dataesr/scientific-paragraphs-categorization huggingface dataset:
S rRNA gene and ITS reads processing Paired 16 S rRNA amplicon sequencing reads were joined (join_-paired_ends QIIME, default) and then quality-filtered and demultiplexed (split_libraries_fastq, QIIME, with max. barcode errors 1 and phred score of 30)  66. The filtered reads were dereplicated (usearch -derep_fulllength)  67  , sorted by copy number (only reads >2 copies were retained) and were checked for chimeras using usearch (usearch Fig. 6  | Validation of candidate genes involved in root colonization of A. thaliana in Rhodanobacter Root179. a Fluorescence microscopy of GFP-labeled Rhodanobacter sp. Root179 (Root179::GFP) colonizing A. thaliana root surface at 14 dpi. Scale: 20 µm, Arrows: accumulation of bacteria in grooves between epidermal cells, Asterisk: root hair emergence site. This experiment was done only one time with no replicate.
First, their genomic DNA was extracted from freshly growing mycelium using a modified CTAB-based protocol, as previously described  70. Genomes were then assembled using CANU v1. 8  71  , and assemblies were polished with Arrow v0. 14. Gene prediction was performed with FGE-NESH v8. 0. 0 72 , with reference matrix Fusarium for the 4 Fusarium strains and matrix Torrubiella hemipterigena for the Plectosphaerella cucumerina strain. Fungal gene annotation was performed using emapper v2  73  but also different specialized tools: BUSCO2 v5. 4. 7  74  (with database asco-mycota_odb10) to identify ascomycetal conserved genes likely involved in primary metabolism, SignalP3 42 v6. 0 to predict genes encoding secreted proteins, EffectorP4 v3. 0  43  (run in fungal mode on predicted secreted proteins) to identify candidate effector-encoding genes and dbCan2 44 v4.
0. 0 to annotate genes encoding carbohydrateactive enzymes. In addition, we used the tool PHIB-Blast (http:// phiblast. phi-base. org/) to identify homologs of fungal differentially expressed genes that have been previously characterized and are referenced in database PHIBase6 v4. 15  45. Functional annotation of the microbial genomes was carried out using the tools emapper v2. 1. 5 and the EggNog database on the protein models associated with each predicted gene  73. From the total EggNog annotation, we used COG categories, GO terms and KEGG orthology groups. To be able to compare the transcription of orthologous genes in the bacterial and fungal communities, two orthology predictions were performed with OrthoFinder v2. 2. 7  50  , using default parameters. Functional annotation of OGs was performed by identifying the best representative sequence in each OG using HMMER v3.
Microbial transcriptome analysis Quality check and trimming of resulting single-end RNA-Seq reads was performed using Trimmomatic v0. 38  74  (options LEADING:20 TRAIL-ING:20 AVGQUAL:20 HEADCROP:10 MINLEN:130). Reads were also deduplicated using USEARCH  67  (options -derep_fulllength -sizeout). The pipeline of analysis used herein is represented in Supplementary Fig. 11. To associate the sequenced reads to genes in our reference genomes, we relied on the pseudo-mapping algorithm Salmon v0. 14. 1  75  , that uses k-mer content to identify the most likely transcript of origin of each read. This high-throughput method was well suited to our dataset, considering the large number of reads sequenced and the size of our reference genome catalog. Prior to read pseudo-mapping, we used all gene coding sequences from the microbial and host genomes to build an index (with command salmon index --perfectHash -keepDuplicates).
e. , normalize the counts) and (  2 ) to detect enough transcripts per gene to compute a Wald test, we did not compute DESeq2 results for strains for which these two conditions were not met. We focused our main interpretations on the 20 most-abundant bacterial strains, to ensure that we only consider transcriptomes that were well covered by the sequencing. For all transcriptomes, perstrain transcripts per million (TPM) values were calculated and their distribution appeared to be normal or quasi-normal (Supplementary Fig. 4 ), confirming the accurate pseudomapping and the possibility to identify differentially expressed genes between roots and matrix. We also inspected the expression of six reference housekeeping genes that (i) were annotated by EggNog in our bacterial genomes; (ii) were present in a single copy in the genomes (no paralog); (iii) which expression has been characterized to be invariable in more than 80% of studies inspected by Rocha and colleagues  76.
