EMBO/soda-nel huggingface dataset:
LncPSCA in the 8q24.3 risk locus drives gastric cancer through destabilizing DDX5 (2021)
Figure 4

Figure 4. lncPSCA interacts with DDX5 and promotes DDX5 degradation.

(A) lncPSCA predominantly locates in the nuclear fraction of gastric cancer cells. In detail, the cytosolic and nuclear fractions of HGC-27 or MGC80-3 cells were separately isolated. RNA was isolated from either the cytosolic fraction or the nuclear fraction. Expression of lncPSCA in the cytosolc and nuclear RNA was measured. Data show one representative example of three biological replicates.

(B) Silver staining of lncPSCA pull-down nuclear proteins of gastric cancer cells.

(C) lncPSCA pull-down followed by Western blot validated its interaction with DDX5 and other candidate proteins identified by Mass spectrometry. GAPDH served as the control.

(D) RIP assays show association of DDX5 with lncPSCA in MGC80-3 and HGC-27 cells. Relative enrichment (means ± SD) represents RNA levels associated with DDX5 relative to an input control from three independent experiments. IgG served as the control. HOTTIP was used as the negative control and SRA as the positive control during RIP. Data show one representative example of three biological replicates.

(E) Western blot analyses of DDX5 protein in gastric cancer cells with the enforced expression of lncPSCA.

(F) Gastric cancer cells after lncPSCA-knockout or stably overexpressing lncPSCA and control cells were treated with cycloheximide (CHX) or vehicle for the indicated periods of time. DDX5 levels were analyzed by Western blot.

(G) Western blot to detect the ubiquitination of DDX5 in HEK293T cells cotransfected with pcDNA-lncPSCA, HA-Ubiquitin and Flag-DDX5 (left panel) or in HGC-27 cells cotransfected with pcDNA-lncPSCA and HA-Ubiquitin (right panel).

(H) Expression of DDX5 was compared between normal and cancer samples in TCGA cohort (left), Shandong Discovery cohort (middle) and Shandong Validation cohort (right).

(I) Knockdown of DDX5 with siRNA (siD-1 and siD-2) substantially reduced the proliferation of gastric cancer cells. Data show one representative example of three biological replicates.

(J) Silencing of DDX5 with shRNA (shD) significantly reduced the proliferation of gastric cancer cells. Data show one representative example of three biological replicates.

(K) Effects of DDX5 knockdown or DDX5 overexpression on the colony formation of gastric cancer cells. HGC-27 or MGC80-3 were seeded into a 6-well cell culture plate and transfected with DDX5 siRNAs (siD-1 and siD-2). The HGC-27 or MGC80-3 cells stably transfected with the DDX5 shRNA (shD) were seeded into a 6-well culture plate. When colonies were visible after 14 days, cells were washed with cold PBS twice and fixed with the fixation fluid. Cells were stained with crystal violet and photos were taken.

Data information: The difference between two groups was calculated using Student's t test. *P< 0.05, **P< 0.01, ***P<0.001.
LncPSCA in the 8q24.3 risk locus drives gastric cancer through destabilizing DDX5 (2021)
Figure 5

Figure 5. lncPSCA-induced degradation of DDX5 releases RNA Pol II from the DDX5-Pol II complex in nucleus.

(A) Western blot for RNA Pol II among proteins pulled down with anti-DDX5 (up) and for DDX5 among proteins pulled down with an anti-Pol II antibody (down).

(B) Global increase in RNA Pol II signals after silencing of DDX5 (lv-shD) in HGC-27 cells. Average RNA Pol II ChIP-seq signal at all RNA Pol II peak regions. Upper subpanel: negative control, Lv-NC; lower subpanel: silencing of DDX5, lv-shD.

(C) Reads per million mapped reads (RPM) of RNA Pol II ChIP-seq signals of genes in human genome.

(D) KEGG Pathway analyses of over-lapped Differential expressed genes (DEGs) in both lncPSCA-overexpression cells and the DDX5-konckdown cells identified multiple pathways involved in cancer biology, with the P53 and MAPK signaling pathways as two of the most markedly changed pathways.

(E) RNA Pol II ChIPseq and RNAseq signals of PMAIP1, CDKN1A, SESN2 and SESN3 in HGC-27 cells. Red bars represent the RNA Pol II ChIPseq signal. Orange or blue bars represent the RNAseq signals of HGC-27 cells overexpressing lncPSCA (lncP) or with silenced DDX5 (shD), respectively.

