A type 1 immunity-restricted promoter of the IL−33 receptor gene directs antiviral T-cell responses

Brunner, Tobias M.; Serve, Sebastian; Marx, Anna-Friederike; Fadejeva, Jelizaveta; Saikali, Philippe; Dzamukova, Maria; Durán-Hernández, Nayar; Kommer, Christoph; Heinrich, Frederik; Durek, Pawel; Heinz, Gitta A.; Höfer, Thomas; Mashreghi, Mir-Farzin; Kühn, Ralf; Pinschewer, Daniel D.; Löhning, Max

doi:10.1038/s41590-023-01697-6

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Published: 03 January 2024

A type 1 immunity-restricted promoter of the IL−33 receptor gene directs antiviral T-cell responses

Nature Immunology volume 25, pages 256–267 (2024)Cite this article

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Abstract

The pleiotropic alarmin interleukin-33 (IL-33) drives type 1, type 2 and regulatory T-cell responses via its receptor ST2. Subset-specific differences in ST2 expression intensity and dynamics suggest that transcriptional regulation is key in orchestrating the context-dependent activity of IL-33–ST2 signaling in T-cell immunity. Here, we identify a previously unrecognized alternative promoter in mice and humans that is located far upstream of the curated ST2-coding gene and drives ST2 expression in type 1 immunity. Mice lacking this promoter exhibit a selective loss of ST2 expression in type 1- but not type 2-biased T cells, resulting in impaired expansion of cytotoxic T cells (CTLs) and T-helper 1 cells upon viral infection. T-cell-intrinsic IL-33 signaling via type 1 promoter-driven ST2 is critical to generate a clonally diverse population of antiviral short-lived effector CTLs. Thus, lineage-specific alternative promoter usage directs alarmin responsiveness in T-cell subsets and offers opportunities for immune cell-specific targeting of the IL-33–ST2 axis in infections and inflammatory diseases.

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Main

Endogenous danger-associated molecules released upon cellular damage, so-called alarmins, act as central orchestrators of inflammation¹. Amongst alarmins, IL-33 stands out as a potent cytokine that triggers pro- and anti-inflammatory responses by engaging its receptor ST2 on immune cells^2,3. ST2, also known as T1 (refs. ^4,5), was first detected on T-helper 2 (Th2) cells and mast cells^6,7,8, and its activation elicited production of type 2 cytokines, suggesting an important role in type 2 immunity^9,10,11. In accordance, disruption of IL-33–ST2 signaling ameliorated type 2 airway inflammation in mice and impaired immunity against nematode infections^6,12,13,14. More recently, studies established ST2 as a marker for type 2 innate lymphoid cells (ILC2s) and demonstrated a critical role of IL-33 for their development and function^15,16. By activating ILC2s and regulatory T (T_reg) cells, IL-33 controls tissue homeostasis, promotes wound healing and mitigates pathology in acute or chronic inflammation^{3,17,18,19,20,21,22}.

IL-33 has also emerged as a key driver of type 1 immune responses. It is released by fibroblastic reticular cells in lymphoid organs and promotes clonal expansion and activation of antiviral CTLs and T-helper 1 (Th1) cells to confer protection against replicating viruses^{23,24,25,26,27}. Moreover, IL-33-mediated amplification of type 1 immune cells was shown to exacerbate tissue damage during graft-versus-host disease (GVHD)^28,29 and contribute to immune dysregulation in systemic inflammatory diseases³⁰.

Considering its multifaceted mode of action, IL-33 is now recognized to amplify pro- or anti-inflammatory T-cell subsets in a context-specific manner^31,32. To accomplish this versatility, it was suggested that transcription of the ST2-coding gene interleukin-1 receptor-like 1 (Il1rl1) requires cell-type specific regulation, such that certain T-cell subsets become sensitive to IL-33 signals dependent on the inflammatory environment^31,32. ST2 is absent from naive T cells but expressed constitutively at high levels by type 2-biased immune cells in which its transcription is controlled by the master-regulator transcription factor of type 2 immunity GATA-3 (refs. ^6,21,33). In contrast, antiviral CTLs and Th1 cells express low levels of ST2 transiently upon infection, and this expression depends on STAT4 and T-bet, key transcription factors of type 1 immunity^24,27. This dynamic expression pattern renders it difficult to study ST2 on type 1 immune cells. Consequently, the molecular mechanism allowing for T-cell lineage-specific ST2 expression patterns has remained enigmatic.

Results

Identification of a type 1 immunity-restricted Il1rl1 promoter

Previous studies have shown that the protein-coding exons of the Il1rl1 gene are preceded by two non-coding exons (exon 1a and exon 1b) located in a distal and proximal promoter region, respectively^4,34 (Fig. 1a). The proximal promoter drives ST2 expression in fibroblasts, whereas the distal promoter mediates ST2 expression in Th2 cells and mast cells^33,35. To assess which promoter is used by type 1-polarized T cells, we generated CTLs, Th1 or Th2 cells in vitro, which all express substantial levels of ST2 (Fig. 1b and Extended Data Fig. 1a–c). Of note, at the per-cell level, type 1 T cells express less ST2 than Th2 cells (Fig. 1b). Thus, to stain ST2, we utilized a multi-step amplification protocol, yielding a more sensitive detection compared to stainings with frequently used ST2 antibodies (Extended Data Fig. 1d,e). By analyzing leader exons of 5’ untranslated regions (UTRs) of ST2-coding transcripts, we found that none of the described promoters could possibly account for the expression of Il1rl1 by type 1-polarized T cells (Fig. 1c). Thus, to map the origin of Il1rl1 transcripts in these cells, we next subjected ST2⁺ CTLs, Th1 and Th2 cells to RNA-sequencing (RNA-seq) analysis. Thereby, we discovered a transcriptional start site (TSS) located ~40 kb upstream of the annotated Il1rl1 gene, which was selectively used in CTLs and Th1 cells (type 1 promoter) (Fig. 1d). This TSS gave rise to two Il1rl1 transcript isoforms with distinct leader sequences but unaltered protein-coding sequences. We refer to the leader exons in these transcripts as exons A, B and D. Further, alternative splicing of exon B to exon C resulted in a transcript that did not contain ST2-coding exons. As expected, the ‘distal’ promoter (exon 1a) presented the primary origin of Il1rl1 transcripts in Th2 cells (type 2 promoter). To assess the usage of the type 1 promoter in vivo, we next transferred naive lymphocytic choriomeningitis virus (LCMV)-specific T-cell receptor (TCR)-transgenic CD4⁺ T cells (Smarta) or LCMV-specific TCR-transgenic CD8⁺ T cells (P14) into wild-type (WT) mice and infected the recipients with LCMV. At day 7 postinfection (d7 p.i.), we reisolated transferred cells and quantified Il1rl1 promoter usage. In contrast to Th2 cells, CTLs and Th1 cells had largely incorporated exons A and B but not exon 1a into 5’ UTRs of ST2-coding transcripts (Fig. 1e,f).

**Fig. 1: A previously unrecognized alternative promoter drives IL-33 receptor expression in antiviral T cells.**

ST2 expression by CTLs and Th1 cells, but not by Th2 cells or T_reg cells, relies on IL-12 and the transcription factors T-bet and STAT4 (Extended Data Fig. 1f–j)^24,27. Hence, we analyzed T-bet- and STAT4 binding as well as activation-induced changes in chromatin accessibility at the Il1rl1 locus in type 1-polarized T cells using publicly available chromatin immunoprecipitation sequencing (ChIP-seq)^36,37,38 and assay for transposase-accessible chromatin sequencing (ATAC-seq)^39,40 data. Although GATA-3 binds predominantly upstream of the type 2 promoter, T-bet and STAT4 binding was detected in the vicinity of exons A and B, at sites that were inaccessible in naive T cells but accessible in LCMV-primed Th1 cells and CTLs (Fig. 1g).

Alternative promoters are abundant⁴¹. We were unaware, however, of other genes with comparably selective alternative promoter usage in type 1 and type 2 immune cells. We thus used RNA-seq data to conduct a genome-wide search to identify additional genes with highly type 1 or type 2 immunity-specific promoters⁴². Il1rl1 was reliably detected when comparing Th2 cells to CTLs and Th1 cells but no other gene with lineage-specific TSS usage was found (Fig. 1h–j). This suggested that such highly restrictive type 1 and type 2 T-cell lineage-specific utilization of alternative first exons is rare, with the Il1rl1 gene potentially representing a unique case.

Il1rl1 promoter usage is conserved between mice and humans

Next, we assessed DNA conservation at the type 1 promoter^43,44,45 and compared it to T-bet- and STAT4-ChIP-seq as well as ATAC-seq data (Fig. 2a). Thereby, we identified a 275-nt-spanning, well-conserved sequence located ~5 kb upstream of exon A (CNS-5), which is bound by T-bet and STAT4 in Th1 cells and is marked by a sharp ATAC-seq peak in CTLs (Fig. 2a). In contrast, the sequence surrounding prominent peaks ~1.5 kb downstream of exon A appears less conserved. Mapping of T-bet and STAT4 binding motifs within CNS-5 indicated that both transcription factors putatively bind in close proximity at sequences almost identical between mice and humans⁴⁶ (Fig. 2b).

**Fig. 2: Alternative promoter usage at the *Il1rl1* locus is conserved between mice and humans.**

To assess whether T-cell lineage-specific promoter usage at the Il1rl1 locus is conserved between mice and humans, we first examined cap analysis of gene expression (CAGE) and transcriptomic data from the FANTOM5 resource^41,47, which provided evidence for a putative TSS upstream of the IL1RL1 gene. In human Th1 cells this site is preceded by T-bet binding sites (Fig. 2c)⁴⁸. Of note, the exon structure closely resembles the exons A and B identified in mice (cf. Fig. 1d). To determine whether this TSS is utilized in primary human CTLs and Th1 cells, we isolated in vivo-differentiated T cells from peripheral blood to quantify IL1RL1 promoter usage (Extended Data Fig. 2a–f). Congruently with mouse T cells, IL1RL1 transcripts of human CTLs and Th1 cells contained exons A and B within their 5′ UTRs, whereas Th2 cells had incorporated exon 1a (Fig. 2d). In summary, we have identified a previously unrecognized type 1 immunity-restricted Il1rl1 promoter, which is instructed by the lineage-associated transcription factors T-bet and STAT4 and orchestrates ST2 expression in CTLs and Th1 cells of humans and mice.

