dataesr/scientific-paragraphs-categorization huggingface dataset:
b Exclusivity (EPPO, 2019) was defined as the percentage of non-target microbial genomes or strains that gave the expected negative result. The number of non-targets was 194,450 genomes of the 2019-NCBI release minus the targets (Supplementary Table  2 ) and the 112 strains of our collection for in vitro test minus the targets as indicated (Table  1 ). c Unexpected perfect match of primers was found on genomic sequences of strain 1188-1 (SAMN03976479) belonging to clade 2b and of strain 77-4C (SAMN23013827) belonging to cluster 2ba-C2. d Unexpected positive result for strains P55 2ba-B and P56 2ba-B classified in cluster 2ba-B by MLSA (Figure  1 , Table  1 ). e Unexpected perfect match of primers was found on genomic sequences of three strains of P. coronafaciens, including the type strain LMG 5060 T (SAMN03976345, SAMN03976475, and SAMN03144492).
f Unexpected positive result for the type strain of P. coronafaciens, strain CFBP 2216 T (homolog of LMG 5060 T ). g Unexpected match with four mismatches was found on genomic sequence of strain G733 (SAMN03976503) belonging to clade 2a. h Unexpected perfect match of primers on genomic sequences of two strains of P. coronafaciens pv oryzae (PG4) (SAMN0397638, SAMN03976382). i Unexpected match with five mismatches was found on genomic sequence of an unclassified strain of Pseudomonas sp. (SAMN05948574). j Unexpected match with three mismatches was found on genomic sequences of two strains belonging to cluster 2a-F, strains 13-429 and 13-139B (SAMN06318302 and SAMN06317916). k 2b* is part of clade 2b without strains belonging to clade 2ba and pathovars pisi, avellanae and coryli (Figure  2 ) l Primers and probe did not match on genomic sequence of strain ICMP 4917 (SAMN03976350) that belongs to the target group (part of clade 2b as defined above).
pepo 2010 2a-D SAMN12288853 P78 France C. pepo 2010 2a-D SAMN12288860 P87 China C. pepo 2011 2a-D SAMN12289083 BS2121 California Cucurbita sp. 2006 2a-E SAMN06317958 ZUM3984 China Cucurbita sp. 2008 2a-E SAMN06318038 CFBP 8692 (P66) Thailand C. pepo 2010 2a-E SAMN12305548 P89 France C. pepo 2011 2a-E SAMN12289141 13-139B Florida Citrullus lanatus 2013 2a-F SAMN06317916 13-429 Florida C. lanatus 2013 2a-F SAMN06318302 ICMP 459 PT (pv. aptata) USA Beta vulgaris 1959 2b SAMN03976295 ICMP 3023 T (pv. syringae) UK Syringa vulgaris 1951 2b SAMN03976255 NCPPB 4273 PT (pv. coryli) Italia Corylus avellana 1995 2b SAMN03481961 P108 Chile C. pepo 2011 2b SAMN12288112 1188_1 California C. pepo nd 2b SAMN03976479 HS191 (pv. syringae) Australia Panicum miliaceum 1969 2ba SAMN03267749 CFBP 8693 (P99) France C.
pepo 2005 2ba-A SAMN12305539 P12832 France C. pepo 2006 2ba-A SAMN12284164 P12854 France C. pepo 2002 2ba-A SAMN12284309 P12855 Chile C. pepo 2007 2ba-A SAMN12284728 P113 Thailand C. pepo 2011 2ba-A SAMN12288386 P118 France C. pepo 2013 2ba-A SAMN12288431 P121 Chile C. pepo 2013 2ba-A SAMN12288432 P123 China C. pepo 2013 2ba-A SAMN12288472 P127 India C. pepo 2013 2ba-A SAMN12288592 P139 China C. pepo 2015 2ba-A SAMN12288625 P22 USA C. pepo 2009 2ba-A SAMN12288683 P5 France C. pepo 2008 2ba-A SAMN12288805 P79 France C. pepo 2010 2ba-A SAMN12289058 P84 China C. pepo 2010 2ba-A SAMN12289072 P12831 (co7A) France C. pepo 2005 2ba-A SAMN12289147 PS711 Serbia Cucurbita sp. 2013 2ba-A SAMN10486866 13-509A Florida Cucurbita sp. 2013 2ba-A SAMN06263769 KL004-k1 Australia C. pepo 2016 2ba-A SAMN23013829 13-C2 Florida C.
