dataesr/scientific-paragraphs-categorization huggingface dataset:
SGT1 (Suppressor of G2 allele of SKP1) is required to maintain R proteins in an inactive but signaling-competent state. Nicotiana tabacum N protein belongs to the Toll-Interleukin Receptor (TIR)-NB-LRR class of R proteins and confers resistance to Tobacco Mosaic Virus (TMV). Using mass spectrometry, confocal microscopy and pathogen assays  Hoser et al. (2013)  showed that SIPK phosphorylates SGT1 and that its phosphorylation state determines its nuclear accumulation and N-mediated resistance to TMV. These data suggest that nucleocytoplasmic partitioning of N protein is determined by MAPK-dependent SGT1 phosphorylation. Wounding of plants can be caused by mechanical stress or by herbivore and insect attack and is therefore a stress that bridges abiotic and biotic responses. Hence, WIPK (wound-induced protein kinase) also functions during pathogen responses in tobacco and phosphorylates WIPK interacting factor (NtWIF)  (Yap et al.
ACKNOWLEDGMENTS This work was supported by the  King Abdullah University of Science and Technology  ( BAS/1/1062-01-0 ,  URF/1/2574-01-01 , and  URF/1/2965-01-01 ). The IPS2 benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). We also would like to thank the reviewers for their useful suggestions.
translucens (49 days following inoculation with the AM fungus) and collected the infiltrated zones and roots after 24 h. Global transcriptional and proteomic analyses were performed on both organs using RNA-seq and LC-MS/MS, respectively. To gain an overview of the differentially expressed transcripts and proteins, we analysed the DEGs and DEPs obtained comparing leaves of C and M plants and root of M plants following pathogen infection (LMX vs LCX; RMX vs RM). In leaves, we detected 28 up-and 69 down-regulated genes (Table  S14 ), and 96 up-and 196 down-regulated proteins (Table  S15 ) (Fig. 6A-D ). Omics data revealed an unexpected metabolic repression when the pathogen was inoculated on mycorrhizal plants. In this LMX vs LCX comparison, no over-represented biological process GO terms were identified at the transcriptomic level; however in LMX plants, the genes and proteins repressed during pathogen infection are mainly involved in AA metabolism (i.
These data support the concept that positive mycorrhizal growth responses arise largely from the increased uptake of growth-limiting nutrients  96. Our work also reports a strong impact exerted by the AM fungus on primary metabolism and phytohormone regulation of the host plant at both the local and systemic level, suggesting that other metabolic pathways are involved in these growth responses. Meyer and colleagues  97  proposed that the increased biomass in Arabidopsis is orchestrated by a combination of metabolites that act synergistically. Among them, sugars such as sucrose, members of the TCA cycle and metabolites involved in membrane/phospholipid biosynthesis such as G3P are involved in the control of plant growth and development. Several wheat genes and proteins involved in these carbohydrate and lipid pathways were strongly induced in both RM and LM plants, suggesting that the increased biomass in AM-infected plants is related to the general mechanisms described for A.
thaliana. In conclusion, both greenhouse and growth chamber experiments demonstrated growth benefits to wheat following AM inoculation. The omics results confirm previous findings (e. g. , improved expression of nutrient transporters), but also provide novel information: a complex pattern of local and systemic changes in gene expression with similarities and differences between roots and leaves, changes in nutrient and AA contents, and effects on seeds. Bars not accompanied by the same letters are significantly different at the 5% level using Tukey's test. At least two processes guarantee the bio-protection effect exerted by AM symbiosis against a leaf pathogen. The bio-protective role of AM symbiosis against crop pathogens has long been recognised on the basis of experimental data showing significant reductions in disease symptoms  8, 32, 33.
Quantitative Real-time was used to measure the expression of nine genes shown to be differentially regulated by RNA-Seq. Single-strand cDNA was obtained as described in Vallino et al. 48. Three biological replicates were conducted for each condition. Quantitative real time PCR experiments and data analysis were carried out as described in Vallino et al. 48. The primer names and corresponding sequences are listed in Table  S1. RNA-seq transcriptome sequencing. The preparation of mRNA libraries was done by TrueSeq Stranded mRNA Sample (Illumina) Preparation. The library quality validation (fragment concentration and length) was performed by Fragment Analyzer using the standard and high sensitivity DNA kits (Agilent Technologies, Santa Clara, CA) and by qPCR (LightCycler 480; Roche Diagnostics, Meylan, France).
