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. 2025 Oct 7;30(6):e70054. doi: 10.1111/gtc.70054

m6A RNA Modification Controls HTLV‐1 Tax and Host Gene Expression

Rei Gibu 1, Kodai Gibu 2,3, Kako Suzuki 1, Yuetsu Tanaka 4, Kaoru Uchimaru 5, Makoto Yamagishi 1,✉
PMCID: PMC12504147  PMID: 41057283

ABSTRACT

Human T‐cell Leukemia Virus Type 1 (HTLV‐1) is a pathogenic human retrovirus that is responsible for intractable diseases such as adult T‐cell leukemia–lymphoma (ATL), a malignancy with a poor patient prognosis. Although recent studies have delineated several genomic, epigenomic, and transcriptomic abnormalities associated with HTLV‐1, to date the importance of epitranscriptomic modifications, particularly N 6 ‐methyladenosine (m6A), remains unclear. Here, we showed that the HTLV‐1 RNA genome undergoes m6A modification, thereby suggesting that these modifications act as bidirectional regulators of both viral and host processes. Moreover, targeted depletion of m6A modification within the viral transactivator HTLV‐1 Tax resulted in markedly destabilized Tax mRNA, attenuated Tax protein abundance, and suppression of downstream expression of host genes including IL2RA and TXN. Overall, these findings suggest that m6A methylation is an essential determinant of the HTLV‐1 life cycle, and understanding it may offer mechanistic insight into viral latency and present novel avenues for therapeutic intervention and prophylaxis.

Our study shows that m6A regulation is critical for controlling HTLV‐1, shaping both viral transcripts and host gene programs, thereby illuminating latency mechanisms and suggesting tractable targets for therapy and prevention.

graphic file with name GTC-30-0-g001.jpg

1. Introduction

Human T‐cell Leukemia Virus Type‐1 (HTLV‐1) is a retrovirus that infects CD4‐positive T cells. HTLV‐1 enters a latent phase post‐infection, and approximately 5% of infected individuals eventually develop conditions such as adult T‐cell leukemia–lymphoma (ATL), HTLV‐1‐associated myelopathy (HAM), or HTLV‐1‐associated uveitis (HU). Relative to other forms of leukemia, ATL is a particularly severe form of hematological malignancy. Moreover, numerous studies have reported specific genomic, epigenomic, and transcriptomic aberrations that are associated with HTLV‐1 (Kataoka et al. 2015; Kogure et al. 2022; Yamagishi et al. 2021; Mizuike et al. 2025; Kobayashi et al. 2014), but to date a detailed understanding of its pathogenic mechanisms remains unclear.

HTLV‐1 expresses two functional genes, tax and hbz, both of which have been extensively studied. The Tax protein acts as a transcriptional activator of other HTLV‐1 genes (Seiki et al. 1985) and is implicated in tumorigenesis and epigenetic alterations (Hasegawa et al. 2006; Ohsugi et al. 2007; Fujikawa et al. 2016). Tax is highly expressed during the early stages of infection but later is suppressed via epigenetic silencing or the emergence of clones containing deletions in the Tax coding region (Taniguchi et al. 2005; Tamiya et al. 1996), thereby leading to clonal proliferation. In contrast, HBZ is expressed during all infection phases and can induce the expression of FoxP3, a master transcription factor for regulatory T cells (Tregs) (Zhao et al. 2011). Furthermore, hbz‐transgenic mice may develop cancer (Satou et al. 2011).

The regulation of major viral factors capable of influencing both viral replication and host‐cell physiology has been characterized mainly on the transcriptional level and has included novel studies of promoter (e.g., long‐terminal repeats, LTRs) (Fujisawa et al. 1985) and enhancer regions (Matsuo et al. 2022). In contrast, much less is known regarding post‐transcriptional control of viral transcripts, which represents a critical unresolved question for elucidating mechanisms governing viral gene expression.

N 6‐methyladenosine (m6A) modification of RNA is characterized by the consensus motif DRACH (D = A/G/U, R = A/G, H = A/C/U). Moreover, it is a reversible process mediated by m6A writers, such as the METTL3 complex, and erasers such as FTO or ALKBH5. In general, m6A reader proteins determine the fate of m6A‐modified RNA by influencing its decay or translation, two key forms of post‐transcriptional regulation (Dominissini et al. 2012; Jia et al. 2011; Zheng et al. 2013; Meyer and Jaffrey 2017; Duan et al. 2024).

Post‐transcriptional regulation via m6A modification is a crucial factor in both cancer and in viral life cycles (McFadden and Horner 2021). For example, the human retrovirus HIV‐1 undergoes m6A modification, which affects nuclear export, virion formation, and latent infection (Lichinchi et al. 2016; Tsai et al. 2021; Mishra et al. 2024). Moreover, in various cancers, abnormal expression of writers, erasers, and readers for m6A modifications has been linked to a variety of features, including tumor invasion, metastasis, and tumor growth (He et al. 2019). Recent studies have reported that HTLV‐1 RNA is modified by m6A, and that some m6A reader proteins are involved in HTLV‐1 RNA regulation (King et al. 2025). However, to date, it remains unclear how m6A‐modified HTLV‐1 RNA affects the control of host genes.

In this study, we investigated m6A modification of the HTLV‐1 genome in HTLV‐1‐infected cell lines and conducted further exploration of its functional implications.

2. Results

2.1. HTLV‐1 RNA Contains m6A Modification

First, we predicted potential m6A modification sites in the HTLV‐1 genome (NCBI Reference Sequence: NC_001436.1) using the sequence‐based prediction tool SRAMP (Zhou et al. 2016). In total, 44 m6A modification sites were predicted in HTLV‐1 genomic RNA, including in structural genes (e.g., gag, pol, and env), regulatory genes (e.g., tax and rex), and accessory genes (e.g., p12, p13, p30, and hbz). Taken together, these findings suggest the potential for m6A modification of both structural and functional gene regions (Figure 1A).

FIGURE 1.

