The mRNA methyltransferase Mettl3 modulates cytokine mRNA stability and limits functional responses in mast cells

Leoni, Cristina; Bataclan, Marian; Ito-Kureha, Taku; Heissmeyer, Vigo; Monticelli, Silvia

doi:10.1038/s41467-023-39614-y

Download PDF

Article
Open access
Published: 29 June 2023

The mRNA methyltransferase Mettl3 modulates cytokine mRNA stability and limits functional responses in mast cells

Nature Communications volume 14, Article number: 3862 (2023) Cite this article

2682 Accesses
4 Citations
2 Altmetric
Metrics details

Subjects

Abstract

Mast cells are central players in allergy and asthma, and their dysregulated responses lead to reduced quality of life and life-threatening conditions such as anaphylaxis. The RNA modification N⁶-methyladenosine (m⁶A) has a prominent impact on immune cell functions, but its role in mast cells remains unexplored. Here, by optimizing tools to genetically manipulate primary mast cells, we reveal that the m⁶A mRNA methyltransferase complex modulates mast cell proliferation and survival. Depletion of the catalytic component Mettl3 exacerbates effector functions in response to IgE and antigen complexes, both in vitro and in vivo. Mechanistically, deletion of Mettl3 or Mettl14, another component of the methyltransferase complex, lead to the enhanced expression of inflammatory cytokines. By focusing on one of the most affected mRNAs, namely the one encoding the cytokine IL-13, we find that it is methylated in activated mast cells, and that Mettl3 affects its transcript stability in an enzymatic activity-dependent manner, requiring consensus m⁶A sites in the Il13 3’-untranslated region. Overall, we reveal that the m⁶A machinery is essential in mast cells to sustain growth and to restrain inflammatory responses.

Metabolic rewiring promotes anti-inflammatory effects of glucocorticoids

Article 10 April 2024

Single-cell analysis reveals context-dependent, cell-level selection of mtDNA

Article Open access 24 April 2024

Disease-associated astrocyte epigenetic memory promotes CNS pathology

Article 20 March 2024

Introduction

Mast cells are tissue-resident immune cells whose functional impact in the context of immune responses besides allergy and asthma is still incompletely understood¹. They respond very rapidly to a large variety of stimuli with the production of immune- and vaso-active mediators such as cytokines, histamine and prostaglandins. Cytokine release by mast cells can have immunoregulatory functions in both health and disease. For instance, apart from the established role for the cytokine IL-13 in allergic inflammation and airway mucus production, the directional secretion of TNF by perivascular mast cells was shown to induce the recruitment of neutrophils during skin inflammation².

The appropriate control of mast cell differentiation and functions requires a network of transcriptional and post-transcriptional mechanisms of regulation of gene expression that allows fast responses to danger signals while limiting excessive damage to the host. How mast cell responses are regulated is still a matter of active investigation. For example, our lab found that chromatin dynamics and epigenetic modifications such as 5-methylcytosine and 5-hydroxymethylcytosine in the genomic DNA are central regulators of mast cell responses and proliferation, respectively^3,4,5. However, what is the role of post-transcriptional modifications in modulating mast cell responses is still unknown.

The RNA modification N⁶-methyladenosine (m⁶A) has important regulatory functions in gene expression. In the context of messenger RNA (mRNA), m⁶A is deposited co-transcriptionally by the action of a multi-subunit methyltransferase complex composed of the enzymatic component Mettl3 and other accessory proteins, such as Mettl14 and Wtap (Wilms tumor 1-associating protein), which are also essential for m⁶A deposition⁶. The functional outcome of m⁶A is mediated by its recognition by RNA-binding proteins primarily of the YTH family, which upon binding frequently affect mRNA stability^7,8. Since it influences basic aspects of post-transcriptional gene regulation, the m⁶A modification also controls various aspects of innate and adaptive immunity, including immune recognition of invading pathogens, cell activation and differentiation⁹. For example, deletion of Mettl3 or Mettl14 in hematopoietic stem cells affected their output in terms of maintenance of cell identity and differentiation^9,10,11. In the myeloid compartment, deletion of Mettl3 in dendritic cells resulted in impaired maturation of these cells in response to lipopolysaccharide (LPS), leading to their reduced ability to stimulate T cell responses¹². In macrophages, deletion of Mettl3 limited the expression of inflammatory cytokines in response to LPS and impaired effector responses, leading to increased susceptibility to bacterial infection and uncontrolled tumor growth^13,14. However, this phenotype appeared to be at least in part dependent on the specificities of the experimental system, since another study found improved immunosuppression of tumor-infiltrating myeloid cells lacking Mettl3¹⁵, while targeting Mettl3 using small molecule inhibitors had a strong anti-tumor activity in acute myeloid leukemia, leading to increased cell differentiation and apoptosis¹⁶. Finally, ablation of Mettl14 led to unrestrained responses of macrophages to bacterial infection, resulting in sustained expression of inflammatory cytokines and death¹⁷. Overall, while these studies highlight a crucial importance of the methyltransferase complex and of the m⁶A modification in the myeloid compartment, the mechanistic aspects remain incompletely understood.

In mast cells, the role of m⁶A in regulating cell differentiation and functions is unknown¹⁸. To address this question, we first optimized CRISPR-Cas9 tools to effectively perform gene editing in mast cells, allowing us to dissect the role of the m⁶A methyltransferase complex in mature mast cells, without affecting cell development. Then, we found that mast cells lacking Mettl3 developed exacerbated effector functions in response to stimulation with IgE and antigen complexes, both in vitro and in vivo. Mettl3 deficiency induced increased stability of inflammatory transcripts, most notably of the Il13 mRNA, and IL-13 expression was diminished upon overexpression of Mettl3. Such effect depended both on the Mettl3 enzymatic activity and on specific adenosines in the 3’-untranslated region (3’UTR) of the Il13 transcript. Overall, our findings point towards a direct link between Mettl3 expression, its enzymatic activity and m⁶A deposition in the regulation of the stability of inflammatory transcripts in mast cells, as well as in sustaining their basal proliferation.

Results

Mettl3 regulates mast cell proliferation and effector functions

We measured the expression of key components of the m⁶A methyltransferase complex in response to stimulation of murine mast cells. Activation with IgE and antigen complexes or PMA and ionomycin induced Mettl3 expression, both at mRNA and protein level (Fig. 1a and Supplementary Fig. 1a, b). An increase in the expression of Mettl3 was also observed upon stimulation of peritoneal-derived mast cells (PMCs) (Fig. 1b and Supplementary Fig. 1c). Since Mettl3 is the catalytic component of the m⁶A methyltransferase complex, we set out to investigate its role in acute mast cell responses.

**Fig. 1: Mettl3 modulates mast cell proliferation and effector functions.**

First, we depleted Mettl3 in mast cells by RNA interference (RNAi). Transient transfection of differentiated mast cells with small-interfering RNAs (siRNAs) against Mettl3 was >95% efficient (Supplementary Fig. 1d) and led to the partial depletion of Mettl3 mRNA and protein after 48 h (Fig. 1c and Supplementary Fig. 1e). Upon stimulation with IgE and antigen complexes, mast cells depleted of Mettl3 showed significantly increased ability to produce the inflammatory cytokines IL-6, TNF and IL-13, measured by intracellular staining (Fig. 1d). Unstimulated mast cells did not express detectable cytokines (Supplementary Fig. 1f, gating strategies are shown in Supplementary Fig. 1g). Overexpression of Mettl3 by lentiviral transduction led to the opposite effect, namely reduced cytokine expression (Fig. 1e and Supplementary Fig. 2a, b), suggesting a role in regulating mast cell effector functions. Overexpression of Mettl3 also modestly enhanced in vitro mast cell differentiation (Supplementary Fig. 2c). Upon depletion of Mettl3, mast cells released the content of pre-stored cytoplasmic granules at a greater extent, further suggesting overall hyper-activation in response to IgE stimulation (Fig. 1f). However, reducing Mettl3 expression also significantly impaired the ability of the cells to proliferate, measured by BrdU incorporation (Fig. 1g). Comparable results were obtained upon deletion of human METTL3 from the human mast cell lines HMC-1.1 and 1.2 (Supplementary Fig. 2d), indicating that the pathways regulated by m⁶A in mast cells are likely to be conserved. These data point towards an important role of Mettl3 in regulating mast cell functions, leading to enhanced effector functions but reduced proliferation capacity.

