dataesr/scientific-paragraphs-categorization huggingface dataset:
However, IFN-γ and IL-2 increased in ICU patients compared to IDU patients (Fig. 2e  and Extended Data Fig. 5a ). In contrast, GzB production progressively increased in stimulated MAIT cells in IDU and ICU patients as compared to controls. Elevated GzB production was also detected in unstimulated MAIT cells from infected patients (Fig. 2f ,g and Extended Data Fig. 5a, b ). Such enhanced cytokine and GzB production in ICU patients was not observed in conventional αβ T, γδ T and CD3 -cells (including NK cells; Extended Data Fig. 5a, b ). While we observed a slight increase in IL-17 and IFN-γ production by MAIT cells in ICU patients when compared to IDU patients, we did not observe any increase of IL-17 + IFN-γ + double-positive MAIT cells in patients with COVID-19 (Extended Data Fig. 5c ). Finally, we analyzed GzB production by lung MAIT cells from endotracheal aspirates (ETAs) and bronchoalveolar lavages (BALs) of ICU patients.
Methods Clinical study design and ethical statements. A first cohort of 102 patients with COVID-19 admitted to Bichat or Cochin hospitals (Paris, France) between 23 March 2020 and 29 May 2020 was included in this clinical study. A second, small cohort of 22 patients with COVID-19 admitted to Bichat or Cochin hospitals between 24 September 2020 and 5 October 2020 was also included in this clinical study. For comparisons, blood from 84 uninfected controls of the QUID-NASH project (n = 30), Etablissement Français du Sang (n = 12) and volunteer donors (n = 42) were mostly collected before the onset of the pandemic (54 of 84). Clinical characteristics of the 182 patients from cohort 1 and 26 patients from cohort 2 are summarized in Extended Data Figs. 1  and 9  and Supplementary Table  1. No statistical methods were used to predetermine cohort size.
The percentage of lung involvement was evaluated on chest computed tomography, with a score of 1 to 5 representing mild to critical pulmonary lesion damage. SAPS II was used to estimate the probability of survival after ICU admission. The ethics committees (detailed below) approved clinical investigations, and informed consent was obtained from each enrolled patient or from their next of kin, in accordance with national laws. Patient's from Bichat Hospital were included in the French COVID-19 cohort (NCT04262921). Ethics approval for this cohort was granted on 5 February 2020 by the French Ethics Committee Comité de Protection des Personnes (CPP)-Ile de France-VI (2020-A00256-33). This cohort is sponsored by Inserm and supported by the REACTing consortium and by the French Ministry of Health (PHRC, 20-0424).
Samples from these patients were derived from samples collected in routine care. For lung fluid samples from Bichat hospital, samples were obtained under authorization from the ethics committee CPP-Ile de France-XI (2020-001457643, 20037-25957 and APHP 200388-COVIDICUS). Other lung fluid samples were obtained from the Regional University Tours Hospital (NCT03379207) and under authorization from the ethics committee CPP-Ile-de-France-VIII (2017-A01841-52). Patients from Cochin Hospital were recruited in the setting of the local RADIPEM biological samples collection derived from samples collected in routine care. Biological collection and informed consent were approved by the Direction de la Recherche Clinique et Innovation and the French Ministry of Research (2019-3677). Investigations with control patients from QUID-NASH were approved by CPP Sud Méditerranée V (18.
Samples were weighted and incubated in PBS (5 ml g -1 ) in the presence of 1 μM DL-dithiothreitol (D5545, Sigma) for 30 min at 4 °C under gentle agitation for cellular dispersion. After centrifugation, cell pellets were filtered through a 100-μm cell strainer. Red blood cells were removed using a red blood cell lysis buffer and then cells were filtered through a 40-μm cell strainer before staining for flow cytometry. Cytokine measurements. IL-6, IL-8 and IL-10 levels were measured in plasma using human CBA Inflammatory Cytokine Kit (BD Biosciences) according to the manufacturer's instructions. Acquisitions were performed on a BD FACSLyric cytometer (BD Biosciences) and raw data analyzed with FCAP Array software v3. 0 (BD Biosciences). For the IFN-α2 quantification, plasma samples were analyzed with the MSD Quickplex using the ultra-sensitive assay S-PLEX human IFN-α2a (K151P3S-1, Meso Scale Diagnostic) using 25 μl of each sample.
