https://orcid.org/0000-0001-9848-2244
Figures
Abstract
N6-methyladenosine (m6A) RNA methylation is one of the most prevalent reversible post-transcriptional RNA modifications and has been recognized as a crucial regulator of host immune responses. Intestinal epithelial cells (IECs) constitute an important component of gastrointestinal mucosal immunity. Interferons (IFNs) play a central role in maintaining intestinal homeostasis, and m6A methylation status influences IFN-mediated cell-intrinsic defense. In this study, we investigated the potential role of m6A RNA modifications in IFN-γ-stimulated IEC-intrinsic defense. We observed significant alterations in the topology of the m6A mRNA methylome in murine IECs following IFN-γ stimulation. A subset of IFN-γ-stimulated immune gene transcripts exhibited increased m6A RNA methylation, including several members of the immunity-related GTPase family M (IRGM) genes. In addition, IFN-γ-responsive long non-coding RNAs may modulate the m6A methylation levels of multiple IFN-γ-stimulated immune transcripts. Enhanced m6A methylation of the Irgm2/3 transcripts was associated with strengthened cell-intrinsic defense against infection by the protozoan parasite Cryptosporidium. Notably, Cryptosporidium infection altered the host m6A mRNA methylome in IECs, thereby counteracting the IFN-γ-mediated defense response. Although the RNA levels of Irgm2/3 genes were upregulated, their m6A RNA methylation levels and protein expression were reduced in infected cells. This effect was associated with host delivery of dsRNAs derived from Cryptosporidium parvum virus 1, a virus harbored in the parasite. Collectively, our findings suggest that m6A methylation of RNA transcripts enhances IFN-γ-mediated IEC-intrinsic antiparasitic defense, while Cryptosporidium has evolved mechanisms to evade this response by suppressing m6A RNA methylation of IFN-γ-stimulated immune genes.
Author summary
Cell-intrinsic defense refers to the ability of an individual host cell to autonomously eliminate invading microbes, a process that is especially important for controlling intracellular pathogens. The cytokine IFN-γ is a key regulator of epithelial cell–intrinsic defense. RNA modifications have emerged in recent years as a critical component of cell biology, including the regulation of host innate immune defense. The most common RNA modification in mRNA is N6 methyladenosine (m6A). The obligate intracellular parasite Cryptosporidium establishes an intracellular but extracytoplasmic infection by forming a membrane-bound vacuole on the apical surface of intestinal epithelial cells (IECs). Because infection is largely restricted to IECs, cell–intrinsic defense mechanisms—particularly those induced by IFN-γ—are essential for clearing the parasite. In this study, we investigated the role of m⁶A-mediated RNA methylation in regulating the IFN-γ–induced, cell-intrinsic anti-Cryptosporidium response in IECs. Our findings indicate that m⁶A-mediated post-transcriptional gene regulation is a key determinant of IFN-γ–induced epithelial antiparasitic defense. We further show that C. parvum has evolved mechanisms to evade host immunity by suppressing m⁶A methylation of host RNAs. These results provide new mechanistic insights into IEC-intrinsic immunity and may inform the development of novel therapeutic strategies.
Citation: Pok C, Gong A-Y, Graham ML, Wang S, Deng S, Sharmin Z, et al. (2026) m6A RNA methylation modulates IFN-γ-stimulated intestinal epithelial cell-intrinsic antiparasitic defense. PLoS Pathog 22(7): e1014442. https://doi.org/10.1371/journal.ppat.1014442
Editor: Tracey J. Lamb, University of Utah, UNITED STATES OF AMERICA
Received: January 5, 2026; Accepted: July 3, 2026; Published: July 20, 2026
Copyright: © 2026 Pok et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within this manuscript, associated supporting information files, or in public repositories. The MeRIP-seq data are deposited in the NCBI database under the accession numbers SRR38251195-SRR38251206. The RNA-Seq data are deposited in the NCBI database under the accession number GSE245591.
Funding: This work was supported by funding from the National Institutes of Health (AI116323, AI136877, and AI177188) to XMC. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Cell-intrinsic immunity (also called cell-autonomous immunity) is the ability of a host cell to eliminate an invasive/intracellular infectious agent at the cellular level. It serves as the first line of host defense against intracellular pathogens [1]. Intestinal epithelial cells (IECs) possess the necessary molecular machinery to mount a cell-intrinsic defense response, constituting an important component of gastrointestinal mucosal immunity. Ligation of their pattern recognition receptors leads to the upregulation of antimicrobial factors, secretion of cytokines and chemokines, and conditioning of immune cells for direct antimicrobial action or instruction of adaptive immune responses [2]. On the other hand, IECs serve as targets of mucosal immune mediators released from immune cells residing at the gastrointestinal mucosa [3]. One group of such cytokines is the family of interferons (IFNs). The IFN family can be classified into three main types: type I (e.g., IFN-α and IFN-β), type II (IFN-γ), and type III (IFN-λ family) [4]. Growing evidence supports the essential role of IFN-γ in antibacterial and antiparasitic immunity, whereas type I and type III IFNs are key to antiviral immunity [5, 6]. The canonical IFN-γ signaling utilizes the JAK/STAT signaling to activate STAT1, resulting in the formation and nuclear translocation of active STAT1 homodimers and transcription of IFN-stimulated genes (ISGs) [7, 8]. Many ISGs are primary effectors of the innate immune response, such as Mx1/MxA, the interferon-Induced proteins with tetratricopeptide repeats (IFIT) family, and the immunity-related GTPase family M (IRGM) genes [9–11]. The protein products of these genes can target different stages of a pathogen’s life cycle [9–11], regulate autophagy formation [12–14], and have been identified as cornerstones of IFN-γ-mediated responses to intracellular pathogens [15].
RNA modifications have gained traction in the past decade as a crucial element in eukaryotic biological processes. Among the most prevalent modifications occurring in approximately 25% of transcripts at the genome level in eukaryotes is m6A methylation [16]. N6-methyladenosine (m6A) is a post-transcriptional modification that can influence aspects of RNA metabolism, such as mRNA degradation, RNA splicing, mRNA stabilization, and translation efficiency [17]. m6A dynamics and functions are executed by three groups of proteins: methyltransferases, demethylases, and m6A-binding proteins [18]. m6A is installed on RNA molecules by the methyltransferase complex, consisting of methyltransferase-like 3 (METTL3) and METTL14 and their cofactor the WT1 Associated Protein [19]. Structural studies revealed that METTL3 primarily functions as the catalytic core, while METTL14 serves as an RNA-binding platform [20, 21]. m6A is mainly removed by the AlkB homolog H5 and fat mass and obesity-associated protein [22]. The m6A modification regulates RNA splicing, translocation, stability, and translation [23–25]. It has been well-recognized as a crucial regulator in T cell homeostasis, inflammation, and immune response [24, 25]. Selectively altering m6A levels, along with other immunotherapies, may be effective management strategies for a variety of immunological diseases.
Cryptosporidium, a coccidian parasite and an NIAID Category B priority pathogen, infects the gastrointestinal epithelium and is a leading cause of infectious diarrhea and diarrheal-related death in children worldwide [26]. The infection can also cause a life-threatening diarrheal disease in AIDS patients [27]. Cryptosporidium attaches to the apical membrane surface of epithelial cells and forms an intracellular but extracytoplasmic vacuole in which the organism remains [28]. Thus, cell-intrinsic defense is critical to the host’s defense against Cryptosporidium infection [29], providing an ideal model to explore intestinal epithelial cell-intrinsic immunity [30]. IFN-γ is key to epithelial cell-intrinsic anti-Cryptosporidium defense [5,6,31].