We considered as differentially expressed all the genes which adjusted P values from DESeq2 are inferior to 0. 05. In addition, we used the shrinkage algorithm apeglm  77  to correct log2-transformed FC values, so differential expression is not over-estimated for transcripts with low read counts. 3. To perform comparative transcriptomic analyses and identify genes that are commonly differentially expressed in roots by bacteria, we conducted a per-strain OG differential expression analysis. We referred to our orthology prediction computed with OrthoFinder. The per-gene read counts obtained from Salmon pseudo-mapping were summed to obtain one read count per OG for each bacterial strain. The resulting aggregated read count values were used to perform independent differential expression analyses with DESeq2.
Plant transcriptome analysis To confirm the suitability of our data processing approach (i. e. , pseudo-mapping of reads on a large multi-species genomic dataset), we also analyzed read counts associated to plant genes. We compared our read count values to those of a previously published RNA sequencing dataset of A. thaliana roots grown in the FlowPot system in the presence of a different synthetic community  9. In this previous study, authors performed Poly-A enrichment prior to RNA sequencing and used a classical mapping algorithm with the A. thaliana genome as a single reference. Read counts resulting from this mapping were downloaded from GEO (GSE160106; file complete_table_3470. txt). Correlation of log-transformed mean read counts (over three samples) between this dataset and ours was performed using a Pearson correlation (computed using function stats.
Primers were designed using BatchPrimer3 80 (155-156-F: 5'-AGCCTTCAAGTCCTGACGAA-3', 155-156-R: 5'-TTTGCTCACATCG CATGTTC-3') their specificity was validated in silico utilizing the EMBOSS software  76, 81. Further validations of primers specificity and efficiency were conducted through PCR and qPCR on DNAs from R179 WT, mutants, and the bacterial and fungal SynComs. Normalization of qPCR results was performed by amplifying the A. thaliana Actin gene (actin2_short_F: 5'-atggaagctgctggaatccac-3', actin2_short_R: 5'-ttgctc atacggtcagcgata-3'). Each qPCR reaction mixture consisted of 5 μL of iQ™ SYBR® Green Supermix (Bio-Rad), 0. 3 μL of 10 μM Forward primers, 0. 3 μL of 10 μM reverse primer, 1 μL of DNA, and MilliQ water, resulting in a final volume of 10 μL. The Bio-Rad CFX Connect Real-Time system was employed with the following cycling program: an initial denaturation step at 95 °C for 3 min, followed by 39 cycles of denaturation at 95 °C for 15 s, annealing at 60 °C for 30 s, and extension at 72 °C for 30 s.
Conservation of root competence genes across the bacterial tree of life To study the conservation of these genes across the bacterial tree of life, we referred to a previously published dataset of 3837 genomes  23  classified into nine phylogenetic groups. Orthology prediction was previously computed for each of these phylogenetic groups independently, defining gene families in these clades. We looked for gene families for which hidden Markov models show similarity to our genes typA, exbD1, pstA, pstB, pstC, and pstS. To do so, we did a similarity search with hmmscan v3. 3 82 using the protein sequences of Root179 as a query, and the gene family hidden Markov models as subjects. For graphical representation, the similarity score of the best hit in each clade was normalized to the top score obtained for one gene.
Statistical analyses Differential expression of genes, OGs, and KEGGS were tested in R 4. 0. 3 with the package DESeq2 1. 30. 1 39 using shrinkage algorithm apeglm 3. 14  77  to correct Log2FC values. Differential relative abundances at the order, class and strain levels were also tested using DESeq2. All volcano plots of gene differential expression and strains/ class/order abundance were done in R with the package Enhanced-Volcano 1. 11. 3 using DESeq2 outputs. Pearson correlations between functions' average differential expression and strains abundance in the root compartment were done in R 4. 0. 3 using the corrplot function from the corrplot package (v0. 92). Linear regressions between functions' average differential expression and strains abundance in the root compartment were done in R 4.