After surface staining, cells were fixated using BD FACS Lysing Solution (BD Biosciences, 349202) according to the manufacturer's instructions. Data acquisition was performed using a BD Biosciences LSR Fortessa. Flow cytometric analyses were performed with FlowJo v10. 6. 2 (Tree Star). For intracellular labeling, thawed PBMCs were treated with DNase (0. 05 mg ml -1 ; D4263, Sigma-Aldrich) in RPMI (61870-010, Gibco) and incubated at 37 °C and 5% CO 2 for 30 min. Depending on the number of PBMCs obtained from each patient, intracytoplasmic staining of cytokines and GzB was analyzed with stimulation with PMA (P-8139, Sigma-Aldrich), ionomycin (I-0634, Sigma-Aldrich) and brefeldin A (B-7651, Sigma-Aldrich) and/or only brefeldin A. Stimulated cells were incubated for 6 h at 37 °C in RPMI medium supplemented with 10% FCS, 1% HEPES, 1% penicillin-streptomycin and stimulated with PMA (25 ng ml -1 ) and ionomycin (1 μg ml -1 ) in the presence of brefeldin A (10 μg ml -1 ).
Acknowledgements We thank all the patients and the physicians, nurses and technician staff who helped with the study. We thank the  Department of Biological Hematology  and the  Department of Biochemistry of Bichat-Claude Bernard University Hospital  for measuring white blood cells and CRP. We are grateful to  M. Diedisheim  for discussion about biostatistics and bioinformatics and the  National Institutes of Health Tetramer Core Facility  for human MR1 tetramers. The laboratories of A. L. and R. C. M. are supported by  ANR-11-IDEX-0005-02   Laboratory of Excellence INFLAMEX  and  Fondation pour la Recherche Médicale  ( EQU201903007779  to A. L. and  EQU201903007816  to R. C. M. ). R. C. M. received a  Université de Paris   COVID-19  grant. The laboratory of A. L. is also supported by  ANR-17-CE14-0002-01 ,  ANR-19-CE14-0020 ,  Fondation Francophone pour la Recherche sur le Diabète and Juvenile Diabetes Research Foundation.
A. L. , C. B. , A. V. -P. and J. -F. G. are supported by the  Recherche Hospitalo-Universitaire  ( RHU) QUID-NASH program  ( ANR-17-RHUS-009 ). We thank  D. Valla  and  A. Brzustowski , coordinator and manager of the RHU QUID-NASH. M. R. and L. Bertrand were supported by  French Ministry of Research grants. A. T. , Z. G. and L. C. were supported by  RHU QUID-NASH. C. R. was supported by  Fondation Francophone pour la Recherche sur le Diabète. P. S. was supported by the  Juvenile Diabetes Research Foundation. We also thank  Servier Medical Art  for the free medical images, licensed under the Creative Commons Attribution 3. 0 Unported License.
16S rRNA Meta-Barcoding and Culturing To analyze the bacterial diversity in the nasopharyngeal cavity using the 16S V3-V4 ribosomal RNA regions, data from our pediatric exacerbation study cohort were used for nasal microbiota analyses through high-throughput next-generation sequencing (NGS) technique. Deep frozen swab specimens (eSwab COPAN LQ Amies) were systematically thawed. For DNA extraction, a special protocol was established using QIAmp DNA micro and QIAmp DNA Microbiome kits from QIAGEN. Further information on the DNA isolation method is available in this article's online repository material. For amplification of the V3 and V4 regions of the bacterial DNA, the 16S gene amplicon sequencing library preparation protocol from Illumina R was used according to Illumina's recommendations.
LEGENDplex TM assay kits were performed according to the manufacturer's instructions. In detail, samples were incubated with capture beads and assay buffer in V-bottom plates overnight with constant shaking at 4 • C. In parallel, standard dilutions were prepared and likewise incubated with beads and Matrix A1. After two washing steps (800 × g for 5 min with 200 µl of wash buffer), beads were incubated with biotinylated detection antibodies for 1 h followed by streptavidin-PE for 30 min at RT on a plate shaker. Plates were washed and beads were resuspended in 100 µl of wash buffer. Samples were stored protected from light at 4 • C until measurement with a BD FACS Canto II flow cytometer. Reactions were performed in duplicate. Data were analyzed via LEGENDplex TM V8. 0 software (BioLegend R ) based on standard curve interpolation and specified as pg/ml.
