EMBO/soda-nel huggingface dataset:
Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks (2019)
Figure 1

Figure 1. Chromatin feature annotation, open regions and gene regulation.

(A) Chromatin features mapped here displayed differences between CLL (patient CLL1) and an NBC donor (H7) as shown for the TCF4 locus, which encodes for a transcription factor from the E protein family. Based on the increased H3K4me1, H3K27ac and ATAC signal, two predicted enhancer loci were marked that became active in CLL. Note that the y-axis for RNA-seq is scaled differently for CLL (8000) and NBCs (100) to visualize that the TCF4 gene was not completely silenced but lowly expressed also in NBCs as evident also from the H3K36me3 mark. Light grey depicts active chromatin region and dark grey the confined enhancer locus coinciding with an open chromatin region. The chromatin state annotation is described in panel B. (B) Chromatin segmentation of co-occurring histone modifications by ChromHMM yielding a model with 12 chromatin states. The indicated emission parameters for the contributions of individual histone marks and the average amount of each state (Mb) for CLL and NBC samples is given. (C) Chord diagram representation of genome wide chromatin state changes. The amount of chromatin change is proportional to the size of the segments with each tick representing 4 Mb of chromatin. Color-coding of chromatin states as in panel B. (D) Distribution of ~24,400 annotated differentially accessible regions (ATAC-seq) in NBC and CLL samples according to the chromatin state annotation. In total 7,605 annotated regions gained an ATAC-seq signal in CLL, while it was lost at 16,790 loci. (E) Part of the computed B cell gene regulatory network showing TCF4 and its deregulated target genes as well as some of the adjacent nodes. The GRN was used to calculate the activity of regulators like TCF4 based on their target gene expression. Color code: TFs, red; target genes, grey; chromatin modifier, blue.
Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks (2019)
Figure 3

Figure 3. Histone modification and nucleosome positioning changes at promoters

(A) Correlation function of H3K4me3 ChIP-seq reads. A broadening of H3K4me3 domains in CLL by 1-2 nucleosomes was detected. Error bars represent the SEM. (B) H3K4me3 peak width distribution at common promoters in CLL and NBC controls. (C) Distribution of nucleosome occupancy calculated from histone H3 ChIP-seq averaged over a 1000 bp window within promoters. H3K4me3 regions displayed higher nucleosome density for CLL as compared to NBC samples. (D) Exemplary region at the TAF13 promoter showing higher H3K4me3 levels upstream of the TSS with lost ATAC signal (grey bar) as compared to the NBC control. (E) TF motif analysis of ATAC signal lost at CLL promoters with broadened H3K4me3 regions. An enrichment for the NFY TF motif was detected. (F) Heatmap of genome-wide histone modification patterns at promoters within -2 to 5 kb around the TSS (x-axis) for an NBC (H3) and a patient (CLL1) sample. Each line on the y-axis corresponds to one promoter. The clustering revealed one cluster with loss of H3K4me3 at bivalent promoters in CLL. (G) TF motif analysis at bivalent promoters that lost H3K4me3 in CLL.
Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks (2019)
Figure 4

