dataesr/scientific-paragraphs-categorization huggingface dataset:
cf. gigantea genomes. A table summarizing the presence or absence of glideosome proteins was visualized using R using the tidyverse package  [151]. Putative TRAP-like proteins were identified by searching for sequences encoding the TSP1 molecular domain (IPR000884) within the two P. cf. gigantea genomes. The predicted structure of each candidate protein was studied, and if necessary partially predicted proteins were re-edited with Genewise  [152]. Schematic representation of TRAP-like proteins was done using BioRender (biorender. com).
Acknowledgements We thank  Roscoff Marine Station  and service mer & observation for help in collecting hosts, in the frame of the  European Marine Biological Resource Centre (EMBRC-ERIC) ,  EMBRC-France  ( ANR-10-INSB-02 );  Pauline Konga  for the ribosomal genes amplifications;  Geraldine Toutirais ,  Cyril Willig ,  Marc Gèze  and the  MNHN Platform (Plateau technique de Microscopie Électronique, Muséum National d'Histoire Naturelle, Paris, France , http:// ptme. mnhn. fr/) for the SEM imaging; and the  Muséum computational cluster  for assemblies/phylogenies.
Funding This work was supported by a grant from the  French Agence Nationale de la Recherche  [LabEx  ANR-10-LABX-0003-BCDiv ], in the program " Investissements d'avenir"  [ ANR-11-IDEX-0004-02 ], by several interdisciplinary Programs of the  MNHN (ATM-Microorganismes, ATM-Génomique et Collections, ATM-Emergence, ATM-Blanc, AVIV department ), and the  CNRS  ( Julie Boisard 's PhD fellowship,  2018-2021 ). This work also benefited from access to the  Station Biologique de Roscoff , an  EMBRC-France  and  EMBRC-ERIC Site. The present work was thus funded in parts by the call  EMBRC-France 2016 (Investments of the Future program , reference  ANR-10-INSB-02 ,  Agence Nationale de la Recherche ).
88, Fig. 3  and Supplementary Fig. 5 ). Models in which monophyletic clustering was enforced on the European lineages showed a decisively lower model fit (ln Bayes factor [BF] ≥ 8. 9, Supplementary Note 4), supporting the hypothesis of transatlantic movement of pandemic viruses, in line with the historical context of human movement near the end of World War I. Altogether, genomic data support a scenario of predominantly local transmission with frequent long-distance dispersal events. Next, we focused on the evolutionary relationships between viruses of the different periods of the 1918 pandemic. For this, we used the same HA sequence dataset, this time to assess whether pandemic peak viruses descended from a single global replacement of pre-pandemic peak viruses. Under such a scenario, the pandemic peak viruses should constitute a single monophyletic lineage.
NGS data analyses. Raw reads were pre-processed using Trimmomatic  31  , with the following settings: LEADING:30 TRAILING:30 SLIDINGWINDOW:4:40 MIN-LEN:40. Reads were initially classified using Kraken2 32. Trimmed reads were then mapped to the A/Brevig Mission/1/1918 (BM) reference genome (GenBank accession numbers AY130766, AF333238, AF250356, AF116575, AY744935, DQ208309-11) using BWA-MEM  33. Upon detection of influenza reads in sample MU-162, we proceeded with de novo assembly using metaSPAdes  34. Scaffolds greater than 750 bp (the smallest BM segment is 830 bp) were blasted against a database of influenza virus sequences and all 8 viral segments were identified. The 8 segments resulting from the de novo assembly were then used as a reference for reference-based mapping of reads generated from sample MU-162 and all other samples included in this study.
To minimize the effects of cross-library sequence bleed-through, we discarded data where samples were co-sequenced with MU-162. Upon trimming, paired end reads were merged using the Clip&Merge tools from EAGER  35. We sorted mapping files and removed duplicates using the SortSam and MarkDuplicates tools from Picard (http://broadinstitute. github. io/picard). We assessed RNA damage using mapDamage v2  36. Base calling for MU-162 was set to 20 reads and 95% agreement whereas for BE-572 and BE-576 to 3 reads and 50% agreement. Recombination analyses for the PA segment were run in Rdp4 using the PA dataset assembled by Worobey et al. 10  We reduced the dataset to unique sequences using FaBox  37  and then chose the 99 most ancient ones to analyze with the MU-162 sequence. We ran Rdp4 38 with all methods (changing the settings of the bootscan approach to a window size of 100).