(F) Validation of candidate downstream genes of the lncPSCA-DDX5 axis identified through the integrated analyses. qRT-PCR was performed for PMAIP1, CDKN1A and SESN2 genes upon overexpression of lncPSCA (lncP), silencing of DDX5 (shD) or lncPSCA konckout. Error bars indicate the SEM. Data show one representative example of three biological replicates.

(G) ChIP-qPCR assays was performed for PMAIP1, CDKN1A and SESN2 genes and demonstrated that significantly decreased RNA Poll II enrichment around TSS of PMAIP1, CDKN1A and SESN2 in various stable lncPSCA konckout GC cell lines. Left subpanel: RNA Pol II ChIP-qPCR fragments a, b and c on PMAIP1, CDKN1A and SESN2 genes; middle and right subpanels: RNA Pol II ChIP-qPCR results of PMAIP1, CDKN1A and SESN2 genes in MGC80-3 (middle) and HGC-27 cells (right). Data show one representative example of three biological replicates.

(H) Correlations between lncPSCA levels and the mRNA levels of the P53 signaling PMAIP1, CDKN1A and SESN2 genes in gastric cancer tissues Discovery cohort (n = 96) and Validation cohort (n = 30). RNA levels were determined by qRT-PCR relative to GAPDH. The r values and P values are from Pearson's correlation analysis.

Data information: The difference between two groups was calculated using Student's t test. The significance of expression association between different genes was calculated using Spearman's correlation. *P<0.05, **P<0.01, ***P<0.001.
The splicing-regulatory lncRNA NTRAS sustains vascular integrity (2022)
Figure 1

Figure 1 | NTRAS is essential for normal endothelial cell function and vital in vivo.

(A) Murine Ntras enrichment in endothelial cells versus other heart-derived cell types (data acquired from GSE95755 ((Quaife-Ryan et al, 2017))) (n = 4 mice).

(B) Changes in lncRNA expression from HUVECs exposed to hypoxia (0.2 % O2 for 12 h or 24 h), determined by ribo-minus RNA deep sequencing (data acquired from GSE107033 ((Neumann et al, 2018))); NTRAS is highlighted (n = 2 independent biological replicates).

(C) Validation of hypoxia-induced NTRAS expression at the indicated time points by RT-qPCR (n = 4 independent biological replicates).

(D) Subcellular localization of NTRAS and control transcripts, assayed by nuclear-cytoplasmic fractionation of HUVECs and RT-qPCR (n = 3 independent biological replicates).

(E) FITC-dextran-based in vitro permeability comparing control and NTRAS-silenced HUVECs (n = 5 independent biological replicates).

(F) Cumulative in vitro sprout lengths of control and NTRAS-silenced HUVECs under basal conditions and VEGFA stimulation (n = 3-10 independent biological replicates).

(G) Cell cycle analysis in Ntras-silenced H5V cells (n = 3 independent biological replicates).

(H) TMR-dextran-based assessment of vascular permeability in vivo, comparing heart homogenates from control and Ntras-silenced mice. Data normalized to organ and body weight (n = 11-12 mice per group). Experimental outline on the left.

(I) Survival of control and Ntras-silenced mice under basal conditions (black, n = 21-23 mice per group) and in hind limb ischemia (HLI; red, n = 8 mice per group). Number of survivors is indicated; experimental outline on the left.

Data information: In (A, C-H) data are represented as mean ± S.E.M. n.s.: non-significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. (A) One-way ANOVA, (C-E, H) two-tailed unpaired t-test, (G) two-way ANOVA, and (I) Mantel-Cox-test.
The splicing-regulatory lncRNA NTRAS sustains vascular integrity (2022)
Figure 2

Figure 2 | NTRAS operates as splicing-regulatory lncRNA.

(A) Separation of mock and proteinase K-treated HeLa nuclear extracts by sucrose density gradient ultracentrifugation followed by NTRAS detection using RT-qPCR. Fractions 1 and 14 represent top and bottom of the gradient, respectively (n = 1).

(B) Enrichment of NTRAS by antisense affinity selection from HeLa cell lysate followed by RT-qPCR, comparing a control and an NTRAS-specific probe (n = 5 independent biological replicates).

(C) Top ten enriched proteins from NTRAS affinity selections, identified by mass spectrometry (n = 5 independent biological replicates)

(D) RT-qPCR-based validation of NTRAS-hnRNPL-interaction by anti-hnRNPL RIPs from HUVEC nuclear fractions (n = 6 independent biological replicates). Representative western blot on the right.

(E) RT-qPCR analysis of NTRAS expression in HUVECs upon silencing of hnRNPL (n = 4 independent biological replicates).

