dataesr/scientific-paragraphs-categorization huggingface dataset:
We further discovered that GLUT1 levels were higher in p62KD-293T cells than in 293T cells, whereas GLUT1 levels were increased in 293T cells with forced LMP1 expression but not in p62KD-293T cells with forced LMP1 expression (Fig  6E ). Moreover, approximately the same amount of GLUT1 protein was pulled down by Flag-p62 in the co-IP analysis as in the CNE2-vector and CNE2-LMP1 cells transfected with Flag-p62 plasmids, although GLUT1 levels were increased in CNE2-LMP1 cells compared with CNE2-vector cells, as determined by an immunoblotting analysis (S3C Fig). These data suggest that LMP1 inhibits GLUT1 autophagic degradation in a p62-dependent manner by disrupting the interaction of GLUT1 with the autophagic cargo receptor p62. In addition, a dramatic decrease in ubiquitinated GLUT1 was observed in CNE2-LMP1 cells compared to that in CNE2-vector cells (Fig  6F ).
(D) NPC-LMP1 and NPC-vector cell lines were treated with the lysosome inhibitor BafA1 for 12 h, proteins were harvested at 0 and 12 h, and GLUT1 expression was measured by immunoblotting. Representative data from 5 independent experiments are shown, and GAPDH was included as a control. (E) Immunoblotting showed that GLUT1 was expressed at higher levels in 293T cells than in p62KO-293T cells, whereas GLUT1 was expressed at higher levels in 293T-LMP1 cells than in 293T cells but was not found at higher levels in p62KO-293T-LMP1 cells compared with p62KO-293T cells. Representative data from 1 of 5 experiments are shown. (F-G). NPC-LMP1 and NPC-vector cell lines cultured in 6-well plates were transfected with hemagglutinin (HA)-tagged Ub (4 μg/well) (F) HA-tagged Ub-K48, HA-tagged Ub-K48R, HA-tagged Ub-K63 or HA-tagged Ub-K63R (G) and then treated with 20 mM MG132 for 6 h prior to harvest.
Cell lysates were immunoprecipitated with anti-HA antibodies and then subjected to WB with an anti-GLUT1 antibody to measure the levels of ubiquitinated GLUT1 proteins (upper panels). WCLs were blotted to evaluate the levels of GLUT1 proteins (lower panels). β-actin expression was used as a protein loading control. The experiment shown is a representative of three independent experiments. https://doi. org/10. 1371/journal. ppat. 1006503. g006 while transfection with pHA-Ub-K63 resulted in a reduction in ubiquitinated GLUT1 in CNE2-LMP1 cells compared to that in CNE2-vector cells (Fig  6G ). Overall, these data suggest that LMP1 disrupts the K48-linked ubiquitination of GLUT1 and the binding of GLUT1 and the autophagic cargo receptor p62 to delay the autolysosomal degradation of the GLUT1 protein.
Statistical analysis Parametric statistics were applied for in vitro data, Student's t-tests were used for comparisons between experimental treatment groups and control groups. The relationships among LMP1, GLUT1, CD33 values and the clinical pathological features were tested using Pearson's chisquare tests. Nonparametric data were analyzed using Mann-Whitney U tests. The Kaplan-Meier method was used to estimate patient survival, and survival differences were estimated using log-rank tests; the cutoff value for each IHC variant was estimated using X-Tile software  [67]. The values are presented as the means ± standard errors of the means (SEM). P was considered statistically significant when < 0. 05. All data were analyzed using SPSS 18. 0 (SPSS, Chicago, IL) and GraphPad Prism 5 software.
(C) NPC-LMP1 and NPCvector cell lines were treated with CHX for 18 h. Proteins were harvest at 0, 3, 6, 12 and 18 h, and the expression of GLUT1 was measured via immunoblotting. Representative data from 5 independent experiments are shown, and GAPDH was included as a control. (D) NPC-LMP1 and NPC-vector cell lines were treated with the lysosome inhibitor BafA1 for 12 h, proteins were harvested at 0 and 12 h, and GLUT1 expression was measured by immunoblotting. Representative data from 5 independent experiments are shown, and GAPDH was included as a control. (E) Immunoblotting showed that GLUT1 was expressed at higher levels in 293T cells than in p62KO-293T cells, whereas GLUT1 was expressed at higher levels in 293T-LMP1 cells than in 293T cells but was not found at higher levels in p62KO-293T-LMP1 cells compared with p62KO-293T cells.