Il1rl1 promoters allow lineage-specific targeting of ST2

Modulation of the IL-33–ST2 axis could represent a promising approach in treating inflammatory diseases⁴⁹. For instance, blockade of IL-33 using therapeutic antibodies has shown encouraging efficacy in clinical trials of asthma and chronic obstructive pulmonary disease⁵⁰. However, due to hard-to-predict effects on the balance between IL-33-mediated inflammation and tissue repair, fine-tuned targeting approaches may offer critical advantages. We thus asked whether lineage-specific promoters can be leveraged to target ST2 expression in a T-cell subset-specific manner. Hence, we retrovirally transduced T cells to express small-hairpin RNAs (shRNAs) targeting distinct Il1rl1 5′ UTRs (Fig. 3a). Selective downregulation of ST2 was achieved in either CTLs and Th1 cells or Th2 cells using shRNAs binding exons A and B or exon 1a, respectively (Fig. 3b,c).

**Fig. 3: Usage of distinct promoters allows T-cell subset-specific targeting of ST2 expression.**

Furthermore, to study the type 1 immunity-restricted promoter in vivo, we generated knockout mice with a deletion of exons A and B (Il1rl1-ExAB^−/−). A second mouse strain lacking exon C (Il1rl1-ExC^−/−) was generated to control for potential effects of exon C-containing transcripts (Fig. 3d and Extended Data Fig. 3a–c). In steady state, both strains harbored normal numbers of T cells at an activation state comparable to WT mice (Extended Data Fig. 3d–i). Likewise, introduced deletions did not affect the composition of splenic innate immune cells (Extended Data Fig. 3j–l). Importantly, deletion of the type 1 promoter severely impaired ST2 expression by in vitro activated CTLs and Th1 cells, whereas Il1rl1-ExAB^−/− Th2 cells differentiated from the same pool of naive CD4⁺ T cells exhibited normal ST2 expression (Fig. 3e,f). As expected, ST2 on Il1rl1-ExC^−/− T cells was not reduced (Fig. 3e,f). Due to a lack of ST2 expression, Il1rl1-ExAB^−/− CTLs and Th1 cells, but not Il1rl1-ExAB^−/− Th2 cells, were unresponsive to IL-33 (Fig. 3g). Further, usage of the type 1 promoter is not limited to T cells, as natural killer (NK) T cells and NK cells of Il1rl1-ExAB^−/− mice failed to express ST2 upon activation ex vivo (Extended Data Fig. 4a–f). To verify that ST2 expression is preserved on type 2-biased immune cells of Il1rl1-ExAB^−/− mice in vivo, we analyzed peritoneal mast cells and lung ILC2s, which use the GATA-3-regulated type 2 Il1rl1 promoter (Extended Data Fig. 5a). These cells indeed displayed normal ST2 expression in Il1rl1-ExAB^−/− mice (Extended Data Fig. 5b–g). Lastly, bone marrow eosinophils and neutrophils of Il1rl1-ExAB^−/− mice expressed ST2 at levels slightly reduced, but largely comparable to WT mice (Extended Data Fig. 5h–l).

To investigate the requirement of the type 1 Il1rl1 promoter for ST2 expression by T cells responding to viral challenge, we infected WT, Il1rl1^−/−, Il1rl1-ExAB^−/− and Il1rl1-ExC^−/− mice with LCMV (Fig. 3h). ST2 surface expression was almost absent from CTLs in Il1rl1-ExAB^−/− mice and was significantly reduced in Th1 cells at day 7 p.i., whereas T_reg cells displayed unaltered ST2 surface levels (Fig. 3i–n). Conversely, Il1rl1-ExC^−/− CTLs and Th1 cells exhibited slightly enhanced ST2 expression (Fig. 3i–l), suggesting that exon C may act as a transcriptional decoy (cf. Fig. 1d). Altogether, we found that targeting of individual Il1rl1 promoters allowed for a selective T-cell lineage-specific manipulation of ST2 expression.

The type 1 Il1rl1 promoter drives antiviral T-cell responses

Next, we studied whether CD8⁺ T-cell responses to LCMV required the type 1 Il1rl1 promoter. At the peak of the response (d7 p.i.), Il1rl1-ExAB^−/− mice harbored substantially reduced numbers of CTLs in spleens and livers, resembling in its extent the impairment observed in Il1rl1^−/− mice (Fig. 4a–c and Extended Data Fig. 6a–c). Diminished CTL counts were largely accounted for by a reduction in CD44⁺CD62L⁻ effector T cells expressing the proliferation marker Ki67 (Fig. 4d–f). Accordingly, CTLs specific for the immunodominant GP_33-41 and NP_396-404 epitopes of LCMV were substantially reduced (Fig. 4g and Extended Data Fig. 6d–f). Ultimately, Il1rl1-ExAB^−/− mice displayed significantly lower numbers of CTLs expressing effector cytokines or cytolytic molecules, and systemic IFN-γ levels were reduced by >70% (Fig. 4h,i and Extended Data Fig. 6g–i). In contrast and as expected, CTL responses of Il1rl1-ExC^−/− mice were comparable to those of WT mice (Fig. 4b,c,e–i and Extended Data Fig. 6b–i).

**Fig. 4: T-cell-intrinsic activity of the type 1 *Il1rl1* promoter is vital for efficient expansion of antiviral T cells.**

To determine whether observed effects were due to a T-cell-intrinsic impairment in ST2 expression, mixed bone marrow chimeras were generated by reconstituting irradiated WT mice with bone marrow from Il1rl1^−/−, Il1rl1-ExAB^−/− or Il1rl1-ExC^−/− mice, each of them mixed 1:1 with WT bone marrow. Following LCMV infection, WT:Il1rl1-ExC^−/− chimeras mounted CTL responses that derived at approximately equal parts from both bone marrow compartments. In contrast, WT bone marrow-derived CTLs outnumbered the CTLs derived from Il1rl1^−/− or Il1rl1-ExAB^−/− bone marrow in the respective chimeras (Fig. 4j–m and Extended Data Fig. 6j,k). Lastly, to study the impact of type 1 promoter-driven ST2 expression on T-cell responses in the absence of any potentially confounding irradiation effects, congenically marked naive Il1rl1-ExAB^−/− P14 CTLs and WT P14 cells were cotransferred into recipients, which were infected with LCMV and analyzed at d10 p.i. Analogously to the data from mixed bone marrow chimeras, Il1rl1-ExAB^−/− and Il1rl1^−/− P14 T cells expanded much less than their respective cotransferred WT P14 T-cell populations (Fig. 4n–r). Similarly, albeit less pronounced, Il1rl1-ExAB^−/− Smarta T cells expanded less than cotransferred WT Smarta cells (Fig. 4s–w and Extended Data Fig. 6n–r). Interestingly, analysis of Smarta cells revealed that ExonAB-deficient, but not WT, Smarta cells used the proximal promoter (exon 1b) (Extended Data Fig. 6s–v). In summary, optimal expansion of antiviral T cells critically depends on T-cell-intrinsic activity of the type 1 Il1rl1 promoter.

The type 1 Il1rl1 promoter drives short-lived effector formation

At the peak of the acute response, the antiviral CTL population is heterogeneous and comprises functionally distinct subsets⁵¹. To delineate the impact of type 1 promoter-driven ST2 expression on CTL differentiation, we sorted activated CD44⁺ CTLs from LCMV-infected WT or Il1rl1-ExAB^−/− mice for combined single-cell gene expression and TCR repertoire analysis. T cells were clustered into six separate populations using nearest neighbor modularity optimization and annotated based on signature gene expression (Fig. 5a,b, Extended Data Fig. 7a,b and Supplementary Table 1). The two dominant clusters showed expression of genes associated with short-lived effector cells (SLECs; Klrg1, Gzma and Id2) or memory precursor effector cells (MPECs; Il7r, Sell and Ccr7) (Fig. 5c). The third cluster was enriched in CTLs expressing Pdcd1 (encoding PD-1) and Lag3, markers of exhausted CTLs⁵², whereas cells of the fourth cluster exhibited higher expression of Tcf7 (encoding TCF-1) and Id3, thus likely presenting stem-like precursors of effector CTLs^53,54. Lastly, the two remaining clusters were enriched in CTLs expressing higher levels of mitochondrial genes or cell cycle-related markers (Mki67, Top2a). A cluster-wise comparison between genotypes revealed that type 1 Il1rl1 promoter disruption affected gene expression in all CTL subsets (Extended Data Fig. 8) but resulted in a particularly pronounced curtailment of SLECs (Fig. 5d). This translated into a 90–95% reduction of SLEC numbers in Il1rl1-ExAB^−/− mice, mirroring the phenotype of Il1rl1^−/− mice (Fig. 5e,f). Further, fewer Il1rl1-ExAB^−/− CTLs exhibited surface expression of the SLEC-associated molecule CXCR3 (Fig. 5g)⁵⁵. Importantly, despite a relative increase in the frequency of MPECs, MPEC counts were slightly decreased (Fig. 5h). Correspondingly, Il1rl1-ExAB^−/− CTLs in mixed bone marrow chimeras featured a pronounced defect in SLEC generation and moderately lower MPEC numbers, similar to Il1rl1^−/− CTLs (Extended Data Fig. 6l,m). In contrast, Il1rl1-ExC^−/− bone marrow-derived CTLs were as proficient as WT cells in populating the SLEC and MPEC compartments. Lastly, the proportion of Il1rl1-ExAB^−/− P14 T cells differentiating into SLECs and/or CXCR3⁺ cells was reduced as compared to adoptively cotransferred WT P14 cells, whereas MPEC counts were largely unaffected (Fig. 5i–l). In line with these results, analysis of Il1rl1-ExAB^−/− and Il1rl1^−/− P14 cells at d30 p.i. revealed a modest decrease in numbers of memory CTLs compared to WT P14 cells (Extended Data Fig. 9a–d). However, both Il1rl1-ExAB^−/− and Il1rl1^−/− P14 cells were able to give rise to both effector memory (T_em) as well as central memory cells (T_cm) (Extended Data Fig. 9e–g) and formed tissue-resident memory cells (Extended Data Fig. 9h–k). Thus, a lack of ST2 signaling leads to a generalized impairment in CTL expansion. This was accentuated in the SLEC compartment during the acute antiviral response and extended in part to the population of circulating memory CTLs, whereas formation of tissue-resident memory cells appeared less ST2 dependent.