0. 3 using the lm function in the package stats (v4. 0. 5). Colonization abilities of wild-type, mutant and complemented strains measured by colony counts were tested using a Kruskal-Wallis test followed by a Dunn's test in R, P values were then adjusted using the P. adjust function in the package stats v3. 6. 2. Colonization ability of wild-type and mutant strains measured by qPCR in flowpots in single or SynCom inoculations were tested using an ANOVA after log-transformation of the data. A subsequent TukerHSD test with a comparison matrix (using the emmeans and contrast functions in R) were used to test for differences between each mutant and the wild type. To perform Gene Ontology (GO) enrichment analyses, the software topGO v2. 34. 0 41 performing Fisher's exact tests) was used using EggNog GO annotations as an input and the Benjamini-Hochberg method for P value correction (FDR).
Acknowledgements This work was supported by funds to S. H. from a  European Research Council  starting grant ( MICRORULES 758003 ), the ' Priority Programme : Deconstruction and Reconstruction of the Plant Microbiota (SPP DECRyPT 2125 )' and the  Cluster of Excellence on Plant Sciences (CEPLAS) , both funded by the  Deutsche Forschungsgemeinschaft. We thank  Paul Schulze-Lefert  for providing feedback regarding this work.
Data availability The community profiling data generated in this study have been deposited in the ENA database under accession code "PRJEB61839" (Root samples 16SrRNA: "ERS15411484", "ERS15411485", "ERS15411486"; Soil samples 16SrRNA: "ERS15411487", "ERS15411488", "ERS15411489"; Root samples ITS: "ERS15411490", "ERS15411491", "ERS15411492"; Soil samples ITS: "ERS15411493", "ERS15411494", "ERS15411495"). The genome of Plectospaerella_cucumerina_MPI-CAGE-AT-0143 generated in this study have been deposited in the ENA database under project ID "PRJEB61839" under sample ID "ERS15396667". The genomes of Fusarium_oxysporum_MPI-CAGE-AT-0013 (Fusarium13), Fusarium_redolens_MPI-CAGE-CH-0216 (Fusar-ium216), Fusarium_oxysporum_MPI-CAGE-CH-0226 (Fusarium226), Fusarium_equiseti_MPI-CAGE-CH-0233 (Fusarium233) have been deposited in the ENA database under accession code "PRJEB50298".
The other genomes have been previously published and were obtained from "TAIR [http://arabidopsis. org]" (A. thaliana), "At-Sphere [http:// at-sphere. com]" (82 bacteria), "Mycocosm [https://mycocosm. jgi. doe. gov]" (17/22 fungi). The raw RNA-seq reads, as well as the reference CDS used for mapping are available at GEO, under accession "GSE231841". All the data generated in this study are provided in the Supplementary Information/Source Data file. Source data are provided with this paper.
Code availability The scripts for the metatranscriptomic analysis pipeline from raw read processing to figure assembly (except differential expression analysis) are available at https://github. com/fantin-mesny/Scripts-from-Vannier-et-al. -2023, https://doi. org/10. 5281/zenodo. 10069685. The scripts for differential expression analysis and DNA community profiling are available at https://github. com/nathanvannierinrae/Scriptsfrom-Vannier-et-al. -2023.
