EMBO/soda-nel huggingface dataset:
Dynamic properties of noise and Her6 levels are optimized by miR-9, allowing the decoding of the Her6 oscillator (2020)
Figure 6

Figure 6. Changes in cell fate decisions in the absence of miR-9 regulation.

(a) Representative examples of triple fluorescent whole mount WM-ISH labelling of gfap (green), elavl3 (magenta) and her6 (grey) domains of expression (top panels) in hindbrain rhombomere6 (r6) in wild-type embryo observed at 34hpf; merged images indicate how the her6 expression domain overlaps with the progenitor zone (NP=gfap(+)/elavl3(-)), transition zone (T=gfap(+)/elavl3(+)) and neurogenic zone (N=gfap(-)/elavl3(+)); (bottom-right panel) schematic representation showing the her6 domain spanning the NP, T and N zones with quantification of distances from dorsal; transversal view; annotations denote dorsal (D) and ventral (V); scale bar 30μm.

(b) Single molecule fluorescent in situ hybridization (smFISH) showing her6 (green), elavl3 (magenta) and dapi nuclear staining (blue) obtained from hindbrain (r6) sections of wild type embryo at 34hpf; head arrows indicates examples of co-existence of transcriptional active sites for her6 and elavl3; scale bar 3μm.

(c) Triple fluorescent WM-ISH for gfap (green) and elavl3 (magenta) and her6 (grey) in the hindbrain (r6) of CTRL (top) and MBSm (bottom) embryos at 52hpf; dorsal (D); ventral (V); scale bar 30μm.

(d) Magnification of inset from merged gfap/elavl3 in (c) showing CTRL (top) and MBSm (bottom) embryos with corresponding NP/T/ N and NP/T zones respectively; dorsal (D); ventral (V); scale bar 30μm.

(e) Normalized intensity mean of elavl3 and gfap along the DV axis spanning the NP/T/N zones in CTRL and NP/T zones in MBSm respectively; region of interest (ROI) delineates high elavl3 versus gfap in CTRL (T and N zones) while in MBSm only the T zone is observed, overlap of elavl3 and gfap intensity mean peaks.

(f-h) Normalized mean of elavl3 and gfap intensities in ROI observed in: (f) uninjected (UI) (5 embryos, 62 slices), (g) control (CTRL) (5 embryos, 35 slices) and (h) MBSm (8 embryos, 62 slices) conditions; bars represent mean and SEM; multiple t-test with Benjamini, Krieger and Yekuteli discovery, significance: p<0.05*, p<0.001***, p<0.0001****.

(i) Transverse sections of double fluorescent WM-ISH for gfap (green) and neuroD4 (blue) in the hindbrain (r6) of CTRL (top) and MBSm (bottom) embryos at 52hpf; dorsal (D); ventral (V); scale bar 30μm.

(j) Chromogenic WM-ISH showing neuroD4 expression intensity in: uninjected (UI) 51/56 embryos (91%), CTRL 50/54 embryos (93%) and mutant (MBSm); 36/65 embryos (55 %); longitudinal view, scale bar 100μm.
Dual roles of HSP70 chaperone HSPA1 in quality control of nascent and newly synthesized proteins (2021)
Figure 3

Figure 3 Effect of HSP70 activity on the solubility of polyubiquitylated proteins

A. Whole lysates of MCF7 and MCF7aTT cells exposed to heat shock and/or proteasome (MG132, 20 μM, 1h) and HSP70 (VER-155008, 50 μM, 1h) inhibitors were prepared as described in Materials and Methods. Total lysate, soluble fractions and insoluble fractions were stained by SYPRO Ruby (left) and quantified (right). (means +/- SD, n = 3, numbers above the bars indicate p values calculated with the two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli).

B. The same total lysates as shown in (A) were assayed for the level of K48-linked polyubiquitin. Actin is shown as a reference. The levels of K48-linked polyubiquitin were quantified from multiple independent repeats. (means +/- SD, n = 5, numbers above the bars indicate p values calculated with the two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli).

