EMBO/soda-nel huggingface dataset:
Inhibition of transcription by dactinomycin reveals a new characteristic of immunogenic cell stress (2020)
Figure 5

Figure 5. Immune-dependent effect of DACT on WEHI 164 tumors growth and sensitization to immunotherapy.

(A-K) 3 x 105 mouse fibrosarcoma WEHI 164 cells were injected subcutaneously (s.c) into the flank of immunocompetent syngeneic Balb/c mice with n mice per group (n=7 for DACT + anti-PD-1, n=8 for DACT, n=9 for Ctr, anti-PD-1, anti-CD4/anti-CD8 and DACT + anti-CD4/anti-CD8). When tumors became palpable, the mice were injected intraperitoneally (i.p.) with injectable solution (Ctr) or with 0.5 mg/kg dactinomycin (DACT). A second injection of chemotherapy was performed four days later. Anti-CD4 and anti-CD8 were administered i.p. at days -1, 0 and 7 days before/after chemotherapy and anti-PD-1 at days 8, 12 and 16 (A). Tumor size was assessed regularly and individual tumor growth curves of DACT versus Ctr (B), DACT + anti-CD4/CD8 versus anti-CD-4/anti-CD-8 (C) and DACT + anti-PD-1 versus anti-PD-1 (F) are depicted. Mean tumor area for each group was calculated and significances were tested using a type II ANOVA test (D, G). Overall survival is depicted and p-values were calculated with a Log-Rank test (E, H). Stars indicate the p-values of each treatment versus Ctr (D, E, G, H) and hashes indicate the p-values of the DACT + anti-CD-4/anti-CD-8 versus DACT alone (D, E) and of DACT + anti-PD-1 versus anti-PD-1 alone (G, H) (*/# p<0.05, **/## p<0.01, ***/### p<0.001). Five naïve mice or the eleven mice that were cured by treatment with DACT alone or in combination with PD-1 blockade were (re)challenged with WEHI 164 cells, and individual tumor growths (I, J), as well as overall survival (*** p<0.001, Log-Rank test) (K), were monitored.
Inhibition of transcription by dactinomycin reveals a new characteristic of immunogenic cell stress (2020)
Figure 6

Figure 6. Role if IFNγ in the anticancer effect of DACT.

(A, B) Mice bearing WEHI 164 sarcomas were treated by systemic (intraperitoneal, i.p.) injections of DACT or PBS as a control (Ctr), and tumor were excised 9 days later for the quantitation of mRNA coding for IFNγ (A). Two independent experiments were conducted with a total of 22 mice in the Ctr group and 23 mice in the DACT-treated group, with each data point indicating one tumor. The cycle threshold of the qRT-PCR of IFNγ was normalized to the one of the housekeeping gene peptidylpropyl isomerase A (Ppia) in each mouse and results are shown normalized with respect to controls as a dot plot depicting mean ± SD . The p-value (* p<0.05) was calculated by means of a Student's t test (B).

(C-F) Balb/c mice with palpable WEHI 164 sarcomas (n=7 mice in Ctr and anti-IFNγ groups; n=8 mice in DACT and DACT+anti-IFNγ groups) received two injections of DACT-based chemotherapy (0.5 mg/kg) as well as multiple injections (3 times per week) of neutralizing IFNγ-specific antibody (C). Tumor growth curves are shown for individual mice (D) and as means (E). Statistical difference between DACT-treated tumors and respective controls, which was calculated with a type II ANOVA (* p<0.05), is lost upon IFNγ neutralization (E). Overall survival of mice is also indicated with statistics to respective controls calculated with a Log-Rank test (* p<0.05) (F).
Inhibition of transcription by dactinomycin reveals a new characteristic of immunogenic cell stress (2020)
Figure 7

Figure 7. Inhibition of transcription and translation by ICD inducers.

