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Cross-talk among writers, readers, and erasers of m6A regulates cancer growth and progression

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Science Advances  03 Oct 2018:
Vol. 4, no. 10, eaar8263
DOI: 10.1126/sciadv.aar8263
  • Fig. 1 RNA methyltransferase METTL14 and demethylase ALKBH5 promote growth and invasion of breast cancer cells.

    Clonogenic assay on scrambled-siRNA– or METTL14-siRNA (METTL14 KD)–transfected (A) or ALKBH5-siRNA (ALKBH5 KD)–transfected (C) MDA-MB-231 cells. Bar graphs below show the number of colonies counted microscopically in 10 different fields. (B and D) Photomicrograph showing migrated (top) and invaded (bottom) MDA-MB-231 cells in scrambled (Scr) or METTL14 KD (B) or ALKBH5 KD (D) cells. Bar graphs show the number of migrated and invaded cells. The data shown are means ± SEM for at least three independent experiments. **P < 0.01; ***P < 0.001; ****P < 0.0001 versus control group, t test. (E and F) Photomicrographs showing representative tumor growth in nude mice injected with 2 × 106 scrambled-siRNA–transfected (control), METTL14-siRNA (METTL14 KD)–transfected (A), or ALKBH5-siRNA (ALKBH5 KD)–transfected (B) MDA-MB-231 cells mixed with Matrigel. Bar graphs show mean tumor volume for the control (n = 8), METTL14 KD (n = 8), and ALKBH5 KD (n = 8) groups at the end of the study on day 21 after implantation of the cells.

  • Fig. 2 METTL14 and ALKBH5 support tumor growth and progression by targeting cell cycle– and TGFβ signaling–associated transcripts.

    (A) Western blot analysis of target genes in scrambled-siRNA–transfected, METTL14-siRNA (METTL14 KD)–transfected, and ALKBH5-siRNA (ALKBH5 KD)–transfected MDA-MB-231, MDA-MB-468, and BT-549 cells using antibodies against the indicated proteins. Membranes were reprobed with β-actin, which served as a loading control. Gel photograph is representative of at least three independent experiments. (B) Western blot analysis of target genes in empty vector (Control) and METTL14 expression vector (METTL14 OE)–transfected and ALKBH5 expression vector (ALKBH5 OE)–transfected MDA-MB-231 cells using antibodies against the indicated proteins. Membranes were reprobed with β-actin, which served as a loading control. Gel photograph is representative of at least three independent experiments. Quantification of band intensities for (A) and (B) is shown in fig. S4 (C and D). (C) Histogram showing cell cycle distribution of scrambled-siRNA (Control), METTL14 KD (top), and ALKBH5KD (bottom) MDA-MB-231 cells. The data shown are means ± SEM of three samples for each treatment and represent three independent experiments. (D) Western blot analysis of scrambled-siRNA–transfected, METTL14-siRNA (METTL14 KD)–transfected, or ALKBH5-siRNA (ALKBH5 KD)–transfected MDA-MB-231 cells using antibodies against the indicated proteins. Vinculin served as a loading control. Gel photograph is representative of at least three independent experiments. PARP, poly(adenosine diphosphate–ribose) polymerase. (E) Histogram showing the number of annexin V+ apoptotic cells in scrambled-siRNA–, METTL14-siRNA (METTL14 KD)–, or ALKBH5-siRNA (ALKBH5KD)–transfected MDA-MB-231 cells. MDA-MB-231 cells were transfected with scrambled-siRNA or METTL14-siRNA/ALKBH5-siRNA for 48 hours before they were stained with propidium iodide (PI) and incubated with annexin V antibody and analyzed by flow cytometry. The data shown are means ± SEM of three samples for each experiment and represent three independent experiments. (F to H) Photomicrograph showing migrated MDA-MB-231 cells in scrambled-siRNA–transfected, METTL14-siRNA–transfected (F), and ALKBH5-siRNA–transfected (H) groups, treated with TGFβ1 recombinant protein. Bar graphs show the number of migrated MDA-MB-231 cells in METTL14 KD (G) and ALKBH5 KD (I) groups. *P < 0.05; ***P < 0.001; ****P < 0.0001 versus control group, t test.

  • Fig. 3 METTL14 and ALKBH5 constitute a positive feedback loop with HuR to regulate the stability of target genes.