FIGURE 1

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Prediction and validation of m6A modification sites in HTLV‐1 RNA. (A) The results of predicting the m6A modification sites in the HTLV‐1 genome. (B) m6A modifications of HTLV‐1 RNA using public MeRIP‐seq data for the HTLV‐1 infected cell line MT‐4. Read‐depth tracks are displayed above; the HTLV‐1 genomic organization is depicted below. (C) The results of RIP‐qPCR using the HTLV‐1‐infected cell line C91/PL. As a negative control, m6A levels were inhibited with the m6A writer inhibitor STM2457. n = 3, mean ± SD.

Next, we investigated m6A modifications of HTLV‐1 RNA using public MeRIP‐seq data for the HTLV‐1 infected cell line MT‐4 (Lichinchi et al. 2016). We extracted HTLV‐1 genome data and reanalyzed m6A modification sites. In doing so, we identified a peak between bases 7109–7439 of the 8507 bp HTLV‐1 genome. This region encodes Tax/Rex, and the MeRIP‐seq data indicated that this coding region contains m6A modifications (Figure 1B). Moreover, approximately 1–3 m6A modification sites have been detected per host gene in previous studies (Dominissini et al. 2012), a finding that is consistent with our secondary data analysis of HTLV‐1 genes.

Based on these results, we conducted RNA immunoprecipitation followed by quantitative PCR (RIP‐qPCR) analyses on the HTLV‐1 infected cell line C91/PL. For RIP‐qPCR, cells were first pre‐treated with the METTL3/14 inhibitor STM2457 72 h before RIP; this sample was used as a negative control. Next, m6A‐modified transcripts were isolated using a mRNA‐specific anti‐m6A antibody, and the resulting precipitated HTLV‐1 tax and hbz mRNAs were quantified. Relative to control IgG samples, DMSO‐treated samples immunoprecipitated with the anti‐m6A antibody showed significant enrichment of tax mRNA as well as NOTCH1 mRNA, consistent with m6A modification of both viral and host transcripts (Figure 1C). However, this enrichment was reduced by STM2457 pretreatment, supporting the specificity of the assay. Taken together, our in silico predictions and the experimental evidence reported here support the hypothesis that HTLV‐1 tax and hbz mRNAs contain m6A modifications.

2.2. Stability of HTLV‐1 Tax Is Downregulated by m6A Depletion

Intracellular m6A‐modified mRNAs are first recognized by the reader protein YTHDF2, then recruited to the CCR4‐NOT deadenylase complex, thereby promoting RNA decay (Du et al. 2016; Dominissini et al. 2012). To examine the functional impact of m6A, C91/PL cells were treated with increasing concentrations of STM2457 to deplete m6A, then we quantified the abundance of viral mRNA present in samples. Unexpectedly, Tax mRNA levels decreased in a dose‐dependent manner, while hbz mRNA levels remained unchanged. In contrast, although we observed hbz mRNA was also m6A‐modified, no differential expression was detected (Figure 2A).

FIGURE 2.

FIGURE 2

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Behavior of HTLV‐1 genes upon treatment with METTL3/14 inhibitor STM2457. (A) C91/PL were treated with the METTL3/14 inhibitor STM2457 for 72 h. Expression levels of HTLV‐1 RNA were evaluated by qRT‐PCR. n = 3, mean ± SD, *p < 0.05. (B) HTLV‐1 mRNA stability was analyzed by RNA decay assay using actinomycin D (n = 3). Time‐lapse kinetics of HTLV‐1 RNA were shown as linear modeling with a Gamma distribution. (C) C91/PL cells were treated with STM2457 for 72 h. The protein expression of Tax was detected by Western blotting. HSP90 served as a loading control. Quantified expression levels are shown below the panel. (D) Western blot analysis of METTL3 and Tax in METTL3 knockdown C91/PL cells. Quantified expression levels are shown below the panel. (E) Tax mRNA levels in METTL3 knockdown cells were measured by qRT‐PCR. (F) C91/PL cells were treated with STM2457, followed by quantification of viral antigen release (p19 gag) by ELISA. n = 5, mean ± SD.

To evaluate how m6A depletion affects viral RNA stability, we exposed C91/PL cells to STM2457, then halted transcription using actinomycin D. Under these conditions, the stability of Tax mRNA was strongly reduced following STM2457 treatment, whereas the stability of hbz mRNA increased (Figure 2B). We also performed immunoblotting in parallel to show that Tax protein levels declined following exposure to STM2457 (Figure 2C).

To corroborate our inhibitor‐based findings, we performed METTL3 knockdown. We generated two independent shRNA‐expressing cell lines and achieved robust METTL3 depletion. In METTL3‐depleted cells, Tax expression was clearly reduced (Figure 2D,E). Concordant changes at the RNA and protein levels support a model in which HTLV‐1 Tax expression is regulated post‐transcriptionally in an m6A‐dependent manner.

2.3. m6A Depletion Impairs Progeny Viral Production

Given that Tax is indispensable for transactivating the HTLV‐1 5′‐LTR promoter, alterations in the abundance of Tax may affect viral production. Next, we attempted to determine whether m6A depletion influences progeny virus production (Cann et al. 1985; Fujisawa et al. 1986). To do so, C91/PL cells were treated with STM2457, after which viral antigen release (p19 gag) was quantified by ELISA. We found that exposure to STM2457 significantly reduced extracellular p19 gag levels (Figure 2F), indicating suppressed viral gene expression. Collectively, these data suggest that the m6A modification plays a critical role in the HTLV‐1 life cycle by sustaining Tax‐mediated transcriptional activation.

2.4. m6A Depletion Alters the Expression of Genes Important for HTLV‐1 Infection

HTLV‐1 Tax interacts with many host factors and significantly affects the regulation of host gene expression. It does so mainly by activating the NF‐κB signaling pathway and reprogramming the epigenetic architecture of the genome (Murakami et al. 1995; Mizuike et al. 2025). Therefore, we investigated changes in host gene expression after a 3‐day STM2457 treatment, using RNA sequencing (RNA‐seq).

