EMBO/sd-character-level-ner huggingface dataset:
Figure 8A A diagrammatic illustration of protein domain organization in MICAL2PV and its mutants. B, MICAL2PV mutants that contain the MO domain retain the activity in suppressing TNT formation. Stable shM2 H1299 cells were transfected with plasmids expressing different MICAL2PV mutants containing a hemagglutinin (HA) tag or the vector control (Ctr). The mutant proteins were detected by Western blotting using an anti-HA antibody , C TNT formation was quantified in transfected cells following confocal microscopy. Expression of MICAL2PV mutants, #1, #2 or #4 significantly reduced TNT formation Three independent experiments were conducted. Data information: Data were analyzed using Student's t-test and shown as mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001, ns, not significant. D MICAL2PV mutants containing the MO domain are active in depolymerizing F-actin. Following transfection of plasmids expressing different HA-MICAL2PV mutants (#1, #2, or #4) into shM2 cells, confocal microscopy was performed following staining with phalloidin (red) and anti-HA antibody (green). F-actin signals were reduced in cells expressing different mutants (HA-positive green cells marked by dashed lines). Scale bar: 20 μm. Data information: nuclei were stained with Hoechst 33342 (blue). E, F MICAL2PV mutants lacking the MO domain does not inhibit TNT formation. Stable shM2 H1299 (E) or A549 (F) cells were transfected with the corresponding MICAL2PV mutants lacking MO domain or control vectors (Ctr). Following transfection, Western blotting was carried out using an anti-HA antibody to detect expression of the mutant proteins, and confocal microscopy was performed to examine TNT formation. 10 images per group were captured for quantification. Three independent experiments were conducted. Data information: Data were analyzed using a one-way ANOVA with Bonferroni post-test and shown as mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001, ns, not significant. G, H The MICAL2PV mutant containing an inactive MO domain does not inhibited TNT formation. Stable shM2 H1299 (G) or A549 (H) cells were transfected with plasmids expressing the MICAL2PV mutant in which the MO domain was inactivated by mutations in the FAD binding site (MOmut: amino acid residues 93-98 GxGxxG mutated to WxWxxW) or the control vector (Ctr). Following transfection, Western blotting and quantification of TNT formation were carried out as in panel E. 10 images were captured for quantification in each group. Three independent experiments were performed. Data information: Data were analyzed using a one-way ANOVA with Bonferroni post-test and shown as mean ± SEM; * P < 0.05, ** P < 0.01, *** P < 0.001, ns, not significant.
a, b) dRagA activation increases sensitivity to starvation.figf8(a, b) dRagA activation increases sensitivity to starvation. Expression of dRagAQ61L using the fat-body-specific Cg-GAL4 driver significantly decreased survival of adult female flies under starvation (b) but not fed (a) conditions, relative to controls (Cg-GAL4 alone). Asterisks indicate significant difference, compared with controls. Data are mean ± s.d., *P 0.05, Students two-tailed t-test (n = 150 flies/genotype/treatment). (c) A proposed model of Rag GTPase in regulation of TORC1 activity. Rag GTPases act independently of and in parallel to TSC-Rheb to activate TOR signalling, possibly by transducing a nutrient-dependent signal. The mechanism of TOR regulation by Rag GTPases is indirect, and probably involves additional unidentified factors.
Fig.Figure 6A Larval gut epithelia in which enterocytes express a marker for polarity and septate junctions, GFP-Dlg using the NP1-Gal4 driver together with or without Atg16B. Expression of Atg16B leads to disruption of lateral plasma membrane (magenta arrows). n=32-44 larvae each genotype. B Smurf gut barrier assay in anesthetised, live larvae. Left, control larvae, right, larvae overexpressing Atg16B in the gut enterocytes under control of the NP1-Gal4 driver. While the distribution of food in the gut of Atg16B-expressing larvae was different from controls, there was no leakage of dye from the gut in any of the animals in three independent experiments (each with n=30 larvae for each genotype).