    (A) Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis showing stability of target genes in scrambled-siRNA– or METTL14-siRNA–transfected MDA-MB-231 cells treated with actinomycin (5 μg) for the indicated hours. Transcript levels in scrambled-transfected cells were normalized to 100% for each time point. The data shown are means ± SEM of three independent experiments (n = 3 biological replicates per experiment). (B and C) Western blot analysis of scrambled-siRNA–transfected, METTL14-siRNA–transfected (B), or ALKBH5-siRNA–transfected (C) MDA-MB-231, MDA-MB-468, BT-549, and HeLa cells using antibodies against the indicated proteins. The data shown are means ± SEM of three independent biological replicates. β-Actin and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were used as loading controls. #, *, **, and $ symbols next to β-actin in (B) and (C) indicate the same loading control as in Fig. 5C. The same loading controls were used because gels were stripped and reprobed for different proteins. Relevant proteins are shown in different figures to maintain the flow of the results. Quantification of band intensities is shown in fig. S5I. (D) qRT-PCR showing enrichment of HuR, METTL14, ALKBH5, and their target genes in MDA-MB-231 cells subjected to RIP using antibody against HuR. The data shown are means ± SEM of six independent experiments. (E) Western blot analysis of the indicated proteins in two sets of scrambled-siRNA– or HuR-siRNA (KD #1 and KD #2)–transfected MDA-MD-231 cells. β-Actin and GAPDH served as loading controls. Gel photograph is representative of at least three independent experiments. Quantification of band intensities is shown in fig. S5J. (F and G) qRT-PCR (F) and Western blot (G) analysis showing HuR expression in MDA-MB-231 cells treated with or without recombinant TGFβ1 (rTGFβ1; 2 ng/ml) using HuR-specific primers and antibody. Bar graph in (G) represents band intensity quantified from all experiments using ImageJ software. The data shown are means ± SEM of three independent experiments. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 versus control group, t test.

  • Fig. 4 m6A methylation analysis of control and METTL14-silenced breast cancer cells.

    (A and B) MeRIP-seq analysis showing the number of peaks (A) and m6A peak–containing transcripts (B) identified in scrambled-siRNA (siCntrl)– and METTL14-siRNA (METTL14 KD)–transfected breast cancer cells. Common m6A-containing genes share at least one common peak, while unique m6A-containing genes share no peak between scrambled-siRNA and METTL14 KD breast cancer cells. (C) TGFβ1, CCNE1, and SMAD3 showing significantly enriched m6A peaks in METTL14 KD MDA-MB-231 cells compared to scrambled-siRNA. Top two tracks represent MeRIP and input for METTL14-siRNA–transfected MDA-MB-231 cells, while bottom two tracks represent MeRIP and input for scrambled-siRNA–transfected MDA-MB-231 cells. bp, base pairs; RefSeq, reference sequence. (D) Pie chart of m6A peak distribution showing proportion of total (top) and unique (bottom) peaks in different regions of genes in scrambled-siRNA and METTL14 KD cells. (E) Ingenuity Pathway Analysis (IPA) using m6A peak–containing genes shows TGFβ as one of the top upstream regulators. (F) Bar graphs show enriched canonical pathways derived from IPA using m6A-containing genes. (G) qRT-PCR showing m6A abundance (normalized to input) of target genes in MeRIP samples from MDA-MB-231 and MCF-7 cells transfected with scrambled-siRNA or METTL14-siRNA (METTL14 KD). The data shown are means ± SEM of three independent experiments (n = 3 biological replicates per experiment).

  • Fig. 5 Cross-talk among writer, reader, and eraser determines m6A levels of target transcripts.

    (A) Western blot analysis of scrambled-siRNA–transfected or METTL14-siRNA–transfected (top) or ALKBH5-siRNA–transfected (bottom) MDA-MB-231, MDA-MB-468, and BT-549 cells using antibodies against the indicated protein. Gel photographs represent results from three independent experiments. Note that METTL14 levels in ALKBH5 KD MDA-MB-231, MDA-MB-468, and BT-549 cells are shown in Fig. 4C. #, *, **, and $ symbols next to β-actin indicate the same loading control as in Fig. 4C. (B) Western blot analysis of scrambled-siRNA–transfected or METTL14-siRNA–transfected (top) or ALKBH5-siRNA–transfected (bottom) MDA-MB-231, MDA-MB-468, BT-549, and HeLa cells using antibodies against YTHDF3. Gel photographs represent results from three independent experiments. Quantification of band intensities for (A) and (B) is shown in fig. S8.

