dataesr/scientific-paragraphs-categorization huggingface dataset:
To test for significance of h S differences, we performed a Kruskal Wallis test followed by Dunn's post-hoc test for multiple comparisons  (Dunn, 1964)  pairwise between the three species, as well as pairwise between the three categories at the within-species level (relict, managed old and young stands). For this purpose, we used the function dunn. test of the package dunn. test v. 1. 3. 5  (Dinno, 2017)  in R version 4. 1. 3 (R Core Team, 2014). To account for multiple comparisons, we adjusted the p-value by applying the Holm method. We carried out the tests for both across the whole population set and within the Upper Rhine Valley.
Analysis of intraspecific genetic structure based on nuclear microsatellites In order to investigate intraspecific genetic structure, we ran STRUCTURE analyses using the standardized genotypic data from 140 populations at 11 nSSRs standardized across projects (AQUAREL-QREG_R nSSR dataset). For within-species analysis, we retained individuals classified as purebreds. After creating one dataset for each of Q. petraea, Q. pubescens and Q. robur, we ran STRUCTURE opting for locprior  (Hubisz et al. , 2009)  by setting the population of origin as location information and keeping all other parameters the same. We further processed the data in CLUMPAK and examined the optimal number of K inferred clusters with the same settings as in the interspecific runs described above. For genetic diversity and differentiation estimates, we included the stands assigned to a given species at the population level and consisted of at least 10 purebreds.
We further subdivided the sample into population groups according to category (relict, managed old or young). Using the R package HIERFSTAT v. 0. 5-10 (Goudet, 2005), we computed observed and expected heterozygosity (H o , H e , respectively), F ST  (Nei, 1987)  and D EST  (Jost, 2008)  as measures of genetic differentiation, as well as measures of genetic diversity. Using the R-package VEGAN v. 2. 5-7  (Oksanen et al. , 2017) , we carried out a Mantel Test to address whether genetic differentiation (F ST ) and geographic distance are correlated. Additionally, we calculated the standardized allelic richness with rarefaction  (Petit et al. , 1998)  with the software ADZE v. 1. 0  (Szpiech et al. , 2008). We compared the genetic diversity pairwise between species, but also between population categories within species by performing Kruskal-Wallis and Dunn's post-hoc tests following the procedure applied for haplotypic diversity (see above).
robur. In contrast to Q. pubescens, the gene pools of Q. petraea and Q. robur appear to be highly homogenous with particularly low F ST -values below 0,01. Isolation by distance is very weak, but significant for both species; for Q. petraea only when analysing the data with more than 20 nSSRs within the AQUAREL and QREG_R nSSR datasets. In general, use of larger marker sets with 20 or more nSSRs might improve the resolution of population genetic analysis if population structure is weak  (Oyler-McCance et al. , 2013). Therefore, despite low differentiation, we conclude that isolation by distance is present in both species. In fact, the pattern of limited differentiation within Q. petraea and Q. robur fits to the location of our study area in the core of both species' ranges, where large population size is expected to prevent genetic drift and efficient gene flow acts towards decreasing genetic differentiation  (Eckert et al.
Acknowledgments We express our gratitude to  Alexander Braun  for his great contribution to the choice of suitable sites for the AQUAREL project ("Origin and adaptation of oaks on relict sites") in Baden-Württemberg, Germany, and  Alsace ,  Grand Est, France , as well as for organizing and carrying out the field work in this area. We thank  Franziska Schlosser ,  Stefan Seegmüller  and  Nikolas Horder  for site selection and tissue samplings for AQUAREL in Rhineland-Palatinate and partly in Alsace,  Grand Est, France. We kindly acknowledge the work of  Isabel Mück  who selected and harvested sites for the same project in Bavaria, Germany. We are grateful to  Michael Schott  for conducting the lab work competently for the project and  Susanne Nowak  for supervising this work. The  AQUAREL  project received funding by the  German Federal Ministry of Food and Agriculture & German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety  due to a resolution of the  German Bundestag in the program "Waldklimafonds ", grant numbers  22WK413501  ( FVA-BW ,  Freiburg, Germany ),  22WK413502  (FAWF,  Trippstadt, Germany ),  22WK413503  (AWG,  Teisendorf, Germany ).
Data availability Genotypic data used for the analysis are available at the Dryad digital repository, https://doi. org/10. 5061/dryad. 4qrfj6qgs (data published for first time here) and https://doi. org/10. 5061/dryad. b64b4 (nSSR data used in earlier publications), as well as in  Neophytou and Michiels (2013)  as supplementary material (cpSSR data used in earlier publications).
