EMBO/soda-nel huggingface dataset:
Kidney Cancer PDOXs reveal patient-specific pro-malignant effects of antiangiogenics and its molecular traits (2020)
Figure 5

Figure 5. Effects of anti-VEGF/R therapies on metastasis.

A-C) Evaluation of lung micrometastases using HE and vimentin-stained lung sections upon antiangiogenic therapies in Ren13BM and Ren28. (A) Representative images of lung metastasis in Ren13BM upon antiangiogenic therapies. (B) Incidence (pie chart, *p < 0.05 by Chi-square) and proportion of mice with 0, 1, 2 or > 3 metastases (mets) per lungs (C) (*p < 0.05 by Mann-Whitney test) are shown.

Anti-VEGF/R treatments exacerbate the invasive capacity quantified as capsular invasion in the tumor kidney interface in some Ren-PDOXs. Moreover, the evaluation of lung metastasis confirms the pro-malignant effects of antiangiogenics in some Ren-PDOXs, recapitulating the inter-patient variable response.
Kidney Cancer PDOXs reveal patient-specific pro-malignant effects of antiangiogenics and its molecular traits (2020)
Figure 7

Figure 7. Validation of ALDH1A3 as a tumor-specific biomarker of pro-invasive behavior.

Representative images of ALDH1A3 expression on pro-invasive (Ren13 and Ren86) and non-pro-invasive (Ren28 and Ren50) tumors.

Confirmation of ALDH1A3 mRNA levels of gene expression in pro-invasive (Ren13 and Ren86) and non-pro-invasive (Ren28 and Ren50) tumors. Average ± SD of 4-6 independent replicates per group were analyzed by Mann-Whitney test *p < 0.05 **p < 0.01.

C,D) Association of pre-treatment ALDH1A3 protein levels by IHC on a series of Ren-PDOX models treated with sunitinib. (C) Representation of ALDH1A3 expression on pro-invasive and non-pro-invasive Ren-PDOX after suntinib treatment (**p < 0.01 by Chi-Square test). (D) Correlation of pre-treatment ALDH1A3 protein levels by IHC with increased tumor invasion found after sunitinib treatment. n=7, Spearman's non-parametric correlation, p=0.0095.

E) Patient characteristics of a series of 15 metastatic ccRCC patients specifically annotated for their type of progression after anti-angiogenic therapy. 60% progressed in preexisting lesions (non-pro-aggressive) while 40% progressed with new lesions (pro-aggressive).

F) Representation of ALDH1A3 score (0=absent, 1=low, 2=high expression) on pre-treatment samples of pro-aggressive and non-pro-aggressive ccRCC patients described above. n=15, Chi-square test for independence p=0.015, and Chi-square test for trend p=0.030 (*).

G) ROC curve for the ALDH1A3 score on 15 ccRCC patients. ROC variation method was used due to categorical predictor factor. AUC= 80.6±13.8, Sensitivity 83.3% [95%CI: 35.9-99.5], Specificity 33.3% [95%CI: 7.5-70.0], empirical p=0.008 by Bootstrapping method (n=1000).

H) Levels of ALDH1A3 differentiate between non-pro-invasive and pro-invasive tumors. High levels of ALDH1A3 in samples before treatment are associated to more invasive tumors after antiangiogenic treatment.

Expanded View Figure legends
Transcription activation depends on the length of the RNA polymerase II C-terminal domain (2021)
Figure 1

Figure 1- CTD shortening hardly changes the transcriptome and RNA metabolism in human cells.

A- Schematic representation of the CTD variants expressed in U2OS cells used in this study.

B- Western blot verifying the expression of the CTD variants RPB1-52R and RPB1-25R in U2OS cells in whole cell extracts. U1 snRNP was used as a loading control.

C- Changes in RNA synthesis upon CTD shortening. MA plot showing RNA synthesis changes in TT-seq datasets upon CTD shortening in U2OS cells in steady state conditions. RPB1-52R cells are used as a control and the data was normalized using spike-in counts. 16214 expressed genes annotated in RefSeq were analyzed. Differentially expressed genes are in red. 473 genes were significantly upregulated (adjusted p value < 0.05, log2 fold change >=1) and 704 genes were significantly downregulated (adjusted p value < 0.05, log2 fold change <=1).

D- Box plot of estimated RNA synthesis rates of expressed RefSeq transcripts (12014 transcripts) based on TT-seq and RNA-seq datasets in RPB1-52R and RPB1-25R cells. Box limits are the first and third quartiles, the band inside the box is the median. The ends of the whiskers extend the box by 1.5 times the interquartile range.