**Fig. 5: Type 1 *Il1rl1* promoter engagement facilitates effector differentiation of CTLs to generate a clonally diverse SLEC population.**

Next, we asked whether ST2 expression by the type 1 promoter drives the selective proliferation of SLEC-differentiated T-cell clones or whether it enforces the differentiation of precursors into SLECs. ST2 is found on both KLRG1⁺ and KLRG1⁻ CTLs of WT mice (Fig. 5m), suggesting IL-33 signaling can occur prior to SLEC differentiation. Further, we integrated single-cell gene expression data with a TCR repertoire analysis. Despite an eight-fold difference in CD44⁺ CTL counts per spleen (Fig. 5n), equivalent numbers of clonotypes were identified in Il1rl1-ExAB^−/− and WT mice when equal numbers of CD44⁺ CTLs were compared (Fig. 5o). This finding suggested that during the acute phase of infection, IL-33 expands activated T cells in a clonotype-unselective manner, which does not substantially alter the TCR diversity amongst the most abundant clonotypes. Of note, the majority of clonotypes identified were represented less than three times per mouse and no clonotype was found more often than seven times (Fig. 5p). In line with previous reports, this indicated that TCR diversity within the CTL population was high during the acute phase of infection⁵⁶. By consequence, the severe reduction in SLEC numbers in Il1rl1-ExAB^−/− mice resulted in reduced SLEC clonotype numbers (Fig. 5q,r). Taken together, without type 1 promoter-driven ST2 expression, most CTL clones achieve basal activation, but fail to develop into fully differentiated SLECs. Thus, type 1 promoter-driven ST2 expression is vital to establish a numerically relevant and clonally diverse population of short-lived antiviral effector CTLs.

RNA profiling indicates a TCR-cooperative role of IL-33

To gain mechanistic insight on how IL-33 signaling modulates T-cell activation and differentiation, we performed a comprehensive analysis of early IL-33 target genes. To this end, naive T cells were differentiated into CTLs, Th1 or Th2 cells, followed by a resting period without antigenic stimulation. Because ST2 signaling is subject to negative feedback mechanisms and oxidation of IL-33 rapidly reduces its activity^57,58, gene expression was analyzed before (0 h) and 2 h after treatment with or without IL-33 (Fig. 6a). Short-term stimulation with IL-33 had a profound effect on the transcriptome of all subsets, and it strongly induced or, in fewer cases, reduced expression of target genes rather than preventing a loss or gain of transcription (Fig. 6b). Whereas many differentially regulated genes were shared between subsets, others were regulated in a lineage-specific manner (Fig. 6c). Gene Ontology-enrichment analysis revealed a broad role of IL-33-responsive genes in T-cell activation, proliferation and differentiation (Fig. 6d). Importantly, IL-33 stimulation of CTLs amplified expression of Tbx21, Zeb2 and Prdm1, encoding transcription factors critical for SLEC differentiation^59,60,61 (Fig. 6b,e). Across the three T-cell subsets we observed a prominent upregulation of genes frequently used as indicators of recent TCR activation (Nr4a1, Cd69 and Batf) (Fig. 6b,e)^62,63,64. Coherently, gene set enrichment analysis showed a significant overlap between IL-33- and TCR-downstream signaling in CTLs (Fig. 6f and Supplementary Table 2). This finding suggested that IL-33 may support TCR stimulation to promote potent antiviral T-cell responses.

**Fig. 6: Transcriptional profiling indicates broad costimulatory and TCR-cooperative functions of IL-33–ST2 signaling in T cells.**

To further investigate the interplay between ST2 and TCR signaling strength, we made use of a genetically engineered LCM virus that differs from the WT counterpart only by a GP-A39C mutation, rendering its GP33 epitope a weak P14 TCR agonist⁶⁵. P14 and Il1rl1^−/− P14 cells were cotransferred into WT recipients, which were subsequently infected with LCMV expressing either the high- or the low-affinity GP33 variant (Fig. 6g). We found that ST2-sufficient and ST2-deficient P14 cells expanded less when primed with the low-affinity ligand (Fig. 6h). Further, P14 cells depended on ST2 for optimal expansion and effector differentiation, irrespective of the TCR stimulation strength (Fig. 6i). Interestingly, the response of WT P14 cells responding to low-affinity virus was comparable to or exceeded the one of high-affinity ligand-primed Il1rl1^−/− P14 cells in terms of total and effector cell progeny, respectively (Fig. 6h,i). This finding suggested that ST2 signals can help reaching effector T-cell responses of critical size even when confronted with low-affinity ligands. Lastly, in comparison to WT P14 cells, Il1rl1^−/− P14 cells yielded slightly fewer MPECs, irrespective of the TCR stimulation strength (Fig. 6j). Of note, the impairment in T-cell expansion and effector differentiation between mice infected with high- or low-affinity GP33-expressing LCMV were unlikely due to any potential differences in inflammation or IL-33 release, as the endogenous NP396-specific T-cell responses to the two LCMV variants were indistinguishable (Fig. 6k).

In summary, these data demonstrate that IL-33–ST2 signaling provides a strong costimulatory signal that can act cooperatively with TCR signaling to promote the expansion and effector cell differentiation of antiviral CTLs.

Discussion

IL-33 has long been recognized as a type 2 immunity-related cytokine^{2,3,6,7,12,31,32}. Over the past decade its important role in promoting type 1 immunity has become widely accepted yet remains mechanistically less well understood, particularly due to a lack of understanding how ST2 expression is regulated in these cells^31,32. Here, we studied the transcriptional regulation of ST2 expression in antiviral T cells and discovered a dedicated type 1 immunity-restricted promoter located ~40 kb upstream of the curated Il1rl1 gene in mice and humans. This type 1 promoter drives ST2 expression by CTLs and Th1 cells in vitro and in viral infections in vivo. As opposed to the previously described type 2 promoter, which is regulated by GATA-3 (ref. ³³) and is utilized by type 2 immune cells and T_reg cells²¹, the identified promoter is controlled by the type 1 immunity-associated transcription factors T-bet and STAT4 and is subject to epigenetic remodeling during type 1 T-cell differentiation. Thus, we provide evidence for a dedicated regulatory genetic element to control ST2 expression selectively in type 1-polarized T cells, as well as NKT and NK cells.

Although the Il1rl1 gene has been studied intensively^8,35,66, the type 1 promoter has remained unrecognized, likely because it is only transiently active, often resulting in a low abundance of ST2-coding transcripts^24,27. The latter renders it difficult to obtain adequate read coverage for a clear definition of exon structures by commonly used RNA-seq techniques⁶⁷—a challenge we approached by analyzing T cells that express a high amount of Il1rl1 transcripts. Subsequently, we have validated the crucial role of this regulatory element in vivo, and by analyzing human T cells have extended the concept to our species.

Above all, our finding was surprising, as to the best of our knowledge no other gene has been identified to date, for which type 1 and type 2 immune cells exhibit a similarly distinct lineage-specific promoter usage. Consistently, our own attempts at identifying genes with an analogous promoter usage were unsuccessful. We acknowledge that technical limitations might have prevented us from identifying such genes. Still, our results suggest that this is not a common feature but represents a fairly unique mechanism to spatiotemporally orchestrate ST2 expression.

IL-33 is an exceptionally potent alarmin, which can act as a pro- or anti-inflammatory cytokine, depending on the local composition of immune cells and their responsiveness to IL-33 (ref. ³²). Likely due to its potential to cause severe inflammation, IL-33 responsiveness requires stringent regulation. Transcription from the type 1 immunity-restricted promoter enables transient ST2 expression by CTLs and Th1 cells in response to inflammatory stimuli^23,24,27, which may serve to prevent continuous activation of cells with a high tissue-destructive potential. In contrast, constitutive type 2 promoter-driven ST2 expression on T_reg cells and ILC2s allows for rapid anti-inflammatory responses to tissue damage^19,20,21,32.

Importantly, this dual mode of action constitutes a major hurdle for the therapeutic modulation of IL-33–ST2 signaling^32,68. We here demonstrate that the usage of distinct promoters offers opportunities for a T-cell subset-specific targeting of ST2 expression. Il1rl1-ExAB^−/− mice exhibit a type 1 immunity-restricted impairment of ST2 expression and display curtailed CTL and Th1 responses against LCMV, whereas ST2 expression by T_reg cells and type 2 immune cells was fully preserved. Of note, in CTLs ST2 expression was almost exclusively dependent on the type 1 promoter, whereas some Il1rl1-ExAB^−/− Th1 cells could compensate for the defect by engaging the proximal Il1rl1 promoter. Nevertheless, the type 1 promoter was critical for optimal expansion of antiviral Th1 cells. T-cell subset-specific targeting approaches could be of interest to modulate IL-33 responses in inflammatory diseases. For instance, IL-33 administration was shown to drive T_reg expansion in the context of GVHD, promoting tolerance induction and disease amelioration^69,70,71. However, IL-33 also augments type 1 alloimmunity by acting as a costimulatory molecule for donor CTLs and Th1 cells^28,29. A targeted disruption of ST2 selectively on type 1 immune cells might minimize the pathological response during GVHD, whereas the protective effects of IL-33 should remain preserved.

Our study provides insight into the role of type 1 promoter-driven ST2 expression and IL-33 signaling in CTL differentiation. scRNA-seq analysis of antiviral CTLs revealed that Il1rl1-ExAB^−/− mice display a pronounced reduction in SLECs. Moreover, clonotype diversity in the SLEC population was high in WT mice and diminished proportionally to cell counts in Il1rl1-ExAB^−/− mice. This suggests that IL-33 can foster the transition of an activated CTL into a cell with potent effector functions rather than selectively expanding a pool of predifferentiated SLECs. Of note, although the effect was strongly magnified in the SLEC compartment, Il1rl1^−/− as well as Il1rl1-ExAB^−/− CTLs showed a generalized reduction in expansion that in most instances also negatively affected MPECs. Consequently, this impairment in primary expansion likely accounts for the lower numbers of circulating memory CTLs 1 month after infection. The formation of tissue-resident memory cells appeared less affected. This finding might suggest a particular importance of IL-33 signals for the generation of antiviral effector CTLs but only to a lower extent for tissue-resident memory CTLs. However, further work is needed to thoroughly test this hypothesis.

