dataesr/scientific-paragraphs-categorization huggingface dataset:
CYLD Interacts with Components of the Centriolar Satellites To uncover novel effectors of CYLD functions, we conducted an unbiased mass spectrometry screen in retinal-pigmented epithelial (RPE-1) cells. As expected  (Elliott et al. , 2016; Kupka et al. , 2016; Schlicher et al. , 2016; Wagner et al. , 2016) , SPATA2 was found as a binding partner. The identified hits were further processed using the STRING public online database (https:// string-db. org), which sorts functional interactions between proteins. Performing a Gene Ontology (GO) analysis unveiled a cluster of microtubule-organizing center (mTOC) elements (Figure  1A ). Among those, four elements of the centriolar satellites (PCM1, CEP131, MIB1, and PIBF1) were isolated (Figure  1B ). Centriolar satellites are small granules that gravitate toward the centrosome in a microtubule-dependent way and coordinate ciliogenesis  (Hori and Toda, 2017).
were prepared and subjected to IP prior analysis by immunoblotting as indicated. The dash (-) indicates an empty vector. Data are representative of two independent experiments. (F) RPE-1 cells transfected with NS siRNA or CYLD-siRNA were lysed, and ubiquitinated proteins were purified by a TUBE pan-ubiquitin pull-down. Samples were treated or not with recombinant CYLD for 30 min and analyzed by immunoblotting as indicated. Data are representative of two independent experiments. (G) FLAG-MIB1 ectopically expressed in HEK293T was purified by IP with FLAG antibodies and eluted using a FLAG peptide, prior incubation in vitro with recombinant CYLD for 0À90 min. Samples were then subjected to immunoblotting. Data are representative of three independent experiments. (H) HEK293T were treated as in (E) and analyzed by immunoblotting as indicated.
Spectra obtained were processed using MASCOT server and compared to UniprotHuman bank for protein identification. All proteins were ranked according to MASCOT score and proteins with at least two peptides above MASCOT threshold or a single peptide of individual score > 50 with a p value better than 10 À5 were conserved. Queries matched correspond to the number of MS/MS specter identified for a protein. Single peptide score (also termed ions score) were calculated following À10Log(P) were P is the probability that an observed match is a random event. Here, the significance threshold (P) chosen was 0. 05. The protein score is calculated as the sum of all unique peptide scores for said protein. Mass spectromety identified proteins were analyzed for Cellular Component (GO) using STRING database (free online software https://string-db.
Immunofluorescence Cells were fixed in 4% paraformaldehyde for 12 minutes at room temperature, incubated with ice-cold methanol for 2 minutes and quenched 10 minutes with PBS/100 mM glycine. Fixed cells were incubated 60 minutes with primary antibodies and 60 minutes with secondary antibodies in PBS 1X-0. 2% BSA-0. 05% Saponin. The following primary antibodies were used: CYLD (Atlas antibodies, HPA050095), MIB1 (Sigma, M5948), gTubulin (Sigma, GTU88), acetylated Tubulin (Santa Cruz Biotechnology, 6-11 B-1), CEP131 (Bethyl, A301-415A), SPATA2 (Bethyl, A302-493A), and CAP350 (Abcam, ab219831). Coverslips were sealed with Prolong gold anti-fade mouting media (Life Technologies) and nuclei were counterstained with 4',6-diamidino-2-phenylindole (DAPI). Confocal Images were acquired with a Nikon A1R confocal microscope using the NIS-Element software.
Structure illumination microscopy (SIM) images were acquired on a Nikon N-SIM microscope (MicroPicell, SFR Francois Bonamy, France). Images were reconstructed in 3D using the NIS-Element Software from Z stacks of 0. 12 mm taken using a 100x oil-immersion lens with a 1. 49 numerical aperture. Images were processed using FIJI software. To disrupt the microtubule, RPE-1 cells were incubated with 2 mg. mL -1 nocodazole for two hours prior to fixation. qPCR RNA was extracted using the RNeasy Mini Kit (QIAGEN) following manufacturer's instructions. Equal amounts of RNA were reverse transcribed using the Maxima Reverse Transcriptase kit (ThermoFisher) and 30 ng of the resulting cDNA was amplified by qPCR using PerfeCTa SYBR Green SuperMix Low ROX (QuantaBio). Data was analyzed using the 2 -DDCt method and normalized by the housekeeping genes ACTB, HPRT1.
