EMBO/soda-nel huggingface dataset:
Coordinated demethylation of H3K9 and H3K27 is required for rapid inflammatory responses of endothelial cells. (2020)
Figure 2

Figure 2. KDM7A and UTX participate in TNF-α-induced NF-κB-p65 signaling pathways leading to the activation of ECs.

(A) Heat map representation of RNA-seq results. The p65-dependent genes (total 123; defined as genes up-regulated more than 2-fold via TNF-α treatment and down-regulated more than 0.75-fold via BAY 11-7082 treatment) were classified based on the alteration patterns by the siRNA knockdown of KDM7A, UTX, and both, using the algorithm "HOPACH".

(B) Three-dimensional (3D) multidimensional scaling (MDS) plots. ECs in the presence or absence of TNF-α ± BAY, siKDM7A, siUTX, or siKDM7A+siUTX were mapped to the 3D space, based on the expression profiles of the TNF-α-responsive genes, by using classical MDS. Each treated cell is represented by a colored dot.

(C-E) Bar plot showing mean mRNA levels of VCAM1, ICAM1, and SELE measured 4 hrs after stimulation of ECs with or without TNF-α ± (siKDM7A+siUTX). Data are shown as means ± SE (n=3). *P < 0.05 compared to TNF-α (-) siKDM7A+siUTX (-). † P < 0.05 compared to TNF-α (+) siKDM7A+siUTX (-). Statistical differences were analyzed by the Tukey-Kramer test.

(F) Western blots for VCAM1, ICAM, SELE, and ACTB in lysates from ECs treated with or without TNF-α (4 hrs) ± (siKDM7A+siUTX).

(G) Representative images (top) and bar plot quantification (bottom) showing the adhesion of calcein-labeled U937 monocytes to ECs treated with or without TNF-α ± (siKDM7A+siUTX). Scale bar represents 250 μm. Graphs are representative of three independent experiments. Data are shown as means ± SD. *P < 0.05 compared to TNF-α (-) siKDM7A+siUTX (-). † P < 0.05 compared to TNF-α (+) siKDM7A+siUTX (-). Statistical differences were analyzed by the Tukey-Kramer test.
Coordinated demethylation of H3K9 and H3K27 is required for rapid inflammatory responses of endothelial cells. (2020)
Figure 3

Figure 3. ChIP-seq revealing the recruitment of KDM7A and UTX to NF-κB-p65 related elements.

(A and B) Scatter plots showing the connection between KDM7A or UTX binding and RNA expression in TNF-α-stimulated ECs. The X and Y axes indicate the log2 ratio of fragments per kilobase of exon per million mapped sequence reads (FPKM) values and the ChIP-seq signals (TNF-α 60min/0min) for KDM7A (A) and UTX (B), respectively. Genes with more than a 1.5-fold increase in FPKM values (light red dots) satisfying a 0.5-fold increase in ChIP-seq signals are indicated as dark red dots. Genes with a 1.5-fold decrease in FPKM values (light blue dots) satisfying a 0.5-fold decrease in ChIP-seq signals are indicated as dark blue dots. These genes are defined as the target genes for KDM7A (A) and UTX (B), respectively. The names of representative genes are shown in the graphs.

(C and D) Metagene representations of KDM7A (C) and UTX (D) ChIP-seq signals in units of read count per million mapped reads at a meta composite of the genomic regions around the TNF-α-up-regulated genes.

(E) De novo motif analyses for KDM7A (top) and UTX (bottom) binding recognition sequences in TNF-α-stimulated ECs. The P-value indicates significant enrichment of the transcription factor binding motif in KDM7A/UTX binding sites, in comparison to a size-matched random background.

(F) Heat map of H3K27ac (green), p65 (yellow), KDM7A (red), and UTX (purple) levels in resting and TNF-α-stimulated ECs. Each row shows ± 10 kb centered on the KDM7A (top) or UTX (bottom) peak. Rows are ordered by max KDM7A or UTX signal in each region. The ChIP-seq signal is depicted by color scaled intensities.

(G and H) Gene tracks of ChIP-seq signals for H3K27Ac, p65, KDM7A, and UTX, and RNA-seq signals around the VCAM1 (G) and SELE (H) loci in TNF-α (-) and TNF-α (+) ECs. ChIP-Seq and RNA-seq signals are visualized by Integrated Genome Viewer (http://software.broadinstitute.org/software/igv/).