C. The soluble and insoluble fractions shown in (A) were assayed for the level of K48-linked polyubiquitin, and polyubiquitylated proteins were quantified (blue line graph, means +/- SEM, n = 4, numbers above the bars indicate p values). The data for bulk protein obtained in (A) were overlaid onto the graph for comparison (red line, means +/- SEM, n = 3).
Dual roles of HSP70 chaperone HSPA1 in quality control of nascent and newly synthesized proteins (2021)
Figure 5

Figure 5 Thermotolerance depends both proteasome and HSP70 activities

A. Similar experiment as described in (Figure 4A) with naïve MCF7 and thermotolerant MCF7aTT cells as indicated. means +/- SEM, n = 3, * p<0.05, **p<0.01 (calculated with the two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli).

B. Same experiment as in (A) with thermotolerant MCF7aTT cells incubated with the HSP70 inhibitor VER-155008 (50 μM, 1h) or MG132 (20 μM, 1h). The signal obtained for ribosomal protein RPS19 and RPL7 are shown for reference, documenting residual polysomes despite the flat UV trace. Right panels are quantifications of K48-ub (means +/- SEM, n = 2), black vertical lines indicate splicing of lanes that were run on different gels due to limitations in lane capacity. The bar graphs represent the P/M ratios (means +/- SD, n = 3, numbers above bars indicate p values calculated with the two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli).

C. Sucrose density gradient fractions described in (B) were assayed for partitioning of HSPA1 and HSPH1 by immunoblotting. HSPA1 data were quantified in a line graph (means +/- SEM, n = 4).

D. Cell lysate was spun through a 30% sucrose cushion to pellet polysomes. Nascent proteins were released from polysomes by dissociating 80S ribosomes with EDTA (see Appendix Figures S3). Soluble and insoluble fractions were prepared by centrifugation and assayed for the level of K48-linked polyubiquitin. The levels of K48-linked polyubiquitin were quantified (means +/- SD, n = 3). Immunoblots within black squares were run on the same gel but irrelevant lanes were removed as indicated by white lines.
Dual roles of HSP70 chaperone HSPA1 in quality control of nascent and newly synthesized proteins (2021)
Figure 6

Figure 6 HSP70 promotes proteasome recruitment to polysomes

A. Trypsin-like proteasome activity determined in fractions as described in Fig. 4A using an in vitro assay. Results are shown relative to the proteasome activity obtained in MCF7 cells maintained at 37 oC (means +/- SEM, n = 4).

B. MCF7 cells were exposed to the proteasome inhibitor MG132 (20 μM) or 42 oC for 1h after which an equal amount of cell lysate was spun through a 30% sucrose cushion. Resulting polysomes were assayed for the levels of 26S proteasome subunits and HSPA1.

C. Cell lysates from MCF7 cells maintained under the conditions indicated were fractionated by sucrose density gradient centrifugation. Polysomal fractions were subjected to immunoprecipitation with RPS19 antibody, and precipitated were analyzed by quantitative LC-MS/MS. The heat map represents normalize spectral abundance factors (NSAF).

D. Total cell lysates were prepared from naïve MCF7 and thermoresistant MCF7aTT cells, and puromycin labeled peptides were pull down as illustrated in the scheme. Precipitates were assayed for the presence of K48-linked polyubiquitin, chaperones, and proteasome subunits by immunoblotting.

E. Partitioning of the 20S proteasome subunit PSMB1 along the sucrose gradient under the indicated conditions was assessed by immunoblotting, black vertical lines indicate splicing of lanes that were run on different gels due to limitations in lane capacity. The line graph on the top shows trypsin-like proteasome activity in the same fractions (means +/- SD, n = 3).
Dual roles of HSP70 chaperone HSPA1 in quality control of nascent and newly synthesized proteins (2021)
Figure 7

Figure 7 Effect of HSPH1 depletion on co-translational quality control in esophageal cancer cells

A. Immunoblot of individual clones of KYSE150 cells in which HSPH1 was disrupted by CRISPR/Cas9 targeting (KYSE150HSPH1-/- cells).