Mettl3 restrains mast cell responses in vivo

To further dissect the role of Mettl3 in mast cells we optimized a CRISPR-Cas9 transfection protocol to efficiently delete genes of interest in primary mast cells. As a proof of principle, we first optimized this approach by ablating the expression of the surface receptor c-Kit, since it is highly expressed by mast cells, and there was no addition of SCF (the c-Kit ligand) in these in vitro cultures. Transfection of Cas9 ribonucleoproteins (RNPs) containing gRNAs directed against the Kit gene led reproducibly to high efficiency of transfection and deletion of the receptor in >90% of the cells (Fig. 2a and Supplementary Fig. 3a). Next, using two different gRNAs directed against the Mettl3 gene, we achieved >60% deletion efficiency, as shown by intracellular staining for the Mettl3 protein (Fig. 2b). T7 endonuclease I assay on cells transfected with one or two gRNAs against Mettl3 also showed the expected pattern of digestion (Fig. 2c and Supplementary Fig. 3b), demonstrating highly efficient CRISPR-Cas9-mediated deletion of genes of interest in mast cells. Deletion of the Mettl3 protein in more than half of the population did not impact the surface expression of c-Kit and FcεRIα (Supplementary Fig. 3c), and it was already sufficient to detect a significant physiologic response, as shown by the increased production of inflammatory cytokines (Fig. 2d, e) and by the reduction in cell proliferation (Fig. 2f), that was consistent with the phenotypes measured in cells in which Mettl3 expression was depleted by RNAi. Mettl3-deleted cells also showed reduced viability (Fig. 2g). Deletion of Mettl3 in mast cells differentiated in the presence of IL-3 + SCF (which induces a more mature phenotype) led to a similar increase in cytokine production and cell death, although the presence of SCF enabled the cells to bypass the defect in proliferation (Supplementary Fig. 3d–f). Mast cells lacking Mettl3 showed increased production of inflammatory cytokines also in response to IgE crosslinking in combination with IL-33 stimulation (Supplementary Fig. 3g). Next, we investigated the in vivo effects of Mettl3 deletion in mast cells using a model of passive cutaneous anaphylaxis (PCA). Mast cells transfected with Cas9 RNPs against Mettl3 were injected intradermally into the ear pinna of c-Kit^W-sh/W-sh mice, that do not have mast cells¹⁹. After repopulation of the tissues, IgE anti-DNP antibodies were injected intradermally followed, 24 h later, by intravenous injection of HSA-DNP antigen and Evan’s blue dye. The extent of dye extravasation induced by mast cell activation was measured by extracting the dye from the tissue and by measuring the OD₆₀₀ of the solution. We found that the extent of PCA was significantly increased in the absence of Mettl3 in vivo, underlining a role for m⁶A-mediated processes in restraining mast cell responses to IgE activation (Fig. 2h).

**Fig. 2: Deletion of *Mettl3* by CRISPR-Cas9 alters mast cell responses.**

Deletion of Wtap and Mettl14 phenocopy Mettl3 ablation

Although Mettl3 is the catalytic subunit of the methyltransferase complex, deletion of the accessory protein Wtap also abrogates m⁶A methylation^6,20. We therefore investigated the impact of deleting Wtap in differentiated mast cells. We took advantage of mice with a Wtap^fl/fl conditional allele²⁰. We transduced bone marrow cells with a lentivirus expressing a GFP-Cre fusion protein to delete Wtap and we measured mast cell activation and proliferation. First, we found that deletion of the Wtap gene was efficient, as shown by intracellular staining for the Wtap protein (Fig. 3a). However, deletion of Wtap destabilized Mettl3 expression, that was reduced in Wtap-deleted mast cells (Fig. 3b). Consistent with the phenotype observed upon depletion of Mettl3, cells lacking Wtap showed strongly reduced proliferation (Fig. 3c), possibly also linked to the reduced expression of Mettl3. Surprisingly, surface staining to assess the expression of the mast cell markers c-Kit and FcεRIα revealed a strong and significant downregulation of the latter (Fig. 3d), an effect that was not observed upon depletion or deletion of Mettl3 (Supplementary Fig. 3c). This observation points towards some m⁶A-independent effects of Wtap, possibly on mRNA splicing^21,22 and precluded the detailed analysis of IgE responses, due to the confounding effects linked to the downregulation of the IgE receptor.

**Fig. 3: Wtap deficiency impairs mast cell proliferation and FcεRI expression.**

Mettl3 exists as a heterodimer with Mettl14^23,24. We deleted Mettl14 in mast cells by CRISPR-Cas9 (scheme in Fig. 4a) and we found that, similarly to the Mettl3 knock-out, deletion of Mettl14 led to decreased proliferation and viability (Fig. 4b, c), and increased cytokine expression (Fig. 4d) in mast cells. We also found that deletion of Mettl14 led to reduced Mettl3 expression (Fig. 4e, f). Similarly, deletion of Mettl3 led to reduced Mettl14 expression, while Wtap levels were increased (Supplementary Fig. 4). Virma expression was also modestly increased, albeit not significantly. These results suggest that the m⁶A machinery complex in its entirety is required to maintain mast cell homeostasis and response to stimuli, and that removal of any of its components affects the stoichiometry of the entire complex in an unpredictable manner.

**Fig. 4: Deletion of Mettl14 phenocopies Mettl3 deficiency.**

Inflammatory transcripts are m⁶A-methylated in stimulated mast cells

To dissect the transcriptome changes that occur in mast cells depleted for Mettl3 we first performed RNA-seq on cells transfected with Mettl3 RNPs (Fig. 5a and Supplementary Data 1). We found that at resting state 214 genes were downregulated and 487 were significantly upregulated in absence of Mettl3 (p value≤0.05 and fold change ≥1.5). Downregulated transcripts included Mettl3 itself and other regulators of mast cell activation and function, such as Hck, encoding a Src family kinase involved in the regulation of mast cell activation and cytoskeletal reorganization, and described to positively regulate the proliferation of mast cells²⁵. Other prominently downregulated genes included Cpe, encoding a carboxypeptidase enzyme, and Il2ra, that was already described to be associated to altered mast cell phenotypes in the mouse²⁶. Several of the upregulated genes instead encoded regulators of cell proliferation and intracellular trafficking, including the tumor suppressor Tspan32 and the actin-binding protein Parvb, concordant with the phenotypes observed in mast cells lacking Mettl3. Basal levels of expression of the Tnf transcript were also increased. Indeed, gene ontology analysis revealed only few significant categories for the downregulated genes, while the upregulated genes were included in categories associated to the cytoskeleton (cell shape, migration, adhesion, intracellular trafficking) and also apoptosis and cell proliferation (Fig. 5b).

**Fig. 5: Analysis of transcript methylation and abundance in mast cells.**

Although some of the dysregulated mRNAs may be direct targets of Mettl3 catalytic activity, the expression of other transcripts may be affected in an indirect manner. To determine which transcripts were specifically methylated in mast cells before or after stimulation, we performed m⁶A-crosslinking, immunoprecipitation and sequencing (miCLIP-seq), which allows the detection of m⁶A at near-nucleotide resolution²⁷. The m⁶A enrichment at different parts of the mRNA of all targets, showed corresponding signals throughout the mRNA, and enrichment at the boundary between the coding sequence and the 3’UTR (Fig. 5c). A prominent methylation signal was also observed within the 5’UTR in both resting and activated cells, consistent with the fact that miCLIP-seq also identifies m⁶Am modifications occurring at the first nucleotide of transcripts²⁷, which may suggest additional roles in the process of translation. Despite many transcripts being methylated in resting mast cells, there was limited overlap (183 transcripts) with genes differentially expressed upon Mettl3 depletion (Fig. 5d). Within these 183 transcripts, the majority (137) were upregulated upon Mettl3 depletion (Fig. 5e), suggesting that they may include transcripts directly affected in their half-life by the m⁶A modification. These included the transcripts encoding the Rho-related GTP-binding proteins Rhob, Rhog and Rhoh, which are regulators of a wide variety of cellular processes including actin cytoskeleton reorganization, the Inf2 gene, involved in actin polymerization and depolymerization, as well as transcriptional regulators (Egr1, Rxra, Sox12), metabolic enzymes (Chst12, Naglu) and regulators of nuclear-cytoplasmic shuttling (Nxt1). Other genes were found to be methylated, but they were not significantly affected by Mettl3 deletion in mast cells. These included the mRNAs encoding the negative regulators of cytokine signaling Socs1 and Socs3 (that were found to be increased in T lymphocytes lacking Mettl3²⁸), suggesting that m⁶A methylation may not be the predominant mechanism of regulation of these transcripts in mast cells. Finally, upon acute stimulation, additional transcripts emerged that were methylated in mast cells, including many inflammatory transcripts such as Il13, Csf1, Tnf, Il6, Csf2, Il3 (Fig. 5f and Supplementary Data 2). Overall, m⁶A methylation by Mettl3 is associated to changes in the expression of genes involved in basic cellular processes.