Whole Proteome Analyses Next, we were interested whether the proteome level of infected NAEsp with subject-derived M. catarrhalis was biologically different between groups. The exposure experiments with M. catarrhalis were firstly lysed, and the extracted proteins were separated by 1D SDS-PAGE before tryptic in-gel digestion. Eluted peptides were analyzed by LC-MS/MS using an EASY-nLC II liquid chromatography system coupled to an LTQ Orbitrap Velos Pro (ThermoFisher Scientific, Waltham, Massachusetts, U. S. A. ). Results obtained during analysis of a pure culture of n = 5 subject-derived M. catarrhalis strains were used to create a spectral library, which was subsequently used to identify spectra obtained from infection experiments. First, the data obtained from infection experiments were searched against the generated spectral library using the tool "SpectraST Search" within the TPP framework.
Obtained pep. xml files where combined to interact. pep. xmls and processed with Peptide Prophet. Resulting ipro. pep. xmls were merged into protein lists using the TPP Tool "Analyze proteins" with activated "Normalize NSP using protein length" option. Finally, the software Philosopher (Nesvizhskii Lab) was used to filter the prot. xml files for an FDR of <0. 01 on peptide and protein level. More details on sample preparation, MS analysis, and data processing can be found in the Supplementary Material.
Bioinformatics Analyses Statistical analyses for LDH and cytokine measurements were performed using GraphPad Prism version 8. 3. 0 for Windows, GraphPad Software, La Jolla, California, U. S. A. (www. graphpad. com). The data were presented as mean and standard error of the mean (SEM). The data were firstly tested for normal distribution using the Shapiro-Wilk test. The majority of the results were not normally distributed. To perform statements regarding significant differences between groups, the following non-parametric test procedures were therefore used: for >2 groups, the Kruskal-Wallis test with Dunn's multiple post-hoc test was used for correction for multiple comparisons; for simple pair comparisons (two groups), the Mann-Whitney U-test was used. The significance level α was set as 0.
The sequence of each OTU was then affiliated with blastn against the Silva v132 database  (31). OTUs with a blast coverage < 95% were removed. Finally, Fasttree  (32)  was used to build a phylogenetic tree from the remaining sequences. All diversity analyses were performed using the R statistical software (v 3. 6. 1) and phyloseq package (https://www. R-project. org/)  (33). α-diversity was computed using the specific richness, the Shannon index (moderately upweights abundant taxa), and the Inverse-Simpson (upweights abundant taxa) index. α-diversity indices were compared with Kruskal-Wallis tests, followed by Dunn's post-hoc tests. β-diversity was computed using the Jaccard, Bray-Curtis (upweights abundant taxa), UniFrac (upweights phylogenetically distant taxa), and weighted UniFrac (upweights phylogenetically distant and abundant taxa) indices after rarefaction to the same number of reads per sample.
Differential Abundances of Taxa Showed Distinct Clusters in the AB, WH, and HC We filtered the taxa (keeping only those present in at least 12% of the samples) before performing a differential abundance analysis on the remaining taxa. In total, we identified n = 24 OTUs with differential abundances in AB, WH, and HC (Supplementary Figures  3A, B  and Supplementary Table  1 ). With reference to the atopic status, we observed n = 42 OTUs with significant differential abundances in atopic AB and WH (Supplementary Figures  3C, D  and Supplementary Table  2 ). The filter applied to the differential abundance analysis and the rationale of the filter was to increase statistical power by pruning the rare taxa whose prevalence was too low in the dataset for any test to be significant. Thus, the 12% prevalence threshold correspond to a taxa being present in at least eight samples.
Nevertheless, we believe that it was important to include the wheezer study group into the analyses, as a defined percentage of this heterogeneous group will show a progress to asthma. Thus, this side information is critically important during interpretation of this dataset, which is necessary for an assessment of biodiversity. Longitudinal analyses of patients of whether bacterial colonization changes or differs between two study visits (exacerbation and phase without clinical symptoms) should be analyzed. In summary, our complex dataset adds important insights into the plasticity of the nasal microbiome during exacerbation of pediatric chronic bronchitis and AB. Despite few limitations, the power of our entire cohort is important. Further ongoing studies and the inclusion of follow-up samples over different time frames would be interesting to address the role of the nasal microbiome in the pathogenesis of wheeze and asthma in order to characterize their impact on disease development.