(F) RPLP0-normalized NTRAS-CA16 motif expression after hnRNPL silencing determined by RT-PCR (n = 4 independent biological replicates).

(G) RNA deep sequencing-based assignment of alternative splicing events in NTRAS- or hnRNPL-silenced HUVECs, using rMATS software. Displayed are changes > 5 % (n = 2 independent biological replicates).

(H) NTRAS and hnRNPL co-regulated alternative splicing (AS) events. Data points with an FDR < 0.05 are circled (n = 2 independent biological replicates).

(I) RT-PCR-based analysis of TJP1 exon 20 inclusion upon silencing of NTRAS in HUVECs (n = 7 independent biological replicates). Representative agarose gels on the right.

(J) RT-PCR-based analysis of TJP1 exon 20 inclusion upon silencing of hnRNPL in HUVECs (n = 4 independent biological replicates). Representative agarose gels on the right.

Data information: In (B, D-F, I, J) data are represented as mean ± S.E.M. n.s.: non-significant, *p < 0.05, ***p < 0.001. (B-F, I, J) two-tailed unpaired t-test.
The splicing-regulatory lncRNA NTRAS sustains vascular integrity (2022)
Figure 3

Figure 3 | NTRAS controls TJP1 splicing and endothelial barrier function.

(A) Scheme depicting the in vitro splicing of a TJP1 exon 19 - exon 20 splice substrate featuring an intronic splicing silencer (ISS). hnRNPL binding sites are highlighted in red.

(B) In vitro splicing efficiency of the TJP1 splice substrate, comparing mock, NTRAS-depleted, and NTRAS-CA16 motif add-back conditions (n = 7-12 independent biological replicates).

(C) Co-precipitation of TJP1 pre-mRNA in anti-hnRNPL RIPs, using nuclear lysates from control and NTRAS-silenced HUVECs (n = 6 independent biological replicates).

(D) Co-precipitation of TJP1 pre-mRNA in anti-hnRNPL RIPs, using nuclear lysates from control and NTRAS-overexpressing cells (n = 5 independent biological replicates). Representative western blot on the right.

(E) Co-precipitation of TJP1 pre-mRNA in anti-hnRNPL RIPs, using nuclear lysates from control and NTRAS-CA16 motif overexpressing cells (n = 5 independent biological replicates). Representative western blot on the right.

(F) RT-PCR-based analysis of TJP1 exon 20 inclusion upon NTRAS overexpression and overexpression of the NTRAS-CA16 motif (n = 8 independent biological replicates).

(G) Endothelial resistance of NTRAS-silenced HUVECs (n = 3-5 independent biological replicates), analyzed by electrical cell-substrate impedance sensing (ECIS).

(H) Endothelial resistance of hnRNPL-silenced HUVECs (n = 3 independent biological replicates), analyzed by ECIS.

(I) Analysis of TJP1 exon 20 inclusion by RT-PCR upon transfection of HUVECs with a control SSO or an SSO masking the exon 20 - intron 20 boundary (E20 SSO) (n = 9 independent biological replicates). Representative agarose gel on the right. Schematic outline at the top right.

(J) Endothelial resistance of control SSO- or E20 SSO-transfected HUVECs (n = 4 independent biological replicates), analyzed by ECIS.

Data information: In (B-J) data are represented as mean ± S.E.M. n.s.: non-significant, *p < 0.05, **p < 0.01. (B, G-J) two-tailed unpaired t-test, (C-E) two-tailed paired t-test, and (F) one-way ANOVA.
The splicing-regulatory lncRNA NTRAS sustains vascular integrity (2022)
Figure 4

Figure 4 | Ntras sustains vascular integrity in vivo.

(A) Validation of Ntras-hnRNPL-interaction by anti-hnRNPL RIPs from H5V lysates, followed by RT-qPCR (n = 4 independent biological replicates). Representative western blot on the right.

(B) RT-PCR-based analysis of murine Tjp1 exon 20 inclusion in cardiac tissue from control and Ntras-silenced mice (n = 8 mice per group). Experimental outline on the left.

(C) Illustration of the murine Ntras locus. Displayed are Ntras and Slc30a1 (GRCm38/mm10). The red box highlights the deleted genomic region (~600 bp) in Ntras∆CA/∆CA mice, comprising the hnRNPL binding motif (~130 bp) and flanking sequences (~440 bp).

(D) Co-precipitation of Ntras in anti-hnRNPL RIPs using whole heart lysates from NtrasCA/CA and Ntras∆CA/∆CA mice (n = 3-4 mice per group). Representative western blot on the right.