Terminal differentiation has been associated with STAT4 signaling and with high levels of T-bet, Blimp-1 and Zeb2 (refs. ^59,60,61). Likewise, the activity of the type 1 Il1rl1 promoter is positively regulated by STAT4 and T-bet. Interestingly, RNA-seq of IL-33 target genes in CTLs demonstrated an induction of Tbx21 (T-bet), Prdm1 (Blimp-1) and Zeb2 expression, thus inferring a positive feedback loop that further reinforces ST2 expression and effector differentiation via T-bet. Besides these cell-intrinsic factors, TCR signaling strength is linked to acquisition of effector properties⁷². Our data show that IL-33 stimulation of CTLs strongly induces the transcription of several TCR-dependent genes. Moreover, IL-33 signals can restore the otherwise suboptimal expansion and effector differentiation of CTLs in response to a low-affinity antigenic peptide. Recent studies demonstrated a loss of IL-33 in lymphoid organs early after LCMV infection, suggesting substantial release during T-cell priming²⁶. Together, this implies that ST2 signaling might act in conjunction with TCR signaling to achieve above-threshold activation required for fully functional effector differentiation.

In summary, we here uncover lineage-specific promoter usage as molecular mechanism governing disparate expression patterns of ST2 in distinct T-cell subsets. Using newly generated knockout mice, we demonstrate that the type 1 immunity-restricted Il1rl1 promoter is essential for fully functional antiviral T-cell responses and critical for the formation of a clonally diverse population of effector CTLs. These findings open new avenues for the modulation and exploitation of IL-33 signaling in type 1 immunity-mediated inflammatory diseases and T-cell-based cancer immunotherapy, respectively.

Methods

Mice

C57BL/6 J mice (WT), LCMV-TCR^tg P14 (ref. ⁷³) and Smarta⁷⁴ mice expressing the congenic markers CD45.1 or CD90.1, respectively, Il1rl1^−/− (ref. ¹²), Il1rl1-ExAB^−/−, Il1rl1-ExC^−/−, Stat4^−/− (ref. ⁷⁵), Tbx21^−/− (ref. ⁷⁶), Il1rl1-ExAB^−/− Smarta, Stat4^−/− Smarta, Tbx21^−/− Smarta, Il1rl1^−/− P14, Il1rl1-ExAB^−/− P14 and Tcrbd^−/− (ref. ⁷⁷) mice were bred under specific-pathogen-free conditions in approved animal-care facilities at the Research Institute for Experimental Medicine of the Charité – Universitätsmedizin Berlin or at the Laboratory Animal Facility of the ETH Zürich (ETH Phenomics Center). Mice were housed in individually ventilated cages with a 12 h light/dark cycle at an ambient temperature of 21 °C and 45% to 65% relative humidity. Mice had ad libitum access to drinking water and chow. Both, male and female mice between 8 and 26 weeks of age were used for experiments. For LCMV infections, experimental groups were age and sex matched. Mice used for scRNA-seq analyses were cohoused for 4 weeks before infection. Animal experiments were performed in accordance with the German or Swiss law for animal protection and were approved by the respective governmental authority (Landesamt für Gesundheit und Soziales Berlin and the Cantonal Veterinary Office of the Canton of Basel; T0058/08, G0111/17, G0206/17, G0245/19).

Generation of Il1rl1-ExAB ^−/− and Il1rl1-ExC ^−/− mice

Il1rl1-ExAB^−/− and Il1rl1-ExC^−/− mice were generated in the Transgenics Core Facility of the Max Delbrück Centrum Berlin using established protocols⁷⁸. In brief, gRNA sequences with minimal predicted off-target effects were identified using the web-based tool CRISPOR⁷⁹. Zygotes were collected from C57BL/6 J mice (Charles River), microinjected with synthetic gRNAs (Integrated DNA Technologies) and recombinant Cas9 protein (Integrated DNA Technologies) and subsequently transferred into pseudo-pregnant C57BL/6 J mice. Resulting F0 offspring mice were screened for successful deletion by PCR amplification of WT or knockout alleles. gRNA and PCR primer sequences are listed in Supplementary Table 4.

Lymphocyte isolation

To isolate lymphocytes, spleens were mechanically disrupted and filtered through 70-µm strainers. Erythrocytes were lysed by 35 min of incubation in erythrocyte lysis buffer (10 mM KHCO₃, 155 mM NH₄Cl, 0.1 mM EDTA, pH 7.5). Livers were collected in PBS/BSA, meshed and centrifugated at 30 g for 2 min to remove debris. Supernatants were subjected to Histopaque density centrifugation (1.083 g ml⁻¹, Sigma-Aldrich) and lymphocytes were collected at the gradient interphase. To stain ILC2s, lungs were cut into small pieces and digested with Collagenase D (0.1 U ml⁻¹) in RPMI1640 (supplemented with 10% fetal calf serum (FCS) and 15 mM HEPES) for 1 h at 37 °C. Afterwards, lymphocytes were isolated by Histopaque density centrifugation (1.083 g ml⁻¹, Sigma-Aldrich). To isolate peritoneal cavity cells, 5 ml cold PBS was injected into the peritoneal cavity of euthanized mice. After a brief massage of the peritoneum, cell-containing liquid was collected and subjected to Histopaque density centrifugation (1.083 g ml⁻¹, Sigma-Aldrich). For analysis of tissue-resident memory T cells, lungs, kidneys and salivary glands were cut into pieces and digested in RMPI1640 + GlutaMax I (Thermo Scientific) medium containing FCS (5% v/v, Thermo Scientific), MgCl₂ (2 µM, Carl Roth), CaCl₂ (2 µM, Carl Roth) and collagenase type I (100 U ml⁻¹, Gibco) at 37 °C for 45 min. Subsequently, tissue was further disrupted using a GentleMACS Dissociator (setting m_Spleen_01.01). Cells were filtered through 70-µm strainers, subjected to erythrocyte lysis and analyzed.

Flow cytometry

Surface stainings of purified lymphocytes were performed using different combinations of antibodies diluted in PBS. A list of antibodies and dilutions used in this study is provided in Supplementary Table 3. Unspecific staining was minimized by blocking with rat immunoglobulin G (Jackson ImmunoResearch) and anti-mouse CD16/32 (2.4G2, DRFZ inhouse production) prior to staining. Dead cells were labeled using Zombie Aqua or Zombie NIR fixable live/dead staining reagents (BioLegend) or by adding propidium iodide (PI) prior to acquisition. For detection of ST2 on murine T cells, lymphocytes were first stained with digoxigenin-conjugated antibody against ST2 (DJ8), followed by a secondary staining with PE- or APC-conjugated anti-digoxigenin Fab fragments (Roche). Further, stainings were enhanced by two rounds of PE- or APC-FASER amplification (Miltenyi Biotec). To identify LCMV-specific T cells, lymphocytes were stained with LCMV GP_33-41 or NP_396-404 peptide-loaded MHC class I (H2-Db) tetramers (PE or APC conjugated, respectively) for 30 min at 37 °C. For detection of transcription factors or Ki67 expression, surface-stained cells were fixed and stained using the FoxP3 staining buffer set (Thermo Scientific). Briefly, cells were fixed with 1x fixation/permeabilization reagent for 30 min at 4 °C and washed with permeabilization buffer. Subsequently, cells were stained with antibodies diluted in permeabilization buffer for 30 min at 4 °C.

For flow-cytometric detection of cytokines, lymphocytes were restimulated with phorbol myristate acetate (5 ng ml⁻¹, Sigma-Aldrich) and ionomycin (5 µg ml⁻¹, Sigma-Aldrich), recombinant LCMV GP_33-41 (1 µg ml⁻¹, Charité Berlin) or LCMV GP_64-79 (1 µg ml⁻¹, Charité Berlin) for 4 h at 37 °C. After 35 min, brefeldin A (5 µg ml⁻¹, Sigma-Aldrich) was added. Restimulated cells were labeled with surface antibodies and fixable live/dead staining reagents, followed by fixation in 2% paraformaldehyde for 10 min at room temperature. Intracellular cytokines were stained with antibodies diluted in PBS containing 0.05% saponin (Sigma-Aldrich) for 30 min at 4 °C and washed before acquisition. Cells were acquired using Canto II or LSRFortessa flow-cytometers (BD) with Diva software (BD). Sorting was performed on Aria and Aria II devices (BD). Cell numbers were determined using MACSQuant (Miltenyi Biotec) or ImmunoSpot (CTL) analyzers. Analyses were performed using FlowJo (v.10.7.1).

Viruses and LCMV infection

LCMV-WE and LCMV-Cl13 strains were propagated on L929 or BHK-21 cells, respectively. Viral titers in stock solutions were determined by immunofocus assay on MC57G cells as described before⁸⁰. In brief, MC57G cells were plated with virus stock dilutions and overlaid with 2% methylcellulose. After 48 h at 37 °C, the confluent monolayer of cells was fixed with 4% formaldehyde, permeabilized with Triton X-100 (1%, v/v) and stained with antibodies against LCMV nucleoprotein. After a secondary staining step with peroxidase-conjugated anti-rat immunoglobulin G antibody, foci were developed by 20-min incubation with OPD substrate (Sigma-Aldrich). Mice were infected intravenously (i.v.) with either 200 plaque-forming units (PFU) of LCMV-WE (mixed bone marrow chimera experiments), 200 PFU LCMV-Cl13 (adoptive transfer experiments, LCMV-Cl13 WT or C6 variant where indicated) or 2 × 10⁶ PFU of LCMV-WE in minimal essential medium (Thermo Scientific).

Adoptive T-cell transfers

For adoptive transfer experiments, TCR-transgenic T cells expressing CD45.1 or CD90.1 were enriched in a negative selection approach. Splenocytes of donor mice were stained with biotinylated antibodies against CD11b, CD11c, CD19, CD25, Gr-1, NK1.1, CXCR3 and CD8a (for isolation of Smarta T cells) or CD4 (for isolation of P14 T cells) followed by incubation with anti-biotin microbeads (Miltenyi Biotec). Subsequently, labeled cells were depleted by magnetic activated cell sorting (MACS) using LS columns (Miltenyi Biotec). 5 × 10⁴ T cells (single transfer experiments), 1 × 10³ P14 T cells or 1 × 10⁴ Smarta T cells (cotransfer experiments) were transferred i.v. into C57BL/6 J mice. For analysis of memory T cells at d30 p.i., 2.5 × 10⁴ P14 cells were transferred. Recipients were infected 1–2 days after transfer and analyzed at indicated timepoints.