  • Fig. 6 m6A reader blocks RNA demethylase activity to regulate m6A levels and expression of target genes.

    (A) m6A-contaning biotin-labeled TGFβ1 mRNA was incubated with 1 μg of FTO in the absence (−) or presence of 1 μg (+) or 2 μg (++) of YTHDF3, followed by mRNA pulldown and Western blot using antibodies against YTHDF3 or FTO. A portion of the sample collected before pulldown served as inputs. Gel photograph represents results from three independent experiments. (B) qRT-PCR showing m6A abundance (normalized to input) of target genes in MeRIP samples from MDA-MB-231 cells transfected with scrambled-siRNA, METTL14-siRNA (METTL14 KD), or METTL14 KD + YTHDF3-siRNA (YTHDF3 KD). The data shown are means ± SEM of three independent experiments. (C and D) qRT-PCR (C) and Western blot (D) analysis of scrambled-siRNA–, METTL14-siRNA (METTL14 KD)–, YTHDF3-siRNA (YTHDF3 KD)–, or METTL14-siRNA + YTHDF3-siRNA–transfected MDA-MB-231 cells using gene-specific primers and antibodies against the indicated proteins. The data shown in (C) are means ± SEM of four independent experiments. Gel photographs in (D) represent results from three independent experiments. Quantification of band intensities for (D) is shown in fig. S8K. (E) Western blot analysis of MDA-MB-231 cells exposed to normoxic and hypoxic conditions for 24 and 48 hours using antibodies against the indicated proteins. β-Actin served as a loading control. Gel photographs represent results from three independent experiments. Quantification of band intensities for (E) is shown in fig. S10A. (F) qRT-PCR showing TGFβ1 m6A abundance (normalized to input) in MeRIP samples (left) and TGFβ1 mRNA levels (right) from MDA-MB-231 cells exposed to normoxic and hypoxic conditions. The data shown are means ± SEM for two (for MeRIP) and three (for expression analysis) independent experiments. (G) qRT-PCR showing m6A abundance (normalized to input) of target gene in breast cancer patients (n = 10) and normal controls (n = 7) using gene-specific primers. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 versus control, t test. ns, not significant. (H) Model showing mechanism by which pro-oncogenic trigger (such as hypoxia) may impair the cross-talk among m6A writer, eraser, reader, and effector proteins (such as HuR), resulting in aberrant target gene expression and, consequently, cancer growth and progression.

  • Table 1 Number of reads generated for samples by MeRIP-seq.

    Sample nameTotal reads (millions)Mapped reads (millions)Mapping rates (%)
    MDA-MB-231 scrambled rep1 input68.2855.4781.23
    MDA-MB-231 scrambled rep1 MeRIP133.05115.9887.17
    MDA-MB-231 scrambled rep2 input37.3228.3776.01
    MDA-MB-231 scrambled rep2 MeRIP45.0737.2982.73
    MDA-MB-231 METTL14 KD rep1 input108.1396.3389.08
    MDA-MB-231 METTL14 KD rep1 MeRIP86.2481.4794.46
    MDA-MB-231 METTL14 KD rep2 input31.2224.0577.03
    MDA-MB-231 METTL14 KD rep2 MeRIP35.2430.0985.38

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/10/eaar8263/DC1

    Table S1. Primer pairs and sequences used in the study.

    Table S2A. Differentially expressed genes derived from comparing METTL14 KD cells versus scrambled-siRNA (control).

    Table S2B. Differentially expressed genes that are highly expressed derived from comparing METTL14 KD cells versus scrambled-siRNA (control).

    Table S3. Differentially expressed genes derived from comparing ALKBH5 KD cells versus scrambled-siRNA (control) using Illumina whole-genome gene expression microarrays [three microarrays for each condition (total of six microarrays) were performed for this comparison].

    Table S4A. Gene set enrichment using DAVID (https://david.ncifcrf.gov/; v6.7, 2015) with 744 differentially expressed genes obtained from METTL14 KD gene expression profiling.

    Table S4B. Gene set enrichment using DAVID with 440 differentially expressed genes obtained from ALKBH5 KD gene expression profiling.