(A-F) Human osteosarcoma U2OS cells were pre-treated with dactinomycin (DACT), bortezomib (BTZ), daunorubicin (DAUN), docetaxel (DOC), doxorubicin (DOXO), epirubicin (EPI), mitoxantrone (MTX), paclitaxel (PACL), vinblastine (VB) and vincristine (VC) at 0.5, 1 and 5 µM ; with cisplatin (CDDP) at 75, 150 and 300 µM, with oxaliplatin (OXA) at 250, 500 and 1000 µM and with crizotinib (CRIZ) at 10, 20 and 40 µM for 1.5 to 2.5 h and followed by an additional hour of treatment in the presence of 100 mM 5-ethynyl uridine (EU). After fixation cells were permeabilized and EU was stained with an AlexaFluor-488-coupled azide. Representative images are shown for each treatment (A). The EU intensity in the nucleus of each condition was ranked between the untreated control (Ctr, 0 % transcription inhibition) and the control that was not incubated with EU (corresponding to 100 % transcription inhibition) (B). Cells were treated for 2.5 h and before fixation and permeabilization. Then, cells were stained with a rabbit anti-fibrillarin antibody followed by a staining with an anti-rabbit AlexaFluor-647- or AlexaFluor-546-coupled secondary antibody as well as with a mouse anti-nucleolin antibody followed by a staining with an anti-mouse AlexaFluor-488-coupled secondary antibody. Then images were acquired and colocalization between both signals was assessed (C). The surface overlap coefficient (SOC) was calculated and ranked between the untreated control (Ctr) and the positive control (DACT) (D). Cells were pre-treated overnight with the aforementioned compounds in complete medium followed by washout and treatment pursued in methionine-free medium for 30 min. Afterwards, the treatments were continued in methionine-free medium supplemented with 25 µM L-azidohomoalanine (AHA) for additional 1.5 h. AHA incorporation was detected after fixation, permeabilization and blocking by the addition of an AlexaFluor-488-coupled azide. Then images were acquired (E) and AHA intensity in the cells was ranked between the untreated control (Ctr, 0 % translation inhibition) and the untreated control without AHA (corresponding to 100 % translation inhibition) (F).

(G, H) Correlation between the transcription measured by EU incorporation and measured by fibrillarin and nucleolin colocalization is depicted with Pearson correlation coefficient (R) and p-value (p) (G). The same parameters are shown for the correlation between transcription measured by EU incorporation and translation measured with AHA incorporation (H).

Data information: Representative images of DACT 1 µM, BTZ 1 µM, CDDP 150 µM, CRIZ 20 µM, DAUN 0.5 µM, DOC 1 µM, DOXO 1 µM, EPI 1 µM, MTX 1 µM, OXA 500 µM, PACL 1 µM, VB 1 µM, VC 1 µM are shown (A, C, E). Scale bars represent 20 µm. One representative experiment among three is shown ± SD and p-values indicting differences to controls were calculated with a Student's t-test: * p<0.05, ** p<0.01, *** p<0.001 (B, D, F).
Inhibition of transcription by dactinomycin reveals a new characteristic of immunogenic cell stress (2020)
Figure 8

Figure 8. Validation of the inhibition of transcription as a hallmark of ICD at large scale.

(A-G) U2OS wild-type cells were treated with a custom made anti-cancer library previously described Bezu et al, 2018() at 3 μM, supplemented with 500 μM oxaliplatin (OXA), 150 μM cisplatin, 50 μM resveratrol and 50 μM spermidine. For assessing transcription, cells were pre-treated for 1.5 h with the library followed by 1 h with the same drugs in which EU was added. For assessing translation, cells were pre-treated with the library for 12 h followed by 30 min in methionine free-medium, before addition of azidohomoalanine (AHA). Percentage of inhibition was calculated and transformed as z-scores. The correlations between transcription inhibition and ICD prediction score (A), peIF2α expression (Β), CALR exposure (C), ATP decrease (D), HMBG1 exodus (E), biological ICD score (F) previously measured and expressed as z-scores (except for ICD prediction score) Bezu et al, 2018(), as well as between transcription and translation inhibitions (G), were calculated with the Pearson method giving the correlation coefficient (R) and corresponding p-values (p). Known immunogenic drugs are indicated with colors: dactinomycin (DACT), mitoxantrone (MTX), doxorubicin (DOXO), daunorubicin (DAUN), OXA, docetaxel (DOC), paclitaxel (PACL), vinblastine (VB), vincristine (VC) and vinorelbine (VR) (A-G).