To counteract host defense, Cryptosporidium has evolved mechanisms to effectively dysregulate the IFN-γ signaling pathway [32]; thus, it can survive host immune attack during the early stages of infection [33, 34]. The Cryptosporidium parvum virus 1 (CSpV1), a non-enveloped RNA virus of the Partitiviridae family, infects C. parvum and other Cryptosporidium spp. including C. hominis [35]. Its genome comprises two distinct double-stranded RNAs (dsRNAs), sized 1,836 bp (CSpV1-dsRdRp) and 1,510 bp (CSpV1-dsCA) [36]. The CSpV1-dsRNAs are not 5’-capped at either end and unlikely to be 3’-polyadenylylated [37]. CSpV1 is likely to be regularly transmitted only by intracellular routes, as it lacks the machinery for cell entry [38]. Importantly, the fecundity of C. parvum is correlated with its intracellular CSpV1 levels [39]. In our recent studies [40], we demonstrated that CSpV1-dsRNAs are present within infected host cells and are associated with attenuation of IFN-γ–mediated antiparasitic defense in IECs.
In this study, we demonstrated that IFN-γ stimulation causes significant alterations in the RNA m6A landscape of murine IECs, involving Mettl3/14 and long non-coding RNAs (lncRNAs). Increased m6A methylation of Irgm2/3 RNAs following IFN-γ stimulation is associated with enhanced cell-intrinsic defense against Cryptosporidium infection. In contrast, Cryptosporidium infection is associated with reduced m6A methylation of Irgm2/3 RNAs in infected IECs, potentially mediated by parasite-derived CSpV1-dsRNAs, coinciding with attenuation of IFN-γ-stimulated epithelial cell-intrinsic defense. Overall, our findings suggest that m6A-mediated post-transcriptional gene regulation may be a key determinant of IFN-γ-mediated epithelial antiparasitic defense, and that C. parvum may, at least in part, evade host immunity by suppressing m6A methylation of host RNAs.
Results
RNA-Seq transcriptomics and m6A mRNA topology of IECs following IFN-γ stimulation
We first examined the landscape of the m6A mRNA methylome in IECs upon IFN-γ stimulation by performing m6A methylated RNA immunoprecipitation sequence (MeRIP-seq). IEC4.1 cells, a transformed but non-tumorigenic intestinal epithelial cell line derived from neonatal mice (5–7 days old) [41], were treated with IFN-γ (10 ng/ml) for 6 h. Total mRNA was then collected and processed for MeRIP-seq as previously reported [42]. We compared the abundance and distribution of m6A peaks on mRNAs between untreated control and IFN-γ-treated cells. IFN-γ stimulation resulted in significant alterations in m6A peaks across 721 mRNAs in the transcriptome, including 590 with increased m6A peaks and 131 with decreased m6A peaks (Fig 1A-1C and S1 Table). The top 50 mRNAs with significant increase or decrease in their m6A peaks are shown in Fig 1A. The complete distribution of m6A sites, regions, and corresponding mRNAs is provided in S1 Table. Among these m6A sites, the majority were in the intronic regions (37.6% at 320 sites), distal regions (33.8% at 287 sites), and promoter regions (16.2% at 138 sites) (Fig 1B, 1C and S2 Table). The remaining peaks were distributed across the exonic (non-coding sequence) regions (7.9% at 67 sites), 3’UTR (3.4% at 29 sites), 5’UTR (0.7% at 6 sites), and downstream regions (0.4% at 3 sites) (Fig 1B, 1C and S2 Table). More sites with increased m6A methylation than with deceased m6A methylation were detected across these regions (Fig 1C). No significant difference in m6A peak alterations was observed between the 5’UTR and 3’UTR regions (Fig 1C). Motif analysis identified the top most enriched sequence motifs associated with newly emerged and lost m6A peaks in IEC4.1 cells following IFN-γ stimulation (Fig 1D). All sequencing data were generated in accordance with MIAME guidelines and were deposited in the NCBI database (with the NCBI accession numbers: SRR38251195 - SRR38251206).
IEC 4.1 cells were treated with IFN-γ (10 ng/mL, 6 h). Total RNAs were collected and processed for m6A methylated RNA immunoprecipitation sequence (MeRIP-seq). (A) IFN-γ stimulation resulted in significant alterations in m6A peaks of mRNAs in the transcriptome. Bar is a schematic representation of the number of mRNAs with either unchanged or altered m6A levels after IFN-γ treatment. Heat map depicts top 50 mRNAs with increased or decreased m6A peaks. (B) Pie chart depicting overall distribution of m6A methylation sites across genomic transcript regions. RefSeq-based coordinates from MeRIP-Seq were converted to genomic coordinates using biomaRt. ChIPseeker was used to assign m6A peaks to genomic regions. (C) Bar graph depicting gain and loss of m6A methylation sites within each transcript region, determined by fold change status. Genomic annotations were derived from ChIPseeker. (D) Motif enrichment analysis showing the top three sequence motifs associated with newly emerged and lost m6A peaks in IEC4.1 cells following IFN-γ stimulation. (E) Gene ontology (GO) analysis depicting biological processes of mRNAs with increased or decreased m6A peaks in IFN-γ-treated-IEC 4.1 cells. p values were calculated based on Kolmogorov-Smirnov test and adjusted by Benjamini-Hochberg method. Data from A-E were derived from three biological replicates for each group (untreated and IFN-γ treated).
Gene ontology analysis of mRNAs with altered m6A peaks identified a broad range of enriched pathways among both newly gained and lost m6A methylation sites. These include immune-related pathways, RNA splicing and translation, mitochondrion functions, and cell proliferation (Fig 1E and S3 Table). Immune-related genes include Gbp5, Zbp1, Irgm3, Gbp6, and Irgm2. Genes involved in RNA translation and splicing include Larp1, Pnn, Zfp36, Bcl3, and Zrsr2. Genes associated with mitochondrion functions include Slc35f6, Gper1, Mmp9, and Triap1. Cell proliferation-related genes included Batf, Batf3, Tgfa, Has2, and B4galt1 (Fig 1E and S3 Table).
We also took a portion of the mRNA isolated from untreated and IFN-γ-treated IEC4.1 cells, as described above, for whole genome RNA sequencing (RNA-Seq) analysis. Consistent with previous studies [43], numerous genes were found to be either upregulated or downregulated following IFN-γ stimulation (Fig 2A and S4 Table). The top 30 induced genes are shown in Fig 2A, and a complete list of differentially expressed genes is provided in S4 Table. Among the upregulated genes are immune-related genes (Gbp2, Irgm3, Irgm2, and Zbp1), stress-responsive genes (Psmb9 and H3c1), and metabolism-related genes (Ido2, and Parp14) (Fig 2B and S5 Table). All sequencing data were generated in accordance with MIAME guidelines and were deposited in the NCBI database (with the GEO accession numbers: GSE245591).