Acknowledgments This work was supported by the  I-Site Montpellier Université d'Excellence  ( MUSE ; project  ECO2THREATS ), the  CNRS  through the  Mission for Transversal and Interdisciplinary Initiatives (MITI) 80 PRIME program , and the  Plant Biology and Breeding department of INRAE. O. C. was recipient of a PhD fellowship from the  CNRS. T. M. was recipient of a PhD fellowship from  INRAE  and  Région Occitanie. We thank  Jiping Liu  from  Cornell University  for the gift of tip2;2-1 mutant line. This study benefited from the  CNRS  human and technical resources allocated to the  Ecotron Research Infrastructure  and from the state allocation " Investissement d'Avenir" AnaEE France   ANR-11-INBS-0001. A CC-BY public copyright license has been applied by the authors to the present document.
008, Table  S4 ). Some miRNAs targeted specific amino acid biosynthesis, for example, miR159c seemed to mostly target serine related mRNAs, whereas miR158a-3p targeted isoleucine, leucine and valine biosynthesis genes (Figure  S7 ). Another highly targeted class of functions was related to energy production and conversion. The taxonomy of the targeted bacteria was investigated, and counts were normalised depending on the amount of each available taxa in the database. Within the rhizobacteria category, the most targeted families were Bacillaceae and Paenibacillaceae (Figure  S8 ). Targeted families within nonplant-associated bacteria were more diverse: Moraxellaceae, Bacillaceae, Pseudomonaceae, Flavobacteriaceae, etc… In summary, plant miRNAs internalised in rhizobacteria are predicted to mainly target genes related to amino acid transport and metabolism, particularly in bacteria from the Bacillaceae and Paenibacillaceae families.
11, https://github. com/BioInfoTools/BBMap). Sequences were then filtered by size: those with a length between 18bp and 27bp were kept. Selected reads were mapped against either A. thaliana (TAIR10/GCA_000001735. 1) or B. distachyon (bd21/GCF_000005505. 1) reference genomes, which are the same as used in miRBase  24. Using BEDTools v. 2. 29. 2  25  with -F 0. 95 argument, a number of reads aligned inside miRNA coordinates were determined, meaning that at least 95% of each read had to belong inside the miRNA coordinates to be considered a valid hit. These assigned miRNAs which were mapped against the reference database were kept for abundance estimation across samples. The remaining reads were normalised using edgeR, with Reads Per Kilobase Million (RPKM). A differential analysis was performed using DESeq2  26  to compare the abundances of each miRNA between 2 conditions (unplanted soil VS rhizosphere).
MiRNA sequences processing Raw reads were trimmed for quality using Trimmomatic v0. 39 (PMID:24695404) to remove the sequencing adapters with the Illumina clip setting parameter set to :5:10:4. and to remove the 5' and 3' flanking sequences containing bases having quality score below 3 and 30, respectively. Common contaminants (Illumina adapters, PhiX spikein sequence) were removed with bbduk (from bbmap v38. 11) ("BBMap" n. d. ) with key parameters k=20 and minkmerhits=1. Remaining reads were filtered by size (nrc tools v1. 3. 2) such that reads having a length between 18 and 27 nucleotides were selected. Selected reads were mapped on the Arabidopsis (TAIR10/GCA_000001735. 1) reference database (i. e. , these genome versions correspond to the same genome versions as used in mirBase (PMID:16381832)) using BWA aln (PMID:19451168) with mismatch=1 and seed=5 parameters.
Number of reads to align inside miRNA coordinates according to the corresponding genome model (i. e. , gff) file were determined with bedtools v. 2. 29. 2 (PMID:20110278) with the -F 0. 95 argument meaning that at least 95% of each read has to be inside the coordinates of miRNAs (from the gff file) to be considered a valid hit. All remaining reads that mapped to these reference databases were selected for downstream miRNA abundance estimation across all samples. Remaining reads were normalised with RPKMs using edgeR v3. 28. 1 (PMID:19910308) with R v3. 6. 3 (R Core Team 2021).