In vivo labeling of T cells

To distinguish between tissue-resident and intravascular T cells, mice were injected i.v. with 3 µg PE-conjugated CD90.2 antibody (30-H12, BioLegend) and sacrificed 3 min after injection.

Mixed bone marrow chimeras

To generate mixed bone marrow chimeras, CD45.1^+/+ WT recipients were lethally irradiated (two doses of 5.5 Gy given in a 6-h interval). One day later, recipients were reconstituted with a 1:1 mixture of CD45.1^+/- WT and CD45.2^+/+ knockout bone marrow cells and splenocytes. After 8 weeks of hematopoietic reconstitution, CTL frequencies of respective donor populations were determined in blood, and mice were infected with LCMV-WE (200 PFU i.v.). Data were analyzed on d10 p.i. and normalized to CTL frequencies before infection.

Legendplex cytometric bead assay

To assess cytokine production by T cells in response to IL-33, 5 × 10⁵T cells were stimulated in 48-well plates with IL-33 (R&D, 10 ng ml⁻¹) for 24 h at 37 °C. Afterwards, individual wells were harvested and centrifuged for 5 min at 350 g. To obtain serum, blood of individual mice was collected using yellow microtainers (BD). Serum and cell-free supernatant were frozen at −80 °C until analysis. Cytokine content was measured using LEGENDplex bead-based immunoassays (BioLegend) according to manufacturer’s instructions and acquired at a Canto II flow-cytometer (BD). Cytokine concentration was extrapolated from standard titrations.

Mouse T-cell cultures

Naive T cells from spleens of indicated mice were preenriched by staining with biotinylated antibodies against CD8a or CD4, followed by incubation with anti-biotin microbeads (Miltenyi Biotec) and subsequent separation by MACS using LS columns (Miltenyi Biotec). Following enrichment, naive (CD62L⁺ CD44⁻CD25⁻CXCR3⁻) CD8⁺ or CD4⁺ T cells were flow-cytometrically sorted and differentiated in the presence of irradiated Tcrbd^−/− splenocytes and antibodies against CD3ε and CD28 (2.5 µg ml⁻¹ each). When naive T cells were isolated from LCMV-TCR^tg mice, cognate LCMV GP_33-41 (P14 mice) or GP_64-79 peptide (Smarta mice, both 1 µg ml⁻¹) were added instead. T cells were cultivated in RPMI1640 + GlutaMax I (Thermo Scientific) medium supplemented with FCS (10% v/v, Thermo Scientific), penicillin (100 U ml⁻¹, Thermo Scientific), streptomycin (100 μg ml⁻¹, Thermo Scientific), gentamycin (10 μg ml⁻¹, Thermo Scientific) and β-mercaptoethanol (50 ng ml⁻¹, Sigma-Aldrich). For CTL and Th1 differentiation, IL-12 (5 ng ml⁻¹), IL-2 (5 ng ml⁻¹, all Miltenyi Biotec) and anti-IL-4 (11B11, 10 μg ml⁻¹, DRFZ inhouse production) were added. For Th2 differentiation, IL-4 (5 ng ml⁻¹), IL-2 (5 ng ml⁻¹, all Miltenyi Biotec), anti-IL-12 (C18.2, 10 μg ml⁻¹) and anti-IFN-γ (XMG1.2, 10 μg ml⁻¹, all DRFZ inhouse production) were added. T cells were split after 2–3 days of culture in a 1:3 ratio with fresh medium containing IL-2 (5 ng ml⁻¹), harvested at day 5 of culture using Histopaque density centrifugation (1.083 g ml⁻¹, Sigma-Aldrich) and cultivated for additional 5 days in identical culture conditions.

Mouse NKT cell and NK cell cultures

Murine NKT cells were preenriched by incubating thymocytes with anti-CD8 and anti-CD62L microbeads (Miltenyi Biotec) followed by subsequent MACS separation using LS columns (Miltenyi Biotec). Enriched cells were stained with PE-conjugated, α-galactosylceramide (α-GalCer)-loaded CD1d tetramers (MBL) and antibodies against TCRβ, CD19 and CD8. CD1d-Tet⁺ TCRβ⁺ CD19⁻CD8⁻ NKT cells were flow-cytometrically sorted and activated in 96-well plates precoated with antibodies against CD3ε and CD28 (2.5 µg ml⁻¹ each). NKT cells were cultivated in CTL/Th1 culture medium as described above. After 2 days of stimulation, NKT cells were transferred to uncoated wells and split in a 1:3 ratio with fresh medium containing IL-2 (5 ng ml⁻¹). Cells were analyzed at day 6 of culture.

To isolate murine NK cells, splenocytes were stained with biotinylated antibodies against CD8, CD4 and B220, followed by incubation with anti-biotin microbeads (Miltenyi Biotec) and subsequent separation by MACS using LS columns (Miltenyi Biotec). CD8-, CD4- and B220-depleted splenocytes were then stained with antibodies against NKp46, TCRβ and streptavidin PE. NKp46⁺ TCRβ⁻ CD8⁻ CD4⁻ NK cells were flow-cytometrically sorted and activated in RPMI1640 + GlutaMax I (Thermo Scientific) medium supplemented with FCS (10% v/v, Thermo Scientific), penicillin (100 U ml⁻¹, Thermo Scientific), streptomycin (100 μg ml⁻¹, Thermo Scientific), gentamycin (10 μg ml⁻¹, Thermo Scientific), β-mercaptoethanol (50 ng ml⁻¹, Sigma-Aldrich), IL-15 (10 ng ml⁻¹), IL-12 (10 ng ml⁻¹) and IL-33 (10 ng ml⁻¹). NK cells were analyzed after 2 days of culture.

Retroviral transduction of T cells

For cloning of shRNA expression vectors, sense- and antisense-shRNA sequences were ordered as phosphorylated oligos with a 5’ SalI restriction overhang (Eurofins Genomics) and annealed by subjecting equimolar amounts of oligos diluted in oligo annealing buffer (100 mM Tris-HCl, 1 M NaCl and 10 mM EDTA, pH 7.5) to a decreasing temperature gradient (95 °C to 25 °C with 1 °C min⁻¹). Oligo sequences are provided in Supplementary Table 4. PQCXIX-GFP target vector⁸¹ was digested by SalI and HpaI restriction enzymes (Thermo Scientific) and dephosphorylated with FastAP alkaline phosphatase (Thermo Scientific). Annealed oligos were ligated using T4 Ligase according to standard protocols (NEB). Heat-inactivated ligation reactions were directly used for heat-shock transformation into Oneshot TOP10 chemically competent Escherichia coli (Thermo Scientific). Single transformed bacterial clones were selected on LB-agar plates (MP Biomedicals) containing ampicillin (100 µg ml⁻¹, Sigma-Aldrich), and plasmid DNA was prepared using QIAprep Spin Plasmid Maxi or Midi kits (Qiagen). Correct plasmid sequences were verified by Sanger-sequencing (Eurofins Genomics). Virus particles were generated by co-transfection of HEK293T cells with shRNA-containing vectors and packaging plasmids pCGP and pECO⁸² using Transporter 5 transfection reagent (Polysciences). For retroviral transduction, mouse T cells were activated in the presence of irradiated APCs and antibodies against CD3ε and CD28 described above. 36–48 h after plating, culture medium was temporarily replaced with virus-containing supernatant, polybrene (8 µg ml⁻¹, Sigma-Aldrich) was added and plates were centrifuged for 90 min at 450 g at room temperature. T cells were incubated at 37 °C for 6–8 h. Afterwards, viral supernatant was replaced with conditioned cell culture medium and cells were split in a 1:3 ratio with fresh IL-2-containing medium. Transduced T cells were analyzed between day 5 and day 7 of culture.

Human T-cell cultures

Human peripheral blood was obtained from the German Red Cross (DRK Berlin; ethics approval EA1/149/12) with consent from donors. For isolation of T cells, blood was first subjected to Ficoll-Paque PLUS density centrifugation (1.077 g ml⁻¹, Cytiva). Interphases were collected, stained with anti-CD4 microbeads (Miltenyi Biotec) and separated using LS columns (Miltenyi Biotec). CD4⁺ T-cell-depleted fractions were used for a MACS enrichment of CD8⁺ T cells using anti-CD8a microbeads (Miltenyi Biotec). CD4-enriched fractions were stained with antibodies against human CD4, CXCR3 and CRTH2 for 15 min at 4 °C followed by a secondary staining with streptavidin PE. CD8-enriched fractions were stained with antibodies against human CD8, CD56, CD62L and CD45RA. In vivo-differentiated Th1 cells were sorted as CD4⁺ CXCR3⁺ CRTH2⁻, and Th2 cells were sorted as CD4⁺ CXCR3⁻ CRTH2⁺. CD8⁺ effector/effector memory T cells were sorted as CD8⁺ CD56⁻CD45RA⁻ CD62L⁻. For activation of human T cells, suspension culture plates were coated with antibodies against human CD3ε and CD28, and sorted T cells were plated in RPMI1640 + GlutaMax I (Thermo Scientific) medium supplemented with FCS (10% v/v, Thermo Scientific), penicillin (100 U ml⁻¹, Thermo Scientific), streptomycin (100 μg ml⁻¹, Thermo Scientific), gentamycin (10 μg ml⁻¹, Thermo Scientific) and β-mercaptoethanol (50 ng ml⁻¹, Sigma-Aldrich). To CTL and Th1 cultures, IL-12 (10 ng ml⁻¹, R&D Systems), IL-2 (10 ng ml⁻¹, R&D Systems) and anti-IL-4 (7A3-3, 10 µg ml⁻¹, Miltenyi Biotec) were added, whereas Th2 cells were cultured in the presence of IL-4 (10 ng ml⁻¹, R&D Systems), IL-2 (10 ng ml⁻¹, R&D Systems), anti-IL-12 (C8.6, 10 µg ml⁻¹, Miltenyi Biotec) and anti-IFN-γ (45-15, 10 µg ml⁻¹, Miltenyi Biotec). Cells were withdrawn from coated plates after 24 h of activation, split after 3 days in a 1:3 ratio with fresh medium containing IL-2 (10 ng ml⁻¹, R&D Systems) and analyzed on day 5 of culture. All analyses were carried out in compliance with the relevant ethical regulations.