(E) RT-PCR-based analysis of TJP1 exon 20 inclusion in hearts from NtrasCA/CA and Ntras∆CA/∆CA mice (n = 9-14 mice per group).

(F) Retinal angiogenesis assessed in P7 NtrasCA/CA and Ntras∆CA/∆CA pups by immunostaining of isolectin B4. Vascularized areas were normalized to total retinal area (n = 4-8 mice per group). Representative micrographs are shown. Scale bars are 200 µm.

(G) FTSC and TMR-dextran in vivo permeability assays, comparing homogenates of hearts from NtrasCA/CA and Ntras∆CA/∆CA mice. Data normalized to organ and body weight (n = 11-18 mice per group).

(H) Quantification of CD45+ cell (red) infiltration into cardiac tissue from NtrasCA/CA and Ntras∆CA/∆CA mice normalized to DAPI. Isolectin B4 (green) was used to label endothelial cells (n = 4-5 mice per group). Representative micrographs are shown. Scale bars are 50 µm. Insets and arrows indicate sites of CD45+ cell infiltration.

(I) Percentage of control and Ntras-silenced mice showing cardiac pathologies (n = 15-17 mice per group). Prevalence is indicated above the bars. H&E micrographs of murine heart sections from control and Ntras-silenced mice are shown. Insets and arrows indicate sites of lymphocytic infiltration. Scale bars are 100 µm.

Data information: In (A, B, D-H) data are represented as mean ± S.E.M. n.s.: non-significant, *p < 0.05, **p < 0.01, ***p < 0.001. (A) two-tailed paired t-test, (B, D-H) two-tailed unpaired t-test, and (I) Chi-square test.
Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit (2021)
Figure 1

Figure 1. σV is activated heterogeneously in response to lysozyme stress.

(A) It is unclear how genetic circuits (centre) tune phenotypic variability in a bacterial population in response to genetic perturbation, environmental stress and history (left) to modulate the fraction of cells that activate a given pathway (right).

(B) Schematic of the σV circuit. In its inactive form σV is bound to its anti-sigma factor RsiV. If there is lysozyme present in the environment, RsiV binds to lysozyme and undergoes a conformational change. Only then the proteases SipS and RasP can cleave RsiV to release σV. Once σV is released from RsiV it can bind to RNA polymerase to redirect transcription to the σV regulon. σV can initiate transcription of its own operon which includes sigV, rsiV, oatA (one of the main lysozyme resistance genes) and yrhK (unknown function).

(C) Time-lapse microscopy of cells containing a PsigV-YFP promoter reporter reveals heterogeneous activation of σV in response to lysozyme stress. The stress (red line) was added between the 200 mins and 300 min time points (at 240 min). The red arrow highlights a cell with a delayed activation of σV. Scale bar: 5 μm.

(D) Time traces of mean YFP fluorescence per cell. Each trace represents a single cell lineage's response to 1 µg/ml lysozyme. (109 traces from two independent experiments). Lysozyme added at the black dashed line.

(E) Figure D replotted to allow examination of variable activation times.

(F) The observed heterogeneity is reduced with increasing stress levels. Each line represents the cumulative fraction of cells (N=~50) with PsigV-YFP values that are higher than the half maximum of their final values (representing cells that have activated).

(G) The fold change in mean YFP fluorescence increases with increasing stress levels, shown for two biological repeats (adjacent boxplots). Each day's fold change distribution consisted of >40 manually corrected single-cell traces. On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers.

Data information: For more information on the number of repeats please see Appendix Table S3.
Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit (2021)
Figure 2

Figure 2. Rapid activation of σV after a first stress application increases survival after a second higher stress.

(A) Schematic of lysozyme application. Cells are either exposed directly to a high concentration of lysozyme (20 µg/ml) for 20 min (top) or exposed first to a short (30 min) lysozyme priming stress (1 µg/ml) before exposure to the higher concentration (bottom).

(B) A priming (30 min) stress of 1 µg/ml lysozyme followed by the high lysozyme stress (20 µg/ml) improves survival. The solid blue lines are the biological repeats for the no priming experiment (n=2) whereas the solid red lines are the biological repeats for the priming experiment (n=4). (Total number of cells shown for No Priming, N = 2013 and Priming, N = 4937).

(C) Surviving cells have higher PsigV-YFP levels after the initial priming stress (1 µg/ml) than perishing cells. The cumulative distributions were normalized by their maximum σV activity and baseline subtracted. Each dashed line is the mean of experiments from n=4 biological repeats. The shaded areas correspond to the mean ± s.d.. For more information on the number of repeats and cell numbers please see the supplementary text.