9 5. 93e-05 Enoyl-CoA hydratase/isomerase Root154 | 1227 3781 13. 8 4. 16e-16 Unknown Root123D2 | 2583 rskA 348 13. 8 6. 31e-06 Anti-sigma-K factor Root1277 | 897 242 13. 8 7. 56e-05 Predicted periplasmic protein (DUF2271) Root154 | 369 181 13. 6 2. 12e-06 Xylose isomerase-like TIM barrel Root70 | 1346 215 13. 4 1. 77e-05 Nucleotidyltransferase Root569 | 2962 pstC 467 13. 4 3. 17e-10 Polypeptide phosphate ABC transporter subunit Root154 | 381 2868 13. 4 3. 25e-15 Lactoylglutathione lyase and related lyases Root70 | 3634 202 13. 2 2. 65e-05 Drug metabolite transporter superfamily Root1277 | 4057 1229 13. 2 7. 44e-09 Amine oxidase flavin-containing A Root154 | 985 filA 135 13. 1 3. 71e-06 Membrane-bound metallopeptidase Root559 | 706 219 12. 9 1. 4e-06 Belongs to the UPF0312 family Root61 | 6 166 12.
6 2. 24e-05 Ribosomal protein bS16 family Root322 | 1193 155 11. 5 1. 93e-05 Major facilitator Superfamily Root322 | 2730 201 11. 5 0. 0005 MFS/sugar transport protein Root149 | 1665 rplQ 123 11. 4 0. 0002 Ribosomal protein L17 Root670 | 456 dctA 883 11. 4 3. 32e-07 Aerobic C4-dicarboxylate transporter Root322 | 1563 136 11. 4 1. 93e-05 Unknown Root149 | 609 atpF 115 11. 3 0. 0002 ATP synthase subunit b Root569 | 2421 aspA 1685 11. 3 1. 13e-14 Aspartate ammonia-lyase Root559 | 1423 rnpA 90 11. 3 5. 94e-05 Ribonuclease P protein component Root559 | 650 99 11. 2 0. 0001 Unknown Root322 | 2077 yigZ 136 11. 2 0. 0001 Uncharacterized protein family UPF0029 Root61 | 2410 hmuT 253 11. 1 5. 36e-06 Periplasmic binding protein Root61 | 2017 62 11. 0 0. 0001 Domain of unknown function (DUF4407) Root61 | 1037 878 11.
| Statistical analysis Statistical analyses were performed with the Prism software (Graph-Pad). Data were presented as median (Quartile1-3). Differences between frozen plasma volumes were analyzed with analysis of variance (ANOVA) or the Friedman test, followed by the appropriate multiplecomparison post hoc (Tukey-Kramer or Dunn) test. The mean bias was calculated using the Bland-Altman method between the test results obtained with the 2 tube types. Compared with the fully filled microtube (1. 6/2 mL), using plasma stored in a small volume (0. 4/2 mL) led to shorter coagulation times with a drop in PT from 14. 1 seconds (13. 1-16. 1 seconds) to 13. 6 seconds (12. 8-15. 3 seconds) (P <. 001) and a drop in aPTT from 36. 0 seconds (32. 4-43. 4 seconds) to 34. 9 seconds (31. 8-42. 4 seconds) (P <.
METHODS We analyzed the potential link among GAD-65/Human insulin/ZnT8 and cow's milk proteins as a trigger for T1DM, using the Protein Data Bank (PDB) database to obtain the structural information of the proteins involved, and the Uniprot website to evaluate the similarity between proteins. The rst step was to obtain the crystal structure data of GAD-65, human insulin, Znt8 and cow's milk proteins (BSA and BLG) from the PDB database. Subsequently, the JSmol tool was used to visualize the structures of these proteins. The next step was to analyze the potential molecular similarities between these proteins using Pairwise Structure Alignment. Pairwise Structural Alignment is a method of comparing two proteins by aligning their amino acid sequences and identifying similarities and differences in their structures.
Reaction products were separated on a 25% polyacrylamide gel, visualized and quantified using a ChemiDoc MP system and Image Lab 6. 1 software (BioRad). The control lane corresponds to the dp8-AF 430 substrate incubated for 900 min without NDST1 or NDST2. A representative gel is shown and histogram data are averaged from two independent reactions. AF 430 , Alexa Fluor 430; AST IV, aryl sulfotransferase IV; dp8, degree of polymerization 8; FACE, fluorophore-assisted carbohydrate electrophoresis; NDST, N-deacetylase/N-sulfotransferase; NSt1, sulfotransferase domain 1; PAP, adenosine-3′,5′-bisphosphate; PNPS, potassium 4-nitrophenyl sulfate. To further quantify the enzymes activities and investigate the deacetylation and resulfation processes kinetically, we next devised a second approach in which NDSTs were incubated for 1 h, with increasing concentrations of (GlcA-GlcNAc) n polymer (170 kDa, Supporting Information: Figure  4 ), either in the presence or the absence of a sulfate donor/regenerating system, to follow the global or the N-deacetylation activity, respectively.