Figure 4. Differential enhancer activity in CLL and NBCs

(A) Overlap of active regions identified in CLL and NBCs by ChromHMM, ATAC peaks or bidirectionally expressed RNA loci labeled as "Bidi". Venn diagram showing the total number of overlapping regions (not area-proportional). (B) Chromatin transitions within differential active states between NBCs and CLL. Heatmap representation of the amount of chromatin (log2 Mb+1) transitioning from a particular state in NBC (rows) to CLL (columns). Transitions were considered for all recurrent active chromatin state regions (states 1, 8 ,9 and 11) present in a minimum of 3 samples even if the consensus state was not an active state. Accordingly, the matrix includes transitions between non-active states at low frequencies. (C) Chromatin states at bidirectionally transcribed predicted enhancers loci. All Bidi loci identified in NBC samples (n = 961) and CLL samples (n = 8530) are shown. The Bidi loci show an enrichment of the states "Active 2 (predicted active enhancer)" and "Active 1 (predicted transcription start sites)". (D) Clustering of samples via expression of bidirectionally regions that are differential between NBCs and CLL and quantified using DESeq2. (E) Volcano plot of differential super-enhancers targeting known leukemia and cancer genes. Examples include SE loss at CDKN1A, PI3KC2B and KMT2B (MLL2) and SE gain at FMOD, CREB3L2, CTLA4, TCF4, LEF1 and BCL2. Points represent non-differential SEs (grey), and differential SEs (FDR < 0.01) with fold change greater than 1 (orange). (F) RNA expression changes of selected genes associated with differential SEs. Top: genes significantly (FDR < 0.05) up-regulated by SEs in CLL. Bottom: Genes significantly down-regulated by SEs in CLL. (G) Comparison of normalized gene expression of histone deacetylases between CLL and NBCs. Histone deacetylases significantly up-regulated in CLL are shown in bold. (H) HDAC activity and its inhibition by panobinostat in B cells from CLL patients (red) in comparison to healthy donors (grey). Error bars indicate standard deviation measured in four biological replicates. (I) Genome browser view of H3K27ac tracks (in grey) at exemplary genes for NBCs and CLL cells 24 hours after mock and after panobinostat treatment. At genes such as CDKN1A (cell cycle control) and KLF13, reduced H3K27ac signal in CLL was increased upon HDAC inhibition to the level found in NBCs. WNT11 is shown as an example of a de novo gain of an active enhancer due to treatment with panobinostat. (J) Heatmap displaying changes in H3K27ac read occupancy in CLL upon panobinostat treatment for 24 h. A general gain of H3K27ac in enhancers upon panobinostat treatment was observed.
Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks (2019)
Figure 5

Figure 5. TF binding sites enriched at differential enhancers between CLL and NBCs.

(A) Plot of the most enriched TF binding motifs in regions that showed gained ATAC-seq signal at ChromHMM states 1, 8, 9 and 11 (predictive for enhancers). Color coding represents different TF classes. The size of the spots is proportional to the percentage of target sequences with a given motif. (B) H3K27ac (left) and DNA methylation (right) at NFAT binding sites. CLL cells showed both an H3K27ac enrichment and DNA-hypomethylation at NFAT target sites, suggesting a higher activity of TFs from the NFAT family in CLL. (C) Same as panel A but for lost ATAC-seq signal. (D) ATAC footprints for E2A, EBF and CTCF motifs from the Homer analysis. The E2A motif footprint (binding site of E protein family TFs like TCF4) displayed an increased binding signal in CLL while sites with the EBF and CTCF motif lost the ATAC signal. (E) Expression of the genes nearest to target enhancers with constitutively bound ("stable") CTCF vs. enhancers that lost CTCF in CLL. Loss of CTCF binding correlated with reduced gene expression. (F) Correlation matrix of simultaneously open regions computed from the scATAC-seq data. For all loci the pair-wise correlation coefficients were calculated and plotted to visualize how different loci were wired to each other. As an example, the TCF4 locus on chromosome 1 is depicted. (G) Enhancer-promoter rewiring at the NFKB2 locus. A switch of interactions between the NFKB2 promoter and two different enhancers in CLL (red line) vs. NBCs (grey line) was observed. Based on the CTCF ChIP-seq analysis both intronic enhancers at the NFKB2 and the FBXL15 gene show (nearby) constitutively bound CTCF, even though the targets of the two enhancers are switched.
Linking aberrant chromatin features in chronic lymphocytic leukemia to transcription factor networks (2019)
Figure 6

