dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by  Veridex LLC. GA and JdB are employees of the Section of Medicine at The Institute of Cancer Research  which is supported by a  Cancer Research UK program  grant and an  Experimental Cancer Medical Centre (ECMC)  grant from  Cancer Research UK  and the  Department of Health  (Ref:  C51/A7401 ). GA and JdB also acknowledge NHS funding to the  NIHR biomedical research centre. The IC2006-04 study was funded by a public grant from the  French government  ( PHRC AOM06156 ). The  IMMC-01 ,  IMMC-06 , and IMMC-38 studies were funded by  Veridex LLC. The sponsors of this study had no role in study design, data analysis, data interpretation, or writing of the report. All authors had full access to all the data and the corresponding author had final responsibility for the decision to submit for publication.
Lineage differentiation in Apc-mutant ESCs correlates with the level of Wnt signaling To attempt the elucidation of the mechanisms underlying lineage differentiation in the context of Wnt activation, we have derived several ES clones from pre-implantation blastocysts carrying different hypomorphic Apc alleles: Apc 1638T/1638T (ApcTT), Apc 1638N/1638T (ApcNT), Apc 1638N/1638N (ApcNN)  [26, 27] , together with Apc +/+ as wild type controls. As previously reported, ApcTT, ApcNT, and ApcNN encode for a gradient of different Wnt signaling dosages  [1, 26] , as also confirmed by TOP-Flash reporter assay  [28]  with ApcNN showing the highest Wnt activity (ApcNN&ApcNT. ApcTT. Apc +/+ )(Figure  1A ). The potential of the Apc-mutant ES cells to differentiate into ecto-, meso-and endodermal lineages was also evaluated and confirmed by the teratoma formation assay followed by immunohistochemistry (IHC) analysis, matching our previous results obtained with ES clones obtained by two rounds of gene targeting by homologous recombination  [1].
We also provide a more detailed picture on how Wnt signaling counteracts Tcf3 function in stem cells by showing that Tcf3 repression, in the context of active Wnt signaling, involves histone modifications at the Tcf3 promoter and the activation of miR-211, which post-transcriptionally stabilizes Tcf3 downregulation. Understanding the downstream effects of Wnt signaling in ESCs is of both fundamental and translational relevance, as it may be exploited to manipulate ESC differentiation towards specific cell lineages. which was further confirmed by qRT-PCR and western blot analysis (Figure  2A  and 2B; and Figure  S1 ). Further qRT-PCR analysis revealed that the observed downregulation is specific for Tcf3 but not for other members of the Tcf/Lef family (Figure  S2A ). Whereas Tcf3 was down-regulated in both ApcNN and Apc Min/Min ESCs, the latter encode for the most severely truncated Apc mutant allele and therefore for a very high level of Wnt signaling, other members of the Tcf/Lef family were exclusively up-regulated in Apc Min/Min ESCs.
Rescuing Tcf3 expression in ApcNN ESCs partially restores neural differentiation It has been previously shown that Tcf3 not only functions as a controller of self-renewal in wild type ESCs, but it is also required for proper neurogenesis in zebrafish, xenopus and mice  [11, 16, 21]. We therefore hypothesized that Tcf3 down-regulation in ApcNN ESCs might mediate the neural differentiation defects observed in these cells. To test this hypothesis, we rescued Tcf3 expression by stably over-expressing its full-length cDNA in ApcNN ES cells (Figure  3A, 3B ). Tcf3 over-expression decreased TOP-Flash reporter activity (Figure  3C ) and, accordingly, reduced the transcript levels of Cdx1 and Brachyury (T), two well-known Wnt downstream targets. Gene expression profiling of Tcf3-expressing ApcNN cells confirmed that Tcf3 effectively reverses the expression pattern of several genes differentially expressed in ApcNN when compared to wild type ESCs (Figure  S3 ).
The immunoprecipitated chromatin was then assessed by qPCR analysis with a panel of specific primers covering a region encompassed between 22 kb to +2 kb from the transcription start site (TSS), as well as 20 kb of the gene body within the Tcf3 locus. In accordance with the observed Tcf3 downregulation in ApcNN cells, we found a decrease in the activating marks H3K4me3 and H3Ac and, to a lesser extent, a slight increase in the repressive marks H3K27me3 and H3K9me3 (Figure  6 ). Similarly, 12 h treatment of wild type ESCs with Wnt3a conditioned medium significantly reduced the H3Ac and H3K4me3 activating marks but had no effect on the H3K27me3 and H3K9me3 repressing marks (Figure  S6 ). These data demonstrate a correlation between Tcf3 expression and histone modifications in its promoter suggesting that Wnt signaling might regulate Tcf3 expression through epigenetic mechanisms.