To overcome this issue, we collected a data set using a T A B L E 1 NDST1 and NDST2 kinetics parameters for conversion of heparosan (NDAc and complete reactions) and N-deacetylated heparosan (NSt reaction) determined following Michaelis-Menten equation (n = 2). Abbreviations: NDAc, N-deacetylase; NSt, N-sulfotransferase. 30°stage tilt and resulting 2D class averages confirmed the presence of novel particle orientations compared to the nontilted data set (Supporting Information: Figure  11 ). The tilted data helped to overcome initial difficulties to reconstruct an ab initio 3D model. We generated 3D reconstructions using either both data sets (tilted and nontilted) or only the tilted micrographs. The latter resulted into a 3D reconstruction of the NDST1 homodimer at a nominal resolution of 4.
We could not identify any obvious substrate channel in between the two catalytic sites that could explain how the catalytic rates are enhanced when the two reactions are coupled (see Figure  6  and Supporting Information: Figures  21  and 22 , in which the position and orientation of the oligosaccharide substrate was drawn on the NDST's sulfotransferase domain, using the murine 3-O-sulfotransferase-1 structure (PDB ID 3UAN) in complex with HS as template  40  ). Within the NDST1 homodimer the NDAc1 and NSt2 (or NDAc2 and NST1) are also 6 nm away from each other, but are more aligned across the dimer than within a single monomer. It is however not clear to ascertain that this could promote catalytic rate enhancement. Rather, it can be assumed that after an initial random encounter, the enzyme remains associated with the substrate and works along the chain, a process that would be more efficient than two random encounters made necessary by the separation of the two domains or the two monomers.
ACKNOWLEDGMENTS The authors are grateful to  Rida Awad ,  Yoan Monneau , and  Damien Maurin  for cloning and initial expression test of SEAP-NDST. The  IBS Electron Microscope  facility is supported by the  Auvergne Rhône-Alpes Region , the  Fonds Feder , the  Fondation pour la Recherche Médicale  and  GIS-IBiSA. We thank  Caroline Mas  for assistance and access to the Biophysical platform. Authors acknowledge  Eleftherios Zarkadas  for data collection and the other staff of the Electron.
Funding information The  Agence Nationale de la Recherche , Grant/Award Numbers:  ANR-18-CE11-0006-01 ,  ANR-21-CE29-0022 ; The " Investissements d'avenir" program Glyco@Alps , Grant/Award Number:  ANR-15-IDEX-02 ; The  CNRS ATIP-Avenir program ;  FRISBI , Grant/Award Number:  ANR-10-INBS-0005-02 ;  GRAL , Grant/Award Number:  ANR-17-EURE-0003.
F I G U R E 5 5 Characterization of the N-deacetylase active site. (A) Superimposition of Agd3 deacetylase (PDB ID: 6NWZ, in transparent mauve) with NDST1 subunit 1 refined structure (orange), and close-up view on the residues of Agd3 involved in metal ion coordination and catalysis conserved in NDST1 (in sticks). NDST1 and Agd3 residues are indicated in orange and mauve, respectively. (B) Analysis of NDST1 H529A mutant (peak II) activity on a fluorescently labeled dp8 by FACE. N-deacetylation step was followed in the presence of 10 mM MnCl 2 without any sulfate donor, then NSt1, PAP, PNPS, and AST IV were added to sulfate any N-glucosamine residues generated. Following the same protocol as above, NDST1 wt was used for the N-deacetylation followed by sulfation by NDST1 H529A mutant instead of NSt1.