(I and J) ChIP-qPCR of p65 (I) and KDM7A (J) at the VCAM1 TSS, normalized to input. Graphs are representative of three independent experiments. Data are shown as means ± SD. *P < 0.05 compared to TNF-α (-). † P < 0.05 compared to TNF-α (+) siKDM7A (-). Statistical differences were analyzed by the Tukey-Kramer test.

(K and L) ChIP-qPCR of p65 (K) and UTX (L) at the SELE TSS, normalized to input. Graphs are representative of three independent experiments. Data are shown as means ± SD. *P < 0.05 compared to TNF-α (-). Statistical differences were analyzed by the Tukey-Kramer test.
Coordinated demethylation of H3K9 and H3K27 is required for rapid inflammatory responses of endothelial cells. (2020)
Figure 4

Figure 4. H3K9me2 and H3K27me3 marks around KDM7A- and UTX-target genes change after TNF-α-treatment.

(A and B) Boxplots of the log2 fold change of ChIP-seq enrichment of H3K9me2 in KDM7A target genes (A) and that of H3K27me3 in UTX target genes (B) with TNF-α treatment (TNF+) and control (TNF-). The horizontal line within the box represents the median sample value. The ends of the box represent the 3rd and 1st quantiles. The whiskers extend from the ends of the box to the outermost data point that falls within the distances computed as follows: 3rd quartile + 1.5*(interquartile range) and 1st quartile - 1.5*(interquartile range). Each dot in the box plot shows data for one single target gene. Genes colored red represent the top five downregulated target genes with TNF-α-treatment, as compared to control. Graphs are representative of two independent experiments.

(C) Gene tracks of ChIP-seq signals for H3K9me2 and H3K27me3 around the VCAM1 locus in TNF-α (-) and TNF-α (+) ECs.

(D and E) ChIP-qPCR showing enrichment (percent input) of H3K9me2 (D) and H3K27me3 (E) around the VCAM1 locus. Primer pairs targeting distinct regions are listed on the X axis. Graphs are representative of three independent experiments. Data are shown as means ± SD. *P < 0.05 compared to TNF-α (-). Statistical differences were analyzed by the Student's t test.

(F) Gene tracks of ChIP-seq signals for H3K9me2 and H3K27me3 around the SELE locus in TNF-α (-) and TNF-α (+) ECs.

(G and H) ChIP-qPCR showing enrichment (percent input) of H3K9me2 (G) and H3K27me3 (H) around the SELE locus. Primer pairs targeting distinct regions are listed on the X axis. Graphs are representative of three independent experiments. Data are shown as means ± SD. *P < 0.05 compared to TNF-α (-). Statistical differences were analyzed by the Student's t test.

(I and J) ChIP-qPCR showing enrichment (percent input) of H3K9me2 and H3K27me3 around the HOXA13 (I) and GATA4 (J) loci. Graphs are representative of three independent experiments. Data are shown as means ± SD. *P < 0.05 compared to TNF-α (-). Statistical differences were analyzed by the Student's t test.

(K and L) ChIP-qPCR showing enrichment (percent input) of H3K9me2 around VCAM1 -11kb (K) and H3K27me3 around SELE -12kb (L). Graphs are representative of three independent experiments. Data are shown as means ± SD. *P < 0.05 compared to TNF-α (-). † P < 0.05 compared to TNF-α (+) siKDM7A (-) or TNF-α (+) siUTX (-). Statistical differences were analyzed by the Tukey-Kramer test.
Coordinated demethylation of H3K9 and H3K27 is required for rapid inflammatory responses of endothelial cells. (2020)
Figure 5

Figure 5. The recruitment of KDM7A and UTX is associated with the formation of TNF-α-induced SEs and their associated chromatin loop formation.

(A) Plot of enhancers defined in TNF-α-treated ECs, ranked by increasing bromodomain-containing protein 4 (BRD4) signal. Super enhancers (SEs) were defined by ROSE (Loven et al, 2013, Whyte et al, 2013), based on the ChIP-Seq signals for BRD4 (Brown et al, 2014). SEs are indicated by dashed lines and are colored yellow. The numbers show the total SEs and their classification based on KDM7A and UTX binding. Representative SE-related genes are indicated in the graph.

