EMBO/soda-nel huggingface dataset:
STAT3-dependent analysis reveals PDK4 as independent predictor of recurrence in prostate cancer (2020)
Figure 7

Fig 7: PDK4 as putative STAT3 target

A. STAT3 and PDK4 stratified subgroups were generated by median splits in MSKCC PCa GSE21032 data set. Pearson correlation between STAT3 and PDK4 is shown. Kaplan-Meier plot shows stratified subgroups. P-values were estimated by Log-rank test and adjusted with Benjamini-Hochberg method. Hi = high, lo = low.

B. Western blot of STAT3, PDK4 and β-TUBULIN proteins in 22Rv1 cells with or without knockdown of STAT3. Ctrl = scrambled control, shSTAT3 = short hairpin knockdown of STAT3.

C. ChIP assay from IL-6 stimulated or non-stimulated 22Rv1 cells with or without knockdown of STAT3 was immunoprecipitated with a STAT3 specific antibody (blue shades) and IgG antibody as negative control (orange shades) followed by qPCR with a promoter- specific primer pair for the PDK4 gene. Bars represent mean ± SD from 2 technical replicates. Precipitated DNA is presented as % of input. One representative experiment is shown. Result of qPCR using primer pair 2 is shown. Ctrl = scrambled control, shSTAT3 = short hairpin knockdown of STAT3, +IL-6 = IL-6 stimulated.

D. Correlation of STAT3, c-MYC and HIF-1α with PDK1-4, PDC- genes (PDHA1, PDHB, PDHX, DLAT, DLD) and TCA/OXPHOS genes (CS, IDH2, IDH3A, SDHB, SDHC, ATP5A1, NDUFS1) in MSKCC PCa (GSE21032). Dot colors represent Pearson correlation (1 = red, -1 = blue), dot sizes represent adj. p-values ≤ 0.05. Only significant correlations are shown. P-values were adjusted with Benjamini-Hochberg method.

Tables and their legends

TCGA PRAD clinical data

n = 397

Table 1: Summary of TCGA PRAD clinical data used for WGCNA

Distribution of clinical variables among patients. PT = pathological tumor staging, pN = pathological lymph node staging.

Table 2: Targeted metabolomics group comparisons

ANOVAs with Tukey multiple comparisons of means between wild type (WT), Ptenpc-/- and PtenStat3pc-/- mice for each metabolite. df = Degree of Freedom, asterisk indicates significant p-values (< 0.05).

Table 3: Primer sequences used for ChIP.

Pro = promoter, _1 and _2 indicate parts of a primer pair.

Expanded View Figure legends
Quantitative imaging of RAD51 expression as a marker of platinum resistance in Ovarian Cancer (2021)
Figure 1

Figure 1. RAD51 assay development and testing on BCC cohort

A Range of example RAD51 nuclear expression score (RAD51NES) values with respective unmixed monochrome fluorescent IHC staining images. EpCAM is used as a tumour marker and an internal sample quality control. Scale bar is 50μm.

B Correlation of RAD51NES with two independent pathologist H‑scores (top left and top right) and correlation of two pathologist with each other (bottom). Pearson correlation.

C Distribution of RAD51 nuclear expression score (RAD51NES) in the BCC cohort. The cohort is divided into RAD51-Low expressing cases (first quartile, Q1 - blue), intermediate cases (interquartile range, IQR - grey) and RAD51-High expressing cases (fourth quartile, Q4 - brown). Dashed line denotes the median RAD51NES in this cohort.

D Survival analysis of the BCC cohort. Kaplan-Meier plots for progression-free survival (PFS) (left) and overall survival (OS) (right) stratified according to fourth quartile (Q4) and first quartile (Q1) of RAD51NES. Log-rank test, shading denotes 95% confidence intervals.

E Number of cases with progression at 12 and 24 months. Chi-square test.
Quantitative imaging of RAD51 expression as a marker of platinum resistance in Ovarian Cancer (2021)
Figure 3

Figure 3. RAD51 does not promote platinum resistance in-vitro but modulates immune-related gene expression

A In-vitro cell survival assay of HGSOC cell lines upon stable RAD51 overexpression. Mean with standard deviation is shown of at least three biological replicates per point. Extra-sum-of-squares F test.

