EMBO/soda-nel huggingface dataset:
p62/SQSTM1 forms protein aggregates degraded by autophagy and has a protective effect on huntingtin-induced cell death. (2005)
Figure 7.

p62 forms a shell around huntingtin protein aggregates. (A) HeLa cells transiently expressing a Flag-tagged NH2-terminal fragment (amino acids 1–171) of huntingtin containing a 68-polyglutamine expansion, Flag–N-HttQ68. After 48 h, the cells were fixed, and Flag–N-HttQ68 was stained green using a Flag mAb. Endogenous p62 was stained red using the p62 pAb. (B) Endogenous p62 forms a shell around aggregated GFP–N-HttQ68. Transiently transfected HeLa cells were fixed 48 h after transfection, and endogenous p62 was detected using a p62 mAb (red). (C and D) The p62 shell surrounding GFP–N-HttQ68 was detected independently of the use of antibodies. (C) GFP–N-HttQ68 and DsRed-tagged p62 were cotransfected into HeLa cells, and images of live cells were obtained after 48 h of expression. The left panel shows one confocal plane of a representative GFP–N-HttQ68 and DsRed-p62 double-positive structure, and the right panel shows the Z-stack of planes with side views of the aggregate at the side and at the top. N, nucleus. (A–C) Nuclei were detected using Draq5. (D) GFP–N-HttQ68 and tdTomato-tagged p62 were cotransfected into HeLa cells, and images of live cells were obtained after 24 h of expression. (E) Transiently expressed Flag–N-HttQ68 and myc-LC3 colocalize in GFP-p62–positive structures. S–GFP-p62 cells were fixed 48 h after transfection, Flag–N-HttQ68 was stained red using a Flag tag mAb, and myc-LC3 was stained far red (blue) using a chicken myc tag pAb (top). The bottom panel shows a live cell image of a GFP–N-HttQ68 aggregate surrounded by a shell containing tdTomato-LC3. Bars, 5 μm.
p62/SQSTM1 forms protein aggregates degraded by autophagy and has a protective effect on huntingtin-induced cell death. (2005)
Figure 8.

Interfering with p62 protein level or function increases cell death induced by mutant huntingtin. (A) GFP-positive cells were scored as live or apoptotic by the appearance of nuclear Draq5 staining. After 48 h of expression, GFP-positive cells attached to the surface with a normal nuclear appearance (open arrowheads) and were scored as live, whereas rounded, partially detached cells with a condensed or fragmented nuclei (arrows) were scored as dead. More than 200 GFP-positive cells in randomly selected microscope views were scored. Bars, 20 μm. (B) The effect of coexpressing either a p62 antisense construct or a deletion construct of p62 lacking the UBA domain on N-HttQ68–induced cell death in HeLa and SHSY-5Y neuroblastoma cells.
Dysregulated mesenchymal PDGFR-β drives kidney fibrosis (2019)
Figure 1

Figure 1: PDGFR-β expression is upregulated in fibrotic human and murine kidneys

(A) Immunoblot detection and its quantification of PDGFR-β in kidney cortex lysates of healthy and diseased human kidneys shows twice as much PDGFR-β abundance in diseased tissue. A stronger signal for phosphorylated PDGFR-β indicates the active status of the receptor. The high α-SMA content in the diseased kidneys shows that they are affected by fibrosis. Bar graphs show means ± SD, healthy n = 5, diseased n = 6

(B) PDGFR-β staining is detectable in mesangium of healthy (a) and diseased (b-d) glomeruli of human kidneys. PDGFR-β positive mesangial cells expand in mesangioproliferating and scarring glomerular changes. Similarly, the population of PDGFR-β positive interstitial cells is discrete in healthy kidney cortex (e) but expands in fibrosis (f-h). Arrows point to selected areas of positive PDGFR-β staining. Scale bar = 50 µm

(C) GFP expression under Pdgfrb-promoter in mice is present in mesangial cells of glomeruli and interstitial cells of cortex and medulla, but not in tubular cells. Nuclei are stained with DAPI (blue). Scale bar = 50 µm

(D) Tissue clearing with Scale and 3D reconstruction of Pdgfrb-GFP reporter mice in a healthy and fibrotic kidney (UUO day 5) shows the expansion of Pdgfrb expressing cells in fibrosis.
Dysregulated mesenchymal PDGFR-β drives kidney fibrosis (2019)
Figure 2

Figure 2: PDGFR-β activation leads to mesenchymal proliferation in vivo and in vitro

(A) Activation of PDGFR-β in renal mesenchymal cells, denoted here as Foxd1Cre::Pdgfrb+/J mice, was achieved by crossbreeding the Foxd1-Cre mouse line (FoxD1GC) with a mouse line with a heterozygous knock-in mouse line with constitutively active Pdgfrb mutant (J) allele (Pdgfrb(S)J) instead of the wt Pdgfrb allele.