lanatus 2013 2ba-B SAMN06263768 13-140A Florida C. lanatus 2013 2ba-B SAMN06256456 14-410 Florida C. lanatus 2014 2ba-B SAMN06270325 14-32 Florida C. lanatus 2014 2ba-B SAMN06257475 14-Gil Florida C. lanatus 2014 2ba-B SAMN06317944 CC457 (P. syringae) France C. melo na 2ba-B SAMN02471568 P12857 France C. pepo 2007 2ba-B SAMN12288124 ZUM3584 Italy Cucurbita sp. 2005 2ba-C SAMN06318381 KFR003-1 Australia C. pepo 2016 2ba-C SAMN23013828 77-4C Australia C. pepo 2016 2ba-C SAMN23013827 ICMP 19117 PT (P. congelans) Germany Poaceae 1994 2c SAMN03976302 B728A (pv. syringae) USA Phaseolus vulgaris na 2d SAMN09235866 ICMP 16925 PT (pv. solidagae) Japon Solidago altissima na 2d SAMN03976256 ICMP 2802 PT (pv. aceris) USA Acer sp. na 2d SAMN03976301 1212 (pv. syringae) UK Pisum sativum na 2d SAMN02471561 HRI-W 7872 (pv.
S1 ; one-way ANOVA, F4,91=1. 28, p = 0. 29). To reduce bias between phased and unphased expression datasets, when the homoeolog expression of allotetraploids was compared with gene expression in diploid parental species, the expression level of each gene in diploid individuals was rescaled by the proportion of reads that can be phased for the same gene in allotetraploid individuals. The overall gene expression pattern of five plant groups in each tissue was visualized by MDS analysis, using the R package edgeR  (version 3. 28. 1; Robinson et al. , 2010)  in the R software environment version 3. 6. 3 (R Core Team, 2020). For both phased and unphased gene expression data, genes with count-per-million (CPM) over one in at least two samples were used for the MDS analysis, and expression levels were normalized with the trimmed mean of M-values (TMM) method.
Differential expression (DE) analysis DE analysis was conducted on both unphased and phased gene expression data with the R package edgeR  (version 3. 28. 1; Robinson et al. , 2010) , using TMM normalized gene expression levels. A negative binomial generalized linear model (GLM) was fitted to each dataset. Pairwise group contrasts were then made for each GLM model, and genewise quasi-likelihood F-tests were conducted to detect expression changes in each contrast. For unphased data, pairwise contrasts were made among the original five groups  (Co2, Cg2, Sd, Sh, and Cbp). For phased data, allopolyploid subgenomes were treated as separate groups (Sd_co, Sd_cg, Sh_co, Sh_cg, Cbp_co, and Cbp_cg) and were compared with the two diploid groups (Co2, Cg2). Genes with an expression foldchange (FC) larger than two and a false discovery rate (FDR) less than 0.
Loss of homoeolog expression The most extreme HEB occurs when one homoeolog is silenced or lost in allotetraploids. To explore the timing and mechanism of the loss of homoeolog expression in Capsella, the number of genes with homoeolog expression loss was counted for each resynthesized or natural allotetraploid. Lowly or occasionally expressed genes were excluded from the analysis to reduce noise from sequencing and phasing. Specifically, if one homoeologous gene had obvious expression (CPM > 5) in all six individuals of the corresponding diploid species, but had almost no expression in one allotetraploid individual (CPM < 0. 5), the case was considered a loss of homoeologous expression. The RNA-sequencing data of natural C. bursa-pastoris generated by this article are available in NCBI Sequence Read Archive (SRA), and can be accessed with BioProject number PRJNA848625.