(D) Lys. from RPE-1 cells treated as in (A) were subjected to a TUBE pan-ubiquitin, TUBE-K 63 , or TUBE-K 48 pull-down, and immunoblotting was performed as indicated. MIB1 (arrowhead) and ubiquitinated MIB1 (MIB1 n ) are indicated. Data are representative of three independent experiments. (E) HEK293T cells were transfected with plasmids encoding for FLAG-MIB1-wild type (WT) or FLAG-MIB1-CS together with HA-Ub-K 48 and HA-Ub-K 63. Cell Lys. were prepared and subjected to IP prior analysis by immunoblotting as indicated. The dash (-) indicates an empty vector. Data are representative of two independent experiments. (F) RPE-1 cells transfected with NS siRNA or CYLD-siRNA were lysed, and ubiquitinated proteins were purified by a TUBE pan-ubiquitin pull-down. Samples were treated or not with recombinant CYLD for 30 min and analyzed by immunoblotting as indicated.
Data are representative of two independent experiments. (G) FLAG-MIB1 ectopically expressed in HEK293T was purified by IP with FLAG antibodies and eluted using a FLAG peptide, prior incubation in vitro with recombinant CYLD for 0À90 min. Samples were then subjected to immunoblotting. Data are representative of three independent experiments. (H) HEK293T were treated as in (E) and analyzed by immunoblotting as indicated. (I) RPE-1 cells were transfected with siRNA for CYLD, MIB1, a combination of CYLD and MIB1, or NS siRNA for 48 h. Cell Lys. were prepared and subjected to immunoblotting as indicated. Data are representative of three independent experiments. (J) Confocal microscopy analysis of RPE-1 cells as in (I). Nuclei were stained by DAPI. Scale bar, 5 mm. Data are representative of three independent experiments.
Ciliated cells were counted (means ± SEM of three independent experiments; n > 100 cells counted per samples; ****p < 0. 0001 [ANOVA]). Cell Reports 27, 1657-1665, May 7, 2019 1663 for advice; M. Fernandez for technical assistance; and the Micropicell facility (SFR SanteFrancois Bonamy, Nantes, France). This research was funded by an International Program for Scientific Cooperation (PICS, CNRS), Fondation ARC pour la recherche sur le Cancer (N. B. ), Ligue nationale contre le cancer comites de Loire-Atlantique, Maine et Loire, Vendee (J. G. , N. B. ), Region Pays de la Loire et Nantes Metropole under Connect Talent Grant (J. G. ), the National Research Agency under the Programme d'Investissements d'Avenir (ANR-16-IDEX-0007), and the SIRIC ILIAD (INCa-DGOS-Inserm_12558). T. D. is a PhD fellow funded by Nantes Metropole and G.
Cryogenic Transmission Electron Microscopy (Cryo-TEM) Cryogenic transmission electron microscopy (Cryo-TEM) micrographs were obtained using a Tecnai F20 TWIN microscope (FEI Company, Hillsboro, Oregon, USA) equipped with field emission gun, operating at an accelerating voltage of 200 kV. Images were recorded on the Eagle 4 k HS camera (FEI Company, USA) and processed with TIA software (FEI Company, Hillsboro, Oregon, USA). Specimen preparation was done by vitrification of the aqueous (HPLC-grade water) solutions on grids with holey carbon film (Quantifoil R 2/2; Quantifoil Micro Tools GmbH, Germany). Prior to use, the grids were activated for 15 s in oxygen plasma using a Femto plasma cleaner (Diener Electronic, Germany). Cryo-samples were prepared by applying a droplet (3 µL) of the solution to the grid, blotting with filter paper and immediately freezing it in liquid ethane using a fully automated blotting device, Vitrobot Mark IV (FEI Company, Hillsboro, Oregon, USA).
X-ray Diffraction (XRD) X-ray diffraction data collection and analysis was carried out on the six nanodispersions prepared, as also described in our recent study  [28]. The in-house X-ray generator (manufactured in Poland by Agilent Technologies (Oxford diffraction) with microfocus sealed tube, CuKα radiation, λ = 1. 54 Å, equipped with a kappa goniometer and a charged coupled detector, diameter 135 mm) installed at the National Hellenic Research Foundation (NHRF) was used. The X-rays were generated at power settings of 50 kV and 0. 8 mA. The samples were mounted on capillaries and exposed to X-rays for 300 sec at a specimen-to-detector distance of 54. 8 cm. The 2D diffraction plots for relative intensity vs. 2θ were calculated and peak assignment was performed using CrysAlisPro software. Further analysis for depicting the collected data in graphical schemes was carried out using Microsoft Excel software.