E- Box plot of estimated RNA degradation rates of expressed RefSeq transcripts (12014 transcripts) based on TT-seq and RNA-seq datasets in RPB1-52R and RPB1-25R cells. Box limits are the first and third quartiles, the band inside the box is the median. The ends of the whiskers extend the box by 1.5 times the interquartile range.

F- Box plot of estimated RNA half-lives of expressed RefSeq transcripts (12014 transcripts) based on TT-seq and RNA-seq datasets in RPB1-52R and RPB1-25R cells. Box limits are the first and third quartiles, the band inside the box is the median. The ends of the whiskers extend the box by 1.5 times the interquartile range.
Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases (2018)
Figure 3

Figure 3. Monocytes, dendritic cells, and B cells express CHIKV proteins during acute CHIKV infection, but only specific monocyte sub-communities undergo expansion. A, overview of the MetaHybridLouvain procedure. B, viSNE layout of CyTOF single-cell events from the same representative sample as Fig 2A, but colored according to the 26 canonical leukocyte phenotypes detected by MetaHybridLouvain. Unique sub-communities of each canonical phenotype are differentiated by the numerals used as point marks. The categorizations from NodLabel (as in Fig 2A) are provided as text labels. Note that over-plotting has been disallowed, so this is a sampling of the single-cell events. C, number of sub-communities detected by MetaHybridLouvain for each of the canonical leukocyte phenotypes. D, viSNE layout of CyTOF single-cell events from the same representative sample as Figure 2A and 3A, but with points colored according to the CHIKV channel. By qualitative comparison with Fig 2A, monocytes, myeloid dendritic cells (mDCs), and B cells (labeled) have the highest CHIKV envelope protein (E2) expression on the cell surface. E, differences in CHIKV E2 protein expression between acute-phase and convalescent-phase samples per MetaHybridLouvain sub-community. Sub-communities are ordered by largest to smallest difference and filtered to sub-communities where the median of the channel means per sample was higher in the acute-phase samples at a significance threshold of FDR < 0.05. Sub-communities are named by their parent canonical community name plus an arbitrary number, up to the numbers given in C. F, heatmap of log10 scaled PBMC sub-community frequencies for all samples (n = 42 patients × 2 timepoints = 84 samples). Clinical variables are depicted for all samples across the top of the heatmap; convalescent post-symptom onset anti-CHIKV antibody titer and viral titer (which was measured during the acute phase) are both in units of log10 dilutions. Hierarchical clustering (using complete linkage) was applied to both samples (X axis) and sub-communities (Y axis). Four major clusters of sub-communities and two major clusters of samples (largely separating acute and convalescent samples) are highlighted. G, fold changes in log10 frequencies for PBMC sub-communities contrasted between acute and convalescent phase samples, filtered to a significance threshold of FDR < 0.05 (moderated paired t-test, mixed effects model). Error bars represent the 95% confidence interval. The acute phase frequency for each sub-community is depicted with the purple heatmap. *, FDR-adjusted P (FDR P) < 0.05; **, FDR P < 0.01, ***, FDR P < 0.001.
Comprehensive innate immune profiling of chikungunya virus infection in pediatric cases (2018)
Figure 4

Figure 4. Marker expression differences between sub-communities of CD14+CD16+ monocytes and CD14+ monocytes. A, relative expression of CD14+ and CD16+ in CD14+CD16+ sub-communities, depicted as boxplots of the mean expression levels for all samples (n = 42 patients × 2 timepoints = 84 samples), indicates that sub-community 1 has a CD14+CD16++ (aka "non-classical") phenotype, while sub-community 2 has a CD14++CD16+ (aka "intermediate") phenotype. Differences shown here are significant at FDR < 0.05 (moderated paired t-test, mixed effects model); for a view of all differences significant at this threshold, see Appendix Fig S3. B, heatmap of relative expression of markers that most differentiate the sub-communities of CD14+ monocytes by difference in mean expression levels for all samples (n = 42 patients × 2 timepoints = 84 samples). Markers are filtered to those different at a significance threshold of FDR < 0.05 (moderated paired t-test, mixed effects model) and fold change > 1.3; for a view of all differences significant at FDR < 0.05, see Appendix Fig S8. The mean intensity of each channel across all sub-communities is shown by the adjacent purple heatmap.
The anti-hypertensive drug Prazosin inhibits glioblastoma growth via the PKCδ- dependent inhibition of the AKT pathway (2016)
Figure 1

Figure 1 - Prazosin inhibits GIC survival.

