dataesr/scientific-paragraphs-categorization huggingface dataset:
(D) Clp1 expression from RNA-seq data of two replicate mTEChi and of 22 mouse tissues; median, log10 scale. BAT stands for brown adipocytes tissue, BMDM for bone marrow derived macrophage, MEF for mouse embryonic fibroblast, mESC for mouse embryonic stem cells and VTA for ventral tegmental area. Duplicate reads were discarded for datasets comparison. (E) Median of d3'UTR ratios of Aire-sensitive genes whose expression in the periphery is tissue-restricted and of Aire-neutral genes, relative to the levels of Clp1 expression (log10 scale) in 22 mouse tissues. mTEChi are represented by a red and gray cross for the Aire-sensitive (Top) and neutral genes (Bottom), respectively; peripheral cells are represented by pink and gray circles. Significance is reached for the Aire-sensitive genes, p=0. 002, Pearson correlation.
Clp1-driven 3'UTR shortening of Aire-upregulated transcripts is associated with higher stability in mTEChi To determine whether the effect of Clp1 on 3'UTR shortening of Aire-upregulated transcripts was associated with higher levels of these transcripts in mTEChi through increased stability, we assessed the stability of all transcripts in these cells. We used Actinomycin D (ActD) to inhibit new transcription  (Sobell, 1985)  and assessed the differences in transcript profiles between treated and untreated mTEChi. We harvested the cells at several timepoints for expression profiling by RNA-seq. Each RNA-seq dataset was normalized to total read counts and we calculated the expression fold-change of each gene in treated versus control samples, therefore reflecting differences in transcript levels in the absence of ongoing transcription.
Therefore, the Clp1-promoted 3'UTR shortening at Airesensitive genes in mTEChi is indeed associated with higher transcript stability. Since short-3'UTR transcripts, in comparison to their longer counterparts, lack miRNA-binding sites specifically targeted by miRNAs known to destabilize transcripts and/or decrease translation, we assumed that the Aire-sensitive genes that underwent Clp1-promoted 3'UTR-shortening escape the post-transcriptional repression mediated by miRNAs in mTEChi. We assessed the level of miRNA-mediated post-transcriptional repression in mTEChi versus mTEClo precursor cells by assessing the differences in expression of sets of genes identified as potentially targeted by miRNAs, in mTEChi versus mTEClo. To this end, we performed a Gene Set Enrichment Analysis (GSEA) between the two mTEC populations for each set of genes potentially targeted by a specific miRNA or miRNA family (as defined by the TargetScan database, Figure  5 -source data 1).
We found 346 miRNAspecific target gene sets that had significantly reduced expression in mTEChi versus mTEClo (Figure 5-figure supplement 1, Figure  5 -source data 1). In sharp contrast, we did not detect any of the miRNA-specific target gene sets that had reduced expression in mTEClo versus mTEChi. This suggests that miRNAs are an important contributor to transcript destabilization in mTEChi and that this effect is similar or less for the same miRNA target sets in mTEClo cells. To determine whether the Clp1-promoted 3'UTR shortening at Aire-sensitive genes can lead to escape from post-transcriptional repression mediated by the miRNAs associated with these 346 target-gene sets, we calculated, among the Aire-sensitive genes subject to Clp1-promoted shortening, the proportion of those that are potentially targeted by one of these miRNAs at a conserved site in the d3'UTR of their longer-transcript isoforms (Figure  5 -source data 1).
The observation that the levels of Clp1 expression and the distributions of the short-3'UTR transcript isoforms of Aire-sensitive genes were similar between Aire-KO and WT mTEChi, strongly suggests that the Clp1-driven 3'UTR shortening mechanism is already in place in mTEChi before Aire is activated. The independence of Aire's action on gene expression and the effect of Clp1 on 3'UTR shortening is also consistent with the lack of direct interaction found between Aire and Clp1. Although these effects of Aire and Clp1 appear decoupled, it is also apparent as noted that sensitivity to these Aire and Clp1 effects have a higher-rate of co-occurrence in the same set of genes. Although we could detect and characterize the effect of Clp1 on the Aire-sensitive genes in mTEChi using annotated pAs from the PolyA_DB 2 database which mainly contains pAs identified from comparisons across peripheral tissues, the precise proportion of Aire-sensitive genes that are subject to differential pA usage and Clp1-driven shortening in mTEChi remains to be precisely defined.