Representative data from 1 of 5 experiments are shown. (F-G). NPC-LMP1 and NPC-vector cell lines cultured in 6-well plates were transfected with hemagglutinin (HA)-tagged Ub (4 μg/well) (F) HA-tagged Ub-K48, HA-tagged Ub-K48R, HA-tagged Ub-K63 or HA-tagged Ub-K63R (G) and then treated with 20 mM MG132 for 6 h prior to harvest. Cell lysates were immunoprecipitated with anti-HA antibodies and then subjected to WB with an anti-GLUT1 antibody to measure the levels of ubiquitinated GLUT1 proteins (upper panels). WCLs were blotted to evaluate the levels of GLUT1 proteins (lower panels). β-actin expression was used as a protein loading control. The experiment shown is a representative of three independent experiments.
05°. The D1B instrument was operated with a wavelength of 2. 521 Å, produced by its pyrolytic graphite monochromator. Graphite filters between the monochromator and the sample reduce the higher-order wavelength contamination, the proportion of λ/2 contamination being ca. 0. 03%, which in the present case is in the limit of observation. Two high-flux neutron patterns were collected at 1. 8 and 10 K, in the ordered and paramagnetic phases, respectively. The temperature evolution of the neutron diffraction pattern was followed from 1. 8 to 10 K, with a heating rate of 0. 04 K/min. Data reduction was carried out using LAMP software  [16]. The single-crystal measurements (D9 and CYCLOPS) were carried out in a sample of 3 × 4 × 4 mm 3 , approximately, which was mounted on a standard aluminum pin.
The data collections in D9 consist of ω scans or ω-2θ scans. The program RACER  [17]  was used to integrate these scans and to correct them for the Lorentz factor. The crystal absorption corrections were made with the DATAP program  [18] , using an estimated absorption coefficient of 0. 608 cm -1 , for a wavelength of 0. 8409 Å, essentially due to the absorption cross section of the chlorine atoms  [19]. The CYCLOPS measurements consisted of an exploration of the Laue patterns, at a specific orientation, as a function of the temperature. One hundred fifteen Laue diffraction patterns were collected during 15 min each with a difference of temperature of 1 K. Two different patterns after and before the nuclear structure transformation can be seen in Fig. S2  in the Supplemental Material  [15].
The refinement of the nuclear and the magnetic structures was performed using the program FULLPROF. [20]. Due to the structural phase transition, the occurrence of two twin domains has been observed. These were included in the data reduction, and the ratio between both domains was refined giving rise to a proportion of 53%:47%. The models for the magnetic structure were deduced from the output of the program BASIREPS  [21]  included in the FULLPROF suite  [20].
D. Neutron diffraction measurements In order to determine an accurate structural model of the Cs 2 FeCl 5 •D 2 O compound, in particular the deuterium positions, which are not easily determined from the x-ray experiments due to the low scattering power of these light atoms, neutron diffraction experiments were necessary  [22]. Room temperature (RT) single-crystal neutron diffraction measurement was carried out at the D9 diffractometer. The obtained model was used as starting point to fit the lowtemperature powder diffraction data collected on the D1B diffractometer. The fit of the data in the paramagnetic phase at low temperature with the room temperature model was not satisfactory, suggesting the occurrence of a nuclear phase transition, similar to that observed in the (ND 4 ) 2 FeCl 5 •D 2 O compound  [6].
This option was explored initially using the Laue diffractometer CYCLOPS  [23]  as a function of temperature and later on, with high-resolution powder neutron diffraction (see Figs. S2 and S3 in the Supplemental Material  [15] ). The comparison between different Laue images shows the appearance of new Bragg reflections below ca. 175 K, which is a fingerprint of the occurrence of a phase transition. The occurrence of this phase transition was corroborated through specific heat measurements, where a weak lambda peak was observed at 173 K (see Fig. S4  in the Supplemental Material  [15] ). After the successful indexing of the lowtemperature neutron powder diffraction, a full data set was collected at ca. 20 K on the D9 diffractometer in order to solve the crystal structure at low temperature in the paramagnetic phase.
C. Magnetic structure at zero magnetic field The neutron diffraction measurements at 1. 8 K (below T N ) and zero magnetic field were carried out in D1B (powder) and D9 (single-crystal) instruments and analyzed in a joint refinement. The observed magnetic contribution, on top of nuclear reflections in the powder pattern, suggests that the magnetic reflections can be indexed with the propagation vector k = (0,0,0). This was confirmed by the survey of the reciprocal space in the single-crystal experiment, proving that the only compatible propagation vector is k = (0,0,0). A total of 1120 independent reflections were collected in D9 at 1. 8 K (the same temperature of the data obtained in D1B). The temperature evolution of the sample was collected on D1B in the temperature range from 1. 8 to 10 K (see Fig.