IEC 4.1 cells were treated with IFN-γ (10 ng/mL, 6 h). Total RNAs were collected and processed for whole genome RNA sequencing analysis (RNA-Seq). (A) Volcano plot depicting differential gene expression in IFN-γ treated-IEC 4.1 cells vs untreated cells by RNA-Seq. Dashed line represents a cutoff of adjusted p < 0.05. Table lists the top 30 upregulated and downregulated genes after IFN-γ treatment. Two-tailed Wald tests were performed for statistical analysis and p value was adjusted by Benjamini-Hochberg method. (B) Gene ontology (GO) depicting the biological processes of upregulated genes in IEC4.1 cells following in IFN-γ stimulation. p values were calculated based on Kolmogorov-Smirnov test and adjusted by Benjamini-Hochberg method. (C) Venn diagram depicting genes with a differential expression level by RNA-seq and with an altered m6A level in their mRNAs by m6A methylated RNA immunoprecipitation sequence (MeRIP-seq) in cells following IFN-γ stimulation. Overlay of genes represents IFN-γ-induced genes with increased m6A status and IFN-γ-downregulated genes with decreased m6A status, respectively. (D) Gene ontology (GO) depicting the biological processes for overlapped genes induced by IFN-γ with increased m6A status. p values were calculated based on Kolmogorov-Smirnov test and adjusted by Benjamini-Hochberg method. Data from A to D were derived from three biological replicates for each group (untreated and IFN-γ treated). (E) IFN-γ-induced expression and m6A RNA levels of selected immune related genes in IEC4.1 cells by quantitative PCR (qPCR). Cells were treated with IFN-γ (10 ng/mL, 6 h) and total RNAs were collected and processed for qPCR or RIP-qPCR using antibody against m6A. (F) IFN-γ-induced expression and m6A RNA levels of Irgm2/3 in 2D mouse intestinal epithelial monolayers by qPCR. 2D intestinal epithelial monolayers from mice were treated with IFN-γ (10 ng/mL, 6 h) and total RNAs were collected and processed for qPCR or RIP-qPCR using antibody against m6A. Data in (E and F) are presented as the mean ± standard deviation from three independent experiments and analyzed by Student’s t test; * p < 0.05, ** p < 0.01, ***p < 0.001, **** p < 0.0001.
Interestingly, comparison of genes with altered RNA expression and those with changes in m6A RNA methylation in IFN-γ-treated cells revealed only a small degree of overlap (Fig 2C and S6 Table). Following IFN-γ stimulation, only 8.2% of genes exhibited both upregulated expression and increased RNA m6A levels, while 1.5% showed both downregulated expression and decreased RNA m6A levels (Fig 2C and S6 Table). The majority of genes with altered RNA m6A levels did not display significant changes in expression following IFN-γ treatment. Representative overlapping genes include Irgm3, Irgm2, Gbp2b, Zbp1, and Batf2 (Fig 2C and S6 Table). Moreover, gene ontology analysis of the 553 genes with both altered expression and m6A methylation revealed board biological processes, including cell adhesion, metabolism, and immune responses (Fig 2D and S7 Table). The IFN-γ-induced increase in RNA m6A levels in selected genes, and its association with RNA expression, was further validated by quantitative PCR (qPCR) in IEC4.1 cells (Fig 2E) and in 2D mouse intestinal epithelial monolayers (Fig 2F).
Involvement of Mettl3/14 and lncRNAs in IFN-γ-induced RNA m6A in IECs
RNA m6A methylation is primarily mediated by the Mettl3-Mettl14 complex in most eukaryotes [44]. We therefore hypothesized that Mettl3/14 physically associates with target mRNAs to mediate m6A methylation. To test this, we performed RIP-Seq using antibodies against Mettl3 and Mettl14 to identify RNAs interacting with these proteins in IEC4.1 cells following IFN-γ treatment. Differential enrichment was analyzed using edgeR, enabling statistical assessment in single-sample comparisons. Our analysis revealed that anti-Mett3 and anti-Mettl14 immunoprecipitation pulled down a similar subset of mRNAs from IFN-γ-treated cells (Fig 3A and S8 Table). Many of these mRNAs overlapped with these exhibiting increased m6A methylation following IFN-γ treatment (Fig 3B and S9 Table). The association of Mettl3/14 with selected IFN-γ-induced mRNAs with increased m6A methylation (e.g., Irgm3 and Irgm2) was further validated by RNA immunoprecipitation-qPCR (RIP-qPCR) analysis (Fig 3C).
IEC 4.1 cells were treated with IFN-γ (10 ng/mL, 4 h). Total RNAs were collected and processed for RIP-Seq using antibodies against Mettl3 an Mettl14. (A) Volcano plot illustrating differential mRNA profiles from RIP-seq with anti-Mettl3 or RIP-seq with anti-Mettl14 of IFN-γ treated-IEC 4.1 cells vs untreated cells. (B) Venn diagram depicting mRNAs associated with Mettl3 and with an altered m6A methylation level in cells following IFN-γ stimulation. Overlay of mRNAs represents these that were enriched with anti-Mettl3 and an increased m6A level, or with a decreased association with Mettl3 and a decreased m6A level. Data (in A and B) were derived from sequencing of a single biological replicate for each group (untreated and IFN-γ treated). (C) IFN-γ-induced association of Irgm2/3 mRNAs with the Mettl3/14 complex in IEC4.1 cells and 2D mouse intestinal epithelial monolayers by RIP-qPCR. IEC4.1 cells and 2D mouse intestinal epithelial monolayers were exposed to IFN-γ (10 ng/mL) for 4 h and total RNA was collected. Association of Irgm2 and Irgm3 mRNAs with the Mettl3/14 complex was assessed by RIP-qPCR with anti-Mettl3 or anti-Mettl14. Data are presented as the mean ± standard deviation from three independent experiments and analyzed by Student’s t test; * p < 0.05, ** p < 0.01, ***p < 0.001, **** p < 0.0001.
RIP-Seq using antibodies against Mettl3 and Mettl14 revealed that many lncRNAs were pulled down from IFN-γ-treated cells (Fig 4A and S10 Table). LncRNAs are known to regulate a wide range of cellular functions [45]. RNA-Seq analysis of IEC4.1 cells following IFN-γ stimulation showed significant alterations in the lncRNA expression profile, including 59 upregulated and 41 downregulated lncRNAs (Fig 4C and S11 Table). The top 20 upregulated and downregulated lncRNAs are listed in S12 and S13 Tables. Notably, several lncRNAs identified in the immunoprecipitation with Mettl3 and Mettl14 antibodies were also upregulated in IFN-γ-treated cells, including Gm12216, Gm20599, Au020206, and 4933412E12Rik (Fig 4D and S12 Table). Their association with the Mettl3 protein was further validated by RIP-qPCR (Fig 4E).