MSR1 triggering activates JNK, which induces a proinflammatory stimulus.Figure 5(A) Immunoblot against MSR1 of TUBE pulldowns of MSR1 WT and KO BMDMs shows increasing amounts of ubiquitylated MSR1 upon alternative activation and further increases upon MSR1 ligation with fucoidan or oxLDL. (B) Msr1+/+ (WT) and Msr1-/- (KO) M0 macrophages and M2(IL-4) macrophages untreated or stimulated with the MSR1 ligands fucoidan or oxLDL (50 μg/ml, 30 min) were analysed for the phosphorylated and the total forms of JNK1/2 and MSR1. ITGAM (CD11b) serves as a loading control. Both fucoidan and oxLDL activate JNK in a MSR1-dependent manner. (C) IL4-activated MSR1 knock-out BMDMs were transfected with WT or K27R MSR1 and treated with 50 μg/ml Fucoidan for 1 hr. Mutation of the ubiquitylation site K27 abolishes MSR1 signalling. (D) qPCR data of Tnfa, Il1b and Ccl2 mRNA levels in WT and MSR1 KO M0 and M2(IL-4) BMDMs shows an MSR1-dependent increase of pro-inflammatory cytokines in response to MSR1 ligation by fucoidan. (E) Inhibition of JNK by JNK-IN8 reduces expression of Tnfa and Ccl2 upon MSR1 ligation, showing that it is JNK-dependent. (F) Flow cytometry analysis of cell surface markers in WT and MSR1-/- M0 macrophages and M2(IL-4) macrophages untreated or stimulated with the MSR1 ligands fucoidan (50 μg/ml, 24 h). Data shows MSR1-dependent increase of the early activation markers CD54, CD69 and CD86 and a decrease of the M2 marker CD301b/Mgl2. CD11b serves as a control.
Figure 2A. Immunoblot analysis of SATB2 expression in GSCs transduced with lentiviral-mediated non-targeting shRNA (shNT) or SATB2 shRNA (shSATB2). B. Cell viability of GSCs transduced with shNT or shSATB2 (n=5). C. EdU incorporation assay of GSCs transduced with shNT or shSATB2. Scale bar: 50 μm. D. Quantification of (C) showing the percentage of EdU+ cells (n=5). E. Tumorsphere images of GSCs transduced with shNTor shSATB2. Scale bar: 100 μm. Quantification of the diameter of tumorspheres formed by GSCs expressing shNT or shSATB2 (F: n=9; G: n=5). G. Quantification of the number of tumorspheres formed by GSCs expressing shNT or shSATB2 (F: n=9; G: n=5). H. In vitro limiting dilution analysis of the tumorsphere formations of GSCs expressing shNT or shSATB2. Silencing SATB2 attenuated the self-renewal capacity of GSCs.
Motor neuron apoptosis in FusΔNLS/ΔNLS mice.Figure 7(A) Representative images of TUNEL assay in spinal cord of FusΔNLS/ΔNLS mice ((panels A4-A6) and Fus+/+ mice (panels A1-A3).(B) Quantification of the total number of TUNEL and DRAQ5 (blue) double-positive cells in FusΔNLS/ΔNLS and Fus+/+ per spinal cord section. N=3 per genotype, (*) p<0.05, by Student's unpaired t-test. (C-D) Immunofluorescence microscopy of spinal cord of Fus+/+ (C) and FusΔNLS/ΔNLS (D) mice showing active-caspase 3 (green), ChAT (red) and DNA (cyan, DRAQ5).(E) Quantification of caspase 3 (Cas3)/ChAT/DRAQ5 triple positive cells in FusΔNLS/ΔNLS mice. N=7 per genotype, (**) p<0.01, by Student's unpaired t-test.
