dataesr/scientific-paragraphs-categorization huggingface dataset:
On the contrary, yield enhancement was associated with a coordinated increase in all of the root and shoot morphological parameters. Inversely, increases of leaf photosynthetic efficiency by A. lipoferum CRT1 negatively correlated with Plotting the absolute values of single characters of the 2015 dataset confirmed that mock-inoculated plants of field L had the smallest root biomass and were among those that had the smallest shoot biomass. Both parameters gained most from the bacterial inoculation (Fig. 3 ). Inversely, their photosynthetic potential was already the highest of all mock-inoculated plants and it gained least from the inoculation, which placed plants of all fields at roughly equal values. These values were nearly optimal for maize  37.
Impact of A. lipoferum CRT1 on maize root and leaf metabolite contents. A total of 92 and 82 metabolites were, respectively, annotated in 6-leaf stage maize root and leaf (most recent mature leaf -forth from the bottom of the plant) samples collected in 2015. These included amino acids, carbohydrates, intermediates of the ascorbate or aldarate metabolisms, members of the TCA or urea cycles, polyols, phenolic compounds and purine or pyrimidine metabolites (Supplementary Table  S1 ). Similar sets of metabolites were detected in mock-inoculated and A. lipoferum CRT1-inoculated plantlets of all fields so that potential differences among treatment groups were quantitative rather than qualitative. A multivariate analysis of the root and leaf metabolite datasets revealed chemical signatures that were specific to sites and A.
Impact of A. lipoferum CRT1 on two-day maize morphology and seed-to-adult plant ratio. Because yield enhancement was not linked to a metabolic signature and did not parallel a modification of the root-to-shoot biomass ratio but rather correlated with plants which were larger in all of their proportions, modifications of seed-to-adult plant ratio by the bacterial symbionts were estimated and plant morphology was analyzed as early as two days after sowing (right after germination, i. e. , radicle emergence). Under controlled laboratory conditions, germination success was greater than 95% and was not affected by A. lipoferum CRT1 seed coating (data not shown). However, two days old inoculated seedlings had longer radicles (0. 13 ± 0. 02 cm and 0. 47 ± 0. 06 cm, respectively, for mock-inoculated and A.
Metabolite content changes in roots (A) or leaves (B) after seed inoculation by A. lipoferum CRT1. Ratios of mean content values of inoculated over mock-inoculated plantlets are listed and expressed as percentage values +/percentage relative standard error. Wilcoxon non-parametric tests were conducted for each metabolite to compare inoculated and non-inoculated plants mean values for each field (FC, L, C and Corg). Only substances displaying significant content changes (*p < 0. 05; ′p < 0. 1) are listed. NA indicates that the metabolite was detected in less than 2 of technical replicate samples. Metabolites were identified by comparison with a standard and are listed in alphabetical order. Unidentified metabolites are named according to their code in the Golm Metabolome Database (http://gmd.
Acknowledgements Anne-Marie Perrois ,  Théo Berthenet ,  Valentin Filippi ,  Marjolaine Durieux ,  Maxence Lacombe ,  Laurie Faisandier  ( Lycée Simone Weil, Le Puy en Velay, France ) and  Leslie Blazère  ( Lycée La Martinière Duchère, Lyon, France ) are thanked for their technical support. Hélène Houdebine  ( Agrauxine SA ) is thanked for coating A. lipoferum CRT1 on the maize seeds. Shallow Sea Farm  ( Corydon, IN, USA ) is thanked for English editing this manuscript. This work was supported by the  Agence Nationale de la Recherche (ANR)  [grant number  ANR-12-Agro-0008 ]. CR was supported by a fellowship from the  Ministère de l' enseignement supérieur et de la recherche (France).
Although rice is a key crop species, few studies have addressed both rice seed physiological and nutritional quality, especially at the tissue level. In this study, an exhaustive "multi-omics" dataset on the mature rice seed was obtained by combining transcriptomics, label-free shotgun proteomics and metabolomics from embryo and endosperm, independently. These high-throughput analyses provide a new insight on the tissue-specificity related to rice seed quality. Foremost, we pinpointed that extensive post-transcriptional regulations occur at the end of rice seed development such that the embryo proteome becomes much more diversified than the endosperm proteome. Secondly, we observed that survival in the dry state in each seed compartment depends on contrasted metabolic and enzymatic apparatus in the embryo and the endosperm, respectively.