Hierarchical clustering of results from the global transcriptome reveals that STM2457 treatment markedly reshapes the overall gene expression landscape of HTLV‐1 infected cell lines (Figure 3A). Next, we conducted a Differential Expression Gene (DEG) analysis and identified 2246 differentially expressed genes. These genes are listed in Table S1 and Figure 3B. We also performed Gene Ontology (GO) analysis, which revealed that some upregulated genes were associated with transcriptional regulation and cell adhesion (Figure 3C). Downregulated genes are associated with nervous system development and protein folding.

FIGURE 3.

FIGURE 3

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Comprehensive analysis of gene expression upon treatment with METTL3/14 inhibitor STM2457. (A) Heatmap of gene expression pattern after exposure to STM2457. Each column represents the control group treated with DMSO and the test group treated with STM (STM2457). (B) Volcano plot showing comparison between control and inhibitor‐treated groups. Genes with increased expression (Log2FC > 0.5, p adj < 0.05) are shown in red, while genes with decreased expression (Log2FC < −0.5, p adj < 0.05) are shown in blue. (C) Results of Gene Ontology and KEGG PATHWAY analyses. Differentially expressed “upregulated genes” (Log2FC ≥ 0.5, p adj < 0.05) and “downregulated genes” (Log2FC ≤ −0.5, p adj < 0.05) are analyzed, respectively. KEGG PATHWAY analysis was performed using both upregulated and downregulated genes. (D) C91/PL and Molt4 cells were treated with STM2457; expression levels of Tax target genes were quantified by qRT‐PCR (n = 3; mean ± SD, *p < 0.05). N.D., not detected. (E) Relative expression levels of viral genes and Tax targets in METTL3‐knockdown C91/PL cells, as measured by qRT‐PCR, are shown in a heatmap. (F) C91/PL cells were cultured for 3 days with the indicated concentrations of STM2457. Cell viability was assessed by WST‐8 assay (n = 3; mean ± SD, *p < 0.05 vs. DMSO).

Next we input all DEGs identified here into a KEGG pathway analysis. The most significantly altered pathway was Human T‐cell Leukemia Virus type 1 infection (Figure 3C and Figure S1). Moreover, IL2RA (CD25), a tumor marker of ATL (Kamihira et al. 1994), TXN (Thioredoxin), which binds Tax at the gene promoter region (Masutani et al. 1996), and VCAM1 (Vascular Cell Adhesion Molecule 1), a key driver of syncytium formation (Hildreth et al. 1997), all of which exhibited significant differential expression. The STM2457‐dependent downregulation of IL2RA and TXN was specifically detected in C91/PL, but not in HTLV‐1‐negative Molt4 cells (Figure 3D). Moreover, METTL3 knockdown confirmed the consistent downregulation of Tax target genes (Figure 3E).

Given that STM2457 suppresses both Tax expression and viral production, we examined its impact on cell growth and observed a dose‐dependent reduction (Figure 3F). Taken together, these results further show that m6A deficiency alters the expression of host genes critical to HTLV‐1 infection.

2.5. CD25 Expression Is Downregulated by STM2457

IL2RA (CD25) is one of the cell surface markers of both HTLV‐1 infected cells and ATL cells, and its expression is dominantly induced by Tax (Ballard et al. 1988). Moreover, Tax is a potent activator of the NF‐κB pathway. Archival ChIP‐seq data (Mizuike et al. 2025) showed binding of Tax and NF‐κB components at the promoter region of IL2RA in the Tax‐expressing C91PL cells (Figure 4A). Moreover, exposure of the C91/PL to STM2457, followed by subsequent qRT‐PCR and flow cytometry experiments, showed that lowering Tax expression also reduced IL2RA transcript abundance and CD25 surface density (Figure 4B,C). Consistent with the results using the inhibitor, METTL3 knockdown also reduced IL2RA expression (Figure 3E). Since IL2RA mRNA stability remained unchanged (Figure 4D), we interpret this finding as meaning that such downregulation is attributable to diminished Tax activity rather than as a direct effect of m6A depletion.

FIGURE 4.

FIGURE 4

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Gene expression changes of HTLV‐1 Tax downstream gene IL2RA (CD25) upon treatment with METTL3/14 inhibitor STM2457. (A) IGV tracks of ChIP‐seq signal for input, Tax, RelA, and RelB at the IL2RA locus in C91/PL cells. (B) IL2RA (CD25) RNA expression level in HTLV‐1‐infected C91/PL cells treated with the METTL3/14 inhibitor STM2457 assessed by qRT‐PCR. n = 3, mean ± SD, *p < 0.05. (C) Cell surface expression of CD25 level was analyzed by flow cytometry (n = 5, mean ± SD). The Y‐axis represents the Mean Fluorescence Intensity (MFI). (D) IL2RA mRNA stability was analyzed by RNA decay assay using actinomycin D (n = 3).

3. Discussion

The impact of m6A modification on HTLV‐1 RNA and its effects on the host have become clearer in the recent past. Furthermore, this study suggests that the HTLV‐1 genome does contain m6A modification, thereby confirming the findings of another report (Figure 1). Moreover, we have experimentally demonstrated that tax mRNA is modified by m6A (Figure 1). This finding is consistent with recent reports investigating the presence of m6A modifications and RNA binding proteins in HTLV‐1 RNA (King et al. 2025). In general, Tax functions as a transcription factor for HTLV‐1 genes. It binds to the 5′ LTR to initiate transcription of structural genes such as gag‐pro‐pol and env, as well as functional genes, including tax itself and p30 (Berneman et al. 1992). Overall, our data suggest that m6A modification may act to regulate HTLV‐1 gene expression, given its potential impact on the expression of structural genes.

