dataesr/scientific-paragraphs-categorization huggingface dataset:
Expression quantitative trait loci (eQTL) analysis We mapped expression quantitative trait loci (eQTLs) using the MatrixEQTL R package  [70] , leveraging our genotyping and expression data  [48]. As for the meQTL mapping, we filtered out SNPs with a minor allele frequency < 5% in the populations studied and kept a final dataset of 10,278,745 SNPs. Age and PC1/PC2 of the genotype matrix were used as covariates in the linear model. Two different models were used: merging all individuals and including ancestry as a covariate, or keeping the two populations separately. We also mapped response quantitative trait loci (reQTLs), using the fold change of expression described above, instead of expression, and the same covariates that we used for the eQTL mapping. For both eQTLs and reQTLs, FDR was computed by mapping eQTLs/reQTLs on 100 datasets with the expression values permuted within each population.
Simulations to infer causality We simulated different scenarios to infer causal relationships between DNA methylation and gene expression. For each scenario, we started by randomly selecting genomic blocks of 1 Mb each along the genome to keep realistic expectations of genetic structure. We next randomly sampled SNPs in these blocks, which we used to simulate methylation and gene expression data. For example, in a scenario where a genetic variant influences DNA methylation variation that, in turn, actively regulates gene expression (see Fig. 4a ), we followed the next steps: (i) G i std ¼ G i -G i À Á sd G i ð Þ (ii) M i ¼ ffiffiffiffi α i p Â G i std þ ffiffiffiffiffiffiffiffiffiffiffiffiffi 1-α i ð Þ p Â ε i (iii) M i std ¼ M i -M i À Á sd M i ð Þ (iv) E i ¼ ffiffiffiffiffiffiffiffiffiffiffiffi ffi γ Â β i p Â M i std þ ffiffiffiffiffiffiffiffiffiffiffiffi γ Â τ i p Â G i std þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 1-γ Â β i þ τ i À Á À Á q Â ζ i where G i is the genotype of the ith sampled variant and G i_std the standardized value of its genotype; M i is the simulated methylation data and M i_std its standardized methylation value; E i is the simulated gene expression data; α i is the proportion of variance of M i that is explained by G i , and γ is a noise parameter that corresponds to the total proportion of variance of E i that is explained by G i and M i.
Mediation analyses For conducting causal mediation analyses, we used a Bayesian approach as implemented in the mediation R package  [90]. Briefly, this approach estimates causal effects of a mediating variable M (DNA methylation) on the relationship between an independent variable X (genetics) and a dependent variable Y (gene expression). In this scenario, the global effect of X on Y can be written as ρ X → Y = τ + α • β, where τ is the specific effect of X on Y, α the specific effect of X on M, and β the specific effect of M on Y. With this, the product α•β represents the mediation effect of G on Y, through M. The mediate function of the mediation R package was used to compute point estimates for average causal mediation effects, as well as 1000 simulation draws of average causal mediation effects.
The empirical distribution of simulated effects was used to fit a normal distribution, which was subsequently used to compute empirical P values for the H 0 hypothesis "α•β = 0. " We used the R function p. adjust with method "fdr" to correct at a FDR = 5%. For comparison purposes with the mediation analyses, we conducted on simulated data a partial correlation approach to test for independence between expression and methylation levels when accounting for genetic variability. We used the pcor. test function from the R package ppcor  [107]  to compute P values of the partial correlation between simulated expression and methylation data.
Availability of data and materials The DNA methylation data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO) under accession code GSE120610  [108]. Genome-wide SNP genotyping, whole exome sequencing, and RNAsequencing data used in this study are available at the European Genome-Phenome Archive (EGA) under accession code EGAS00001001895  [109].
Notes 1. http://presse. inserm. fr/therapie-cellulaire-de-linsuffisance-cardiaque-premiere-implantation-decellules-cardiaques-derivees-de-cellules-souches-embryonnaires-humaines/17502/ 2. In this article, the term "biobank" is used with a wide definition that is not restricted to a definition stemming from national and international regulations. First of all, this text builds on the context of the early 2000's when such regulations were still in the making. In 2005, in the huES world, the only institutionalized biobank was the UK stem cell bank from Glyn Stacey. But, huES lines were nonetheless derived, stored and circulated across countries. In this text, we use the definition of  Milanovic, Pontille, and Cambon-Thomsen (2007, 3). When referring to biological samples they write that there is no consensus on terminology in regard to these resources, which are designated by diverse terms: collections, biological resource centres, biological databases, biorepositories, biobanks, to name only a few.