Beclin‐1 knockdown affects kinetochore–microtubule attachment.Figure 3(A) Inter‐kinetochore distance is reduced in unaligned chromosomes of Beclin‐1‐depleted cells. The distance between each kinetochore pair was quantified in siRNA‐treated and control cells treated with nocodazole for 3 h before fixation. Cells were stained with anti‐CREST antibody and DAPI. Quantification of the distance between kinetochore pairs (indicated by coloured bars) is reported at the bottom and done on sister kinetochores present in the same focal plane. One asterisk indicates significant result (P=0.0365). Graphs represent three independent experiments (error bars=s.d.). (B) Depletion of Beclin‐1 leads to defects in K‐MT attachment. siRNA‐treated cells were cooled on ice before fixation to reveal stable k‐fibres and then stained with anti‐α‐tubulin (red) and anti‐CREST (green) antibodies. Numbers point to magnified areas and indicate the mode of attachment of k‐fibres to kinetochores (1, bi‐oriented; 2, unattached; 3, mono‐oriented kinetochores). Scale bar, 10 μm. DAPI, 4′,6‐diamidino‐2‐phenylindole; NS, not significant; siRNA, small interfering RNA.
Figure 2SDS-PAGE of RNA pulldown assays using biotin-labelled sense/antisense probes against GUARDIN from whole-cell lysates of A549 cells indicating putative GUARDIN-binding proteins (left); protein identities with high probabilities were determined by mass spectrometry (right). RNA pulldown assays interrogating putative GUARDIN-associated proteins identified in (A) from whole-cell lysates of A549 and HAFF cells. BRCA1, BARD1 served as positive controls and β-actin served as negative controls. RNA immunoprecipitation (RIP) assays against IgG/LRP130 antibodies in whole-cell lysates of A549 cells. Subcellular localization of GUARDIN and its co-localization with LRP130. RNA FISH for GUARDIN (red) and IF for LRP130 (green) in A549 cells with either shCtrl or shGUARDIN. Nucleus were counterstained with Hoechst (blue). RNA pulldown assays using biotin-labelled sense/antisense probes against GUARDIN from whole-cell lysates of A549 cells. GUARDIN levels were measured by RT-PCR and co-precipitated LRP130 and PGC1α detected by Western blotting. BRCA1 and β-actin served as positive and negative controls, respectively. RIP assay using IgG/PGC1α antibodies from whole-cell lysates of A549 cells. GUARDIN, LRP130 and PGC1α levels were measured as per (E). Two-step IP assays in whole-cell lysates of A549 cells transfected with FLAG-tagged PGC1α. First phase IPs were conducted with FLAG antibodies (left) and following elution with FLAG peptides, eluates were further subjected to second phase IPs with LRP130 antibodies (right). Samples were subjected to Western blotting and qPCR analysis for LRP130, PGC1α and GUARDIN, respectively. Co-immunoprecipitation (co-IP) between LRP130 and PGC1α in A549 cells after 48h transduction with shCtrl or shGUARDIN. LRP130 was precipitated and samples subjected to Western blotting analysis for LRP130, PGC1α and β-actin as loading control. Mammalian two-hybrid assays between pACT-LRP130 and pBIND-PGC1α in A549 cells after 48h transduction with shCtrl or shGUARDIN. Samples were subjected to the luciferase activity assays. LRP130/PGC1α and p21 protein expression was measured by Western blot in A549 and HAFF cells after 48h transduction with shCtrl or shLRP130 (top left) or shPGC1α (bottom left) as indicated. qPCR assays for p21 mRNA levels were performed in parallel (right panels). Western blotting analysis of LRP130, PGC1α and p21 protein levels in HAFF and A549 cells after 48h transduction with shCtrl or shGUARDIN.