5 (1:50) (Biolegend), Ly51-PE (1:800) (Biolegend), and I-A/E-APC (1:1,200) (eBioscience). Sorting of mTEChi/lo (CD45 À Ly51 À I-A/E high/low ) or, for lentigenic mice, of mTEChi +/-for GFP expression, was performed on a FACSAria III instrument (BD Bioscience). For Clp1 staining, cells labeled for membrane antigens were fixed in (3. 7%) formaldehyde for 15 min, permeabilized in (0. 5%) saponin for 15 min, and incubated with an antibody to Clp1 (1:100) (clone: EPR7181, GeneTex, GTX63930) and an Alexa Fluor 488-conjugated goat polyclonal antibody to rabbit (1:200) (TermoFisher, A11008). Cells were analyzed on an Accuri C6 instrument (BD Bioscience). All compensations were performed on single-color labeling of stromal cells and data analysis was done using the BD Accuri C6 Analysis software.
RNA-seq and d3'UTR ratios Total RNA was isolated by TRIzol extraction (ThermFisher). Only high-quality RNA (with RIN values around or over 8) was used to generate polyA-selected transcriptome libraries with the TruSeq RNA Library Prep Kit v2, following the manufacturer's protocol (Illumina). Sequencing was performed using the Illumina HiSeq 1000 machine and was paired-end (2 Â 100 bp) for mTEChi and Aire-KO mTEChi isolated from pooled thymi (deposited in the GEO database as GSE140683), and for mTE-Chi and mTEClo isolated from the same mice (deposited in the GEO GSE140815). Sequencing was single-end (50 bp) for transfected and knockdown HEK293 cells (deposited in the GEO database as GSE140738 and GSE140993), actinomycin D-treated mTEChi (as GSE140815), and mTEChi isolated from Clp1 lentigenic mice (as GSE140878).
RNA libraries from thymic cells isolated from lentigenic mice or actinomycin D-treated mTEChi were constructed with the Smarter Ultra Low Input RNA kit (Clontech) combined to the Nextera library preparation kit (Illumina). Paired-end (100 bp) datasets were homogenized to single-end (50 bp) data by read-trimming and concatenation. Lower quality reads tagged by the Illumina's CASAVA 1. 8 pipeline were filtered out and mapped to the mouse or human reference genome (mm9 or hg19) using the Bowtie aligner  (Langmead et al. , 2009). For the multi-tissue comparison analysis, duplicate reads were removed with the Samtools rmdup function (1000 Genome Project Data Processing  Subgroup et al. , 2009). For read counting, we used the intersectBed and coverageBed programs of the BEDtool distribution  (Quinlan and Hall, 2010)  with the -f one option.
It enabled the count of reads strictly contained in each exon of the Refseq genes whose annotation GTF file was obtained from the UCSC Table Browser, in choosing mm9 mouse (or hg19 human) genome/Genes and Gene Predictions/RefSeq Genes/refGene. GTF files for mice and humans are provided in Figure  1 -source data 2. Differential fold-change expression between two datasets was computed using DESeq  (Anders and Huber, 2010)  and gene expression was quantified in each sample as reads per kilobase per million mapped reads (RPKM). For d3'UTR ratio calculation, we counted the reads as above but with a GTF file that we generated as follows: First, from the UCSC Table Browser, we chose mm9 mouse (or hg19 human) genome/Genes and Gene Predictions/RefSeq Genes/refGene, and we obtained one file with coding exon features only and another file with 5' and 3'UTR features.
We combined these two files into a single one and split the 3'UTR annotations into distal 3'UTR (d3'UTR) and proximal 3'UTR annotations at the sites of alternative pAs that we identified in parsing the PolyA_DB 2 database (http:// exon. umdnj. edu/polya_db/v2/). A proximal pA was validated when its genomic location from the PolyA_DB 2 database differs from 20 bp at least to the genomic location of the UCSC annotated 3'UTR distal boundary or distal pA. In case of multiple proximal pAs, the most proximal one was considered. Then, we fused into a single feature the proximal 3'UTR annotations with the last coding exons (exon CDS) and formatted the file to fit a GTF format. d3'UTR annotation and GTF files for mice and humans are provided in Figure  1 -source data 1. For each Refseq gene corresponding to an independent NM_Refseq ID, we divided the RPKM expression of the d3'UTR region by the expression of the upstream region of the last 3' exon to generate the d3'UTR ratio used as a measure of pA usage.
Multi-tissue comparison analysis First, an RNA-seq database of mouse tissues was assembled in collecting 22 RNA-seq datasets generated from polyA-selected RNA and Illumina sequencing. The raw read sequences were obtained from the GEO database: GSE36026 for bone marrow, bone marrow derived macrophage (BMDM), brown adipocytes tissue (BAT), cerebellum, cortex, heart, intestine, kidney, liver, lung, olfactory, placenta, spleen, testes, mouse embryonic fibroblast (MEF), mouse embryonic stem cells (mESC), E14. 5 brain, E14. 5 heart, E14. 5 limb and E14. 5 liver; GSM871703 for E14. 5 telencephalon; GSM879225 for ventral tegmental area VTA. Reads of the VTA dataset, over 50 bp in length, were trimmed for homogeneous comparison with our RNA-seq data. We processed each collected dataset for gene expression profiling and d3'UTR ratio computing as above.