Bioinformatic analysis of transcriptome data. Mapping and alignment of sequence reads was done with Burrows-Wheeler transform (BWA-MEM algorithm) using the BWA-MEM algorithm. Reads were subsequently aligned to T. aestivum coding sequences (CDS) (IWGSC1. 0 + popseq. 29) and non-coding RNA (ncRNA). Subsequently, sequence counts were collected by Samtools idxstats filtering of bam alignment files for reads with MAPQ ≥ 3. Differentially expressed genes (DEG) were called via DESeq. 2 1. 10. 0 Bioconductor package using local fit and betaPrior parameter set to False. Independent filtering was enabled  106. A false discovery rate threshold of 0. 05 was set for DEG calling. Sample clustering and principal component analyses were performed upon variance stabilizating transformation of expression data.
Transcripts were considered differentially expressed when the adjusted FDR values are below 0. 05 and the logarithmic fold change over 0. 5 or when the logarithmic fold change was over 2. The transcriptomics data related to roots and leaves of mycorrhizal plant and roots and leaves of mycorrhizal plants infected by X. translucens have been deposited in the European Bioinformatics Institute (EMBL-EBI) ArrayExpress database (https://www. ebi. ac. uk/arrayexpress) with the dataset identifier E-MTAB-5898, while the transcriptomic data from control plants and control plants infected by X. translucens with the dataset indentifier E-MTAB-5891  73. Unless otherwise stated, further graphical outputs were generated with custom R and Python scripts.
The samples were acquired using internal lock mass calibration on m/z 371. 1010 and 445. 1200. Proteomic data processing and bioinformatic analysis. Mass spectrometer raw files were analyzed by MaxQuant (version 1. 5. 3. 28 and 1. 5. 3. 30) with the match between runs (matching time window of 2 min) and label free quantification (LFQ) options selected. Tandem MS spectra were searched against UniProt T. aestivum (Version 2015-10, 100,800 entries), Uniprot Rhizophagus irregularis (Version 2015-10, 29,847) and Uniprot Xanthomonas translucens (Version 2015-10, 14,378 entries). Trypsin/P was chosen as the protease, cysteine carbamidomethylation was set as fixed modification, and oxidation of methionine and acetylation of the N-terminal as variable modifications. Peptide tolerance was set to 4. 5 ppm, while MS/MS tolerance was set to 0.
5 Da. Peptide-spectrum matches (PSMs) and proteins were validated with 1% FDR. Only PSMs with a minimum length of 7 amino acids were kept. The raw data was first processed by Perseus (MaxQuant package) and the incorrected protein identifications (contaminants, decoy and "only identified by site" entries) were removed from the main data frame. LFQ intensities were Log2 transformed. Only protein groups detected in almost two of three biological replicate samples sharing the same treatment and tissue were considered unambiguously identified and were used for assessment of significant change. Missing value were imputed using the R package 'imputeLCMD' (https://cran. rproject. org/ web/packages/imputeLCMD/). The experimental quality was checked using the multi-scatter plots tool for the analysis of Pearson correlation between the samples.
To determine the differentially expressed proteins (DEPs) among considered conditions, we performed a multiple sample test using Anova with a permutation-based false discovery rate (FDR) cutoff of 0. 01. For the annotation of the unknown proteins a blast search was made against the Uniprot database viridiplantae (Version 2015-10, 3398870 entries), taking the first hit with a valid annotation. For the GO analysis of DEPs we used agriGO v2. 0 (Tian et al. 2017) and for the cluster analysis, we used the "Profile plot" tool of Perseus program. Aminoacid analysis. HPLC-grade water, HPLC-grade methanol (MeOH), formic acid, aspartic acid (Asp), asparagine (Asn), glutamine (Gln), glutamic acid (Glu), serine (Ser), threonine (Thr), cystine (Cys), alanine (Ala), proline (Pro), valine (Val), methionine (Met), tyrosine (Tyr), leucine (Leu), phenylalanine (Phe), tryptophan (Trp), lysine (Lys), histidine (His) arginine (Arg) ornithine (Orn), pipecolic acid (Pip), citrulline (Cit), GABA and two deuterated internal standards (L-Phenyl-d5-alanine and L-alanine 15 N) were purchased from Sigma-Aldrich Co.
