dataesr/scientific-paragraphs-categorization huggingface dataset:
6e ). Indeed, both SMURF2 and SMURF2(FF) repressed SMAD2 phosphorylation, but not the SMURF2(EE) mutant (Fig. 6f ). Next, we evaluated the consequence of SMURF2 phosphorylation on MAPK activation. As expected, expression of SMURF2 or SMURF(FF) decreased β-TrCP levels (Fig. 6g )  49. Furthermore, and in complete contrast to TβR signalling, both SMURF2 and SMURF2(FF) enhanced MAPK activation whereas SMURF2(EE) abrogated downstream MAPK activation (Fig. 6h ). 9] [20]. To further investigate the potential role of the HGF/c-Met axis and EMT in bladder cancer we probed the TCGA Bladder Cancer data set (BLCA n = 408) against our established EMT signature  50. A high EMT score indicates a sample, portends toward a more mesenchymal-like phenotype, and vice versa. Importantly, HGF, c-Met and p-c-Met expression all correlated with the EMT signature (Fig.
Following immunoprecipitation of MYC-SMURF2 lysates were resolved by SDS-PAGE and probed with indicated antibodies. g 293 T cells transfected with wild-type MYC-SMURF2 or indicated SMURF2 mutants, HA-tagged ubiquitin and/or Flag-tagged c-SRC. Following immunoprecipitation of MYC-SMURF2 lysates were resolved by SDS-PAGE and probed with the indicated antibodies ARTICLE NATURE COMMUNICATIONS | https://doi. org/10. 1038/s41467-019-12241-2 (four out of eight) while no lung metastasis was detected in TβR inhibitor-treated mice (Supplementary Fig. 14g-j ). Importantly, however, the remaining tumour maintained a high tumour proliferation capacity. Taken together, these data indicate that TβR inhibition induces a change in cell morphology limiting the invasion capacity, but not the proliferation, of a subpopulation of bladder carcinoma cells.
Cells were either treated with or without 5 ng/mL of HGF (Calbiochem) for 2 hours before harvesting. Cells were harvested by incubating cells with dissociation buffer (Gibco) for 10 min and centrifuged at 300 × g for 5 min. Cell pellets were snap frozen with liquid nitrogen and sent to Fullmoon Biosystems (Sunnyvale, CA, USA) for processing, hybridisation and phospho-array detection of the TGF-β Signalling Phospho-Specific Antibody Array (Fullmoon Biosystems, Sunnyvale, CA, USA). Fluorescence intensity was scanned with an Axon GenePix 4400 A microarray scanner (Axon, Sunnyvale, CA, USA) at 760 nm wavelength. Each slide was analysed by the software GenePix Pro 7126 and phosphorylation ratio was calculated from the raw data with phospho value/ unphospho-value (provided by Fullmoon Biosystems).
Microarray data analysis. HGF-treated NBT-II bladder carcinoma cells at 0, 2, 4, 6, 9, 24, 48 h were subjected to microarray gene expression profiling on Affymetrix Rat Gene 1. 0 ST platform. The data had been deposited in Gene Expression Omnibus (GEO) with the accession GSE104740. The preprocessing of microarray was performed using Partek software version 5. 0 (Partek Incorporated). Probeset intensities were normalised using RMA algorithm. Log2 probe intensities of below 6 were deemed low expression and were filtered off from the analysis. Significant differentially expressed genes were chosen with criteria of FDR 0. 01 and a minimum fold change of 1. 2. Mass spectrometry analysis. Samples were treated by in-gel digestion prior to MS analysis. In brief, samples were reduced in 10 mM dithiothreitol (DTT) for 1 h at 56 °C followed by alkylation with 55 mM iodoacetamide (Sigma) for 45 min in the dark.
The raw files were processed with MaxQuant version 1. 5. 2. 8 with preset standard settings for SILAC labelled samples and the requantify option was activated  61. Carbamidomethylation was set as fixed modification while methionine oxidation, protein N-acetylation and phosphorylation (STY) were considered as variable modifications. Search results were filtered with a false discovery rate of 0. 01. Statistical analysis. Pathway enrichment analyses were performed using signature from Molecular signature database (Msigdb v5. 0) and ssGSEA projection method implemented in R 3. 3. 1 bioconductor package GSVA 1. 20. 0. P value is computed by two-tailed unpaired t test. Nuclear cytoplasmic fractionation assay. NBT-II cells were seeded at a density of 0. 5 × 10 6 cells per 10 cm cell culture-treated dish (NUNC) and grown for 4 days to form colonies.