To avoid center-to-center biases, we removed from the sequence assemblies the duplicate reads that could arise from PCR amplification errors during library construction. For multi-sample comparison analysis, our mTEChi datasets were also subjected to removal of duplicate reads. Next, the tissue-specificity (one tissue of restricted expression) or selectivity (two-to-four tissues of restricted expression) of the Aire-sensitive genes was characterized by using the specificity measurement (SPM) and the contribution measurement (CTM) methods  (Pan et al. , 2013). For each gene, the SPM and the CTM values were dependent on its level of expression in each tissue. If no read was detected in a tissue, the latter was excluded from the comparison. For tissues of similar type, only the one showing the highest level of gene expression was included in the comparison.
CLIP-seq analysis The location of the 3' end processing complex on the transcripts of Aire target genes was tracked by measuring the density of reads that map to these transcripts in PAR-CLIP sequencing data of RNAs bound to the endogenous CSTF2 from the GEO database (GSM917676). We processed these assemblies of RNA-mapped reads (as wig files) using the sitepro program (CEAS distribution)  (Shin et al. , 2009)  to infer the read density at the vicinity of proximal and distal pAs of transcripts of Aire target genes in HEK293 cells. The genomic location of these proximal and distal pAs extracted from the PolyA_DB 2 database (Figure  2 -source data 1) was loaded into sitepro as bed files of Aire-sensitive and neutral genes identified from RNA-seq differential expression of Aire versus control-transfected HEK293 cells.
Microarray gene expression profiling Total RNA was prepared from harvested HEK293 cells knockdown for CLP1, HNRNPL, DDX5, DDX17, PARP1, PRKDC, SUPT16H, PABPC1, CPSF6 and the control LacZ gene using TRIzol (Ther-moFisher). Single-stranded DNA in the sense orientation was synthesized from total RNA with random priming using the GeneChip WT Amplification kit (Affymetrix). The DNA was subsequently purified, fragmented, and terminally labeled using the GeneChip WT Terminal Labeling kit (Affymetrix) incorporating biotinylated ribonucleotides into the DNA. The labeled DNA was then hybridized to Human Gene ST1. 0 microarrays (Affymetrix), washed, stained, and scanned. Raw probe-level data (. CEL files deposited in the GEO database as GSE141118) was normalized by the robust multiarray average (RMA) algorithm and summarized using the R-package aroma.
Individual probe-level microarray analyses For each HEK293 sample of a microarray comparison, an expression file of individual probes of each gene on the array was generated using aroma. affymetrix, as described in (Figure  3 -source data 1). Probes with expression values over the background in each sample, had their hg19 genomic location retrieved from the Affymetrix individual probe dataset (NetAffx Web site), and were mapped to the d3'UTR annotation file generated as described in the 'RNA-seq and d3'UTR ratios' section. The d3'UTRs ratios were then computed for the genes having at least two individual probes in their d3'UTR regions by dividing the specific d3'UTR expression by the whole-transcript expression. We performed the individual probe d3'UTR mapping and d3'UTR ratio calculation using an adaptation of our R-implementation of PLATA  (Giraud et al.
miRNA potential target gene identification We used the TargetScan 6. 2 database (http://www. targetscan. org/vert_61/) to generate a list of miRNAs or miRNA families and their potential target genes harboring miRNA conserved sites in 3'UTRs  (Friedman et al. , 2009). We further selected from these potential target genes those for which the miRNA conserved sites are specifically located within the cleavable d3'UTRs. The miRNAs and their potential target genes with miRNA conserved sites within their 3'UTRs or d3'UTRs are provided in Figure  5 -source data 1.
Gene set enrichment analysis (GSEA) GSEA was first used to test the effect of candidate gene knockdown on the expression of Aire-sensitive genes. The overlap between the transcripts impacted by Aire in HEK293 cells and those impacted by the knockdown of CLP1, HNRNPL, DDX5, DDX17, PARP1 was tested by the GSEA software  (Subramanian et al. , 2005)  (Broad Institute). The Aire-sensitive genes were identified from RNA-seq data of control and Aire-transfected HEK293 cells. For this analysis, the top 30% of genes the most sensitive to Aire were considered. GSEA was also used to test the potential involvement of miRNAs or miRNA families in the regulation of sets of genes in mTEChi versus mTEClo. To this end, we performed RNA-seq profiling of mTEChi and mTEClo that were isolated from the same pool of 4 thymi of B6 mice.