Metabolome Analysis by Gas Chromatography Coupled to Mass Spectrometry (GC-MS) For each tissue (i. e. , embryo, endosperm), metabolite samples were obtained starting from three replicates of 100 rice seeds manually dissected in embryo and endosperm fractions. 100 embryos and 100 endosperms were grinded with mortar and pestle under liquid nitrogen for the embryos and with a Cyclotec TM 1093 Sample Mill (FOSS, Hillerød, Danemark) for the endosperms. All samples were lyophilized and around 20 mg dry weight (DW) of each sample were placed in 2 ml Safelock Eppendorf tubes (Eppendorf AG, Hamburg, Germany). All analysis steps including extraction, derivatization, analysis, and data processing were adapted from the original protocol described by  Fiehn et al. (2008)  and following the procedure described by  Avila-Ospina et al.
(2017). The extraction solvent was prepared by mixing water:acetonitrile:isopropanol at the volume ratio 2:3:3 allowing to extract metabolites with a broad range of polarities. For derivatization step, N-methyl-Ntrimethylsilyl-trifluoroacetamide (MSTFA; Sigma-Aldrich) was used in silylation procedure of metabolites. Samples were analyzed on an Agilent 7890A gas chromatograph coupled to an Agilent 5975C mass spectrometer. Raw Agilent datafiles were converted in NetCDF format and analyzed with AMDIS (Automated Mass Deconvolution and Identification System; http://chemdata. nist. gov/mass-spc/amdis/). A home retention indices/ mass spectra library built from the NIST, Golm, and Fiehn databases and standard compounds was used for metabolites identification. Peak areas were then determined using the QuanLynx software  (Waters, Milford, USA)  after conversion of the NetCDF file in MassLynx format.
RNA Isolation and Microarray Analyses Total mRNAs were isolated from three replicates of 100 embryos and 50 endosperms and hybridizations on the Affymetrix GeneChip R Rice Genome Array (Affymetrix, Santa Clara, CA, USA) were performed as previously described  (Galland et al. , 2014a). To obtain presence/absence calls for each probe, we normalized the CEL files by the MAS5 algorithm (Affymetrix). The CEL files were then normalized with the GC-RMA algorithm using the "gcrma" library available from the R Bioconductor suite of open-source softwares  (Huber et al. , 2015). To determine differentially expressed genes in the embryo and endosperm transcriptomes, we performed a usual two group t-test that assumes equal variance between groups. The raw P-values were adjusted by the Bonferroni method.
We considered a gene as differentially expressed if adjusted-value is < 0. 01. To establish the Pearson correlation, we plotted the embryo against the endosperm normalized mean probe intensity. All raw CEL files are available from the Gene Expression Omnibus under the accession GSE43780 (for the embryo: GSM1071216, GSM1071217, GSM1071204; for the endosperm: GSM1071199, GSM1071201, GSM1071210).
LC-MS/MS Analysis Peptide separation by NanoLC was performed as described previously  (Bonhomme et al. , 2012). Eluted peptides were analyzed on-line with a Q-Exactive mass spectrometer (Thermo Electron) using a nano-electrospray interface. Peptide ions were analyzed using Xcalibur 2. 1 with the following data-dependent acquisition parameters: a full MS scan covering 300-1,400 range of mass-to-charge ratio (m/z) with a resolution of 70,000 and a MS/MS step (normalized collision energy: 30%; resolution: 17,500). MS/MS Step was reiterated for the 8 major ions detected during full MS scan. Dynamic exclusion was set to 45 s. A database search was performed with X!Tandem  (Craig and Beavis, 2004). Enzymatic cleavage was declared as a trypsin with two possible misscleavage. Cys carboxyamidomethylation was set to static modifications.
Met oxydation was set as possible modifications. Precursor mass and fragment mass tolerance were 10 ppm and 0. 02 Th, respectively. The 7th annotation of the Rice Genome Annotation Project database  (Kawahara et al. , 2013; 66,338 proteins)  and a contaminant database (trypsin, keratins) were used. Only peptides with an E-value smaller than 0. 1 were reported. Peptide quantification was performed by extracted ion current (XIC) using MassChroQ software  (Valot et al. , 2011). A 5 ppm precision windows was set for XIC extraction. We eliminated the peptide ions not specific of a single protein and, since a peptide ion was detected several times in one biological sample, we summed the Total Ionic Current (TIC) area under peak corresponding to the same peptide ion. We also removed peptide ions that were not reliably detectable by keeping only peptide ions detected at least twice out of the three biological replicates.