(B and C) Metagene representations of KDM7A (B) and UTX (C) ChIP-seq signals in units of read count per million mapped reads, at a meta composite of SEs and typical enhancers (TEs). TEs were obtained from the pre-compiled HUVEC ngsplotdb (https://github.com/shenlab-sinai/ngsplot).

(D) Pirate plots showing ChIA-PET interaction changes at control-specific, common, and TNF-α-specific SEs. The Y axis indicates the log2 ratio of ChIA-PET signals (TNF-α 60 min/0 min). The central bar indicates the average.

(E and F) Bean plots showing ChIA-PET interaction changes at KDM7A (E) and UTX (F) binding sites. The Y axis indicates the log2 ratio of ChIA-PET signals (TNF-α 60 min/0 min). The central bar indicates the median.

(G and H) ChIA-PET interactions of active RNA pol II around the VCAM1 (G) and IL8 (H) loci integrated with the ChIP-seq profiles of KDM7A, UTX, and BRD4, and RNA-seq profiles in TNF-α (-) and TNF-α (+) ECs. ChIA-PET interactions were visualized by the WashU Epigenome Browser (http://epigenomegateway.wustl.edu/browser/). Interactions detected by ChIA-PET are depicted with purple lines. Red bars show TNF-α-specific SEs.

(I-K) Gene tracks of ChIP-seq signals for H3K9me2 and H3K27me3 around VCAM1 (I), IL8 (J), and SELE (K) in TNF-α (-) and TNF-α (+) ECs. Red bars show TNF-α-specific SEs.
Coordinated demethylation of H3K9 and H3K27 is required for rapid inflammatory responses of endothelial cells. (2020)
Figure 6

Figure 6. KDM7A and UTX are important for leukocyte adhesion in mice.

(A) Drug administration protocol for intravital microscopy (IVM) experiments. Male, 10 week old C57BL/6 mice (n=3/group) were pretreated with vehicle or mixed inhibitor (Daminiozed+GSKJ4, 50 mg/kg each) at 16 hrs and 1 hr prior to TNF-α treatment. Inflammation was induced by an intraperitoneal injection of TNF-α (5 μg/mouse) and IVM was performed 4 hrs later.

(B) Representative snapshot from the IVM analysis of leukocyte adhesive interactions in the femoral arteries. Margins of vessels are indicated with dashed lines. White spots represent fluorescently labeled leukocytes visualized by the intravenous injection of rhodamine 6G.

(C) Quantitative analyses of leukocyte adhesive interactions in the femoral arteries. Data are shown as means ± SE (n=3/group). *P < 0.05 compared to the vehicle + TNF-α group. Statistical differences were analyzed by the Student's t test. See also Movies EV1 and 2.
Whole-genome analysis of TET dioxygenase function in regulatory T cells (2021)
Figure 1

Figure 1. Dysregulated transcriptional program in Treg cells in the absence of Tet2 and Tet3.

(A) Principal component analysis of WT CD4+ naïve T cell (gray), WT Treg cells (blue), and Tet2/3 DKO Treg cells (red). The percent of variation explained by principle components 1 (PC1) and 2 (PC2) are shown in parenthesis.

(B) Scatter plots showing the comparison of mean gene expression between WT Treg cells vs CD4+ naïve T cells (upper panel) and WT Treg cells vs Tet2/3 DKO Treg cells (lower panel). Differentially expressed genes (FDR ≤ 0.05 and fold-change ≥ 2) are shown in color (blue: WT Treg cells; black: CD4+ naïve T cells; red: Tet2/3 DKO Treg cells) and selected genes are labeled in the plots. The scale is log2(FPKM+1).

(C-E) Gene set enrichment analysis of gene expression changes between WT Treg cells vs CD4+ naïve T (upper panels) and WT Treg cells vs Tet2/3 DKO Treg cells (lower panels) for the hallmark gene sets IL2/STAT5 signaling (C), E2F Target genes (D) and Treg signature gene set that are upregulated in Treg cells compared to conventional T cells (GSE7460) (E). The green line represents the enrichment score with enrichment in WT Treg cells shown to the left and enrichment in CD4+ naïve T (upper panels) or Tet2/3 DKO Treg cells (lower panels) shown to the right. Overlap of genes in the given gene set is shown below and denoted by black ticks.