We ran GSEA on the DESeq-normalized read counts of the mTEChi versus mTEClo comparison using the list of miR-NAs and of their potential target genes harboring miRNA conserved sites in 3'UTRs as generated through parsing the TargetScan 6. 2 database (above). A 'classic' scoring scheme was used for the GSEA analysis. Gene sets of 15 to 5000 genes were considered for the analysis. Null read count values were removed. To determine the genes that contribute the most to the observed miRNA-specific gene set reduction in mTEChi, we performed a leading edge analysis in including all miRNAspecific gene sets with FDR q-values <0. 05 (Figure  5 -source data 1). We found that these leading edge genes were potentially targeted by a number of different miRNAs, whose median was 4. We selected for representation the leading edge genes targeted by more than four different miRNAs (>=5).
Acknowledgements We thank  Dr Sheena Pinto  for her useful comments and suggestions, that have helped improve this paper. We thank  Drs. D Mathis  and  C Benoist  ( Harvard Medical School ) for Aire-KO ( B6 ) mice. We thank the members of the  'Homologous recombination' and the 'Genomic'IC' core facilities  ( Cochin Institute ) for lentigenic generation and RNA-seq data production. This work was supported by the  Agence Nationale de la Recherche (ANR)   2011-CHEX-001-R12004KK  (to MG), the  European Commission CIG  grant  PCIG9-GA-2011-294212  (to MG) and by the ' Investissements d'Avenir' program  managed by the  ANR  to the  France Genomique national infrastructure   ANR-10-INBS-09  (FC) and the  French Institute of Bioinformatics   ANR-11-INBS-0013  (CB). CG and Y-CL were supported by fellowships from the  Fondation pour la Recherche Medicale FDT20150532551  and  ING20121226316 , respectively.
CG, NJ and MG designed the study and wrote the manuscript; CG and NJ performed most of the experimental work; FP performed bioinformatics analyses and experiments, notably coimmunoprecipitations; FP, Y-CL and MG performed bioinformatics analyses; CB provided us with computing resources on the IFB national service infrastructure in bioinformatics and help with script optimization, OU and NF with RNA-seq and microarray datasets, and  DER  with lentivirus materials and editing of the manuscript.
Agence Nationale de la Recherche Research  grant  2011-CHEX-001-R12004KK   Matthieu Giraud European Commission Career Integration  Grant  PCIG9-GA-2011-294212   de la Recherche Investissements d'Avenir ANR-10-INBS-09 Fanny Coulpier Agence Nationale de la Recherche Investissements d'Avenir   ANR-11-INBS-0013.
(F) Proportion of Aire-sensitive genes with d3'UTR ratios < 0. 25 or>0. 25 in Aire-KO mTEChi among those with d3'UTR ratios < 0. 25 or>0. 25 in WT mTEChi. (G) Median of d3'UTR ratios calculated from RNA-seq data for Aire-sensitive genes with tissue-restricted expression in mTEChi and 22 mouse tissues. Duplicate reads were discarded to allow more accurate dataset comparison. Cell types were arranged in ascending order based on the median of the d3'UTR ratios of their Aire-sensitive TRA genes. (H) Scatterplot of d3'UTR ratios of 762 TRA genes in mTEChi and their respective tissue of expression. The online version of this article includes the following source data and figure supplement(s) for figure 1: Source data 1. d3'UTR annotation files in mice and humans for RNA-seq and microarray analyses.
However, somewhat mysteriously ST66 has almost never been reported in the literature among Klebsiella infections for nearly one century. Here, we describe a severe CAI caused by a Kp ST66/K2 strain, providing evidence for the persistence, even if largely silent, of this reference sublineage. Further, we show that the virulence plasmid of this 're-emerged' strain now possesses a conjugative apparatus gene cluster, prompting concern of possible dissemination of hypervirulence genes to other sublineages of Kp, including multidrug-resistant ones. and annotated with PROKKA (https:// github. com/ tseemann/ prokka). The blood isolate (SB5881) was also subjected to long-read sequencing (Oxford Nanopore Technologies) and a hybrid assembly was obtained using Unicycler 0. 4. 4  [16]. Multilocus sequence typing (MLST, 7 genes) and core genome MLST (cgMLST, 629 genes) was performed using BIGSdb at the Institut Pasteur Klebsiella MLST website (http:// bigsdb.
pasteur. fr/ klebsiella/)  [3]. Antimicrobial resistance and virulence genes were detected using KLEBORATE (https:// github. com/ katholt/ Kleborate) and BIGSdb. Plasmid replicons were searched using PlasmidFinder (https:// cge. cbs. dtu. dk/ services/ PlasmidFinder/) and plasmids were compared using EasyFig 2. 2. 2. For phylogenetic analysis, short-reads from the four isolates and from AJ210 were mapped to the reference genome Kp52. 145 using a Burrows-Wheeler aligner, and the final whole-genome alignment was used to infer a maximumlikelihood tree using IQ-TREE 1. 6. 3 (model K3Pu+F, 1000 bootstraps).