Cells were pre-treated with 5 ng/ml of HGF (Calbiochem) alone or HGF with 8 µM A83-01 (Tocris Bioscience) before harvesting. Cells were trypsinized and the number of cells in each sample was quantified. Equal number of cells was pelleted for each sample. Fractionation was performed using Qproteome cell compartment kit (Qiagen), according to manufacturer's protocol. Site directed mutagenesis. These PCR reactions were performed using Quik-Change XL kit by Agilent Technologies (catalogue no. 200517-4) according to manufacturer's instructions. The presence of mutants was confirmed by Sanger sequencing. Modelling the HECT: C2 domain complex of SMURF2. The NMR structure of the C2 domain (PDB ID: 2JQZ) and the X-ray crystal structure of the HECT domain (PDB ID: 1ZVD) of SMURF2 protein were downloaded from the Protein Data Bank  29, 32.
A total of 10 structure clusters were generated after docking of the two domains. The representative structure from the top cluster with the best score as computed by the scoring scheme used in HADDOCK was selected as an initial model (Fig. S7C ). It has also been previously suggested by Wiesner et al. 32  that the C-lobe in HECT could rotate around the well-known flexible linker connecting the N-and C-lobes in order to facilitate a more favourable conformation of the two lobes for C2 interaction as compared with the state observed in the crystal structure of HECT (PDB ID: 1ZVD). So, as a further optimisation to our initial docked model, the C-lobe was rotated around the linker ( 628 GLGKID 632 ) by gradually changing their backbone dihedral angles in steps. This was done till the region from C-lobe, which has the residues predicted to be involved in interaction with C2 was in close proximity to the docked C2 domain (Fig.
S7D ). Subsequently, the complex structure was refined by energy minimisation to obtain the final representative docked HECT-C2 complex state structure of Smurf2 (Fig. S7E ). System setup for molecular dynamics simulations. The NMR structure of SMURF2 WW3 domain in complex with a SMAD7-derived peptide (PDB ID: 2LTZ) was downloaded from the Protein Data Bank  47. The lowest energy state model (Model 1) from the ensemble of structures was selected for simulation. The effective length for WW3 domain was 37 residues (297-333, residue numbering as per Uniprot ID: Q9HAU4) and for the SMAD7 peptide was 15 residues (203-217, residue numbering as per Uniprot ID: O15105). The refined HECT-C2 complex state obtained from docking (Fig. S7E ) was used as a starting state for simulation of this system. The phosphorylated state of Y314 in WW3-SMAD7 complex and Y434 in HECT-C2 complex was respectively modelled in these structures which collectively resulted in a total of four systems for simulation.
The N-and Cterminal of the separate chains in all the complexes was capped with ACE and NME functional groups. The complexes were then placed in a cuboid box whose dimensions were set by ensuring a minimum distance of 8 Å between any solute atom and the edge of the box. TIP3P water model was used for solvation, and all the systems were neutralised by adding an appropriate number of counter ions using the TLEAP module of AMBER14  64, 65. Molecular dynamics simulations. MD simulations were performed in AMBER14 using PMEMD module and employing the all-atom ff99SB force field 66. The amber library files for Phosphotyrosine were downloaded from http://research. bmh. manchester. ac. uk/bryce/amber wherein the force field parameters for the modified amino acid were derived from the work by Craft and Legge 67.
3 software, using RT 2 SYBR Green/ROX qPCR master mix. Data analysis was done by the 2 -ΔΔCt method on the manufacturer's Web portal. http:// pcrdataanalysis. sabiosciences. com/pcr/arrayanalysis. php. Quantitative real-time PCR. Cells were collected, washed twice in PBS and RNA was isolated using GeneJet RNA extraction kit (Thermo Scientific) qRT was performed using Taqman probes (USP26) from Applied Biosystems according to manufacturer's recommendations. Reactions were carried out on a ABI 7900 or 7500 FAST sequence detector (Perkin Elmer). Relative mRNA values are calculated by the ΔΔCt method. GAPDH or 18 S were used as internal normalisation controls where specified. The following QRT primers were used: PAI-1-5´-AAGG-CACCTCTGAGAACTTCA-3´, 5´-CCCAGGACTAGGCAGGTG-3´. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We appreciate the assistance in technical support and facility usage from the  Histopathology Facility/Advanced Molecular Pathology Lab  (AMPL), IMCB; and  Dr. R. A. Jackson  for editorial assistance. We thank  Dr. Adelene Sim  for her assistance during C2-HECT model building. This work was funded by research grants from  Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR ) core funding and  Joint Council Office Development Programme 1234e00018 , the  National Research Foundation Singapore  and the  Singapore Ministry of Education  under its  Research Centers of Excellence initiative  and the  Ministry of Education Academic Research Fund Tier  1 grants ( T1-2013 Sep-10 ) and ( T1-2014 Oct-08 ). We thank  A*STAR-BMRC  for computing support.