B In-vitro colony forming assays comparing RAD51-overexpressing and control HGSOC cell line, Caov-3, after treatment with increasing doses of carboplatin. Mean and SD of four biological replicates (left) a representative experiment (right). P-value for a comparison between cell lines for each drug treatment condition is indicated above the bars. T-test.

C Gene Set Enrichment Analysis of RAD51-overexpression vs control HGSOC cell-lines (n=4). The top ten enriched pathways in RAD51-overexpression cell lines are listed on the right, ranked by the lowest false-discovery rate (FDR). The corresponding Enrichment Score (ES) is shown. See Table EV1 for details on all data points. Colouring denotes the top two enriched pathways. GO - Gene Ontology.

D Gene Set Enrichment Analysis of RAD51-High vs RAD51-Low HGSOC tumours from a TCGA cohort. The top ten enriched pathways in RAD51-High tumours are listed on the right, ranked by the lowest false-discovery rate (FDR). See Table EV3 for details on all data points. Colouring denotes genesets related to immune response pathways. ES - Enrichment Score.

E Volcano plot for fold changes of immune genes enriched or depleted in RAD51-High tumours vs RAD51-Low tumours from TCGA. T-test; dashed line denoted threshold of significance, Bonferroni corrected for multiple testing.

F Immune genes enriched in RAD51- High and RAD51-Low tumours across four EOC mRNA cohorts (TCGA, AOCS, MGH, Duke).

G Example unmixed fluorescent IHC images demonstrating the presence of CD8+ T cells in the Tumour (T) and Stroma (S) in a RAD51-High and a RAD51-Low tumour from the BCC cohort. Scale bar is 50μm.

H CD8+ cytotoxic T-cell (Tcyt) infiltration analysis in the BCC cohort. Absolute tumour cytotoxic T-cell infiltration numbers and tumour/stroma (T/S) cytotoxic T-cell number ratio in RAD51-High and -Low cases (left). Bar is median. Mann-Whitney test.

Figure Expanded View Legends
Hepatic stellate cell-expressed endosialin balances fibrogenesis and hepatocyte proliferation during liver damage. (2015)
Figure 1

Upregulation of endosialin (EN) expression by activated hepatic stellate cells (HSC) in early liver fibrogenesis

A EN qRT–PCR of murine liver sinusoidal endothelial cells (LSEC), Kupffer cells (KC), hepatocytes (Hep), and HSC (n = 3 per cell type).

B EN qRT–PCR of human hepatocytes (n = 1) and HSC cell line LX-2 (n = 3).

C EN immunofluorescence staining of LX-2 cells. Scale bar: 25 μm.

D EN qRT–PCR of normal liver (NL) and fibrotic liver (FL,n = 8 each), and active or inactive cirrhotic liver (CL+/−, n = 10/ n = 2).

E-H EN immunohistochemistry staining of human normal liver (E), fibrotic liver (F), and cirrhotic liver (G, H). Weak EN expression in portal tracts of normal liver (E). Abundant EN expression detected in activated HSC in fibrous septa of fibrotic (F), active cirrhotic (G), and inactive cirrhotic liver (H). Scale bars: 500 μm.

I-N Higher magnification of EN-stained normal liver (I), fibrotic liver (K), and (active) cirrhotic liver (M) compared to alpha-smooth muscle actin (αSMA) immunohistochemistry on serial slides (J, L and N). Scale bars: 100 μm.

O EN/αSMA mRNA ratio in normal liver, fibrotic liver, and active cirrhotic liver.

P Collagen 1a (Col1a) expression in normal, fibrotic, and active cirrhotic liver determined by qRT–PCR.

Data information: Data are expressed as mean ± s.e.m.; P-value (significant < 0.05) determined by Student's t-test.
Hepatic stellate cell-expressed endosialin balances fibrogenesis and hepatocyte proliferation during liver damage. (2015)
Figure 2

Reduced fibrosis in EN-deficient mice (ENKO) in chronic liver damage

A, B qRT–PCR for EN (A) and αSMA (B) of liver lysates from single high-dose CCl4- or oil-treated wild-type (WT) mice (n = 2 per time point).