B. Effect of disrupting HSPH1 on stress sensitivity. KYSE150 and KYSE150HSPH1-/- cells were exposed to heat stress (45 oC) (means +/- SD, n = 3).

C. KYSE150 and KYSE150HSPH1-/- cells were exposed to the indicated stress conditions and cell viability was assessed by staining dead cells with trypan blue. Graphs represent means +/- SD, n = 3, numbers above bars indicate p values calculated with the two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli.

D. KYSE150 and KYSE150HSPH1-/- cells, either naïve or subjected to the aTT temperature shifting scheme, were exposed to the indicated temperature and cell lysates were separated by sucrose density gradient centrifugation. K48-linked polyubiquitin was detected by immunoblotting of gradient fractions (left panel). Signals were quantified and displayed as a line graph (right panel, means +/- SEM, n = 2). The red boxes highlight the pronounced differences in polysomal polyubiquitylation between KYSE150 and KYSE150HSPH1-/- cells.

E. Whole lysates of cells KYSE150 and KYSE150HSPH1-/- cells exposed to the indicated temperature conditions (aTT scheme and heat shock) were fractionated into soluble and insoluble fractions as described in Materials and Methods. Insoluble fractions were assayed for the level of K48-linked polyubiquitin (left panel). Total levels of HSPA1 and HSPH1 are shown for reference. K48 ubiquitin signals were quantified and displayed in a bar graph (means +/- SD, n = 4).

F. Lysates of cells KYSE150 and KYSE150HSPH1-/- cells exposed to the indicated temperature conditions (aTT scheme and heat shock) were obtained and polysomes were isolated by centrifugation through a 30% sucrose cushion. Whole cell lysates (left panel) and the polysomal pellets (right panel) were assayed by immunoblotting for the levels of HSPH1, HSPA1 and proteasome subunits.
Dual roles of HSP70 chaperone HSPA1 in quality control of nascent and newly synthesized proteins (2021)
Figure 8

Figure 8 Expression of HSPA1 and HSPH1 in human carcinomas

A. Representative micrographs of tissue sections derived from oral squamous cell carcinomas (OSCC) and esophageal squamous cell carcinomas (ESCC) stained with the indicated antibodies by immunohistochemistry (IHC). Top panels show routine H&E staining. Black squares point to regions magnified in the adjacent panels to the right.

B. IHC staining of HSPA1 and HSPH1 was performed for the indicated numbers of normal, OSCC and ESCC tissues. Staining intensity was scored as described in Materials and Methods.

C. Kaplan-Meier plots showing the correlation between HSPH1 mRNA expression level and the survival of patients with ESCC. The expression data was obtained from and visualized with KM Plotter (www.kmplot.com, (Nagy et al, 2018) using the Pan Cancer algorithm at default settings.

D. KYSE150 and KYSE150HSPH1-/- cells were injected into nude mice, and xenograft tumor growth was monitored over time (left panel; mean tumor volume +/- SEM, n = 4 with 5 animals per n). Mean tumor weights at the end of the experiments were determined (means +/- SD, n = 4 with 5 animals per n, number above the bar graph represent p values, * p<0.05, **p<0.01 (calculated with the two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli)).