In silico analysis of miRNA targets To predict potential plant miRNA targets in rhizospheric bacteria, a workflow inspired from psRNAtarget( 35 ; PMID:21622958; 19802588), which is used to find miRNA targets in plant genomes, was implemented (https://github. com/jtremblay/MiRNATarget). This tool is based on specific pairing patterns between plant miRNA and target  36  and implements SSEARCH36 (from the fasta36 v36. 3. 8 37 package) which uses Smith-Waterman local alignment algorithm, resulting in the most optimal alignments. Multiple targets for each miRNA can be identified. Based on the psRNAtarget rules for miRNA-target recognition, an e-value was calculated for each alignment and only those with an e-value ≤ 5 were retained. The position of the targeted region was noted with respect to the neighbouring coding DNA sequences (CDS): the target sequence could be inside a CDS, at the 5' or 3' untranslated region (UTR) or in other UTRs, intercistronic regions, overlapping CDSs or elsewhere, further away from any CDS.
If the target sequence was in the latter category, i. e. , too far from any CDS, it was discarded from the analysis, because it was considered that a miRNA in that area would not affect mRNA translation. For functional analysis, the closest CDS to the miRNA target region was investigated. The CDSs of 3837 bacterial genomes  22  were predicted using Prodigal v2. 6. 3  38. Cellular function of these CDSs were inferred by alignment with ortholog genes referenced in the KEGG database (PMID:10592173; 31441146), identified with DIAMOND blastp v2. 0. 8  39. Only results with an e-value < 1e -10 were kept for downstream analyses. For taxonomic analyses, the number of targets in a certain family was normalised by the abundance of this family in the whole dataset.
Acknowledgments We want to thank  Hervé Vaucheret  and  Taline Elmayan  ( INRAE, France ) for sharing their Arabidopsis mutant seeds with us. We also thank  Christophe Penno  for the helpful discussion. This work was financially supported by a  CNRS MITI (Mission pour les Initiatives Transverses et Interdisciplinaires   2020-2021 ) and a  IEA (International Emerging Actions) grant  ( France ), the  French region of Brittany , a  CRSNG  and a  FRQNT grant (Canada ). We acknowledge the  National Research Council Canada's New Beginnings Initiative  funds awarded to JT and EY. We acknowledge  Compute Canada  for access to the Graham compute cluster.
Bacterial strains and growth conditions C. insecticola strains were cultured at 28°C in Yeast-Glucose medium (YG: 5 g/L yeast extract; 1 g/L NaCl; 4 g/L glucose) for routine use or in minimal media (MM) supplemented with various carbon, sulphur or nitrogen sources for the Tn-seq screens (Supplementary Dataset s1). C. insecticola RPE75 is a spontaneous rifampicin-resistant derivative of the wild-type strain RPE64  [12]. For standard molecular microbiology purposes, E. coli strains DH5, HB101, WM3064, MFDpir, S17-1pir and their derivatives were grown in LB medium (5 g/L yeast extract, 10 g/L tryptone, 5 g/L NaCl) at 37°C. Growth of the MFDpir and WM3064 strains that are ∆dapA derivatives, auxotroph for diaminopimelic acid (DAP) synthesis, required the supplement of 300 µg/mL DAP to the medium.
Tn-seq screening of the Himar1 transposon library for growth with different nutrients An aliquot of the Tn-seq library was 100-fold diluted to reach a suspension of 2x10 8 cfu/mL. Hundred µL of this dilution was inoculated into 20 mL of a growth medium, supplemented with Rif and Km, to obtain an initial inoculum of 10 6 cfu/mL (OD600nm≈0. 0015). Eleven growth conditions were tested: YG medium and ten different minimal media (MM) supplemented with various carbon, sulphur or nitrogen sources. The assembly from stock solutions and the composition of the MM media are provided in Supplementary Dataset s1. Cultures were incubated at 28°C, with shaking at 180 rpm. When the cultures reached an OD600nm≈1, corresponding to approximately 9 to 10 generations of multiplication, bacteria were collected by centrifugation at 4 000 rpm for 20 minutes at 4°C and the pellets were stored at -20°C until DNA extraction.
The mutant library was grown in three different standard growth media: a rich medium (YG) and a defined minimal medium (MM) with glucose or succinate as carbon source, and cultures were subjected to Tn-seq. The essential genome was determined using the Hidden Markov Model (HMM) in Transit software  [20]  and considering only the genes that are common to the three conditions as part of the essential gene set. This analysis classified the majority of the genes (92 %) as non-essential and 498 (8 %) protein-coding genes as essential (Fig. 1 ) (Supplementary Dataset s2). This proportion of the C. insecticola genome identified as its essential genome is very similar to what is found in other bacteria  [32]. These 498 genes were mostly located on the chromosome 1 (464 genes or 93 % of total). Another 22 genes (4.
s1 ; Supplementary Dataset s3). If essential genes shared by seven out of the eight species are considered, the number of common genes increases to 231 (Fig. s1 ) and the number of pairwise shared essential genes ranges from 195 to 412 genes with as a mean 291 genes (Fig. s2 ). Among the 37 clusters of orthologous genes that are preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this this version posted September 7, 2023. ; https://doi. org/10. 1101/2023. 09. 07. 556630 doi: bioRxiv preprint 13 essential in at least four of the considered species but not in C. insecticola, we found for five clusters that the C. insecticola genome lacked the corresponding gene. In 26 cases, the gene was present but non-essential and in six cases, the gene was duplicated in the genome.
