dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential protein content of oviductal EVs across the estrous cycle Unlike in our previous study where we analyzed the protein content of the oEVs at post-ovulatory stage  [9] , here we analyzed the oEVs at different stages of the bovine estrous cycle. Mass spectrometry analysis revealed differential protein abundance of EVs along the bovine estrous cycle. Among the 336 identified clusters of proteins, 170 were differential among different stages of the estrous cycle (p-value < 0. 05, ratio < 0. 5 or > 2). Complete lists of identified and differentially abundant proteins are shown in Additional file 1 (Tables  S6-S8 ). A principal component analysis of normalized weighted spectra data of the top 200 proteins with highest differences across the dataset revealed a clear separation of the protein profiles for the 4 stages (Fig.
05) (Additional file 2: Figure  S5 ). For miRNA, Table  3  shows differential miRNAs among stages with 8 differentially regulated miRNAs (20 sequences) between S4 vs S1, while      and Stage 4 (hsa-mir-10b-5p, hsa-miR-423-3p; hsa-miR-449, hsa-miR-375, hsa-miR-24-3p, hsa-miR-429, hsa-miR-148a-3p, hsa-miR-34b-3p, hsa-miR-200b-3p, hsa-miR-92a-3p, hsa-miR-151a-3p, hsa-miR-30d-5p, hsa-miR-125b-5p). For this purpose, human orthologous miRNAs were used for analysis in the DIANA-TarBase v7 tool. The selected miRNAs, pathways involved and the number of genes and gene symbols of the target genes in each pathway are shown in Table  4  and Additional file 3 (Table  S9-10 ). This data was used for cluster analysis based on presence of interactions of target genes and pathways by DIANA-miRPath v3. 0. The depicted heatmap shows the 13 selected miRNAs versus pathways based on significance levels (Fig.
9b ). Dysregulation of mir-449a has been associated with defective cilia in the oviduct and infertility. Mir-449a was more abundant in S1 compared to the rest of the stages, which may downregulate a set of target genes in S1 in contrast to the other stages of the cycle. To investigate the possible target genes for mir-449a, DIANA tool was used with two different databases (TarBase and TargetScan). TarBase showed 512 target genes for mir-449a, while Tar-getScan showed 283 genes. Comparative analysis of these two lists of target genes for mir-449a provided 32 common target genes that were used for further functional analysis using ToppCluster tool. Network in Fig. 9c  shows the target genes (in red) for mir-449a and the network for GO enriched terms for those target genes. Furthermore, GO terms associated to embryo development, embryonic morphogenesis and stem cell division were identified.
Protein cargo in oviductal EVs is under hormonal regulation Proteomic analysis showed differential protein profiles across the different stages of the estrous cycle, demonstrating a strong effect of the steroid hormones on oEVs protein cargo. Of the 336 proteins identified in oEVs, 170 were differentially abundant across the estrous cycle and could be grouped in different clusters of proteins based on similar profiles over the cycle stages. Our data are in line with recent studies showing a hormonal effect on the protein cargo of exosomes/EVs derived from the uterus. Greening et al. ,  [19]  showed that the exosome protein cargo derived from a human endometrial cell line was also under hormonal regulation. In this case, the authors treated endometrial cells with estrogen (E) or estrogen plus progesterone (E + P) during culture to mimic the hormonal profile in the proliferative and secretory phases of the menstrual cycle.
Based on the results from the Bioanalyzer and the obtained mRNA-sequencing data, we conclude that bovine oEVs isolated with the protocol used in our study are containing full-length mRNAs and ncRNAs. The profile of the total RNA looked similar to total RNA isolated from cells or tissues. Furthermore, the analysis of exon coverage of the RNA-seq data (Additional file 2: Figure  S12 , IGV Sashimi plots) suggested the presence of complete undegraded transcripts. In addition, this analysis for some selected genes suggested the existence of splicing variants of mRNAs identified in oEVs. Functional annotation revealed clusters of related terms with highest enrichments scores for the 500 most abundant transcripts in S1, were associated to protein translation and transport, extracellular vesicles, gene expression, response to stress and immune response and response to steroid hormones.