Data availability The molecular dynamics simulation trajectories and the coordinates files of the molecular models reported in the paper are available from the Figshare repository (https://figshare. com/articles/_/9044549). The NBT-II transcriptomic data are available from the GEO repository with the accession GSE104740. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [1]  partner repository with the dataset identifier PXD014736. All other source data are available from the corresponding authors on reasonable request and/or are included with the manuscript (as figure source data or Supplementary Information).
Genetic mapping and genomic prediction Significant GWAS results that were replicated in both years are highlighted here in the main text, with figures in the main text showing the results from 2014 because its sample size was larger. All remaining Manhattan plots are found in the supplementary material (Figs. S8  and S9 ). The position, effect size and list of genes within 100 kb of each significant SNP for both the 2014 and 2016 datasets are found in the supplementary material (Tables  S3  and S4 ). We found a single, strong association signal on chromosome 16 for the concentrations of catechin, epicatechin, and procyanidins B1, B2 and C1 (Fig. 3 ). In each case, we found no evidence of allelic heterogeneity, which means the GWAS signal we detected is likely driven by a single variant at this locus.
Apple germplasm Apple cultivars used for this study originated from the Nova Scotia Fruit Growers' Association (NSFGA) Cultivar Evaluation Trial (CET) based at Agriculture and Agri-Food Canada's (AAFC) Kentville Research and Development Centre in Nova Scotia, Canada as described previously  44. Phenotype data were collected from fully mature trees from the CET in 2014, and we refer to this data set as the "2014 data". Fruit skin color was scored by eye as the percentage of red blush covering the fruit surface, and flesh browning was assessed on fruit cut longitudinally and exposed to air for 40 min, followed by scoring on a scale from 1 (no browning) to 6 (severe flesh browning). Scab resistance/susceptibility was scored as a binary trait according to information obtained from breeders upon introduction of cultivars into the orchard.
Following measurements taken at harvest, the remaining fruit were kept at 0. 8-1 °C in stacked, perforated plastic bins covered in plastic sheets. After 1 month of storage, fruit were removed for phenolic analyses. The distributions of phenolic phenotypes were visualized using the "geom_boxplot" function in the ggplot2 R package  54. Next, these phenotypes were correlated with fruit skin color using Pearson's product-moment correlation test, flesh browning using Spearman's rank correlation test, and scab resistance presence using the Wilcoxon rank sum test. P values are reported after Bonferonni correction for multiple comparisons for all of these comparisons. All statistical analyses were performed in R  55. Of the 136 cultivars evaluated in 2014, 85 were evaluated again in 2016. The 2016 phenotype data were collected from 85 cultivars from the CET that were grafted onto M.
9 rootstock in the spring of 2012 and planted in an adjacent orchard in the spring of 2013. We refer to this dataset as the "2016 data". Cultivars were planted in two different randomized blocks with a single tree in each block. The orchard was maintained to industry standards and no supplemental irrigation applied. Trees were hand thinned in mid-July to adjust crop load to commercial standards of one fruit per cluster, with 10 to 15 cm between each fruit. Fruit were harvested based on maturity assessment using a starch-iodine or starch-tosugar conversion test, seed color, background fruit skin color, and presence of fruit drop. Following harvest, fruit were kept at 0. 8-1 °C in stacked, perforated plastic bins covered in plastic sheets to retain moisture. After 1 month of storage, fruit were removed for phenolic analyses.