Accordingly, activation of Wnt signaling in wild type ESCs either by Wnt3a conditioned medium (CM) or by GSK3 inhibition, confirmed that miR-211 is a novel Wnt-regulated microRNA in mouse embryonic stem cells (Figure  7B  and 7C ). In silico analysis with three software packages, namely Miranda  [39] , Targetscan  [40]  and PicTar  [41] , pointed to several potential miR-211 target genes predicted by all three programs. To narrow down the list of potential targets, qRT-PCR analysis was performed on wild type ESCs compared with ApcNN (Figure  S7A ) as well as on wild type ESCs treated with Wnt3a CM (Figure  S7B ). We excluded those predicted targets that showed up-regulation upon Wnt signaling. Based on these results Sox11, Sf3b1 and Tcf3 were selected for further analysis. Several stable ESC clones were generated which ectopically over-express miR-211 in an otherwise wild type background (Figure  S7C ).
Microarray analysis RNA was isolated using the RNeasy Mini Kit (QIAGEN) from cells lysed directly on the plate; a DNase step on the column was performed according to manufacturer's instructions. RNA quality was controlled by RNA 6000 Nano LabChip kit (Agilent Technologies). RNA was labeled using the GeneChip One-Cycle Target Labeling kit, hybridized to MOE430 2. 0 arrays (Affymetrix) according to manufacturer's instructions. For data analysis, CEL files were uploaded and normalized using MAS 5. 0 algorithm in Expression Console software (Affymetrix, Inc). Expression analysis was performed using Partek Genomics Suite 6. 5 ((Partek Inc. , St. Louis, MO) and Excel 2010 (Microsoft). A robust empirical method coupled with a validation step using qRT-PCR was used to confirm the modulation of gene expressions between different genotypes.
DNA methylation analysis ESCs were cultured on 0. 1% gelatin-coated dishes without MEFs for 2 passages and genomic DNA was isolated using DNeasy Blood & Tissue Kit (Qiagen). 1 mg of genomic DNA was used in bisulfite conversion reaction using EZ DNA Methylation Kit (Zymo Research) according to the manufacturer's instructions. Converted DNA was amplified by PCR using specific primers (Table  S2 ) designed with Methyl Primer Express Software 1. 0 (Applied Biosystems) or MethPrimer software  [57]. The PCR amplification was carried out using KAPA2G Robust HotStart Taq DNA polymerase (Kapa biosystems) and PCR conditions were: 95uC for 3 min and 39 cycles of 95uC for 15 s, 57 or 53uC for 15 sec and 72uC for 15 sec, followed by 10 min at 72uC. PCR products from Region A, B and D were employed in direct sequencing using ABI BigDye Terminator and ABI 31306l genetic analyzer (Applied Biosystems).
Acknowledgments We thank  Wilfred F. van Ijcken  and  Wilfrid Richer  for performing gene array analysis,  Simona Rossi  for performing miRNA array analysis,  Wolter Oosterhuis  for assistance with the teratoma analysis,  Nina Solberg  for kindly providing the Tcf3-promoter-constructs,  Juanjiangmeng Du  and  Rosalie Joosten  for technical assistance, and  Frank van der Panne  for assistance with figure preparation.
This study was supported among others by grants from the  Erasmus MC  and the  EU   FP6  and  FP7 consortia Migrating Cancer Stem Cells program  (MCSCs; www. mcscs. eu)and from  TuMIC  (integrated concept of tumour metastasis (http://itgmv1. fzk. de/www/tumic/tumic_main. htm ), and by the  Erasmus MC Stem Cell Institute (ESI). GAC is supported as a Fellow at the  University of Texas MD Anderson Research Trust  and as a  University of Texas System Regents Research Scholar. Work in his laboratory is partially supported by the  NIH , the  DoD , the  Laura and John Arnold Foundation , and the  RJK Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Total RNAs were extracted from each tissue using TRIzol® Reagent (Invitrogen, Carlsbad, CA, USA). RNA integrity was verified by gel electrophoresis and RNA quantity was determined on a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies Inc. , Wilmington, DE, USA). A pool of RNAs from each tissue (1 µg each) was used as a template for cDNA synthesis and 454 sequencing (454 Roche GS FLX Titanium, ½ Pico Titer Plate GATC Biotech SARL, Mulhouse, France). In parallel, RNAs from larvae antennae and palps, female ovipositors and female antennae were independently used as templates for Illumina sequencing (one channel for the two adult samples, one channel for the larvae sample, single read 51 pb lenght, HighSeq2000; GATC Biotech). All the data generated in this project have been deposited in LepidoDB (http://www.