Only for snRNA, a partial grouping of S1-S2 and S3-S4 samples was observed. Despite no clear separation, higher differences in sncRNA were also found between S1 and S4 than between the rest of stages analyzed. Regarding the miRNA content in oEVs, miRNAs represented with 11-18% of total read counts in the small RNA data set the most abundant RNA type after the fragments derived from rRNAs and tRNAs. Their importance as regulators of gene expression is well known as well as their important role in embryo development and embryo-maternal communication  [53]. To date, dynamic changes of miRNA expression have been shown in both gametes and early embryos in different mammalian species  [54] [55] [56]. Here, we demonstrated that oEVs miRNA content, which may be transferred to both gametes and embryos during the early reproductive events, is also dynamic and slightly under hormonal regulation.
Pooled barcoded libraries were run on two lanes of a single flow cell generating between 4 and 11 million single-end reads (125 bp) per sample. The obtained sequence reads (Fastq files) were analyzed with an established analysis pipeline integrated in a local Galaxy installation  [81] a tt h e Animal Physiology group, ETH, Zurich. First, the sequence reads were processed with Trim Galore! to remove low quality from the 3′ end, adapter sequences, and sequences below a minimal length of 50 nt after the trimming steps. Subsequently, removal of PCR duplicates was done using FastUniq tool. Then, the tool Trimmomatic was used to cut 9 bases from the start of the read (HEADCROP) as these bases are derived from the molecular code contained in the adapter sequence. To control the performance of the processing steps, all Fastq files were quality checked after each processing step with FastQC (v0.
This count table was the basis for the subsequent statistical analysis. From a total of 20 samples in mRNA seq-dataset, 2 outliers (S1_R5; S4_R3) were removed because the low integrity of mRNA (RIN < 5) and other 2 outliers (S2_R4, S3:_R1) were removed because of the presence of high amount of universal adaptor after clipping. Then, the cutoff was set to 3. 93 CPM and for at least 2 out of 16 libraries. The analysis of differential mRNA expression was performed using BioConductor package EdgeR  [83]. Data normalization was performed on library size (TMM normalization)  [84]  and with GLM robust (estima-teGLMRobustDisp)  [85]  function. For comparison of the experimental groups, the following contrasts were set: S2 versus S1; S3 vs. S1 and S4 vs. S1. An adjusted p-value (false discovery rate, FDR) of 0.
1% was used as threshold for significance of differentially abundant mRNA sequences. RNA-Seq data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO SuperSeries GSE110444 (mRNA data: GSE110399 and ncRNA data: GSE110443). Access SuperSeries RNA data: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE110444 Access Subseries mRNA data: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE110399 Small RNA library preparation, sequencing and data analysis RNA was isolated from the same 20 EVs samples (5 replicates, 4 stages) but using miRNeasy Mini kit (QIA-GEN) according to the manufacturer's instructions. A total of 444 ng RNA was used for the preparation of each small RNA library using NEXTflex™ Small RNA-Seq Kit v3 (Bioo Scientific). Sequencing of the small RNA libraries was conducted on an Illumina HiSeq 2500 instrument at the FGCZ.
g2. bx. psu. edu/). The resulting table contained the unique sequences and the number of reads per sample. This count table was filtered to remove sequences with negligible read counts, comprising sequencing errors or sequences with very low evidence for potential expression by using CPM per sample filtering tool  [82]  (https://www. bioconductor. org/packages/devel/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide. pdf. ) The minimum library size and potential CPM cutoff (Counttable statistics, in-house tool) was calculated and the cutoff set to 8. 41 CPM (corresponding to an average of 20 reads per library) for at least 3 out of 20 libraries. This count table was the basis for the subsequent statistical analysis and to annotate the sequences. From a total of 20 samples in small RNA seq-dataset, 2 outliers (S2_R1; S4_R5) were removed because of the low integrity of mRNA (RIN) and the presence of high amount of universal adaptor after clipping).
Then, the cutoff was set to 8. 41 CPM and for at least 3 out of 18 libraries. Small RNA sequences were annotated based on BLAST (Basic Local Alignment Search Tool) searches against a number of sequence databases. The collection of BLAST databases contained sequences from miRBase (bovine, human, and porcine precursor and canonical mature miRNAs), transcript sequences from NCBI and Ensembl, including non-coding RNAs, as well as tRNA and piRNA cluster sequences retrieved from the NCBI Bos taurus UMD 3. 1. 1 GFF3 file and http://www. smallrnagroup. uni-mainz. de/piRNAclusterDB. html, Rosenkranz D. piRNA cluster database: a web resource for piRNA producing loci. Nucleic Acids Research 2016 44(D1):D223-D230, respectively. The current miRBase release 21 contains 808 precursors and 793 mature bovine miRNAs.