Statistical analysis Statistical analysis was performed using GraphPad Prism version 6. 00 for Windows (GraphPad Software, La Jolla California USA, www. graphpad. com) and SPSS statistics. All data were first tested for normality (D'Agostino &Pearson omnibus normality test). Normally distributed data were presented as means and SEM. Students T-test was used to test for statistically significant differences between two groups. ANOVA with Tukey and/or LSD posthoc testing was used for statistical comparison of more than two test groups. If normality did not hold, non-parametric analysis was used accordingly. Mann-Whitney U-test for comparison between 2 groups and Kruskal-Wallis ANOVA with Dunn's posthoc testing for comparison among more than 2 groups. Significance levels were defined as *p < 0.
As there are ties in the data, exact p-values could not be computed so the test was run a second time, with a bootstrap (nboots = 10000) yielding the same results. The uncorrected Welch two-sample ttest showed that the difference in means between the two populations was significant for the untransformed data (t = -7. 3009, df = 7. 688, p = 0. 0001024; Mean for silent death = 35. 27273, sudden = 341. 12500) and also for the transformed data by taking the square root (t = -9. 8127, df = 16. 234, p = 3. 126e-08; Mean for silent death = 4. 398886, Mean for sudden death = 18. 235005). Cluster analysis was done to extract an objective breakpoint between the two groups, which yielded a value of a total of 150 climbing efforts / 700 min and produced scree and cluster plots showing that two groups explain 100% the variability.
Software The Pixelman  [9]  software package developed in Prague University is designed to readout the chip. The package has a very flexible modular architecture, that can be extended by software plugins (i. e. additional software that extends functionality). In our project we used two ways to communicate and control detector: (1) Pixelman Graphical User Interface (GUI) (allows to visualize the acquired frames and to write data into the text files); (2) Python scripting plugin (gives a possibility to integrate Pixelman libraries inside other data acquisition (DAQ) systems).
Depending on the crystal position with respect to the beam core (σ X ≃ 0. 6 mm), in stationary condition the number of particles from the halo reaching the edge of the crystal is varying from few to hundreds protons at every turn (23 µs). The acquisition of each pixel matrix of the detectors is performed through an optical link in USB 2. 0 protocol using a unique PC connected to each FITPixes. Figure  5  shows the scheme of the data transferring. Using a python script the data frame by frame (fired pixel coordinates and number of counts) is transferred each minute to a new data file to the CERN-DFS storage. With a BASH script running on the user PC a quasi-online analysis can be performed producing about each minute a suitable information for the MD: analyzed data is converted into the beam images and profiles, and saved on the user PC.
Triggering and synchronization A plastic scintillator placed on the beam line was used as a trigger. The scintillator gave a signal per each crossing particle. The maximum DAQ rate of the system, which consists of 4 detectors, driven by the Python script, is about 2 Hz (2 acquisition frames per second for each detector) or 500 ms of the time delay between two consecutive frames, that can be improved for the future. In order to be sure that all detectors are starting from the same trigger signal and that they are all ready to collect the data a delay of 1 s between two trigger signals was applied. Once all Timepixes are configured, we send a command simultaneously to all detector to be ready for the acquisition and wait for the arriving of the trigger signal. In such a way we provide the time synchronization between detectors.
Acknowledgments The authors are grateful to other members of the  UA9 collaboration  for their support and their contribution to the development of the experimental apparatus used in this study. This research was partially conducted in the scope of the  IDEATE International Associated Laboratory (LIA). We would like to thank  Erik Heijne  for his helpful advices in the preparation of this paper,  Luca Foggetta  and  Claudio Di Giulio  for the studies on the DAQ synchronization. The authors warmly acknowledge the support of  Michael Campbell  from the Medipix collaboration for the readout electronics.
| Statistics We calculated a priori the number of donors to include using a standard method for a clinical trial. We estimated that the BR would mark a real change if it reduced cell loss by 40%. With this hypothesis, a sample size of 49 corneas randomized to 1 of 2 groups was necessary. The details are set out in the supporting information. Normality of continuous-data distribution was analyzed using the Shapiro-Wilk test, with a non-normality threshold set at 5%. Normally distributed data were described by their mean and standard deviation. Continuous non-normally distributed variables were summarized as median (10-90 percentiles). The nonparametric Wilcoxon signed-rank test was used when the variable followed a non-normal distribution, and a t test when the variable followed a normal distribution.
| EC density is higher after storage in the BR Corneas in both groups had similar ECD at retrieval (D2), with 2607 ± 340 vs 2577 ± 357 cells/mm 2 for the BR and OC, respectively (P =. 243). At D26, ECD was significantly higher in the BR with 2350 ± 322 vs 2061 ± 412 cells/mm 2 in OC (P <. 001). The mean difference was 290 cells/mm 2 (95% confidence interval [CI]   198-382), or 12% more ECs in the BR. We calculated EC loss daily, assuming that the rate of decay (λ) was constant over the 26 days (with ECD t2 = ECD t1 *e λ (t2-t1) ). It was 0. 44% per storage-day (95% CI 0. 44-0. 45) for the BR and 0. 93% (95% CI 0. 86-1. 01) for OC (P <. 001). In addition, cell loss was also calculated from the individual data of each cornea. It was 0. 41% per storage-day (95% CI 0. 32-0. 50) for the BR and 0.
