dataesr/scientific-paragraphs-categorization huggingface dataset:
For classical RT-QPCR analysis (comparaison with fluidigm data and primer optimization), two microliters of cDNA (dilution 1:10) were used for amplification. RT-QPCR mixtures contained PCR buffer, dNTP (0. 25 mM), MgCl2 (2. 5 mM), forward and reverse primers (final concentration of 150, 300 or 600 nM), 1 U of HotGoldStar polymerase and SYBR Green PCR mix as per the manufacturer's recommendations (Eurogentec, Seraing, Belgium). Amplification was performed as follows: 95°C for 10 min; 40 cycles of 95°C for 15 s, 62°C for 1 min and 72°C for 30 s; finally, 95°C for 1 min and 55 °C for 30 s. The RT-QPCRs were performed using a LightCycler480 machine (Roche; Mannheim, Germany) and data were extracted using LightCycler480 software. The amount of plant RNA in each sample was normalized using actin (Os03g50890) for rice as internal control.
Gene expression was measured for 94 genes identified in the literature. Out of the 92 genes initially selected that were known to be transcriptionally regulated in the different targeted processes (Table  S2 ), only 88 were analyzed (Table  S3 ). Baseline correction (using linear derivative) and assessment of cycle threshold (Ct) values were performed by the BIOMARK HD software (Fluidigm). Fluidigm gene expression was calculated by the 2-ΔΔCt method  (Livak and Schmittgen, 2001)  using the 3 most stable references genes EF1a, EF4a and UBQ5 with the R package "fluidigr". Genes for which expression was not detected in the entire data sample have been deleted from the analysis.
Data analysis and statistics All analyses were made using R (R Core Team, 2019). For clustering analysis, a Principal Component Analysis (PCA) was performed using the R package "FactomineR". Clustering of gene expression and heatmap visualization were done with the R package "gplots. heatmap" and "ggplot2". For statistical analysis, we first identified significant differences between mock and treated plants in the gene expression analysis. Analysis of distribution of expression value (2-ΔΔCt) revealed that at least one of the distribution was not normal, thus we evaluated differences between conditions using non-parametric Wilcoxon's tests (with a Bonferroni correction). Second, in order to identify marker genes, for each gene we compared fold change values between treatments. Fold change were calculated by dividing values of the expression of each gene for each treatment by the mean of the gene in the corresponding mock condition.
Next, to identify marker genes specifically responding to only one or two treatments, we compared fold changes obtained from each gene in each treatment using a Tukey HSD (see Methods). When considering specificity of expression in this manner in our dataset, we found 37 genes specifically responding to one or two treatments out of the nine treatments covered by our RNA set. Beyond the genes considered as markers of one of our 9 treatment, we detected at least one marker gene for each treatment (OsABI2 for ABA; OsCKX2 for CK; OsCHI for defense, DREB2A/WRKY7 for Drought; OsJAZ5/OsJAZ11 for JA and OsPBZ1 for SA treatment). However, we did not identify specific marker genes for Auxin and Ethylene treatment or Nitrogen stress (Figure  3 ; Supplementary Table. S3 ).
Discussion Despite the increasing availability of Omics tools and methods, especially for transcriptomic analysis (e. g RNAseq), the high cost and the access to large computational resources often result in expensive, time-consuming analysis of a limited number of RNA samples. However, RT-QPCR gene expression is relatively easy to perform, even by non-expert users and constitute a fast and accessible method to access plant transcriptome (Fassbinder-Orth, 2014). Here we report a new database of key response genes of rice involved in different major processes in leaves. Thanks to the Fluidigm technology, we access for the first time to the simultaneous transcriptomics regulation of 88 marker genes under 9 different physiological situations. Overall, our study confirms a majority of the regulation data found in the bibliography (~ 64%) (Figure  2 ; Supplementary Table.