Furthermore, m6A modification appears to influence host genes, as evidenced by the decreased IL2RA (CD25) expression observed along with lower Tax expression (Inoue et al. 1986). Moreover, the results of our RNA decay assays using a METTL3/14 inhibitor found no changes in IL2RA mRNA stability. This suggests that observed decreases in IL2RA expression may be due to reduced Tax expression rather than the direct effect of the inhibitor. Because m6A participates in the global regulation of host as well as viral transcripts, inhibition of the METTL3/14 writer complex is expected to exert broader, transcriptome‐wide effects (Figure 3). Taken together, these findings indicate that m6A modifications contribute to the stability of tax and affect gene networks regulated by Tax.

In this study, we observed important differences in RNA stability between tax and hbz, both of which undergo m6A modification. As frequently reported in tRNA studies, RNA modifications can alter RNA secondary structures (Lorenz et al. 2017). For example, research on HIV‐1, a retrovirus that is similar to HTLV‐1, has shown that introducing mutations at m6A modification sites in RRE RNA, which prevent m6A modification, can alter its binding affinity to Rev, reduce its nuclear export efficiency, and decrease HIV‐1 replication levels (Lichinchi et al. 2016). Lichinchi et al. suggested that m6A modification might directly interact with Rev or otherwise change the secondary structure of RRE RNA, thereby affecting Rev binding. When taking these reports together, it is plausible that differences in m6A‐binding proteins, which depend on the presence and location of m6A modifications, can result in compromised RNA stability. According to Protein Atlas data (Uhlén et al. 2015), the gene expression levels of m6A reader proteins such as YTHDC1, YTHDC2, HNRNPA2B1, HNRNPC, and IGF2BP3 differ among activated CD4 T cells. However, although observed IGF2BP3 RNA levels were quite low, they were almost absent in Naïve CD4+ T cells but only slightly expressed in HTLV‐1‐infected HuT102 cells. Finally, since IGF2BP family proteins have been reported to contribute to the stabilization of m6A‐modified mRNA within the nucleus (Huang et al. 2018), IGF2BP3 might be involved in the stabilization mechanism of tax mRNA (Table S2). Since this study primarily relies on the METTL3/14 inhibition, the roles of m6A readers, including YTHDF1 and YTHDC1, may also be involved in regulating HTLV‐1.

In the future, identifying m6A modification sites within the HTLV‐1 genome, predicting secondary structures, and identifying major binding m6A readers will all be crucial for elucidating the comprehensive post‐transcriptional regulatory mechanisms mediated by m6A modifications in HTLV‐1. Therefore, understanding the gene expression mechanisms of HTLV‐1 genes may contribute insight into the mechanisms of HTLV‐1 latent infection and the development of infection prevention modalities.

4. Experimental Procedures

4.1. Cell Culture

The HTLV‐1‐infected cell line C91/PL and T cell line Molt4 were cultured in RPMI1640 medium (Thermo Fisher Scientific Inc., MA, USA). Lenti‐X 293T cells (Clontech Laboratories, CA, USA) were cultured in DMEM (Nissui Pharmaceutical Co. Ltd., Tokyo, Japan). Both media were supplemented with 10% FBS (Thermo Fisher Scientific Inc., MA, USA) and 1% Penicillin/Streptomycin (Thermo Fisher Scientific Inc., MA, USA). All cells were maintained in 5% CO2 at 37°C.

4.2. Establishment of METTL3 Knockdown Cell Line

To produce shMETTL3‐expressing lentivirus, 4 μg shMETTL3 lentiviral vector, 2.5 μg pCAG‐HIV‐gagpol, and 2.5 μg pCMV‐VSV‐G‐RSV‐rev were mixed in 1 mL Opti‐MEM (Thermo Fisher Scientific, Waltham, MA, USA) and 18 μL polyethylenimine (PEI) was added. After incubation for 20 min at room temperature, the mixture was applied to pre‐cultured HEK293FT cells and incubated at 37°C with 5% CO₂ for 4 h. The medium was then replaced with fresh DMEM containing 10% FBS, and cells were cultured for an additional 48 h. Supernatants were collected, passed through a 0.45‐μm filter, and concentrated by centrifugation at 12,000 × g for 2 h at 4°C. C91/PL cells were infected by spinoculation at 2000 × g for 2 h at 35°C. Infected cells were selected with blasticidin S (Thermo Fisher Scientific) to establish METTL3‐knockdown lines. The shRNA‐expressing lentiviral vectors targeted the following sequences: control (shCtrl), 5′‐CCTAAGGTTAAGTCGCCCTCG‐3′, shMETTL3#1, 5′‐GCTGCACTTCAGACGAATTAT‐3′, and shMETTL3#2, 5′‐GCAAGTATGTTCACTATGAAA‐3′. pLV‐EGFP:T2A:Bsd‐U6 were obtained from VectorBuilder (Chicago, IL, USA).

4.3. STM2457 Treatment

The METTL3/14 inhibitor STM2457 was purchased from Selleck Chemicals (TX, USA). Cells were collected 72 h after the addition of the inhibitor and subjected to RT‐qPCR (Final Conc. 0, 10, 25, 50 μM), WB (Final Conc. 0, 1, 10, 50 μM), RIP (Final Conc. 25 μM), RNA decay assay (Final Conc. 25 μM), FCS (Final Conc. 25, 50 μM), and RNA‐seq (Final Conc. 50 μM).

4.4. RT‐qPCR

Reverse transcription was performed using 4 μL of ReverTra Ace qPCR RT Master Mix (TOYOBO CO. LTD., Osaka, Japan) with 1 μg of RNA in a thermal cycler. For each well, 5 μL of THUNDERBIRD Next SYBR qPCR Mix (TOYOBO CO. LTD., Osaka, Japan), 0.25 μL of forward and reverse primers, 2.5 μL of dH2O, and 2 μL of the reverse transcription product were mixed. Gene expression levels were measured using a real‐time PCR system (Thermal cycler Dice, Takara Bio Inc., Shiga, Japan; CFX Duet Real‐Time PCR System, Bio‐Rad Laboratories Inc., CA, USA).