(A) Volcano plot depicting lncRNAs associated with the Mettl3/14 complex in IFN-γ-treated IEC4.1 cells. Cells were exposed to IFN-γ (10 ng/mL) for 4 h and RNA was collected for RIP-seq with anti-Mettl3 or RIP-seq with anti-Mettl14. Data were derived from sequencing analysis of a single biological replicate for each group (untreated and IFN-γ treated). Differential expression statistics were computed using edgeR enabling statistical testing in single-sample comparisons. (B) A volcano plot showing lncRNA expression profiles in IEC4.1 cells following IFN-γ stimulation. Cells were treated with IFN-γ (10 ng/mL) for 4 h and total RNAs were collected for RNA-Seq. Data were derived from sequencing of three biological replicates for each group (untreated and IFN-γ treated). (C) Venn diagram depicting lncRNAs with an altered expression levels and associated with the Mettl3/14 complex in IEC4.1 cells following IFN-γ stimulation. Overlay of lncRNAs represents these that were induced with an enriched association with the Mettl3/14 complex, or that were suppressed with a decreased association with Mettl3/14, in cells following IFN-γ stimulation. (D) Table listing top 5 lncRNAs with an increased expression level and enriched with the Mettl3/14 complex in IEC4.1 cells following IFN-γ stimulation. (E) Validation of IFN-γ-induced enrichment of the top 5 lncRNAs with the Mettl3/14 complex in IEC4.1 cells by RIP-qPCR. Cells were exposed to IFN-γ (10 ng/mL) for 4 h and total RNA was collected. Association of selected lncRNAs with the Mettl3/14 complex was assessed by RIP-qPCR with anti-Mettl3 or anti-Mettl14. Several unrelated lncRNAs, including Dancr, Snhg4, and NR_038009, were used as negative controls. (F) IFN-γ-induced enrichment of Nostrill with the Mettl3/14 complex in IEC4.1 cells by RIP-qPCR. Cells were exposed to IFN-γ (10 ng/mL) for 4 h followed by RIP-qPCR for Nostrill enrichment using anti-Mettl3 or anti-Mettl14. U90926 and NR_045064 were used as negative controls. (G) Knockdown of Nostrill partially blocked IFN-γ-induced m 6A methylation of Irgm3 and Irgm2 mRNAs in IEC4.1 cells. Cells were transfected with si_Nostrill or si_Control (24 h), then treated with IFN-γ (10 ng/mL, 6h), followed by RIP-qPCR for Irgm3 and Irgm2 using anti-m6A. Data in (E, F, and G) are presented as the mean ± standard deviation from three independent experiments and analyzed by Student’s t test; * p < 0.05, ** p < 0.01, ***p < 0.001, **** p < 0.0001.
We previously demonstrated that induction of the lncRNA Nostrill promotes the expression of Irgm3 and iNos in IECs in response to IFN-γ stimulation [43]. We next investigated whether Nostrill is specifically involved in Mettl3/14-mediated m6A methylation of Irgm2/3 following IFN-γ stimulation. RIP-qPCR using anti-Mettl3/14 antibodies confirmed the presence of Nostrill, but not the two unrelated control lncRNAs U90926 and NR_045064, in immunoprecipitates from IFN-γ–treated IEC4.1 cells (Fig 4F). Interestingly, knockdown of Nostrill using siRNA (siR_Nostrill) reduced IFN-γ-induced m6A methylation of Irgm2/3 (Fig 4G). Although Nostrill did not reach statistical significance in the Mettl3/Mettl14 RIP-Seq dataset, this may reflect the limited statistical power of single-sample comparisons and the possibility that not all associated lncRNAs were captured.
m6A RNA methylation modulates IFN-γ-stimulated cell-intrinsic defense against Cryptosporidium infection
We next investigated the effects of inhibiting m6A methylation on IFN-γ-stimulated cell-intrinsic antimicrobial defense. To this end, we employed an infection model of IECs with Cryptosporidium, a protozoan parasite that infects the gastrointestinal epithelium and other mucosal surfaces in humans and is an important cause of diarrheal disease in young children and AIDS patients [46]. This parasite infects IECs and resides within a specialized intracellular but extracytoplasmic vacuole [47]. IFN-γ-stimulated intrinsic immunity represents a frontline host defense against Cryptosporidium infection [48]. Consistent with previous studies [43, 49], we found that treatment of IEC4.1 cells with IFN-γ enhanced cell-intrinsic defense against Cryptosporidium infection, as evidenced by a decreased infection burden in the IFN-γ-treated cells compared with untreated controls (Fig 5A). Knockout of Mettl3 in IEC4.1 cells (IEC4.1-Mettl3-/- cells) using a CRISPR/Cas9 approach (S2 Fig) partially impaired IFN-γ-mediated cell-intrinsic defense, as indicated by a higher infection burden in IFN-γ-treated IEC4.1-Mettl3-/- cells compared with IFN-γ-treated wild-type cells (Fig 5A and 5B). Consistent with our previous findings [43], treatment of IEC4.1 cells with siR_Nostrill partially inhibited IFN-γ-mediated anti-Cryptosporidium defense.
(A) Knockdown of Mettl3 partially blocked IFN-γ-stimulated epithelial cell-intrinsic defense against C. parvum infection. IEC 4.1 and IEC4.1-Mettl3-/- cells were exposed to C. parvum for 24 h with/or without IFN-γ treatment (5 ng/mL, added at 4 h post infection). C. parvum infection burden was measured through qPCR for Cryptosporidium cp18s mRNA. (B) Representative immunofluorescent staining images of C. parvum infection in IEC 4.1 and IEC4.1-Mettl3-/- cells from A. Blue depicts DAPI while green depicts C. parvum. (C) m6A methylation and RNA stability of Irgm3 and Irgm2 in IEC 4.1 and IEC4.1-Mettl3-/- cells. Cells were treated with IFN-γ (10 ng/mL, 4 h) followed by RIP-qPCR for Irgm3 and Irgm2 using anti-m6A. For RNA stability assay, cells were pretreated with IFN-γ (10 ng/mL, 4 h) and then treated with Actinomycin D (10 ug/ml) followed by RNA extraction at 30-minute intervals for a duration of 2 h to quantify RNA expression of Irgm3 or Irgm2 by qPCR. (D) Knockdown of Mettl3 partially blocked IFN-γ-induced Irgm3 expression at the protein level but not at the RNA level. IEC 4.1 and IEC4.1-Mettl3-/- cells were exposed to IFN-γ (10 ng/mL) for 8 h followed by measurement of Irgm3 at the RNA level (by qPCR) and at the protein level (by Western blot). Densitometric levels of positive bands were quantified and are expressed as the ratio to Gapdh. (E) Knockdown of Irgm3 partially blocked IFN-γ-stimulated cell-intrinsic defense against C. parvum infection. IEC 4.1 and IEC4.1-Irgm3-/- cells were exposed to C. parvum for 24 h with/or without IFN-γ treatment (5 ng/mL, added at 4 h post infection). C. parvum infection burden was measured through qPCR for cp18s. Data in (A to E) are presented as the mean ± standard deviation from three independent experiments and analyzed by Student’s t test; * p < 0.05, ** p < 0.01, ***p < 0.001, **** p < 0.0001. CP = C. parvum.
To further investigate the mechanisms by which m6A methylation regulates IFN-γ-promoted cell-intrinsic anti-Cryptosporidium defense, we examined its effects on the stability of Irgm2/3 mRNAs in IEC4.1 cells following IFN-γ stimulation. While knockout of Mettl3 significantly decreased IFN-γ-induced m6A methylation of Irgm2/3 mRNAs, their stability was comparable between IEC4.1 and IEC4.1-Mettl3-/- cells after IFN-γ treatment (Fig 5C). We next assessed Irgm2/3 protein levels in IEC4.1 and IEC4.1-Mettl3-/- cells after 8 h of IFN-γ stimulation. Although mRNA levels remained similar between the two groups (Fig 5D), protein levels in IFN-γ-treated IEC4.1 cells were significantly higher than those in IEC4.1-Mettl3-/- cells exposed to the same amount of IFN-γ (Fig 5D). These findings suggest that m6A RNA methylation is required for efficient Irgm2/3 protein expression in IECs in response to IFN-γ stimulation. Importantly, knockout of Irgm3 in IEC4.1 cells (IEC4.1-Irgm3-/- cells) using a CRISPR/Cas9 approach (S2 Fig) partially impaired IFN-γ-mediated cell-intrinsic defense against Cryptosporidium infection, as indicated by a higher infection burden in IFN-γ-treated IEC4.1-Irgm3-/- cells compared with IFN-γ-treated wild-type cells (Fig 5E).