The representational analysis technique described by Bertaut was used to determine the possible magnetic structures compatible with the (nonstandard setting) I 2/c space group and propagation vector k = (0,0,0)  [24]. Four irreducible representations (irreps) of the group G k ,( 1 ,-4 ) and the corresponding set of basis vectors for each irreducible representation were determined using the program BASIREPS  [21]. The magnetic representation M for the magnetic site [Wyckoff position 8f for Fe  (1) ] can be decomposed as a direct sum of irreps [see Eq. (  1 )]. In the present case, where k = (0,0,0), all the Fourier coefficients must be real and in   1 ) ion located at (-0. 12188, 0. 0041, -0. 3748) 8f Wyckoff position in the space group I 2/c with k = (0,0,0). The symmetry elements are written according to Seitz's notation.
ACKNOWLEDGMENTS The authors are grateful to the  Institut Laue Langevin  for the neutron beam-time allocated (doi: 10. 5291/ILL-DATA. 5-41-829 ). Partial funding for this work is provided by the  Ministerio de Economía y Competitividad (MINECO)  through project  MAT2015-68200-C2-2-P  and  Centro Universitario de la Defensa de Zaragoza  through  CUD 2013-17. Authors would like to acknowledge the use of  Servicio General de Apoyo a la Investigación-SAI, Universidad de Zaragoza.
GP2 and ECAD define four populations in the human fetal pancreatic epithelium that develop sequentially Recently, GP2 was identified as a novel cell surface marker of the immature pancreatic progenitor cells derived from hPSC  (Ameri et al. , 2017). Furthermore, additional data indicate that ECAD levels are tightly modulated during endocrine differentiation  (Gouzi et al. , 2011). Therefore, we analyzed GP2 and ECAD expression levels in the CD45 -CD31 -EPCAM + fraction at 9. 4WD. GP2 expression in CD45 -CD31 -was restricted to the EPCAM + fraction (Figure  2 -figure supplement 1). GP2 and ECAD expression segregated the CD45 -CD31 -EPCAM + fraction into four distinct populations: GP2 hi ECAD + (named GP2 hi ), GP2 + ECAD + (named GP2 + ), GP2 -ECAD + (named GP2 -), and the GP2 -E-CAD low (named E low ) (Figure  2A ).
Acinar and endocrine functions segregate within the GP2 and ECAD populations To characterize the four epithelial populations described above, we sorted the GP2 hi , GP2 + , GP2 - and E low populations and performed global transcriptomic analyses combined with RT-qPCR analyses at 9 and 11WD. As a non-epithelial control, we included the CD45 -CD31 -EPCAM -fraction (population M) (Figure  1A ). Due to limitations in cell numbers, the GP2 hi population was only sorted at 11WD. Principal component analysis (PCA) on the sorted populations at 9WD revealed three clusters. PC1 separated the epithelial populations (GP2 hi , GP2 + , GP2 -and E low ) from the mesenchymal fraction (M), while PC2 segregated the E low population from the GP2 hi , GP2 + and GP2 -populations (Figure  3A ). Gene Set Enrichment Analysis (GSEA) using Gene Ontology database indicated that digestion was the most represented biological process in the GP2 hi and GP2 + populations while endocrine functions (insulin and peptide secretion, hormone secretion) were enriched in the E low population (Figure  3B , Figure  3 -figure supplement 2).
6WD. DOI: 10. 7554/eLife. 27564. 015 Figure  6. Molecular characterization of the GP2 -CD142 -SUSD2 -and E low GP2 -CD142 -SUSD2 -subsets. (A, B) RT-qPCR for CHGA, NEUROD1, NKX2-2 and INS on sorted GP2 -CD142 + SUSD2 -(GP2 -CD142 + ), GP2 -CD142 -SUSD2 -(GP2 -CD142 -), E low GP2 -CD142 -SUSD2 + (E low SUSD2 + ) and E low GP2 -CD142 -SUSD2 - (E low SUSD2 -) subsets at 8. 6 and 10-12WD. (C) RT-qPCR for NEUROG3 at 8. 4, 8. 6, 9 and 11-13WD in the GP2 -CD142 + SUSD2 -(GP2 -CD142 + ), GP2 -CD142 -SUSD2 -(GP2 -CD142 -), E low GP2 -CD142 -SUSD2 + (E low SUSD2 + ) and E low GP2 -CD142 -SUSD2 -(E low SUSD2 -) subsets. (D, E) Single cell RT-qPCR Figure  6  continued on next page surface markers were used in a limited number of studies on pancreas development and mainly in rodents. It was found that a combination of CD49f and CD133 antibodies can be used to enrich fraction in mouse pancreatic progenitors expressing NEUROG3-expressing cells with some data on human fetal pancreas.