(H) The inhibition of transcription was assessed for the negative and positive ICD hits identified with the predictive algorithm (Figure 1). U2OS cells were treated with the agents at concentrations corresponding to their IC60: 1 μM dactinomycin (DACT), 50 μM topotecan, 1 μM becatecarin, 0.5 μM trabectedin, 5 μM UCN-01, 30 μM mycophenolate mofetil, 30 μM nonoxynol-9, 25 μM dactolisib, 2.5 μM β-lapachone, 5 μM 5-fluorodeoxycytidine and 2 μM RH-1 for 1.5 h followed by 1 h with EU. The percentage of transcription inhibition was calculated and the coefficient of correlation (R) and associated p-value (p) between the percentage of inhibition and the theoretical ICD score was calculated using the Pearson method.

(I-L) The 50,000 compounds of the NCI-60 library were annotated for different parameters including transcription and translation inhibition. The predicted ICD score was calculated with a previously described model built on artificial intelligence Bezu et al, 2018(). Empirical cumulative distribution is plotted in black for all compounds and in red for the compounds falling into the categories of interest which are transcription inhibitors (n=31) (I), translation inhibitors (n=25) (J), as well as two other random categories used as controls, PARP inhibitors (n=4) and antimetabolites (n=49) (K, L). The p-values calculated with a Kolmogorov-Smirnov test are indicated on each graph.
SWI/SNF chromatin remodeling controls Notch-responsive enhancer accessibility (2019)
Figure 1

Figure 1 - H3.3 levels increase at the E(spl)-C in Notch-ON nuclei.

A, C, E Live imaging of histone-GFP (green) and ParB-mCherry (magenta) expressed in larval salivary gland nuclei using 1151-Gal4. H3.3-GFP levels are increased at the E(spl)-C in the presence of constitutively active Notch, NΔECD (Notch-ON), compared to control Notch-OFF nuclei expressing LacZ (A). The same is seen with H3.3core-GFP (E), but there is little change in H3-GFP between Notch-OFF and Notch-ON nuclei (C). ParB-mCherry binds to its cognate int DNA sequence inserted within the E(spl)-C [8,64]. Yellow dotted box contains E(spl)-C and yellow arrow indicates position of E(spl)-C on chromosome. Scale bars (white) = 5 µm.

B, D, F Quantifications of relative fluorescence intensity of histone-GFP and ParB-mCherry across the E(spl)-C in Notch-OFF (upper) and Notch-ON (lower) conditions. Mean+/-SEM; nnuclei = 7, 6, 5, 8, 9, 11 and nglands = 5, 5, 5, 5, 5, 4 where each gland represents a biological replicate (from top to bottom).
SWI/SNF chromatin remodeling controls Notch-responsive enhancer accessibility (2019)
Figure 2

Figure 2 - The BRM complex is required for Su(H) recruitment in Notch-ON nuclei.

A, C Effects from depleting chromatin remodelers and histone chaperones, as indicated (wide range shown in A, BRM complex components shown in C), on recruitment of Su(H)-GFP in Notch-ON nuclei (expressing NΔECD) of larval salivary glands. w RNAi is a control and BrmK804R is expression of dominant-negative Brm. Different OSA RNAi stocks used in C are denoted by (1) and (2). In all conditions except Moira RNAi (A), Snr1 RNAi and BrmK804R expression (C), nuclei exhibit a bright accumulation of Su(H)-GFP at a single locus when imaged live. Scale bars (yellow) = 5 µm.

B, D Percentage of Notch-ON nuclei retaining a single clear band of Su(H)-GFP when the indicated RNAi is co-expressed with NΔECD. For each genotype, 5 nuclei from each of 10 glands were scored (50 nuclei total). **** A significant fraction of nuclei lost the fluorescent band when core components of the BRM complex were perturbed; p<0.0001, two-tailed Fisher's exact test calculated using the raw (non-percentage) scoring data.
SWI/SNF chromatin remodeling controls Notch-responsive enhancer accessibility (2019)
Figure 3

Figure 3 - The BRM complex is required for chromatin accessibility at Notch-responsive regions.