(B) Representative Ki67 immunofluorescence staining (green) in wt and Foxd1Cre::Pdgfrb+/J mice, showing increased proliferation in the transgenic mice. Glomeruli are outlined with circles, arrowheads point to Ki67 positive interstitial cells. Nuclei are stained with DAPI (blue). Scale bar = 50 µm.

(C-E) Quantification of proliferating Ki67-positive cells specifically in glomeruli (C), interstitium (D) and tubules (E) in Foxd1Cre::Pdgfrb+/J mice (black bars) and wt mice (white bars) of 6, 14, 25 and 35 weeks of age. Foxd1Cre::Pdgfrb+/J mice exhibited increased proliferation of glomerular and interstitial cells, whereas tubular epithelial cell proliferation was not altered.

Data in C, D and E are shown as means ± SD of n = 3 animals per group.

(F) FoxD1-reporter mice (Foxd1::tdTomato) confirmed the expansion of mesenchymal cells in 14 weeks old Foxd1Cre::Pdgfrb+/J mice both in glomeruli and interstitium. Circles outline glomeruli. Scale bar = 50 µm.

(G) Quantification of FoxD1-Tomato positive cells in the cortical interstitium in 14 weeks old Foxd1Cre::Pdgfrb+/J and wt mice confirmed the significantly increased expansion of mesenchymal cells by 42% in Foxd1Cre::Pdgfrb+/J mice. Cells were counted in six viewfields at 40x magnification. Bar graphs show means ± SD of n = 3 mice per group.

(H) Isolated primary fibroblasts and mesangial cells from Foxd1Cre::Pdgfrb+/J mice have higher proliferation rates assessed by bromodeoxyuridine (BrdU) incorporation assay compared to cells from wt mice. Bar graphs show means ± SD, n = 4 per group, *: p≤0.05 compared to wt of same time point.
Dysregulated mesenchymal PDGFR-β drives kidney fibrosis (2019)
Figure 3

Figure 3: Activation of PDGFR-β in mesenchymal cells in Foxd1Cre::Pdgfrb+/J mice results in progressive mesangioproliferative glomerulonephritis and mesangial glomerulosclerosis

A) PAS staining of glomeruli of 25 weeks old wt and of 6 and 35 weeks old Foxd1Cre::Pdgfrb+/J mice shows pathological changes of the glomeruli in Foxd1Cre::Pdgfrb+/J mice.

(B) Glomerular tuft size of Foxd1Cre::Pdgfrb+/J mice was significantly increased compared to wt mice during the time course.

(C) Immunhistological staining of α-SMA and its histomorphometric quantification (D) reveals expansion of activated mesangial cells in the glomerular tuft of in the transgenic mice in the time course.

(E) Immunhistological staining of collagen I and its histomorphometric quantification (F) shows that collagen I deposits in an increasing manner in Foxd1Cre::Pdgfrb+/J mice during the time course.

Scale bars = 50 µm. Bar graphs show mean ± SD of n = 3 animals per group. *: p≤0.05 compared to same wt of same time point.
Dysregulated mesenchymal PDGFR-β drives kidney fibrosis (2019)
Figure 4

Figure 4: Activation of PDGFR-β in mesenchymal cells in Foxd1Cre::Pdgfrb+/J mice results in progressive interstitial fibrosis, inflammation and tubular damage

(A) Histological stainings and its histomorphometric quantification (B) of kidney cortex for α-SMA of wt (25 weeks) and Foxd1Cre::Pdgfrb+/J mice of an age of 6 and 35 weeks show expansion of myofibroblasts in the interstitium of the transgenic mice.

(C) Collagen I staining and its histomorphometric quantification (D) show the progression of interstitial fibrosis in the time course.

(E, F) The tubular stress marker Lipocalin-2 (LCN2) is not expressed in early time points but increases in time course in late stages when fibrosis is more prominent, as seen on histological staining (E) and mRNA level (F).