Geneconv, bootscan, lard and 3seq identified Brevig as a recombinant (MU-162 backbone with a ca. 100 bp originating in another virus). PhyML  39  was finally run on the backbone and recombinant region. The influenza positive samples were analyzed for human RNA preservation. The sequencing data was processed using EAGER  35. Sequencing reads were inspected with FastQC before merging and adapter trimming with AdapterRemoval V2. 2. 1a. Pre-processed reads were then aligned to the human transcriptome reference (Gencode, Release 34), using BWA with a minimum quality score of 0 and a maximum edit distance of 0. 01. The MarkDuplicates method was used for duplicate processing (Broad Institute) as well as DamageProfiler v0. 3. 8 and Qualimap  40  to investigate the read length distribution and the damage patterns.
In agreement with the ethics committee of the Charité, Berlin, human reads were removed from the sequencing files prior to uploading them to the European Nucleotide Archive. Filtered reads were mapped to the human RefSeq genome assembly GRCh38. p13 (GCF_000001405. 39) using BWA-MEM  33. Unmapped reads were extracted using SAMtools 41 v1. 3. 1 and then extracted by name from the original fastq files using seqtk (https://github. com/lh3/seqtk).
Evolutionary analyses and simulations Datasets. For the HA and NA segments, the newly generated pandemic H1N1 sequence data were complemented with the collection of human, swine and avian H1 and N1 sequences previously used by Worobey et al. 11. The A/London/1/ 1919 isolate  9  was also added to the HA data. The newly sequenced samples from Berlin differ from each other in HA at two positions (alignment positions 356 and 1600). BE-576 is polymorphic at these positions with ~40% of the reads exactly matching the BE-572 HA sequence. It can hence not be excluded that BE-576 is identical to BE-572, and that the majority rule consensus base calls at these positions reflect the amplification of potential RNA damages/RT or PCR errors. For this reason, all HA analyses were run in duplicate, once with the majority rule consensus sequence, and once with these positions represented by the appropriate ambiguity code.
For the other segments, except for NS, the new 1918 pandemic flu data were complemented with the human H1N1 viruses, the classical swine flu strains and Eastern plus Western avian lineages from the final datasets used by Worobey et al. 10  downloaded from Dryad. For NS, all human and swine lineages selected by Worobey et al. 10  were used, but only a subset of the avian taxa was taken along to not have to enforce monophyly constraints on the human seasonal and pandemic lineages when accounting for the host-specific evolutionary rates (see below). The region in PA identified as of uncertain origin in the Brevig Mission sequence was not included (masked) for analysis. Evolutionary analyses. Sequences were aligned with MAFFT v7. 313  42  and Aliview v1. 19  43  was used for manual refinement of the alignments.
Maximum likelihood (ML) trees for all datasets were reconstructed using IQTREE 2. 0 44 with a general time-reversible (GTR) nucleotide substitution model  45  and a discretized gamma distribution to model among-site rate heterogeneity. Bootstrap support was estimated using the ultra-fast bootstrap procedure with 1000 pseudo-replicates  46. Time-measured phylogenies were estimated using BEAST v1. 10 20 with the same substitution model settings as for the ML trees. The skygrid model was used as a flexible demographic tree prior  47. To accommodate host-specific rates in the estimation of divergence times, we employed a molecular clock model that allows for different evolutionary rates in lineages of different host species  10. The standard host-specific local clock (HSLCstd) model specifies the avian influenza virus rate as the background rate, and assumes a different rate for the human (and swine) lineage, and yet another separate rate for the swine lineage specifically.
We used BEAGLE 3  49  for efficient likelihood computation and simulate the MCMC chains sufficiently long to ensure stationarity and mixing as diagnosed using Tracer v1. 7. 0  50. We summarized posterior tree distributions in the form of maximum clade credibility (MCC) trees and visualize these trees using FigTree (http://tree. bio. ed. ac. uk/software/figtree/). Selection analyses were performed using codon substitution model approaches implemented in HyPhy  51. Specifically, we tested for episodic (diversifying) selection and relaxed selection along the branch ancestral to human seasonal viruses in the human H1N1 lineage using aBSREL  52  and RELAX  53  respectively. For this purpose, we used the human lineage subtrees extracted from the BEAST MCC trees estimated using the HSLCext model.