E. Macroscopic images of representative tumors derived from KYSE150 and KYSE150HSPH1-/- cells (shown are tumors from two of the four independent experiments).
Evidence that the human cell cycle is a series of uncoupled, memoryless phases (2019)
Figure 1

Figure 1. Variation and lack of correlation among cell cycle phase durations in single human cells. a, Diagram of the cell cycle composed of G1, S, G2, and M phases (not to scale). Phase durations were quantified by time-lapse fluorescence microscopy using a PCNA-mCherry reporter to identify four discrete events during the lifetime of an individual cell (see main text and Experimental Procedures). Images were acquired every 10 min. b, Mean phase durations in RPE, U2OS, and H9 cell lines. Error bars represent standard deviations. c, Coefficient of variation (CV) of phase durations. d, Percentage of the total variation in cell cycle duration contributed by individual phases. e, Correlations between individual cell cycle phase durations. Sample sizes were adequate to detect correlations (see Experimental Procedures). n = 125 (RPE), 130 (U2OS), 113 (H9). R2, square of Pearson correlation coefficient.
Evidence that the human cell cycle is a series of uncoupled, memoryless phases (2019)
Figure 3

Figure 3. Lack of coupling among cell cycle phases under perturbation. a, Schematic of shortening G1 by Myc overexpression. RPE cells infected with retrovius harboring a taxmoifen-inducible Myc-overexpression construct. b, Shift in phase durations of RPE cells overexpressing Myc. c, Pairwise correlation between cell cycle phase durations of RPE cells overexpressing Myc. d, Schematic of prolonging S phase by replication stress using aphidicolin. Asynchronously proliferating RPE cells were treated with 50 ng/mL aphidicolin for 8 hours, washed with PBS, and then replenished with fresh media. Only cells whose S phase overlaped with the 8-hour treatment window for at least 1.8 hours were analyzed. e, Shift in phase durations of RPE cells treated with 50 ng/mL aphidicolin. f, Pairwise correlation between cell cycle phase durations under aphidicolin treatment. g, Schematic of prolonging all phases by incubating cells at 34°C. h, Shift in phase durations of RPE cells incubated at 34°C. i, Pairwise correlation between phase durations for cells incubated at 34°C. j, Schematic of prolonging G1 by DNA damage using NCS. Asynchronously proliferating RPE mother cells were treated with 25 ng/mL NCS, and their daughter cells were analyzed for a full cell cycle. k, Shift in phase durations of RPE cells treated with NCS. l, Pairwise correlation between phase durations for cells treated with NCS. In panels b, e, h, and k, boxplots representing the distributions of phase durations in untreated cells are underlaid for comparison. *, P < 1 × 10-5; **, P < 1 × 10-10; ***, P < 1 × 10-20, 2-sided Kolmogorov-Smirnov test. Number of cells: Myc, n = 116; aphidicolin, n = 115; 34°C, n = 122; NCS, n = 119.
Evidence that the human cell cycle is a series of uncoupled, memoryless phases (2019)
Figure 4

Figure 4. A model for heritable factors governing the rate of cell cycle phase progression. a, Alternative models for inheritance of molecular factors governing the durations of cell cycle phases. b, Simulation of the "strength of coupling" as a function of the number of unique phase-coupling molecule types under the many-for-all model. Each simulation generated 200 cells for which an R2 value was calculated. R2 values were averaged across 200 simulations. The shaded area represents the standard deviation of R2 across the simulations. c, Simulation of coupling strength as a function of the number of unique phase-coupling and phase-specific factors. Phase-coupling factors have shared control over a pair of cell cycle phases, whereas phase-specific factors affect only one cell cycle phase. Strength of coupling is represented by mean R2 value as in panel b. d, same as in b, but simulating the effect of perturbing a single phase-coupling factor by significantly increasing its abundance or activity. Perturbation was simulated by increasing the abundance of a phase-coupling factor by 10 fold. e, Same as in c, but simulating the effect of increasing a phase-coupling factor by 10 fold (see Method Details). f, Schematic of prolonging all phases by adding CDK2 inhibitor. RPE cells were treated with 2 μM CVT-313 and the durations of each phase were quantified for a full cell cycle. g, Shifts in phase durations for RPE cells treated with 2 μM CVT-313. A boxplot representing the distributions of durations in untreated cells is underlaid for comparison. *, P < 1 × 10-5, 2-sided Kolmogorov-Smirnov test. (n = 117 cells). h, Pairwise correlation between cell cycle phase durations upon treatment with CVT-313. i, Schematic of shortening phases by overexpression of cyclin D. Cyclin D was overexpressed in RPE cells and the durations of each phase were quantified for a full cell cycle. j, Shifts in phase durations for RPE cells overexpressing cyclin D. A boxplot representing the distributions of durations in untreated cells is underlaid for comparison. *, P < 1 × 10-5, 2-sided Kolmogorov-Smirnov test. (n = 113 cells). k, Pairwise correlation between cell cycle phase durations upon overexpression of cyclin D. P indicates P-value.
Non-invasive lung cancer diagnosis by detection of GATA6 and NKX2-1 isoforms in exhaled breath condensate. (2016)
Figure 1