The analysis of differential abundance of sequences was performed using BioConductor package EdgeR  [83]. To normalize the read count data, the dataset was processed with TMM normalization  [84]  and GLM robust (estimateGLMRobustDisp)  [85]. For comparison of the experimental groups, the following contrasts were set: S2 versus S1; S3 vs. S1, and S4 vs. S1. An adjusted p-value (false discovery rate, FDR) of 5% was used as threshold for significance of differentially expressed sequences (separately for ncRNA, rRNA, snoRNA, snRNA, tRNA, and miRNA sequences). Expression values (counts per million, cpm) of identified differentially expressed sequences (DES) were further processed in R to perform hierarchical cluster analysis by samples and DES [Gregory R. Warnes et al. , https://CRAN. R-project.
org/package=gplots 2016]. Small RNA-Seq data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO SuperSeries GSE110444 (mRNA data: GSE110399 and ncRNA data: GSE110443). Access SuperSeries RNA data: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE110444 Access Subseries ncRNA data: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE110443.
Nano LC-MS/MS analysis Peptide mixtures were analyzed by nanoflow liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS). All experiments were performed on a LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) coupled to an Ultimate® 3000 RSLC Ultra High Pressure Liquid Chromatographer (Dionex, Amsterdam, The Netherlands) controlled by Chromeleon Software (version 6. 8 SR11; Dionex, Amsterdam, The Netherlands). Six microliters of each sample were loaded on trap column for desalting and separated using nano-column as previously described by Labas et al. [87]. Three nanoLC-MS/MS analyses were performed for each in vivo and in vitro EVs preparations. Data were acquired using Xcalibur software (version 2. 1; Thermo Fisher Scientific, San Jose, CA). The instrument was operated in positive data-dependent mode.
Data processing and statistical analysis MS/MS ion searches were performed using Mascot search engine v 2. 2 (Matrix Science, London, UK) via Proteome Discoverer 2. 1 software (ThermoFisher Scientific, Bremen, Germany) against a locally maintained copy of NCBInr (571,109 Mammalia entries, download January 2016). The parameters used for database searches included trypsin as a protease with two missed cleavages allowed, and carbamidomethylcysteine, oxidation of methionine and N-terminal protein acetylation as variable modifications and peptide charge 2 and 3+. The tolerance of the ions was set at 5 ppm for parent and 0. 8 Da for fragment ion matches. Mascot results obtained from the target and decoy databases searches were subjected to Scaffold 4 software (v 4. 4. 7, Proteome Software, Portland, USA) for validation.
Peptide identifications were accepted if they could be established at over 95. 0% probability as specified by the Peptide Prophet algorithm  [88]. A false discovery rate was calculated as < 1% at the peptide or protein level. For comparative analysis, we employed Scaffold Q+ software (version 4. 4, Proteome Software, Portland, USA) for spectral counting label-free quantitative method with "Weighed Spectra" option on protein clusters. A t-test was performed to characterize changes between in vivo and in vitro EVs preparations. Differences were considered statistically significant for t-tests (phase of cycle comparison) at p-value< 0. 05. Limits of an average normalized weighted spectra (NWS) of ≥5 and fold change/ratio of ≥2 were included to increase validity of any comparisons made. MS data have been deposited to the ProteomeXchange Consortium via the PRIDE  [89]  partner repository with the dataset identifier PXD008851 and https://doi.
Data mining and bioinformatics analysis Gene symbols and Entrez Gene IDs (bovine and putative human orthologs) were mapped for all transcripts, small RNA and protein identifications and analyzed using bioinformatics custom tools integrated in a local Galaxy  [81]  installation. Custom database tools (NCBI annotation mapper, Mammalian Ortholog and Annotation database, MOADb; Bick J, ETH Zurich, unpublished results 2017) were used to assign known or putative human orthologous genes. Human gene identifiers or symbols were used for subsequent functional annotation. Functional analysis of transcripts and proteins contained in EVs was performed using DAVID 6. 8 Functional Annotation (https:// david. ncifcrf. gov) [90, 91]. DIANA-miRPath v3. 0 and Tar-Base v. 7 was used for gene target analysis from candidate miRNA and further functional analysis of target genes and clustering.