Effective gene editing by homology-directed repair in mast cells

Our RNA-seq analysis was performed on cells transfected with RNPs at high efficiency, but still containing a remaining population of untargeted cells. To better investigate the mechanistic impact of Mettl3 on the mast cell transcriptome, we developed a system to allow us to select the cells that successfully underwent Mettl3 gene deletion within a live mixed population. We therefore resorted to a knock-out/ knock-in approach based on mechanisms of homology-directed repair (HDR). We designed a reporter construct to be used as donor DNA template, containing a ZsGreen reporter gene followed by a polyA-STOP cassette and flanked by homology arms of ~400 bp in length, targeting either the Kit (exon 2) or Mettl3 (exon 1) gene (Supplementary Fig. 5a). The donor plasmid was either directly co-transfected together with RNPs, or it was used as a template to generate single-stranded DNA (ssDNA), that was then transfected with Cas9 RNPs. When targeting the Kit gene, we found that ssDNA provided more efficient and reproducible gene deletion, while at the same time correctly inserting the reported ZsGreen cassette in ~11% of the cells (Supplementary Fig. 5b). To confirm insertion specifically within the Kit locus, we sorted ZsGreen⁺ c-Kit^–, ZsGreen^– c-Kit^– and ZsGreen^– c-Kit⁺ cells and we extracted the genomic DNA. No ZsGreen⁺ c-Kit⁺ cells could be sorted in sufficient numbers, suggesting that very few cells, if any, inserted the ZsGreen reporter cassette on one allele while at the same time retaining one wild-type allele, and that the most frequent event was the deletion of the Kit gene on both alleles. Analysis of the genomic DNA using PCR primers located across the expected region of recombination confirmed the presence of the ZsGreen-polyA-STOP cassette within the Kit locus only in ZsGreen⁺ c-Kit^– cells (Supplementary Fig. 5c). Next, we repeated the same knock-out/ knock-in procedure to delete the Mettl3 gene while at the same time inserting the reporter cassette. We found that after transfection ~5% of the cells expressed ZsGreen (Supplementary Fig. 5d), and that the ZsGreen⁺ cells had increased cytokine expression by intracellular staining, a phenotype consistent with the one initially observed in Mettl3-depleted cells (Supplementary Fig. 5e). However, the low percentage of correctly targeted cells limited the possibility of using these cells for further downstream analyses.

To improve the efficiency of genome editing, we added truncated Cas9 target sequences (tCTSs) to the ends of the HDR template (Fig. 6a). This was reported to enable shuttling of the template to the cell nucleus, enhancing HDR efficiency²⁹. Cas9 RNPs were further stabilized by the addition of polyglutamic acid²⁹. Upon transfection of mast cells, the combination of these improvements led to a 6.8-fold increase in Mettl3 gene targeting efficiency (from ~5% to ~34% ZsGreen⁺ cells) (Fig. 6b). To confirm specific deletion of the correct gene, we sorted the ZsGreen⁺ population from cells transfected with the Kit-targeting template (c-Kit KO cells) and Mettl3-targeting template (Mettl3 KO cells). Staining for Mettl3 confirmed ablation of the Mettl3 protein in Mettl3 KO cells, compared to c-Kit KO cells, and vice versa (Fig. 6c). Consistent with our previous results, Mettl3 KO cells showed significant increased expression of inflammatory cytokines and reduced proliferation (Fig. 6d, e), establishing a role for Mettl3 in modulating basal cell growth and in restraining mast cell responses upon stimulation with IgE and antigen.

**Fig. 6: Efficient gene replacement with truncated Cas9 target sequences templates in mast cells.**

Mettl3 deficiency affects the expression of inflammatory transcripts

Expression of inflammatory cytokines is central to the outcome of immune responses. Since we found that cytokine transcripts were m⁶A-methylated in stimulated mast cells (Fig. 5f and Supplementary Data 2), to further investigate the mechanism(s) underlying the phenotype observed in the absence of Mettl3 on inflammatory responses, we sorted c-Kit KO and Mettl3 KO cells generated using tCTS-HDR constructs, and we performed gene expression analysis using the NanoString “myeloid innate immunity” panel in cells either stimulated with IgE and antigen for 4 h or left resting (Supplementary Data 3), therefore complementing our RNA-seq dataset obtained from unstimulated cells. IgE and antigen complexes induced the expression of many inflammatory cytokines and chemokines in both c-Kit KO and Mettl3 KO cells, as expected (Fig. 7a, b). Direct comparison of c-Kit KO and Mettl3 KO mast cells revealed that Mettl3 targeting did not substantially affect mast cell identity or activation state (Fig. 7c), as shown also by the largely unaltered expression of immune-related transcripts by RNA-seq in the absence of stimulation. Concordant with the fact that the most established functional outcome of m⁶A is the destabilization of the methylated transcripts⁹, we observed that most differentially expressed inflammatory transcripts were upregulated in stimulated mast cells in the absence of Mettl3, and only very few were downregulated (Fig. 7d). Among the most differentially expressed genes were many transcripts encoding for cytokines and chemokines, including Tnf, Il13 and Il6, a result consistent with the increased expression of the corresponding cytokines observed by intracellular staining in cells lacking Mettl3. Other upregulated cytokines included Csf2, Il3 and Il2 (Fig. 7e), although the latter was expressed at only very low levels in unstimulated cells. Serpine1 encodes for a protein known to induce mast cell-dependent airway inflammation and tissue remodeling in a mouse model of asthma³⁰, further highlighting a role for Mettl3 in restraining mast cell inflammation. Very few genes were downregulated in Mettl3 KO cells, and only modestly (Fig. 7f). The differential expression of some of the upregulated genes was validated in independent samples (Fig. 7g). Importantly, of the 45 most upregulated genes in stimulated Mettl3 KO cells, 26 (57%) were found to be methylated, as determined by miCLIP-seq (red asterisks in Fig. 7e). These data suggest an important role for m⁶A methylation in downmodulating the expression of inflammatory transcripts in mast cells.

**Fig. 7: Mettl3 modulates the expression of inflammatory transcripts in mast cells.**

Mettl3 directly targets inflammatory transcripts and modulates their stability

Next, we asked whether the effect of Mettl3 on cytokine expression was direct. We cloned the 3’UTR of Il13, Il6 and Tnf downstream a luciferase reporter gene. Co-transfection of the reporter vectors together with a Mettl3 expression plasmid significantly reduced luciferase expression from all constructs (Fig. 8a), pointing towards a direct role for Mettl3 in modulating transcript stability and/or translation. To assess whether the methyltransferase enzymatic activity of Mettl3 was indeed necessary to impact cytokine expression, we mutated the DPPW motif (amino acids 395-398) to APPA, which is known to impair Mettl3 methyltransferase activity^31,32. Both proteins were highly expressed in transduced mast cells (Fig. 8b). Overexpression of wild-type Mettl3 in mast cells by lentiviral transduction led to the expected reduction in cytokine expression (Fig. 8c, d and Supplementary Fig. 6a). The overexpression of the catalytically inactive APPA mutant showed instead variable effects, likely due to the expression of high basal levels of endogenous wild-type Mettl3, that might be competing with the overexpressed protein for the methyltransferase complex. However, for the most part we observed no significant reduction in cytokine production, suggesting that the catalytic activity of Mettl3 is required to modulate the expression of inflammatory transcripts (Fig. 8c, d). Since the presence of m⁶A in mature, cytoplasmic mRNAs is predominantly associated with reduced stability of the methylated transcripts, we measured the stability of the endogenous Il13 and Tnf transcripts after treatment of wild-type or Mettl3-KO cells with actinomycin D (Fig. 8e). We found that in absence of Mettl3 the stability of both endogenous transcripts was increased, establishing Mettl3 as a key regulator of the expression of inflammatory cytokine in mast cells.

**Fig. 8: Mettl3 deletion stabilizes inflammatory transcripts in mast cells.**

IL-13 expression and Il13 transcript stability were strongly affected by Mettl3 deletion or overexpression. To determine if the regulation of IL-13 expression depended on the enzymatic activity of Mettl3 on the 3’UTR of the Il13 transcript we performed luciferase reporter assays using wild-type Mettl3 or its APPA mutant. Since the variability in cytokine expression observed in mast cells overexpressing the APPA mutant was likely to be due to the presence of high levels of endogenous wild-type Mettl3, we first deleted the endogenous METTL3 gene in HEK293T cells by CRISPR-Cas9 (Fig. 8f), and then used these cells for luciferase assay. In these conditions of reduced endogenous METTL3 expression, luciferase assays using a reporter construct containing the Il13 3’UTR showed reduced expression upon co-transfection of wild-type Mettl3, but not its APPA mutant (Fig. 8f), further suggesting a direct role for the catalytic activity of Mettl3 in modulating Il13 expression/ stability through its 3’UTR.