Figure 1. Em/Ad expression ratios of GATA6 and NKX2-1 as LC biomarkers.

A In silico analysis of human GATA6 and NKX2-1 shows similar gene structures (top) with two promoters (grey boxes) driving the expression of two distinct transcripts (middle and bottom); exons as black (non-coding) and white (coding) boxes. GATA6, GATA Binding Factor 6; NKX2-1, NK2 homeobox 1; Em, Embryonic; Ad, Adult.

B Box plots of the Em/Ad expression ratios of GATA6 (left) and NKX2-1 (right) in FFPE lung tissue sections from controls (Ctrl, n=61) or lung cancer (LC, n=51) patients. Isoform-specific expression of the indicated genes was analyzed by qRT-PCR after total RNA isolation from tissue samples. Each point represents one sample.

C Box plots of Em/Ad of GATA6 (top) or NKX2-1 (bottom) show that high Em/Ad ratios in LC samples are maintained among ethnic groups (left) and gender (right). GER, samples collected in Germany; MEX, samples collected in Mexico.

D Box plots of Em/Ad of GATA6 (left) or NKX2-1 (right) in FFPE lung tissue sections from controls or LC patients. Samples were staged according to the TNM Classification (UICC, 7th edition).

The five-number summary and the statistical test values from each plot in Fig 1 are shown in the Tables EV1-2.
Non-invasive lung cancer diagnosis by detection of GATA6 and NKX2-1 isoforms in exhaled breath condensate. (2016)
Figure 2

Figure 2. Development and validation of a simple clinical score for LC diagnosis.

A Box plots of the Em/Ad expression ratios of GATA6 (left) and NKX2-1 (right) in EBCs from controls (Ctrl, n=65) or lung cancer (LC, n=48) patients (training set). Each point represents one sample.

B Learning of the SVM on the training set of EBCs. Left, the log2-transformed Em/Ad expression ratios of GATA6 (x-axis) and NKX2-1 (y-axis) detected in the EBCs of the training set were plotted. The SVM combined the Em/Ad of GATA6 and NKX2-1 of each sample and created a linear classifier. The solid line represents the decision boundary of this classifier, which reliably separates control (green dots) and LC (red dots) samples. Filled circles, correctly classified samples; open circles, wrongly classified samples. Right, box plot of the LC score on the training set of EBCs.

C External validation of the LC score. Left, the log2-trasnformed Em/Ad expression ratios of GATA6 and NKX2-1 detected in an independent set of blinded collected EBCs were plotted as in (B). The SVM based classifier confirmed its discriminatory power between control and LC samples. Right, box plot of the LC score on the validation set of EBCs.

D ROC analysis confirmed the increase in performance of the LC score based classification on the validation set of EBCs (black line) over a classification relying only on Em/Ad expression ratios of GATA6 (pink line) or NKX2-1 (blue line). The orange diamond represents the optimal operating point of the SVM classifier, i.e. the point on the curve with maximal Youden´s J index.

E Performance of the LC score on the validation set of EBCs. PPV, prospective predictive value; NPV, negative predictive value; TPR, true positive rate or sensitivity; TNR, true negative rate or specificity.