C Early activated and long-term-activated HSC isolated by differential density gradient centrifugation (8.26% versus 11.5% density layer). qRT–PCR for EN from early activated (8.26%) and long-term-activated HSC (11.5%) from 4-week CCL4-treated mice and vehicle control mice (n = 3–4 per data point).

D EN expression in liver lysates from 2-, 4-, and 6-week CCl4-treated mice (n = 5–8) and vehicle control mice (n = 3) determined by qRT–PCR.

E-G METAVIR score of 2-, 4-, and 6-week CCl4-treated mice (F–G) Col1a and F4/80 immunofluorescence of 6-week CCl4-treated and vehicle control WT (F) and ENKO (G) mice.

H Mean Col1a+ area per high power field (HPF) shown as percentage of the total area (n = 5–8).

I Western blot analysis for collagen 1a (Col1a), platelet-derived growth factor receptor beta (PDGFRβ) and β-actin of total liver protein lysates from 6-week CCl4-treated or control vehicle WT and ENKO mice (n = 3–5).

J-L Double immunofluorescence of 6-week CCl4-treated and vehicle control WT (J) and ENKO (K) mice for desmin and αSMA (L) shown as percentage of the total area (n = 5–8).

M Serum liver enzymes (GPT/ALT and GOT/AST) from 2-, 4-, and 6-week CCl4-treated WT and ENKO mice (n = 5–8).

N qRT–PCR of endosialin (EN), αSMA, and collagen 1a1/6a1 from lentiviral-mediated endosialin knockdown (shEN) and control-transfected (CTL) human hepatic stellate cells (LX-2).

O Lentiviral-mediated endosialin knockdown (shEN) and control (CTL) in LX-2 cells (n = 3). Magnification 10×.

Data information: Scale bars: 250 μm. CTL = vehicle control mice. Data are expressed as mean ± s.d. (A–D), or s.e.m. (E, H, L and M); P-value (significant < 0.05) determined by Student's t-test. Source data are available online for this figure.
Hepatic stellate cell-expressed endosialin balances fibrogenesis and hepatocyte proliferation during liver damage. (2015)
Figure 3

Negative regulation of hepatocyte proliferation by HSC-expressed endosialin

A Representative images of Ki-67 immunohistochemistry after 2, 4, and 6 weeks of CCl4 treatment of WT and ENKO mice (n = 5–8 mice per group and time point). Scale bars: 75 μm.

B Quantitation of Ki67+ hepatocytes/HPF after 2, 4, and 6 weeks of CCl4 treatment.

C Time course of EN expression after partial hepatectomy (2/3 PHx) determined by qRT–PCR of total liver lysates (n = 5/time point, sham: n = 3).

D Representative images of Ki-67 immunohistochemistry 2 days after 2/3 PHx of WT and ENKO mice (n = 5–9 mice per group and time point).

E Quantitation of Ki-67+ hepatocytes/HPF determined 1, 2, 4, and 8 days after 2/3 PHx.

F Number of mitotic figures 2 days after 2/3 PHx of WT and ENKO mice (n = 5–9 mice per group and time point).

G qRT–PCR analysis of total liver lysates 1 day after PHx for IGF2.

H Western blot analysis of phosphorylated insulin-like growth factor receptor 1 (pIGFR1), phosphorylated insulin receptor substrate 1 (pIRS1) and β-actin 2 days after PHx.

I Western blot analysis of phosphorylated and total extracellular signal-regulated kinase (ERK) and AKT 2 days after PHx.

J, K qRT–PCR of IGF2 in isolated liver sinusoidal endothelial cells (LSEC)(J) or hepatocytes (HEP)(K) 1 day after PHx.

L EdU-positive hepatocytes (in %) after 24-h stimulation with conditioned medium (CM) from either WT or ENKO HSC (3, 5, or 7 days of activation). HGF (40 ng/ml) and TGF-β (10 ng/ml) serve as positive or negative control, respectively.

M EdU-positive WT and ENKO hepatocytes (in %) after stimulation with IGF2 (100 ng/ml). HGF (40 or 10 ng/ml) and TGF-β (10 ng/ml) serve as positive or negative control, respectively.