Global transcriptomic analysis Total RNA was obtained from Trizol-preserved (Invitrogen, France) sorted cells by chloroform extraction, quantified and assessed for quality by Agilent-2100 Bioanalyzer (Agilent, Santa Clara, CA). cDNAs were prepared and amplified using Ovation Pico WTA System V2 kit (NuGEN Technologies, San Carlos, CA, USA) and hybridized onto GeneChip Human Gene 2. 0 ST Array (Affymetrix,Santa Clara, CA, USA). Quality Control and normalization (RMA) were conducted using Bioconductor Project software (https://www. bioconductor. org), which provides log2 transformed expression values. Data were extracted from raw CEL files using a custom GeneChip library file (CDF file) provided by http://brainarray. mbni. med. umich. edu/CustomCDF (Dai et al. , 2005).
Transcriptomic statistical analysis Two-sample comparisons were made using two-tailed Student t-test. Transcripts for which any p-value was above 0. 05 were filtered out, leaving 6444 transcripts for further analyses. We compared expression of these genes in each cell population using Gene Set Enrichment Analysis (GSEA) software and the 'Biological Process' database of the Gene Ontology Consortium  (Subramanian et al. , 2005). Biological processes with FDR q-value <0. 05 were considered significant. Cut off to define the 'specific enriched genes': Each population (GP2 hi , GP2 + , GP2 -, E low and M) was compared to the other populations using twotailed Student t-test. Genes were considered as enriched in a specific population when overexpressed (p-value<0,05 and Fold Change > 2) in a population compared to any of the others.
The list of the 'specific enriched genes' is displayed in supplementary file 1b and c. Expression pattern of the enriched fetal genes in the adult pancreas: We use data from the single-cell RNA-seq  (Segerstolpe et al. , 2016). The normalized data are available on ArrayExpress (http://www. ebi. ac. uk/arrayexpress/ experiments/E-MTAB-5060). Next, we generated heatmaps displaying the expression of the fetal 'specific enriched genes' in the adult acinar, alpha, beta, epsilon, gamma and ductal cells. Heatmaps were generated by the 'heatmap2' function from gplots R package (https://cran. r-project. org/web/packages/gplots/) on standardized log2 expression values, with Pearson correlation as the distance function.
Acknowledgements We would like to thank  Delphine Bredel  ( INSERM U1016 ) for technical assistance and  Ana Cumano  ( INSERM U668 ) for supports. This work was supported by the  HumEn  project funded by the  European Commotion's Seventh Framework Programme for Research , (agreement No  602587 ) (RS and HS) and the  Foundation Bettencourt Schueller  (to RS). The  RS laboratory belongs to the Laboratoire d'Excellence consortium Revive  and to the  Departement Hospitalo-Universitaire (DHU) Autoimmune and Hormonal disease. The research leading to the results on the hiPSC has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement n  ˚115439 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution.
To generate in silico hypotheses on Syk signaling propagation, we developed a method allowing to rank paths between Syk and its targets. We first annotated the network according to experimental datasets. We then combined shortest path computation with random walk processes to estimate the importance of individual interactions and selected biologically relevant pathways in the network. Molecular and cell biology experiments allowed to distinguish candidate mechanisms that underlie the impact of Syk on the regulation of cortactin and ezrin, both involved in actin-mediated cell adhesion and motility. The Syk network was further completed with the results of our biological validation experiments. The resulting Syk signaling sub-networks can be explored via an online visualization platform.
These approaches, however, allowed only to establish a comprehensive list of direct and indirect Syk targets. In this study, we use the data produced in these investigations to reconstruct a Syk-based signaling network and to identify the intermediary pathways via which the signal propagates from Syk to its effectors in this network. Phospho-proteomic studies provide data sets of phosphorylated proteins with a significant "fold change" in differential experiments. Comparably to the gene data sets in transcriptomic studies, these protein targets can be used to build networks by using comprehensive interaction databases (hypergeometric test  [20] ; GSEA  [21] ; DAVID  [22] ; enrichment maps  [23] ). On the one hand, the set of identified targets is incomplete and intermediate variables and interactions are needed to describe systems-level functioning.