Figure 6. Integration of chromatin state changes, TF binding and gene expression

(A) Relation of chromatin state changes and gene expression changes. For 81% of differentially expressed genes, a change in a regulatory chromatin feature was observed. (B) Scheme of chromatin feature changes and associated TFs identified in this study (Appendix Table S2). (C) Pathway analysis of identified core TFs and their target genes from our B cell GRN. Significantly enriched gene sets (p < 0.05) relevant to CLL pathophysiology were retrieved (BCR signaling, NF-κB signaling and MAPK signaling). (D) CLL GREN. TFs identified here, associated chromatin modifiers and differentially expressed target genes were integrated into a network. A part that includes TCF4 and EBF1 is shown. The different TSSs of the TCF4 gene (Fig. 1A) were combined into a single gene target for the corresponding SE. Several enhancers of EBF1 target genes were active in NBCs (H402, H441, H464, H1000) but became silenced in CLL. As an example, H464 and SNX22 are highlighted in yellow. These enhancers represent candidate enhancers for involving EBF1 binding. Color code: active enhancers, dark, light and medium green for CLL only ("C"), only in NBCs ("H") and both cell types ("CH"), respectively; TFs, red; target genes, grey; chromatin modifier, blue. (E) Analysis of regulation of SNX22 by the intronic H464 enhancer shown in panel B. This enhancer lost its ATAC signal at the predicted EBF1 binding site. EBF1 ChIP-seq analysis validated that EBF1 is indeed lost at this site. Although H3K27ac at the locus was only slightly reduced, transcription of SNX22 was largely reduced. These findings are consistent with a mechanism where EBF1 binding drives gene expression of SNX22 by binding to H464.

Expanded View Figure legends
A high-throughput RNAi screen for detection of immune-checkpoint molecules that mediate tumor resistance to cytotoxic T lymphocytes. (2015)
Figure 1

Luc-CTL assay design used for identification of immune-checkpoint molecules

Assay principle: RNAi was performed with luciferase-expressing cells that were challenged with or without CTLs and bi-specific antibody. Before readout, cell supernatant was removed and the remaining intact cells were lysed to measure the residual-cell-associated luciferase. To identify immune-checkpoint regulators, the difference between normalized luciferase measurements for conditions with CTLs and without CTLs was calculated. siRNAs enhancing CTL cytotoxicity would only reduce normalized luciferase levels under conditions with CTLs; hence, the difference between luciferase measurements will be > 0.

Luc-CTL assay performed at different T cell to MCF7 cell ratio with PBMC-derived CD8+ T cells and anti-EpCAM x CD3 bi-specific antibody (○). Anti-CD19 × CD3 bi-specific Ab (▪) was used as a specificity control since CD19 is a B-lymphocyte-specific antigen and therefore this bsAb fails to cross-link tumor to T cells. Lower luciferase intensity indicates higher lysis. Error bars denote ± SEM.

Luc-CTL assay was performed with MCF7 cells transfected with control or PD-L1-specific siRNAs and cocultured with or without CTLs and bsAb. For each condition, the luciferase activity of PD-L1-siRNA-treated cells was normalized to that of the control siRNA; n = 8. Error bars denote ± SEM.

Comparison between the Luc-CTL assay (▪) and the classical chromium (Cr)-release assay (○) with MCF7 as target cells and survivin-specific T cells as effector cells at varying effector to target (E:T) ratios. Error bars denote ± SEM.
A high-throughput RNAi screen for detection of immune-checkpoint molecules that mediate tumor resistance to cytotoxic T lymphocytes. (2015)
Figure 2

Layout and analysis of the RNAi screen used to identify immune-modulatory tumor genes

A Workflow and experimental layout of the RNAi screen which was performed thrice, each time in duplicates, along with an additional CTG-based viability screen to filter out lethal genes from the final hit list. Hits were analyzed after data normalization using the cellHTS2 package.

B Correlation between replicates for screen 2 for both viability and toxicity sets, as determined by the Pearson rank correlation test.