CONCLUDING REMARKS Starch and SSPs were long associated with the endosperm storage function. Thus, it was remarkable to pinpoint, in our proteomic dataset, the presence of glutelins and starch biosynthesis enzymes at non-negligible level also in the embryo (Figures  4, 7 ). These results refine and expand previous proteomic results on whole developing rice seeds  (Koller et al. , 2002). Altogether, this would also support the supernumerary embryo hypothesis with both ancestral tissues being equipped with different molecular apparatus before divergence. Classically, the inner SE has been seen as a dead storage tissue since the central parts of the endosperm undergo PCD  (Young and Gallie, 2000). Yet, we could show that, as expected, the embryoless endosperm (aleurone and SE) showed an important translational activity however, the embryo display a higher protein synthesis (Figures  6B, C ).
ACKNOWLEDGMENTS We are thankful to  Loïc Fontaine  and  Jean-Benoît Morel  ( UMR BGPI, CIRAD, Montpellier, France ) for supply of rice Nipponbare seeds. We acknowledge  Dr Francisco Cubillos Riffo  ( Laboratorio de Microbiologia y Biotecnologica Aplicada, Centro de Estoduis en Ciencia y Tecnologia de los Alimentos, Santiago, Chile ) for help with statistical analysis. Thanks to  Olivier Langella  and  Thierry Balliau , from  PAPPSO (Plateforme d'Analyse Protéomique de Paris Sud-Ouest ), for making the proteomic data available on PROTICdb. The author gratefully acknowledges the support of  K. C. Wong Education Foundation ,  Hong Kong  for the visit of DH at IJPB. Thanks to the  Saclay Plant Sciences (SPS) LabEx supporting IJPB ,  IPS2  and  GQE-Le Moulon  ( ANR-10-LABX-0040-SPS ).
More recently, an ST4 genome obtained from a laboratory rodent in Singapore was published  [22] ; its size was 12. 91 Mb and a set of 5,713 protein-coding genes was predicted. There were no detailed analyses of the genome structure or of the genes themselves. In 2015, draft assemblies for the nuclear genomes of STs 2, 3, 4, 6, 8, and 9 were released to public databases. None of these assemblies have been annotated with predicted genes and no analyses have been reported to date beyond their use in a microbiome analysis  [13]. Here, we report a draft genome sequence and transcriptome analysis of Blastocystis ST1, NandII strain from a symptomatic human, and compare it to the published genomes of Blastocystis ST7  [21]  and ST4  [22]. We first provide a general overview and statistics for the NandII genome and perform high-level comparisons between the 3 genomes.
Secretome and degradome A stringent, 4-step approach identified a total of 89 genes confidently predicted to encode secreted proteins in Blastocystis ST1 (S1 Data). The corresponding predicted number for ST7 was 307  [21] , although those results were based only on SignalP predictions, a less stringent approach than the one used here. Most of the 89 secreted proteins predicted in ST1 are also present in ST7, with the exception of a metallophosphoesterase and GH2 and GH33 glycosyl hydrolases. Several of the 89 genes were not predicted as secreted in ST7 because their signal peptides were not part of the predicted gene models. More than half of these putative secreted proteins have a predicted role in posttranslational modification and protein turnover (Fig  5 ). We compared the secretory signal peptides of the different STs and were unable to identify a unifying characteristic apart from a core of 10 hydrophobic amino acids (S2 Fig).
The total number of proteases encoded in ST1 is 243, with 198 in ST4 and 210 in ST7 (S2 Data) (Fig  6 ). All 3 STs encode aspartic, cysteine, metallo, serine, and threonine proteases. The most prevalent protease genes are those of the cysteine type, constituting between 39% (ST7) and 47% (ST4) of the degradome. Undoubtedly, many cysteine proteases play roles that are conserved in eukaryotes, such as lysosomal function, autophagy, and ubiquitination, but others have been implicated in host-parasite interactions  [48] [49] [50]. All 3 STs show extensive gene expansions of the cysteine protease families C1, C13, and C19. A large number of C1 genes is fairly common. Less common is the number of C13 genes found in ST1 (  16 ), ST4  (11) , and ST7  (11). Most protist genomes contain fewer than 5 C13 genes and many just have a single version (see MEROPS database, merops.
sanger. ac. uk,  [51] ). The only protist known to approach the number of C13 genes seen in Blastocystis is the sexually transmitted human excavate Trichomonas vaginalis  (10) , in which at least 1 type of C13 protein has been implicated in trichomonal cytoadherence  [52]. While Blastocystis appears to have an elevated number of C19 genes compared with other protists (MEROPS database), currently there is no indication that these genes are involved in anything other than standard intracellular removal of ubiquitin molecules. Other protease families that exhibit large differences between STs are C56, C95, C97, and M16 (S2 Data). The biological significance, if any, of these differences is currently unknown. The most striking disparity between the STs is seen in metallo-type proteases (Fig 6, S2 Data).