(G, H) A slight, but significant increase in interstitial ErHr3 positive stained macrophages was observed, particularly at later stages (G). For evaluation, ErHr3 positive cells were counted on 20 view fields on 40x magnification (H).

Scale bars = 50 µm. Bar graphs show means ± SD of n = 3 animals per group. *: p≤0.05 compared to same wt of same time point
Dysregulated mesenchymal PDGFR-β drives kidney fibrosis (2019)
Figure 5

Figure 5: Reduced kidney function in Foxd1Cre::Pdgfrb+/J mice

(A) Creatinine clearance was measured in mice of an age of 10, 14, 25 and 35 weeks and decreased continuously in Foxd1Cre::Pdgfrb+/J mice, with a predicted complete loss of renal function 62 weeks in transgenic mice using linear regression analyses as described previously (Steiger et al, 2018).

10 weeks n = 5 wt and n = 4 Foxd1Cre::Pdgfrb+/J, 14 weeks n = 2 per group, 25 weeks n = 8 wt and n = 8 Foxd1Cre::Pdgfrb+/J, 35 weeks n = 3 per group

(B) The blood urea nitrogen (BUN) concentrations were higher in Foxd1Cre::Pdgfrb+/J mice compared to wt mice, apart from 25 weeks old group. Bar graphs show means ± SD, 10 weeks n = 7 wt and n = 5 Foxd1Cre::Pdgfrb+/J, 14 weeks n = 3 per group, 25 weeks n = 11 wt and n = 12 Foxd1Cre::Pdgfrb+/J, 35 weeks n = 3 per group
Dysregulated mesenchymal PDGFR-β drives kidney fibrosis (2019)
Figure 6

Figure 6: The Foxd1Cre::Pdgfrb+/J mice develop progressive renal anemia and are unable to regulate erythropoiesis under pathological stimuli

Erythrocyte numbers (A), hematocrit (B) and hemoglobin content (C) decrease in Foxd1Cre::Pdgfrb+/J mice continuously. (D) EPO levels shown as delta from respective wt littermates. Horizontal lines are means ± SD of n = 3 per group. *: p≤0.05 compared to same wt of same time point.

(E) RNA in-situ hybridization (RNAScope) of Epo (green) and Pdgfrb (purple) in renal kidney cortex of 35 week old mice shows that EPO-producing cells co-express Pdgfrb. Foxd1Cre::Pdgfrb+/J mice have less EPO-producing cells (arrows) than wt mice. Scale bar = 25 µm

(F) After induction of anemia by blood taking, the erythrocyte numbers recovered after one week in wt mice, whereas Foxd1Cre::Pdgfrb+/J mice were not able to recover their erythrocytes numbers at all. Data are means ± SD of n = 5 per group.
Frequent lack of repressive capacity of promoter DNA methylation identified through genome-wide epigenomic manipulation (2018)
Figure 1

Fig. 1. Genome-wide manipulation DNA methylation with ZF-D3A. (A) Schematic representation of the ZF-D3A doxycycline inducible system. (B) Genome browser representation of the SOX2 locus. The promoter is defined as the unmethylated region spanning the SOX2 transcriptional start site in MCF7-control. Genomic distribution of (C) identified ZF-D3A binding sites, and (D) differentially methyl- ated regions (DMRs) between MCF7-control and ZF-D3A +dox. (E) Shown for all DMRs identified between ZF-D3A +dox and MCF7-control is: ∆mCG (difference in the methylation level [ratio of C base calls to total base calls for all CG dinucleotides in region, mCG/CG] between ZF-D3A +dox and MCF7-control), ZF-D3A ChIP-seq signal (normalized ChIP-seq read density, ±1kb flanking DMR center), CpG dinucleotide density (CpG/100bp, ±1kb flanking DMR center), and classification of each DMR as promoter or non-promoter located. (F) DNA methylation level in MCF7-control and ZF-D3A +dox cells flanking (±1 kb, 100 bp bins) the center of ZF-D3A ChIP-seq peaks identified in ZF-D3A +dox. (G) Genomic distribution of unmethylated regions (UMRs) in MCF7-control. (H) DMRs and UMRs with an intersection of ≥1 bp.
Frequent lack of repressive capacity of promoter DNA methylation identified through genome-wide epigenomic manipulation (2018)
Figure 2