Cryptosporidium infection suppresses Irgm RNA m6A methylation in infected IECs potentially mediated by parasite-derived CSpV1-dsRNAs
Many pathogens have evolved strategies to evade host immune defense [28]. Similarly, Cryptosporidium infection disrupts the IFN-γ signaling pathway in host cells [32], enabling the parasite to evade immune attack during the early stage of infection [30]. In our previous studies we demonstrated that C. parvum infection induced significant alterations in m6A peaks across 118 regions corresponding to 80 genes in the transcriptome [42]. Specifically, although the RNA levels of Irgm2 and Irgm3 were increased in IEC4.1 cells following C. parvum infection, m6A modifications in their 3’UTRs were reduced in infected cells [42]. Consistently, we observed increased Irgm2/3 RNA levels (Fig 6A) companied by decreased m6A methylation (Fig 6B) in cells following Cryptosporidium infection. Interestingly, despite the elevated mRNA levels, Irgm2 and Irgm3 protein levels remained low in infected cells (Fig 6C). These findings suggest that C. parvum infection reduces m6A methylation of Irgm2 and Irgm3 mRNAs, thereby suppressing their protein expression in infected cells.
(A) C. parvum infection induces Irgm3 and Irgm2 RNA expression in IEC 4.1 cells. Cells were exposed to infection for 24 h and RNA expression of Irgm3 and Irgm2 was quantified by qPCR. (B) C. parvum infection suppresses Irgm3 and Irgm2 RNA m6A methylation in IEC 4.1 cells. Cells were exposed to infection for 24 h and m6A methylation of Irgm3 and Irgm2 was measured by RIP-qPCR using antibody to m6A. (C) C. parvum infection causes suppression of Irgm3 and Irgm2 at the protein level in IEC4.1 cells. Cells were exposed to infection for 24 h and 48 h. Cell extracts were collected followed by qPCR and Western blot for Irgm2 and Irgm3. Cells treated with IFN-γ for 8 h were used for positive control and Gapdh was also blotted as an internal control of equal loading. Representative gel images were shown. (D) Effects of CSpV1-dsRdRp + -dsCA transfection on IFN-γ-induced Irgm2/3 RNA m6A methylation in IEC4.1 cells. Cells were transfected with CSpV1-dsRdRp + -dsCA (200 ng each, 24 h) followed by IFN-γ treatment (5 ng/mL, 24h). RNA was collected and processed for RIP-qPCR using anti-m6A antibody. (E) Association of CSp-V1-dsRNAs with Mettl3 in IEC 4.1 cells following C. parvum infection. Cells were infected with C. parvum (24 h), RNA was extracted and analyzed by RIP-qPCR using anti-Mettl3 antibody. Presence of CSpV1-dsRdRp and -dsCA, as well as several other RNAs of parasite-origin (Cdg7_FLc_1000, Cdg_FLc_0990, Cgd5_440, and Cgd_4270), was assessed using qPCR. (F) Association of CSp-V1-dsRNAs with Mettl3 in 2D intestinal epithelial monolayers following C. parvum infection. Monolayers were infected with C. parvum (24 h), RNA was extracted and analyzed by RIP-qPCR using anti-Mettl3 antibody. (G) Effects of CSpV1-dsRdRp + -dsCA transfection on IFN-γ-stimulated cell-intrinsic defense against C. parvum infection. IEC4.1 cells were transfected with CSpV1-dsRdRp + -dsCA (200 ng each, 24 h). After transfection, cells were infected with C. parvum and IFN-γ was added at 4 h post C. parvum infection. Cells were cultured for additional 20 h and RNA was collected, followed by qPCR for cp18s. Data A-G are presented as the mean ± standard deviation from three independent experiments and analyzed by Student’s t test; * p < 0.05, ** p < 0.01, ***p < 0.001, **** p < 0.0001.
To elucidate the molecular mechanisms by which C. parvum reduces m6A methylation of Irgm2/3, we investigated whether parasite-derived effectors contribute to this process. In our previous studies, we identified several RNAs with low protein-coding potential in infected host cells [40]. Notably, double-stranded RNAs (dsRNAs) from CSpV1, a member of the family Partitiviridae harbored by C. parvum and many other Cryptosporidium spp, were detected in infected host cells and may modulate host cell function [40]. We therefore examined the m6A methylation levels of Irgm2/3 in IEC4.1 cells following transfection of these parasite-derived RNAs. Transfection with CSpV1-dsRNAs (CSpV1-dsRdRp or CSpV1-dsCA), but not other selected parasite RNAs, resulted in a reduction of m6A methylation on Irgm2/3 transcripts (Fig 6D).
To determine how CSpV1-dsRNAs reduce m6A methylation of Irgm2/3 in IECs, we investigated whether these viral dsRNAs directly interact with Irgm2/3 mRNAs and thereby interfere with Mettl3/14-mediated m6A methylation. RIP assays detected CSpV1-dsRNAs in immunoprecipitates from infected IEC4.1 cells using anti-Mettl3/14 antibodies (Fig 6E). In contrast, such associations between CSpV1-dsRNAs and Irgm2/3 mRNAs or the Mettl3/14 complex were not observed for other selected parasite-derived RNAs (Fig 6E). Consistently, the interaction between Mettl3/14 and CSpV1-dsRNAs was also detected in 2D intestinal epithelial monolayers following Cryptosporidium infection (Fig 6F). Sequence alignment analysis further identified potential complimentary regions between CSpV1-dsRNAs and Irgm2/3 mRNAs (S3 Fig), supporting the possibility of direct RNA-RNA interactions.
We next investigated the effects of CSPv1-dsRNAs on IFN-γ-stimulated cell-intrinsic antimicrobial defense. Transfection of CSPv1-dsRNAs into IEC4.1 cells partially inhibited IFN-γ-induced m6A methylation of Irgm2/3 mRNAs (Fig 6G). Consistent with this effect, transfection of CSp-V1-dsRNAs also partially impaired IFN-γ-mediated cell-intrinsic defense against Cryptosporidium infection, as evidenced by a higher infection burden in IFN-γ-treated IEC4.1 cells transfected with CSp-V1-dsRNAs compared with IFN-γ-treated control cells (Fig 6G).
m6A RNA methylation and IFN-γ stimulated cell-intrinsic anti-Cryptosporidium defense in human IECs
We further investigated the role of m6A RNA methylation in IFN-γ stimulated cell-intrinsic defense using human intestinal epithelial HCT-8 cells [50]. The human orthologs of murine Irgm2 and Irgm3 are IRGM and IRGC, respectively [14]. We designed PCR primers to assess the expression levels of IRGC and IRGM in HCT-8 cells. Both genes exhibited only modest increases in expression following IFN-γ stimulation or C. parvum infection (Fig 7A). Nevertheless, we observed a significant increase in m6A methylation of IRGM in IFN-γ-treated cells (Fig 7B), with a similar increasing trend in infected cells (Fig 7B). Consistent with these findings, IFN-γ treatment significantly reduced the infection burden in HCT-8 cells (Fig 7C). Silencing IRGM and IRGC using siRNAs resulted in a higher infection burden compared with cells treated with a non-specific siRNA control (Fig 7C). Furthermore, knockdown of IRGM and IRGC largely abolished the IFN-γ-mediated suppression of infection in HCT-8 cells (Fig 7C).