Acknowledgements The study was supported by grant  2019-00806  from the  Swedish Research Council  to ML and  Nilsson- Ehle research grant from the  Royal Physiographic Society in Lund  to TD. ML also acknowledges support from the  Erik Philip-Sörensen Foundation. TD was supported by the  Sven and Lilly Lawski Foundation. Computation and data handling were provided by the  Swedish National Infrastructure for Computing (SNIC)  at Uppmax, partially funded by the  Swedish Research Council  through grant agreement no. 2018-05973. We thank  Barbara Mable  for her critical comments on the manuscript. Finally, we thank  Elsa Sundkvist  and  Mattias Vass  for their help with seed collecting and counting.
Polyploidy assessment To analyse polyploidy, liver sections were stained with Hoechst 33342, after which a dataset with nuclei size and circularity was extracted using QuPath. Then, by using R software, a dataset containing only hepatocyte nuclei was generated based on size (>30 µm) and circularity (>0. 8). Nuclear ploidy was assessed automatically, by a specific macro developed with Image J software, as previously described  (Gentric et al. , 2015). Hepatocyte polyploidy is presented as the ratio between the number of polyploid cells and total hepatocytes.
Liver sphingomyelin and ceramide analysis Lipids were extracted from the liver (1 mg) as described by Bligh and Dyer in dichloromethane/methanol (2% acetic acid)/water (2. 5:2. 5:2 v/v/v). Internal standards were added (Cer d18:1/15:0, 16 ng; SM d18:1/12:0, 16 ng). The solution was centrifuged at 1500 rpm for 3 min. The organic phase was collected and dried under azote, then dissolved in 50 μl MeOH. Sample solutions were analyzed with an Agilent 1290 UPLC system coupled to a G6460 triple quadripole spectrometer (Agilent Technologies). MassHunter software was used for data acquisition and analysis. A Kinetex HILIC column was used for LC separations. The column temperature was maintained at 40°C. The mobile phase A was Acetonitrile; and B was 10 mM ammonium formate in water at pH 3. 2. The gradient was as follows: from 10% to 30% B in 10 min; 100% B from 10 to 12 min; and then back to 10% B at 13 min for 1 min to reequilibrate prior to the next injection.
The flow rate of the mobile phase was 0. 3 ml/min, and the injection volume was 5 μl. An electrospray source was employed in positive ion mode. The collision gas was nitrogen. Needle voltage was set at +4000 V. Several scan modes were used. First, to obtain the naturally different masses of different species, we analyzed cell lipid extracts with a precursor ion scan at 184 m/z and 264 m/z for SM and Cer, respectively. The collision energy optimums for Cer, SM were 25 eV, 45 eV respectively. Then, the corresponding SRM transitions were used to quantify different phospholipid species for each class. Two 9 MRM acquisitions were necessary, due to important differences between phospholipid classes. Data were treated with QqQ Quantitative (vB. 05. 00) and Qualitative analysis software (vB. 04. 00).
Microarray based transcriptome profiling Liver RNA extraction was performed as mentioned previously and checked for integrity using Bioanalyzer. Microarray experiment and data normalization were performed by the transcriptomic core facility at Cochin Institute. Briefly, gene expression profiling was carried out on 5 biological replicates per condition. After RNA quality validation with Bioanalyzer 2100, RNA was reversed transcribed following the manufacturer protocol (GeneChip® WT Plus Reagent Kit, Thermofisher). After fragmentation and biotin labelling, cDNA was hybridized to GeneChip® Clariom S Mouse (Thermofischer) and scanned using the GCS3000 7G. The images were analyzed with Expression Console software (Thermofischer). Raw data were normalized using the Robust Multichip Algorithm (RMA).
Statistics and RMA normalization were performed using TAC4. 0 software (Thermofisher). Downstream analysis was done to identify differentially expressed (DE) genes based in comparison with control conditions. The DE genes were selected based on the corrected p-value for False Discovery Rate (FDR p-value < 0. 05) and the fold change from OGT LKO compared to controls (up-regulated genes: fold change ≥ 2, down-regulated genes: fold change ≤ -2). Venn diagram was performed using the interactive tool 'Venny 2. 1. 0' (Oliveros et al. 2007 (Oliveros et al. -2015)). Up-regulated and down-regulated genes were annotated using Reactome gene set. Functional enrichment of specific pathways in the gene set was performed using Fisher's exact test and FDR correction. Pathways were considered significantly enriched at an FDR < 0.