C–E Graphical summary of gene function related to modification of T cell-mediated tumor lysis and cell viability for screens 1, 2, and 3, respectively. Positive score = reduced cancer cell viability; negative score = increased viability. X-axis: influence on cell viability without addition of T cells. Y-axis: influence on cell viability with addition of T cells. Appropriate immune-suppressive (PD-L1, CEACAM-6, GAL-3) and lethality (UBC, PLK-1) controls, along with few positive (GRM4, GRK5) and negative (CCR9, IL8) candidate immune-modulatory hits are highlighted herein.
A high-throughput RNAi screen for detection of immune-checkpoint molecules that mediate tumor resistance to cytotoxic T lymphocytes. (2015)
Figure 4

CCR9 knockdown sensitizes tumor cells to immune attack

A MCF7 cells were transfected with the described siRNA sequences and harvested after 72 h for mRNA and protein estimation using RT–PCR (upper) and immunoblot (lower) analysis, respectively. GAPDH and beta-actin were used as controls for RNA and protein normalization, respectively.

B Luc-CTL cytotoxicity assay with PBMC-derived CTLs and bi-specific Ab as effector population and MCF7 as target cells, which were transfected with individual (s1–s4) or pooled CCR9 siRNA sequences. PD-L1 and non-specific control siRNAs were used as positive and negative controls, respectively, for CTL-mediated cytotoxicity.

C, D Cr-release assay showing % specific lysis of MCF7 cells by survivin-specific T cells at different ratios upon CCR9 knockdown (C) or overexpression (D). MCF7 cells were transfected with either CCR9 siRNA s1 (Δ), pooled siRNA sequences (○), positive control PD-L1 (□), and non-specific control siRNA (▪) (C) or with pCMV6-AC-His control vector (▪) and pCMV6-AC-His-CCR9 expression construct (○) (D) 72 h prior to the assay.

E Cr-release assay showing % specific lysis of MDA-MB-231 breast tumor cell line by survivin-specific T cells at different ratios upon CCR9 knockdown (○) in comparison to the control knockdown (▪).

F, G Cr-release assay showing lysis of patient-derived melanoma cells (M579-A2) by tumor-infiltrating lymphocytes (TIL 412) (F) or lysis of PANC-1 pancreatic adenocarcinoma cells by patient-derived pancreatic TIL 53 (G) at different E:T ratios upon CCR9 (○) or control (▪) knockdown.

Data information: All experiments were performed in triplicates and are representative of at least three independent experiments. Error bars denote ± SEM, and statistical significance was calculated using the unpaired, two-tailed Student's t-test.
A high-throughput RNAi screen for detection of immune-checkpoint molecules that mediate tumor resistance to cytotoxic T lymphocytes. (2015)
Figure 5

Tumor-specific CCR9 impedes Th1-type immune response

A, B ELISpot assay showing IFN-γ (A) and granzyme B (B) secretion by survivin-specific T cells, as spot numbers, upon CCR9 knockdown (black bars) in MCF7 cells compared to the control knockdown (white bars). T cells (TC) alone (grey bars) were used as control for background spot numbers.

C Luminex assay showing cytokine levels in the supernatant from the coculture of survivin-specific TC and either CCR9hi MCF7 (transfected with CCR9-specific siRNA) or CCR9lo MCF7 (transfected with control siRNA) cells.

D Phospho-plex analysis showing the phospho-STAT levels in survivin-specific TC upon encountering CCR9hi or CCR9lo MCF7 cells. Log2 ratio of mean fluorescent intensity (MFI) of the respective analytes to the unstimulated TC is plotted herein.

E Immunoblot analysis showing the phospho-STAT1 levels in the CCR9hi-treated, CCR9lo-treated or unstimulated TC using the phospho-specific STAT1 (pTyr701) antibody. Beta-actin was used as the loading control.