A Genomic region encompassing the E(spl)-C; green graphs indicate ChIP enrichment for Su(H) in Kc167 cells (Log2 scale is -0.5 to 2.9, data published previously in [4]); gene models are depicted in dark blue. Positions of primer pairs used in qPCR experiments are indicated with black arrows. Abbreviations are as follows: "igr" = intergenic region, "tr" = transcribed region and "enh" = enhancer.

B, C Chromatin accessibility in Notch-OFF (B) and Notch-ON (NΔECD expression, C) salivary gland nuclei measured by ATAC-qPCR; fold enrichment at the indicated regions compared to a "closed ctrl" region. Expression of dominant-negative Brm, BrmK804R, led to reduced accessibility of E(spl)mβ-HLH and E(spl)m3-HLH enhancer regions in Notch-OFF conditions, and to a more widespread reduction in accessibility in Notch-ON conditions. "Eip78C enh" corresponds to the ecdysone receptor-binding region of the Eip78C enhancer, which is highly accessible but not Notch-responsive; "Rab11 tr" and "Mst87F tr" represent highly and lowly-expressed control genes respectively. Mean +/- SEM; n = 3; * p<0.05 with two-tailed Welch's t-test comparing LacZ and BrmK804R samples.

D Chromatin accessibility in salivary gland nuclei depleted for Su(H) by RNAi, measured by ATAC-qPCR; fold enrichment at the indicated regions compared to a "closed ctrl" region. Accessibility is increased across most of the E(spl)-C compared to controls expressing LacZ. Control primer regions are as in B and C. Mean +/- SEM; n = 3; * p<0.05 with two-tailed Welch's t-test compared to LacZ controls.
SWI/SNF chromatin remodeling controls Notch-responsive enhancer accessibility (2019)
Figure 4

Figure 4 - The BRM complex is required for Su(H) recruitment and Notch-dependent transcription in Kc167 cells.

A Effect of brm and Snr1 RNAi on brm and Snr1 cDNA levels respectively, measured by reverse transcription-qPCR in Kc167 cells; percentage cDNA compared to GFP RNAi. The knockdowns are highly effective, with only 1-2% of brm and Snr1 cDNA remaining detectable. Mean +/- SEM; n = 3.

B, C Knockdown of components of the BRM complex reduces Su(H) recruitment both in Notch-OFF (B) and Notch-ON (C) conditions. Fold enrichment of Su(H) occupancy at the indicated positions detected by ChIP, relative to input, in Kc167 cells treated with brm, Snr1 or GFP RNAi as a control. Notch-ON conditions (C) were induced by 30 minutes of EGTA treatment. Mean +/- SEM, n = 3 (B); Mean, n = 2 (C); * p<0.05 with one-tailed student's t-test compared to GFP RNAi control.

D Effect of brm RNAi on E(spl)mβ-HLH (mβ) and E(spl)m3-HLH (m3) induction by Notch activation (EGTA treatment) measured by reverse transcription-qPCR; shown as fold difference to lacZ RNAi control. Mean +/- SEM; n = 3.

E Effect of Brm dominant-negative on expression of E(spl)mβ-HLH (mβ) and E(spl)m3-HLH (m3) measured by reverse transcription-qPCR. Expression was analyzed in stable cell lines containing pMT-inducible BrmWT or BrmK804R in the absence (left, uninduced) or presence of copper sulfate (right, Cu2+ induced). The response of E(spl)mβ-HLH and E(spl)m3-HLH to Notch activation ("N-On" = EGTA treatment vs. "N-Off" = PBS control) was reduced in the BrmK804R-expressing cells compared to BrmWT-expressing cells, only when induced with copper (right graph). Mean, n = 2 (left); Mean +/- SEM, n = 3 (right); * p<0.05 with one-tailed student's t-test comparing BrmWT and BrmK804R.