ACKNOWLEDGMENTS We very much appreciate our colleagues at the  Microbiology Department of Institute of Medical Laboratory Diagnostics of the Helios University Hospital Wuppertal of Witten/Herdecke University, Germany , for their endless support. We thank  Claudia Hirschfeld  for assistance with the MS measurements and the support during spectral library building. We are grateful to the  INRAE MIGALE bioinformatics (MIGALE, INRAE, 2020. Migale bioinformatics Facility , doi:  10. 15454/1. 5572390655343293E12 )for providing statistical analyses, help, and computer resources. Finally, we would like to thank the children and parents who were motivated and willing to support the asthma and allergy science to search for a better understanding of asthma development and exacerbation. Without their incredible support, we could never have implemented our experimental ideas into practice.
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (45) partner repository with the dataset identifier PXD024694. Project Name: The rhinobiome of exacerbated preschool wheezers and asthmatics: insights from a German pediatric exacerbation network, Project accession: PXD024694. Moreover, the metagenomics data set are uploaded and have the following project number: BioProject ID: PRJNA714100.
The assembly was merged into a single haplotype genome assembly using HaploMerger2 20 (Figure  S2 , Extended data) and polished using two rounds of Hapo-G with the Illumina reads. To increase the contiguity of the assembly to a chromosomescale level (Table  S1 , Extended data), we aligned Hi-C pairedend reads to the polished haploid assembly with bwa -mem  21  (BWA, RRID:SCR_010910). Because Hi-C captures conformation via proximity-ligated fragments, paired-end reads are first mapped independently (as single-end reads) and subsequently paired in a later step. Hi-C reads and alignments contain experimental artifacts so the alignments need some additional processing. We use alignment filtering method using Arima Genomics pipeline (https://github. com/ArimaGenomics/map-ping_pipeline) and applied the script "filter l" to each bam file (Read1 and Read2) and afterwards paired the filtered single-end Hi-C reads using "two_read_bam_combiner.
pl". Then, with Picard tools (https://broadinstitute. github. io/picard/), we added read groups to the combined BAM file (with the command AddOrReplaceReadGroups) and discarded any PCR duplicates present in the paired-end BAM file (with the command Mark-Duplicates). We scaffolded the assembly with the Hi-C data using SALSA2  22  and obtained 138 scaffolds. Only scaffolds larger than 35kb were kept resulting in a final assembly of 112 scaffolds (Table  S3  and Figure  S10 , Extended data). The largest scaffolds were manually checked for missassembly using the sequencing information and the synteny with the ten Tribolium castaneum chromosomes (cf. comparative genomics section). Transcriptome assembly RNA-seq reads from six transcriptomes derived from different developmental stages (embryos, larvae, pupae, and adults), sexes (females and males) and public data of two RNA-seq samples generated from pooled bacterial infected T.
molitor (PRJNA646689, Underlying data) were assembled using Velvet  23  (Velvet, RRID:SCR_010755) version 1. 2. 07 and Oases  24  (Oases, RRID:SCR_011896) version 0. 2. 08 with k-mer size set to 81 and 63 for the in-house and public RNA-seq reads, respectively (Table  S2 , Extended data). The first five bases of contigs 5' and 3' ends were removed. The sequences were masked for low-complexity using DustMasker (version 1. 0. 0 from the BLAST 2. 10. 0 package) and only contigs larger than 150bp with more than 75% of unmasked bases were kept. To address the problem of merged chimeric contigs, a post-processing of Oases contigs has been done. Assembly tools often erroneously merge sequences into one single contig and Oases is prone to this behaviour. To address this problem, we used an in-house script that splits chimeric contigs.
Splitting a contig into regions where different ORFs appear, or regions where abrupt shifts in read coverage occur, could streamline the gene-prediction process. Based on combined resources such as the pileup-coverage, the research of ORFs (TransDecoder https://github. com/TransDecoder/TransDecoder/releases) and domains, this tool aims to split contigs sequences with different functional sites form different contigs. Reads were mapped to the contigs with BWA-mem and the consistent paired-end reads were selected. Chimeric contigs were identified and split (uncovered regions) based on coverage information from consistent paired-end reads. Moreover, open reading frames (ORF) and domains were searched using respectively TransDecoder and CDDsearch (Conserved Domain Database, RRID:SCR_ 002077). We only allowed breaks outside ORF and domains.