Data information: Scale bars (D, E): 100 μm. Data are expressed as mean ± s.e.m. P-value (significant < 0.05) determined by Student's t-test. Source data are available online for this figure.
Alternative splicing coupled mRNA decay shapes the temperature-dependent transcriptome (2020)
Figure 2

Figure 2. Temperature-dependent poison events globally regulate GE

(A) Temperature-dependent poison events in Hnrnpdl (left, see also Figure 1C), Hnrnph3 (middle) and Cirbp (right). For each target, on top a simplified exon-intron structure is given and below Sashimi plots show the distribution of raw sequencing reads. Exon-Exon junction reads are indicated by the numbers connecting the exons. Below, sequence conservation across placental species is indicated. Hnrnpdl exhibits a heat-included exon that leads to a PTC, Hnrnph3 a cold-skipped exon that leads to a frameshift, and in Cirbp, heat leads to inclusion of an alternative transcript end, which leads to formation of a PTC.

(B) For each gene from A, the triplicate normalized read counts in DMSO (left y-axis, green) and the percentage of the poison isoform in CHX (right y-axis, orange) are plotted at the two temperatures. Line represents mean PSI.

(C) Correlation of poison isoform inclusion and GE levels for all RBPs with temperature-dependent AS-decay skipped exon events. Shown are the log2 fold change (FC) in GE versus the ∆PSI of the poison isoform between the CHX samples. Top shows the GE change between the DMSO samples and bottom between the CHX samples. R² and P (deviation from zero slope) are indicated. N=24.

(D) GFP splicing reporter. The GFP encoding sequence is interrupted by an intron and contains destabilizing mRNA (AU-rich) and protein (PEST) elements (based on a luciferase reporter by (Younis et al., 2010)). Insertion of an alternative exon makes GFP expression splicing-dependent, as exon inclusion (with or without a STOP, red asterisks) abolishes the GFP signal (see Figure EV2B).

(E) Splicing reporters, containing only an intron (GFP ctrl, #1) or additionally temperature-dependent exons with ~1kB surrounding introns. Representative radioactive RT-PCRs (bottom) confirm cold-induced inclusion of Ythdf3 exon 7 (#2) and human Synerg exon 21 (#3) after 24 hours at the indicated temperatures. Representative images are derived from three independent gels. See SourceDataForFigure2E for uncropped versions of these gels.

(F-G) GFP expression was quantified using FACS and is shown relative to GFP ctrl. In (F) transfected cells were incubated at the indicated temperatures for 24 hours (n=3, mean +/- SD). In (G) cells were incubated directly after transfection for 12 hours at 39°C (blue) or 33°C (red) and then shifted for 2, 4, 8 and 12 hours (n=3, mean +/- SD).

Data information: See also Figure EV2.
Alternative splicing coupled mRNA decay shapes the temperature-dependent transcriptome (2020)
Figure 3

Figure 3. Temperature-dependent AS-decay is evolutionarily conserved

(A) SR protein specific temperature-dependent AS-decay. Sashimi plots are shown for heat-induced poison exon inclusion for Srsf2 (left) and cold-induced poison isoform formation for Srsf10 (right). Quantification of GE and AS are presented in the center (as in Figure 2B), lines represent mean.

(B) Poison exon inclusion for Srsf2 (left) and Srsf10 (right) in a 24h temperature-rhythm in human cells. Hek293 cells were pre-entrained with square-wave temperature cycles (12h 34°C/ 12h 38°C) for 48h. For the last 24h, cells were treated with DMSO or CHX every 4h and harvested after 4h and analyzed by splicing sensitive RT-PCR (n=3, mean ± SD). White area: 34°C; Red area: 38°C. In Hek293 cells, inclusion of Srsf10 exon 3 is coupled to polyadenylation making it a weak decay target.

(C) AS-decay of human targets at 34°C (blue) and 38°C (red) was investigated after a square-wave temperature regime (see B). RNAs were harvested after 56 and 68 hours, respectively (n=3, mean ± SD). Statistical significance was determined by 2-way ANOVA and is indicated by asterisks: adjusted p values: not significant (ns), **p<0.01, ***p<0.001, ****p<0.0001.

(D) Intron conservation of alternative cassette exons. Shown are average placental conservation scores of introns surrounding all alternative exons (black, N=46,901), AS-decay exons (yellow, N=453) and temperature-regulated AS-decay exons (green, N=139).