(B) Histogram representing phosphokinase array results from TG1-C1 GSC treated with 10 µM of DDPM (in 1% DMSO; green bars) or DMSO alone (grey bars). Results are shown as the mean pixel intensity of the signal from triplicates within the same experiment (± s. d. ), analyzed with the ImageJ software. The phosphorylation of WNK1-T60 was significantly decreased by DDPM (black arrow). Black rectangle illustrates modifications in the phosphorylation status of Akt S473 and T308 required for Akt activation. Activated Akt was shown to phosphorylate T60 in WNK1. (C) WNK1 and phospho (p)-T60 WNK1 levels were determined by Western blotting and appropriate antibodies in extracts from proliferating (P) and quiescent (Q) TG1 and TG1-C1 GSC. GAPDH expression was used as a loading control. Blots are representative out of at least two independent experiments.
These data indicate that DDPM and S0859 are probably not competing for the same binding sites on target proteins. To distinguish between NBCe1 and NBCn1 contributions to the protective effect of S0859 on quiescent GSC, we used DIDS (4,4'-Diisothiocyanatostilbene-2,2'disulfonate), a non-selective inhibitor of anion transport stilbene derivative that blocks NBCe1 and all other Na +coupled HCO3 -cotransporters, but not NBCn1. Blockade of NBCn1 is poor even at concentrations of 500 µM of DIDS  [34]. The effect of DIDS was assessed on quiescent GSC treated with DDPM. As shown in Supplementary Figure  9 , TG1 GSC were highly sensitive to DIDS even in the absence of DDPM. TG1-C1 cells were less sensitive to the compound since higher concentrations of DIDS were required to induce cell death (compare graphs in left and right panels in Supplementary Figure  9 ).
This differential sensitivity is in agreement with the higher expression levels of, at least, NBCe1 in these cells compared to TG1 GSC (Figure  6A  and 6B ). However, the effects of DDPM were not even partially reversed in the presence of DIDS (Supplementary Figure  9  left and right panels). Thus, either NBCn1 is not involved in DDPM cytotoxicity on GSC or the cytotoxicity of DIDS alone, namely on quiescent TG1 GSC, masks any protective effect. The protective effect of S0859 was further assessed on TG1 GSC-derived macro-spheres. Pretreatment of such neurospheres with S0859 (100 µM) was again able to partially preserve TG1 GSC from DDPM cytotoxicity (Figure  6H , left and right panels). Altogether these data suggest that Na + /HCO3 -cotransporter cell surface expression and activity deregulation is mediating DDPM cytotoxicity on quiescent glioblastoma GSC.
Data are from two independent cultures (± SEM). Numbers of cells are indicated above the plots. Ordinary one-way ANOVA and Tukey's multiple comparisons test. * p < 0. 05, **** p < 0. 0001. (D) Quiescent TG1 and TG1-C1 cells were treated with DDPM (10 µM, 2 hours in 0. 1% DMSO used as vehicle). Control cells were incubated only in the presence of 0. 1% DMSO. At the end of treatment, HCO3 -transport activity was measured as described in (C). Data are from two independent cultures (± SEM). Numbers of cells are indicated above the plots. Ordinary one-way ANOVA and Tukey's multiple comparisons test. **** p < 0. 0001. (E) Surface membrane levels of NBCe1 and NBCn1 were determined by Western blotting in quiescent (Q) TG1 and TG1-C1 GSC treated for 2 hours with DDPM (10 µM in 0. 1% DMSO used as vehicle) or in DMSO alone (0.
It was thus conceivable, that DDPM, by reducing p-T60 levels in the N-terminus of WNK1, relieves the regulatory effect of WNK1 on NBCe1 and/or other related cotransporter-cell surface expression level and function, and that the excessive activation of these proteins leads to necrotic death. Data presented in this study confirm this hypothesis since differences in HCO3 -transport activity were measured between proliferating and quiescent GSC with lower activity levels present in quiescent cells. Moreover, DDPM treatment of quiescent cells resulted in a significant increase in NBC cotransporter function associated to an increase in cell surface levels of NBCe1 and, to a lesser extent, NBCn1. A combined treatment of DDPM with S0859, an inhibitor of NBC Na + /HCO3 -cotransporters including NBCe1 and NBCn1, significantly reduced the cytotoxic effect of the compound.