Most mammalian mRNAs have four or less sites that are actually methylated, and only in a proportion of mRNA molecules³³. By miCLIP-seq, we found that the Il13 transcript contained regions within its 3’UTR that were methylated in murine mast cells. Within these methylated regions, we identified and mutated three putative DRACH motifs (D = G/A/U, R = G/A, H = A/U/C), since they are the potential sites of m⁶A methylation in the Il13 transcript (Fig. 8g). We then used a tethered reporter construct, containing the 3’UTR of Il13 followed by 5 box B sites. The box B DNA results in a stem-loop structure in the nascent RNA that is recognized with high affinity by the λ peptide, a 22 amino acids sequence forming the RNA-binding domain of the λ phage antiterminator protein N^34,35. Such interaction has already been extensively used to tether desired protein activities to the 3’UTR of genes^36,37. This construct was co-transfected in HEK293T lacking endogenous METTL3 expression, together with a plasmid expressing Mettl3 fused to the λΝ peptide. We found that the expression of λΝ Mettl3 was sufficient to reduce the expression of a reporter containing the wild-type Il13 3’UTR. Mutation of the DRACH motifs proximal to the stop codon in any combination was sufficient to ablate the negative effect of Mettl3 transfection on the Il13 3’UTR, confirming that the effect of Mettl3 is direct and mediated through the DRACH sites (Fig. 8h). Overall, our findings indicate that Mettl3 restrains IL-13 expression in mast cells by acting directly on the Il13 3’UTR in an enzymatic activity-dependent manner.

Discussion

The role of the mRNA methyltransferase complex in mast cells was so far unexplored. One of most prominent phenotypes that we observed in mast cells lacking Mettl3 was their increased responses to acute stimulation. This was at least in part linked to the direct effect of Mettl3 on the half-life of inflammatory transcripts like Tnf and Il13. Both TNF and IL-13 are central cytokines that mediate mast cell effector functions and tissue inflammation, as shown in mice lacking either Tnf or Il13 specifically in mast cells^2,38. Indeed, mast cell-derived TNF was shown to be crucial for neutrophil extravasation and recruitment in various models of skin inflammation². Similarly, mast cell-derived IL-13 in response to mechanical skin injury (scratching) influenced the phenotype of dendritic cells in the skin, inhibiting Th1-type cell responses in models of dermatitis³⁸. Understanding how these cytokines are regulated can therefore lead to a better comprehension of mechanisms underlying inflammatory diseases.

By focusing on the 3’UTR of the Il13 transcript we found that mechanistically the effect of Mettl3 on this transcript was mediated by specific DRACH sites in the 3’UTR and depended on the enzymatic activity of Mettl3. In general, cytokine transcripts can be regulated by multiple RNA-binding proteins (RBPs) in a complex interaction landscape able to provide temporal control to cytokine expression. However, the RBPs involved in the regulation of cytokine mRNA stability are only partially known, and the extent of functional cooperation or interference among the different RBPs in this process is also not fully understood. For instance, expression of TNF is prominently regulated at the post-transcriptional levels by the RBP tristetraprolin (TTP), and whether altered methylation of Tnf also affects binding of other RBPs remains to be established. Indeed, the presence of m⁶A reduces the ability of the methylated RNA to form secondary structures, thereby potentially affecting binding of other RBPs, even those that do not recognize m⁶A directly. We found that the m⁶A reader Ythdf2 could be detected as bound to the Il13 3’UTR in stimulated cells (Supplementary Fig. 6b–d). However, other RBPs may preferentially bind m⁶A-methylated RNA because of its increased accessibility, rather than m⁶A recognition, making difficult to discern between these different possibilities when studying mRNA:RBPs interactions^8,39,40. Similar to other systems^20,28, we found that the transcripts encoding the negative regulators of cytokine signaling Socs1, Socs3 and Cish were methylated in mast cells. However, we did not observe the anticipated stabilization of these transcripts upon deletion of Mettl3, suggesting that the m⁶A-mediated mechanisms of regulation of these transcripts are not predominant in mast cells, at least in the absence of acute stimulation.

Consistent with the deletion of Mettl3, ablation of Wtap and Mettl14 also led to reduced mast cell proliferation. This is consistent with observations from other cell types, like T lymphocytes. In these cells, both Wtap and Mettl3 deficiency impaired Treg functionality, causing colitis²⁰. However, the reduction of surface FcεRI expression appeared to be Wtap-specific. Wtap is not only an essential component of the m⁶A RNA methyltransferase complex, but it is also involved in regulating alternative splicing of specific targets (such as genes with short introns and high GC content²¹), possibly in part through its interaction with the Wilm’s tumor 1 (WT1) protein²². However, a final answer on the extent of the putative m⁶A-independent functions of Wtap is currently out of reach, since it would require uncoupling the m⁶A-related and -unrelated functions of Wtap. It is also worth to point out that we did not measure global m⁶A changes in each individual experimental setup, therefore it remains possible that levels of m⁶A may be depleted at variable extent depending on the experimental system (RNAi, CRISPR-Cas9 with or without HDR, deletion of Mettl3 vs. Mettl14).

Deletion of Mettl14 phenocopied the effect of deleting Mettl3 in mast cells also in terms of cell survival and cytokine production. However, it also affected the expression of Mettl3 itself, leading to its depletion. The expression of other components of the methyltransferase complex was also affected, suggesting that changes in the stoichiometry of the various components affect the complex in its entirety. Although we were able to show that at least on some transcripts (most notably Il13) the effect of Mettl3 is direct and mediated by m⁶A methylation at specific DRACH sites, the effect of deleting especially other components of the complex may be linked, at least in part, to the indirect reduction in Mettl3 expression.

Overall, with this work, we were able to establish the importance of the m⁶A methyltransferase complex in modulating mast cell functions, at least in part mediated by the regulation of cytokine transcript stability. The future challenge will be to perform all the meticulous work required to understand how and why each transcript is bound and regulated by its own ‘constellation’ of RBPs and how these proteins interact with each other to achieve such combinatorial post-transcriptional regulation⁴¹.

Methods

Mice, murine mast cells and human mast cell lines

Bone marrow-derived mast cells (BMMCs) were differentiated from the bone marrow of 6-8 week old C57BL/6 mice by culturing whole bone marrow cells for at least 4 weeks (until the percentage of FcεRIα⁺ c-Kit⁺ cells was ~95%) in the presence of IL-3 (made in-house), as previously described^3,4. Kit^Wsh/W-sh mice lacking mast cells were obtained from The Jackson Laboratory¹⁹. When indicated, hematopoietic precursors were enriched from the bone marrow by negative magnetic selection using a lineage-cell depletion kit (Miltenyi Biotec). To generate mast cells lacking Wtap, bone marrow from Wtap^fl/fl mice²⁰ were transduced with a lentiviral vector expressing the Cre recombinase fused to an EGFP reporter. EGFP⁺ cells were then sorted for downstream analyses. Peritoneal-derived mast cells (PMCs) were obtained from mouse peritoneal cells isolated by intraperitoneal lavage and enriched for c-Kit expression using magnetic beads (Miltenyi Biotec). Cells were expanded for at least 1 week in the presence of IL-3 and 30 ng/ml recombinant SCF (Peprotech). All animal studies were performed in accordance with Swiss Federal Veterinary Office guidelines and approved by the Cantonal animal experimentation committee, Dipartimento della Sanità e della Socialità Cantone Ticino (authorization number TI10/19). Housing conditions for the animals were 12 h dark/light cycles, 20-24 °C temperature and 50-65% humidity. The HMC-1.1 and 1.2 human mast cell lines were kindly provided by Joseph Butterfield and were cultured as described⁴².

Mast cell activation

BMMCs and PMCs were stimulated for 4 h with the following: 1 µg/ml IgE-anti DNP antibody (Sigma) and 0.2 µg/ml HSA-DNP (Sigma) or 20 nM PMA (Sigma) and 2 µM ionomycin (Sigma), and/or 0.1 ng/ml recombinant IL-33 (Biolegend). When used in combination, IL-33 and HSA-DNP were added simultaneously. HMC-1.1 and 1.2 cells were stimulated for 4 h with 20 nM PMA and 2 µM ionomycin.

Flow-cytometry analysis of surface markers and intracellular cytokine staining

Surface staining was performed using the following antibodies: CD117 (c-Kit)-APC or APC/Cy7, FcεRIα-PE, CD11b (Mac-1)-Pacific Blue (all from Biolegend) and Ly-6G (Gr-1)-PE-Cyanine7 (eBioscience). All antibodies for surface staining were used at a dilution of 1:200. For intracellular cytokine staining, cells were stimulated with 1 μg/ml IgE–anti-DNP antibody and 0.2 μg/mL HSA-DNP antigen (both from Sigma) for 3.5 h, with the addition of brefeldin A in the last two hours of stimulation, as previously described^3,4. The following antibodies were used: IL-6-PE or APC, TNF-α-PE/Cy7 (both from Biolegend) and IL-13-PE (eBioscience). All antibodies used in this study are listed in Supplementary Table 1. Flow-cytometry data were collected on a FACS Symphony A5 or Fortessa (BD Biosciences) and analyzed by FlowJo version 10 (BD Biosciences).