RNA isolation and qRT-PCR

To isolate RNA for qRT-PCR analysis or bulk RNA-seq, 10⁵–10⁶ T cells were harvested and lysed in RA-1 buffer (Macherey & Nagel). Total RNA was purified using the Nucleospin RNA XS Micro kit (Macherey & Nagel) according to manufacturer’s instructions, without addition of carrier RNA. For qRT-PCR analysis, RNA was transcribed into cDNA utilizing Taqman Reverse Transcription Reagents (Applied Biosystems). cDNA was then subjected to qRT-PCR analysis using PowerUp SYBR Green or Taqman Fast Advanced Mastermix reagents (Applied Biosystems). Primer sequences and Taqman probes are listed in Supplementary Table 4. Amplifications were performed in triplicates by using a QuantStudio 7 device (Applied Biosystems) and expression levels were quantified with the ΔΔCt-method by normalizing target gene expression to levels of Hprt (mouse) or GAPDH (human).

Single-cell RNA library preparation, sequencing and analysis

Single-cell suspensions of CD90⁺ CD8⁺ CD44⁺ T cells were obtained by flow-cytometrical sorting of CD19-depleted splenocytes from LCMV-infected WT and Il1rl1-ExAB^−/− mice at day 7 p.i. with LCMV-WE (2 × 10⁶ PFU). Sorted T cells of individual mice were barcoded using TotalSeq-C anti-mouse Hashtags (anti-mouse Hashtag 1, 2 and 3, all BioLegend). T cells of each genotype were then pooled and applied to the 10x Genomics workflow for cell capturing. For the preparation of scRNA gene expression (GEX), TCR and CiteSeq libraries, the Chromium Next GEM Single Cell 5’ Library & Gel Bead Kit v1.1 as well as the Chromium Single Cell 5’ Feature Barcode Library Kit were used in conjunction with Chromium Controller (10x Genomics). After cDNA amplification the CiteSeq libraries were prepared separately using the Single Index Kit N Set A (10x Genomics). TCR target enrichment was performed using the Chromium Single Cell V(D)J Enrichment Kit for mouse T cells (10x Genomics). Final GEX and TCR libraries were obtained after fragmentation, adapter ligation and final Index PCR using the Single Index Kit T Set A. The Qubit dsDNA HS assay kit (Life Technologies) and a Qubit 2.0 Fluorometer were used for library quantification. Fragment sizes were determined using a Fragment Analyzer device with the NGS Fragment Kit (1–6,000 bp) (Agilent). Sequencing was performed on a NextSeq2000 device (Illumina) using P2 Reagents v3 (200 cycles) with the recommended sequencing conditions for 5’ GEX and barcode libraries (read 1: 26 nt, read 2: 98 nt, index1: 8 nt, index 2: n.a.) and on a NextSeq500 device (Illumina) using a Mid Output v2 Kit (300 cycles) for TCR libraries (read 1: 150 nt, read 2: 150 nt, index 1: 8 nt, index 2: n.a., 20% PhiX spike-in). Raw data were processed using cellranger-3.1.0 with refdata-gex-mm10-2020-A and refdata-cellranger-vdj_GRCm38_alts_ensembl-mouse-2.2.0 as reference. Mkfastq, count and vdj were used in default parameter settings with 3,000 expected cells for demultiplexing, detection of intact cells, quantification of gene expression, antibody capture as well as assembly and quantification of T-cell receptor sequences.

The cellranger output was further analyzed in R using the Seurat package (version 4.0.0)⁸³. Hashtag sequences of three individual Il1rl1-ExAB^−/− and WT mice were imported and combined. Centered log ratio transformation was used for normalization. Seurat’s default method was used for scaling. Features with correlation coefficients >0.85 to Gm42418, Malat1, AY036118 and Lars2 were removed from the count matrix. Hashtag demultiplexing (representing the three biological replicates per genotype) was performed based on Seurat’s HTODemux with the parameter ‘positive-quantile’ at 0.99. Doublets and untagged cells were filtered out. Cells with expression values for Cd8a or Cd8b1 and Cd3g, Cd3d or Cd3e, with >200 and <4,500 features, and <10% UMI for mitochondrial genes were kept for further analysis.

After ranking by residual variance, 3000 variable genes were determined. The genes encoding TCR variable regions (Trav, Trbv, Trdv and Trgv) were removed. 30 principal components were computed and stored. UMAP and t-distributed stochastic neighbor embedding were run using the first 15 principal components. Transcriptionally similar clusters were identified using shared nearest neighbor modularity optimization, with a resolution of 0.35. For visualization, cells of the Il1rl1-ExAB^−/− condition were down-sampled to match the number of cells in the WT condition. Signature genes were identified using the FindAllMarkers function in default parameter settings (only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25). Heatmaps and dotplots for the single-cell data were plotted with Seurat’s DoHeatmap and DotPlot function, respectively, using default settings. Identified clusters were annotated based on the expression of key markers for CD8⁺ T-cell subsets and cell functions. Two Klrg1 expressing clusters were merged to the SLEC cluster. Further, two clusters were merged to form the Mito^hi cluster based on their expression of mitochondrial genes. After combining the stated clusters, signature genes were identified again using the same method as described above. Cluster size was defined as the number of cells in one cluster. Relative cluster sizes were calculated by analyzing the number of cells in one cluster per genotype divided by the total number of cells. For feature plots, the expression of single features was plotted on the UMAP by using Seurat’s default function FeaturePlot with the option “keep.scale=‘all’”. For differential expression analysis between clusters the FindMarkers function was used.

For the analysis of TCRs, immune profiles were integrated using identical cellular barcodes. For cells with more than one contig for the heavy or light TCR chain the most abundant, productive contig was chosen. Cell numbers were equalized by subsampling the larger condition. Cells without TCR annotation were excluded from the analysis. The R package immunarch (version 0.6.6)⁸⁴ was used for clonality analysis after downsampling to the WT condition.

RNA-seq

Smarta and P14 T cells used for bulk RNA-seq experiments were differentiated as described above. To enrich for ST2-expressing CTLs and Th1 and Th2 cells, ST2⁺ T cells were flow-cytometrically sorted at day 10 of culture. For global analysis of IL-33-responsive genes, T cells were harvested at day 10 of culture and rested for 3 days in the presence of IL-2 (5 ng ml⁻¹) and IL-7 (5 ng ml⁻¹, both Miltenyi Biotec), without irradiated splenocytes or cognate peptide. At day 13, IL-12 (5 ng ml⁻¹) was added to CTLs and Th1 cells to trigger ST2 expression. At day 14 of culture, T cells were subjected to Histopaque density centrifugation (1.083 g ml⁻¹, Sigma-Aldrich) and stimulated in conditioned medium with IL-33 (10 ng ml⁻¹, R&D Systems) for 2 h. When used for RNA-seq, quality of the isolated RNA was assessed with a Fragment Analyzer System (Agilent). All processed samples showed high RNA integrity (RQN > 8). cDNA libraries were prepared using the Smart-seq v4 mRNA Ultra Low Input RNA Kit (Clontech) with up to 10 ng RNA (IL-33-stimulated T cells) or TrueSeq stranded total RNA library kit (Illumina) with up to 1 μg of RNA (ST2-enriched T cells) according to manufacturer’s instructions. Paired-end sequencing (2 × 75 bp) of cDNA libraries was performed on an Illumina NextSeq500 device using the NextSeq 500/550 High output Kit v2. Obtained reads were mapped to the mm10 genome (annotation release GRCm38.p6) using Tophat2 (ref. ⁸⁵) and Bowtie2 (ref. ⁸⁶) with very sensitive settings. Read counts were determined with featureCounts⁸⁷. DESeq2 (ref. ⁸⁸) was used in RStudio for differential gene expression analysis. The DESeq2 count matrix was pre-filtered for genes with ≥100 summarized read counts across the analyzed samples. A gene was considered as differentially expressed when log₂ fold change > 1.0 and P adjusted < 0.01. AnnotationDbi⁸⁹, EnhancedVolcano⁹⁰, ComplexHeatmap⁹¹, pheatmap⁹² and ggplot2 (ref. ⁹³) were used in RStudio for data visualization.

For PCA and sample distance calculation, a blind variance stabilizing transformation was performed on the unnormalized counts across all samples. Sample distance plots are based on pairwise calculation of the Pearson correlation.

Gene set enrichment and overrepresentation analysis

For gene set enrichment analysis, the R package clusterProfiler⁹⁴ was used. A gene list ranked by log₂ fold change containing all expressed genes served as input and was tested for enrichment of biological process gene sets from the gene ontology resource^95,96.

For overrepresentation analysis, differentially expressed genes were split into up- and downregulated genes. Overrepresentation analysis was performed against biological process gene sets from the gene ontology resource using a one-sided hypergeometric test with BH correction. All expressed genes in the respective conditions were used as a background gene list. The results were simplified to reduce overlaps between ontology terms by using the clusterProfiler::simplifyGO function with a cutoff of 0.7.

Analysis of alternative transcription start sites

Raw RNA-seq reads of indicated T-cell subsets were aligned using hisat2 (version 2.2.1)⁹⁷ and assembled using Cufflinks (version 2.2.1)⁹⁸ in RABT mode with EnsEMBL annotation release 67. Assemblies were then merged into a new reference annotation with the public reference using the cuffmerge function. The resulting annotation was used for an analysis with the R package ProActiv⁴². The getAlternativePromoters function was used with standard parameters except for minAbs = 5. Only results with false discovery rate < 0.01 were considered.

Processing of published RNA-seq, ChIP-seq and ATAC-seq data

Fastq files of published RNA-seq datasets were obtained from the NCBI Sequence read archive and aligned using hisat2 with default settings⁹⁷. ChIP- and ATAC-seq data sets were downloaded from the NCBI GEO Database and if required crossmapped to the mm10 genome using CrossMap⁹⁹. All NGS data tracks were visualized in the IGV browser¹⁰⁰.