4.5. m6A RNA Immunoprecipitation (RIP)

Pre‐cultured cells were collected and extracted RNA using TRIzol reagent (Thermo Fisher Scientific Inc., MA, USA). Dynabeads Protein G (Thermo Fisher Scientific Inc., MA, USA) (50 μL) were washed with the RIP Buffer [10 mM Tris–HCl (pH 7.2), 150 mM NaCl, 0.1% NP‐40], then incubated with 2 μg of antibody control IgG (#2729), which was purchased from Cell Signaling Technology Inc. (MA, USA); Anti‐m6A antibody (#202003) was purchased from Synaptic Systems (Göttingen, Germany) for 10 min with gentle mixing to bind the Dynabeads and antibody. The cell lysate supernatant was added to the antibody‐bound magnetic beads and incubated with gentle mixing (4°C, Overnight) for immunoprecipitation. The beads were then washed four times with RIP Buffer, and the RNA bound to the magnetic beads was extracted and purified. RT‐qPCR was then performed to examine the presence of m6A modifications.

4.6. RNA Decay Assay

C91/PL cells exposed to the METTL3/14 inhibitor (STM2457) for 72 h were collected and seeded into a 12‐well plate at a density of 1 × 105 cells/well. Actinomycin D (Nacalai Tesque, Kyoto, Japan) was added to achieve a final concentration of 5 μg/mL. Cells were then collected at 0 h (without Actinomycin D) and at 7, 24, and 48 h after the addition of Actinomycin D. RNA was extracted and subjected to RT‐qPCR.

4.7. Western Blotting

The cultured cells were washed with PBS and suspended in RIPA Buffer [10 mM Tris–HCl (pH 7.4), 1% NP‐40, 0.1% Sodium Deoxycholate, 0.1% Sodium Dodecyl Sulfate (SDS), 150 mM NaCl, 1 mM EDTA] supplemented with a protease inhibitor cocktail (Nacalai Tesque, Kyoto, Japan). The suspension was incubated on ice for 20 min, then centrifuged at 14,000 rpm for 20 min at 4°C, and the supernatant was collected. Protein concentration was measured using a Protein Assay (Bio‐Rad Laboratories Inc., CA, USA), and the protein supernatant was added to Sample Buffer [10% Glycerol, 3% SDS, 65 mM Tris–HCl (pH 6.8), 0.01% BPB (MeOH), 15% 2‐Mercaptoethanol] to achieve the desired concentration. After heating at 100°C for 5 min, the samples were spun down and electrophoresed on 12.5% acrylamide gels at 150 V for 1.5 h. The proteins were then transferred to a 0.2 μm PVDF membrane at 45 V for 2 h. The membrane was blocked with 5% skim milk/PBST [20 mM Tris–HCl (pH 7.5), 150 mM NaCl, 0.1% (w/v) Tween20] for 30 min at room temperature, and incubated with primary antibodies (Tax Ab clone: Lt‐4; HSP90 Ab #4874, Cell Signaling Technology Inc., MA, USA) overnight at 4°C. After washing three times with PBST, secondary antibodies (ECL Peroxidase labeled anti‐rabbit (#NA934VS); ECL Anti‐Mouse IgG, Horseradish Peroxidase linked whole antibody (#NA931V), Cytiva, Tokyo, Japan) were incubated for 1 h at room temperature. Following three washes with PBST, protein detection was performed using ECL Select Western Blotting Detection Reagent (Cytiva, Tokyo, Japan). Protein detection was carried out using Chemidoc (Bio‐Rad Laboratories Inc., CA, USA), and semi‐quantitative analysis was performed using ImageLab (Bio‐Rad Laboratories Inc., CA, USA) with the quantified data.

4.8. p19 gag ELISA

C91/PL cells were seeded at 1 × 104 cells/well in a 24‐well plate and exposed to the METTL3/14 inhibitor (STM2457) for 72 h. After centrifugation (600 × g, 3 min), the supernatant was removed. The cells were then reseeded into a 12‐well plate and cultured with STM2457 for an additional 48 h. Following culture, the cells were centrifuged twice (1000 × g, 5 min), and the supernatant was collected for ELISA samples. ELISA was performed using the RETROTEK HTLV p19 Antigen ELISA (ZeptoMetrix, NY, USA) according to the manufacturer's protocol. After washing each well, samples diluted 500‐fold were added and incubated (37°C, 2 h). The wells were then washed six times with plate wash buffer, and the detector antibody was added and incubated (37°C, 1 h). After washing the wells, peroxidase working solution was added and incubated (37°C, 1 h), followed by another six washes. Finally, substrate solution was added and incubated (RT, 30 min), then stop solution was added, and absorbance at 450 nm was measured using the GloMax Explorer Multimode Microplate Reader (Promega, WI, USA).

4.9. CD25 Expression Analysis

After exposing C91/PL cells to the METTL3/14 inhibitor (STM2457) for 72 h, the cells were collected and centrifuged (1000 × g, 2 min), then washed with FACS Buffer. The cells were centrifuged again (1000 × g, 2 min), and the supernatant was removed. FACS Buffer (100 μL) was added, and the cells were stained with CD25‐APC (clone bc96, BioLegend, CA, USA) for 30 min at room temperature. After staining, the cells were centrifuged (1000 × g, 2 min), and the supernatant was removed. The cells were washed with FACS Buffer, and 7‐AAD (BioLegend, CA, USA) was added. CD25 expression was measured using the BD FACSymphony A1 (Becton, Dickinson and Company, NJ, USA). Measurement data were analyzed using FlowJo (v10.10) to calculate the Mean Fluorescence Intensity (MFI).