(F) Examples of expression levels of Treg signature genes Foxp3, Il10ra, Nrp1, Ikzf4 and Ccr6; and examples of expression levels of genes upregulated in Tet2/3 DKO Treg cells Eomes, Ccl4, Chek1 and Fn1. The Y-axis shows FPKM (Fragment Per Kilobase per Million reads), the X-axis shows the different cell types. Error bars show mean ± S.D. from two (CD4+ naïve T cells) or four (WT and Tet2/3 DKO Treg) biological replicates.
Whole-genome analysis of TET dioxygenase function in regulatory T cells (2021)
Figure 2

Figure 2. 5hmC correlates with gene expression.

(A) Anti-CMS dot blot assay assessing the global amount of 5hmC levels in CD4+ naïve T cells and WT Treg cells. Data are representative of three biological replicates.

(B) Heatmpas of differentially hydroxymethylated regions (DhmRs) between CD4+ naïve T cells and WT Treg cells.

(C) Scatter plot of 5hmC changes (Y-axis) and gene expression changes (X-axis) between CD4+ naïve T and WT Treg cells. DhmRs associated with a differentially expressed gene are denoted in color (blue: WT Treg cells; black: CD4+ naïve T cells).

(D) Genome browser view of CMS-IP data showing CMS (5hmC) distribution at Il2ra and Foxp3 locus for CD4+ naïve T cells and WT Treg cells. Hydroxymethylated regions (hmRs) and DhmRs are shown on the top; conservation (cons) is shown at the bottom.

(E) The graphs show CMS (5hmC) signal (RPM) in DhmRs denoted by the shaded region in D for IP and Input in CD4+ naïve T cells and WT Treg cells. FDRs are calculated based on the Benjamini-Hochberg correction of P-values determined using a negative binomial distribution (see methods). Error bars show mean ± S.D. from two independent biological replicates.

(F) The graphs show the expression levels of Il2ra (upper panel) and Foxp3 (lower panel) in CD4+ naïve T cells and WT Treg cells. Error bars show mean ± S.D. from two (CD4+ naïve T cells) or four (WT Treg) independent biological replicates.
Whole-genome analysis of TET dioxygenase function in regulatory T cells (2021)
Figure 3

Figure 3. Treg-specific DNA demethylation patterns are impaired in the absence of Tet2 and Tet3.

(A) Principal component analysis of DNA methylation data at 17,937,401 CpGs for CD4+ naïve T cells (gray), WT Treg cells (blue) and Tet2/3 DKO Treg cells (red). The percentage of variation explained by each component is shown in parentheses.

(B) Scatter plots of DNA methylation data showing the comparisons between WT Treg vs CD4+ naïve T cells (left) and WT Treg vs Tet2/3 DKO Treg cells (right). The differentially methylated CpGs (DM-CpGs) are shown in color (blue: WT Treg; black: CD4+ naïve T cells; red: Tet2/3 DKO) and the numbers are shown on top of each graph.

(C) K-means clustering of DNA methylation data for all the samples using CpGs differentially methylated between CD4+ naïve T cells vs WT Treg cells and WT Treg vs Tet2/3 DKO Treg cells (left); the number (N) of CpGs in each cluster is shown in parenthesis. Middle: CpG loci proximal to selected genes are annotated. Right: the average DNA hydroxymethylation levels at each CpGs as measured by CMS-IP.

(D) The average DNA methylation levels at selected consensus transcription factor-binding motifs (gray: CD4+ naïve T cells; blue: WT Treg cells; red: Tet2/3 DKO Treg cells). Standard error is denoted in transparency of the corresponding colors.

(E) Genome browser view of DNA methylation patterns of Hopx and Il10 loci for CD4+ naïve T cells, WT Treg cells and Tet2/3 DKO Treg cells. Differentially hydroxymethylated regions (DhmRs) and differentially methylated CpGs (DM-CpGs) are shown on top of the tracks. Conservation track (Cons) and denotation of all CpGs are shown at the bottom. WGBS data are from two biological replicate samples.
Whole-genome analysis of TET dioxygenase function in regulatory T cells (2021)
Figure 4

Figure 4. Changes of chromatin accessibilities in the absence of Tet2 and Tet3.

(A) Principal component analysis of WT CD4+ naïve T cell (gray), WT Treg cells (blue), and Tet2/3 DKO Treg cells (red). The percent of variation explained by principle components 1 (PC1) and 2 (PC2) are shown in parenthesis.

(B) Scatter plot of ATAC-seq data for WT Treg vs Tet2/3 DKO Treg cells. Differentially accessible regions (DARs) are shown in color and the total number of DARs (N) is shown on the graph.