Glucose, 3-hydroxybutyric acid (HBA), mannitol, succinate, myo-inositol or rhamnose were used as the only carbon sources. Taurine was used as sole carbon source, or as sole nitrogen or sulphur source, or as sole carbon/nitrogen/sulphur source. Tn-seq data were obtained for each growth condition, and we used the resampling method analysis from the Transit software package to identify the fitness defect genes for each condition, using minimal medium with glucose or YG rich medium as the reference control condition (Supplementary Dataset s4). By these comparisons, we identified sets of condition-dependent fitness defect genes for each growth conditions (Fig. 4 ; Fig. s3 ; Supplementary Dataset s4). Examples are amino acid biosynthesis genes that are not fitness defect genes in rich medium, but are fitness defect genes in MM lacking any source of amino acids.
Acknowledgements This work beneficiated from financial support by  Saclay Plant Sciences-SPS , by the  Agence Nationale de la Recherche , grant  ANR-19-CE20-0007 , and by a  JSPS-CNRS   Bilateral Open Partnership Joint Research Project  and a  CNRS International Research  Project to Y. K. and P. M. Y. K. was supported by the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT) KAKENHI  ( 18KK0211 ,  21K18241 ,  22H05068 ). T. O. was supported by the  JSPS   Research Fellowship for Young Scientists  ( 14J03996 ,  20170267  and  19J01106 ). J. L. , G. L. and R. J. were supported by Ph. D. fellowships from the  French Ministry of Higher Education, Research, and Innovation. Tn-seq sequencing and data treatment were performed by the I2BC high-preprint (which was not certified by peer review) is the author/funder.
Genome wide association studies and characterization of alleles Genotyping data from a panel of 52 diverse genotypes was obtained from our previous study  [36]. A genome-wide association study (GWAS) was conducted using 1. 9M high-quality single nucleotide polymorphisms (SNPs), and 45,000 randomly selected SNPs from the initial 1. 9M datasets. The two SNP densities were tested to highlight the effect of SNP density on GWAS power. The SNP density plot was generated using the "CMplot" package  [44]  in R4. 0. 23. GWAS was performed using two statistical models: Fixed and random model circulating probability unification (FarmCPU) and mix linear model. The FarmCPU algorithm has enhanced statistical power and computational efficiency and can mitigate false associations in GWAS  [36]. To identify SNPs significantly associated with traits in each environment, the adjusted p-value was utilized.
5), as shown in Figures  3D  and 3E. These findings suggest that utilizing high-density SNP data and employing the FarmCPU model enhances the accuracy and power of GWAS in D. alata. The 1. 9M SNPs dataset was thus kept for downstream analyses. Figure  3. Effect of SNP density and model selection on GWAS. A) subplot A displays the SNP density at a lower level, with 45,000 SNPs. The plot with 1. 9 M SNPs was presented by Dossa et al. [36]. This plot illustrates the distribution and density of SNPs across the genome, indicating the coverage and resolution of the genotyping data used in the GWAS analysis B) The Manhattan plot in subplot B represents the GWAS associations obtained using the lower SNP density. The Manhattan plot shows the genomic positions of SNPs on the x-axis, with their corresponding association p-values on the y-axis.
Moreover, our diversity panel exhibits sufficient variation for multiple quality traits, enabling association mapping. The presence of a high degree of recombination in D. alata, as suggested in a previous report  [36] , underscores the importance of utilizing high SNP density for GWAS in yam. We validated this by conducting GWAS using low-density (45,000 SNPs) and high-density (1. 9 million SNPs) SNP datasets. Our findings confirmed that high SNP density significantly improves the GWAS results in D. alata. This high resolution allowed for more precise identification of associations between genetic markers and the studied traits. In addition, our results also demonstrated that the multi-locus FarmCPU model outperformed the single-locus MLM model in terms of identifying key associations between genetic markers and the studied traits.