Simulations. We performed two sets of sequence simulations. First, we simulated sequence data according to the BEAST estimate obtained under the HSLCstd model. We simulated 20 replicate data sets under this scenario (with the same size as the HA data set) and then evaluated the performance of both non-clock maximum likelihood reconstruction and BEAST estimation using the extended HSLC model. Second, we simulated sequence data using the same procedure, but now according to the BEAST estimate obtained under the HSLCext model and evaluated the performance of both non-clock maximum likelihood reconstruction and BEAST estimation using the standard HSLC model. Simulations were performed using piBUSS  54.
Acknowledgements B. V. and S. L. were supported by postdoctoral fellowship grants of the  Research Foundation-Flanders (Fonds voor Wetenschappelijk Onderzoek ,  12U7121N  and  12R4718N , respectively). M. T. P. G. acknowledges  Danish National Research Foundation Award   DNRF143  for funding. This work was funded in part by the  Intramural Research Program  of the  National Institute of Allergy and Infectious Diseases  of the  NIH  (D. M. M. and J. K. T. ). MAS was supported by  US National Institutes of Health  grants  HG006139  and  AI135995. M. W. was supported by the  Bill and Melinda Gates Foundation  ( INV-004212 ) and the  David and Lucile Packard Foundation. VJS was supported by the  University of Zurich 's  University Research Priority Program  "Evolution in Action: From Genomes to Ecosystems".
Data availability All raw reads generated for this study have been deposited to the European Nucleotide Archive under project number PRJEB41631 (sample numbers ERS5447402-413, ERS6621911, and ERS5549335-ERS5549361). Human reads were removed as described in the methods section prior to uploading. Kraken2 results for all specimens can be visualized through Krona plots at https://zenodo. org/record/4384755. Nucleotide alignments for the 8 viral segments are available at https://zenodo. org/record/ 4384715. Source data are provided with this paper.
P. zopfii and P. wickerhamii were isolated from raw sewage and sludge from both aerobic and anaerobic wastewater treatment plants. P. wickerhamii is likely the most abundant species in human sewage  [34] , and it was suggested that the primary and secondary sewer system is a place of growth and reproduction for Prototheca. Prototheca spp. are generally killed by chlorination in the liquid effluent from settling ponds, but in certain cases of inadequate treatment they may escape into rivers  [12]. These earlier observations led us to initiate a pilot bioinformatic analysis of shotgun metagenomic sequencing data for wastewater samples available in the National Center for Biotechnology Information Sequence Read Archive (NCBI SRA) database. The initial results detected P. zopfii/P. bovis microalgae in two wastewater metagenomic datasets from the USA and Pakistan, respectively (Libisch et al.
RNA isolation and transcriptional analysis Total RNA was extracted from cell cultures or mouse skin tissues using TriPure Isolation Reagent ® (Roche, Merck) and treated with DNase I (0. 05 U/ml; Clontech). RNA was reversetranscribed with « Transcriptor Reverse Transcriptase » (Roche) and transcripts were amplified and quantified using the LightCycler-FastStart DNA Master Plus SYBR Green I kit on a LightCycler 480 instrument (Roche). Oligonucleotides were designed with Primer 3 and-Blast software and purchased from Merck (Sigma-Aldrich) or Eurogentec. Primer sequences to amplify human IL-8 were 5'-CTCTTGGCAGCCTTCCTG-3'(Forward) and 5'-TTGGGGTCCAGACAGAGC-3'(Reverse). Forward and reverse primer sequences for mouse gene amplification were indicated in Supplemental Table  1. Samples were normalized to the expression of the control housekeeping gene GAPDH.
For Affymetrix analysis, RNA was extracted using the NucleoSpin RNA kit (Macherey-Nagel), according to the manufacturer's instructions. The concentration and the quality of extracted RNA was determined for each sample using a 2100 bioanalyzer system (Agilent technologies). Probes were prepared and hybridized to the Affymetrix Human Genome U219 Array as recommended by the manufacturer. Equal amount and quality of RNA was used for each sample, constituting the first step of normalization. Data were obtained from measurements of the relative signal strength for probes containing ~30,000 transcripts. The raw intensity values are background corrected, log2 transformed and then quantile normalized according to the RMA algorithm. Then, a linear model is fit to the normalized data to obtain an expression measure for each probe set on each array.