Sequence processing and assembly 454 and Illumina data were processed by removing adapters and by trimming low quality regions. Data were first analyzed with FastQC v. 0. 10. 0 (www. bioinformatics. babraham. ac. uk/projects/fastq c) that provided information on sequence quality and identified over-represented sequences within libraries. Over-represented sequences and low quality regions were removed with, respectively, Cutadapt  [38]  and PRINSEQ v 0. 17. 3. [39]. Sequences shorter than 20 bp long were also removed from all data sets. A first step of de novo assembly was performed on the Illumina reads with Trinity assembler (release 2012-01-25  [40] ). Then, the processed sequences from 454 sequencing were added to the Trinity contig set and used as input in MIRA assembler v 3. 2. 1. using as parameters de novo assembly method, est assembly type, accurate quality, Sanger sequencing technology  [41].
Transcriptome analyses and gene annotation The contigs were compared to the NCBI non redundant protein database (NR, version January 20 th 2013) using BLASTX, with a 1e -8 value threshold. BLAST2GO was used for the Gene Ontology (GO) annotation (GO association done by a BLAST against the NCBI NR database)  [42]. Contigs were translated to peptides using FrameDP 1. 2. 0  [43]  with three training iterations and using Swissprot (398. 181, August 2009) as the reference protein database. GO annotation was then completed with Interproscan annotation of translated peptides. Olfactory gene tran-scripts were searched within the assembly with available lepidopteran OBP, CSP, OR, IR and SNMP amino acid sequences (see Phylogenetic analyses) as queries using TBLASTN. Enzyme-encoding genes were searched using carboxylesterase sequences (for candidate pheromone-degrading enzymes) and desaturase/acetyltransferase/reductase sequences (for pheromone biosynthesis enzymes) from Lepidoptera  [44, 45].
The sequences that matched with the queries were further assembled using Cap3  [46] , when possible, to obtain longer contigs. Resulting sequences were reversely compared to NCBI NR database using the BLASTX application to confirm annotation and their translation was manually verified or corrected. OBPs, CSPs and esterases were searched for the presence of a signal peptide using SignalP 4. 0  [47] , secondary structures were predicted using the Psipred server  [48] , and logos were generated using WebLogo  [49]. Transmembrane domains of candidate ORs were predicted using the HMMTOP 2. 0  [50].
sexta  [26, 60]. Signal peptide sequences were removed from the data set, which contained 182 sequences (256 amino acid positions for each). The CSP data set contained 19 sequences from S. nonagrioides and sequences from S. littoralis  [24, 25, 54] , B. mori  [61] , H. melpomene  [56] , H. virescens  [62]  and Papilio xuthus  [63]. As for OBPs, signal peptide sequences were removed. The data set contained 124 sequences (103 amino acid positions for each). In the IR dataset, 10 S. nonagrioides candidate IR sequences were added to sequences identified in S. littoralis  [54, 64] , B. mori  [16] , and D. plexippus  [55]. Since IRs are well conserved in insects, IR sequences from non-Lepidoptera species (D. melanogaster, Apis mellifera and Tribolium castaneum  [16] ) were also included in the data set.
In addition, D. melanogaster iGluR sequences were included, and the final data set contained 179 sequences (618 amino acid positions for each). Amino acid sequences were aligned with MAFFT v. 6  [65]  using the FFT-NS-2 algorithm and default parameters, except for the OR sequences that were aligned using MUSCLE  [66]  as implemented in Seaview v. 4  [67]. The alignments were manually curated to remove highly divergent regions. Phylogenetic reconstructions were carried out using maximum likelihood. For each data set, the LG+I+G substitution model  [68]  was determined as the best-fit model of protein evolution by ProtTest 1. 3  [69]  following Akaike information criterion. Rate heterogeneity was set at four categories, and the gamma distribution parameter and the proportion of invariable sites were estimated from the data set.