Cells were then collected and washed with PBS. For each experiment, 20. 000 events were acquired by flow cytometry using Novocyte Cytometer (Ozyme). Data were analysed using the Novoexpress Software. Total ROS levels were measured by using Cellrox Deep red Reagent (Thermofisher) at a final concentration of 2. 5 M and then incubated for 30 minutes at 37°C. Data were analysed using the Novoexpress Software. For ARE-luciferase activity, cells were transfected with a 3xARE-Luciferase reporter  (Wang et al. , 2006)  and a β-J o u r n a l P r e -p r o o f galactosidase plasmid using Lipofectamine 2000 and OptiMEM media. After overnight incubation, medium was changed to low glucose (5mM) for 24 hours. Luciferase assay was performed after cell lysis. β-galactosidase assays were performed for normalization of 3xAREluciferase activity.
Acknowledgements The authors would like to thank  Dr. Hélène Gilgenkrantz  and  Pr. Valérie Paradis  ( Centre de Recherche sur l'Inflammation (CRI), Université Paris Cité ) for helpful discussions. We also would like to thank the  Animal Facility  from the  Institut Cochin Inserm   U1016 , and in J o u r n a l P r e -p r o o f particular  Mathieu Benard  for taking excellent care of the mice. We also thank the  Institut Cochin platforms: the Genomic platform (GENOM'IC) , the  Cytometry and Immunobiology platform (CYBIO) , the  "Imagerie du vivant platform" (PIV)  ( Isabelle Lagoutte Franck Lager  and  Gilles Renault ) and the  Microscopy Platform  ( Thomas Guilbert ) for fibrosis quantification. Lipidomic profiling was done in  MetaToul-Lipidomique Core Facility  ( I2MC, Inserm 1297, Toulouse, France ) from  MetaToul  (Toulouse metabolomics & fluxomics facilities, www.
Financial statements P. O-P. was supported by the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie Grant Agreement No 675610  (Current address:  Drug Transport  and  Tumour Metabolism Lab ,  MRC London Institute of Medical Sciences, London, UK)  and L. P was supported by  AFEF  ( Association Française pour l'Etude du Foie ) and by  INSERM  (Poste d'accueil). C. P. acknowledges the support of grants from the  National Agency for Research (ANR)  ( ANR-20-CE14-0038   IMAGINE ;  ANR-20-CE14-HEPATOMORPHIC ,  ANR RHU QUID NASH).
To characterize the chromatin landscape allowing Svb binding, we investigated whether Svb-bound regions displayed recognizable histone mark patterns. In a first step, we made use of dataset available in Drosophila cells devoid of Svb. [34] [35] [36] [37] [38]  Then we performed our own ChIPseq analysis in S2 cells. As detailed in the following, the two complementary approaches gave similar results. To allow higher sensitivity in peak detection, we used Svb-ACT peaks detected with relaxed parameter (MACS q value 10e À1 , n = 6,939). A strong enrichment in histone 3 acetylated on K27 (H3K27ac) was found across regions to which Svb can bind (Figures  2A  and 2B ). Notably, H3K27ac enrichment directly correlated with Svb-ChIP signal (see heatmap intensity ''gradients'' in Figure  2A ). H3K27ac marks open chromatin, in particular active enhancers,  37, 39, 40  which are also enriched in histone 3 methylated on K4.
41, 42  Consistently, regions bound by Svb were enriched in H3K4me1 and H3K4me3 that accumulated on both sides of the peak (Figures  2A  and 2B ). In contrast, H3K27me3 that marks repressive chromatin  43  was depleted at Svb binding regions (Figure  2A ), as best seen from the dataset taken from the literature. Although a similar behavior was also observed in our experimental conditions, the latter dataset of weaker quality was not retained for further analysis. Therefore, these results obtained with different sets of data (integrated or experimental) suggest that Svb binds active enhancers. We further tested this hypothesis by analyzing Svb binding to all regions acting as enhancers in naive S2 cells (Figures  2C  and 2D ), as defined by short for self-transcribing active regulatory region sequencing (STARR-seq).