Immunohistofluorescence Immunohistofluorescence experiments were performed on cryopreserved mouse skin samples. Five-micrometer sections were cut from a tissue block. Sections were fixed in acetone/ methanol and staining was performed using monoclonal antibodies against Mouse Gr1 (1:100; BD Biosciences; 550291) and vimentin (1:100; Abcam; ab92547). These stainings were detected using appropriate secondary antibodies (goat anti-rat IgG Alexa 488 conjugated secondary antibody for Gr1 and Alexa 568-conjugated goat anti-rabbit for vimentin). Sections were observed using a NIKON Ci microscope. The images were captured using a NIKON DS-Ri2 and processed with NIS Elements software. For quantitative analyses, the intensity of Gr1 staining was measured in five representative areas for each section using the Image J software (National Institute of Health, USA).
pylori strain, using three different epithelial gastric cell lines, a non-cancerous (HFE-145), a diffuse subtype of GC (AGS) and an intestinal subtype of GC (MKN74). We then invalidated Nrf2 in the three cell lines by CRISPR-cas9 to assess its implication in EMT, notably through proteomic analysis. Finally, we assessed Nrf2 levels in H. pylori-infected human gastric tissues samples and using the UALCAN database. Moreover, we evaluated in those tissues the correlation between Nrf2 and the EMT-TF ZEB1 as well as in the overall survival probability of GC patients using the Kmplot molecular database.
After 10 min of incubation at RT, the solution was added in a 96 well containing 10,000 cells plated the day before in a 100 µL culture medium. At 2-3 days post-transfection, cells were trypsinized and half of them were cultured while the other half was pelleted, lysed and used as PCR template using Phire Tissue Direct PCR Master Mix (ThermoFisher Scientific). The PCR amplification of the targeted area was conducted following supplier instruction with primers 5 -CAAACGGGGTCATGACTGGT-3 and 5 -TCCCAGATCAGACGTCAGGT-3 for exon 3 and with primers 5 -CCCACTTCCCACCATCAACA-3 and 5 -ACCTGGGAGTAGTTGGCAGA-3 for exon 1. Sequencing of PCR products was performed by Eurofins Genomics and Sanger data were used to quantify Indels reflecting gene KO with DECODR algorithm (decodr. com [27]  accessed on 19 November 2019) or ICE algorithm (ice.
Immunofluorescence Cells were seeded on glass coverslips in 24-well plates at a density of 50,000 cells/well for 24 h prior to infection. Immunofluorescence staining was performed as previously described  [11, 29]. Primary antibodies used were rabbit anti-ZEB1 (1:100, Bethyl Laboratories, Souffelweyersheim, France A301-922) and rabbit anti-SNAIL (1:50, StemCell Technology, Vancouver, Canada 28193HCI30). The secondary antibody used was goat anti-rabbit Alexa-488-labelled (1:250, Thermo Fisher Scientific A32731). Images were taken using ×40 (numerical aperture, 1. 3) oil immersion objective on an Eclipse 50i epi-fluorescence microscope (Nikon, Minato-ku, Tokyo, Japan) with Nis Element acquisition software. Fiji software was used for the relative quantification of the signal intensity on digital images  [30].
Differential Proteomic Analysis Cells were plated in 6-well plates at a density of 200,000 cells/well for 24 h and total proteins (three replicates for each cell lines) were extracted using Mem-PER TM Plus Membrane Protein Extraction Kit (Thermo Fisher Scientific) according to the manufacturer's recommendations. The entire protocol is described in the Supplementary File S1. Briefly, protein samples were prepared for protein digestion and submitted to a nLC(C18)-MS/MS analysis (Orbitrap Fusion Lumos, Thermo Fisher Scientific) and Label-Free Quantitative Data Analysis. After validation and normalization, protein ratios between Nrf2-KO and WT for each cell line were calculated. Proteins with an abundance ratio >1. 5 or <0. 65 and an adjacent p-value < 0. 05 were considered respectively to be significantly upregulated or downregulated in Nrf2-KO compared to WT cells.
Prior to bioinformatics analysis, the resulting protein list was filtered to eliminate common contaminants. Protein network analyses were conducted using the STRING database (http://string-db. org accessed on 18 January 2021). All networks were visualized with Cytoscape (http://www. cytoscape. org/ accessed on 18 January 2021). Categorical annotations of proteins were supplied from Uniprot (https://www. uniprot. org/ accessed on 18 January 2021).
In Silico Database Analysis UALCAN cancer database (http://ualcan. path. uab. edu/ accessed on 28 June 2022)  [32]  was used to investigate gene expression levels of NRF2 (gene NFE2L2) in normal gastric tissues and H. pylori-infected gastric tumors. Overall survival probability of GC patients was analyzed using the KMplot database tool (www. kmplot. com accessed on 28 June 2022)  [33]. The JetSet probes used were 239952_at (ZEB1) and 201146_at (NFE2L2, NRF2). Analysis was conducted on all GC cases, or according to their Lauren classification status: diffuse type and intestinal type GC. High or low expression levels in patients' samples were determined according to the software's best cut-off value auto-setting and p-values were calculated by a log-rank test.