Fig. 2. Induced methylation is frequently transient and preferentially lost from H3K4me3 marked regions. (A) Line histogram of DMR methylation levels in each sample. Kernel density estimate smoothed scatterplot comparison of DMR methylation levels in MCF7-control and (B) ZF-D3A +dox, or (C) ZF-D3A dox-wd. (D) Line histogram of UMR methylation levels in each sample. Scatterplot compari- son of UMR methylation levels in MCF7-control and (E) ZF-D3A +dox, or (F) ZF-D3A dox-wd. (G) Heat- maps of DMRs (ordered by decreasing H3K4me3 read density) showing: DNA methylation levels (±2.5 kb), differences (ΔmCG), classification as promoter or non-promoter located, CpG dinucleotide density (±2.5 kb), and histone modification ChIP-seq read density (±2.5 kb); (H) As in (G) but for the 6,000 DMRs with the lowest H3K4me3 levels, ordered by decreasing H3K4me1 read density, and DMR classification as enhancer or non-enhancer in MCF7 (ENCODE).
Frequent lack of repressive capacity of promoter DNA methylation identified through genome-wide epigenomic manipulation (2018)
Figure 3

Fig. 3. DNA methylated by ZF-D3A is bound by H3K4me3 and initiated RNA polymerase II. (A) H3K4me3 ChIP-seq read density flanking (±1 kb, 50 bp bins) the centre of DMRs with ∆mCG >0.2 (top), and H3K4me3 ChIP-seq peaks with ∆mCG <0.2 (bottom). (B) H3K4me3 ChIP-seq normalized read counts in DMRs with ∆mCG >0.2, from H3K4me3 ChIP-seq in ZF-D3A no-dox and ZF-D3A +dox cells. Black line represents equal normalized read counts in ZF-D3A no-dox and ZF-D3A +dox cells; blue line represents a linear regression of the data. (C) Comparison of UMR ∆mCG and difference in H3K4me3 ChIP-seq normalized read counts between ZF-D3A no-dox and ZF-D3A +dox. Blue line represents a linear regression of the data. (D) DNA methylation levels of UMRs that intersect with H3K4me3 ChIP-seq peaks in ZF-D3A no-dox and ZF-D3A +dox cells, for genomic DNA or DNA immunoprecipitated with an anti-H3K4me3 antibody. (E) DNA methylation levels of UMRs that intersect with phos- pho-ser5 RNA polymerase II ChIP-seq peaks in ZF-D3A no-dox and ZF-D3A +dox cells, for genomic DNA or DNA immunoprecipitated with an anti-phospho-ser5 RNA polymerase II antibody.
Frequent lack of repressive capacity of promoter DNA methylation identified through genome-wide epigenomic manipulation (2018)
Figure 4

Fig. 4. DNA methylation is lost in the absence of ZF-D3A by an active process. (A) Western blot measurement of ZF-D3A protein abundance upon doxycycline induction and withdrawal. (B) Box and whisker plots of the DNA methylation levels of the 121 CpG sites in the DACH1 promoter region through- out doxycycline induction and withdrawal. Whiskers indicate 1.5 times the interquartile range or the most extreme data point, whichever is lower. (C) Average DNA methylation level at the 121 CpG sites in the DACH1 promoter region throughout doxycycline induction followed by doxycycline withdrawal and cell cycle inhibition by growth in doxycycline-free media containing different cell cycle inhibitors (Lovastatin, RO-3306, thymidine). (D) DMR mCG and hmCG levels in MCF7-control and ZF-D3A +dox cells, ordered by the difference in DMR hmCG level (hmCG/CG) between MCF7-control and ZF-D3A +dox cells, and heatmap representation of H3K4me3 levels in ZF-D3A +dox cells flanking (±2.5 kb, 50 bp bins) the centre of the DMRs. (E) Heatmap of hmCG levels flanking (±2.5 kb, 50 bp bins) the centre of the DMRs, ordered by DMR hmCG level in MCF7-control cells.
Frequent lack of repressive capacity of promoter DNA methylation identified through genome-wide epigenomic manipulation (2018)
Figure 5