Read mapping All candidate chemosensory genes as well as the genes encoding candidate enzymes were used to perform unique read mapping of each Illumina library (female ovipositors, female antennae, larval palps and antennae). Each of the 85 individual gene mapping alignments was inspected for uniquely mapped reads. Read counts were normalized between libraries according to the size of the library with the DESeq package  [73]  implemented in R  [74].
S. nonagrioides reference transcriptome and annotation We generated a de novo transcriptome of S. nonagrioides using transcriptomic data sets obtained from 454 and Illumina sequencing. These sequencing platforms were combined since they lead to substantial differences in read length and they are supposed to recover different sequence types from a sample, thus we expected their combination to enhance the quality of the final assembly. In addition, the availability of Illumina libraries from isolated organs allows performing read mapping for expression studies. The 454 data set (995,424 processed sequences) was obtained from adult (male and female) and larvae antennae, larvae palps, adult brains and female ovipositors (1,004,420 raw reads) (Table  2 ). The Illumina data sets were obtained from female antennae (53,623,491 processed sequences), female ovipositors (71,544,332 processed sequences) and pooled larvae antennae and palps (190,673,453 processed sequences) (Table  2 ).
6%), and 16,280 predicted proteins (73. 5%) translated from these regions showed similarity to known proteins when compared to the non-redundant protein database. Figure  1  represents the distribution of the S. nonagrioides contigs in GO terms. Among the 51,999 contigs, 11,369 (21. 8%) corresponded to at least one GO term. As observed in other lepidopteran transcriptomes  [25, 26] , a large number of transcripts could not be associated with a GO term (78. 2%). Among those associated to a GO-term, 9,961 were assigned to a molecular function (87. 6%), 6,635 to a putative biological process (58. 4%) and 4,836 to a cellular component (42. 5%) (Figure  1 ). In the molecular function category, the terms "binding" and "catalytic activity" were the most represented (respectively 62. 3% and 35. 5%), as previously observed in the transcriptomes of S.
Most of them, including the two GOBPs and the three PBPs, were observed to be highly expressed in female antennae. Some of them were also highly expressed in the larval chemosensory organs (Table  4 ), some others were not, and it could be speculated that these latter participate in adult specific behaviors. Interestingly, four OBPs were clearly expressed in the female ovipositors and 32 reads from the ovipositor library could be mapped on one PBP (Table  4 ). Table  3 : List of S. nonagrioides contigs putatively involved in odorant binding. Signal peptides were determined using SignalP 4. 0  [47]  and α-helice structures were predicted using the Psipred server  [48]. In each box, the number of uniquely mapped reads is indicated (read counts were normalized between libraries according to the size of the library with the DESeq package  [75] ).
the genus level as a Bordetella sp. (score 1. 7). To identify the Bordetella species, DNA from the isolate was extracted by using the QIAamp DNA Kit (QIAGEN, Hilden, Germany). Partial PCR amplification and sequencing of 16S rRNA gene was performed as described  (7). Sequence analysis showed that the isolate was closely related to B. hinzii LMG 13501 (99. 0% homology), which was isolated from the blood of a patient who died of septicemia in 2000  (2). The 16S rRNA sequence of our isolate (B. hinzii L135) has been deposited in GenBank under accession no. JX188059. A phylogenetic analysis of the new sequence and sequences of other bacteria in the genus Bordetella is shown in the Figure. B. hinzii is a causative agent of respiratory tract illnesses in birds and has been described as an emerging and opportunistic pathogen in immunocompromised patients; and in patients with AIDS, cystic fibrosis, and fatal septicemia (1-5).
Acknowledgments We thank  Annick Bernard  and  Linda Hadjadj  for technical assistance. This study was supported by the  French National Research Agency; CERoPath (Community Ecology of Rodents  and their  Pathogens in Southeast Asia  Project  ANR 07 BDIV 012 );  Infectiopôle Sud ;  Center for Excellence on Agricultural Biotechnology ; the  Science and Technology Postgraduate Education and Research Development Office ; the  Office of Higher Education Commission, Ministry of Education  ( AG-BIO/PERDO-CHE ); and the  Center  of.
Phenotyping Clusters of each fruit-bearing offspring were harvested at maturity during at least two years (Table  1 ). End parts of clusters were discarded and 100 berries randomly sampled and weighted to estimate mean berry weight (MBW). Seeds were extracted from 25 random berries to determine mean seed number per berry (MSN), total seed fresh weight per berry (TSFW), mean seed fresh weight (MSFW) and the percentage of seed dry matter after drying at 80°C for 72 hours (%SDM). The residuals obtained by linear regression using SAS/STAT® software v9. 1. 3 (SAS Institute Inc. , Cary, NC) of MBW on either MSN or TSFW (RESN and RESFW, respectively) were used as additional traits for QTL analyses.