Statistics Results are expressed as mean ± S. E. M of at least three independent experiments. Statistical tests were carried out using the GraphPad Prism software version 8. 0. 2. The Student's t-test was used for the two-groups comparisons and one-way ANOVA with Tukey's or Dunnet's test as a post hoc test was performed for multiple comparisons. To evaluate the modulation of Nrf2 upon infection, three gastric epithelial cell lines (HFE-145: non-cancerous, AGS: diffuse type GC and MKN74: intestinal type GC) were infected with H. pylori P12 strain for 1, 5 or 24 h. We observed a significant increase in Nrf2 activity measured by the ARE transcriptional activity 1 h post-infection (hpi) (Figure  1A ). However, 24 hpi, the activity of Nrf2 was normalized to basal levels in the non-cancerous cell line and significantly inhibited in the cancerous ones (Figure  1A ).
Knock-Out of Nrf2 Favors Decrease in Epithelial Proteins Expression and Increase in Mesenchymal Ones In our proteomic analysis dataset, we specifically looked for proteins described as epithelial (Figure  4A ) and mesenchymal (Figure  4B ) markers in the EMTome database  [35]. Comparing Nrf2-KO vs. WT cells, we noted that different proteins were impacted by the KO of Nrf2 in the non-cancerous or the cancerous cell lines. We observed that many epithelial markers were under-expressed while many mesenchymal markers were overexpressed in Nrf2-KO cells for the three cell lines and especially in the non-cancerous and intestinal type well-differentiated cell lines compared to the poorly differentiated diffuse type. Therefore, the invalidation of Nrf2 leads to a decrease in epithelial markers and an increase in mesenchymal ones, suggesting a role of Nrf2 in maintaining an epithelial phenotype.
However, by separating the glands into two parts with the top composed of pit and isthmus and the bottom composed of neck and base, we observed an almost significant (p = 0. 06) decrease in nuclear positive cells in the neck and base of H. pylori positive tissue compared to non-infected tissue (Figure  6B ). To corroborate our results in another patient cohort, we checked the expression of NRF2 using the UALCAN database, which confirmed that NRF2 expression is decreased in H. pylori-infected gastric mucosa (Figure  7A ). However, ZEB1 expression is not significant different between normal or H. pylori-infected gastric mucosa (Figure  7B ). Given that gastric mucosa histology differs by anatomical region, we assessed the status of Nrf2, ZEB1 and Ki67 in gastric epithelial cells of the two histologically and functionally different regions: (i) fundic or body type comprising the largest part of the stomach and containing fundic glands secreting gastric acid and (ii) pyloric type, which contains antro-pyloric glands that secrete alkaline mucous.
10x Genomics Chromium Next GEM Single-Cell V(D)J Kit Data Processing and Analysis 10X Genomics data is analyzed using the Cell Ranger software, provided by 10x Genomics free of charge. Cell Ranger allows (1) sequencing raw data demultiplexing, (2) quality control of the raw data obtained, (3) raw data alignment to the reference genome, and (4) data matrix preparation for further in-depth analyses using dimensionality reduction methods. Three Cell Ranger pipelines are now available: cellranger count (for transcriptome and feature data), cellranger vdj (for AIRR data) and cellranger multi (which does the integrative analysis of transcriptome, AIRR, and feature data). Loupe Brower and Loupe VDJ Browser, which are also available via the 10x Genomics website, provide a complementary set of analysis tools.
Check raw data files (see Note 47). 3. Perform standard quality control, and trim sequencing reads for bad quality reads/nucleotides and adapter sequences by using tools such as Trim Galore (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore) and Cutadapt  [7]. The main arguments expected by TraCeR are as follows: <file_1>: FASTQ file containing #1 mates from pairedend sequencing or all reads from single-end sequencing. If paired-end sequencing is used, provide #2 mates after the #1 mates FASTQ file. <cell_name>: Name of the cell chosen by the user. This name will be used in file names and labels. <output_directory>: Directory for output. The cellspecific output from the assemble mode will be found in <output_directory>/<cell_name>. TraCeR also accepts several options, which are detailed at https://github.