Acknowledgements We would like to thank to  Professor Susanne Ulbrich  ( ETH Zurich, Animal Physiology ) for kindly allow us to use her labs to prepare the RNA-seq libraries and the  Functional Genomics Center Zurich (FGCZ)  for performing Illumina sequencing. Authors are grateful to  Jochen Bick  ( ETH Zurich ) for his bioinformatics support using Galaxy custom tools. Moreover, the authors thank to  Lucie Combes-Soia  (PAIB,  INRA ) for the help with mass spectrometry analysis. Additionally, the authors thank to  Ana Clara Mendes de Ávila  ( FZEA-University of São Paulo ) for helping with miRNA RT-PCR and Nanoparticle analysis and also, to  Anastasiia Garanina  ( Laboratoire Biologie Cellulaire et Microscopie Electronique, Faculté de Médecine, Université François Rabelais, Tours, France ) for helping with TEM.
Funding This work was supported by the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills fellowship , under grant agreement  n°267196. This work was also supported by  INRA   CI PHASE grant , by  COST Action-EPICONCEPT-FA1201  and  COST Action-SLAAM-BM1308. The high-resolution mass spectrometer was financed (SMHARTproject no. 3569 ) by the  European Regional Development Fund (ERDF) , the  Conseil Régional du Centre , the  French National Institute for Agricultural Research (INRA)  and the  French National Institute of Health and Medical Research (Inserm).
Availability of data and materials All data generated during this study are included in this published article and its supplementary information files. Moreover, proteomic data are available in PRIDE partner repository with the dataset identifier PXD008851 and https://doi. org/10. 6019/PXD008851. ( https://www. ebi. ac. uk/pride/ archive/). RNA-Seq data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO SuperSeries GSE110444 (mRNA data: GSE110399 and ncRNA data: GSE110443). Access SuperSeries RNA data: https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE110444 Access Subseries mRNA data:https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE110399 Small RNA-Seq data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO SuperSeries GSE110444 (mRNA data: GSE110399 and ncRNA data: GSE110443).
Quantification of plant traits To follow the kinetics of plant growth, four plants per replicate were analyzed at 10 (T1), 17 (T2), 24 (T3), 33 (T4) and 37 (T5) days after sowing (DAS). Standard parameters were recorded: number of leaves per plant, shoot and root fresh weight, plant leaf areas, plant height and root length. Statistical analyses were performed using the R software  [38]. Data were compared between healthy and diseased plants using linear models [LMM; function "lmer", package "lme4",  [39] ]. Pairwise comparisons of least square means (LSMeans) were performed using the function "lsmeans" [package "lsmeans",  [40] ] and the false discovery rate (FDR) correction for p-values  [41].
Symptom development and clubroot severity measurement Disease severity was assessed in inoculated plants during the vegetative stage of plant growth at 0 (T1), 7 (T2), 14 (T3), 23 (T4) and 35 (T5) days after inoculation (DAI) with P. brassicae, corresponding to 10, 17, 24, 33 and 45 DAS, respectively. Clubroot severity was recorded using the scale: 0, no visible swelling; 1, very slight swelling usually confined to lateral roots; 2, moderate swelling on lateral roots and taproot; 2+, severe clubs on all roots, but some roots remain present; and 3, no root left, only one big gall. A disease index (DI) was calculated as described by  [42] : DI = (n1*25 + n2*50 + n2+*75 + n3*100)/N, where "ni" is the number of plants in the symptom class "i" and N is the total number of plants tested. Disease data were analyzed using a likelihood ratio test on a cumulative link model  [40]  [CLMM; function "clmm", package "RVAideMemoire",  [43] ].
Pairwise comparisons of LSMeans were then computed. To measure the hypertrophy of infected root, taproot width was also assessed at each date of sampling at 1 cm under the soil surface. Taproot width data were compared between non-inoculated (or healthy) and inoculated (or diseased) plants using a linear model [LMM; function "lmer", package "lme4"]. Pairwise comparisons of LSMeans [function "lsmeans", package "lsmeans"] and FDR correction for p-values were then performed.