(C) K-means clustering of ATAC-seq data for DARs between WT Tregs and Tet2/3 DKO Tregs. ATAC-seq data for CD4+ naïve T cells is from Yoshida et al, 2019. The number of regions in each cluster is denoted.

(D) Heatmap of transcription factor binding motifs in each K-means cluster. The significance of enrichment (-log10(FDR)) is denoted by color scale. Transcription factor families of particular interest are highlighted on top of the heatmap.

(E) Genome browser view of chromatin accessibilities at Hopx and Il10 loci for CD4+ naïve T cells, WT Treg cells and Tet2/3 DKO Treg cells. DhmRs, DM-CpGs and DARs (shaded) are shown on top of the tracks. Conservation track (Cons) is shown at the bottom.

(F) Expression levels of Hopx and Il10 in CD4+ naïve T cells, WT Treg cells and Tet2/3 DKO Treg cells. Error bars show mean ± S.D. from two biological replicate samples.
Whole-genome analysis of TET dioxygenase function in regulatory T cells (2021)
Figure 7

Figure 7. Enrichment of STAT motifs in regions that are more accessible with Vitamin C treatment.

(A) Scatter plot of pairwise comparison of chromatin accessibility changes between TGFβ + RA + VitC vs TGFβ iTregs showing the top identified motifs.

(B) CD4+ naïve T cells were sorted from Foxp3-IRES-eGFP reporter mice, labeled with cell proliferation dye cell trace violet and differentiated into iTreg cells with TGFβ alone or TGFβ + RA + VitC with different amounts of IL-2. Cells in the cultures were analyzed daily by flow cytometry. The two upper panels show representative dot plots of Foxp3 expression vs cell proliferation for TGFβ alone and TGFβ + RA + VitC iTregs with 100 U/ml IL-2 from day 2 to day 9. The two lower panels show representative dot plots of Foxp3 expression vs cell proliferation for TGFβ alone and TGFβ + RA + VitC iTregs with 0 U/ml IL-2 from day 2 to day 9.

(C) Graph showing the percentage of Foxp3+ cells at each day for TGFβ iTregs and TGFβ + RA + VitC iTregs with either 0 U/ml IL-2 or 100 U/ml IL-2. Error bars show mean ± S.D. from three biological replicates.

(D) Graphs depicting the percentage of viable cells for TGFβ iTregs (left) and TGFβ+RA+VitC iTregs (right) differentiated with 0 U/ml, 1 U/ml, 5 U/ml and 10 U/ml IL-2. Error bars show mean ± S.D. from three biological replicates.
Whole-genome analysis of TET dioxygenase function in regulatory T cells (2021)
Figure 8

Figure 8. Vitamin C facilitates the binding of STAT5 and maintains IL2Rα expression in iTregs.

(A-B) CD4+ naïve T cells were sorted from Foxp3-IRES-eGFP reporter mice and differentiated into iTreg cells with TGFβ alone or TGFβ + RA + VitC in the presence of 100 U/ml IL-2 in cultures. On day 6, cells were restimulated without IL-2 or with 3, 5, 10 and 100 U/ml IL-2 at 37°C for 1hr, and stained for phospho-STAT5. Representative histogram overlay of phospho-STAT5 staining for TGFβ and TGFβ + RA + VitC iTregs after IL-2 restimulation (A). Graph depicting the geometric mean fluorescent intensity (MFI) of phospho-STAT5 in TGFβ and TGFβ + RA + VitC iTregs after IL-2 restimulation; each dot represents cells from an individual mouse (B). Data are from seven biological replicates. P-values are calculated using two-tailed paired Student's t test and error bars show mean ± S.D.

(C) ChIP-qPCR showing STAT5 occupancy at Foxp3 CNS2 and IL2Rα IN1b regions in TGFβ and TGFβ + RA + VitC iTregs without or with 3 U/ml IL-2 restimulation. GMPR is a negative control region. Data are from two biological replicates.

(D-E) Representative histogram overlay (D) and geometric MFI (E) of IL2Rα staining for TGFβ and TGFβ + RA + VitC iTregs on day 3 to day 6 after differentiation in the presence of 100U/ml IL-2. Data are from three biological replicates. P-values are calculated using two-tailed paired Student's t test and error bars show mean ± S.D.