A. Viability analysis of GICs treated with the α-AR antagonists prazosin, ARC 239, doxazosin, BMY 7378, and terazosin. GICs were treated with the antagonists or corresponding vehicles for 72 h, and viability was assayed using WST-1. *P<0.05, n=4, two-sided Mann–Whitney U-test.

B. Quantification of GIC survival using trypan blue exclusion after 24 h and 72 h of treatment with prazosin. *P=0.0286, n=4, two-sided Mann–Whitney U-test.

C. Viability analysis of patient-derived GICs (TG1, TG16, GBM5, GBM44) and NSCs (NSC24, NSC25, NSC5031, NSC8853) treated with prazosin for 72 h. *P=0.0286, n=4, two-sided Mann–Whitney U-test.

D. Analysis of the sphere-forming capabilities of GICs using the extreme limiting dilution assay. Cells were seeded in presence of vehicle or 30 µM prazosin (PRZ). Sphere formation was scored 10 days post-seeding. Frequency of sphere-forming cells: Control=1/3.88 (lower 8.61, upper 1.95); prazosin 1/248 (lower 1003, upper 62), n=12, p= 1.13 10-10. Overall test for difference in stem cell number between groups.

E. Viability analysis of GICs treated with prazosin after sorting cells according to their expression of EGFR, and the neural stem cell markers CD15 and CD133. Prazosin inhibits cell viability regardless of CD133 or CD15 expression. *P=0.0286, n=4, two-sided Mann–Whitney U-test.

F. Imunocytochemical staining of NSCs and GICs cultured in media favoring neuronal (ß3-tubulin), astroglial (GFAP) or oligodendroglial (O4) differentiation (Diff media). ß3-tub: ß3-tubulin.

G. Viability analysis of differentiated NSCs and GICs treated with prazosin for 72 h. Diff: differentiation. *P=0.0286, n=4, two-sided Mann–Whitney U-test.
The anti-hypertensive drug Prazosin inhibits glioblastoma growth via the PKCδ- dependent inhibition of the AKT pathway (2016)
Figure 4

Figure 4 - Prazosin induces GIC apoptosis independently from α-AR.

A. Immunoblotting for pro-caspase-3 (pro-CASP3) and active caspase-3 (CASP3) in GICs treated with prazosin (PRZ) or vehicle (V) demonstrating that prazosin activates caspase-3. ZVAD, a caspase inhibitor, prevents prazosin-induced caspase-3 activation. kDa: kilodaltons.

B. Immunoblotting for pro-caspase-9 (pro-CASP9) and active caspase-9 (CASP9) in GICs treated with prazosin (PRZ) or vehicle (V) demonstrating that prazosin does not activate caspase-9.

C. Viability analysis of GICs treated with prazosin for 24h in the presence or absence of ZVAD, a caspase inhibitor. ZVAD counteracts prazosin-induced GIC death. *P=0.0286, n=4, two-sided Mann–Whitney U-test.

D. Prazosin induces glioblastoma cell apoptosis in vivo. GFP+ and GFP- cells were analyzed from tumors in vehicle or prazosin-treated mice. Annexin V / DAPI staining was used to identify apoptotic cells by FACS. Tumors were initiated by GBM44 GICs implantation.

E. Dose-response curve of prazosin on GIC survival (24h treatment). EC50 was determined with curve fit using nonlinear regression.

F. Viability analysis of GICs treated with prazosin for 24h in the presence or absence of cirazoline, a subtype agonist of the α-ARs. Cirazoline did not alter GIC survival and counteracted only poorly prazosin-induced GIC death. *P<0.05, n=4, two-sided Mann–Whitney U-test.

G. Membrane binding assays demonstrating the absence of prazosin binding sites in GIC membrane preparations. Positive control shows that prazosin binds to membrane preparations of yeast expressing 1-AR. PRZ: prazosin. CRZ: cirazoline.

H. Immunoblotting for phosphorylated ERK1/2 (P-ERK1/2) and total ERK1/2 following prazosin treatment for 30 min. Prazosin induces ERK1/2 phosphorylation in GICs.

I. SRE-luciferase reporter activity analysis of GICs treated with prazosin in absence or presence of U0126 (10 µM), an inhibitor of the ERK-activating kinase MEK. U0126 prevented prazosin-induced SRE-luciferase activation in GICs.

J. Viability analysis of GICs treated with prazosin for 24 h in the presence or absence of U0126 (10 µM), an inhibitor of the ERK-activating kinase MEK. U0126 did not counteract prazosin-induced GIC death. Blockade of prazosin-induced ERK1/2 phosphorylation by U0126 was confirmed by immunoblotting (insert). PRZ: prazosin.
The anti-hypertensive drug Prazosin inhibits glioblastoma growth via the PKCδ- dependent inhibition of the AKT pathway (2016)
Figure 5

Figure 5 - Prazosin induces GIC apoptosis in a PKC-dependent manner.