The divergence in the number of genes belonging to this type is entirely attributable to the complete absence of subfamily M23B genes in the ST4 genome, while both ST1 and ST7 have 29 members. The M23 family is composed of metallopeptidases involved in the lysing of peptidoglycans in bacterial cell walls for either defense or feeding (MEROPS database). What functions they possess in Blastocystis and why they are absent from ST4 are open questions. Differences in the number of the S54 rhomboid serine proteases were also identified, with ST1 having 4 genes, ST4 having 3, and ST7 having only 1. These transmembrane peptidases play a role in the invasion of host cells in the alveolates Toxoplasma, Cryptosporidium, and Plasmodium, while in the amoebozoan Entamoeba histolytica, the single rhomboid protease is vital to immune evasion via the cleavage of lectins during receptor capping  [53] [54] [55].
Quite significantly, the glycogen synthase enzyme belongs to the GT5 database of carbohydrate-active enzymes (CAZy) family, meaning the animal hosts of Blastocystis can be rejected as possible LGT sources, as they all contain the very different GT3-type enzymes. Given that alveolates also employ α-glucan metabolism  [86] , it is likely that α-glucan metabolism was present in the ancestor of stramenopiles. The presence of the indirect debranching enzyme (iDBE), absent from red and green algae, suggests that this network is not a secondary endosymbiosis relic from Archaeplastida. Coat carbohydrates. Previous spectrophotometric analyses have shown that the surface coat of Blastocystis contains α-D-mannose, α-D-glucose, N-acetyl-α-D-glucosamine, α-Lfucose, chitin, and sialic acid  [92]. Our analyses have confirmed Blastocystis STs 1, 4, and 7 encode a eukaryotic-type chitin synthase (GT2) and 2 eukaryotic-type chitinases (GH18) orthologous to proteins found in other taxa, indicating that this organism is capable of synthesizing and degrading this polysaccharide.
Genome sequencing and assembly The Blastocystis ST1 genome was sequenced using 454 pyrosequencing and Illumina technologies. Illumina sequencing entailed generation of a mate paired-end library with an insert size of 3 kb. Following the filtering steps described above, 1,865,740 454 and 55,617,444 Illumina reads remained. The 454 and Illumina reads were assembled using the Ray genomic assembler software  [107]. Only contigs above 200 bps were retained for further analysis. Using different combinations of 454 and Illumina reads, 3 different assemblies were created. 2F was generated from just Illumina reads and contained 13,338 contigs. 2E was generated from all reads and contained 29,673 contigs. 2D was generated just from 454 reads. The software Minimus2 from the AMOS package  [108]  was used to merge the 3 into a single assembly with 14,304 contigs.
Bacterial contamination The genome assembly for Blastocystis ST1 was interrogated for the presence of contigs composed of bacterial reads. For contigs of less than 500 bps, BLASTx and BLASTn  [49]  searches were performed. All contigs with a percent identity of 95% and higher against entries in the nr and nt NCBI databases were considered contaminants. In total, 305 contigs were removed. Large contigs were split into 5,000-bp pieces while those less than 5,000 bps in length were kept intact. All the pieces were compared, using BLASTx, against a specialized database consisting of protein sets from all publicly available bacterial genomes and protein sets from 28 eukaryotic genomes drawn mainly from Chromalveolata and Archaeplastida. Pieces that only had matches for bacterial proteins were putatively designated as derived from bacterial contamination.
Estimation of genome coverage In the absence of an experimentally determined genome size for ST1, the extent to which the final assembly represents the full genome was determined roughly using 2 methods. The first method entails taking a set of 679 conserved protein sequences derived from RNA-Seq data and matching them against the genome assembly. Of the conserved transcripts, 5. 4% were not found in the genome assembly, suggesting that the current assembly encompasses 94. 6% of the genome or at least the gene space of the genome  [110]. The second strategy for estimating genome coverage was based on a method first used in the Conus bullatus genome paper  [111]  to estimate genome size. A database of 31. 8 million 90-bp Illumina reads was used in BLASTn searches of the assembled contigs. Read coverage for contigs was estimated as (number_of_hits Ã read_length)/length_of_contig.