Expanded View Figure Legends:
An amphipathic Bax core dimer forms part of the apoptotic pore wall in the mitochondrial membrane (2021)
Figure 4

Figure 4. Pore-forming activity of Bax proteins with mutations that would reduce the α2-α5 dimer interaction with membranes

(A) NMR structure of Bax (α2-α5) dimer and lipid bilayer-interacting residues. α helices are shown as cylinders. In the α4 and α5 helices, some of the lipid bilayer-interacting nonpolar, polar and positively charged residues are shown as orange, green and blue sticks, respectively, including the R89, F93, F114, A117 and S118 that were mutated to negatively charged residues to disrupt the interaction with lipid headgroups (red ovals) or acyl chains (orange curves). The longest axis (L) of the dimer is tilted 60° from the bilayer normal (N) to maximize the nonpolar interaction with the lipid acyl chains while allowing R89 to interact with the polar lipid headgroups.

(B-C) Fluorescent dye release from the mitochondria-mimic liposomes by the indicated wild-type (wt) or mutant Bax in the presence (B) or absence (C) of tBid or by tBid alone (C) was measured by quenching the fluorescence of the released dyes by the dye-specific antibodies outside the liposomes during a time course. The fraction of dye release was normalized to that by detergent. The data shown were obtained from n = 3 independent replicates using the same preparations of the proteins and liposomes.

(D) Digitonin permeabilized BMK Bax-/-/Bak-/- cells expressing SMAC-mCherry in the mitochondria intermembrane space (50 μL, 500,000 total cells) were incubated with 25 nM of Bax and 2 nM cBid for the indicated time at 37°C in a 96-well plate. The samples were centrifuged for 10 min and separated into supernatant and pellet fractions. SMAC-mCherry release was calculated as the fraction of total (supernatant + pellet) mCherry fluorescence coming from the supernatant fraction. The data was normalized to the percent SMAC-mCherry release of wt Bax and cBid at 60 min. Each symbol represents the normalized SMAC-mCherry release for one single replicate (n = 3 or more independent replicates). Where applicable the data was fit with the [Agonist] vs. response - Variable slope equation in Graphpad Prism 8.0.1. The dotted lines represent the 95% confidence interval of the fit.
Iron supplementation is sufficient to rescue cancer-induced muscle wasting and function. (2022)
Figure 1

Fig. 1. Iron deficiency induces skeletal muscle atrophy

(A-B) Hemoglobin (A) and hematocrit (B) levels of healthy subjects and cachectic cancer patients presenting a body weight loss superior to 10% of initial body weight (19 healthy subjects, 17 cachectic patients).

(C) TFR1 mRNA levels in muscle biopsies from cancer-patients of late stage cachexia (19 healthy subjects, 17 cachectic patients with at least 10% total body weight loss);

(D) Gastrocnemius weight in mice subjected to iron deprivation by feeding with an iron deficient diet (IDD) combined to a phlebotomy (PHL) (n=5-6);

(E) TFR1 mRNA levels in the liver of mice subjected to iron deprivation by feeding with an iron deficient diet (IDD) combined to a phlebotomy (PHL) (n=5-6);

(F) TFR1 mRNA levels in the gastrocnemius of mice subjected to iron deprivation by feeding with an iron deficient diet (IDD) combined to a phlebotomy (PHL) (n=5-6);

(G) Cross-sectional area of skeletal muscle fiber transfected with shSCR (scramble) and shTFR1 (n=3-4) and representative picture of a shTFR1 transfected fiber. Scale bar=50µm.

(H) Cross-sectional area of skeletal muscle fibers transfected with TFR-pHuji (n=4) and representative picture of a TFR-pHuji transfected fiber. Scale bar=50µm.

(I-J) Diameter of TfR1 (I) or NCOA4 (J) knocked down C2C12 myotubes at day 3 post-transfection (n=7 and n=3 respectively).

(K) Diameter of C2C12 myotubes after 48h treatment with Deferoxamine 100µM (DFO), bathophenanthroline disulfonate 100µM (BPS), or apo-transferrin (Apo-Tf).

(L) Reprensentative pictures and diameter measurements of human myoblast-derived myotubes after 48h treatment with DFO 100µM (n=3). Scale bar=50µm.

Data information: For all data, n represents the number of biological replicates. Statistical significance was calculated by unpaired, two-tailed Student's T-test. Data are mean ± SEM. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.