All parameters such as the wavelength and the bandpass were set according to the recommendations of the instrument Cookbook. The phosphorous content determination was performed as reported by Chen and Toribara 107. Detection of carbonylated proteins. Carbonylated proteins were detected in all samples collected at 1 dpi and 14 dpi, as described previously  71. Protein carbonylation index (arbitrary units) was measured as ratio between the optical density (OD) obtained from the whole lane of the immunoblot and the OD of Coomassie stain. For each set (Leave and Roots) data (means ± SD, n = 4) were subjected to one-way analysis of variance (ANOVA) and post-hoc Tukey's test.
Acknowledgements This work was supported by the  MIC-CERES  ("Microbial eco-compatible strategies for improving wheat quality traits and rhizospheric soil sustainability") Project ( FC  Project  ID 2013-1888 ;  AF  Project  ID 1301-003 ) jointly supported by  Agropolis Fondation  (through the  "Investissements d'avenir " programme with reference number  ANR-10-LABX-0001-01 ) and  Fondazione Cariplo Research in P. B. laboratory's was also supported by  60%  (2017) Project (University of Torino). F. O. is a student of the  PhD School in 'Life Sciences and Biotechnology' at Insubria University. The authors express their thanks to the  Montpellier Genomix  for help in the pre-processing of raw RNaseq data, to  Francesco Venice  for his support in the phylogenetic tree building and to the  Functional Genomics Center Zurich (FGCZ)  for highly valuable technical support.
C. Raman Microspectroscopy Cells (~ 3 x 10 3 /cm 2 ) were seeded and incubated on (20mm diameter) CaF2 windows (Crystan Ltd, UK) for 24 hrs for both control and exposure to chemotherapeutic drug or nanoparticles. Medium was then removed and samples were rinsed twice with sterile PBS and covered with solutions of the appropriate drug solution or nanoparticle suspension concentration. After each incubation period cells were washed twice with sterile PBS and fixed in formalin (10 %, 15 min). A Horiba Jobin-Yvon LabRAM HR800 spectrometer with a 785 nm, 300 mW diode laser as source, Peltier cooled 16-bit CCD, 300 lines/mm grating and 100 μm confocal hole, was used for this work. Cells were measured either air dried or in water immersion, and spectra were acquired from the three cell locations: cytoplasm, nuclear and nucleolar in the range from 400 cm -1 to 1800 cm -1 with an x100 objective (LCPlanN, Olympus (dry), or LUMPlanF1, Olympus (immersion)) for 30 s two times, to finally produce a data set of 30 points per cell location for each cell line, over a total of 90 different cells.
D. Data Processing and Analysis Raman spectral pre-processing and analysis were performed in Matlab using algorithms developed in house. Prior to analysis, background was subtracted using a NCLS (non-negatively constrained least squares) algorithm, spectra were smoothed (Savitsky-Golay filter 3th order, 11 points), baseline corrected (fifth order polynomial) and vector normalised. Principal components analysis (PCA) was employed as an unsupervised multivariate approach to analyse data and the effects of the drug or nanoparticles in each cell localisation. The order of the PCs denotes their importance to the dataset and PC1 describes the highest amount of variation. Partial least squares regression (PLSR), a statistical regression technique which reduces the dimensionality of the data and correlates information, here represented by the Raman spectra, to values of the drug concentration inside the cells, and the time evolution, was employed to track the temporal and dose dependent evolution of the spectral signatures in the subcellular regions.
DNA samples with known genotype were used as control. Biological samples included 590 samples of DNA, 582 of serum, and a further 528 of serum collected 1 year after the first sample. Patient data were categorized into biological and clinical data and asthma severity according to the Guidelines for the Diagnosis and Management of Asthma, NHLBI 2007. Healthy individuals (HI) and asthmatic patient characteristics are detailed in Table  1.
HLA-G next generation sequencing The HLA-G gene was sequenced from position -1983 to +3447 by next-generation sequencing (NGS). A 5,430 pb long amplicon was generated by PCR; primer sequences were 5′AGGAGCTGACA CAGGAGGAA3′ and 5′CAGCTGAGCAGTGACCACAT3′; and amplification was performed using the Long Range PCR Kit (Qiagen, France). PCR fragments were sequenced using an NGS platform (MiSeq, Illumina, The Netherlands). HLA-G NGS data were analyzed through PolyPheMe, specific software developed by the Xegen Company (France). HLA-G allelic assignment at eight digits was based on the HLA sequences listed in the official IMGT/HLA database  (30). Regulatory regions were typed at low and high resolutions. Both low and high resolutions of regulatory region haplotypes were defined using PHASE and an EM algorithm implemented in the Gene[Rate] computer tools  (31).