The five-number summary and the statistical test values from each box plot in Fig 2 are shown in Table EV1.
Splicing factor ESRP1 controls ER-positive breast cancer by altering metabolic pathways. (2018)
Figure 1

Figure 1. High ESRP1 expression correlates with poor prognosis in estrogen receptor positive (ER+) but not in estrogen receptor negative (ER-) breast cancer

A BreastMark microarray platform; Kaplan-Meier curves of overall survival (OS), demonstrating that high expression of ESRP1 (red line) is associated with poor prognosis in ER+ breast cancer. A log rank test was used to calculate P=2.8 e-07 (n= 934, number of events= 216).

B BreastMark microarray platform; Kaplan-Meier curves of overall survival (OS) of ER- breast cancer. A log rank test was used to calculate P=0.4481 (n= 322, number of events= 130).

C BreastMark microarray platform; correlation of ESRP1 expression with overall survival in tamoxifen-treated patients. A log rank test was used to calculate P=1.218, e-06 (n= 210, number of events= 49).

D BreastMark microarray platform; correlation of ESRP1 expression with overall survival in chemotherapy-treated patients. A log rank test was used to calculate the P=0.28 (n= 129, number of events= 21)

E The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) RNA-seq dataset; Kaplan-Meier curves of overall survival (OS) in ER+ breast cancer, demonstrating that high expression of ESRP1 (red line) is associated with poor prognosis in ER+. A log rank test was used to calculate P=0.00011 (n= 656, number of events= 62).

F The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) RNA-seq dataset; Kaplan-Meier curves of overall survival (OS) in ER- breast cancer. Red-high ESRP1 expression; Black-low ESRP1 expression. A log rank test was used to calculate P=0.19 (n=100, number of events=17).
Splicing factor ESRP1 controls ER-positive breast cancer by altering metabolic pathways. (2018)
Figure 2

Figure 2. Knockdown of ESRP1 decreases malignant and tumor growth significantly in endocrine-resistant ER+ breast cancer models.

A RT-qPCR confirmed the stable knockdown of ESRP1 expression in endocrine-resistant cells using a shRNA lentiviral system (Mission TRC human shRNA constructs-Sigma); 2-control (LCC2 cells transduced with pLKO.1 control vector) and 2C1 and 2C3 (LCC2 cells transduced with pLKO.1 shRNA ESRP1). Clones were selected for expression verification (clone 1 and clone 3); 9-control (LCC9 cells transduced with pLKO.1 control vector), 9C2 and 9C3 (LCC9 cells transduced with pLKO.1 shRNA ESRP1. Clones were selected for expression verification (clone 2 and clone 3). * represents P <0.05; statistically significant. Data (mean ± SD) were calculated using two-way ANOVA based on three independent biological replicates. B Western blot analysis using ESRP1 antibody GTX 131373 (GeneTex). GAPDH is used as the reference control.

C Relative cell density was determined by crystal violet assay. Cells were treated with TAM

(10-6 M) or ICI (10-9 M) for 6 days in the presence and absence of E2 (β-estradiol; 10-10 M). Data (mean + SD) were calculated using two-way ANOVA based on three independent biological replicates.

D Cell viability was determined using Cell Biolabs CytoSelect™ Cell Transformation Assay using 2-control, 2C3, 9-control and 9C2 cells in CSM-charcoal stripped media, E2 (β-estradiol; 10-10 M) and 10% fetal bovine serum. Data (mean + SD) were calculated using two-way ANOVA based on three independent biological replicates. E Impact of ESRP1 knockdown on in vivo tumor growth. Five million cells (2-control, 2C3, 9-control and 9C2) were implanted into mammary fat pads of athymic mice (five mice per group) in the presence of supplemental estrogen. Tumors were measured weekly using calipers for external measurements. Tumor volume was calculated as L x W2/2, where L is length and W is width (note the different scale for tumor volumes). Data (mean ± SD) were calculated using two-way ANOVA (n= 5 mice per group).