, 2013; Iwai et al. , 2007)  compared to ET which can mediate resistance even in rice seedling  (Nahar et al. , 2011). Altogether, these observations plead for a better and integrated comparison of pathogen defense and JA/SA/ET/ABA responses. In conclusion, we gathered 88 genes transcriptionally regulated during key rice leaf processes. Monitoring their transcriptional regulation under different treatments allowed us to confirm previous regulation known in the bibliography and to discover new gene regulations. Additionally, this pool of genes represents a unique database for researchers to rapidly access and explore at low cost rice leaf transcriptome with Fluidigm technology or with classical RT-QPCR analysis. For future experience, the available gene expression dataset produced here can be used for any transcriptomic analysis on rice leaves to characterize the impact of unknown stresses by comparing it to our treatments.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-021-04355-9. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Crystallographic structure determination Diffraction data were collected on the PROXIMA-1 beamline at the synchrotron SOLEIL (Saint-Aubin, France)  52. A crystal of holo-native Mmp10 with peptide substrate (Mmp10-SAM-peptide structure) belonging to the space group P6 3 was detected, and diffraction data were collected to 2. 4 Å with phases obtained through multiwavelength anomalous diffraction (MAD). High-energy remote data were collected using an X-ray wavelength of 0. 97857 Å and were scaled with a dataset collected at the iron absorption peak at 1. 72200 Å. Diffraction images were recorded using an EIGER-X 16M detector, processed with XDS using the XDSME package  53, 54  and corrected for anisotropy using STARANISO  55. The experimental phasing searched for one FeS cluster site, treated as a super-atom, and one cobalt site using SHARP/AutoSHARP 56.
Acknowledgements This work was supported by the  European Research Council  ( ERC consolidator grant   617053 ),  ANR  ( ANR-17-CE11-0014 ) and the  CHARMMMAT Laboratory of Excellence  ( ANR-11-LABX0039 ). We express appreciation for use of the EPR facilities available at the  French EPR network  ( IR CNRS3443 ) and the  Aix-Marseille University EPR  centre. Use of the beamline PROXIMA-1 at the synchrotron  SOLEIL (Saint-Aubin, France)  is also acknowledged.
Data availability Atomic coordinates and structure factors for the crystal structures reported in this work have been deposited to the Protein Data Bank under the following accession numbers: 7QBS (Mmp10-SAM-peptide structure), 7QBT (Mmp10 MTA_1 structure), 7QBU (Mmp10 MTA_2 structure) and 7QBV (Mmp10-SAH structure).
DNA sequencing and phylogenetic analysis of aecial clusters Prior PCR amplification, DNA was extracted from dried single aecial clusters using… Aecial sequences were assembled and aligned using the QIAGEN CLC Genomics Workbench v. 20. 0. 4 (https://digitalinsights. qiagen. com) and apparent misalignments manually examined. After trimming, all sequences were aligned and a consensus sequence generated for each sample. The Basic Local Alignment Search Tool (BLASTN) were used to find similarities between sequences in GenBank, NCBI  (Altschul et al. , 1997). Rust specie identity of samples was determined by the maximum identity observed compared to sequences available in GenBank. A maximum-likelihood phylogenetic tree was generated using the Neighbor Joining construction method with bootstrap values generated from 1000 replicates.
Population genetic analyses of uredinial isolates The "poppr" package version 2. 9. 3 implemented in the R environment was used for genetic analyses of sexually derived uredinial isolates from three populations, i. e. , Spain, England and Switzerland, and from three cereal hosts, i. e. , wheat, barley, and rye  (Kamvar et al. , 2014; Kamvar et al. , 2015). Population genetic analyses included number of observed MLGs showing genotypic richness, G/N number of MLGs divided by number of samples, Simpson's index (lambda) measuring genetic diversity  (Simpson, 1949) , evenness distribution of genotype abundance (E5)  (Grünwald et al. , 2003) , and Nei's unbiased gene diversity (Hexp)  (Nei, 1978). Analyses of molecular variance (AMOVA) and subsequent significant tests were performed on clone corrected data to evaluate molecular differences among populations and host of origin  (Excoffier et al.
, 1992). The fixation index (Fst) measuring pairwise divergence between populations, the number of effective migrants (Nm = [(1/Fst)-1]/4) and Nei´s genetic distance were assessed using GenAlEx 6. 503 with 9999 permutations and bootstraps  (Wright, 1965; Nei, 1972; Peakall and Smouse, 2006; Peakall and Smouse, 2012). Pearson´s correlation coefficients (ρ) were calculated to measure the linear correlation between Fst, Nm and Nei's genetic distance values. The population structure of related sexually derived uredinial isolates was analysed by Discriminant Analysis of Principal Components (DAPCs) using the "adegenet" package version 2. 1. 5 implemented in the R environment  (Jombart, 2008; Jombart et al. , 2010). The DAPC, which transforms the MLG genetic data using PCA and subsequently performs a discriminant analysis (DA) on the PCs retained, is a multivariate non-parametric method without any predetermined population genetic model with respects to Hardy-Weinberg equilibrium or linkage disequilibrium  (Jombart et al.