serum shla-g Protein Measurement Soluble HLA-G (sHLA-G), i. e. , peripheral HLA-G expression, was quantified in sera from 582 asthmatic patients and 528 sera collected 1 year after the first sample. All serum samples were stored at -80°C prior to analysis. Measurement of soluble isoforms sHLA-G1 and sHLA-G5 was performed in duplicate on serum samples using the ELISA test (Biovendor ® , Czech Republic; capture antibody: MEM-G/9) according to the manufacturer's protocol. association of HLA-G alleles and haplotypes with asthmatic Features and Peripheral shla-g Dosage Missing data led to the exclusion of the concerned sample from further analyses. No multiple imputation was used. Genetic data frequencies between HI and asthmatic patients were compared with Chi-square tests. Associations between patients' asthmatic features, sHLA-G expression, and genetic polymorphism were tested with logistic regressions when the predicted variable was categorical and general linear regression when the predicted variable was quantitative.
HLA-G Full-length sequence Phylogenetic analysis HLA-G full-length sequences from position -1983 to +3447 were aligned using the multiple sequence alignment tool MUSCLE in the Molecular Evolutionary Genetics Analysis (MEGA) software version 7. 0  (32). Evolutionary analyses were conducted in MEGA7. Evolutionary relationships among HLA-G full-length sequences were inferred using the neighbor-joining (NJ) method with 1,000 replicates. Evolutionary distances were computed using the p-distance method, and units were the number of base differences per site. Analysis involved gorilla HLA-G genomic sequence as an outgroup (accession number CU104658. 1).
47) 9 (0. 77) 0. 455 H49 T C T G A G A A C G C C T A G A G * A C A A C G G C G C G G C D C G C C C A C G UTR6 G C A C A G G T A C A T C G G T A A * A C C A G G G A A C G T G I T C A T G A C G UTR7 G Variation in splicing sites was studied for alleles sharing identical or similar UTR haplotypes using the Human Splicing Finder program  (34). This program combines different algorithms to identify and predict the effect of mutations on acceptor and donor splice site motifs.
TF binding site Variations between HLA-G haplotypes Thirty-four TFs were identified to bind differentially to each 5′URR sequence by in silico analysis (Tables  5  and 6 ), their classification is provided in Table  7 ; their specific expression in blood and lungs according to NCBI and TiGER databases is given in Table  6. Interestingly, haplotype grouping based on their TF binding site profiles mirrored that observed on the phylogenetic tree (Table  6 ; Figure  1 ). Hence, haplotypes displayed different TF binding site profiles, notably in haplotype H23 (UTR3-G*01:04:01/G*01:04:04): the transition G>A at position -1155 impaired progesterone receptor (PR) and SOX5 binding sites. Besides this unique feature, this haplotype displayed similarities with H16 (UTR7-G*01:01:03:03) and H10 (UTR2-G*01:06/G*01:05N/G*01:01:02:XX).
DiscUssiOn HLA-G genetic polymorphism is extensively described in different human populations worldwide. However, there are only limited data describing the synergic effect of these SNPs, i. e. , haplotypes, in clinical studies. We fully characterized HLA-G haplotypes by NGS from position -1983 to +3447 both in a cohort of 330 HI and in 580 patients from a French asthmatic multicenter cohort. sHLA-G was quantified using the ELISA test (MEM-G/9) in sera from 582 asthmatic patients and in 528 serum samples collected 1 year after the first sample. Associations between patients' asthmatic features, sHLA-G expression, and genetic polymorphism were tested with logistic regressions. H21_UTR5_G*01:03:01:02 0. 0098 0. 0087 0. 0085 0. 0084 0. 0013 0. 0012 0. 0013 0. 0002 0. 0013 0. 0012 0. 0002 0. 0012 0.
Evolutionary distances were computed using the p-distance method, and units were the number of base differences per site. The percentage of trees in which the associated taxa clustered together is shown next to the branches. The sum of branch length equals 0. 01616800 base differences per site. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test is shown next to the branches. The tree was drawn to scale, with branch lengths in the same units (base differences per site) as the evolutionary distances used to construct the phylogenetic tree. Analysis involved gorilla HLA-G genomic sequence as an outgroup (accession number CU104658. 1).
(33) and Tissue-specific Gene Expression and Regulation (TiGER) databases(33). NCBI data included the "HPA RNA-seq normal tissues" project, i. e. , RNA-seq performed on 27 different tissue samples from 95 human individuals and the "Illumina bodyMap2 transcriptome" project, i. e. , transcription profiling by high-throughput sequencing of RNA from 16 different human tissues.