Data information: In all the cases, experiments were performed in triplicate with at least two independent repeats. Mean ± SEM are shown herein, unless stated otherwise, with statistical significance assessed using unpaired, two-tailed Student's t-test. Source data are available online for this figure.
A high-throughput RNAi screen for detection of immune-checkpoint molecules that mediate tumor resistance to cytotoxic T lymphocytes. (2015)
Figure 6

Tumor-specific CCR9 interacts directly with T cells inducing prominent changes in the gene expression signature

A ELISA showing CCL25 levels in cell lysates from indicated tumor cell lines. CCR9 knockdown (k.d.) in MCF7 cells was achieved using specific shRNA (see Materials and Methods).

B Cr-release assay showing % specific lysis of MCF7 cells by survivin TC upon CCL25 (□) or CCR9 (○) inhibition using specific siRNAs in comparison to the control siRNA (▪). Mean ± SEM are depicted herein.

C MCF7 cells were transfected with control or CCR9-specific siRNAs, and 48 h later, the supernatants (CCR9lo or CCR9hi SSN, respectively) were used to culture survivin TCs overnight. Supernatant-treated TCs were then used as effector cells against CCR9lo or CCR9hi MCF7 tumor cells in the Cr-release assay along with wild-type MCF7 cells. Mean ± SEM are depicted herein.

D Cr-release assay showing % specific lysis of MCF7 cells that were pre-treated with or without pertussis toxin (PTX), or knocked down for CCR9 using specific siRNA. Mean ± SEM are depicted herein.

E, F MCF7 cells transfected with control siRNA (CCR9hi) or CCR9 siRNA (CCR9lo) were cocultured with survivin TCs for 12 h. Gene microarray was performed with the total RNA extracted from purified T cells after the coculture. Volcano plot (E) illustrating fold change (FC; log2) in gene expression intensities compared with P-value (−log2) between CCR9hi- and CCR9lo-treated TCs. Horizontal bar at y = 4.32 represents a statistical significance of P = 0.05 (genes in gray below this line did not reach significance). LogFC cutoff at ± 0.5 is represented by the vertical lines. Heatmap representation of the top upregulated (LogFC > 0.5) and downregulated (LogFC < −0.85) genes (F) with P ≤ 0.05. Individual replicates per sample group are shown herein.
A high-throughput RNAi screen for detection of immune-checkpoint molecules that mediate tumor resistance to cytotoxic T lymphocytes. (2015)
Figure 7

<italic>In vivo</italic> inhibition of CCR9 significantly reduces tumor outgrowth in response to adoptive TIL therapy

A Cr-release assay showing TIL 209-mediated lysis of CCR9+ M579-A2 (transduced with control shRNA) or CCR9− M579-A2 cells (transduced with CCR9-specific shRNA). Curves represent mean ± SEM.

B Scheme for the in vivo mouse experiment involving the s.c. injection of CCR9+ (shControl) or CCR9− (shCCR9) M579-A2 tumor cells in the left and right flank, respectively, of the NSG mice. Following this, at d2 and d9, mice received i.v. injection of TIL 209 in PBS (n = 7) or PBS alone (control group for tumor growth; n = 3) and measured for tumor growth.

C, D Tumor growth curves showing mean ± SEM tumor volume of CCR9+ or CCR9− M579-A2 tumors in TIL-treated mice (C) or the PBS alone group (D). Statistical difference was calculated using the unpaired one-sided Mann–Whitney U-test.
TRIM50 promotes NLRP3 inflammasome activation by directly inducing NLRP3 oligomerization (2022)
Figure 1

Figure 1. TRIM50 directly interacts with NLRP3.

A. Exogenous immunoprecipitation assay of the interaction between TRIM50 and NLRP3.

B. Endogenous immunoprecipitation assay of the interaction between TRIM50 and NLRP3 in mouse peritoneal macrophages.

C. TRIM50 and NLRP3 proteins were expressed and obtained from the in vitro translation system and their interaction were further analyzed.

D. Co-IP assay of the interaction between NLRP3 and TRIM50 after stimulation with nigericin for 0, 15, 30 and 60 min.

E. Co-IP assay of the interaction between NLRP3 and TRIM50 in mouse peritoneal macrophages primed with LPS for 6h and treated with ATP for 30min.