Sema3C, but not Sema3Cp60, acts through Neuropilin-1 and PlexinD1 to suppress endothelial cell adhesion.Figure 6HUVECs were transfected with siRNA against PlexinD1 or control NS (non-silence) siRNA. qPCR analyses and Western blotting were performed after 48 h (n = 3; paired Student's t-test, NS siRNA vs. PlexinD1 siRNA, ***P = 0.0004). Cell lysates were subjected to SDS-PAGE and probed with anti-PlexinD1. HUVECs were transfected with siRNA against PlexinD1 and plated on gelatin-coated coverslips. Confluent monolayers were treated with Sema3C-, Sema3Cp60- or control-conditioned medium for 30 min. Cells were fixed and stained with Alexa-Fluor 488-conjugated phalloidin to visualize F-actin. Arrows indicate Sema3C-induced cell membrane ruffling. Scale bar, 20 μm. Cell repellence was quantified as the number of cells with membrane ruffling normalized to the number of nuclei (n = 5; mean ± s.e.m., paired Student's t-test, **P = 0.001). Silencing of Nrp-1 by lentiviral particles expressing shRNA. After culturing 72 h in selection medium, RNA was harvested for qPCR analysis. NS shRNA vs. Nrp-1 shRNA: #1, *P = 0.017 and #2, *P = 0.02; n = 3; mean ± s.e.m. Cell lysates were subjected to SDS-PAGE and probed with anti-Nrp-1. Depletion of Nrp-1 abolished the response to Sema3C. Sema3Cp60 had no effect on HUVEC adhesion. Arrows indicate membrane ruffling. Scale bar, 20 μm. Cell repellence was quantified as the number of cells with membrane ruffling normalized to the number of nuclei (n = 5; mean ± s.e.m.; paired Student's t-test, ***P = 0.0003). HUVECs were treated with Sema3C-conditioned medium for 10 min and lysed, and Nrp-1 was immunoprecipitated. Bound Sema3C was detected by Western blotting. Schematic model of Sema3C signaling through Nrp-1 and PlexinD1 receptors in endothelial cells.
Figure 1First, we generated databases of all the putative ORFs encoded by the genomes of 109 different bacteria. The database of M. pneumoniae was used to perform the shotgun MS and RNA-Seq studies that were aimed at evaluating the coverage and performance of experimental approaches in the discovery of SEPs. In a parallel, experiment-independent manner, RanSEPs performed in silico predictions of potential novel proteins in the database. Results coming from both experimental and computational approaches are integrated in a validation step using a set of 570 SEPs characterized both in this work and in previous studies. Finally, RanSEPs predictions for the 109 bacterial genomes are combined together to assess the functional diversity and importance of predicted SEPs. The second part of the figure highlights how RanSEPs functions. In step 0 (grey box), RanSEPs detects annotated standard proteins (purple) and SEPs (yellow). By BlastP, non-conserved standard and SEP proteins are detected (pink and light pink, respectively). In parallel, protein features are computed and filtered by Recursive Feature Elimination. These features are combined with general features of biological interest. In step 1 (yellow box), RanSEPs randomly subsets annotated standard and small proteins into a positive (green and yellow), a feature (blue and yellow) and a negative (pink and light pink) set from the bulk of non-conserved sequences. During step 2 (blue box), specific features that vary with each iteration are appended. In step 3 (purple box), the labelled positive and negative sets are divided into training and test sets. Step 4 (green box) consists of collecting the classifiers and classification task results, and computing the final statistics and scores for all the sequences. Step 0 is only run once and then it is out of the iteration process. Steps 1 to 3 are repeated as many times as iterations selected by the user. Step 4 is computed at the end to integrate the results of each iteration.
Overview of data modalities and their consistency.Figure 1(A) Overview of datasets, research sites, and molecular modalities that were analyzed in the study. (B) The number of cell lines having data for the 9 types of modalities that were analyzed in the study. (C) The number of cell lines for which data were available for each of the modality types. (D) Correlation of the different types of data modalities of cancer cell lines profiled at multiple research sites. Spearman correlation was calculated between identical cell lines for the shared set of genes that were overlapping between any two datasets. Gray distributions show the correlation of non-identical cell lines between datasets from various research sites for comparison. Ng and Nc indicate the median [ranges] of the number of genes and cell lines, respectively, across the pairwise comparisons made between datasets from different research sites. More details on the breakdown of Nc and Ng by data modality and research site is available in Appendix Figure S5B and S6, respectively, and Appendix Figure S7C shows the correlation P-values adjusted for the sample size (Ng). For the point mutation view, only those genes having mutations with an associated functional consequence were considered in the Matthews correlation analysis. Only those datasets for which the mutation profiles were obtained using the whole-exome sequencing technology were considered in this study. Horizontal lines mark the median value. Target addiction score (TAS), Drug Sensitivity Score (DSS), Gene dependency (FUNC), protein phosphorylation (PHOS), protein expression (PEXP), gene expression (GEXP), copy number variation (CNV), point mutation (MUT) and methylation (METH) profiles.