4.10. RNA‐Seq

Total RNA of C91/PL cells was extracted using TRIzol Reagent (Thermo Fisher Scientific Inc., MA, USA) and quantified and qualified by Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA), NanoDrop (Thermo Fisher Scientific Inc., MA, USA), and 1% agarose gel. Twenty nanograms of total RNA with RIN value above seven was used following library preparation. The library preparation and sequencing were processed and analyzed by GENEWIZ. The libraries with different indices were multiplexed and loaded on an Illumina HiSeq instrument according to the manufacturer's instructions (Illumina Inc., CA, USA). Sequencing was carried out using a 2 × 150 bp paired‐end (PE) configuration; image analysis and base calling were conducted by the HiSeq Control Software (HCS v2.2.38 or later) + OLB + GAPipeline‐1.6 (Illumina Inc., CA, USA) on the HiSeq instrument. For quality control, to remove technical sequences, including adapters, PCR primers, or fragments thereof, and quality of bases lower than 20, pass filter data of fastq format were processed by Trimmomatic (v0.30; Bolger et al. 2014) to be high‐quality clean data. For mapping, HISAT2 (v2.0.1; Kim et al. 2019) was used to index the reference genome sequence. Finally, clean data were aligned to the reference genome via software HISAT2. For differentially expressed gene analysis, HTSeq (v0.6.1; Anders et al. 2015) estimated gene and converted read counts to transcripts per million (TPM) from the pair‐end clean data. Gene Ontology analysis was performed by DAVID Bioinformatics Resources (https://david.ncifcrf.gov/; Huang et al. 2009; Sherman et al. 2022).

4.11. MeRIP‐Seq Secondary Analysis

Sequence data were retrieved from the DDBJ Sequence Read Archive (SRA) in SRA format (accession numbers: SRR2648293, SRR2648294, SRR2648296, SRR2648297) and converted to FASTQ files using SRA‐tools (v2.9.6; https://github.com/ncbi/sra‐tools). Quality control and adapter trimming were performed using fastp (v0.23.2; Chen et al. 2018). The processed reads were mapped to the human reference genome (hg38, obtained from NCBI) using HISAT2 (v2.2.1; Kim et al. 2019). Peak calling was conducted using MACS2 (v2.2.7.1; Zhang et al. 2008).

4.12. Data Analysis

Statistical significance was determined using Student's t‐test. All statistical analyses were performed using the statistical software R (ver 4.4.1; R Core Team 2024) or Microsoft Excel. Time series data from the RNA decay assay were analyzed using generalized linear modeling (GLM) with a Gamma distribution to accommodate non‐normality and heteroscedasticity. Statistical significance was assessed by calculating 95% confidence intervals. Integrative Genomics Viewer (IGV) tool was used for visualizing and interpreting the results of ChIP‐seq.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Results of KEGG PATHWAY analysis. Genes compatible with the “human T‐cell leukemia virus 1 infection” pathway are highlighted with asterisks.

GTC-30-0-s001.pdf (465.1KB, pdf)

Table S1: List of differentially expressed genes under STM2457 treatment in C91/PL cells.

Table S2: Normalized TPM data of m6A‐related genes in activated naive CD4+ T‐cells and HTLV‐1‐infected cell line HuT102.

GTC-30-0-s002.xlsx (269.7KB, xlsx)

Acknowledgments

This study was supported by AMED under Grant Numbers JP25fk0108672, JP25wm0325056, JP25ama221534, JSPS KAKENHI Grant Numbers JP24K02317, and JST SPRING, Grant Number JPMJSP2108. Computations were partially performed on the NIG supercomputer at ROIS National Institute of Genetics.

Gibu, R. , Gibu K., Suzuki K., Tanaka Y., Uchimaru K., and Yamagishi M.. 2025. “ m6A RNA Modification Controls HTLV‐1 Tax and Host Gene Expression.” Genes to Cells 30, no. 6: e70054. 10.1111/gtc.70054.

Funding: This work was supported by the Japan Agency for Medical Research and Development (JP25fk0108672, JP25wm0325056, JP25ama221534), the Japan Society for the Promotion of Science (JP24K02317), and the Japan Science and Technology Agency (JPMJSP2108).