02G085400 Threonine protein kinase G Cooking time rs2792 583131 Dioal. 01G006400 Dioal. 01G006500 intergenic Tripeptidyl peptidase Proprotein convertase subtilisin R R rs821713 13328572 Dioal. 09G034300 intronic PPR repeat domain containing protein R rs1751594 24585447 Dioal. 18G073800 downstream Calcium-responsive transcription coactivator R rs1128334 14577742 Dioal. 12G041900 intergenic Plant protein of unknown function domain containing protein R Dioal. 12G042000 Sec14 protein-like R rs363258 14556249 Dioal. 04G067700 intronic ABL interactor-like protein 2 R CYTOCHROME P450 Dioal. 09G012400 (phenylalanine n-monooxygenase R rs754523 815494 intergenic (CYP79A2)) Dioal. 09G012500 Transposon protein R rs615299 9054657 Dioal. 07G056700 Dioal. 07G056800 intergenic Uncharacterized UPF0114 domain containing protein Glutathione s-transferase THETA 1 R R Dioal.
Acknowledgments We thank all stakeholders who participated to the surveys and workshops mentioned here. We thank  Ben Boswell  for English reviewing and the three anonymous reviewers for their very helpful comments. We thank also  Prs. Ahcèn Kaci ,  Khaled Abbas  and  Amar Mebarkia  for participating in workshop moderation in Algeria, and the student  Ms. Bensebsi Ichrak  for the participation and organization of the Algerian workshop.
Proteolysis analyses The degree of the protein hydrolysis (DH) was assessed at the end of each digestive phase as described in Hellebois, Fortuin, Gaiani, and Soukoulis (2022) using the OPA method (i. e. free -NH 2 group quantification). Capillary SDS-PAGE was used to investigate the peptide cleavage of the protein before and after fermentation, and at the end of the gastric and intestinal phases. The SDS-PAGE was carried out using a Bioanalyzer 2100 and Protein 80 chip kit (Agilent Technologies, Santa Clara, CA, United States) following the manufacturer's instructions. The gel was reconstructed using 2100 Expert software (Agilent Technologies, Santa Clara, CA, United States). To determine the production of peptides from the milk proteins throughout the digestion, 10 mL aliquots of homogeneous gastric and intestinal chymes prepared in duplicate, were centrifuge-filtered using Amicon tubes (Merck, Darmstadt, Germany) equipped with a filter cutoff of 10 kDa.
Then, two technical replicates of 1 mL filtrates obtained from each replicate were vacuum-dried (CentriVap, Labconco, Kansas City, MO, United States) prior to performing the nano-liquid chromatographymass spectrometry (nano-LC-MS) analyses. All replicates were solubilised and diluted with a 0. 1% vol. formic acid (FA) solution. Peptide separation was performed on a NanoLC-425 Eksigent system coupled with a TripleTOF 6600+ mass spectrometer (SCIEX, Darmstadt, Germany) as previously described  (Ribeiro et al. , 2020). The 30 most intense ions were selected to perform fragmentation in high sensitivity mode using the automatically adjusted system of rolling collision energy. The data acquired were converted and submitted to the Mascot search engine (Matrix science, London, UK) for peptide identification.
The searches were restricted to the cow milk protein database in Uni-protKB (released on 27 October 2022; 25,681 sequences) using the Mascot Daemon interface (version 2. 6. 0. Matrix Science, London, UK). For all research, the parameters were as follows: a mass tolerance value of 10 ppm for the precursor, 0. 02 Da for the fragment ions; no proteolytic cleavage specificity; no missed cleavages; no static modifications; Met oxidation, pyroglutamic acid at the N-term Gln, and Ser/Thr phosphorylation as variable modifications. To eliminate false positives, peptide spectrum matches (PSMs) were filtered using the target decoy database with a false discovery rate (FDR) at a peptide level of 1%, corresponding to a 99% confidence score. In Mascot settings, as matches are usually random to very short peptides, a minimal peptide length was kept to a minimum of 7 amino acids.
The MSMS spectra of the two technical replicates were merged into a single search. Data analysis and handling was further computed. Only the proteins identified with a significant Mascot-calculated confidence of 95%, a minimum of two peptides with at least one unique one per protein and a protein and peptide score ≥50 were retained. The Venn diagram representation was constructed using the significant peptide sequences (score ≥50) in the gastric and intestinal digesta using OriginLab 2019b. The potential bioactivities of the detected peptide sequences were compared to the Milk Bioactive Peptide Database  (Nielsen, Beverly, Qu, & Dallas, 2017 , http://mbpdb. nws. oregons tate. edu/). The proteomics data were deposited in the Proteo-meXchange Consortium via the PRIDE partner  (Deutsch et al.