Ethical statement In this study, the research was done on data that was already available. We used the leftover of the sample (the bacterial strains) to conduct our research. In no way was the patient's care changed by our study. Accordingly, in France this type of research is out of the scope of the decree no. 2016-1537 of 16 November 2016 implementing law no. 2012-300 of 5 March 2012 on research involving human subjects. The patient data has been included in a database called Outcomerea, for which a declaration for research was approved by the French competent body CNIL (Commission Nationale Informatique et Libertés), and received a CNIL certificate number 999 262 v2. In this context, we have the authorization to use the patient's data.
Funding information This work was supported financially by the  French Government 's  Investissement d'Avenir program Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  ( ANR-10-LABX-62-IBEID ). C. R. was financially supported by the  MedVetKlebs  project, a component of European Joint  Programme One Health EP , which has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 773830.
Results and discussion Numerical simulation results. We performed a series of 3D simulations of impacts that produce a Chicxulub-scale crater, using the iSALE3D shock physics code  18, 19. The simulations assumed a flat, two-layer target comprising crust and mantle and considered four different impact angles (90 (vertical), 60 , 45 and 30 ) and two impact speeds (12 and 20 km/s). Further model details are described in the "Methods" section. Our simulations provide insight into crater asymmetries diagnostic of impact angle and trajectory in the absence of any target asymmetry (Figs. 2  and 3 , Supplementary Figs. 3 4 5 and Supplementary Movies 1-4). In our vertical impact simulation (Supplementary Fig. 3 ), crater formation is axially symmetric and consistent with previous two-dimensional (2D) numerical simulations that employed an axially symmetric geometry  2, 3, 17.
Methods Numerical simulations. The Chicxulub impact was simulated using the iSALE3D shock physics code  18, 19. Tabular equations of state generated using ANEOS  38  with input parameters for dunite  39  and granite  40  were used to describe the thermodynamic response of the mantle and crust, respectively. The impactor was also modelled as a granite sphere, with a density of 2650 kg/m 3 , because of a current limitation of iSALE3D that does not allow for more than one boundary between materials per grid cell. The actual Chicxulub impactor density is not known. Although a carbonaceous chondrite composition has been proposed  41, 42  , the bulk porosity of the Chicxulub asteroid prior to impact is undetermined. The Murchison (CM2) carbonaceous chondrite meteorite has a bulk density only 10% less than our impactor density, suggesting that our assumed impactor density is reasonable.
Moreover, for a given impactor mass our simulation results are not expected to be sensitive to the assumed impactor density (or other impactor material properties). Material strength was modelled using an approach appropriate for geological materials  43. The choice of model parameters was based on previous vertical impact simulations using iSALE2D  3, 4, 17  and the similar SALEB code  44, 45  and oblique impact simulations of the early stages of the Chicxulub impact  2, 46. A flat, two-layer target was employed, with a crustal thickness of 33 km. Material number limitations precluded inclusion of a rheologically distinct sedimentary layer in the target; however, Lagrangian tracer particles allowed material at this stratigraphic level to be tracked during the simulation, as well as the peak shock pressure and provenance of peak-ring materials.
Acknowledgements We gratefully acknowledge the developers of iSALE3D, in particular  Dirk Elbeshausen  and  Kai Wünnemann  (www. isale-code. de). We thank the reviewers for their constructive feedback that improved the paper. Funding support is acknowledged from the  Science and Technology Facilities Council  (grants  ST/N000803/1  and  ST/S000615/1 ) and the  Natural Environment Research Council  (grants  NE/P011195/1  and  NE/P005217/1 ) as well as the  National Science Foundation  (grants  OCE-1737351 ,  OCE-1450528 ,  OCE-1736826 ). This work was performed in part using the DiRAC Data Intensive service at Leicester, operated by the  University of Leicester IT Services , which forms part of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). The equipment was funded by  BEIS  capital funding via STFC capital grants  ST/K000373/1  and  ST/R002363/1  and  STFC DiRAC Operations  grant  ST/R001014/1.