Transmitting Editor: Haruhiko Siomi

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Anders, S. , Pyl P. T., and Huber W.. 2015. “HTSeq—a Python Framework to Work with High‐Throughput Sequencing Data.” Bioinformatics 31, no. 2: 166–169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ballard, D. W. , Böhnlein E., Lowenthal J. W., Wano Y., Franza B. R., and Greene W. C.. 1988. “HTLV‐I Tax Induces Cellular Proteins That Activate the Kappa B Element in the IL‐2 Receptor Alpha Gene.” Science 241, no. 4873: 1652–1655. 10.1126/science.241.4873.1652. [DOI] [PubMed] [Google Scholar]
  3. Berneman, Z. N. , Gartenhaus R. B., Reitz M. S. Jr., et al. 1992. “Expression of Alternatively Spliced Human T‐Lymphotropic Virus Type I pX mRNA in Infected Cell Lines and in Primary Uncultured Cells From Patients With Adult T‐Cell Leukemia/Lymphoma and Healthy Carriers.” Proceedings of the National Academy of Sciences of the United States of America 89, no. 7: 3005–3009. 10.1073/pnas.89.7.3005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bolger, A. M. , Lohse M., and Usadel B.. 2014. “Trimmomatic: A Flexible Trimmer for Illumina Sequence Data.” Bioinformatics 30, no. 15: 2114–2120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cann, A. J. , Rosenblatt J. D., Wachsman W., Shah N. P., and Chen I. S.. 1985. “Identification of the Gene Responsible for Human T‐Cell Leukaemia Virus Transcriptional Regulation.” Nature 318, no. 6046: 571–574. 10.1038/318571a0. [DOI] [PubMed] [Google Scholar]
  6. Chen, S. , Zhou Y., Chen Y., and Gu J.. 2018. “fastp: An Ultra‐Fast All‐In‐One FASTQ Preprocessor.” Bioinformatics 34, no. 17: i884–i890. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Dominissini, D. , Moshitch‐Moshkovitz S., Schwartz S., et al. 2012. “Topology of the Human and Mouse m6A RNA Methylomes Revealed by m6A‐Seq.” Nature 485, no. 7397: 201–206. [DOI] [PubMed] [Google Scholar]
  8. Du, H. , Zhao Y., He J., et al. 2016. “YTHDF2 Destabilizes m6A‐Containing RNA Through Direct Recruitment of the CCR4–NOT Deadenylase Complex.” Nature Communications 7, no. 1: 12626. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Duan, M. , Liu H., Xu S., et al. 2024. “IGF2BPs as Novel m6A Readers: Diverse Roles in Regulating Cancer Cell Biological Functions, Hypoxia Adaptation, Metabolism, and Immunosuppressive Tumor Microenvironment.” Genes & Diseases 11, no. 2: 890–920. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Fujikawa, D. , Nakagawa S., Hori M., et al. 2016. “Polycomb‐Dependent Epigenetic Landscape in Adult T‐Cell Leukemia.” Blood 127, no. 14: 1790–1802. [DOI] [PubMed] [Google Scholar]
  11. Fujisawa, J. , Seiki M., Kiyokawa T., and Yoshida M.. 1985. “Functional Activation of the Long Terminal Repeat of Human T‐Cell Leukemia Virus Type I by a Trans‐Acting Factor.” Proceedings of the National Academy of Sciences 82, no. 8: 2277–2281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Fujisawa, J. , Seiki M., Sato M., and Yoshida M.. 1986. “A Transcriptional Enhancer Sequence of HTLV‐I Is Responsible for Trans‐Activation Mediated by p40 Chi HTLV‐I.” EMBO Journal 5, no. 4: 713–718. 10.1002/j.1460-2075.1986.tb04272.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hasegawa, H. , Sawa H., Lewis M. J., et al. 2006. “Thymus‐Derived Leukemia‐Lymphoma in Mice Transgenic for the Tax Gene of Human T‐Lymphotropic Virus Type I.” Nature Medicine 12, no. 4: 466–472. [DOI] [PubMed] [Google Scholar]
  14. He, L. , Li H., Wu A., Peng Y., Shu G., and Yin G.. 2019. “Functions of N 6‐Methyladenosine and Its Role in Cancer.” Molecular Cancer 18: 1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hildreth, J. E. , Subramanium A., and Hampton R. A.. 1997. “Human T‐Cell Lymphotropic Virus Type 1 (HTLV‐1)‐Induced Syncytium Formation Mediated by Vascular Cell Adhesion Molecule‐1: Evidence for Involvement of Cell Adhesion Molecules in HTLV‐1 Biology.” Journal of Virology 71, no. 2: 1173–1180. 10.1128/JVI.71.2.1173-1180.1997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Huang, D. W. , Sherman B. T., and Lempicki R. A.. 2009. “Systematic and Integrative Analysis of Large Gene Lists Using DAVID Bioinformatics Resources.” Nature Protocols 4, no. 1: 44–57. [DOI] [PubMed] [Google Scholar]
  17. Huang, H. , Weng H., Sun W., et al. 2018. “Recognition of RNA N 6‐Methyladenosine by IGF2BP Proteins Enhances mRNA Stability and Translation.” Nature Cell Biology 20, no. 3: 285–295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Inoue, J. , Seiki M., Taniguchi T., Tsuru S., and Yoshida M.. 1986. “Induction of Interleukin 2 Receptor Gene Expression by p40x Encoded by Human T‐Cell Leukemia Virus Type 1.” EMBO Journal 5, no. 11: 2883–2888. 10.1002/j.1460-2075.1986.tb04583.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Jia, G. , Fu Y. E., Zhao X. U., et al. 2011. “ N 6‐Methyladenosine in Nuclear RNA Is a Major Substrate of the Obesity‐Associated FTO.” Nature Chemical Biology 7, no. 12: 885–887. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kamihira, S. , Atogami S., Sohda H., Momita S., Yamada Y., and Tomonaga M.. 1994. “Significance of Soluble Interleukin‐2 Receptor Levels for Evaluation of the Progression of Adult T‐Cell Leukemia.” Cancer 73, no. 11: 2753–2758. 10.1002/1097-0142(19940601)73:11<2753::aid-cncr2820731117>3.0.co;2-x. [DOI] [PubMed] [Google Scholar]
  21. Kataoka, K. , Nagata Y., Kitanaka A., et al. 2015. “Integrated Molecular Analysis of Adult T Cell Leukemia/Lymphoma.” Nature Genetics 47, no. 11: 1304–1315. [DOI] [PubMed] [Google Scholar]
  22. Kim, D. , Paggi J. M., Park C., Bennett C., and Salzberg S. L.. 2019. “Graph‐Based Genome Alignment and Genotyping With HISAT2 and HISAT‐Genotype.” Nature Biotechnology 37, no. 8: 907–915. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. King, E. M. , Midkiff A., McClain K., Kim S., and Panfil A. R.. 2025. “YTHDF1 and YTHDC1 m6A Reader Proteins Regulate HTLV‐1 Tax and hbz Activity.” Journal of Virology 99: e02063‐24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kobayashi, S. , Nakano K., Watanabe E., et al. 2014. “CADM1 Expression and Stepwise Downregulation of CD7 Are Closely Associated With Clonal Expansion of HTLV‐I–Infected Cells in Adult T‐Cell Leukemia/Lymphoma.” Clinical Cancer Research 20, no. 11: 2851–2861. [DOI] [PubMed] [Google Scholar]