(33) and Tissue-specific Gene Expression and Regulation (TiGER) databases(33). NCBI data included the "HPA RNA-seq normal tissues" project, i. e. , RNA-seq performed on 27 different tissue samples from 95 human individuals and the "Illumina bodyMap2 transcriptome" project, i. e. , transcription profiling by high-throughput sequencing of RNA from 16 different human tissues. *01:01:03:03 MG825347 30 (4. 73) 73 (6. 27) 0. 179 Total of haplotypes with frequency above 0. 3% 592 (93. 3) 1,104 (94. 8) Significant p-values (chi-square test) are in bold. 4 Ribeyre et al. HLA-G Haplotypes and Asthma Frontiers in Immunology | www. frontiersin. org February 2018 | Volume 9 | Article 278 in.
Statistical analysis Prolificacy was analysed by logistic regression using the multinom procedure of the nnet package of R software (R Core Team 2016). An Exact Fischer's test was performed as a comparison between treatments for the number of kids born. Weights, ADG, morphometric parameters, carcass composition, total litter weight and milk production on the first 100 days of milking were analysed with Type 3 ANOVA using Anova procedure of library car of R software (R Core Team 2016). Bonferroni test was performed as post hoc suitable for unbalanced data. Age at the days of measurement was used as a covariable for analysis of weight and morphometric parameters. The duration between parturition and first weighing after parturition was used as a covariable for analysis of weight at parturition.
001; Table  1 ) whereas the growth rate of group H was 14% higher (P <. 001, Table  1 ). From 120 to 215 days of age (P4), the ADG of group H was 31% higher than that of group C (Table  1 ) whereas the ADG of group L was 22% lower. From birth to 230 days of age (P5), the ADG of group L was 23% lower than group C with a decrease of 47% of concentrate consumption (P <. 001; Table  1 ) and the ADG of group H was 14% higher than that of group C with a raise of 36% of concentrate consumption (P <. 001; Table  1 ). At 230 days of age, group L weighed 20% and 29% less than groups C and H, respectively (Table  1 ). The lightest kids weighed 25. 0, 31. 0 and 34. 4 kg for L, C and H groups, respectively. For the L group, 48% of the kids weighed over 30 kg, 36% weighed between 27 and 30 kg and 16% weighed less than 27 kg (data not shown) (Figure  2 ).
Acknowledgements We gratefully acknowledge IEPL ( Domaine de Méjusseaume ,  Le Rheu , France) support of this work, the staff of the experimental farm, the staff of the  laboratories of UMR MoSAR and Saint-Gilles , as well as Laurent Drouet (MixScience, France),  Jessie Guyager  ( Neovia,France), Cédric Faure (CCPA, France ),  Romain Bidaux  ( Cargill, France ) and  Christophe Garnier  ( Techna, France ) for their help and support of this study.
In silico design of Ap7-derived hamster-adapted strains The Ap7M hamster-adapted strain was created by introducing 10 mutations in the first subgenomic fragment (FI) used for virus production with the ISA method (see Fig. 1 ). FI region involves nucleotide genome positions 1-3919 that code for proteins C to NS2A. Among the 14 changes described in strain Ap7 (ref. 9 ), six NS and four synonymous (S) mutations were introduced into the genome of the YFV strain Asibi 34 (Genbank accession number (AN): AY640589). NS changes were located in the E (Q27H, D28G, D155A, K323R and K331R) and NS2A (T48A) proteins. We could not identify the synonymous mutation described in the NS1 coding region (theoretically in position 3274 on the complete coding sequence (CDS)). In position 4864 (NS3 coding region), our reference strain was similar to strain Ap7 and not to the original Asibi/hamster p0 (Ap0) strain described by McArthur  15.
9%) and included a NS change in the NS2A protein (T105A). The corresponding hamster died of viral infection so no specific phenotype could be attributed to those mutations. Those results globally confirm that variants at positions E/Q27H, E/D28G and E/D155A can strongly modify virulence in hamsters. Location and structural impact of mutations Q27H, D28G and D155A in the E protein of YFV strain Asibi The amino-acid substitutions Q27H and D155A were identified as sufficient to induce a pathogenic phenotype in hamsters for the Asibi strain (see above). In the structure of the YFV E protein (Asibi strain, pdb accession code 6EPK), both residues Q27 and D155 are in domain I and their side chains are involved in polar interactions that could affect the conformation of the protein (Fig. 4 ). Residues Q27 and D28 are at a β-turn connecting strand B 0 and C 0 of domain I.