Hdac1-deleted thymic lymphoma cell lines depend on DOT1L activity.Figure 4Immunoblots showing HDAC1 status and H3K79me/H3K9ac levels in nuclear lysates of Hdac1-proficient (p53-null) and Hdac1-deficient thymic lymphoma cell lines. The top four and bottom two panels are from separate lysates of the same cell lines. Mass spectrometry analysis of H3K79 methylation in the cell lines from panel A. Mean and individual data points of two independent cell lines; H3K79me0 is the predominant state, the y axis is truncated at 80% for readability. p<0.01 (**) and p<0.001 (***) by two-way ANOVA. Immunoblot showing H2BK120 ubiquitination levels in Hdac1-proficient and -deficient cell lines (two independent lines each). Growth curves of Hdac1-proficient and -deficient cell lines that were left untreated or were infected with empty virus (pLKO) or shRNAs against Dot1L and selected with puromycin. Growth curves were determined by a series of resazurin assays, which measure metabolic activity, starting four days post-infection. Error bars indicate the range of two replicates from independent cell lines. Inhibitor dose-response curves of the two DOT1L inhibitors EPZ-5676 (Pinometostat) and SGC-0946 in Hdac1-proficient and -deficient cell lines. Cell viability was measured by a resazurin assay after three days of treatment and measurements were normalized to DMSO-treated cells. Two independent cell lines are plotted separately; error bars indicate the range of two biological replicates.
Single-Molecule RNA Fluorescence In Situ Hybridisation (smRNA FISH) captures transcript distributions of core clock genes in mouse fibroblasts at multiple time points.Figure 1(A) smFISH targeting Bmal1, Cry1 and Nr1d1 in wild-type NIH 3T3 cells at 17h, 29h and 25h after synchronisation with Dex, respectively. Nuclei are stained with DAPI (blue). Each fluorescent dot (white) corresponds to a single transcript. Segmented cell boundaries are delineated in grey. The blue and white channels represent maximum z-projections. Scale bar: 20μm. (B) Number of Bmal1, Cry1, and Nr1d1 transcripts per cell as a function of time after treatment with Dex. These data combine all smFISH hybridizations from the 4h sampled Nr1d1/Cry1 and Bmal1/Cry1 experiment. Each dot shows the average over one replicate from an independent slide (Methods). The solid lines represent fits using a two-harmonic cosinor model (Equation (1), Methods) for each gene individually. (C) The inferred peak phase (angular component of the graph) and max-to-min fold change (radial component) from the fit of Equation (1). (D) Distributions of Nr1d1, Cry1 and Bmal1 transcripts at 21h, 29h and 37h after synchronisation with Dex. μ represents the mean of the distribution, CV represent the coefficient of variation (standard deviation / mean), and N is the number of cells at the given time point. Total number of cells analysed for the Nr1d1/Cry1 pair: 21h - 449; 25h - 414; 29h - 521; 33h - 463; 37h - 477; 41h - 429. Total number of cells analysed for the Bmal1/Cry1 pair: 17h - 465; 21h - 490; 25h - 504; 29h - 436; 33h - 407; 37h - 454; 41h - 404.
Gain of miR-21-3p function activates inflammation in HaCaT human keratinocytes.Figure 4(A) Proportion of inflammatory- and immune processes- associated mRNA in regard to the total number of significantly deregulated mRNA in miR-21-3p mimic versus scrambled control treated HaCaT cells. (B) Go term enrichment analysis based on the significantly up- and down-regulated mRNA in miR-21-3p mimic treated compared to scramble control treated HaCaT cells. (C) RT-qPCR analysis of IL6, IL1B, IL1RAP, PTGS2, CCL5, CXCL10 and CASP14 levels in HaCat cells transfected with a miR-21-3p mimic or a scrambled sequence (control). IL6: control vs miR-21-3p mimic P = 0.05; IL1B: control vs miR-21-3p mimic P = 0.005; IL1RAP: control vs miR-21-3p mimic P = 0.008; PTGS2: control vs miR-21-3p mimic P = 0.050; CCL5: control vs miR-21-3p mimic P = 0.001; CXCL10: control vs miR-21-3p mimic P = 0.012; CASP14: control vs miR-21-3p mimic P = 9E-05. N=3 biological replicates, one representative experiment is shown out of three independent replicates.