F. Confocal microscopy assay of the interaction between TRIM50 and NLRP3 in THP1 cells with stimulation by LPS and ATP. Scale bars: 10μm.

G. Schematic diagram of human TRIM50 (WT) and its truncation mutants was presented(top), and the interaction of TRIM50 (TRIM50 truncation mutants) and NLRP3 was detected by IP assay.

H, I. Schematic diagram of human NLRP3 (WT) and its truncation mutants (top), and immunoprecipitation assay of the interaction of NLRP3 (NLRP3 truncation mutants) with TRIM50 in HEK293T cells.

Data information: Data are representative of three biological replicates with similar results.
TRIM50 promotes NLRP3 inflammasome activation by directly inducing NLRP3 oligomerization (2022)
Figure 2

Figure 2. TRIM50 promotes NLRP3 inflammasome activation.

A. Immunoblot analysis of the NLRP3 protein level in the mouse peritoneal macrophages transfected with Si-Trim50.

B. qRT-PCR analysis of Nlrp3 and IL-1β mRNA levels in LPS-primed Si-Trim50 transfected mouse peritoneal macrophages.

C, D. Immunoblot analysis of Caspase1-P20 (C) and Gasdermin D (D) of WT or Trim50-/- mouse peritoneal macrophages after LPS stimulation for 6h and ATP treatment for 30min.

E, F. Immunoblot analysis of Caspase1-P20 (E) and Gasdermin D (F) of WT or Trim50-/- BMDM cells after LPS stimulation for 6h and ATP treatment for 30min.

G, H. ELISA analysis of IL-1β, TNF-α and IL-6 in the supernatant of WT or Trim50-/- peritoneal macrophages and BMDM cells with the activation of NLRP3 inflammasome. n=3

I. LDH analysis of WT or Trim50-/- mouse peritoneal macrophages with the activation of NLRP3 inflammasome. n=3

Data information: Band densities were quantitated by 'Image J' software and normalized to GAPDH. P-values are shown, two-tailed Student's t-test (A, C-F) or two-way analysis of variance (ANOVA) (B, G, H and I). Data are representative of three biological replicates with similar results. (mean ± SD in A-I).
TRIM50 promotes NLRP3 inflammasome activation by directly inducing NLRP3 oligomerization (2022)
Figure 3

Figure 3. TRIM50 reduces the ubiquitination of NLRP3.

A. Immunoprecipitation analysis of the NLRP3 ubiquitination in HEK293T cells transfected with HA-ubiquitin, His-NLRP3 and Flag-TRIM50 plasmids.

B. Immunoprecipitation analysis of the endogenous NLRP3 ubiquitination in WT or Trim50-/- BMDM cells.

C. Immunoprecipitation analysis of NLRP3 ubiquitination in HEK293T cells transfected with NLRP3, HA-Ubiquitin, TRIM50 or TRIM50 (ΔRING) mutant plasmids.

D. Immunoblot analysis of the ASC oligomerization in cross-linked cytosolic pellets (insoluble) and the whole cell lysate (soluble) in HEK293T cells with nigericin treatment for 30 min.

E. Immunoblot analysis of NLRP3 protein levels in cycloheximide (CHX) treated HEK293T cells.

F. Immunoblot analysis of NLRP3 in HEK293T cells transfected with NLRP3 plasmid and TRIM50 or TRIM50 (ΔRING) mutant, followed with the further treatment with CHX for 0h, 4h or 8h.

G. Immunoblot analysis of NLRP3 in mouse peritoneal macrophages, followed with the further treatment with CHX for 0h, 4h or 8h.

Data information: Band densities were quantitated by 'Image J' software and normalized to GAPDH. P-values are shown, two-way ANOVA (E-G). Data are representative of three biological replicates with similar results. (mean ± SD in E-G).