 2 + ir e f 2 f 2 ir z f z f A e z e z 4 4 4 A F - e F + e + 10 0.1 0.110 0.1 F + F 10 Fezf2 Fezf2e + Fezf2 r e i r 2 0 0 0 0 0 0 A i 20 A

OTII lo 3 3 ** mTEC 10 103 10 : Aire : Aire : Aire 98 1.4 37 62 36 63 1 0 0 0 0 0 0 2 10 102 102 10

0 0 0 Numbers(10 F IN A L F I0N A L - m T E C h i In v e rs é R ip O T 2 b is 0 102 103 104 105 0 102 103 104 105 0 102 103 104 105 0 A + A + A + : Fezf2 : Fezf2 : Fezf2 V 2 V 2 V 2 G - 5 5 5 T T T 10 10 10 6 0 0 0 0 0 O O O Gated on Aire mTEC O O O ) 60 m m m P P P 4 I I ** I N b m T E C D C L K 1 + in A ire - b is 30 33 R R R + 4 2 4 4 Control RipmOVAxOTII OTII DCLK1 10 10 10 8 0 0 0 ) 5 5 5

3 8 hi 10 10 10 mTEC 3 10 103 103 -PE/Cy7 * 3 0 0300 0 0 : Aire : Aire 4 4 : Aire 10 10 104 60 0.6 7 62 26 40 2 10 102 102 4 0 0 0 ** 3 3 10 10 103 4 0 0 0

0.1 38 8 Numbers(10 EpCAM : EPCAM : EPCAM : EPCAM 2 3 4 5 0 0 10 10 10 10 0 102 103 104 105 0 102 103 104 105 0 102 102 102 : Fezf2 : Fezf2 : Fezf2 A ++ A + - A + + AireV 2 AireV 2 V Aire2 II Abs Fezf2-APC O T O T O T Numbers(10 m O m O m O 0 0 0 IP -IP IP + + 00 RFezf2R Fezf2R Fezf2 2 3 4 5 2 3 4 5 0 102 103 104 105 0 10 10 10 10 A + 0 10 10 10 10 V 2 : DCLK1 : DCKL1 : DCKL1 O T RipmOVAxOTIIO II Abs DCLK1-APC m IP R OTII bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 6 A B H3K27me3 H3K27me3 H3K4me3 H3K4me3 14 Aire-dependent *** 15 Aire-dep 1.5 Fezf2-dependent 1.0 ** Fezf2-dep 12 *** Aire/Fezf2-independent Aire/Fezf2-indep 10 ** All genes 0.8 10 All genes 1.0 8 ** 6 0.6 5

0.5 Signalin gene 4 Average enrichment Average 0.4 enrichment Average 2 -1 0 1 2 3 4 -3 -2 -1 0 1 2 3 window TSS Signalin 1kb Upstream to TSS (kb), 3kb of Meta-gene, Relative distance to TSS (kb) Downstream to TTS (kb) C D lo H3K27me3 Gated on mTEC H 3 K 2 7 m e 3 H3K27me3 H3K4me3 100 1.51 5 0 0 0 8 ) 1.2 RipmOVAxOTII

80 4 1.01 0 0 0 0 7 OTII 60 1.0 % of Max 40 0.55 0 0 0 6

20 (10 MFI 0.8 Events(%max) 00 5 0 p II i T 2 3 4 5 R 0 10 10 10 10 O : H3K27me3 0.6 4 H3K27me3-APC H3K4me3 H 3 K 4 m e 3 im m a tu re s in v e rs é R ip O T 2 3 Signalin gene 100 4 0 0 0 0 0.4 II Abs 4 * ) 2

80 4 OTII 3 030 0 0 0.2 60 window TSS Signalin 1kb 1 RipmOVAxOTII 2 0 0 0 0 % of Max 2 40

1 0 0 0 0 20 (10 MFI 1 Events(%max) 0 00 2 3 4 5 0 10 10 10 10 : H3K4me3 ip + Aire-dependent Aire/Fezf2-independent R 2 T RipmOVAxOTIIO H3K4me3-FITC Fezf2-dependent All genes OTII E bioRxiv preprint doi: https://doi.org/10.1101/2020.11.29.399923; this version posted November 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 7