(A) Expression of human IRGC and IRGM in HCT-8 cells in response to C. parvum infection and IFN-γ stimulation. Cells were exposed to C. parvum infection for 6 h or IFN-γ stimulation (10 ng/mL) for 4 h followed by qPCR for IRGC and IRGM. (B) IRGM RNA m6A methylation in HCT-8 cells in response to C. parvum infection and IFN-γ stimulation. Cells were exposed to C. parvum infection for 6 h or IFN-γ stimulation (10 ng/mL) for 4 h followed by RIP-qPCR for IRGM using anti-m6A. (C) Knockdown of IRGC and IRGM in HCT-8 cells blocked IFN-γ-stimulated cell-intrinsic defense against C. parvum infection. HCT-8 cells were transfected with si_Control, si_IRGM or si_IRGC (25 μM each) for 24 h. Cells were then exposed to C. parvum for 24 h with/or without IFN-γ treatment (5 ng/mL, added at 4 h post infection). C. parvum infection burden was measured through qPCR for cp18s. Data in (A - C) are presented as the mean ± standard deviation from three independent experiments and analyzed by Student’s t test; * p < 0.05, ** p < 0.01, ***p < 0.001, **** p < 0.0001.
Discussion
Although m6A has recently emerged as an important regulator of host immune responses, the underlying molecular mechanisms remain incompletely defined [51]. Cell-intrinsic defense is critical for controlling Cryptosporidium, as a defining feature of infection is the formation of an intracellular yet extracytoplasmic vacuole on the apical surface of infected IECs. Although long-term acquired resistance depends on α/β T cells [52], IFN-γ–mediated epithelial cell-intrinsic immunity is indispensable for early host defense [31]. IFN-γ signaling is well known to induce transcription of numerous ISGs in IECs, many of which encode effector molecules essential for epithelial intrinsic immunity [53]. Our findings indicate that, in addition to transcriptional activation, IFN-γ signaling modulates m6A methylation of selective Irgm transcripts, thereby contributing to IEC-intrinsic defense against Cryptosporidium. Disruption of global m6A RNA methylation through Mettl3 knockout diminishes IFN-γ–induced m6A modification of Irgm transcripts and results in increased Cryptosporidium burden in IECs. Thus, regulation of RNA m6A methylation following IFN-γ stimulation represents an additional layer of control in epithelial cell-intrinsic antiparasitic defense.
Interestingly, only a small subset of ISGs, such as Irgm2 and Irgm3, exhibits increased m6A methylation in IECs following IFN-γ stimulation, suggesting that IFN-γ signaling regulates RNA m6A methylation in a gene-specific manner. In most cell types, m6A marks are deposited by the METTL3/METTL14 complex, which is recruited to target RNAs in response to specific stimuli [54]. However, the mechanisms governing recruitment of the METTL3/METTL14 complex to distinct RNAs upon activation of different signaling pathways remain poorly understood. Effecter molecules may act as guides to direct METTL3/METTL14 to specific RNAs, thereby enabling gene-specific m6A modification. Our data support the possibility that specific lncRNAs serve as such guides for IFN-γ–induced m6A methylation of ISGs. LncRNAs are known to function as signals, decoys, guides, or scaffolds by interacting with DNA, RNA, and proteins, and several have been shown in other systems to integrate into the METTL3/METTL14 complex following extracellular stimulation [55–60]. Therefore, lncRNAs may mediate gene-specific recruitment of METTL3/METTL14, enabling selective m6A methylation of IFN-γ–responsive ISGs. Among the lncRNAs upregulated in IECs following IFN-γ stimulation, several are physically associated with the METTL3/METTL14 complex, with Nostrill being of particular interest. Knockdown of Nostrill disrupts the association of METTL3/METTL14 with Irgm2 and Irgm3 transcripts and reduces their IFN-γ-induced m6A methylation. Consequently, Nostrill knockdown attenuates IFN-γ-mediated cell-intrinsic anti-parasitic defense in IECs.
RNA m6A methylation influences multiple aspects of RNA metabolism, including mRNA degradation, splicing, stability, and translation efficiency [60]. Our data suggest that the impact of IFN-γ–induced m6A methylation on the stability of ISG transcripts is limited. Instead, m6A methylation is closely associated with Irgm2/3 protein expression in IECs following IFN-γ stimulation. Knockout of Mettl3 did not significantly alter Irgm2/3 RNA levels in IECs in response to IFN-γ stimulation; however, their protein abundance was markedly reduced compared with wild-type cells subject to the same stimulus. Whether this effect of m6A RNA methylation on Irgm2/3 protein expression is mediated through regulation of translation and/or protein degradation remains unclear. MeRIP-seq analysis revealed that some IFN-γ–induced m6A sites within ISG mRNAs are located near the translation initiation region in the 5′UTRs. The 5′UTR plays a central role in regulating translation initiation, largely through interactions with eukaryotic initiation factor 4E (eIF4E) [61, 62]. The positioning of these m6A marks is consistent with a role for m6A RNA methylation in modulating translational efficiency. Moreover, knockout of Irgm3 partially impaired IFN-γ–mediated defense against Cryptosporidium, resulting in a significantly higher parasite burden in IFN-γ–treated IEC4.1-Irgm3 ⁻ / ⁻ cells compared with treated wild-type cells. These findings highlight a critical role for the Irgm family members in IFN-γ–stimulated cell-intrinsic defense against Cryptosporidiuminfection.
Notably, we observed a similar role for m6A RNA methylation in regulating IFN-γ-mediated cell-intrinsic antiparasitic defense in human IECs. This finding provides new mechanistic insight into species-specific differences in IFN-γ–regulated gene expression, as highlighted in previous studies [63,64]. In particular, transcription of human IRGM and IRGC is not strongly induced by IFN-γ, in contrast to mice [65, 66]; nevertheless, IRGM and IRGC remain essential for IFN-γ–mediated control of intracellular pathogens in humans [67]. Our data support a model in which IFN-γ regulates IRGM/IRGC-mediated host defense in human IECs through an m6A-dependent post-transcriptional mechanism. IRGM and IRGC are required for epithelial cell-intrinsic defense against C. parvum infection in human HCT-8 cells, as knockdown of them impairs IFN-γ–mediated resistance to Cryptosporidium. Moreover, increased m6A methylation of IRGM and IRGC transcripts following IFN-γ stimulation supports the notion that IFN-γ modulates IRGM/IRGC-mediated host defense through m6A-dependent mechanisms in human IECs.
To counteract host defense, Cryptosporidium has evolved strategies to disrupt the IFN-γ signaling pathway [32, 34], enabling the parasite to withstand immune pressure during the early stages of infection. In our previous work [40], we demonstrated that CSpV1-dsRNAs are present in infected host cells and attenuate IFN-γ–mediated antiparasitic defense. In the present study, we found that Cryptosporidium infection alters m6A RNA methylation patterns in IECs, including a reduction in m6A methylation on key ISG transcripts such as Irgm2 and Irgm3. Transfection of IECs with CSpV1-dsRNAs recapitulated these effects: CSpV1-dsRNAs reduced m6A methylation of ISGs, including Irgm2 and Irgm3, and inhibited the IFN-γ–induced Irgm3 protein expression. Similarly, CSpV1-dsRNA transfection in IEC4.1 cells partially impaired IFN-γ–dependent m6A methylation of Irgm2/3 transcripts and reduced IFN-γ–driven cell-intrinsic defense, as indicated by higher parasite burdens compared with IFN-γ–treated control cells. These findings suggest a previously unrecognized immune evasion strategy: Cryptosporidium employs CSpV1-dsRNAs to suppress m6A methylation of specific IRG transcripts, thereby undermining IFN-γ–mediated epithelial cell-intrinsic defense. Nevertheless, further studies are needed to clarify the potential involvement of global cellular stress responses induced by infection or dsRNA transfection in regulating RNA m6A methylation and translation.