Data information: For more information on the number of repeats please see Appendix Table S3.
Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit (2021)
Figure 3

Figure 3. The observed σV heterogeneity can be explained by a simplified σV circuit. (A) Schematic of the σV circuit. In the figure R (orange) is the anti-sigma factor RsiV, R* (orange) is RsiV bound to lysozyme, S (red) is signalling peptidase and RP (light blue) is RasP the site-2 protease. For more information on the activation mechanism see Figure 1B. (B, C) RNA-seq experiment on WT (JLB130) and ΔsigV (JLB154) strains, showing quantification of the absolute expression of individual genes in the presence and absence of lysozyme stress. The shaded grey box represents a ± 5 fold change. DEseq was used to identify genes which were differentially expressed with a 5% p-value cutoff between the WT and ΔsigV mutant in response to lysozyme treatment (default DEseq test was used). (B) Only the sigV operon is strongly activated (>5 fold change) in response to lysozyme stress in WT (JLB130), as previously reported (Guariglia-Oropeza and Helmann 2011). (C) No genes were strongly upregulated in ΔsigV (JLB154) by lysozyme stress.

(D) Effect of the overexpression of individual components of the σV pathway (Biological repeats: WT: n=8, sigV+: n=4, rsiV+: n=3 oatA+: n=4, yrhK+: n=3, sipS+: n=3 and rasP+: n=3, pbpX+: n=6) on the fraction of activated cells. Only overexpression of sigV, rsiV or oatA changed the observed dynamics compared to WT. The histograms of the shown data are shown in Appendix Figure S16.

(E) Deleting oatA did not alter the σV activation dynamics. However, deleting sigV or rsiV resulted in no further activation of σV in response to 1 ug/ml lysozyme. n>=3 biological repeats for all data shown. The histograms of the shown data are shown in Appendix Figure S17.

(F) Schematic of simplified σV circuit with only σV and RsiV, where σV (green) activates its own expression and that of its anti-sigma RsiV (orange, R).

Data information: Bars correspond to the mean ± s.d.. For more information on the number of repeats please see Appendix Table S3.
Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit (2021)
Figure 5

Figure 5. Phenotypic variability is tuned by additional copies of components of the σV circuit.
(A) Schematic of genetic perturbations to σV circuit. Second copies of σV components were added under the control of the PsigV promoter. ? = PsigV-sigV (2xsigV), PsigV-sigVrsiV (2xsigV-rsiV), or PsigV-rsiV (2xrsiV).

(B) Model simulations predict that the 2xsigV or 2xsigV-rsiV strains have a homogenous σV response to lysozyme, while the 2xrsiV strain has increased heterogeneity. Bars correspond to mean (N=999 simulations) ± s.d. (1000 bootstraps).

(C) As predicted, the 2xsigV or 2xsigV-rsiV stains have a homogenous σV response to 1 µg/ml lysozyme, while the 2xrsiV strain has increased heterogeneity. Each bar plot is the average of three biological repeats. The bars correspond to the mean ± s.d..

Data information: For both (B) and (C), bars represent the fraction of cells that have activated σV 30 min after treatment with lysozyme. σV activated cells were defined as those cells that had passed a threshold of the WT mean before lysozyme application plus six standard deviations.For more information on the number of repeats please see Appendix Table S3.
Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit (2021)
Figure 6

Figure 6. The σV circuit has a memory of previous stress.

Stress is removed from an activated system at time 0 and reapplied at a time indicated by the dashed line, lysozyme concentrations are indicated by the purple traces above time course plots.

(A) Simulations predict that after a break in stress, on reapplication of stress all cells turn on σV immediately and homogeneously (N=99).

(B, C) With increasing intervals between stresses, response heterogeneity reappears in the model, indicating a loss of memory. (C) Each line represents the cumulative fraction of cells (N=99) with σV concentration values that are higher than the half maximum of their final values (representing cells that have activated). If the interval between stress is increased long enough, (800 au), the heterogeneity is the same as if the cells had not experienced a prior lysozyme stress (control).

(D) Experiments confirm memory in the σV circuit. For short intervals between stress applications, cells respond immediately and homogeneously. Each line corresponds to one of 48 single-cell traces.

(E, F) For very long intervals between stress (12h), the heterogeneity is the same as if the cells had not experienced a prior lysozyme stress (control). N>50 single-cell traces.

Data information: For more information on the number of repeats please see Appendix Table S3.

Extended View Figures