Intracellular staining for components of the m⁶A complex

Intracellular staining for Mettl3, Wtap and Virma was performed as previously described²⁰. Briefly, cells were washed once with PBS and stained with LIVE/DEAD-Fixable Aqua Dead Cell Stain (ThermoFisher Scientific) for 20 min at room temperature. To fix and permeabilize cells, the Foxp3/Transcription Factor Staining buffer (eBioscience) was used according to manufacturer’s instructions. Following permeabilization, cells were incubated 40 min at room temperature with either 4 µg/ml of anti-METTL3 antibody (clone EPR18810, Abcam) or 10 µg/ml of anti-Wtap monoclonal antibody (clone 4A10G9, Proteintech). The anti-Virma antibody (clone D4N8B, Cell Signaling Technology), was used at 0.7 µg/ml. Secondary antibody staining was carried out for 30 min at room temperature using either Alexa Fluor 647 anti-rabbit IgG (H + L) (Sigma) (for Mettl3 and Virma) or Alexa Fluor 647 (or 594) goat anti-mouse IgG (H + L) (ThermoFisher Scientific) (for Wtap staining). All secondary antibodies were used at a 1:400 dilution.

Degranulation assay

Degranulation assay was performed as previously described³. Briefly, 0.5-1.5 ×10⁶ BMMCs were pre-treated with 1 µg/ml IgE-anti DNP antibody overnight at 37 °C. Cells were then resuspended in Tyrode’s buffer (10 mM HEPES, 129 mM NaCl, 5 mM KCl, 15.05 mM BSA, 1.8 mM CaCl₂, 2 mM MgCl₂, 5 mM glucose) and stimulated in a 96-well U-bottom plate with 0.2 μg/ml HSA-DNP for 1 h at 37 °C. 20 nM PMA and 2 μM ionomycin were used in parallel as positive control of the stimulation. 10 µl of supernatant were collected in a flat-bottom 96-well plate while cell pellets were lysed in 0.5% Triton X-100 in Tyrode’s buffer. 10 µl cell lysate were collected in a second flat-bottom 96-well plate. 50 μL β-hexosaminidase substrate (4-nitrophenyl N-acetyl-β-D-glucosaminide 4 mM, Sigma) were then added to flat-bottom 96-well plates for 1 h at 37 °C and the reaction was stopped with 150 μL of 0.2 M glycine (pH 10.7). Finally, absorbance was read at 405 nm and the percentage of degranulation was calculated as the ratio between the absorbance of the supernatants on the total absorbance of the supernatants plus cell lysates.

Reverse-transcriptase quantitative PCR (RT-qPCR)

Total RNA was isolated using TRIreagent (MRC) followed by purification using the Direct-zol RNA mini prep kit (Zymo Research). qScript cDNA SuperMix (Quanta Bioscience) was used for retrotranscription and PerfeCTa SYBR Green FastMix (Quanta Bioscience) was used to amplify target genes. Primer sequences are listed in Supplementary Table 2. qPCR reactions were run on an ABI 7900HT Fast Real-Time PCR system (Applied Biosystems) or a QuantStudio 3 Real-Time PCR System (ThermoFisher Scientific). Data analysis was performed using the 2^-ΔΔCt or 2^-ΔCt method.

Cell proliferation and viability

BrdU incorporation assay was used to measure mast cell proliferation using an APC BrdU Flow Kit (BD Bioscience), according to the manufacturer’s instructions and as previously described^3,4. Cells were left to incorporate BrdU for 8–16 h at 37 °C. For cell viability assay, cells were washed once with PBS and stained with LIVE/DEAD-Fixable Aqua Dead Cell Stain (ThermoFisher Scientific) for 20 min at room temperature.

Western blots

BMMCs were lysed in RIPA buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% SDS, 140 mM NaCl) supplemented with a cocktail of protease inhibitors (Sigma). Protein concentration was measured using a Pierce BCA assay (ThermoFisher Scientific). Protein samples (50 μg) were run on 8% SDS/polyacrylamide gels and blotted on a PVDF membrane using a wet transfer system and a methanol-based transfer buffer (20 mM Tris, 150 mM glycine, 20% methanol). Blocking was performed with 5% milk in TBST (5 mM Tris pH 7.3, 150 mM NaCl, 0.1% Tween-20) for 60 min at room temperature with gentle shaking. Specific for Mettl14 detection, blocking was performed with 5% BSA in TBST. Immunodetection was performed using primary antibodies at a 1:1000 dilution overnight at 4 °C. The specific antibodies used are listed in Supplementary Table 1. This was followed by washing and incubation with the corresponding HRP-conjugated secondary antibody (ThermoFisher Scientific) at a 1:10,000 dilution 1 h at room temperature. Blot development was performed using the ECL Prime Western Blotting Detection Reagent (Amersham) and analyzed with a blot imager (GE, Amersham Imager 680). Protein band intensity was quantified using the ImageJ software, version 1.53e.

Plasmids

Plasmids were generated using standard cloning techniques. The human METTL3 gene was subcloned from Addgene vector 53739²³ into pScalps (Addgene vector 99636)⁴, while the murine Mettl3 gene was amplified from cDNA obtained from BMMCs, cloned into the pScalps vector and finally tagged with FLAG-HA. To generate the Mettl3-APPA mutant, residues D395 and W399 of Mettl3 were mutated to alanines using the Quick Change II XL site-directed mutagenesis kit (Agilent) according manufacturer’s instructions. The luciferase reporter plasmids containing the full-length 3’UTR of the cytokine genes Il13, Il6 and Tnf were generated by PCR using genomic DNA from BMMCs as a template and cloned downstream of the luciferase reporter gene in the pmirGLO dual luciferase vector (Promega). For tethering assays, the lambda (λ) N peptide was cloned at the N-terminus of Mettl3, while 5 box B binding sites were cloned downstream the Il13 3’UTR. Five box B sequences were shown to be sufficient to induce a strong effect in reporter constructs³⁶. Mutations of the DRACH motifs within the 3’UTRs of Il13 were introduced using the Quick Change II XL site-directed mutagenesis kit (Agilent) according to manufacturer’s instructions. The vector expressing the EGFP-Cre fusion protein was generated by subcloning EGFP-Cre from pLV-EGFP-Cre (Addgene number 86805)⁴³ into pScalps. To generate the templates for homology-directed repair (HDR) experiments, pBluescript II Ks(+) was used to clone the ZsGreen reporter gene from pLVX-EF1α-IRES-ZsGreen1 (Clontech) followed by the addition of the SV40 PolyA-Stop sequence from the pCAG-b3STOPb3-ZsGreen plasmid (Addgene number 51266)⁴⁴. The resulting vector was then used to clone homologous arm sequences from the Kit and Mettl3 loci, to generate the final donor templates for homologous recombination as previously described^29,45.

Lentivirus preparation and mast cell transduction

HEK293T cells were transfected with a lentiviral vector together with the packaging vectors psPAX and pMD2.G (Addgene plasmids 12260 and 12259 by Didier Trono). Lentiviral particles were purified from supernatant of transfected HEK293T cells by sucrose gradient (10 mM Tris-HCl pH 7.5, 100 mM NaCl, 1 mM EDTA, 25% sucrose) followed by ultracentrifugation (2.5 h, 1,000,000 × g at 4 °C). Alternatively, viral particles were concentrated using a PEG-8000 solution as described⁴⁶. 25×10⁴ cells were seeded in a 48-well plate and 10-25 µl of virus per transduction were used. After at least 72 h, GFP-expressing cells were sorted using a FACSaria (BD Bioscience) or selected by puromycin (2 μg/ml for 48-72 h).

siRNA transfection

siRNA transfection was performed using the 100 µl Neon Transfection System Kit (ThermoFisher Scientific). Briefly, 1×10⁶ differentiated BMMCs (week 4-6 of in vitro culture) were washed once with PBS and resuspended in 100 µl of Buffer R. 100 µl of cell suspension were added to 200 pmol of Mettl3 custom siRNA or 200 pmol of siGLO fluorescent oligonucleotide transfection control (all from Dharmacon). SiRNA sequences are listed in Supplementary Table 2. Cells were electroporated with one pulse at 1600V and 30 ms of width. After transfection, cells were kept for 24 h in antibiotic-free cell culture medium prior to downstream analyses.