(E) Quantification of Srsf2 AS-decay in hamster, rabbit and chicken cells (n=2, mean ± SD). Statistical significance was determined by 2-way ANOVA, *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Data information: In (C) and (E) Box plots show median PSI of validation PCRs, whiskers show min to max. Individual data points are shown as circles. See also Figure EV3.
Alternative splicing coupled mRNA decay shapes the temperature-dependent transcriptome (2020)
Figure 4

Figure 4. Temperature-dependent AS-decay is necessary for rhythmic GE

(A) WT Hek293 and cell lines lacking either Srsf2 or Srsf10 exon 3 (Δpoison exon) through CRISPR/Cas9-mediated genome editing (see also Figure EV4) were temperature entrained at 3 consecutive days of square-wave temperature rhythm (12 hours at 34°C and 38°C, respectively). Time points of harvest are indicated by arrows.

(B, C) Rhythmic Srsf2 (B) and Srsf10 (C) productive mRNA levels (relative to Gapdh) were analyzed by RT-qPCR. For each clone, expression is normalized to time point 52h, (B) n = 3, (C) n =4, mean ± SEM. For Srsf10 two independently generated clones were investigated (∆E3 A and B). Statistical significance was determined by unpaired t-test and is indicated by asterisks: p values: **p<0.01, ****p<0.0001.

(D, E) Comparing temperature-induced expression of the core clock components Bmal1 (D) and Per1 (E) in WT and Srsf10 ∆E3 cells (relative to Gapdh, normalized to time point 52h). Statistical significance was determined by unpaired t-test and is indicated by asterisks: p values: *p<0.05, ***p<0.001, ****p<0.0001. n=3, mean ± SEM.

Data information: White area: 34°C; Red area: 38°C. See also Figure EV4.
Alternative splicing coupled mRNA decay shapes the temperature-dependent transcriptome (2020)
Figure 5

Figure 5. Temperature-dependent AS-decay of SR proteins is conserved in plants

(A) Normalized GE values of plant SR proteins in a 48-hour time course. In the first day, plants were kept at 20°C (red) and shifted to 4°C (blue) for the second day. Plants were under a 12-hour dark/light regimen (gray/yellow bars). Warm- and cold-expressed genes are indicated.

(B) A. thaliana SR proteins divided in their subfamilies and classified as warm- or cold-expressed.

(C) Sashimi plots as shown in Figure 2A (without conservation score) for decay-inducing AS events upon cold (At-SCL33, left) or warm temperature (At-SR34, right). Data shown represent, from top to bottom, warm-dark, warm-light, cold-dark and cold-light conditions as indicated on the right of the plots.

(D) For validation, A. thaliana were incubated for 3 days either at 20°C or 4°C and RNA was investigated for gene expression (green, left y-axis) and poison isoform formation (orange, right y-axis). Gene expression of At-SCL33 (left) or At-SR34 (right) is shown relative to Ipp2. Lines represent mean, individual data points are shown.

(E) Correlation of poison isoform inclusion and GE levels for temperature-dependent poison skipped exon events. Shown are the log2 fold change (FC) in GE versus the ∆PSI of the poison isoform. Decay targets were identified by using data from CHX-treated plants. R² and P(deviation from zero slope) are indicated. N=80 (see main text and methods for details).

Data information: See also Figure EV5A-D.
Alternative splicing coupled mRNA decay shapes the temperature-dependent transcriptome (2020)
Figure 6

Figure 6. AS-decay generates GE rhythms in vivo

(A) Top: Rose plot visualizing the acrophases (based on rhythmic genes in the liver transcriptome from (Atger et al., 2015)) of genes exhibiting heat-induced (orange, n=797) and cold-induced (blue, n=828) upregulation of GE in mouse hepatocytes, respectively. Bottom: Acrophases of genes with cold-induced (blue, n=55) and heat-induced (orange, n=47) poison exon events, respectively. Numbers indicate respective ZT, with the left half representing night (at which mice are active and have an elevated body temperature) and the right half representing day (at which mice are asleep with lower body temperature). The black circles represent expected normal distribution if data would be randomly sampled.

(B) Correlation of rhythmic Srsf10 expression (orange, left y-axis) and exon 3 inclusion (black, right y-axis) in mouse liver samples from the indicated ZTs (n=4, mean +/- SEM). Quantifications were obtained by RNA-seq analysis of datasets from (Atger et al., 2015).