Genetic diversity and variance of sexual populations Prior analysis, the suitability of the SSR markers was assessed by generating a genotype accumulation curve that plots the number of multilocus genotypes (MLGs) against the number of loci (Supplementary Figure  1 ), which showed that the 19 loci applied in the present study were sufficient to discriminate between individuals in the dataset. Population genetic analyses was performed on a subset of 192 single stem rust isolates collected from wheat, rye and barley. The 19 SSR markers identified 135 multilocus genotypes (MLGs) (Table  2 , Supplementary Table  3 ). The allelic genotypes of each of the 192 isolates and their host of origin and corresponding MLG at the 19 SSR loci are provided in Supplementary Table  4. The highest number of MLGs was detected in the Spanish population followed by UK and Switzerland.
■ EXPERIMENTAL SECTION Mi-Filter Script. The mi-filter script was written in python and is available at the Github repository (https://github. com/ stengellab/mi-filter. git). It is tailored to xQuest  17  output tables but can, in principle, be applied to cross-linking MS data sets obtained by any of the established cross-link-identification software platforms such as MeroX,  18  Xlinkx,  19  Xi, 20 pLink2,  21, 22  or RNPxl. 23 It selects proteins from the input data set, which contain at least one mono-or intra-protein link and subsequently filters for inter-protein cross-links within this list. It also calculates a decoy ratio (using the ratio of the target and decoy links) at each ld-Score cutoff for monolinks and inter-protein and intra-protein cross-links separately. In detail, the mi-filter script works as follows: it filters the input files for a specified ld-Score, then concatenates the input data frames, and, if specified, filters for biological replicates of cross-linking sites (therefore, input files must be sorted by biological replicates).
In the next step, it adds a "decoy" column to the concatenated data frame. In the "XLtype" column, strings are replaced in a way that only three types of cross-link are left: monolinks and intra-protein and interprotein cross-links. Proteins without a monolink or an intraprotein cross-link are then filtered out by the parameter "-mi" when running the mi-filter program. 26S Proteasome Cross-Linking Data Set. Purification of yeast 26S proteasomes was performed as described in a previous study. 24 S. cerevisiae cells (YYS40; MATa rpn11::RPN113FLAG-HIS3) were grown for 48 h and harvested in the stationary phase. The purification of 3XFLAG-tagged 26S proteasome was carried out by affinity purification using M2 anti-FLAG beads (Sigma A2220). After incubation for 1. 5 h at 4 °C, the proteasome was eluted with FLAG peptide.
1% formic acid for 4 min, 5-35% acetonitrile in 0. 1% formic acid in 75 min, and 35-80% acetonitrile in 1 min). Full scan mass spectra were acquired in the Orbitrap at a resolution of 120,000, a scan range of 400-1500 m/z, and a maximum injection time of 50 ms. Most intense precursor ions (intensity ≥5. 0 × 10 3 ) with charge states 3-8 and monoisotopic peak determination set to "peptide" were selected for MS/MS fragmentation by CID at 35% collision energy in a data-dependent mode. The duration for dynamic exclusion was set to 60 s. MS/MS spectra were analyzed in the iontrap at a rapid scan rate. For the cross-link identification of the 26 proteasome in a "proteome-wide setting," a database was compiled which contained the 34 proteins of the 26S proteasome plus the 200 most abundant proteins in S.
cerevisiae as annotated in the PAX database (https://pax-db. org/). MS raw files were subsequently converted to centroid files and searched using xQuest in ion-tag mode. Cross-links were exported as. tsv files with the filter settings ΔS = 95 and a max. ppm range from -5 to 5, containing all (nonunique) identifications. The mi-filter was applied to different ld-Score cut-offs (20, 25, 28, and 32) before comparing the ratio of target to decoy hits for each data set before and after mi-filtering (Supplementary Data 1 and Figure  3A, B ). Pre-60S Ribosome XL-MS Data Set. The data set consists of biological triplicate measurements of 12 different pre-60S ribosomal particles, which were enriched using affinitytagged RBFs and were collected as part of another study. 25 The mi-filter was applied to different ld-Score cut-offs (20, 25, 28,  and 32) and target to decoy hits compared before and after mifiltering (Supplementary Data 2 and Figure  3C, D ).