Here we focus on the identification of signaling pathways from a single source to a collection of targets identified in phospho-proteomic studies. In this context, the classical shortest paths approach can be inaccurate and does not take into account alternative paths, while the kshortest paths extension generates a collection of ranked alternative paths, but relies on wellseparated weights between arcs to be effective  [27]. Steiner trees enable the identification of the smallest set of edges allowing to connect a set of nodes: it may lead to longer individual paths but will reduce the number of interactions when considering the dataset as a whole, with the drawback of further reducing the number of alternative paths  [25]. Suboptimal solutions of the Steiner tree problem were computed for a lymphoma network  [32]  but the functional significance of disparate solutions was not appraised.
Furthermore, IPA is a proprietary software with a private database. Despite their limitations, each of these methods provides valuable information and it is useful to combine several approaches with ad-hoc adjustments when analyzing networks in relation with specific data. In this paper we assembled the interaction network of signaling pathways controlled by Syk in breast cancer cells, exploiting existing phospho-proteomic studies. To reconstruct and analyze the signaling network, we propose a novel methodology that combines the shortest paths methods with random walk processes. We defined interaction weights based on functional annotations and experimental datasets. Mainly, we pinpointed phosphorylation-based interactions leading directly to targets whose changes of phosphorylation are significant and interactions whose sources were identified targets.
Results Starting with the set of Syk-dependent targets identified by phosphoproteomics, we identified a network explaining the propagation of the signal from Syk to its targets. In order to generate this network we interrogated comprehensive pathways databases, extracted significantly enriched pathways and consistently merged them by avoiding duplicate interactions and nodes. The result of this assembly was a connected but very large network, too difficult to analyze and containing many unessential interactions. Here, we propose several methods allowing to prune this network and to extract significant paths from it. Several paths connecting Syk to functionally important targets were biologically validated.
Selection of Syk targets in breast cancer cells To bootstrap the reconstruction of a comprehensive network of Syk downstream signaling in breast cancer cells, we analyzed tyrosine phospho-proteomic data acquired in two independent mass spectrometric studies using breast cancer cell lines with modified Syk catalytic activity or protein expression. On the one hand, we selected from our own study the proteins lost or gained in tyrosine-phosphorylated protein complexes of the Syk-positive MCF7 cells treated with a pharmacological Syk inhibitor (further referenced as the MCF7 dataset)  [19]. On the other hand, we identified the proteins with modified tyrosine phosphorylation after exogenous Syk expression in the Syk-negative MDA-MB-231 cells (further referenced as the MDA231 dataset)  [17]. Post-treatment procedures of the original phospho-proteomic data are detailed in the Materials and Methods section.
From these two studies; we respectively selected 265 and 487 proteins as Syk targets (S1 and S2 Tables). Only 64 proteins were found in common, reflecting the complementarity of the two original phospho-proteomic studies. Indeed, the phospho-tyrosine enrichment prior to mass spectrometry as well as the experimental cell models used in those studies are different (see the Materials and methods section). We analyzed the two datasets separately to evaluate whether they point to distinct or similar cell signaling pathways.
Identification of overrepresented pathways We searched for enriched pathways in the lists of Syk targets, using pathways from the KEGG database  [37]. We selected pathways which contain at least one of the proteins from the target lists and used a Fisher exact test to assess their enrichment. As we are only interested in overrepresented pathways, we removed the underrepresented ones: i. e; the ones for which the ratio "number of proteins from the target list per total number of proteins" is lower than the same ratio in the background list. Within the two rather poorly overlapping datasets, we found among the most enriched KEGG pathways those related to cell-cell and cell-substrate adhesions, actin cytoskeleton regulation and apoptosis (S3 and S4 Tables). This observation is consistent with the reported role of Syk on cell adhesion, motility, proliferation and death in breast cancer cells  [19, [38] [39] [40] [41] [42] [43].
Network reconstruction We assembled and explored a prior-knowledge interaction network to extract candidate mechanisms underlying the datasets. While such networks are often assembled using complete pathway or interaction databases, here we focused on previously identified enriched pathways. Using pathways rather than individual interactions will enable the extension of sub-networks with relevant interactions in their neighborhood for further analysis. By restricting our full network to a subset of pathways, some coverage may be lost but it allows to reduce the amount of irrelevant interactions and to better assess the relevance of the identified pathways. Hereafter we show that the enriched pathway can be used to identify candidate mechanisms. We also extended our search by using the Pathway Commons database  [44].