8% trifluoroacetic acid in water (solvent A) and 0. 68 % trifluoroacetic acid in acetonitrile (solvent B) with a solvent elution gradient as follows: 0 min: 2% B, 2 min: 2% B, 22 min: 6% B, 30 min: 12% B, 60 min: 35% B, 62 min: 100% B, 64 min: 100% B, 65 min: 2% B, re-equilibrating 10 min before next injection. Injection volume was 30 µL, and detection was 200-600 nm on PDA, extracting chromatograms at 280 nm (total phenolics), 320 nm (total hydroxycinnamates (HCA)), 360 nm (total flavonols) and 520 nm (total anthocyanins). We also quantified total fluorescence with 228 nm as excitation and 324 nm as emission, which individually detects catechin, epicatechin, procyanidin B1, procyanidin B2 and procyanidin C1. For phloretin-like compounds, we detected and extracted from the UV profiles at 280 nm for the 2014 data because numerous cultivars showed extra phloretin-like peaks not fully captured by the specific compounds quantified using standards listed below.
Genotype Calling Genotype data for the CET were generated via genotyping-by-sequencing (GBS)  57  as described previously  44  except that reads were aligned to the most recent reference genome version GDDH13 Whole Genome v1. 1  58. Raw VCF files were filtered using VCFtools  59  to include only bi-allelic SNPs with a minor allele frequency (MAF) >1%. The two VCF files were merged using a custom perl script that preferentially kept SNPs generated from the PstI-EcoT22I file because these SNPs tended to have higher coverage. SNPs were imputed using LinkImputeR  60  , allowing for PositionMiss(0. 7), Sample-Miss(0. 7), and Depth (6), which resulted in 154,153 SNPs with an imputation accuracy of 0. 956. Finally, two final genotype tables were produced, one for the samples phenotyped in 2014 (i.
The significance threshold for MLMM was determined separately for the 2014 and 2016 data, using the "simpleM" package in R  63  , which calculates the effective number of independent tests (M eff ). The significance threshold was drawn as -log 10 (α/M eff ) where α was set to 0. 05 (dashed line in GWAS plots). If a SNP crossed the M eff threshold and was deemed significantly associated with a phenotype, a 100 kbp window centered on that SNP was explored for candidate genes using the Genome Database Rosaceae (GDR)  64. Keyword searches for genes on chromosomes with significant GWAS results were also completed using GDDH13 Whole Genome v1. 1 (https://www. rosaceae. org/search/genes). Genomic prediction was performed using the "x. val" function in the R package PopVar  65. The rrBLUP model was selected and 5-fold (nFold = 5) cross-validation was repeated 3 times (nFold.
Acknowledgements This research was supported in part by funding from the  Canada Research Chairs program  (SM), the  National Sciences and Engineering Research Council of Canada (SM) , and  A-Base  funding ( NOI-1767 ) from  Agriculture and Agri-Food Canada  (JS). ZM was supported by  NSF   1546869. The authors gratefully acknowledge the  Nova Scotia Fruit Growers' Association  and the  Farm Services team at AAFC-Kentville  for establishing and maintaining the orchards studied here, and the  Ministry of Education  and  AAFC  for the PhD fellowship provided to  YiHui Gong. We thank  Drs. John Delong  and  Peter Toivonen  for critical review of this manuscript.
| Statistical analyses To test the representativeness of crown assessment we first calculated Pearson's correlations between T Def and each insect guild damage estimated with the leaf sample collected in the same focal oak trees. For each response variable (total crown defoliation T Def , guild diversity using a Shannon index and guild-specific damage or abundance), we first built a beyond optimal linear mixed effect model including Site (Aurignac vs. Lamothe), Tree diversity (Pure vs. Mixed stands), Location (forest interior vs. forest edge) as fixed effects as well as every two-ways interactions. We declared the forest patch (n = 16) as a random factor to account for variance arising from nonindependent plots within the same patch. Data on leaf miners, leaf gallers, leaf tiers, leaf rollers and sap feeders were recorded as count data.
For these response variables, we used generalized mixed effect models with a Poisson error family and log-link. In a second model, we replaced the categorical factor plot diversity (pure vs. mixed) by actual tree species richness as continuous variable (ranging from 1 to 7 tree species). Models were built using lmer function in lme4 package  (Bates, Mächler, Bolker, & Walker, 2015)  in R version 3. 4. 4 (2018-03-15). For each response variable, we applied model selection based on information theory. We ranked the 18 resulting models according to their Akaike's Information Criterion corrected for small (1) T Def = P ACL × P DefL + 1 -P ACL × P DefS AICc. According to our sample size, models with ΔAICc < 2 can be interpreted as competing models with no evidence for one being better than the other(s)  (Burnham & Anderson, 2002).