A. PKCδ expression analysis in two patient-derived GICs and NSCs by immunoblotting. GICs express higher levels of PKCδ than NSCs. kDa: kilodaltons.

B. PKCδ by immunostaining (green). Nuclei were stained with DAPI (blue). Enhanced PKCδ punctate nuclear expression in prazosin-treated (PRZ, 5µM) GICs, as compared to vehicle-treated GICs. PRZ: prazosin. Scale bar: 10 µm.

C. Total PKCδ (PKCδ 78kDa) and cleaved PKCδ (PKCδ 41kDa) expression analysis in GICs by immunoblotting. Prazosin (PRZ) induces PKCδ cleavage into a 41kDa active fragment. 2 µM rottlerin (Rott), a PKCδ inhibitor, inhibits prazosin-induced PKCδ cleavage. V: vehicle. kDa: kilodaltons.

D. Pro-caspase-3 (Pro-CASP3) and activated caspase-3 (CASP3) expression analysis in GICs by immunoblotting. 2 µM rottlerin (Rott) inhibits prazosin (PRZ)-induced caspase-3 activation. V: vehicle. kDa: kilodaltons.

E. Inhibition of PKCδ by rottlerin prevents, in a concentration-dependent manner, prazosin-induced GIC death. Viability analysis of GICs treated with prazosin for 72 h in the presence or absence of rottlerin. *P=0.0286, n=4, two-sided Mann–Whitney U-test.

F. Inhibition of PKCδ by the specific anti-PKCδ RACK peptide δV1.1 (10 µM) counteracts prazosin-induced GIC death. Viability analysis of GICs treated with prazosin for 72 h in the presence or absence of δV1.1. *P < 0.005 for Prazosin 1 and 5 µM and P<0.001 for Prazosin 10 µM by two-tailed unpaired Student’s t-test, n=4.

G. Decreased PKCδ expression using shRNA counteracts prazosin-induced GIC death. Viability analysis of GICs transduced with scrambled or PKCδ shRNA and treated with prazosin for 72 h. *P < 0.005 by two-tailed unpaired Student’s t-test, n=4.

H. Prazosin inhibits AKT phosphorylation in GICs. LY294002 (LY, 30 µM), an inhibitor of the PI3K/AKT pathway, was used as positive control. Terazosin, which does not affect GIC viability, does not alter AKT phosphorylation (see the corresponding cell viability counting in Fig EV4C). Phosphorylated AKT (P-AKT) and total AKT (AKT) expression analysis in GICs by immunoblotting. V: vehicle. kDa: kilodaltons.

I. Prazosin (10 µM) does not inhibit AKT phosphorylation in NSCs. Analysis by immunoblotting. V: vehicle. kDa: kilodaltons.

J. β-catenin expression, a downstream target of AKT, is decreased in prazosin-treated GICs, as opposed to NSCs. Analysis by immunoblotting. V: vehicle. kDa: kilodaltons.

K. L. Inhibition of PKCδ using rottlerin (Rott, 2 µM) or δV1.1 (10 µM) counteracts prazosin inhibition of AKT phosphorylation. Analysis by immunoblotting. V: vehicle. kDa: kilodaltons.
The anti-hypertensive drug Prazosin inhibits glioblastoma growth via the PKCδ- dependent inhibition of the AKT pathway (2016)
Figure 6

Figure 6 - Prazosin inhibits glioblastoma growth in vivo in a PKC-dependent manner.

A. Schematic representation of the experimental design.

B. Bioluminescent in vivo images of tumors in mice grafted with GICs expressing either scrambled or PKCδ shRNA. All mice were treated with prazosin (PRZ) for 45 days.

C. Quantification of the bioluminescent signals showing that decreased expression of PKC counteracts prazosin inhibition of tumor growth. Fold change in total flux represents the ratio: total flux after treatment/total flux prior treatment. *P=0.0007, n=8, two-sided Mann–Whitney U-test.

D. Schematic representation of the molecular mechanisms underlying prazosin-induced GIC apoptosis. See text for details.
RNF26 temporally regulates virus-triggered type I interferon induction by two distinct mechanisms. (2014)
Figure 1

RNF26 is an E3 ubiquitin ligase for MITA.