(C) Representative Western blot of SRSF10 protein levels from mouse liver nuclear extracts (NX) from different ZTs. The different SRSF10 variants are highlighted on the right. SRSF10-fl and SRSF10-2 are the result of differential last exon usage. hnRNP L was used as a loading control. The asterisks could represent hyperphosphorylated SRSF10-fl through higher CLK activity during the day.

(D) Quantification of SRSF10-fl + SRSF10-2 relative to ZT0 and hnRNP L (mean of at least 3 mice ± SEM). Student's unpaired t test-derived p values ***p<0.001.

Data information: Grey areas indicate night time (dark). See also Figure EV5E-G.
A bacterial protein promotes the recognition of the Legionella pneumophila vacuole by autophagy (2013)
Figure 2

Vacuoles harboring legA9 mutant bacteria significantly evade fusion with lysosomes and escape recognition by autophagy in WT BMDMs. (A) Representative images from 6 h infected WT BMDMs with JR32 or legA9 mutant bacteria. Nuclei were stained blue with DAPI and L. pneumophila stained green with L. pneumophila antibody. Lyso‐tracker red was used to stain acidified lysosomes. White arrows show L. pneumophila colocalization with lysotracker. (B) Percent colocalization of L. pneumophila with lyso‐tracker. (C) Representative images from 6 h infected WT BMDMs with JR32 or legA9 mutant bacteria. LC3 is stained red with LC3 antibody. White arrows show L. pneumophila colocalization with LC3. (D) Percent colocalization of L. pneumophila with LC3. Hundred bacteria were scored from each coverslips. Images were captured with the 60× objective and magnified 3×, scale bar = 10 μm. (A–D) Data are presented as mean + SD of n = 3 and are representative of three independent experiments. Asterisks indicate significant differences (**p < 0.01, ***p < 0.001; two‐tailed t‐test).
A bacterial protein promotes the recognition of the Legionella pneumophila vacuole by autophagy (2013)
Figure 3

Induction of autophagy in macrophages restricts legA9 mutant replication, and rescues colocalization with LC3 and fusion with lysosomes. BMDMs were untreated or pretreated with rapamycin (Rap) for 1 h and kept throughout the infection. BMDMs were infected with L. pneumophila JR32 or the leg A9 mutant for (A) 1 or (B) 24 h with an MOI of 0.5. CFUs were scored at these time points. (C, D) Percent colocalization of L. pneumophila with (C) lyso‐tracker and (D) LC3. Hundred bacteria were scored from each coverslips. (A–D) Data are presented as mean + SD of n = 3 and are representative of three independent experiments. Asterisks indicate significant differences (**p < 0.01; two‐tailed t‐test).
A bacterial protein promotes the recognition of the Legionella pneumophila vacuole by autophagy (2013)
Figure 4

BMDMs derived from Atg5−/− mice are defective in clearing the WT L. pneumophila and the legA9 mutant. (A) BMDMs derived from WT and Atg5−/− mice were infected with L. pneumophila JR32 or legA9 mutant at an MOI of 0.5. Data are shown as mean ± SD of n = 3 and are representative of two independent experiments. Asterisks indicate significant differences (*p < 0.05; two‐tailed t‐test. (B) Western blot showing the absence of Atg 5 in the knockout. Data are representative of three independent experiments.
A bacterial protein promotes the recognition of the Legionella pneumophila vacuole by autophagy (2013)
Figure 5

Macrophage vacuoles‐containing the legA9 mutant have reduced ubiquitin compared with vacuoles harboring parent L. pneumophila. (A) Representative images from 6 h infected BMDMs with JR32 or legA9 mutant bacteria. Polyubiquitin is stained red. White arrows show L. pneumophila colocalization with polyubiquitin. (B) Percent colocalization of L. pneumophila with polyubiquitin. Hundred bacteria were scored from each coverslips. Images were captured with the 60× objective and magnified 3×, scale bar = 10 μm. Data are presented as means + SD of n = 3 and are representative of two independent experiments. Asterisks indicate significant differences (*p < 0.05, **p < 0.01, ***p < 0.01; two‐tailed t‐test).