- + CD69 matureC D 4 + CCD4D 6 9 - T thymocytesH Y M U S C D 4 + R A T E A Stomach B Splenic CD4+Foxp3- T cells 1 5 2 0 15 WT 20 CNS (3.1%)Ovary ΔMHCII E11.5-E18 mTEC (6.2%) (5.5%) Testis 101 0 o v a ry * Brain (7.2%) (9.3%) * * 101 0 *** ** te stis 55 * *** Cerebellum (3.1%) b la d d e r *

lu ngFrequency(%) Kidney (4.1%) Liver 00 00 T T T T T T T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 4 T 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 W 1 1 W 1 W 1 W 1 W 1 W 1 W 1 W 1 W 1 W 1 W 1 W 1 (12.4%) th y m u s W K W W K W K W K W K W K W K W K W K W K W K W K K W K K K K K K K K K K K K 2 K 4 5 6 8 9 0 1 2 3 4 7 2 2 3 4 4 5 5 6 6 C D8 88 + C 9D96 9 - 0T0H Y M1 1U S 2 2 3 3 4 4 7 7 2 3 4 5 6 8 C D 89+ R A0T E 1 2 3 4 7 b 3 b b b b b 1 1 1 1 1 1 b 3 b b b b b 1 1+ 1 1 1 1 1 1 1 1 1 1 Placenta (3.1%) V b b b V b V b V b V b V b b 1 b 1 b 1 b 1 b 1 b 1 V b b b V b V b V b V b V b b b b b b b V V V V - V V V +b b b b b b V V V V VSplenicV VCD8V b TV bcellsV b V b V b V b live r V CD69 mature CD8V V thymocytesV V V V V V V V V V V V V V V V V 202 0 202 0 Mammary gland in te s tin e *** Bladder * (5.2%) d u d o e n u m (4.5%) s p le e n ** Spleen 101 0 101 0 Colon/Intestine (9.3%) m a m m a ry g la n d (9.6%) p la c e n ta

Thymus Frequency(%) Duodenum k id n e y Lung 00 00

(7.6%) T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 T 4 1 1 1 1 1 1 1 W 1 W 1 W 1 W 1 W 1 W 1 1 1 1 1 1 1 1 1 1 1 1 1 1 (3.8%) c e re b e llu W mK W W K W K W K W K W K K K K K K K W K W W K W K W K W K W K W K W K W K W K W K W K T(5.9%)o ta l= 1 0 0 2 K 4 5 6 8 9 0 1 2 3 4 7 2 K 4 5 6 8 9 0 1 2 3 4 7 2 3 4 5 6 8 9 0 1 2 3 4 7 2 3 4 5 6 8 9 0 1 2 3 4 7 b 3 b b b b b 1 1 1 1 1 1 1 1 1 1 1 1 b 3 b b b b b 1 1 1 1 1 1 V b b b V b V b V b V b V b b b b b b b V b b b V b V b V b V b V b b 1 b 1 b 1 b 1 b 1 b 1 V V V V V V V V b V b V b V b V b V b V V V V V V V b b b b b b b ra in V V V V V V V V V V V V V V V V V V V V C N S C D S to m a c h E +5 WTS p l e n o W T WT mTECΔMHCII WT or mTECΔMHCII ΔMHCII splenocytes splenocytes splenocytes p e rte p o id s fin a l mTECS p le n o K 1 4 p o id s ra te 0.150 .1 5 * 5 0 i.v. WT 0 .1 0 0.10 ΔMHCII -5 mTEC 6 wks **** Body weight/ 0.050 .0 5

Tissue infiltration? (g) Weight 0 -/- -10 0 .0 0 Rag2 recipients T 4 1 0 1 2 3 4 5 6 W K Bodyweight change (%) o o n n le e weeks p l p s s

ΔMHCII WT mTEC >0.06 mm² + + F splenocytes splenocytes -5 G Splenic T cells CD4 T cells CD8 T cells 0.03-0.06 mm² F IN A L - C D 3 C D 4 C DF I8N A L - P H E N O C DF4IN A L - P H E N O C D 8

6 1 0 0 0 0) 0 0 5 0 0 0 0 0 1 5 0 0 0 0

5 5 <0.03D a ta 1 mm² 10 ** 15 * WT 1 0 0 4 0 0 0 0 0 * 100 4 ΔMHCII 1 0 0 0 0 0 mTEC > 0 .0 6 3 0 0 0 0 0 10 * 5 0 0 0 0 0 3 0 .0 3 -0 .0 6 5 * 2 0 0 0 0 0 < 0 .0 3 2 5 0 0 0 0 5 0 5 * .0 .1 50 .2 .3 .4 .5 .6 1 0 0 0 0 0 M M M M M M M * 1 * E E E E E E E * livecells (10 S S S S S S Numbers10 / S 0 0 0 00 0 * F F 3 3 4 4 8 8 IF IF E E I I E E E E D D D D D D E E V V 0 A A IV IV R R A A I I R R C C C C C C I I N N T T I I 0 N N T T O O O O T 4 T 4 T 4 4 C C T 4 C C T 4 1 1 1 T 1 A A M M Folliclesarea (%) 1 1 A A M M W W K W K W K W E E K W K 4 E E K T 4 T 1 M M 1 M M W W K 4 K T 4 T 1 1 W W K K