  25. Kogure, Y. , Kameda T., Koya J., et al. 2022. “Whole‐Genome Landscape of Adult T‐Cell Leukemia/Lymphoma.” Blood 139, no. 7: 967–982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lichinchi, G. , Gao S., Saletore Y., et al. 2016. “Dynamics of the Human and Viral m6A RNA Methylomes During HIV‐1 Infection of T Cells.” Nature Microbiology 1, no. 4: 1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lorenz, C. , Lünse C. E., and Mörl M.. 2017. “tRNA Modifications: Impact on Structure and Thermal Adaptation.” Biomolecules 7, no. 2: 35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Masutani, H. , Hirota K., Sasada T., et al. 1996. “Transactivation of an Inducible Anti‐Oxidative Stress Protein, Human Thioredoxin by HTLV‐I Tax.” Immunology Letters 54, no. 2–3: 67–71. [DOI] [PubMed] [Google Scholar]
  29. Matsuo, M. , Ueno T., Monde K., et al. 2022. “Identification and Characterization of a Novel Enhancer in the HTLV‐1 Proviral Genome.” Nature Communications 13, no. 1: 2405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. McFadden, M. J. , and Horner S. M.. 2021. “ N 6‐Methyladenosine Regulates Host Responses to Viral Infection.” Trends in Biochemical Sciences 46, no. 5: 366–377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Meyer, K. D. , and Jaffrey S. R.. 2017. “Rethinking m6A Readers, Writers, and Erasers.” Annual Review of Cell and Developmental Biology 33, no. 1: 319–342. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Mishra, T. , Phillips S., Zhao Y., Wilms B., He C., and Wu L.. 2024. “Epitranscriptomic m6A Modifications During Reactivation of HIV‐1 Latency in CD4+ T Cells.” MBio 15, no. 11: e02214–e02224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Mizuike, J. , Suzuki K., Tosaka S., et al. 2025. “Rewired Chromatin Structure and Epigenetic Gene Dysregulation During HTLV‐1 Infection to Leukemogenesis.” Cancer Science 116, no. 2: 513–523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Murakami, T. , Hirai H., Suzuki T., Fujisawa J., and Yoshida M.. 1995. “HTLV‐1 Tax Enhances NF‐Kappa B2 Expression and Binds to the Products p52 and p100, but Does Not Suppress the Inhibitory Function of p100.” Virology 206, no. 2: 1066–1074. 10.1006/viro.1995.1029. [DOI] [PubMed] [Google Scholar]
  35. Ohsugi, T. , Kumasaka T., Okada S., and Urano T.. 2007. “The Tax Protein of HTLV‐1 Promotes Oncogenesis in Not Only Immature T Cells but Also Mature T Cells.” Nature Medicine 13, no. 5: 527–528. [DOI] [PubMed] [Google Scholar]
  36. R Core Team . 2024. R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing. https://www.R‐project.org. [Google Scholar]
  37. Satou, Y. , Yasunaga J. I., Zhao T., et al. 2011. “HTLV‐1 bZIP Factor Induces T‐Cell Lymphoma and Systemic Inflammation In Vivo.” PLoS Pathogens 7, no. 2: e1001274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Seiki, M. , Inoue J. I., Takeda T., Hikikoshi A., Sato M., and Yoshida M.. 1985. “The p40x of Human T‐Cell Leukemia Virus Type I Is a Trans‐Acting Activator of Viral Gene Transcription.” Japanese Journal of Cancer Research GANN 76, no. 12: 1127–1131. [PubMed] [Google Scholar]
  39. Sherman, B. T. , Hao M., Qiu J., et al. 2022. “DAVID: A Web Server for Functional Enrichment Analysis and Functional Annotation of Gene Lists (2021 Update).” Nucleic Acids Research 50, no. W1: W216–W221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Tamiya, S. , Matsuoka M., Etoh K. I., et al. 1996. “Two Types of Defective Human T‐Lymphotropic Virus Type I Provirus in Adult T‐Cell Leukemia.” Blood 88, no. 8: 3065–3073. [PubMed] [Google Scholar]
  41. Taniguchi, Y. , Nosaka K., Yasunaga J. I., et al. 2005. “Silencing of Human T‐Cell Leukemia Virus Type I Gene Transcription by Epigenetic Mechanisms.” Retrovirology 2: 1–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Tsai, K. , Bogerd H. P., Kennedy E. M., Emery A., Swanstrom R., and Cullen B. R.. 2021. “Epitranscriptomic Addition of m6A Regulates HIV‐1 RNA Stability and Alternative Splicing.” Genes & Development 35, no. 13–14: 992–1004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Uhlén, M. , Fagerberg L., Hallström B. M., et al. 2015. “Tissue‐Based Map of the Human Proteome.” Science 347, no. 6220: 1260419. [DOI] [PubMed] [Google Scholar]
  44. Yamagishi, M. , Kubokawa M., Kuze Y., et al. 2021. “Chronological Genome and Single‐Cell Transcriptome Integration Characterizes the Evolutionary Process of Adult T Cell Leukemia‐Lymphoma.” Nature Communications 12, no. 1: 4821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Zhang, Y. , Liu T., Meyer C. A., et al. 2008. “Model‐Based Analysis of ChIP‐Seq (MACS).” Genome Biology 9: R137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhao, T. , Satou Y., Sugata K., et al. 2011. “HTLV‐1 bZIP Factor Enhances TGF‐β Signaling Through p300 Coactivator.” Blood 118, no. 7: 1865–1876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Zheng, G. , Dahl J. A., Niu Y., et al. 2013. “ALKBH5 Is a Mammalian RNA Demethylase That Impacts RNA Metabolism and Mouse Fertility.” Molecular Cell 49, no. 1: 18–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Zhou, Y. , Zeng P., Li Y. H., Zhang Z., and Cui Q.. 2016. “SRAMP: Prediction of Mammalian N 6‐Methyladenosine (m6A) Sites Based on Sequence‐Derived Features.” Nucleic Acids Research 44, no. 10: e91–e91. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: Results of KEGG PATHWAY analysis. Genes compatible with the “human T‐cell leukemia virus 1 infection” pathway are highlighted with asterisks.

GTC-30-0-s001.pdf (465.1KB, pdf)

Table S1: List of differentially expressed genes under STM2457 treatment in C91/PL cells.

Table S2: Normalized TPM data of m6A‐related genes in activated naive CD4+ T‐cells and HTLV‐1‐infected cell line HuT102.

GTC-30-0-s002.xlsx (269.7KB, xlsx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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