Construction of plasmids for WT and mutant WNK1 overexpression Full length synthetic gene for human WNK1 (GenBank accession No. NM_018979. 3) assembled from oligonucleotides and/or PCR products and cloned into a pMA plasmid was purchased from GeneArt. WNK1 coding sequence was PCR amplified with primers including attB1 and attB2 sites for Gateway cloning (Thermo Fisher Scientific). AttB1-WNK1-attB2 amplified fragments were gel-purified (Macherey-Nagel NucleoSpin Gel and PCR clean-up kit). The WT WNK1 coding sequence was then introduced via a BP recombination reaction into the pDONR 207 vector. Point mutations to obtain T60A, T60/D, E and K233M mutants were generated in WNK1 coding sequences in the pDONR 207 vector with the QuickChange II XL Site-Directed Mutagenesis kit following the instructions of the manufacturer (Agilent Technologies).
Following a washing step with washing buffer provided with the kit, membranes were incubated with provided secondary antibodies at room temperature for 2 hours and then in the presence of a solution containing streptavidin coupled to horseradish peroxidase (HRP) at room temperature for 30 min. Luminescent signals obtained after incubation with revealing solution, were detected with the Image Quant LAS 4000 imaging system (GE Healthcare Life Science). Spots were quantified with the ImageJ software.
Cleared suspensions were quantified with the DC Protein Assay from Bio-Rad. Equivalent amounts of proteins (25-50 µg) were subsequently analyzed in 4-15% gradient Mini-PROTEAN TGX pre-cast gels from Bio-Rad and immunoblotting with appropriate antibodies listed in Supplementary Table  2. Specific antibody binding was detected by horseradish peroxidase-conjugated antirabbit or anti-mouse secondary antibodies. Proteins were visualized with the ECL Prime reagent (GE Healthcare Life Sciences) and the Image Quant LAS 4000 imaging system (GE Healthcare Life Science). Quantification of protein bands corrected to loading controls was done with the ImageJ software. For extraction of total membrane proteins, cells were collected and lysed in a lysis buffer prepared with 10 mM HEPES pH 7. 4 (Thermo Fisher Scientific) and 2 mM EGTA (Sigma), completed with 1x protease inhibitor cocktail (Roche).
ACKNOWLEDGMENTS We thank  R. Vauchelles  for training and providing access to the confocal imaging platform "Plateforme www. oncotarget. comd 'Imagerie Quantitative (PIQ)" (Faculty of Pharmacy, Strasbourg-Illkirch, France)  and  N. Vitale  ( INCI, CNRS UPR3212, Strasbourg, France ) for providing vectors for shRNA expression. We would also like to acknowledge  C. Ebel  for technical assistance on the flow cytometry platform of the  IGBMC (Strasbourg-Illkirch, France ) and  Chantal Fitterer  ( INCI, CNRS UPR3212, Strasbourg, France ) for technical support with intracellular pH imaging experiments. Finally, we warmly thank  N. Grant  for critical reading of the manuscript.
FUNDING This work was supported by multiple grants from  La Ligue Contre le Cancer  (project (MZ) and PhD funding for WC) and has been performed within the LABEX  ANR-10-LABX-0034_Medalis. Additional funding was from  Agence Nationale de la Recherche (ANR) (JH) ,  Université de Strasbourg  and  Centre National de la Recherche Scientifique (CNRS).
Recent work has suggested that the performance of prediction models for complex traits may depend on the architecture of the target traits. Here we compared several prediction models with respect to their ability of predicting phenotypes under various statistical architectures of gene action: (1) purely additive, (2) additive and dominance, (3) additive, dominance, and two-locus epistasis, and (4) purely epistatic settings. Simulation and a real chicken dataset were used. Fourteen prediction models were compared: BayesA, BayesB, BayesC, Bayesian LASSO, Bayesian ridge regression, elastic net, genomic best linear unbiased prediction, a Gaussian process, LASSO, random forests, reproducing kernel Hilbert spaces regression, ridge regression (best linear unbiased prediction), relevance vector machines, and support vector machines.
When the trait was under additive gene action, the parametric prediction models outperformed non-parametric ones. Conversely, when the trait was under epistatic gene action, the non-parametric prediction models provided more accurate predictions. Thus, prediction models must be selected according to the most probably underlying architecture of traits. In the chicken dataset examined, most models had similar prediction performance. Our results corroborate the view that there is no universally best prediction models, and that the development of robust prediction models is an important research objective. The effectiveness of genomic prediction depends on the accuracy of estimation of the genetic value of individuals with yet-to-be observed phenotypes 1. Various factors affect the accuracy of estimated genomic breeding values (GEBVs) and, hence the expected response to genomic selection.