WT mTECΔMHCII splenocytes splenocytes H I Lung-infiltrating T cells J Lung-infiltrating CD4+ T cells + F IN A L C D 3 C D 4 C D 8 Lung-infiltrating CD8 T cells % in filtra tio n p o u m o n F IN A L C D 4 a c tiv é m eFmINoAryL C D 4 C D 6 9 /C D 4 4 F IN A L C D 8 a c tiv é m eFmINoAryL C D 8 C D 6 9 /C D 4 4

Lung 6 5 0 0 0 0) 0

4 0 5 6 6 ) 5 8 1 0 2 0 0 0 0 0 6 40 ´ 8 8 0) 0 0 0 4 0 0 0 0

5 4 2 4 8 ** 4 0 0 0 0 04 ** 3 0 * 30 6 ´1 0 6 6 0 0 0 0 3 0 0 0 0 3 0 0 0 0 03 * 6 6 3 2 0 6 20 4 ´1 0 1 0 0 0 0 0 4 0 0 0 0 2 0 0 0 0 Lung 2 0 0 0 0 02 4 1 * 4 2

1 0 1 0 0 0 0 0 6 2 ´1 0 1 0 0 0 0 10 1 * 2 2 0 0 020 * 1 livecells (10

Numbers10 / 0 0

% of infiltration%of 0 livecells (10 0 0 livecells (10 0 Numbers10 / 0 0 3 3 4 4 8 8 Numbers10 / T 4 0 D D D D D D 0 0 1 0 W C C C C C C é é e e - - - - K r r + + 9 9 é é e e + + 4 4 4 iv v i i 9 9 v v ir ir 9 9 9 9 T T T t ti o o 6 6 6 6 i i 6 6 1 1 1 c D D t t o o 6 6 W K W K W K c m m D D c c D D D D a z é é C C a z m m C C C C + + é é C C T 4 + + T 4 + + + + 1 m m 4 4 4 4 m m 4 4 W 4 4 1 4 4 4 % inSGfiltra ti o n G S K T 4 4 4 W K T 4 4 4 4 1 D D W D D 1 D D D D K C C C C W K C C C C 4 T 4 T 4 T 1 T 4 1 1 W 1 W 2 0 SG-infiltrating T cells W K K W K S G F IN A L C D 3 C D 4 C D 8 K 20 * 6 ) 1 5 0 0 0 0 5 1.5 WT ΔMHCII SG 101 0 1 0 01.00 0 0 mTEC SG-infiltrating CD4+ T cells SG-infiltrating CD8+ T cells S G F IN A L C D 4 a c tiv é m e mSoGry F IN A L C D 4 C D 6 9 /C D 4 4 S G F IN A L C D 8 a c tiv é m e mSo Gry F IN A L C D 8 C D 6 9 /C D 4 4

6 5 0 0 0 0 4 0 0 0 0 ) 6 3 0 0 0 0 8 0 0 0 1 0 0 0 0 ) 0.5 * 4 3 10 4 3 0.09 8 0 8 0 0 0 % of infiltration%of 0 3 0 0 0 0 6 0 0 0 8 T 4 1mm 1 0 3 6 W livecells (10 2 0 0 0 0 K Numbers10 / 0 2 3 3 4 4 8 8 6 0 0 0 D D D D D D 6 WT C C C C C C 2 0 0 0 0 4 0 0 0 T 4 T 4 T 4 1 1 1 W W W 2 4 K K K ΔMHCII 4 040 0 mTEC 1 0 010 0 * 1 0 0 0 0 2 0 0 0 1 2 * 2 020 0 0.08

livecells (10 0 0 0 0 Numbers10 / livecells (10 Numbers10 / 0 0 0 0 - - é é e e - - é é e e + + r r + + 9 9 v v ir ir 9 9 9 9 iv v i i 9 9 i i 6 6 t ti o o 6 6 6 6 t t o o 6 6 c c D D D D c c m m D D D D m m a z C C a z é é C C C C é é C C 4 + + T 4 + + + + T m m + + 4 4 1 m m 4 4 1 4 4 W 4 4 4 4 W 4 4 4 4 4 K K T 4 4 4 K T D D 1 D D 1 D D W D D W K C C K C C C C C C T 4 + + T 4 T 4 1 T 4 1 1 W 1 W W K K CD4 T cell infiltration CD8 T cell infiltration W K K

WT mTECΔMHCII

Liver Lung Pancreas Kidney Colon Salivary glands