Proteome-Wide Cross-Linking Data Set. The data set contains biological triplicate measurements of cell lysate in Saccharomyces cerevisiae and was collected as part of another study. 16 xQuest results from this paper were directly downloaded and the cross-linked sample using equimolar concentrations (1×) of BS3 as a cross-linker was chosen for further analysis. The mi-filter was applied to different ld-Score cut-offs (20, 25, 28, and 32) and target to decoy hits compared before and after mi-filtering (Supplementary Data 3 and Figure  3E, F ). Mapping of Filtered Cross-Links. Cross-link networks were visualized with xiNET. 26 ata Availability. The MS raw files, the cross-link databases, and the original xQuest result files have all been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  27  with the project accession number PXD031215.
cerevisiae cell lysate  16  (Figure  3E, F ). We first had a closer look at the relative abundance of proteins that were filtered out by the mi-filter, taking the pre-60S ribosomal particle data set as an example. 25 Here, proteins for which a mono-or intra-protein link was detected are in average of significantly higher abundance than proteins without    31  that is, increasing levels of agreement between the measured experimental and in-silico-generated reference spectra. The relative proportion of detected decoy hits to all detected hits (y-axis, right) versus the respective ld-score setting (x-axis) is plotted on the right y-axis (symbols). Figure  3. Comparison of false-positive assignments for inter-protein cross-links with and without the mi-filter. Inter-protein cross-links are shown for three different types of datasets representing typical experimental set-ups including the 26S proteasome from S.
We, therefore, took the totality of MS and MS/MS spectra that we had experimentally obtained from a sample of crosslinked 26S proteasome and searched it in a proteome-wide setting (i. e. , against a large protein database; see Experimental Section for details) using a constant score cutoff with and without the application of the mi-filter (Figure  4 ). Our data show that the application of the mi-filter did not only lead to a significant reduction of detected decoy hits. When mapped again onto the published high-resolution cryogenic electron microscopy structure of the S. cerevisiae 26S proteasome (PDB 4CR2), over 90% of our mi-filtered interprotein cross-links fall within 35 A°(Supplementary Figure  2 ), indicating that our mifiltered cross-links are also structurally accurate.
While we have used the xQuest cross-linking software in this manuscript as an example to identify cross-links, our mi-filter can in principle be applied to any cross-linking software. We therefore suggest its use as a tool to minimize false-positive assignments for inter-protein links prior to FDR estimation using the respective workflow of choice. Taken together, our mi-filter greatly enhances the reliability of individual inter-protein cross-links in any type of crosslinking data and therefore their ability to provide reliable and biologically relevant positional information as a source of a PPI.
* sı Supporting Information The Supporting Information is available free of charge at https://pubs. acs. org/doi/10. 1021/acs. analchem. 2c00494. Relative abundance of inter-protein cross-links with and without additional mono-or intra-protein cross-link; mapped distances of all inter-protein cross-links within the mi-filtered 26S proteasome data set at Id 25; and mapping of identified cross-links with and without mifiltering (PDF).
| Sampling of additional date palms and Phoenix spp. In the first part of this study, we explored the intra-named type genetic variability of barnī through genome sequencing of 23 barnī palms sampled in al-'Ul a region, analyzed along with the genomes of 12 other date palms (Table  1 ; Figure  1 ; Dataset S1). Specifically, three accessions from other regions, having either the same name or a name that may also be used to refer to barnī, were included. The first one is a "barnī," identified as such by the owner, sampled in the oasis of Khaybar located 200 km from al-'Ul a (Figure  1 ). The second one is also named "barnī" and is grown in al-Kamil, in Oman. The third is a GenBank accession (from  Hazzouri et al. , 2019)  of a date palm cultivated in the UAE (al-Shiwayb) and called "mabrouma" by its owner, easily associated with the name given in al-'Ul a to a category of barnī dates, mabr um (see Results).