Average level of heterozygosity The average level of heterozygosity for ST1 was determined by aligning trimmed genomic reads to the genomic assembly with Bowtie 2. 3. 1  [113]. The program mpileup from SAMtools  [114]  was used to determine the read depth at each position of the assembly as well as the breakdown of the 4 possible bases. An in-house perl script was used to parse the mpileup data. Positions with less than 10X coverage were ignored. A site was deemed to be heterozygous if at least 2 different bases were present and there were at least 2 (or 3) reads with the different bases. The number of heterozygous sites was then divided by the total number of sites (with !10X coverage).
Gene modeling A set of 679 protein sequences was generated from the ST1 RNA-Seq clustered transcripts and used to train AUGUSTUS  [115]  on a repeat masked assembly  [116]. The training set contained protein sequences with the following criteria: at least 1 intron, less than 80% identical at the amino acid level to any other sequence in the set, and a start site based on highly similar eukaryotic protein sequences. The script optimize_augustus. pl was also run to create speciesspecific meta parameters. A set of 32,269 Trinity  [106]  assembled RNA-Seq transcripts were used to generate a hints file for donor/acceptor intron sites. Subsequent gene modeling generated 5,637 gene models, which were then corrected with PASA  [117]  based on RNA-Seq transcripts. Because of the unusual nature of termination codons in Blastocystis  [25] , it was necessary to set the parameter stopCodonExcludedFromCDS to true.
Of the final gene model set, 58% was examined manually at some point to check various aspects such as intron boundaries, stop codons, and chimeric models. The degree to which intron boundaries were confirmed by RNA-Seq data was determined by mapping RNA-Seq data to the genome assembly using Bowtie2  [113]. Using the intron boundary positions and the alignment positions of individual reads from the SAM file, an in-house perl script calculated that 70. 3% of the intron boundaries were confirmed by 5 or more RNA-Seq reads (60. 1% with 10 or more RNA-Seq reads).
Functional annotation To assist in the manual annotation of the gene models, various information was generated for each model. The models were compared against the conserved domain database  [118]  and domain hits as well as Pfam  [119]  hits were extracted from the results. The models were also compared against the NCBI protein database as well as the protein data set for ST7 using BLASTp. SignalP results were generated using a locally installed version of SignalP. The genome browser Genomeview  [120]  was chosen for annotation because of its relative ease in setting up and populating with information via gff files and the ability to alter the information using custom scripts. The genome browser also included tracks for genome reads mapped to the scaffolds (Bowtie2  [113]  and SAMtools  [114] ), genome read map coverage, RNA-Seq reads mapped to the scaffolds, and RNA-Seq map coverage.
Because of the possibility of missed genes, the intergenic spacers were identified and the sequences compared against the NCBI protein database (nr) as well as the ST7 protein data set using BLASTx. Potentially missed genes were mapped to the scaffolds and displayed in the genome browser for confirmation and correction through manual annotation. The ST7 protein data set was also compared to the gene models for ST1 using BLASTp and the results mapped to the scaffolds as a genome browser track.
Intron categories For Blastocystis ST1, the analysis of different intron categories was based on the final annotation of the genome, incorporating data from RNA-Seq and extensive manual curation. To analyze introns in the Blastocystis ST7 genome, the available annotation of the genome could not be relied on because it proved to be inaccurate in many cases. Therefore, analysis was restricted to introns that have direct support from EST sequences. Available ESTs were aligned to the genome sequence using STAR  [123]  and introns indicated by the alignments were identified and manually corrected if needed. Homologs of protein subunits specific for the minor spliceosome were identified by BLAST (BLASTp, tBLASTn) using sequences of previously defined subunits from Homo sapiens and plants. The identity of the candidate orthologs was confirmed by reciprocal BLASTp searches against the nr database at NCBI.
Incorrect or missing gene models in Blastocystis ST1 were corrected or added to the annotation of the genome. Most of the identified homologs in Blastocystis ST7 are either represented by incorrect gene models or completely missing from the annotated genes but could be found in the assembly with tBLASTn. Models for snRNA in both STs were identified using the program cmscan from the Infernal package  [124].