Quantification and statistical analysis

Statistical analysis on untransformed or log₂-transformed values was performed using GraphPad Prism (v10.0.3). Normal distribution was tested using Shapiro–Wilk and Kolmogorov–Smirnov tests. Unpaired or paired two-tailed Student’s t-tests were used when two groups were compared with respect to one parameter. More than two groups were analyzed by one-way ANOVA with Tukey’s post hoc test for multiple comparison. For comparisons of more than one parameter between two or more groups, two-way ANOVA with Tukey’s post hoc tests (unpaired samples) or two-way repeated measures ANOVA with Šidák’s post hoc tests (paired samples) were performed as indicated in the figure legends.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Generated single-cell and bulk RNA-seq data are provided via the Gene Expression Omnibus (GEO) under accession codes GSE204695 and GSE204693. Published data are accessible via the following GEO accession codes: ChIP-seq: GSM550303 (STAT4), GSM998272 (T-bet), GSM523226 (GATA-3), GSM776557 (human T-bet); ATAC-seq: GSE120532 (CD4⁺ T cells) and GSE111902 (CD8⁺ T cells). PhyloP conservation tracks are provided by the UCSC Sequence and Annotation database https://hgdownload.soe.ucsc.edu/goldenPath/mm10/phyloP60way/ (refs. ^43,101). Mast cell and ILC2 RNA-seq data are available at the NCBI Sequence read archive via Run ID SRR7549295 (ILC2s) and SRR6155875 (mast cells). FANTOM5 CAGE-seq data and CAGE-associated transcript data are available from the FANTOM5 collection (https://fantom.gsc.riken.jp/5/)^47,102,103. Genome releases GRCm38.p6/mm10 and GRCh37.13/hg19 are accessible at Ensembl (http://www.ensembl.org/index.html). Mouse data can be inspected in the UCSC genome browser: https://genome.ucsc.edu/s/agloehning/BrunnerServeetal2023. Source data are provided with this paper.

Code availability

Code related to the data analysis has been deposited to GitLab (https://agloehninggitlab.gitlab.io/BrunnerServe-Type1-ST2/).

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Acknowledgements

We thank J. Kirsch, T. Kaiser (Flow Cytometry Core Facility, DRFZ, Berlin), G. Guerra, K. Lehmann, V. Holecska, I. Panse, C.L. Tran (DRFZ, Berlin), C. Scholl, A. Leschke (Transgenics Core facility, MDC, Berlin), T. Abreu Mota, M. Ji-Lu (University of Basel), A. Greco (DKFZ, Heidelberg) and A.N. Hegazy (Charité, Berlin) for technical assistance and advice. This work was supported by the German Research Foundation (DFG grants LO 1542/5-1 and LO 1542/4-1 to M.L.), the Swiss National Science Foundation (Sinergia grant CRSII3_160772/1 to M.L. and D.D.P., project grant 310030_185318/1 to D.D.P.), the Willy Robert Pitzer Foundation (Pitzer Laboratory of Osteoarthritis Research, 21-033 to M.L.), the Dr. Rolf M. Schwiete Foundation (Osteoarthritis Research Program, 2021-035 to M.L.) and the state of Berlin and the European Regional Development Fund (ERDF 2014–2020 and EFRE 1.8/11 to M.F.M.). S.S. is a member of the Berlin Institute of Health at Charité – Universitätsmedizin Berlin. T.M.B., M.D. and N.D.H. were fellows of the International Max Planck Research School for Infectious Diseases and Immunology.

Funding

Open access funding provided by Deutsches Rheuma-Forschungszentrum Berlin (DRFZ).

Author information

These authors contributed equally to this work: Tobias M. Brunner, Sebastian Serve.

Authors and Affiliations

Experimental Immunology and Osteoarthritis Research, Department of Rheumatology and Clinical Immunology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
Tobias M. Brunner, Sebastian Serve, Jelizaveta Fadejeva, Philippe Saikali, Maria Dzamukova, Nayar Durán-Hernández & Max Löhning
Pitzer Laboratory of Osteoarthritis Research, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
Tobias M. Brunner, Sebastian Serve, Jelizaveta Fadejeva, Philippe Saikali, Maria Dzamukova, Nayar Durán-Hernández & Max Löhning
Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany
Sebastian Serve
Division of Experimental Virology, Department of Biomedicine, University of Basel, Basel, Switzerland
Anna-Friederike Marx & Daniel D. Pinschewer
Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
Christoph Kommer & Thomas Höfer
BioQuant Center, University of Heidelberg, Heidelberg, Germany
Christoph Kommer & Thomas Höfer
Therapeutic Gene Regulation, German Rheumatism Research Center (DRFZ), a Leibniz Institute, Berlin, Germany
Frederik Heinrich, Pawel Durek, Gitta A. Heinz & Mir-Farzin Mashreghi
Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
Ralf Kühn

Authors

Tobias M. Brunner
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Sebastian Serve
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Anna-Friederike Marx
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Jelizaveta Fadejeva
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Philippe Saikali
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Maria Dzamukova
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Nayar Durán-Hernández
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Christoph Kommer
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Frederik Heinrich
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Pawel Durek
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Gitta A. Heinz
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Thomas Höfer
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Mir-Farzin Mashreghi
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Ralf Kühn
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Daniel D. Pinschewer
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Max Löhning
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Contributions

T.M.B., S.S. and M.L. conceptualized the study. T.M.B., S.S., A.F.M., D.D.P. and M.L. interpreted the results. T.M.B., S.S. and M.L. wrote the manuscript. T.M.B., S.S., A.F.M., J.F., P.S., M.D. and N.D.H. conducted experiments. T.M.B., S.S., F.H., P.D., G.A.H. and M.F.M. performed and analyzed RNA-seq and scRNA-seq experiments. T.M.B. and R.K. designed and generated Il1rl1-ExAB^−/− and Il1rl1-ExC^−/− mice. C.K., T.H. and D.D.P. provided expertise and advice. All authors reviewed and edited the manuscript.

Corresponding authors

Correspondence to Tobias M. Brunner or Max Löhning.

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Competing interests

The authors declare the following competing interests: D.D.P. is a founder, shareholder and advisor to Hookipa Pharma, commercializing arenavirus vectors. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Jean-Philippe Girard, Stephen Jameson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: S. Houston, in collaboration with the Nature Immunology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 ST2 expression by CTLs and Th1 cells is regulated by T-bet and STAT4.

a, Experimental scheme of in vitro T-cell differentiation. b, Representative T-bet and GATA-3 stainings of differentiated T cells with expression intensity depicted as geometric mean index (GMI), normalized to isotype control stainings (gray) (n = 4). c, Cytokine expression of polarized T cells upon restimulation (n = 4). d,e, In vitro differentiated Th1 and Th2 cells were stained with PE-conjugated ST2 antibodies (clones DIH9 and RMST2-2) or with a digoxigenin-conjugated ST2 antibody (clone DJ8) followed by secondary anti-digoxigenin-PE staining and two rounds of Faser amplification. Representative FACS plots (d) and quantification (e) of ST2 stainings (n = 3). f-i, Representative histograms (f,h) and quantification (g,i) of ST2 surface expression by in vitro differentiated WT, STAT4- or T-bet-deficient T cells, or T cells activated in the absence of IL-12 (n = 4). Stainings with secondary reagents without primary ST2 antibody served as staining controls (ctrl) (dotted line, bottom). j, ST2 expression by splenic T_reg cells in WT, Stat4^-/- or Tbx21^-/- mice (WT, Stat4^-/-: n = 3; Tbx21^-/-: n = 4). Data represent two independent experiments and are presented as mean ± SD with each dot representing one mouse (j) or one culture performed with T cells from individual mice (c,e,g,i). P was determined using one-way ANOVA with Tukey’s post hoc test (g,i,j).

Source data

Extended Data Fig. 2 Flow-cytometric sorting and analysis of human T cells.

a, Gating strategy for the sorting of human CXCR3⁺ CRTH2^- Th1 cells and CXCR3⁻ CRTH2⁺ Th2 cells. b, Gating strategy for the sorting of human CD8⁺ T cells. c,d, Representative histograms and quantification of T-bet (c) and GATA-3 (d) expression by activated T cells at day 5 of culture (n = 3). e,f, Representative histograms and quantification of IFN-γ (e) and IL-13 (f) expression by T cells stimulated with PMA/ionomycin at day 5 of culture (n = 3). Data represent two independent experiments and are presented as mean ± SD with each dot representing one culture performed with T cells from individual donors (c-f). P was determined using one-way ANOVA with Tukey’s post hoc test (c-f).

Source data

Extended Data Fig. 3 Generation of Il1rl1-ExAB^-/- and Il1rl1-ExC^-/- mice.

a, Schematic depiction of the gene-targeting approach for the generation of Il1rl1-ExAB^-/- and Il1rl1-ExC^-/- mice. b, RNA-seq coverage and splice junction tracks of ST2⁺ CTLs showing the areas of deletions (gray), T-bet binding sites and ATAC-seq peaks. c, Representative genotyping PCRs to identify heterozygous and homozygous mutant mice. Chromatograms depicting the sequence of joined DNA segments as analyzed by Sanger-sequencing. d-l, Analysis of adaptive and innate immune cells in spleens and lymph nodes (LN) of Il1rl1-ExAB^-/+, Il1rl1-ExAB^-/- and Il1rl1-ExC^-/- mice. d, Representative staining of CD4 and CD8 on splenic T cells. e,f, Frequencies (e) and absolute cell counts (f) of CD8⁺ T cells, CD4⁺ T cells and B cells. g, Representative staining of CD62L and CD44 on splenic CD8⁺ T cells. h, Frequency of effector and central memory CD8⁺ T cells. i, Frequencies and absolute cell counts of splenic T_reg cells. j, Gating strategy for the analysis of innate immune cells. k,l, Frequencies (k) and absolute cell counts (l) of splenic neutrophils, eosinophils, macrophages (MΦ), conventional dendritic cells (cDCs) and natural killer (NK) cells. Data represent two independent experiments and are presented as mean ± SD with each dot representing one mouse (WT, Il1rl1-ExAB^-/-, and Il1rl1-ExC^-/-: n = 4, Il1rl1-ExAB^-/+: n = 5; T_reg cell analysis: WT, Il1rl1-ExAB^-/-: n = 8 and Il1rl1-ExC^-/-: n = 6; NK cell analysis: WT, Il1rl1-ExAB^-/- and Il1rl1-ExC^-/-: n = 4). P was determined using one-way ANOVA (i,k,l) or two-way AONVA (e,f,h) with Tukey’s post hoc test.