(B) Metaprofiles of histone marks from our experimental ChIPseq centered on Svb-ACT peaks. Because of poorer signal/noise ratio, data for H3K27me3 were not kept for analysis. (C) Intersection between Svb-ACT peaks and active enhancers identified by STARR-seq. 44  The Venn diagram shows the overlap between Svb-ACT-bound regions (green, n = 6939) and active enhancers (brown, n = 5,499). Metaprofiles of 6 kb centered on Svb peak midpoint show the average enrichment of Svb-ACT (green), STARR signal (brown), and H3K27ac (blue) for all active enhancers, or Svb-bound and Svb-unbound enhancers. (D) Genome browser view of a characteristic genomic region, showing the behavior of H3K27ac (GSM890121, Kc cells), H3K27ac (this study, S2 cells), and ATACseq (GSE103177, S2 cells) around STARR-seq identified enhancers (S2 cells), bound or not by Svb-ACT.
We analyzed chromatin accessibility around F7-BS using available Assay for Transposase-Accessible Chromatin with high throughput sequencing (ATAC-seq) data from S2 cells  46  and observed that F7-BS that can be bound by Svb in S2 cells are open, while non-bound sites are often in a closed conformation (Figure  4C ). Of note, the binding status of F7-BS in embryos has no effect on the chromatin accessibility in S2 cells (a vs. b and c vs. d). We further analyzed histone post-translational modifications (using published datasets) to inspect the chromatin landscape around F7-BS. This analysis showed that Svb can bind to F7-BS (a and b) that bear hallmarks of active enhancers: higher H3K27ac, H3K4me1, and H3K4me3 and lower H3K27me3, as compared to non-bound F7-BS (Figures  4D-4F ). Again, this is independent of Svb ability to bind these sites in another context (e.
The enhancer-promoter pairs were then ranked according to such net changes in 3D contacts between the two conditions. For each decile from the list, we estimated the enrichment for Svb-bound or Svb-unbound enhancers (Figure  5B  and STAR Methods). Svb-bound enhancers were enriched among the enhancers on which the most significant change in 3D contacts was scored in Svb-REP compared to Svb-ACT, unlike for unbound enhancers (Figure  5B ). These data thus show that Svb impinges on chromatin organization, as monitored by changes in long-range enhancers-promoters 3D contacts. Next, we assessed whether Svb affected the chromatin landscape at a local scale. First we confirm that, among accessible chromatin regions defined by ATAC-seq, Svb can bind only to regions that display high H3K27ac mark, a key mark of active enhancers,  39  while unbound regions have no detectable H3K27ac (Figure  S3A ).
(E) Subset of genes from cluster 1 with their associated functions. (F) Venn diagram showing how Svb target gene sets evolve during intestinal cell differentiation. Note the similarity between Svb target genes in ISCs and EBs, as opposed to Svb targets in ECs. Svb target genes were identified in ISCs, EBs, and early and mature ECs (eECs and mECs, respectively) using the same pipeline (see Figure  S5 , Tables  S1  and S2 ). (G) Heatmaps showing the expression of the 195 ISC target genes in ctrl, Svb-ACT, and svb-RNAi conditions, in ISC, EB, and mEC (top to bottom). For progenitors (ISCs and EBs) and ECs, transgenes were expressed thanks to esg ts and Myo ts respectively. (H-J) CATaDa data  50  allow studying the dynamics of chromatin accessibility through the lineage. (H, I, J) are IGV genome browser screenshots of representative loci of an ISC target gene (stg, H), an EB target gene (egfr, I) and a mEC target gene (Cyp9b2, J), showing CaTADA signal in ISC (green), in EB (yellow), and in EC REP/control ratio (Figure  5E ).
These cells were isolated by fluorescence-activated cell sorting (FACS) from dissected guts of control animals, upon RNAi-mediated svb depletion and expression of Svb-ACT or Svb-REP 3Kmut (an engineered form of Svb-REP insensitive to Pri and thus not convertible into Svb-ACT  25  ). The manipulation of Svb function in the adult gut was specifically targeted either to progenitor cells (ISC and EB) or to ECs (eEC and mEC), using cell-type-specific drivers and temporal control (Figure  6B ; Jiang et al. and Micchelli et al. 51, 52  ). To identify primary targets of Svb, we purified cells at 6 days of induction, a time point where the phenotypes are still limited, with Svb-ACT initiating proliferation of progenitor cells and Svb-REP promoting EC differentiation (Figure  S5 ). We also verified that our RNA-seq datasets were consistent with available transcriptomic data  53  and indeed represented distinct cell types along the ISC to EC differentiation route (ISC, EB, and EC Figure  S6A ).