(A) Overexpression of RNF26 promoted polyubiquitination of MITA. The 293 cells (5×106) were transfected with Flag-MITA (5 µg) and HA-Ub (1 µg) together with a control or RNF26 expression plasmid (0.5, 1, 2 µg). Twenty-four hours after transfection, cells were subjected to immunoprecipitation (IP) under denatured conditions with anti-Flag and the immunoprecipitates were analyzed by immunoblots with anti-HA (upper panel) or anti-Flag (lower panel). The whole cell lysates were analyzed by immunoblots with anti-HA (upper panel), anti-Flag (middle panel) and anti-RNF26 (bottom panel). Ub, ubiquitin. (B) RNF26-mediated polyubiquitination of MITA depends on the enzymatic activity of RNF26. The 293 cells (5×106) were transfected with Flag-MITA (5 µg) and HA-Ub (1 µg) together with RNF26 or its mutants (1 µg each). IP under denatured conditions and ubiquitination assay was performed as described in (A). (C) UbcH5 mediated polyubiquitination of MITA by RNF26. The RNF26 and MITA proteins were obtained by in vitro transcription and translation, then incubated with biotin-Ub, E1 and the indicated E2s. Polyubiquitination of MITA was examined by immunoblot analysis with HRP-streptavidin (top panel). The inputs of RNF26 and MITA were analyzed by immunoblots with anti-MITA and anti-RNF26 (bottom panels). (D) RNF26 but not its enzymatic inactive mutants targeted MITA for polyubiquitination in vitro. MITA, RNF26 and its mutants were obtained by in vitro transcription and translation. Biotin-Ub, E1, UbcH5 and MITA were incubated with RNF26 or its mutants, followed by ubiquitination and immunoblot analysis as described in (C). All experiments were repeated for at least three times with similar results.
RNF26 temporally regulates virus-triggered type I interferon induction by two distinct mechanisms. (2014)
Figure 2

RNF26 interacts with MITA.

(A) RNF26 interacted with MITA. The 293 cells (5×106) were transfected with the indicated plasmids (5 µg each). Twenty-four hours later, cells were lysed and cell lysates were immunoprecipitated with anti-Flag or control IgG. The immunoprecipitates and whole cell lysates were analyzed by immunoblots with anti-HA or anti-Flag. (B) RNF26 was physiologically associated with MITA. THP-1 cells (2×107) were infected with SeV or HSV-1 for the indicated time points or left uninfected. Cells were lysed and immunoprecipitation and immunoblot analysis was performed with antibodies against the indicated proteins. NC, negative control. (C) RNF26 is localized to the ER and mitochondria. HeLa cells (2×105) were transfected with the indicated plasmids (0.5 µg each). Twenty-four hours after transfection, the cells were stained with Mito-Tracker Red for 15 minutes or left untreated. The cells were fixed with 4% paraformaldehyde and subjected for confocal microscopy analysis. (D) Subcellular distribution of RNF26. The 293 cells (5×107) were infected with SeV for the indicated time points or left uninfected. Subcellular fractionation assays were performed and the cellular fractions were analyzed by immunoblots with antibodies against the indicated proteins. (E) RNF26 and MITA were colocalized to the ER. HeLa cells (2×105) were transfected with the indicated plasmids (0.5 µg each). Eighteen hours after transfection, cells were infected with SeV or HSV-1 for 6 hours or left uninfected, followed by ER-Tracker Blue/White staining for 30 minutes. Cells were fixed with 4% paraformaldehyde and subjected for confocal microscopy analysis. All experiments were repeated for at least three times with similar results.
RNF26 temporally regulates virus-triggered type I interferon induction by two distinct mechanisms. (2014)
Figure 3

RNF26 promotes polyubiquitination of MITA at K150.

(A) RNF26 mediated polyubiquitination of MITA at K150. The 293 cells (5×106) were transfected with HA-Ub (1 µg) and RNF26 (1 µg) together with Flag-MITA or the indicated mutants (5 µg each). Twenty-four hours after transfection, cells were subjected IP under denatured conditions with anti-Flag and immunoprecipitates were analyzed by immunoblots with anti-HA (upper panel) or anti-Flag (lower panel). The whole cell lysates were analyzed by immunoblots with anti-Flag or anti-RNF26 as indicated. (B) RNF26 targeted MITA for polyubiquitination at K150 in vitro. RNF26, MITA and its mutants were obtained by in vitro transcription and translation. Biotin-Ub, E1, UbcH5 and RNF26 were incubated with MITA or its mutants. The ubiquitination of MITA was examined by immunoblot analysis with HRP-streptavidin (top panel). The inputs of RNF26 and MITA were analyzed by immunoblots with anti-MITA and anti-RNF26 (bottom panels). All experiments were repeated for at least three times with similar results.