In the two human orthotopic LUAD models a significant reduction of tumor size was observed (Supplementary information Fig. S2A, B ). A larger number of metastatic animals was also observed in the 'H1975_LUAD mice': 6/10 in the control H1975 group versus 10/10 in the H1975-EIF3F group (Supplementary information Fig. S2C ). In the 'H460_LUAD mouse' model the large tumor size led to premature animal death before metastasis could be evaluated. In the LLC_LUAD immuno-competent mouse model of metastasis, we observed a significant increase in the number of hepatic tumors following EIF3F-luc-LLC cells intra-venous injection (Supplementary information Fig. S2D ) The link between EIF3F overexpression and increased hepatic metastasis was also explored in human samples in silico using 'TCGA database  [9] ', focusing on the cohort of patients with lung adenocarcinoma (LUAD TCGA cohort; http://cancergenome.
The Fig. 3  EIF3F reprograms the proteome of mice orthotopic human lung tumors and promotes cell migration. a Proteome analysis of EIF3F lung tumors compared to CTL lung tumors (N = 4). Ingenuity pathway core analysis (IPA; Qiagen). The bar graph of a was generated using Ingenuity Pathway analysis (IPA; Qiagen) software from the analysis of the raw data of the differential proteomics study, obtained between EIF3F-A549 and A549 orthtotopic lung tumors. The proteins with a different expression between the two types of tumors were organized based on pre-defined categories suggested by IPA (as for example 'EIF2 signaling'). Then, proteins with different expression were assigned to these IPA-categories based on the IPA-experts curated database, and the categories were ranked according to their frequency of identification in the proteome (the -log(p value); top axis value).
In complete agreement with our cell biology findings, we demonstrated that overexpression of EIF3F promotes metastasis in vivo using three mouse models of human or murine LUAD. While the anti-growth effect of EIF3F was important in these models, the prometastatic potential was weaker despite the important migratory effect observed in vitro. The molecular mechanisms linking EIF3F overexpression and metastasis include (i) proteome remodeling with specific upregulation of the machinery involved in cell migration, invasion, and energy metabolism and (ii) nuclear activities including the partnering with STAT3 transcription factor and the regulation of a cluster of 34 genes involved in cell migration, invasion, and energy metabolism. This gene module identified in our study provides a molecular signature for EIF3F overexpression that could be used for bioinformatic studies on large datasets of tumor transcriptomes and proteomes.
To conclude, our multi-omic, bioenergetic and cell biology study provides a better understanding of EIF3F biological function in the nucleus, beyond protein translation. We provide a unique set of data demonstrating that EIF3F regulates the metastatic process by remodeling the proteome, the transcriptome and the bioenergetics of cancer cells. Using Chip-seq, we also demonstrate that EIF3F binds to and regulate the expression of a cluster of 34 metastasis-promoting genes. This discovery extends our knowledge of the molecular mechanisms of metastasis that causes 90% of deaths from cancer. Therefore, our findings suggest that EIF3F could be considered as a novel effector of cell migration and metastasis, and its molecular link with STAT3 fosters the need to pursue investigations on the role of the EIF3F-STAT3 signaling in cancer.
Mitochondrial network immunostaining Cells were seeded into Nunc Lab-Tek chamber slide at 5000 cells per chamber. Briefly, cells were fixed with 4% paraformaldehyde then permeabilized with 0. 15% Triton X100 before 45 min of 10% bovine serum albumine saturation. Overnight incubation of primary antibodies anti-Tom20 (abCam) was performed before fluorescent-secondary antibody incubation. Cells were finally rinsed twice with PBS. Fresh PBS was added to the chambers to maintain cells integrity. Fluorescence microscopy was performed on a Zeiss Axio Observer microscope equipped with the Vivatome system with a 63× oil spring-loaded objective. The images were acquired using AxioVision (6D acquisition and vivatome modules). A minimum of 30 different cells obtained from three different experiments was randomly selected per experimental condition, and the analysis of mitochondrial morphology was performed using Morphostryder (Explora Nova).