F Knockdown of actin-related subunit, BAP55, reduces Su(H) recruitment in both Notch-OFF (PBS treatment) and Notch-ON (EGTA treatment) conditions. Fold enrichment of Su(H) occupancy at the indicated positions detected by ChIP, relative to input, in Kc167 cells treated with Bap55 or lacZ RNAi as a control. Mean +/- SEM; n = 3; * p<0.05 with one-tailed student's t-test compared to lacZ RNAi control.

G Effect of Bap55 RNAi on E(spl)mβ-HLH (mβ) and E(spl)m3-HLH (m3) expression levels measured by reverse transcription-qPCR in Notch-OFF (PBS treatment) and Notch-ON (EGTA treatment) conditions. Expression level of RpII215 is shown as a control gene. Mean +/- SEM; n = 3; * p<0.05 with one-tailed student's t-test compared to lacZ RNAi control.
SWI/SNF chromatin remodeling controls Notch-responsive enhancer accessibility (2019)
Figure 5

Figure 5 - Nucleosome turnover at Su(H)-bound enhancers is increased in Notch-ON cells and is dependent on the BRM complex.

A Nucleosome turnover measured by CATCH-IT-qPCR; fold enrichment over input samples compared to Sec15 tr control region. Su(H)-bound enhancers show increased nucleosome turnover in response to Notch signaling. Notch signaling is activated in Kc167 cells by 6 hours of copper induction of pMT-NICD with copper excluded in the control. Positions of E(spl)-C primers are shown in Fig 3A; the remaining primers are control non-Notch-responsive regions. Mean, n = 2.

B brm RNAi reduces nucleosome turnover at Notch-responsive regions. CATCH-IT-qPCR results as in A after brm or lacZ RNAi as a control. Mean +/- SEM; n = 5; * p<0.05 with one-tailed student's t-test comparing brm and lacZ RNAi.

C Brm is required for Notch-responsive nucleosome turnover. CATCH-IT-qPCR results after brm or lacZ RNAi as in B and pMT-NICD expression as in A. Mean +/- SEM; n=3; * p<0.05 with two-tailed student's t-test compared to control (lacZ RNAi Notch-ON bars are compared to lacZ RNAi Notch-OFF bars and brm RNAi bars are compared to their respective lacZ RNAi control bars).

D brm RNAi reduces incorporation of histone H3.3. V5 ChIP-qPCR in Kc167 cells after lacZ or brm RNAi treatment in cells with H3-V5 or H3.3-V5 expression induced from the pMT promoter by 3 hours of copper treatment, shown as fold enrichment over input samples. Mean +/- SEM; n = 3. * p<0.05 with two-tailed Welch's t-test compared to lacZ RNAi control.
Combinatorial transcription factor activities on open chromatin induce embryonic heterogeneity in vertebrates (2020)
Figure 1

Figure 1. Chromatin accessibility during early development.

A. Genome browser view showing chromatin accessibility (ATAC-seq) and ChIP-seq (p300 and H3K4me3) profiles at stage 9, 10½, 12 and 16 at sox2 gene locus. The number at the left (black line) indicates the Y-axis scale of the profile. ATAC-seq peaks were found at promoter (H3K4me3) and enhancer (p300-bound) regulatory regions. B. Chromatin accessibility and p300 binding at differential ATAC-seq peaks visualized using K-means clustering. C. Box plots showing pair-wise sequential-stage comparisons of fold change in accessibility (ATAC-seq) and corresponding changes in gene expression (RNA-seq dataset) (Owens et al, 2016). The data represents two biological replicates. The central band within the boxplot represents the median (50th percentile), the box represents the range between the first and third quartile (25th-75th percentile), and the whiskers show 1.5 x the interquartile range (IQR).
Combinatorial transcription factor activities on open chromatin induce embryonic heterogeneity in vertebrates (2020)
Figure 2

Figure 2. Chromatin accessibility in animal cap (AC) and dorsal marginal zone (DMZ).