Overall, our findings highlight a new role for m6A RNA methylation in IFN-γ–induced anti–C. parvum defense, providing new mechanistic insight into the interplay among IFN-γ–mediated epithelial cell-intrinsic defense, m6A regulatory machinery, and host antiparasitic immunity. Future investigations should further define how lncRNAs regulate m6A RNA methylation on specific ISGs and how these modifications influence the protein expression of individual ISG transcripts, as well as more broadly elucidate the role of m6A in IFN-γ–mediated cell-intrinsic immunity.
Methods
Ethics statement
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health under the Assurance of Compliance Number A3348-01. All animal experiments were done in accordance with procedures (protocol number #23–074) approved by the Institutional Animal Care and Use Committee of Rush University Medical Center.
Sequencing and bioinformatics
Total RNAs were isolated by Trizol reagent and purified by the RNeasy Mini Kit (QIAGEN 74104). The BGI Americas Corporation (Cambridge, MA) carried out all transcriptome sequencing for RNA-seq, MeRIP-seq, and RIP-Seq with anti-Mettl3/14. For bioinformatics, raw reads were trimmed to remove Truseq adapters and bases from the 3’ end with quality scores less than 20 using cutadapt [68]; trimmed reads shorter than 40 bp were discarded. Trimmed reads were aligned to the Mus musculus (house mouse) genome assembly GRCm39 (mm39) from Genome Reference Consortium [GCA_000001635.9 GCF_000001635.27] using STAR [69]. The expression level of ENSEMBL genes was quantified using FeatureCounts [70]. The bioinformatic analysis of MelRIP-Seq data was performed as described in our previous study [42].
Differential expression statistics was computed using edgeR [71, 72], on raw expression counts obtained from quantification. Normalized expression level was computed as log2 CPM (counts per million), including a TMM normalization and batch effect correction. Comparisons were made between the untreated and IFN-γ-treated groups. In all cases p-values were adjusted for multiple testing using the false discovery rate (FDR) correction of Benjamini and Hochberg [73]. Heatmaps, m6A topology charts, gene ontology (GO) analysis, and enhanced volcano plots, were also generated within the R programming language. Predicted RNA-RNA structure analysis was generated using ViennaRNA within the R programming language as well [74, 75]. An absolute LogFC (Log Fold Change) value of greater than 1.5 and p value of less than 0.05 were used to generate the volcano plots.
C. parvum and cell lines
C. parvum oocysts of the Iowa strain were purchased from a commercial source (Bunch Grass Farm, Deary, ID). The IEC4.1 cell line was a kind gift from Dr. Pingchang Yang (McMaster University, Hamilton, Canada) [41]. Culture media (DMEM/F12 medium 1:1, Fisher Scientific) were supplied with 10% FBS (Ambion) and antibiotics (100 IU/ml of penicillin and 100 µg/ml of streptomycin). HCT-8 cells were acquired from a commercial source (American Type Culture Collection ATCC) and cultured in RPMI 1640 (ATCC), 10% FBS Horse (ATCC), 50 ug/mL Gentamycin (Gibco), with antibiotics (100 IU/ml of penicillin and 100 µg/ml of streptomycin). Stable IEC4.1 cells deficient in Mettl3 and Irgm3 were generated through transfection with the CRISPR/Cas9 KO and the HDR plasmids (Santa Cruz Biotechnology), respectively, validated by absence of the corresponding protein, and according to the manufacturer’s instructions and as previously described [76]. The primers used to screen the CRISPR/Cas9 KO cell lines are listed in S13 Table. All cultured cells were maintained at 37°C in a 5% CO2 incubator.
2D Intestinal epithelial cultures
2D monolayers were isolated and cultured as previously described [77]. Briefly, small intestines were opened longitudinally and washed with ice-cold Ca2+ and Mg2+ free PBS, then were cut into 1–2 mm fragments and washed with ice-cold Ca2+ and Mg2+ free PBS 3 times. The cut fragments were incubated in ice-cold 2 mM PBS/EDTA at 4°C for 30min with gentle rotation followed by vigorous shaking until the PBS solution was mostly opaque with dislodged crypt and villus particles. Large tissue fragments were removed through a 100-µm cell strainer (Becton-Dickinson Bioscience, Franklin Lakes, NJ). The pass through was centrifuged 150g for 5 min at 4°C and the pellet was collected as the intestinal epithelium.
Infection models
For experiments using IEC4.1 and HCT-8 cells or 2D monolayers, viable C. parvum oocysts were used. These oocysts were treated with 1% sodium hypochlorite and were added to their respective culture medium. The infection was established by combining oocysts and host cells in a 1:1 ratio. The cell cultures were then incubated at 37°C for 4 h to facilitate parasite attachment and invasion. After this, the cells were thoroughly washed with DMEM-F-12 medium three times to remove any free parasites. The cells were further cultured for varying time periods based on the experimental requirements. qPCR for Cryptosporidium 18s (cp18s) mRNA and immunofluorescence microscopy were used to assay C. parvum infection as previously reported [40]. Primer details are listed in S13 Table.
qPCR
For quantitative analysis of RNA expression, comparative real-time PCR was performed as previous reported [42, 77] using the SYBR Green PCR Master Mix (Applied Biosystems, Carlsbad, CA). The sequences for all the primers described above are listed in S13 Table.
siRNAs
Custom-designed RNA oligos against Nostrill, IRGM, IRGC, and a scrambled RNA were synthesized by Sigma. siRNAs were transfected into either IEC4.1 cells or HCT-8 cells with Lipofectamine RNAimax according to the manufacturer’s protocol (Invitrogen). Details, including the sequences of siRNAs, are described in the S13 Table.
Western blot
Cellular lysate was isolated using the standard approach [78]. Protein concentration of each cell lysate was determined and subsequently analyzed by Western blot. The antibodies anti-Irgm3 (Cell Signaling Technology), anti-Irgm2 (Thermo Scientific), anti-Mettl3 (Abcam), and anti-Gapdh (Santa Cruz Biotechnology) were used for blotting. Precision Plus Protein Dual Color Standards (Bio-Rad) was used as a marker in blotting.
RNA stability
RNA stability assay was performed by qPCR as previously reported [79]. Briefly, cells were pre-treated with IFN-γ (10 ng/mL, 4 h, R&D System) and transcription was then blocked using actinomycin D (10 µg/ml, Sigma); RNAs were isolated at various time points after actinomycin D treatment. Real-time PCR was then performed using 500 ng of template cDNA for each mRNA gene of interest. Each sample was run in triplicate. The relative abundance of each mRNA was calculated using the ΔΔCt method and normalized to Gapdh. The relative amount of mRNA at 0 h following actinomycin D treatment was arbitrarily set to 1. Curve fittings of the resultant data were performed using Microsoft Excel and the half-lives of the RNAs calculated.