    Table S5A. Upstream regulators predicted by the Ingenuity Pathway Analysis (www.ingenuity.com) software with 744 DEGs of METTL14 KD gene expression profiling.

    Table S5B. Upstream regulators predicted by the Ingenuity Pathway Analysis (www.ingenuity.com) software with 440 differentially expressed genes of ALKBH5 KD gene expression profiling.

    Fig. S1. Efficient KD of methyltransferase complex proteins and ALKBH5 inhibits cell viability and invasion of cancer cells.

    Fig. S2. METTL14 and ALKBH5 promote growth and progression of cancer cells without affecting the viability of normal cells.

    Fig. S3. Cancer-associated genes are differentially expressed in METTL14/ALKBH5-silenced breast cancer cells.

    Fig. S4. METTL14 and ALKBH5 regulate expression of genes involved in cell cycle, EMT, and angiogenesis.

    Fig. S5. METTL14 and ALKBH5 regulate TGFβ1 and HuR expression.

    Fig. S6. HuR-binding sites and m6A motif (RRACH) in 3′UTRs of METTL14/ALKBH5 target genes.

    Fig. S7. Transcriptome-wide MeRIP-seq analysis shows m6A peaks in target transcripts.

    Fig. S8. METTL14 and ALKBH5 regulate m6A levels of target genes by constituting a positive feedback loop and inhibiting YTHDF3.

    Fig. S9. ALKBH5-YTHDF3 and METTL14-YTHDF3 axes regulate growth and migration of cancer cells.

    Fig. S10. METTL14 and ALKBH5 do not show significantly different expression and association with overall survival in cancer patients.

    References (44, 45)

  • Supplementary Materials

    This PDF file includes:

    • Table S1. Primer pairs and sequences used in the study.
    • Table S2A. Differentially expressed genes derived from comparing METTL14 KD cells versus scrambled-siRNA (control).
    • Table S2B. Differentially expressed genes that are highly expressed derived from comparing METTL14 KD cells versus scrambled-siRNA (control).
    • Table S3. Differentially expressed genes derived from comparing ALKBH5 KD cells versus scrambled-siRNA (control) using Illumina whole-genome gene expression microarrays three microarrays for each condition (total of six microarrays) were performed for this comparison.
    • Table S4A. Gene set enrichment using DAVID ( https://david.ncifcrf.gov/; v6.7, 2015) with 744 differentially expressed genes obtained from METTL14 KD gene expression profiling.
    • Table S4B. Gene set enrichment using DAVID with 440 differentially expressed genes obtained from ALKBH5 KD gene expression profiling.
    • Table S5A. Upstream regulators predicted by the Ingenuity Pathway Analysis ( www.ingenuity.com) software with 744 DEGs of METTL14 KD gene expression profiling.
    • Table S5B. Upstream regulators predicted by the Ingenuity Pathway Analysis ( www.ingenuity.com) software with 440 differentially expressed genes of ALKBH5 KD gene expression profiling.
    • Fig. S1. Efficient KD of methyltransferase complex proteins and ALKBH5 inhibits cell viability and invasion of cancer cells.
    • Fig. S2. METTL14 and ALKBH5 promote growth and progression of cancer cells without affecting the viability of normal cells.
    • Fig. S3. Cancer-associated genes are differentially expressed in METTL14/ALKBH5-silenced breast cancer cells.
    • Fig. S4. METTL14 and ALKBH5 regulate expression of genes involved in cell cycle, EMT, and angiogenesis.
    • Fig. S5. METTL14 and ALKBH5 regulate TGFβ1 and HuR expression.
    • Fig. S6. HuR-binding sites and m6A motif (RRACH) in 3′UTRs of METTL14/ALKBH5 target genes.
    • Fig. S7. Transcriptome-wide MeRIP-seq analysis shows m6A peaks in target transcripts.
    • Fig. S8. METTL14 and ALKBH5 regulate m6A levels of target genes by constituting a positive feedback loop and inhibiting YTHDF3.
    • Fig. S9. ALKBH5-YTHDF3 and METTL14-YTHDF3 axes regulate growth and migration of cancer cells.
    • Fig. S10. METTL14 and ALKBH5 do not show significantly different expression and association with overall survival in cancer patients.
    • References (44, 45)

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