Genome annotation Repeated sequence masking. Low complexity regions of the assembly were masked with the DustMasker 25 algorithms (version 1. 0. 0 from the BLAST 2. 10. 0 package). Transposable elements (TEs) and other repeats were annotated and masked using RepeatMasker  26  (RepeatMasker, RRID:SCR_012954) version open-4. 0. 5 with rmblastn 26 version 2. 10. 0+. The assembly was compared to classified sequences of the RepeatMasker complete database 20150807. We set the custom library RepeatMasker. lib of version 4. 0. 5 to the -lib parameter  27. Transcriptome and proteome alignments. mRNA contigs from the eight samples were aligned to the assembly in a two-step strategy. First, BLAT  28  (BLAT, RRID:SCR_011919) (version 36 with default parameters) was used for fast localizing genomic regions and the best match of each contig was kept.
A second local alignment was performed with Est2Genome 29 (version 5. 2 with default parameters). Aligned contigs with overlap higher than 80% and more than 95% identity were retained. Additionally, proteomes of four other Coleoptera (T. castaneum (Herndon et al. , 2020), Ontophagus taurus, Asbolus verrucosus, Dendroctonus ponderosae) and T. molitor proteins from UniProt  30  database were aligned to the genome in a two-step strategy. First, using BLAT (version 36 with default parameter) matches with score higher than 90% of the best match score were retained. Second, alignments were refined using Genewise 31 (version 2. 2. 0 default parameters) and proteins with more than 50% of their length aligned onto the assembly were kept.
Gene predictions. To identify the gene structure, the transcriptomic and protein alignments were combined using Gmove  32  (Gmove, RRID:SCR_019132) (Note S2, Extended data). Protein alignments from the five coleoptera were merged into a single file and provided to Gmove (-prot parameter). We also set transcriptomic alignments from eight different samples (Table  S2 , Extended data) to the -rna parameter and activated the -score option to keep the gene model with the highest score. Based on T. castaneum gene features, we set the maximal size of intron and minimal size of exons to 150,000bp and 3bp using the -m and -e parameters, respectively. To prevent false positive gene predictions due to a large number of single-exon transcripts, sample-specific single-exon transcripts were removed before running Gmove.
Several criteria were applied sequentially to filter the gene predictions. We used HMMER  33  (Hmmer, RRID:SCR_005305) (version 3. 2. 1, June 2018) to find pfam domains, DIAMOND  34  (DIAMOND, RRID:SCR_016071) version 0. 9. 24 for protein searches against ncbi-nr database, RepeatModeler  35  (Repeat-Modeler, RRID:SCR_015027) version 2. 0. 1 for ab initio repeats screening and TransposonPSI 36 to compare predicted models with transposable elements. Gmove initially predicted 24,870 genes, 27% of which were intronless. While we are more confident in multi-exon gene predictions, we were cautious with intronless genes corresponding potentially to transposable elements or false positive predictions caused by the fragmented alignments of transcripts. To solve this problem, we launched HMMER (version 3.
2. 1 with e-value set to 10e-5) for detecting known pfam domains and a DIAMOND analysis against ncbi-nr database for protein hits (version 0. 9. 24 with -evalue 10e-5, -unal 0). Furthermore, Repeat-Modeler version 2. 0. 1 was used for screening ab initio repeats in the T. molitor assembly and 46. 77% of the genome was masked. Then, we focused on overlaps between the predicted genes and repeats, using commands from BEDtools. Genes with exons highly covered by repeats (>90%) were automatically classified as repeats. In parallel, we used transposonPSI. pl to align the virtual cDNA proteins of the 24,870 predictions against the TransposonPSI_08222010 library. We selected the single best transposonPSI match for each protein (from file proteins. fasta. TPSI. topHits) and tagged the corresponding genes as transposable elements.
At this point, we excluded genes (single and multi-exon) that were either highly covered by repeats (RepeatModeler) or TE tagged (TransposonPSI) without any blastp/pfam hit. We also excluded intronless genes that were predicted only by RNA-seq evidence (not any Coleoptera protein overlap) and at the same time composed of >80% untranslated regions (ratio UTR/(UTR+CDS)) without any pfam/blastp hit. Additionally, we searched for overlaps between predicted intronless genes and CDS of protein or mRNA evidence. Then, we discarded any intronless gene accomplishing none of the following conditions: (i) A gene that is predicted from at least one mRNA and one protein evidence. (ii) A gene that is predicted from mRNA transcripts of at least two different samples and (iii) A gene that is predicted from at least a T.