RIP and MeRIP
Formaldehyde crosslinking RIP was performed as described [80]. Briefly, lysates were precleaned with PBS-washed Magna ChIP protein A + G magnetic beads (Millipore). The precleaned lysates were then diluted with whole cell extraction buffer, mixed with specific antibody-coated beads, and then incubated with rotation at 4°C for 4 h. Samples were then washed four times with whole cell extraction buffer containing protease and RNase inhibitors. The collected immunoprecipitated RNP complexes and input were digested in an RNA PK buffer pH 7.0 (100 mM NaCl, 10 mM Tris-HCl pH 7.0, 1 mM EDTA 0.5% SDS) with protease K and incubated at 50°C for 45 min with end-to-end shaking at 400 rpm. Formaldehyde cross-links were reversed by incubation at 65°C with rotation for 4h. RNA was extracted from these samples using Trizol according to the manufacturer’s protocol (Invitrogen) and treated with a DNA-free DNase Treatment and Removal I Kit according to the manufacturer’s protocol (Ambion). The presence of RNA was measured via qPCR. Gene-specific PCR primer pairs are listed in S13 Table. The following antibodies were used for RIP analysis: Normal IgG rabbit (Cell Signaling Technology), anti-Mettl3 (Cell Signaling Technology) and anti-Mettl14 (Cell Signaling Technology)
For MeRIP, total RNAs were isolated by Trizol according to the manufacturer’s protocol (Invitrogen) and m6A RIP was performed with the following commercially available kits: Dynabeads mRNA Purification Kit (Invitrogen), NEBNext Magnestium RNA Fragmentation Module Kit (New England Biolabs), Monarch Spin RNA Cleanup Kit (New England Biolabs), and EpiMark N6-Methyladenosine Enrichment Kit (New England Biolabs) that contains a rabbit monoclonal antibody specific for m6A.
Synthesis of CSpV1-dsRNAs and transfection
Synthesis of CSpV1-dsRNAs and transfection was performed as previously described [40]. For specific details of the specific primers and RNA sequences, see S13 Table.
Statistical analysis
Statistical analysis was performed using edgeR or GraphPad Prism 5 (GraphPad Software). Data are given as mean ± SEM from at least three independent experiments or three biological replicates as indicated. The two-tailed unpaired Student’s t test was used for comparisons between two groups. When more than two groups were being compared, one-way ANOVA followed by Tukey’s HSD test was used. The statistical analysis of RNA-Seq, RIP-Seq, and MeRIP-Seq data is described in the Sequencing and Bioinformatics section. p values < 0.05 were considered statistically significant.
Supporting information
S1 Table. Full list of mRNAs with increased or decreased m6A methylation levels in IEC4.1 cells following IFN-γ stimulation as revealed by MeRIP-Seq using anti-m6A antibody.
https://doi.org/10.1371/journal.ppat.1014442.s001
(XLSX)
S2 Table. Genomic distribution of m6A sites in IEC4.1 cells following IFN-γ stimulation.
https://doi.org/10.1371/journal.ppat.1014442.s002
(XLSX)
S3 Table. The GO ontology of mRNAs with significant m6A alteration in IEC4.1 cells following IFN-γ stimulation.
https://doi.org/10.1371/journal.ppat.1014442.s003
(XLSX)
S4 Table. mRNA gene expression profiles revealed by RNA-Seq in IEC4.1 cells with and without IFN-γ treatment.
https://doi.org/10.1371/journal.ppat.1014442.s004
(XLSX)
S5 Table. The GO ontology of mRNAs with significant alterations in their expression in IEC4.1 cells following IFN-γ stimulation.
https://doi.org/10.1371/journal.ppat.1014442.s005
(XLSX)
S6 Table. Full list of mRNAs with altered m6A peaks and RNA expression levels in IEC4.1 cells following IFN-γ stimulation.
https://doi.org/10.1371/journal.ppat.1014442.s006
(XLSX)
S7 Table. The GO ontology of overlapping mRNAs with altered RNA expression and those with changes in m6A RNA methylation in IFN-γ-treated IEC4.1 cells.
https://doi.org/10.1371/journal.ppat.1014442.s007
(XLSX)
S8 Table. Full list of mRNA profiles revealed by RIP-Seq using anti-Mettl3 and anti-Mettl14 in IEC4.1 cells following IFN-γ stimulation.
https://doi.org/10.1371/journal.ppat.1014442.s008
(XLSX)
S9 Table. Full list of mRNAs associated with Mettl3 protein (as revealed by RIP-Seq with anti-Mettl3) and with an altered m6A methylation level (as revealed by MeRIP-Seq) in IEC4.1 cells following IFN-γ stimulation.
https://doi.org/10.1371/journal.ppat.1014442.s009
(XLSX)
S10 Table. The lncRNA profiles revealed by RIP-Seq using anti-Mettl3 and anti-Mettl14 in IEC4.1 cells following IFN-γ stimulation.
https://doi.org/10.1371/journal.ppat.1014442.s010
(XLSX)
S11 Table. The lncRNA gene expression profiles revealed by RNA-Seq in IEC4.1 cells with and without IFN-γ treatment.
https://doi.org/10.1371/journal.ppat.1014442.s011
(XLSX)
S12 Table. Full list of lncRNAs associated with the Mettl3/14 complex (as revealed by RIP-Seq with anti-Mettl3 or anti-Mettl13) and with an altered expression level (as revealed by RNA-Seq) in IEC4.1 cells following IFN-γ stimulation.
https://doi.org/10.1371/journal.ppat.1014442.s012
(XLSX)
S13 Table. List of PCR primers (used for qPCR, RIP-qPCR, and cloning CSpV1-dsRNAs) and siRNA sequences.
https://doi.org/10.1371/journal.ppat.1014442.s013
(XLSX)
S1 Fig. Overlay of mRNAs from RIP-Seq with anti-Mettl14 and MeRIP-Seq in IEC4.1 cells following IFN-γ stimulation.
Venn diagram depicting mRNAs associated with Mettl14 and with an altered m6A methylation level in IEC4.1 cells following IFN-γ stimulation. Cells were treated with IFN-γ (10 ng/mL, 4 h). Total RNA was collected and processed for MeRIP-Seq using anti-m6A antibody. mRNA was used for RIP-Seq using antibodies against Mettl3 an Mettl14. Overlay of mRNAs represents these that were enriched with anti-Mettl14 and an increased m6A level, or with a decreased association with Mettl3 and a decreased m6A level. Data of MeRIP-Seq were derived from sequencing of three biological replicates for each group (untreated and IFN-γ treated) but data of RIP-Seq with anti-Mettl14 was from a single biological replicate for each group.
https://doi.org/10.1371/journal.ppat.1014442.s014
(TIF)
S2 Fig. Generation of stable IEC4.1 cells with deficient in Mettl3 or Irgm3 using the CRISPR/Cas9 approach.
Stable IEC4.1 cells with deficient in Mettl3 or Irgm3 were generated through transfection of cells with the CRISPR/Cas9 KO and the HDR plasmids for knockout of Mettl3 and Irgm3, respectively. Plasmids were obtained from Santa Cruz Biotechnology. Deletion was verified by using PCR primers covering the designed regions of the genes and by Western blotting.
https://doi.org/10.1371/journal.ppat.1014442.s015
(TIF)
S3 Fig. Predicted RNA - RNA Base Pairing Alignment between CSpV1-dsRNAs and Irgm2/3 mRNAs for potential direct interactions.
Predicted RNA-RNA base pairing alignments between CSpV1-dsRNAs and Irgm2/3 using RNAduplex, a ViennaRNA package in R to identify favorable intermolecular interactions. Lines indicate complementary base pairing with the local minimum free energy (MFE) shown for the strongest interaction site between the two RNAs.
https://doi.org/10.1371/journal.ppat.1014442.s016
(TIF)
Acknowledgments
We thank Jeffrey Schneider, Ph.D., Josefina Ramos-Franco, M.D., Ph.D., Edward Barker, Ph.D., Pradeep Dudeja, Ph.D., David L. Williams, Ph.D. for their comments and suggestions for experimental design and bioinformatic analysis. The IEC4.1 cells were from Dr. Pingchang Yang (McMaster University, Hamilton, Canada).
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