REAGENT or RESOURCE SOURCE IDENTIFIER Graphical representations were made with ComplexHeatmap. 82  Gene set enrichment analysis (GSEA) Gene set analysis was performed with fgsea software (v1. 18. 0). 69  Gene lists are those obtained after differential expression analysis (ACTvsCTL, REPvsCTL and ACTvsREP) and were sorted in descending logFC order. Enrichment was tested on gene lists associated with Svb-ACT act or Svb-REP peaks (Figure  S2 ). The population considered for each experiment are shown in the legends of Figure  S2.
Hi-C analysis Aggregation analysis of Figure  5  was performed as previously in 1D/2D plots  48, 49  to compare genomic contacts in cells expressing either Svb-REP or Svb-ACT. Hi-C data were aggregated onto pairs of enhancers identified by STARR-seq  44, 47  and distant target transcription start sites. Enhancer-promoter pairs were then ranked depending on net variations of 3D contacts between Svb-REP and Svb-ACT and the ranked quantiles of pairs were assessed for enrichment depending on presence or absence of Svb binding or not using a Fisher exact test. 3D contacts were calculated by assessing the levels of contacts of any enhancer with any TSS that is in the proper range of distances, here between 40 and 200 kb. Aggregation analysis of Figure  S4  was based on differential quantification of H3K27ac REP-ACT (log2(Quantif_H3K27ac_REP)-log2(Quantif_H3K27ac_ACT)) for an HiC resolution of 1kb.
To make the aggregated plots on R, the HicAggR package developed by the Cuvier Team (https://github. com/CuvierLab/HicAggR) was used. The matrices Hi-C are extracted with a resolution of 1kb. This resolution is defined by binning the genome into regular intervals or bins of a given length (1kb in this instance). Each sub-matrix is quantilized between 1 and 500, i. e. vectorized and sorted, and the values are scaled between 1 and 500. To do this, the percentiles of the values are calculated and then these percentiles are multiplied by 500, so that the maximum value of the sub-matrix obtains 500 and the minimum value 1. Unassigned values are omitted. Quantization serves as a metric for estimating the rank of each contact pixel relative to its sub-matrix. The signal of the pixel center (3x3), which represent the interaction between the two sites of interest, has been quantified and presented as Boxplot (Figure  5F ).
CATaDa analysis CATaDa chromatin accessibility data from ISCs, EBs, and ECs were obtained from  50  (Figure  6 ). Per-GATC fragment counts were RPM normalized and visualized in Integrative Genomics Viewer (IGV). GFF signal files were converted to bigwig format using kentUtils bedGraphToBigWig (https://github. com/ENCODE-DCC/kentUtils) and profiles of RPM chromatin accessibility scores across transcriptional start sites were plotted using deeptools computeMatrix and plotProfile functions. Data of chromatin accessibility at individual promoter regions for global comparison was extracted using 2kb regions centered on the TSS, means were compared using Kruskal-wallis test with Dunn's multiple comparison. The populations considered for each cell type are shown in Figure  6F.
ACKNOWLEDGMENTS We thank  Marion Aguirrebengoa  ( BigA platform ) for her bioinformatic expertise,  Emmanuelle Na ¨ser  from the Cell sorting service of the TRI genotoul platform, and  Hugues Parrinello  from the MGX sequencing platform. We thank all the members of the  Payre laboratory  for discussions on the manuscript. This work was supported by  ANR  (Chrononet, to F. P. 's team; Helico, O. C. 's team),  Fondation pour la Recherche  Medicale (FRM, grant  DEQ20170336739  to F. P. 's team and  DEQ20160334940  to O. C. 's team), and an FRBT grant. A. M. -F. , A. R. , and D. D. were supported by fellowships from  Ligue contre le Cancer. C. I. was supported by  FRM  and  UniversiteToulouse 3 Paul Sabatier. We would like to thank the  MCD unit  for covering the publication fees.