Transfection of CRISPR-Cas9 ribonucleoproteins (RNPs)

One or two different crRNAs were selected per gene to be targeted (Supplementary Data 4). 400 pmol of each crRNA were combined with 400 pmol of tracrRNA-ATTO-550 in 10 µl reaction in Nuclease Free Duplex buffer (IDT). Oligonucleotides were subsequently annealed by boiling and cool-down to room temperature to generate gRNA complexes. Cas9 RNPs were prepared immediately before transfection by combining 0.75 µl of each gRNA with 1.5 µl TrueCut Cas9 Protein v2 (5 µg/µl, ThermoFisher Scientific) in a total volume of 3 µl and incubating 20 min at room temperature. Transfection was performed using the 10 µl Neon transfection System Kit (ThermoFisher Scientific). Briefly, 1×10⁶ cells were washed once with PBS and resuspended in 10 µl of Neon electroporation Buffer R. Cells were added to RNPs and incubated 5 min at room temperature. Electroporation was performed using one pulse at 1600V and 30 ms of width for BMMCs, and one pulse at 1700V and 20 ms of width for HEK293T and HMC 1.1 and 1.2 cells.

T7 endonuclease I assay

Genomic DNA was isolated from 5 × 10⁶ to 10 × 10⁶ mast cells using the DNeasy Blood & Tissue Kit (Qiagen). T7 endonuclease I assay to screen for mutations at selected targeted genes was carried out as previously described⁴⁷. Briefly, PCR primers (Supplementary Table 2) were designed to amplify a DNA fragment of ~1000 bp around the area targeted by the gRNAs using the high fidelity KOD Hot Start DNA polymerase (Novagen). Then, 15 µl of the PCR reaction were denatured and reannealed. Finally, 100 U of T7 endonuclease I enzyme (New England Biolabs) were added to each reaction, incubated at 37 °C for 15 min and run on an agarose gel to assess the extent of digestion of the parental band.

Preparation of double- and single-strand DNA (dsDNA and ssDNA) templates for homology-directed repair (HDR)

pBluescript plasmids containing a ZsGreen-PolyA-Stop cassette flanked by homologous arms were used to generate templates for HDR. The homology arms corresponded to regions upstream and downstream the gRNA target sites as previously described⁴⁵. These plasmids were either directly electroporated as donors of dsDNA or used as templates to generate ssDNA. To generate ssDNA templates, PCR primers were designed to amplify ~400 bp of homology arms flanking the gRNA target sites. The Guite-it Long ssDNA Production system (TakaraBiotek) was then used according to manufacturer’s instructions. For truncated Cas9 target sequences (tCTS) templates, truncated Cas9 target sequences were added at the ends of the HDR templates by PCR, as previously described²⁹. PCR reactions were carried out using the PrimeSTAR Max DNA polymerase (TakaraBiotek) and NucleoSpin Gel and PCR Clean-UP were used to purify PCR amplicons. HDR template were concentrated to 1 µg/µl using a speed-vac concentrator. All HDR template sequences are listed in Supplementary Data 4.

Gene replacement by CRISPR-Cas9

For gene replacement based on HDR templates, 5 µg of plasmid DNA templates or 1 µg of ssDNA were used. For tCTS PCR templates, 0.75 µl of each gRNA (2 gRNAs in total) were premixed with 150 µg of poly-L-glutamic acid (PGA, Sigma) as previously described²⁹. 1.5 µl of TrueCut Cas9 Protein v2 (5 µg/µl, Thermo Fisher Scientific) were then added to the gRNA-PGA complexes and incubated 20 min at room temperature before addition of 750 ng of tCTS PCR template. The different RNP-HDR template complexes were then added to 1×10⁶ cells in buffer R and electroporation was performed using the 10 µl Neon transfection System Kit (ThermoFisher Scientific) using one pulse at 1600V and 30 ms width. When needed, ZsGreen⁺ cells were then sorted using a FACSaria (BD Bioscience).

Expression of inflammatory transcripts by digital profiling

Cells were either left resting or were stimulated for 4 h with IgE and antigen complexes. Purified RNA (40 ng) was hybridized to the NanoString nCounter Myeloid Innate Immunity Panel codeset for 16 h at 65 °C. Following hybridization, 30-35 µl of sample were added to the nCounter cartridge and analyzed using an nCounter SPRINT Profiler according to manufacturer’s instructions. Data analysis was carried out using the nSolver Advanced Analysis Software v4.0 (NanoString Technologies). Differentially expressed genes were considered significant when p value ≤ 0.05 and fold change ≥1.5.

RNA-sequencing

Differentiated BMMCs were transfected with Mettl3 RNPs (four independent biological replicates). After verifying (by intracellular staining) that a minimum of 70% Mettl3 depletion was achieved, total RNA was extracted using Zymo-Spin IICR columns (Zymo Research). After poly-A mRNA enrichment, sequencing of Tecan Revelo mRNA libraries using Illumina Novaseq 6000 (2 ×50-bp reads) was outsourced to the Next Generation Sequencing Platform at the University of Bern (Switzerland). Quality control was performed using fastqc v.0.11.9 and RSeQC v.4.0.0⁴⁸. Reads were mapped to the reference genome (Mus_musculus.GRCm39.107) using HiSat2 v.2.2.1⁴⁹. Counts were generated using featureCounts v.2.0.1⁵⁰ and corrected for batch effects using the removeBatchEffect function in R⁵¹. Differential expression analysis was performed using the Omics Playground platform⁵². Gene ontology (GO) analysis was performed using DAVID⁵³. Data visualization was performed with RStudio version 4.1 and Integrative Genomics Viewer (IGV) 2.16.0.

Luciferase assays

HEK293T cells (1×10⁶) were seeded in a 6-well plate. After 24 h, cells were transfected with 3.5 μg of pmirGLO plasmid containing the 3’UTR of Il13, Il6 or Tnf and 1 μg of pScalps-FLAG-HA-Mettl3 or empty control plasmid. After 36-48 h, cells were lysed and analyzed using the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer’s protocol. Luciferase activity was measured using a GloMax Luminometer (Promega). HEK293T cells lacking METTL3 were also used for luciferase assays when indicated. Briefly, cells were transfected with Cas9 RNPs to delete human METTL3 using gRNAs listed in Supplementary Data 4. Four days after transfection, and after measuring the extent of METTL3 deletion by intracellular staining ( ~ 60-70% deletion), 1×10⁶ METTL3-KO HEK cells were seeded in a 6-well plate. For tethering assays, 3.5 μg of pmirGLO reporter plasmid containing the 3’UTR of Il13 with BoxB were co-transfected with 1 μg of λN-FLAG-HA-Mettl3 expression vector and analyzed 36–48 h post-transfection.

ELISA

BMMCs were stimulated with IgE and antigen complexes for 6 h, followed by the collection of the cell supernatant. ELISA assay to measure IL-13 concentration was performed using an IL-13 mouse ELISA kit (Invitrogen), following instructions from the manufacturer.

Passive cutaneous anaphylaxis (PCA)

In vivo PCA experiments were performed as previously described³. Ear pinnae of Kit^Wsh/W-sh mice lacking mast cells (The Jackson Laboratory)¹⁹ were injected intradermally with 1×10⁶ mast cells. Female mice of 6–8 weeks of age were used. After 2 weeks, ears were sensitized with intradermic injection of 1 μg IgE-anti-DNP antibody. The day after sensitization, mice were challenged with 250 μg HSA-DNP antigen, resuspended in Evans blue dye (10 mg/ml), injected intravenously. Ears were collected and incubated in formamide at 65 °C overnight to retrieve the extravasated blue dye, which correlates with local mast cell activation. Intensity of the dye was measured using a spectrophotometer at optical density 600 nm (OD₆₀₀).