Further, three sets of clonal accessions consisting of one quartet and two triplets were sampled to calibrate the analyses aiming at identifying whether those palms represent a single clonal line (Table  1 ; Figure  1 ; Dataset S1; Figure  S2 ). A quartet of barnī clones was constituted by sampling one accession a first time in 2019 (barnī_00036) and re-sampling the same palm 2 years later (barnī_00036A), along with two of its offshoots (barnī_00036r1 and barnī_00036r2). The two triplets were constituted by a male palm and a female variety (ḏakar_00254 and 'as ela_00169A, respectively) sampled along with two of their offshoots (ḏakar_00254r1/ḏakar_00254r2 and 'as ela_00169r1/'as ela_00169r2, respectively). In the second part of the study, we assessed the diversity of date palms in al-'Ul a by analyzing three unique genomes from this region (barnī_000268, ḏakar_00254 and 'as ela_00169A) along with 112 Phoenix spp.
genomes from previous studies (Dataset S1)  (Flowers et al. , 2019; Gros-Balthazard et al. , 2017 , 2021; Hazzouri et al. , 2015). The detailed protocol for read processing, genome alignment, variant calling and filtering may be found in Methods S1. Briefly, we filtered reads based on quality and length before aligning them to the Barhee BC4 reference genome assembly  (Hazzouri et al. , 2019). We carried out low depth sequencing and performed population genetic analyses to identify the intra-varietal genetic variability by computing the genotype likelihoods from short read alignments. For the second part of our study, we called variants from higher coverage data using GATK v4. 2. 0. 0  (McKenna et al. , 2010)  and filtered sites and genotypes based on several criteria detailed in Methods S1.
We Further, genetic distances among those samples were computed using ngsDist v1. 0. 10  (Vieira et al. , 2016). ANGSD was used to compute genotype posterior probabilities with the same filtering options as above and downsampling the sites to obtain $10,000 sites. Bootstrap replicates (n = 100) using blocks of 20 sites were generated, and fastME v2. 1. 5 (Guindon & Gascuel, 2003)  was employed to compute the trees with default parameters. The consensus function from ape R package  (Paradis & Schliep, 2019)  was run to obtain a consensus tree where nodes found in 90% of the 100 bootstrap trees were represented. Finally, TreeDyn v198. 3  (Chevenet et al. , 2006)  was used for plotting with mid-point rooting via the phylogeny. fr web interface  (Dereeper et al. , 2008). with a cross-validation of 100, and a principal component analysis (PCA) was run with the pcadapt  (Luu et al.
, 2017 ) R package v4. 3. 3 filtering out SNPs with minor allele frequencies below 5%. A maximum likelihood tree was generated using RAXML-NG  (Kozlov et al. , 2019)  v0. 9. 0. To do so, 20 maximum likelihood tree searches were performed using 10 random and 10 parsimony-based starting trees. The best scoring topology was picked and checked for robustness by performing 100 bootstrap replicates. F I G U R E 2 The fraction of heterozygote sites for each date palm accession was calculated using pixy  (Korunes & Samuk, 2021). Finally, admixture tests were performed using the admixr R package v0. 9. 1  (Petr et al. , 2019)  (Methods S2). To gain insight into the maternal origins of the date palms from al-'Ul a, a bootstrapped chloroplast DNA tree was constructed using the Neighbor-joining method with the phangorn v2.
| What lies behind the name barnī? To study the genetic variability within the barnī, we sequenced the genomes of 35 date palms, including 23 barnī from al-'Ul a collected in the four agricultural contexts (Figures  1  and 2 ), and obtained between 18 and 122 million of paired reads of size >76 bp (Table  1 ; Dataset S2). Sequencing reads from these runs and those retrieved from GenBank SRA were mapped to the Barhee BC4 genome assembly  (Hazzouri et al. , 2019) , resulting mostly in low coverage alignments, ranging from 2. 9Â to 20. 5Â (9. 3Â on average; Dataset S2). We assessed whether 26 barnī and mabrouma date palms from Saudi Arabia, Oman and the UAE are genetically identical using the King-robust kinship estimator  (Waples et al. , 2019)  (Figure  4a ; Dataset S3), a principal component analysis (Figure  4b ), and a tree based on genetic distances (Figure  4c ; Figure  S3 ).
482, while kinship estimates for the mother palm and two offshoots ranged between 0. 481 and 0. 499 (Figure  4a ; Dataset S3). Sequences of two clone triplets (ḏakar and 'asel a) that each consisted of a palm tree and two of its offshoots each yielded a kinship estimate of 0. 499. Since these samples are known to be clonal, we attribute any differences in kinship estimates from the expectation of 0. 5 to be attributable to our low coverage approach. In the PCA, the accessions from each triplet/quartet of clones overlap (Figure  4b ), while they group together in separate clades in the genetic distance tree (Figure  4c ; Figure  S3 ), as expected given their genetic identity.