Measurement of pathogen DNA amount in roots by real-time qPCR Plant root colonization by P. brassicae was also monitored by quantitative PCR. The predicted 18S gene was used to estimate P. brassicae DNA amount per ng of total extracted DNA. A portion of this gene sizing 164 bp was amplified with the primers PbK1F/PbK1R (5'-TTGGGTAATTTGCGCGCCTG-3'/5'-CAGCGGCAGGTCATTCAACA-3'). All reactions were performed in 20 µL qPCR reaction using 10 µL of SYBR Green Master Mix (Roche), 1 µL of DNA (2. 5 ng) and 0. 08 µL of each primer (100 µM). Amplification conditions were as follows: 5 min at 95°C, followed by 45 two-step cycles at 95°C (10s) and 60°C (40s). Standard curves were constructed using serial dilutions of P. brassicae DNA extracted from resting spores. A linear model [LMM; function "lmer", package "lme4"] was used to analyze the pathogen DNA quantification data.
Analysis of MiSeq sequencing data After reads assembly, sequences were processed with GnS-PIPE bioinformatics platform developed by GenoSol platform and optimized for amplicons analysis  [49, 50]. The reads were filtered and eliminated if they harbored one or more ambiguities (Ns) or an average quality score below 30. A PERL program was applied to obtain strict dereplication (i. e. clustering of strictly identical sequences). After this initial quality filtering step, the reads were aligned with INFERNAL alignments  [51]  and clustered at 97% sequence similarity into operational taxonomic units (OTU) using another PERL program. All single-singletons (reads detected only once and not clustered) were then deleted in order to eliminate PCR chimeras and large sequencing errors. These final sequences were used to produce rarefaction curves.
Statistical analysis on phyla counts To identify phyla enriched in rhizosphere and root microhabitats compared to unplanted soil and to compare phyla composition between samples collected from healthy and diseased plants, we employed linear statistics on Relative Abundances (RA) values (log2 > 5‰ threshold) using a script developed from the R package "Limma"  [17]. Differentially abundant phyla between two samples were calculated using moderated t-tests. The resulting p-values were adjusted for multiple hypotheses testing using the Benjamini-Hochberg (BH) correction.
Detection of differentially enriched OTUs EdgeR is a workflow largely based on the free open-source R language and Bioconductor software  [56]. This workflow was originally used to analyze count-based differential expression of RNA sequencing as part of transcriptome studies  [57]  and was recently adapted to metagenomic data analysis  [26]. OTU counts without at least a mean of one read per sample were removed from the analysis. To normalize the data for each sample OTU count, the trimmed mean of M values normalization method (TMM) was used according to the method described by  [58]. A Log2transformation was performed on the normalized data for statistical comparisons. Threshold, normalization and transformation steps were performed using a custom R script. To identify differentially abundant genus in bacterial and fungal communities between sampling dates and treatments (non-inoculated or inoculated) in root or soil samples, EdgeR was used to fit a model with treatment (non-inoculated or inoculated) * sampling date (T1 to T5) terms to the count data in each compartment by using glmFit and glmLRT with tagwise dispersion and to test for significant effects of each term.
Acknowledgment The authors thank  Christine Lariagon  and  Yannick Lucas  for their technical assistance. They also thank  Hector Mougel  for helping to design to Fig 9 and  Maxime Hervé  for his advises on statistical analyses. The authors would like to thank the  BrACySol biological resource centre (INRA, IGEPP Ploudaniel, France)  for providing the seeds used in this study. This work has benefited from the involvement of bioinformatics service of the GenoSol platform from the  INRA (French National Institute for Agronomic Research) of Dijon.
Transcriptome analysis Gene identifiers are from the Coffee Genome Hub (coffee-genome. org). For microarray analyses, total RNA was extracted from whole seeds as described in  Joët et al. (2014). However, since the embryo represents less than 1% of total seed mass, it was assumed that its contribution to total RNA population was negligible. Microarrays were performed at MGX transcriptomic platform (Montpellier-GenomiX, Institut de Génomique Fonctionnelle, Montpellier) using the 15k PUCE CAFE and the protocols described in  Privat et al. (2011). Microarray data are publicly available at http://www. ncbi. nlm. nih. gov/geo/ (GEO accession number GSE107949). For 454 pyrosequencing, total RNA was extracted from separated endosperm and embryos using the RNeasy™ Lipid Tissue Kit (Qiagen). cDNA were tagged independently (Titanium kit, Roche Diagnostics) then mixed for sequencing by GATC Biotech AG (Germany) using GS FLX Titanium (Roche).