QTL detection Parental and consensus genetic maps were already available (Table  1 ). Since only a partial genetic map was available for population MTP3346 (10 genomic regions spanning less than a quarter of the genome), the results for this population were provided as additional files and only mentioned to complement the other results. A summary of map features is given in Additional file 2: Table  S2 ). QTL detection was performed on parental and consensus maps as follows. Composite interval mapping (CIM) was applied to parental maps using WinQTLCart 2. 0  [52] , with cofactors selected through forward and backward (FB) regressions. LOD thresholds corresponding to a genome-wide type I error rate of α = 5% were determined using 1,000 permutations of traits over marker data, with cofactors reselected for each permuted data set, using QTLCartographer 1.
Candidate genes To identify the most probable candidate genes, we used a double approach. First, consulting the available literature or databases, we established a list of genes possibly involved in berry weight according to their putative function (e. g. cell replication, water transport, or cell wall metabolism), as well as to the role of their homologue on fruit weight in other species or to published results obtained for expression, transformation or association genetics in grapevine. We then checked whether these candidate genes colocalized with the detected QTLs in our mapping populations. In particular, we explored two gene families involved in fruit weight QTLs cloned in tomato, the CNR and P450 78A families  [57, 58]. To search for homology and genome position, we used the BlastP algorithm to match the proteic sequence against the NCBI Vitis refseq_protein database.
Secondly, as a complementary approach, we looked for the limited number of genes with a putative function relevant to berry weight, within the numerous genes present under the QTLs, even in the absence of published results in grapevine or cloned QTLs in other fleshy fruit species. Taking into account a review of fine mapped QTLs  [59] , showing that causal polymorphisms were found within 3 cM of LOD peaks, we extracted positional candidate genes from a +/-3 cM interval around the LOD peak of each stable BLUP QTL for berry weight on consensus maps. The physical interval limits were extrapolated from the positions of flanking SSR markers. Accession numbers, positions and putative functions of predicted mRNA were extracted from ref_12X_top_level. gff3. gz, last modification 03/15/2012 downloaded at ftp://ftp.
In particular, an additional stable QTL was found for RESFW (8-18%) on LG 13, where none was detected for MBW. Stable berry weight QTLs showed substantially different patterns between consensus and parental maps. Noticeably, the QTL on LG 13, unstable on the consensus map, was stable for the Syrah parent. The stable parental QTLs for MBW were found also for RESN and/or RESFW, but four additional stable parental QTLs were found for residual values, on LGs 1, 11, 17 and 18. Among them, the QTL for RESN on LG 1 (6-9%) revealed a new locus involved in berry weight variation that was found neither in consensus maps nor with MBW or RESFW. No stable QTL for raw or residual berry weight data was found on consensus or parental maps in population MTP3346.
colocalized with the major berry weight QTL, while the second matching gene did not colocalize with a QTL (LOC100249034, LG1:381267. 382921, 71% identity on 99% of the sequence). The cloning of another fruit weight QTL in tomato, fw2. 2  [69] , led to the identification of the LOC101245309 gene, encoding a cell number regulator. The three best matches on Vitis refseq database were on LG3, where no QTL was found. However, three other genes were annotated as predicted cell number regulator-like in grapevine genome, among which LOC100246592 colocalized with a berry size QTL, on LG8 (Additional file 16: Table  S12 ). In addition, the best match of the maize ZmCNR2 gene from the same family, potentially involved in tissue growth  [60] , was obtained for LOC100250939, encoding a plant cadmium resistance 2-like protein, which colocalized with the parental-only berry growth QTL on LG1.
Acknowledgements This work was supported by the  European Union  [grant numbers  ICA4-2001-10065   MASTER  project, 513960  FLAVO  project] and the  Institut National de la Recherche Agronomique. We thank the  CTIFL  for growing the MTP3346 population in their greenhouse, the  INRA experimental station of Vassal  for the controlled crosses and the first growing of ungrafted populations in the field, the  INRA experimental station of Chapitre  for subsequent growing in the field,  Alain Bouquet  for creating, managing and partly phenotyping the MTP3140 population,  World Wine Vineyards  for helping with overgrafting,.