Figure 4 (A) Concentrations of cytokines or chemokines in the BAL of days 5 and 9 post sub-lethal influenza virus infection as determined by protein bead array Data information: Data in (A) are obtained from one independent experiment with 4 mice per group, and results of selected analytes IFNγ, CXCL10, IL-6, IL-13 and IL-17 are reconfirmed in a separate independent experiment by a customized protein bead array Significance i was determined by unpaired student's T-test. n.s. = not significant Mice were superinfected as described in Fig 3A. (B) Concentrations of IL-17 and IL-22 in BAL of mice, as determined by ELISA Data informatio Data in (B are representative of two independent experiments with 3-4 mice per group. All data are represented as mean ± S.E.M. Significance i was determined by unpaired student's T-test. n.s. = not significant Mice were superinfected as described in Fig 3A (C) Concentrations of IL-17 and IL-22 in BAL of mice 12 h after with 2x105 cfu of S. pneumoniae serotype 19F alone (-Flu), or following superinfection (+Flu), assessed by ELISA Data information Dat i (C) are representative of two independent experiments with 3-4 mice per group. All data are represented as mean ± S.E.M. Significanc was determined by unpaired student's T-test. n.s. = not significant
Figure 5(A) Immunoblot analysis of the indicated proteins in WT and Cdkn2a-/-;p53-/- placentas, on day E14.5 (two independent placentas out of four from each genotype are shown). RT-PCR analysis of expression of Mmp2 in murine Cdkn2a-/-;p53-/- and in WT placentas on day E14.5 (n>=4 fields of view for each genotype). Values are means + SEM of at least three experimental repeats. Data information: Values are means + SEM of at least three experimental repeats. Statistical significance was determined by unpaired two-tailed Student's t-test (*) p < 0.05; (**) p < 0.01; (***) P<0.001. RT-PCR analysis of expression of Mmp9 in murine Cdkn2a-/-;p53-/- and in WT placentas on day E14.5 (n>=4 fields of view for each genotype). Values are means + SEM of at least three experimental repeats. Data information: Values are means + SEM of at least three experimental repeats. Statistical significance was determined by unpaired two-tailed Student's t-test (*) p < 0.05; (**) p < 0.01; (***) P<0.001. (D, E) Representative images (D) and quantification (E) of in-situ gelatin zymography of the labyrinth zone of murine WT and Cdkn2a-/-;p53-/- placentas on day E14.5. Scale bar, 50 μm. Data information: Values are means + SEM of at least three experimental repeats. Statistical significance was determined by unpaired two-tailed Student's t-test (*) p < 0.05; (**) p < 0.01; (***) P<0.001. (F, G) Representative images (F) and quantification (G) of in-situ zymography of human normal third trimester post-partum placentas and placentas from IUGR-complicated pregnancies of the same gestational age (n>=9 fields of view of normal and IUGR placentas). Scale bar, 100 µm. Data information: Values are means + SEM of at least three experimental repeats. Statistical significance was determined by unpaired two-tailed Student's t-test (*) p < 0.05; (**) p < 0.01; (***) P<0.001. (H) Immunoblot analysis of the protein content of p-p65, p65, p-STAT3 and STAT3 in normal placentas (n=4) and placentas from IUGR-complicated pregnancies (n=4). Each lane represents one independent placenta. (I) Quantitative RT-PCR analysis of expression of MMP2, MMP9, IL6, and the JAK-STAT targets IFNAR1 and IFNAR2, in normal placentas and placentas from IUGR-complicated pregnancies. Results are from four human normal placentas and four placentas from IUGR-complicated pregnancies. Data information: Values are means + SEM of at least three experimental repeats. Statistical significance was determined by unpaired two-tailed Student's t-test (*) p < 0.05; (**) p < 0.01; (***) P<0.001.