Unfortunately, the genetic architecture of most complex traits remains unknown for animal breeders and evolutionary geneticists, so a search for robust and stable prediction models is important. The objective of this study was to compare predictive accuracy of several parametric and non-parametric genomic prediction models for quantitative traits simulated under various forms of gene actions (additive, additive-dominance, additive-dominance-epistasis and pure epistasis). Predictive accuracy of the all models was also assessed with a real chicken dataset.
Genetic variance components. In order to compute genetic variance components based on Cockerham  22  , we assumed that each pairs of QTLs were independent, and the additive and non-additive genetic variances were as in Table  4. Table  5  shows the partition of variance relative to the total variance explained by each source of genetic variation accounted for traits. Real Data. The data set consisted of records on 1,351 broiler chickens provided by Aviagen Ltd (Aviagen Ltd, Newbridge, UK) for three traits: body weight (BW), ultrasound of breast muscle at 35 days of age (BM), and hen-house egg production (HHP) defined as the total number of eggs laid between weeks 28 and 54 per bird. Phenotypic records for BW and BM were pre-corrected for a combined effect of sex (525 males and 826 females), hatch contemporary group of parents and pen in the growing farm, whereas phenotypic records for HHP.
Additive δ α = + - = pq a d q p pq 2 [ ( ) ] 2 a 2 2 2 Dominance δ = pqd [2 ] d 2 2 Additive × Additive δ αα = ∑p q 4 ( ) aa i k ik 2 2 Additive × Dominance δ αδ = ∑p q q 2 ( )  were pre-adjusted for hatch effects. All individuals were genotyped with a 600 K Affymetrix SNP chip (Affymetrix, Inc. , Santa Clara, CA, USA). More precisely, 580,954 SNP genotypes were available in the dataset. Markers with MAF < 1% were removed and missing genotypes for the remaining SNPs were imputed using the Beagle software  23. All SNPs were subsequently kept if they presented a genotype call rate >95% and were in Hardy-Weinberg equilibrium. Individuals were kept if their genotype call rate >95%. Deviation from Hardy-Weinberg equilibrium was assessed by the Pearson's chi-square test with a significance threshold of 10 -6.
After edits, 354,364 autosomal SNPs remained for the analysis. Mean MAF was equal to 0. 27. Only SNPs on 28 chromosomes were considered, covering 919 Mb of the Gallus gallus genome. The PLINK software  24  was used to edit the data. ad i k l ikl 2 2 Dominanc × Additive δ δα = ∑p p q 2 ( ) da i j k ikl 2 2 Dominanc × Dominanc δ δδ = ∑ ( ) p p q q dd i j k l ijkl 2 2 Genome-assisted prediction model. The performance of 14 different prediction models that differ with respect to assumptions regarding distribution of marker effects was evaluated. The parametric models included GBLUP  25, 26  , ridge regression BLUP (rrBLUP)  27, 28  , the least absolute shrinkage and selection operator (LASSO)  29, 30  , the elastic net (EN)  31  , Bayesian ridge regression (BRR)  5, 31, 32  , BL  33  , BayesA  27, 34  , BayesB  27, 34  , and BayesC  27, 34.
In addition, the following non-parametric models were evaluated: reproducing kernel Hilbert space regression (RKHS)  [35] [36] [37]  , SVM  38  , relevance vector machine (RVM)  39  and Gaussian Processes (GP)  39, 40  and random forest (RF). Although GBLUP and the GP use similar approaches, GP which is often used in machine learning, predict the value for an unseen point from training data and defined as a collection of random variables  40, 41. To implement the BayesA, BayesB, BayesC, BRR, BL, and RKHS, we used BGLR R package developed by Pérez and de los Campos  42  and the glmnet function from the glmnet R-package were used for LASSO and EN  43. The rvm, ksvm functions from the kernlab package  44  were used to predict genomic breeding values for RVM, SVM, and GP. In addition, we used the mixed.
solve function from rrBLUP package  28  to perform GBLUP and rrBLUP and the randomForest option from the e1071 package  45  for RF. To compare the performance of the different prediction models, we used 20 replicates of a five-fold cross-validation scheme as described in Pérez-Cabal et al. 46. The data were divided into training (80%) and testing (20%) sets. The training set was used to fit the models and the testing set to measure performance of the prediction models. The procedure was repeated 20 times at random, yielding 100 cross-validation runs. For each cross-validation scenario, three criteria were measured: (i) predictive accuracy defined as the correlation between phenotypic values and the predicted genomic values (r ) y GEBV , , (ii) the "empirical" accuracy defined as the correlation between true breeding values (TBV) and predicted genomic breeding values (r TBV GEBV , ) (because of unknown TBV, this criterion was not used in the chicken data set) and, (iii) a test for empirical prediction bias done by regressing phenotypes (simulated and real) on the GEBVs.