The reaction products HS and HP could therefore, by local sequestration, prevent further heparosan modifications. SEC, mass photometry, and cryo-EM analysis concurringly suggested that NDST1 forms different oligomeric assemblies in solution. NDST1 has been previously shown by FRET to colocalize with its inactive variant NDST1B, which lacks the region F I G U R E 6 Visualization of the NDAc (gold) and NSt (green) catalytic sites in the refined NDST1 homodimer structure. The three basic clusters contained in each NDST1 monomer are colored in blue and the region missing in NDST1B variant is shown in red. Orientation of the substrate in the NSt catalytic site was identified after superimposition with the murine 3-O-sulfotransferase-1 structure (PDB ID 3UAN). NDAc, N-deacetylase; NDST, Ndeacetylase/N-sulfotransferase; NSt, N-sulfotransferase.
Anti-TPO developed at the 1-month time point in individual AA23. These data reveal that a subset of patients acutely infected with influenza develop autoantibodies that were undetectable at baseline. A subset of ACA has functional receptor blocking activity. We used cell-based cytokine blocking assays to assess whether patient sera with high MFI values for ACA inhibit receptor signaling in vitro (Figure  4 ). For each cytokine blocking assay, patient sera were selected based on an MFI value > 5 SD above the HC average and > 3,000 MFI. A total of 39 samples met these criteria and were available for assessment of blocking activity (Stanford ICU, n infected = 20, n noninfected = 2; ARDS, n = 8; Giessen/Marburg influenza, n = 4; and Athens influenza, n = 5). From pSTAT induction blocking assays, blocking activity for at least 1 cytokine was observed in 11 of 39 ACA + samples analyzed across the 5 cohorts (28%, see Table  2  for summary and clinical characteristics).
Methods Serum and plasma samples. Serum or plasma samples were obtained following informed consent and cryopreserved at -80°C until antibody profiling was performed. Stanford ICU patient samples. The Stanford ICU biobank is a collection of whole blood samples prospectively obtained from individuals admitted to Stanford Hospital ICU with at least 1 risk factor for ARDS (e. g. , sepsis, aspiration, and/or trauma). Exclusion criteria included routine postoperative patients and severe anemia. Clinical data were abstracted from the medical record by study staff blinded to autoantibody levels. Infection status was determined through retrospective chart review by 3 physicians blinded to autoantibody levels. Positive or negative infection status was defined by a consensus of at least 2 of 3 physicians as previously described  (13).
Samples were collected between February 2015 and November 2018, and all patient samples and data collected were compliant with the Stanford IRB (n = 167, Stanford IRB no. 28205; Supplemental Table  1 ). Giessen and Marburg acute respiratory illness patient samples. Serum samples were obtained from hospitalized individuals at 2 academic centers in Germany (Giessen and Marburg). Serum samples from patients admitted to the ICU between April and May 2020 for COVID-19 symptoms but who tested negative by PCR (n = 19) and serum samples from patients hospitalized with Influenza A and pneumonia between January 2018 and March 2020 (n = 25) were obtained from the Philipps University Marburg (IRB no. 57/20). Serum samples from patients with ARDS (n = 17) were obtained from the University of Giessen and were collected between July 2016 and January 2020 (IRB no.
The percentage of pSTAT + cells was compared between the stimulated and unstimulated condition. To develop each assay, cells were stimulated at different cytokine concentrations to determine the final working concentration. The lowest concentration at which maximal stimulation was observed was selected for the final blocking assays (Supplemental Figure  7 ). Patient sera positive by array for anti-IFN-α2, -IFN-α7, -IFN-α8, -IFN-γ, -IL-6, and -GM-CSF antibodies were assessed using blocking assay conditions and U937 (ATCC CRL1593) and THP-1 (ATCC TIB-202) cell lines, as summarized in Supplemental Table  9. Cells were assessed on a BD LSR II analyzer and analyzed using FlowJo software version 10. 8. A complete list of cytokines, blocking antibodies, staining antibodies, vendors, and catalog numbers can be found in Supplemental Table  10.