Label-free quantitative proteomics This analysis was performed by the proteomics core facility at University of Bordeaux (https://proteome. cgfb. u-bordea ux. fr/en). The steps of sample preparation, protein digestion and nanoliquid chromatography-tandem mass spectrometry analysis on Q Exactive were performed as previously described  [48]. For protein identification, Sequest HT and Mascot 2. 5 algorithms through Proteome Discoverer 1. 4 Software (Thermo Fisher Scientific Inc. ) were used in batch mode by searching against a merge of protein databases: Homo sapiens and Mus musculus (121 170 entries, Reference Proteome Set, release 2017_05). Databases were downloaded from http://www. uniprot. org/ website. Two missed enzyme cleavages were allowed. Mass tolerances in MS and MS/MS were set to 10 ppm and 0.
02 Da. Oxidation of methionine, acetylation of lysine, and deamidation of asparagine and glutamine were searched as dynamic modifications. Carbamidomethylation on cysteine was searched as static modification. Peptide validation was performed using Percolator algorithm  [49]  and only 'high confidence' peptides were retained corresponding to a 1% false discovery rate (FDR) at peptide level. Raw LC-MS/MS data were imported in Progenesis QI (version 2. 0; Nonlinear Dynamics, a Waters Company) for feature detection, alignment, and quantification. All sample features were aligned according to retention times by manually inserting up to fifty landmarks followed by automatic alignment to maximally overlay all the two-dimensional (m/z and retention time) feature maps. Singly charged ions and ions with higher charge states than six were excluded from analysis.
All remaining features were used to calculate a normalization factor for each sample that corrects for experimental variation. Peptide identifications (with FDR < 1%) were imported into Progenesis. Univariate one-way analysis of variance (ANOVA) was performed within Progenesis LC-MS to calculate the protein p-value according to the sum of the normalized abundances across all runs. Only proteins with a p-value cut-off ≤ 0. 05 were validated. A minimum of two peptides matched to a protein, and a ≥1. 2-fold change in relative abundance between the two conditions (N = 4 in each group) were used as the criteria for identification as a differentially expressed protein. Noticeably, only nonconflicting features and unique peptides were considered for calculation at protein level. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [12]  partner repository with the dataset identifier PXD010097.
Proteins were clusterized according to their functions by using the KEGG analysis in the search tool for retrieval of interaction between genes and proteins (STRING) database. More global analysis of the data was performed via use of IPA (Qiagen). We used the 'Core Analysis' package to identify relationships, mechanisms, functions, and pathways relevant to a dataset. We also used the 'regulators' package to identify predicted regulators of the proteomic changes. Comparative analyses were also performed with IPA using the 'Comparative Analysis' module.
Mitochondrial DNA quantification After collecting the cultured A549 cells, the total DNA was extracted using a DNeasy Blood & Tissue Kit (Qiagen) and the total RNA was extracted using an RNeasy Plus Mini Kit (Qiagen). First strand cDNA was synthesized in a 20 μl volume using 1 μg of total RNA, with 50 units of M-MuLV Reverse Transcriptase (RNase H -) and Oligo d(T) 16 (Applied Biosystems). Real-time quantitative PCR for nuclear DNA gene RNA12S (nDNA) and mtDNA gene RNA16S was performed using IQ TM SYBR Green Supermix (Bio-Rad) on a thermocycler CFX96 Touch Real-Time PCR system (Bio-Rad), as described previously  [50]. Normalized units to the endogenous references (RPLP0 and GUSB) were calculated using the CFX Manager Software program.
with 3, 3′-diaminobenzidine. Tissue sections were counterstained with hemalum. Slides were scanned by using a digital slide scanner (Pannoramic Scan; 3D HISTECH Ltd, Budapest, Hungary) with a Zeiss objective (Plan Apochromat 40×; numerical aperture 0. 95; ZEISS, Oberkochen, Germany) and a highresolution color camera (CIS VCC-FC60FR19CL, 4MP, CIS Corporation, Japan). The images were read by using the Pannoramic Viewer software (3D HISTECH Ltd, Budapest, Hungary).
Hemalun, eosin, and safran stainings Lung tissues taken from mice were fixed and embedded in paraffin and 4-μm sections were prepared. The sections were stained with HES using standard procedures for proper lung orientation and morphological assessment. Slides were scanned by using a digital slide scanner (Pannoramic Scan; 3D HISTECH Ltd, Budapest, Hungary) with a Zeiss objective (Plan Apochromat 40×; numerical aperture 0. 95; ZEISS, Oberkochen, Germany) and a high-resolution color camera (CIS VCC-FC60FR19CL, 4MP, CIS Corporation, Japan). The images were read by using the Pannoramic Viewer software (3D HISTECH Ltd, Budapest, Hungary).