We also calculated model R 2 to estimate model fit, and AICc weight. We calculated variable importance as the sum of AICc weights of every models containing this variable as a predictor. Variable importance corresponds to the probability that a given variable is included in the best model  (Burnham & Anderson, 2002; Symonds & Moussalli, 2011). However, it does not represent the probability that an explanatory variable is a good predictor of the response variable. We therefore estimated model parameter coefficients and their 95% CI using model averaging. Model comparison was done using the dredge and model. avg functions in the MuMIn package in R  (Bartoń, 2018).
research leading to these results received funding from the European Union Seventh Framework Programme(FP7/2007(FP7/ -2013)   )    under grant agreement n°265171 for the FunDivEurope project. The PhD of VG was funded by INRA and the Aquitaine Region. We would like to thank the technicians from INRA Toulouse for field assistance and the climbers for collecting oak leaves (Guillaume Gauthier from LiberTree association and the team from INRA Avignon). We also thank Sylvie Ladet for maps and Michel Goulard for help with sta-, MD and AV devised the conceptual idea of the study and designed the experimental sampling; VG, LB collected field data; WH designed the database; VG and BC conducted the statistical analyses; AV and VG led the writing of the manuscript; All coauthors made a significant contribution to the final manuscript.
Being properly annotated and publicly accessible, the model becomes reproducible and subject to scrutiny that can enhance its quality and assist in verifying its validity. This will also allow for the model's timely revision and update when new information becomes available to both researchers and end-users  (Barnes, 2010; LeVeque, 2013). There are multiple online platforms providing storage and facilitating version control for model code, including the popular repositories GitHub (https://github. com/) and Code Share (https://codeshare. io/). Sharing of code and programs should be a goal whenever possible.
ACKNOWLEDGMENTS The ideas presented here have been developed over the course of several years thanks to frequent discussions, misunderstandings, and disagreements with colleagues and engagement with interested conservation managers. Particular thanks go to  Andrew Woolnough  ( Victoria, Australia ),  John Virtue  ( South Australia, Australia ), Alberto Puente (Spain), and  César Ayres (Spain). A. Brandt ,  M. Barron , and  A. Gormley  (M. Whenua -L. Research) reviewed a previous version of the manuscript and provided feedback that improved it. Thanks to three anonymous reviewers and the editors, whose constructive feedback helped improve this manuscript. M. L. is supported by the  French ANR  through  LabEx TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-002-02 ), by the  French Midi-Pyrénées Region  (Project  CNRS   121090 ), and by the  FRAGCLIM Consolidator Grant , funded by the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement number  726176 ).
Funding information European Union's  Horizon 2020 , Grant/Award Number:  726176 ;  French ANR ,  Grant/Award Numbers :  ANR-10-LABX-41 ,  ANR-11-IDEX-002-02 ;  French ꁆMidi-Pyrénées Region , Grant/ Award Number: CNRS 121090; ꁆEuropean  Research Council , Grant/Award Number: 726176 conservation management and decision-making. required to clarify the scope of modeling applications and to facilitate the impact and acceptance of models by practitioners.
Experimental evaluation of susceptibility to Diplodia sapinea For D. sapinea inoculations, we used the pathogen strain Pier4, isolated from P. nigra cones in Pierroton, France (May 2017) and maintained on malt-agar medium. The identity of this strain as D. sapinea, was confirmed by its ITS sequence (ITS1-F and ITS4 see  Gardes and Bruns 1993) , and blasting against the NCBI nucleotide data base (Benoît Laurent, personal communication). We sampled a total of 453 branches, from 151 genotypes (i. e. one branch from each of three replicate trees per genotype) in the CLONAPIN common garden representing all differentiated maritime pine gene pools. On several days between June 12 th and July 31 st 2018, one lateral branch per tree was cut from 30 randomly selected trees and taken to the laboratory for inoculation.
Climatic Data Summary climate data for the years 1950-2000 were retrieved for 32 variables from Worldclim  (Hijmans et al. , 2005 ) and a regional climatic model  (Gonzalo, 2007)  for the 11 non-Spanish and the 22 Spanish populations, respectively. Climate variables included monthly mean, highest, and lowest temperatures and mean monthly precipitation. Gonzalo's (2007) model was favored for climate data in Spain because it considers a much denser network of meteorological stations than Worldclim, which is known to underperform in this region (see  Jaramillo-Correa et al. , 2015).