A. Genome browser view of AC and DMZ accessibility profiles for ectoderm-expressed (tfap2a and grhl3) and organizer-expressed (gsc and chrd) marker genes. B. Boxplots showing differential gene expression (AC versus DMZ) and associated ATAC-seq signals (Two biological replicates). The central band within the boxplot represents the median and the whiskers show 1.5 times the range between the first and third quartile (IQR). C. Hierarchical clustering of AC and DMZ ATAC-seq data on H3K4me3-positive (top) and p300-positive (bottom) ATAC-seq peaks. D. Heat map showing accessibility signal (log1p of fold over background) at p300-positive ATAC-seq peaks surrounding pluripotency genes (ventx1/ventx2, pou5f3 and sox2). The row labeled 'random' shows accessibility signals at random genomic loci. E. Single-cell ATAC-seq UMAP projection of cells derived from gastrula stage embryos (stage 10½), colored by cluster. F. Genomic tracks showing aggregated accessibility of single-cell ATAC-seq clusters at the t (tbxt), tfap2a, gsc, ctcf, lhx1 and sox11 loci.
Combinatorial transcription factor activities on open chromatin induce embryonic heterogeneity in vertebrates (2020)
Figure 6

Figure 6. Induction of organizer gene expression in AC cells.

A. Heat map showing log2 fold expression changes of differentially expressed genes in AC tissues overexpressing Foxb1, Foxb1-Eomes, Eomes, Irx3, Irx3-Otx2, Otx2 and Lhx8. B. Correlation heat map of overexpression RNA-seq samples and single cell clusters. C. Fold enrichment of genes with zygotically defined (ZyD) H3K4me3 at their promoter in AC with transcription factor overexpression. Asterisk indicates hypergeometric p-value <= 0.01.

Expanded View Fig. legends
Molecular flexibility of DNA as a key determinant of RAD51 recruitment (2019)
Figure 1

Figure 1. The experimental setup.

A. Depiction of the experimental setting. A biotinylated DNA oligonucleotide is immobilised onto a surface plasmon resonance (SPR) CM5 chip via biotin-streptavidin interaction. RAD51 protein is injected over the DNA-coated SPR matrix to measure polymerisation kinetics. Throughout this period, association and dissociation of RAD51 take place simultaneously. Following protein injection (stop), running buffer is injected to measure dissociation kinetics.

B. Wild-type (WT) RAD51 SPR curves for ssDNA dN-50 and dsDNA dN-50p. RAD51 was injected at 150 nM in the presence of 2.5 mM ATP (pH 7.5) and 10 mM CaCl2. The normalised mean number of WT RAD51 bound to respective DNA oligonucleotides (N) are plotted versus time, as measured by SPR, following the equation N = S / (L * (MRAD51 / MDNA)), where S is the signal in RU units (1 RU ~ 50 pg/mm2), L is the amount of DNA ligand immobilised onto the experimental flow cell (RU), MRAD51 is the molecular weight (kDa) of RAD51 (~ 37 kDa) and MDNA is the molecular weight (kDa) of the immobilised DNA molecule.
Molecular flexibility of DNA as a key determinant of RAD51 recruitment (2019)
Figure 2

Figure 2. Kinetics of WT RAD51 binding to ssDNA and dsDNA oligonucleotides of varying length.

A. Biotinylated ssDNA oligonucleotides of indicated lengths were separately immobilised onto SPR CM5 chips via biotin-streptavidin interaction. Wild-type (WT) RAD51 was injected at the indicated concentrations to measure association and dissociation kinetics.

B. The dotted and solid curves show the normalised mean number of RAD51 bound to the ssDNA oligonucleotides, as measured by SPR (see Fig. 1 for equation) and the ODE model fits (see Fig. 3A for model description), respectively.

C. As in A, except biotinylated dsDNA molecules of indicated lengths were used to measure association and dissociation kinetics.

D. As in B, the dotted and solid curves show the mean number of RAD51 bound to the dsDNA oligonucleotides, as measured by SPR and the ODE model fits (see Fig. 3B for model description), respectively.