Fig. 5. Forced promoter methylation is frequently insufficient for transcriptional repression. (A) Scatter plot of the difference in UMR DNA methylation levels, versus the fold change in mRNA abun- dance of UMR-associated expressed genes, between MCF7-control and ZF-D3A +dox. Point color indicates the gene differential expression significance: red indicates FDR <0.01, black indicates FDR >0.01. Trend line (green) was fitted using a generalized additive model, and contour lines represent the relative density of UMRs. (B) Distribution of the ratios of mRNA levels between ZF-D3A +dox and MCF7-control, for expressed genes that have an associated UMR with a ∆mCG >0.3. Column color indicates the class of mRNA abundance ratios between ZF-D3A +dox and MCF7-control: blue = no decrease; green = small decrease; red = decrease. (C) Representative genes with high induction of promoter DNA methylation in ZF-D3A +dox and no reduction in mRNA abundance.
Frequent lack of repressive capacity of promoter DNA methylation identified through genome-wide epigenomic manipulation (2018)
Figure 6

Fig. 6. Heterogeneity in gene expression between cells does not reflect differences in DNA methylation. Fluorescence distribution of cells stained with RNA-FISH probes targeting (A) GAPDH or (B) PSME1 mRNA before and after FACS. mRNA quantitation after FACS sorting of (C) GAPDH by RT-qPCR, and (D) PSME1 by RNA-seq. DNA methylation levels of single CpGs at the promoter region of (E) GAPDH, and (F) PSME1, for the low (x axis) and high (y axis) expressing sorted cell populations. (G) Genome browser representation of the GAPDH (top) and PSME1 (bottom) promoter regions showing DNA methylation in MCF7-control and low and high expressing sorted cell populations. (H) Distribution of per-read methylation levels (blue line) for selected ZF-D3A +dox WGBS reads (≥10 cytosines in the CpG context) that align within robustly methylated DMRs (DMR mean methylation level [mCG/CG] of 0.4-0.6 in ZF-D3A +dox and <0.1 in MCF7-control), and average CG methylation levels from ZF-D3A +dox WGBS data of the DMR genomic intervals covered by the selected reads (red line).
m6A RNA methyltransferases Mettl3/14 regulate immune responses to anti-PD-1 therapy (2020)
Figure 1

Figure 1. Depletion of Mettl3 or Mettl14 sensitizes CT26 and B16 tumors to immunotherapy

A, B. Immunoblotting were performed to validate Mettl3 or Mettl14 expression levels in CT26 and B16 cells as indicated. Gapdh served as a loading control.

C, D. Tumor volume was monitored for control and Mettl3 or Mettl14-depleted tumors with treatment as indicated in CT26 colon cancer and B16 melanoma, respectively. Data are mean ± s.e.m of the indicated number of mice in each group. n, the numbers of mice. * P < 0.05; * * * P < 0.001 by Student's t-tests.

E, F. Survival analysis of control tumors and those with depleted genes were recorded as indicated in CT26 colon cancer and B16 melanoma, respectively. Data are mean ± s.e.m. of the indicated number of mice in each group. n, the numbers of mice. * P < 0.05; * * P < 0.01; * * * P < 0.001 by Student's t-tests.
m6A RNA methyltransferases Mettl3/14 regulate immune responses to anti-PD-1 therapy (2020)
Figure 2

Figure 2. Mettl3 or Mettl14 deficiency enhances tumor-infiltrating CD8+ T cells and cytokine production.

A. Percentage of tumor-infiltrating T cells, Treg and NK cells were identified by flow cytometry from CT26 tumors as indicated. Each spot represents one mouse. * P < 0.05; * * P < 0.01 by Student's t-tests.

B. Representative images of CD8 by IHC staining. Tissue sections from BALB/c mice bearing the indicated knockout of genes with treatment of PD1 antibody. Scale bars, 50 μm.

C. Percentage of Granzyme B-expressing CD8+ T cells from control and Mettl3 or Mettl14 deficient CT26 tumors. Each spot represents one mouse. * P < 0.05; * * P < 0.01 by Student's t-tests.

D, E. Mice bearing control and Mettl3 or Mettl14 null tumors were treated with CD8-depleting antibody and PD-1 antibody or PD-1/GVAX as indicated. Tumor volume was measured over time points. n, the numbers of mice. * P < 0.05; * * P < 0.01; * * * P < 0.001 by Student's t-tests.

F, G. IFNγ production in serum (F) and Intratumor (G) from BALB/c mice by ELISA. The results are representatives of at least three independent experiments. n, the numbers of mice. Data are mean ± s.e.m. * P < 0.05 by Student's t-tests.
m6A RNA methyltransferases Mettl3/14 regulate immune responses to anti-PD-1 therapy (2020)
Figure 3

Figure 3. Identification of target genes of Mettl3 and Mettl14 by RNA-seq and m6A-seq.