Chicken dataset. The results obtained with chicken data on predictive accuracy and bias indicated that GP and GBLUP consistently produced the least biased, most precise, and most stable estimates of predictive accuracy for HHP and BM (Fig. 5  and Table  6 ). For BW, BayesA and BayesB, and LASSO yielded the highest predictive accuracies, and LASSO was at least as good as or ever better than BayesA and BayesB in terms of unbiasedness. RKHS performed best the among non-parametric models. Other prediction models performed inconsistently across the traits and suffered varying degrees of over-or under-prediction and numerical instability. In general, all models tended more to over predict yet-to-be observed phenotypes than to under predict, whereas in the simulations, most models tended to under predict measured phenotypes.
Results A stereotypical PI4P and PI(4,5)P 2 pattern at the SAM To reveal the pattern of PI4P and PI(4,5)P 2 at the shoot apex, we generated transgenic lines with a reporter for PI4P (P4M domain from the Legionela protein SiDM: P4M SiDM ) and PI(4,5)P 2 (PH domain from Rat Phospholipase C, PLC: PH PLC ) fused to the yellow fluorescent protein mCitrine  [23, 24]. These biosensors were expressed under the control of the pPDF1 promoter, which restricts expression in the epidermis and is rather homogeneously active across the different SAM regions  [17]. To extract signal intensity in each cell, we used the MorphoGraphX software  [25]  and generated corresponding heatmaps (see Methods). Using this pipeline, we could also obtain heatmaps of Gaussian curvature, based on the membranederived meristem topography, allowing us to distinguish four zones in the shoot apex (Additional file 1: Figure  S1 ).
Image analysis The maps and quantifications of meristem curvature and signal intensity at cellular levels were obtained using the MorphographX (MGX) software (http://www. mpipz. mpg. de/MorphoGraphX/  [25] ) according to a previously described procedure  [27]. The curvature maps displayed in the figures correspond to Gaussian curvatures with a neighboring of 15 μm. The signal intensity maps were generated by extracting the fluorescent signal at the surface of the meristem (up to 2 μm from the meristem surface) and by projecting it on the cellular mesh. Quantification of signal intensity in the central zone, peripheral zone, boundary and organs were done by seeding the four domains in MGX and extracting the corresponding signal intensity. Extraction of meristem curvature and signal intensity along a transect was done using ImageJ (https://sites.
2a ; Fig. S1 ; Ref. 22). To explore the origin and evolution of inversion P 1 , we investigated the presence of this inversion in other species of the clade. PCR amplification of inversion breakpoints showed that inversion P 1 was polymorphic in H. numata (Hn) across its Amazonian range, and was also found fixed in all population of H. pardalinus (Hp), a non-sister species deeply divergent from H. numata within the silvaniform clade (Fig. 1b  and Table  S3 ). All other taxa including the sister species of H. numata and that of H. pardalinus were positive only for markers diagnostic of the ancestral gene order. Furthermore, a 4kb duplication associated with P 1 in Hn was also found in whole genome sequence datasets for all Hp individuals and no other taxon (Fig. 1b ; Table  S2 ; Fig. S10 ).
Acknowledgements The authors thank  Mathieu Chouteau ,  Violaine Llaurens ,  Marianne Elias ,  Stéphanie Gallusser ,  César Ramírez ,  Benigno Calderón ,  Moisés Abanto ,  Lisa de Silva ,  Armando Silva , for help during fieldwork,  Gerardo Lamas  for help with research permits in Peru,  Florence Piron-Prunier ,  Agnès Bulski ,  Guillaume Achaz , and  Mark Blaxter , for help with lab and analytical work. Analyses were conducted with the support of bioinformatic platforms Genotoul (Toulouse) and MBB (Montpellier). This research was conducted under SERFOR research permits from the  Peruvian Ministry of Agriculture , and was supported by  ANR  Grant  HYBEVOL  ( ANR-12-JSV7-0005 ) and  European Research Council Grant MimEvol  ( StG-243179 ) to MJ. The authors declare no competing interests. P.