Measurement of RNA stability

BMMCs (2×10⁵) were seeded in a 48-well plate and incubated with 1 μg/ml IgE–anti-DNP (Sigma) for 30 min followed by stimulation with 0.2 μg/ml HSA-DNP (Sigma) for 30 min. Actinomycin D (10 μg/ml, Sigma) was then added for different time points (15, 30, 60, and 120 min), prior to RNA isolation and RT-qPCR. mRNA decay rate was calculated by non-linear regression curve fitting (one phase decay) using Prism version 9 (GraphPad).

miCLIP

The miCLIP experiments were performed exactly as previously described^20,54. Briefly, BMMCs (6×10⁷) were either left resting or stimulated with 2 nM PMA (Sigma) and 200 nM ionomycin for 2 h, and total RNA was extracted. The poly-A fraction was purified twice from 3 independent biological replicates using the Dynabeads mRNA purification kit (Ambion) according to the manufacturer’s instructions. After fragmentation with an RNA fragmentation reagent (5 min at 70 °C, Invitrogen), the samples were incubated with 6 μg of anti-m⁶A antibody (Synaptic Systems) in immunoprecipitation buffer (50 mM Tris-HCl, pH 7.4, 100 mM NaCl, 0.05% NP-40) for 4 h at 4 °C, and crosslinked twice with 150 mJ/cm² of UV light (254 nm) in a UV-Stratalinker. After incubation with 50 μl protein A beads for 1 h at 4 °C, antibody–RNA complexes were washed twice with high-salt buffer (50 mM Tris-HC, pH 7.4, 1 M NaCl, 1 mM EDTA, 1% Igepal CA-630, 0.1% SDS, 0.5% sodium deoxycholate) and twice with polynucleotide kinase (PNK) wash buffer (20 mM Tris-HCl, pH 7.4, 10 mM MgCl₂, 0.2% Tween 20). After 3′-end dephosphorylation using PNK beads, the 3′-adapter was ligated with T4 RNA ligase (ligation buffer: 200 mM Tris-HCl, pH 7.8, 40 mM MgCl2, 4 mM DTT). Next, the samples were radioactively labeled, subjected to electrophoresis and transferred onto a Protan BA85 Nitrocellulose Membrane (Whatman). The areas of the membrane containing RNA-crosslinked antibody were excised and the RNA recovered by proteinase K digestion and purification. SuperScript IV reverse transcriptase (Invitrogen) was used for reverse transcription, followed by ligation of the 5′-adapter with T4 RNA ligase (New England Biolabs). After first-strand cDNA synthesis using the Phusion High Fidelity PCR Master Mix (New England Biolabs) and size-selection (ProNex Size-Selective Purification System, Promega), libraries were amplified. Sequencing (50 bp, single-end) was performed on a NextSeq1000 sequencer.

CLIP read processing

Data processing was performed as previously described²⁰. Reads were de-multiplexed based on the experimental barcode and adapter sequences were removed from the read ends (Flexbar v.3.5.0). Unique molecular identifiers (UMIs) were trimmed and added to the read names. Individual samples were mapped to the genome (assembly v.GRCm39) and its annotation (GENCODE release M32) using STAR (v.2.7.6a). Reads were de-duplicated if they had identical UMIs. Significant crosslink sites at single-nucleotide resolution were called using PureCLIP (v.1.3.1) with default parameters, and individual crosslink sites within a distance of 8 bp of each other were merged into wider binding regions (peaks). For assigning a host gene to each PureCLIP peak, transcript annotations were taken from GENCODE release M32, mm39. The metagene plots were generated using the R package Guitar.

RNA immunoprecipitation-RT-qPCR

BMMCs (1×10⁷) were stimulated with 2 nM PMA (Sigma) and 200 nM ionomycin (Sigma) for 2 h. Cells were washed with ice-cold PBS and lysed in lysis buffer (20 mM Tris-HCl, pH7.5, 0.25% NP40, 150 mM NaCl, 1.5 mM MgCl₂) supplemented with 1 M DTT, protease inhibitor cocktail (Sigma) and 0.16 U/µl RNase inhibitor (Promega). 1 mg of protein lysate was pre-cleared with 20 µl Dynabeads Protein A (30 mg/ml, Invitrogen) at 4 °C for 1 h on a rotating wheel. 6 µg anti-YTHDF2 (clone EPR20318, Abcam) or normal rabbit IgG (Cell Signaling Technology) antibody was incubated with 50 µl Dynabeads Protein A (30 mg/ml, Invitrogen) overnight at 4 °C. Pre-cleared lysate was incubated with the antibody-coupled beads at 4 °C for 4 h. Antibody-RNA complexes were recovered on a magnetic stand (Life Technologies) and washed 3 times with ice-cold lysis buffer with RNase inhibitor. RNA was isolated using TRI reagent (MRC) and purified using the Direct-zol RNA mini prep kit (Zymo Research). Reverse transcription was carried out using qScript cDNA SuperMix (Quanta Biosciences) and RT-qPCR was performed using PerfeCTa SYBR Green FastMix (Quanta Bioscience) as described above. Primer sequences are listed in Supplementary Table 2.

Data analysis

Statistical analysis was performed with Prism version 9 (GraphPad).

Reporting summary

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

Data availability

All data generated in this study are provided in the Supplementary Information and Source data files. Sequencing and Nanostring data generated in this study are also available as super-series in the GEO database under accession code GSE228615. Source data are provided with this paper.