A. Volcano plot of differentially expressed genes obtained by DESeq2 analysis in Mettl3 or Mettl14 null tumors compared to control tumors. Significantly upregulated or downregulated genes are plotted in red and blue points, respectively. n.s, non-significant.

B. Venn diagrams showing 202 significantly co-upregulated genes and 28 significantly co-downregulated genes in the indicated tumors.

C. Meta-enrichment analysis summary for 202 significantly co-upregulated genes as indicated in (C).

D. Consensus m6A motifs and P value identified by HOMER from two biological replicates, Student's t-tests.

E. Schematic workflow for analysis of Mettl3 and Mettl14 downstream genes and identified genes or peaks number.

F. Representative genes with m6A sites generated by integrative genomics viewer. Data is representative of duplicates with similar results. Red represents reads coverage of IP sample and blue represents reads coverage of input sample. Rectangular cyan shade represents the m6A peaks located on transcripts.

G. m6A enrichment of Stat1 and Irf1 was examined by m6A RIP-qPCR in control, Mettl3, or Mettl14 depleted CT26 tumors as indicated. Ctla4 functioned as a m6A negative control (Wang et al., 2019) Data are mean ± s.d. * * P < 0.01 by Student's t-tests.

H. Immunoblots of p-Stat1 (phosphorylated), Stat1 and Irf1 were carried out in the indicated tumors in triplicate with Gapdh as a loading control.

I. Tumor growth from CT26 cells with Mettl3, Mettl14, Mettl3/Stat1, Mettl3/Irf1, Mettl14/Stat1 or Mettl14/Irf1-depleted genes and control under treatment of PD-1 antibody as indicated. n, the numbers of mice. Data are mean ± s.e.m of the indicated number of mice in each group.

* P < 0.05; * * P < 0.01 by Student's t-tests.
m6A RNA methyltransferases Mettl3/14 regulate immune responses to anti-PD-1 therapy (2020)
Figure 4

Figure 4. Tumor cells with knockout of Mettl3 or Mettl14 exhibit enhanced response to IFNγ.

A. Cellular proliferation analysis of Mettl3 or Mettl14 depleted and control CT26 cells treated with indicated combinations of cytokines for 48h. The mean ± SD of three replicates is shown. * P < 0.05; * * P < 0.01 by Student's t-tests.

B. BALB/c mice bearing Mettl3 or Mettl14 deficient and control tumors were treated with IFNγ -blocking antibody and PD-1 antibody as indicated. Tumor size was measured over time. n, the numbers of mice. Data are mean ± s.e.m of the indicated number of mice. * P < 0.05; * * P < 0.01; * * * P < 0.001 by Student's t-tests.

C. Quantitative RT-PCR was performed to identify transcriptional changes of the IFN-γ response gene expression (n=3). Data are shown as the relative fold change (RFC, color coded bar).

D, E. mRNA stability of Stat1 and Irf1 were measured by qRT-PCR in tumor cells treated with IFN-γ and actinomycin D. Mean ± SD of n=3. * * P < 0.01; * * * P < 0.001 by Student's t-tests.

F. Validation the effect of knock out of Ythdf1-3 using western blotting, Gapdh served as a loading control.

G. qPCR analysis of Stat1 and Irf1's expression in the indicated depletion of CT26 cells with/without stimulation of IFNγ. Mean ± SD of n=3. * P < 0.05 by Student's t-tests.

H. Western blot analysis of Mettl3 and Mettl14 in overexpressed CT26 cells. Gapdh served as a loading control for each.

I, J. qPCR analysis of the mRNA stability of Stat1 and Irf1 in the indicated CT26 cells treated with IFNγ and actinomycin D. Mean ± SD of n=3. * P < 0.05; * * P < 0.01; * * * P < 0.001 by Student's t-tests.
m6A RNA methyltransferases Mettl3/14 regulate immune responses to anti-PD-1 therapy (2020)
Figure 5

Figure 5. The negative correlation of METTL3, METTL14 and Stat1 in human pMMR-MSI-L CRC colon tissue.

A, B. The protein level of STAT1 was negatively correlated with METTL3 and METTL14 in human pMMR-MSI-L CRC colon tissue (r2=-3.2477 for METTL3, r2=-2.7491 for METTL14). Each dot represents one tumor tissue.

C. Schematic showing the functional and molecular mechanisms of Mettl3 and Mettl14 in antitumor immunotherapy.

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