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Extended Data Fig. 4 Type 1 Il1rl1 promoter deficiency abrogates ST2 expression by in vitro activated NKT cells and NK cells.

a-c, Thymic CD1d(α-GalCer-loaded)-Tetramer⁺ NKT cells were flow-cytometrically sorted from WT, Il1rl1 ^-/- or Il1rl1-ExAB^-/- mice and stimulated in vitro with antibodies against CD3ε and CD28 in CTL/Th1 culture medium for 6 days. a, Representative FACS plots showing the purity of NKT cells after MACS pre-enrichment and after FACS sorting. b,c, Representative FACS plots (b) and quantification (c) of ST2 expression by NKT cells (WT: n = 5, Il1rl1^-/-: n = 4, Il1rl1-ExAB^-/-: n = 6). d-f, Splenic NKp46⁺ NK cells were flow-cytometrically sorted from CD4-, CD8- and B220-depleted splenocytes and stimulated with IL-12 + IL-33 for 48 h. d, Representative FACS plots showing the purity of NK cells after FACS sorting. e,f, Representative FACS plots (e) and quantification (f) of ST2 expression by NK cells (WT: n = 6, Il1rl1^-/-: n = 3, Il1rl1-ExAB^-/-: n = 6). Data are pooled from two independent experiments and are presented as mean ± SD with each dot representing one mouse (c,f). P was determined using one-way ANOVA with Tukey’s post hoc test (c,f).

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Extended Data Fig. 5 ST2 expression by ILC2s, mast cells, eosinophils, and neutrophils is largely unaffected by the type 1 Il1rl1 promoter deletion.

a, RNA-seq coverage tracks and detected splice junctions at the Il1rl1 locus of ILC2s¹⁰⁴ and mast cells¹⁰⁵. chr1:40,377,000-40,465,500; GRCm38.p6/mm10 is shown. b, Gating strategy for the analysis of Lin⁻CD45⁺ CD3⁻ CD127⁺ ILCs. c, Representative histograms showing ST2 expression by KLRG1⁺ IL-18R^- ILC2s isolated from lungs of WT or Il1rl1-ExAB^-/- mice (n = 4). d, Frequencies of ST2⁺ ILC2s and ST2 expression intensities (MFI). e, Gating strategy for the analysis of c-kit⁺ FcεRIa⁺ peritoneal mast cells. f, Representative histograms showing ST2 expression by peritoneal mast cells isolated from WT or Il1rl1-ExAB^-/- mice. g, Frequencies of ST2⁺ mast cells and ST2 expression intensities (MFI) (n = 4). h, Gating strategy for the analysis of bone marrow eosinophils and neutrophils. i, Representative FACS plots showing ST2 expression by eosinophils from WT, Il1rl1^-/- and Il1rl1-ExAB^-/- mice. j, Frequencies of ST2⁺ eosinophils and ST2 expression intensity of ST2⁺ eosinophils. k, Representative FACS plots showing ST2 expression by neutrophils from WT, Il1rl1^-/- and Il1rl1-ExAB^-/- mice. l, Frequencies of ST2⁺ neutrophils and ST2 expression intensity of ST2⁺ neutrophils (WT: n = 5, Il1rl1^-/-: n = 4, Il1rl1-ExAB^-/-: n = 4). Results are presented as mean ± SD with each dot representing one mouse. Data are representative of two experiments. P was determined using two-tailed t-tests (d,g,j,l) or one-way ANOVA with Tukey’s post hoc test (j,l, left panels).

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Extended Data Fig. 6 The type 1 Il1rl1 promoter drives expansion and activation of antiviral T cells.

a-i, WT, Il1rl1^-/-, Il1rl1-ExAB^-/- and Il1rl1-ExC^-/- mice were infected with LCMV-WE and analyzed on d7 p.i. (WT: n = 9, Il1rl1^-/-: n = 6, Il1rl1-ExAB^-/-: n = 8, Il1rl1-ExC^-/-: n = 7). a, Experimental outline. b,c, Representative FACS plots (b) and quantification (c) of liver CTLs. d-f, Representative FACS plots (d) and quantification of ST2 expression (e) and LCMV-Tetramer⁺ CTLs in livers (f). g-i, Representative FACS plots (g) and quantification (h,i) of effector molecule⁺ CTLs restimulated with LCMV GP_33-41. j-m, Irradiated WT recipients (CD45.1⁺) were reconstituted with WT (CD45.1⁺ CD45.2⁺) and Il1rl1^-/-, Il1rl1-ExAB^-/- or Il1rl1-ExC^-/- (all CD45.2⁺) bone marrow, infected with LCMV-WE and analyzed on d10 p.i. (WT+Il1rl1^-/-: n = 6, WT+Il1rl1-ExAB^-/- and WT+Il1rl1-ExC^-/-: n = 7). j, Experimental outline. k, Counts of LCMV GP_33-41-specific CTLs. l,m Frequencies and counts of SLECs (l) and MPECs (m). n-r, Smarta cells (CD90.1⁺) were cotransferred with Il1rl1^-/- or Il1rl1-ExAB^-/- Smarta cells (CD90.1⁺ CD90.2⁺) into WT mice (CD90.2⁺). Recipients were infected with LCMV-Cl13 and analyzed at d10 p.i. (Il1rl1-ExAB^-/- Smarta: n = 6, Il1rl1^-/- Smarta: n = 5). n, Experimental outline. o,p, Representative FACS plots showing CD4⁺ T-cell populations before transfer (o) and after infection (p). q,r, Frequencies (q) and counts (r) of Smarta cells in livers. s-v, Smarta, Il1rl1^-/- Smarta or Il1rl1-ExAB^-/- Smarta T cells (CD90.1⁺) were cotransferred into WT mice (CD90.2⁺). Recipients were infected with LCMV-Cl13 and analyzed at d8-9 p.i. s, Experimental outline. t,u, Representative FACS plots (t) and quantification of ST2 expression (u) (n = 6). v, Transferred cells were sorted and Il1rl1 first exon usage was quantified (Smarta: n = 4 with two samples <LOQ in Exon 1a and 1b reactions; Il1rl1-ExAB^-/-: n = 4 with three samples <LOQ in Exon AB reaction; CTL, Th2, and NIH3T3 controls: n = 1). Data represent one (j-v), two (a-f) or three (g-i) independent experiments and are presented as mean ± SD with each dot representing one mouse. P was determined using two-tailed t-tests (u), one-way ANOVA with Tukey’s post hoc test (c,e,h,i), two-way ANOVA with Tukey’s post hoc test (f) or two-way RM ANOVA with Šidák’s post hoc test (k,l,m,q,r).

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Extended Data Fig. 7 scRNA-seq profiling of antiviral T cells in WT and Il1rl1-ExAB^-/- mice.

a, Gating strategy for the flow-cytometric sorting of CD44⁺ CTLs from spleens of LCMV-infected WT and Il1rl1-ExAB^-/- mice. b, Heatmap displaying all analyzed CTLs and the top ten marker genes per cluster.

Extended Data Fig. 8 Gene expression comparison of WT and Il1rl1-ExAB^-/- CTLs in each scRNA-seq cluster.

Heatmaps displaying all genes differentially expressed between CTLs from LCMV-infected WT and Il1rl1-ExAB^-/- mice in each CTL cluster identified by scRNA-seq (n = 3) (log2 fold change > 0.5; P adjusted < 0.05). P was determined using two-sided Wilcoxon rank sum test with Bonferroni correction.

Extended Data Fig. 9 Efficient CD8 memory T-cell formation depends on T-cell-intrinsic ST2 signaling.

a-k, P14, Il1rl1^-/- P14 or Il1rl1-ExAB^-/- P14 T cells (all CD45.1⁺) were adoptively transferred into WT mice (CD45.2⁺). Recipients were infected with LCMV-Cl13 (200 PFU) and analyzed at d30 p.i. (P14: n = 6, P14 Il1rl1^-/-: n = 6, P14 Il1rl1-ExAB^-/-: n = 5). a, Experimental outline. b-d, Representative FACS plots (b) and quantification (c,d) of memory P14 cells in indicated organs. e-g, Representative FACS plots (e) and quantification (f,g) of splenic effector memory (Tem, CD44⁺ CD62L⁻) and central memory (Tcm, CD44⁺ CD62L⁺) T-cell subsets. h,i, Representative FACS plots (h) and quantification (i) of P14 cells labeled in vivo by i.v. injection of CD90.2-PE antibody prior to sacrificing mice. j, Representative FACS plots showing CD69 and CD103 expression in i.v.⁺ and i.v.^- P14 cells. k, Counts of i.v.⁺ and i.v.^- P14 cells in spleens, lungs, salivary glands and kidneys of infected animals. Results are presented as mean ± SD with each dot representing one mouse. P was determined using one-way ANOVA with Tukey’s post hoc test (c,d,f,g,k).

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Supplementary information

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Supplementary Tables 1–4

Supplementary Table file including four tables (tabs): 1: scRNA-seq data related to Fig. 5 and Extended Data Figs. 7 and 8. 2: RNA-seq data related to Fig. 6. 3: List of antibodies. 4: List of gRNA and primer sequences.

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Brunner, T.M., Serve, S., Marx, AF. et al. A type 1 immunity-restricted promoter of the IL−33 receptor gene directs antiviral T-cell responses. Nat Immunol 25, 256–267 (2024). https://doi.org/10.1038/s41590-023-01697-6

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Received: 24 January 2023
Accepted: 02 November 2023
Published: 03 January 2024
Issue Date: February 2024
DOI: https://doi.org/10.1038/s41590-023-01697-6

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Abstract

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Main

Results

Identification of a type 1 immunity-restricted Il1rl1 promoter

Il1rl1 promoter usage is conserved between mice and humans

Il1rl1 promoters allow lineage-specific targeting of ST2

The type 1 Il1rl1 promoter drives antiviral T-cell responses

The type 1 Il1rl1 promoter drives short-lived effector formation

RNA profiling indicates a TCR-cooperative role of IL-33

Discussion

Methods

Mice

Generation of Il1rl1-ExAB −/− and Il1rl1-ExC −/− mice

Lymphocyte isolation

Flow cytometry

Viruses and LCMV infection

Adoptive T-cell transfers

In vivo labeling of T cells

Mixed bone marrow chimeras

Legendplex cytometric bead assay

Mouse T-cell cultures

Mouse NKT cell and NK cell cultures

Retroviral transduction of T cells

Human T-cell cultures

RNA isolation and qRT-PCR

Single-cell RNA library preparation, sequencing and analysis

RNA-seq

Gene set enrichment and overrepresentation analysis

Analysis of alternative transcription start sites

Processing of published RNA-seq, ChIP-seq and ATAC-seq data

Quantification and statistical analysis

Reporting summary

Data availability

Code availability

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Generation of Il1rl1-ExAB ^−/− and Il1rl1-ExC ^−/− mice