References

Cildir, G., Pant, H., Lopez, A. F. & Tergaonkar, V. The transcriptional program, functional heterogeneity, and clinical targeting of mast cells. J. Exp. Med. 214, 2491–2506 (2017).
Article CAS PubMed PubMed Central Google Scholar
Dudeck, J. et al. Directional mast cell degranulation of tumor necrosis factor into blood vessels primes neutrophil extravasation. Immunity 54, 468–483.e465 (2021).
Article CAS PubMed Google Scholar
Leoni, C. et al. Dnmt3a restrains mast cell inflammatory responses. Proc. Natl. Acad. Sci. USA 114, E1490–E1499 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Montagner, S. et al. TET2 regulates mast cell differentiation and proliferation through catalytic and non-catalytic activities. Cell Rep. 15, 1566–1579 (2016).
Article CAS PubMed PubMed Central Google Scholar
Monticelli, S. & Leoni, C. Epigenetic and transcriptional control of mast cell responses. F1000Res 6, 2064 (2017).
Article PubMed PubMed Central Google Scholar
Ping, X. L. et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res. 24, 177–189 (2014).
Article CAS PubMed PubMed Central Google Scholar
Patil, D. P., Pickering, B. F. & Jaffrey, S. R. Reading m(6)A in the transcriptome: m(6)A-binding proteins. Trends Cell Biol. 28, 113–127 (2018).
Article CAS PubMed Google Scholar
Zaccara, S. & Jaffrey, S. R. A unified model for the function of YTHDF proteins in regulating m(6)A-modified mRNA. Cell 181, 1582–1595.e18 (2020).
Article CAS PubMed PubMed Central Google Scholar
Shulman, Z. & Stern-Ginossar, N. The RNA modification N(6)-methyladenosine as a novel regulator of the immune system. Nat. Immunol. 21, 501–512 (2020).
Article CAS PubMed Google Scholar
Lee, H. et al. Stage-specific requirement for Mettl3-dependent m(6)A mRNA methylation during haematopoietic stem cell differentiation. Nat. Cell Biol. 21, 700–709 (2019).
Article CAS PubMed PubMed Central Google Scholar
Cheng, Y. et al. M(6)A RNA methylation maintains hematopoietic stem cell identity and symmetric commitment. Cell Rep. 28, 1703.e6–1716.e6 (2019).
Article Google Scholar
Wang, H. et al. Mettl3-mediated mRNA m(6)A methylation promotes dendritic cell activation. Nat. Commun. 10, 1898 (2019).
Article ADS PubMed PubMed Central Google Scholar
Tong, J. et al. Pooled CRISPR screening identifies m(6)A as a positive regulator of macrophage activation. Sci. Adv. 7, eabd4742 (2021).
Yin, H. et al. RNA m6A methylation orchestrates cancer growth and metastasis via macrophage reprogramming. Nat. Commun. 12, 1394 (2021).
Article ADS CAS PubMed PubMed Central Google Scholar
Xiong, J. et al. Lactylation-driven METTL3-mediated RNA m(6)A modification promotes immunosuppression of tumor-infiltrating myeloid cells. Mol. Cell 82, 1660.e10–1677.e10 (2022).
Article Google Scholar
Yankova, E. et al. Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia. Nature 593, 597–601 (2021).
Article ADS CAS PubMed PubMed Central Google Scholar
Du, J. et al. N(6)-adenosine methylation of Socs1 mRNA is required to sustain the negative feedback control of macrophage activation. Dev. Cell 55, 737.e7–753.e7 (2020).
Article Google Scholar
Bataclan, M., Leoni, C. & Monticelli, S. RNA-binding proteins and RNA methylation in myeloid cells. Immunol. Rev. 304, 51–61 (2021).
Grimbaldeston, M. A. et al. Mast cell-deficient W-sash c-kit mutant Kit W-sh/W-sh mice as a model for investigating mast cell biology in vivo. Am. J. Pathol. 167, 835–848 (2005).
Article CAS PubMed PubMed Central Google Scholar
Ito-Kureha, T. et al. The function of Wtap in N(6)-adenosine methylation of mRNAs controls T cell receptor signaling and survival of T cells. Nat. Immunol. 23, 1208–1221 (2022).
Article CAS PubMed Google Scholar
Horiuchi, K., Kawamura, T. & Hamakubo, T. Wilms’ tumor 1-associating protein complex regulates alternative splicing and polyadenylation at potential G-quadruplex-forming splice site sequences. J. Biol. Chem. 297, 101248 (2021).
Article CAS PubMed PubMed Central Google Scholar
Little, N. A., Hastie, N. D. & Davies, R. C. Identification of WTAP, a novel Wilms’ tumour 1-associating protein. Hum. Mol. Genet. 9, 2231–2239 (2000).
Article CAS PubMed Google Scholar
Liu, J. et al. A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat. Chem. Biol. 10, 93–95 (2014).
Article ADS CAS PubMed Google Scholar
Wang, P., Doxtader, K. A. & Nam, Y. Structural basis for cooperative function of Mettl3 and Mettl14 methyltransferases. Mol. Cell 63, 306–317 (2016).
Article CAS PubMed PubMed Central Google Scholar
Hong, H. et al. The Src family kinase Hck regulates mast cell activation by suppressing an inhibitory Src family kinase Lyn. Blood 110, 2511–2519 (2007).
Article CAS PubMed PubMed Central Google Scholar
Deho, L. et al. Two functionally distinct subsets of mast cells discriminated By IL-2-independent CD25 activities. J. Immunol. 193, 2196–2206 (2014).
Article CAS PubMed Google Scholar
Linder, B. et al. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat. Methods 12, 767–772 (2015).
Article CAS PubMed PubMed Central Google Scholar
Li, H. B. et al. M(6)A mRNA methylation controls T cell homeostasis by targeting the IL-7/STAT5/SOCS pathways. Nature 548, 338–342 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Nguyen, D. N. et al. Polymer-stabilized Cas9 nanoparticles and modified repair templates increase genome editing efficiency. Nat. Biotechnol. 38, 44–49 (2020).
Article CAS PubMed Google Scholar
Jo, A. et al. Mast cell-derived plasminogen activator inhibitor type 1 promotes airway inflammation and remodeling in a murine model of asthma. J. Allergy Clin. Immunol. 142, 294.e5–297.e5 (2018).
Article Google Scholar
Bujnicki, J. M., Feder, M., Radlinska, M. & Blumenthal, R. M. Structure prediction and phylogenetic analysis of a functionally diverse family of proteins homologous to the MT-A70 subunit of the human mRNA:m(6)A methyltransferase. J. Mol. Evol. 55, 431–444 (2002).
Article ADS CAS PubMed Google Scholar
Fustin, J. M. et al. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell 155, 793–806 (2013).
Article CAS PubMed Google Scholar
Khong, A., Matheny, T., Huynh, T. N., Babl, V. & Parker, R. Limited effects of m(6)A modification on mRNA partitioning into stress granules. Nat. Commun. 13, 3735 (2022).
Article ADS CAS PubMed PubMed Central Google Scholar
Legault, P., Li, J., Mogridge, J., Kay, L. E. & Greenblatt, J. NMR structure of the bacteriophage lambda N peptide/boxB RNA complex: recognition of a GNRA fold by an arginine-rich motif. Cell 93, 289–299 (1998).
Article CAS PubMed Google Scholar
Mogridge, J., Mah, T. F. & Greenblatt, J. A protein-RNA interaction network facilitates the template-independent cooperative assembly on RNA polymerase of a stable antitermination complex containing the lambda N protein. Genes Dev. 9, 2831–2845 (1995).
Article CAS PubMed Google Scholar
Gehring, N. H., Neu-Yilik, G., Schell, T., Hentze, M. W. & Kulozik, A. E. Y14 and hUpf3b form an NMD-activating complex. Mol. Cell 11, 939–949 (2003).
Article CAS PubMed Google Scholar
Wang, X. et al. N(6)-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).
Article CAS PubMed PubMed Central Google Scholar
Leyva-Castillo, J. M. et al. Mast cell-derived IL-13 downregulates IL-12 production by skin dendritic cells to inhibit the TH1 cell response to cutaneous antigen exposure. J. Allergy Clin. Immunol. 147, 2305.e3–2315.e3 (2021).
Article Google Scholar
Liu, N. et al. N(6)-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 518, 560–564 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
Wu, B. et al. Molecular basis for the specific and multivariant recognitions of RNA substrates by human hnRNP A2/B1. Nat. Commun. 9, 420 (2018).
Article ADS PubMed PubMed Central Google Scholar
Monticelli, S. Emerging roles for RNA-binding proteins in T lymphocytes. Immunol. Lett. 246, 52–56 (2022).
Article CAS PubMed Google Scholar
Sundström, M. et al. Functional and phenotypic studies of two variants of a human mast cell line with a distinct set of mutations in the c-kit proto-oncogene. Immunology 108, 89–97 (2003).
Article PubMed PubMed Central Google Scholar
Truitt, J. M. et al. Inhibition of IKKβ reduces ethanol consumption in C57BL/6J mice. eNeuro 3, ENEURO.0256-16.2016 (2016).
Hermann, M. et al. Binary recombinase systems for high-resolution conditional mutagenesis. Nucleic Acids Res. 42, 3894–3907 (2014).
Article ADS CAS PubMed PubMed Central Google Scholar
Roth, T. L. et al. Reprogramming human T cell function and specificity with non-viral genome targeting. Nature 559, 405–409 (2018).
Article ADS CAS PubMed PubMed Central Google Scholar
Lo, H. L. & Yee, J. K. Production of vesicular stomatitis virus G glycoprotein (VSV-G) pseudotyped retroviral vectors. Curr. Protoc. Hum. Genet. Chapter 12, Unit 12.17 (2007).
Google Scholar
Leoni, C., Bianchi, N., Vincenzetti, L. & Monticelli, S. An optimized workflow for CRISPR-Cas9 deletion of surface and intracellular factors in primary human T lymphocytes. PLoS ONE 16, e0247232 (2021).
Article CAS PubMed PubMed Central Google Scholar
Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).
Article CAS PubMed Google Scholar
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Article CAS PubMed PubMed Central Google Scholar
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Article CAS PubMed Google Scholar
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Article PubMed PubMed Central Google Scholar
Akhmedov, M., Martinelli, A., Geiger, R. & Kwee, I. Omics Playground: a comprehensive self-service platform for visualization, analytics and exploration of Big Omics Data. NAR Genomics Bioinform. 2, lqz019 (2020).
Article Google Scholar
Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Körtel, N. et al. Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning. Nucleic Acids Res. 49, e92 (2021).
Article PubMed PubMed Central Google Scholar

Download references

Acknowledgements

The authors would like to thank David Jarrossay and Laurent Perez for invaluable technical support, Pamela Nicholson (Next Generation Sequencing Platform, University of Bern) for RNA-seq and Simone Moro for help with the bioinformatic analyses. The HMC-1.1 and 1.2 human mast cell lines were kindly provided by Joseph Butterfield. This work was supported in part by the German Research Foundation grants HE3359/8-1 (#444891219) and HE3359/7-1 (#432656284) (to V.H.), the Fondazione Aldo e Cele Daccò, and the Swiss National Science Foundation grant 310030L_189352 (to S.M.).

Author information

These authors contributed equally: Cristina Leoni, Marian Bataclan.

Authors and Affiliations

Institute for Research in Biomedicine, Università della Svizzera italiana (USI), 6500, Bellinzona, Switzerland
Cristina Leoni, Marian Bataclan & Silvia Monticelli
Institute for Immunology, Biomedical Center, Faculty of Medicine, Ludwig-Maximilians-Universität in Munich, 82152, Planegg-Martinsried, Germany
Taku Ito-Kureha & Vigo Heissmeyer
Research Unit Molecular Immune Regulation, Helmholtz Zentrum München, 81377, Munich, Germany
Vigo Heissmeyer

Authors

Cristina Leoni
View author publications
You can also search for this author in PubMed Google Scholar
Marian Bataclan
View author publications
You can also search for this author in PubMed Google Scholar
Taku Ito-Kureha
View author publications
You can also search for this author in PubMed Google Scholar
Vigo Heissmeyer
View author publications
You can also search for this author in PubMed Google Scholar
Silvia Monticelli
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

C.L., M.B. and T.I.-K. designed, performed and analyzed experiments. T.I.-K. and V.H. provided essential reagents and protocols. S.M. overviewed the project. C.L. and S.M. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Cristina Leoni or Silvia Monticelli.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Peer Review File

Description of Additional Supplementary Files

Supplementary Data 1

Supplementary Data 2

Supplementary Data 3

Supplementary Data 4

Reporting Summary

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Leoni, C., Bataclan, M., Ito-Kureha, T. et al. The mRNA methyltransferase Mettl3 modulates cytokine mRNA stability and limits functional responses in mast cells. Nat Commun 14, 3862 (2023). https://doi.org/10.1038/s41467-023-39614-y

Download citation

Received: 26 August 2022
Accepted: 16 June 2023
Published: 29 June 2023
DOI: https://doi.org/10.1038/s41467-023-39614-y

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.