dataesr/scientific-paragraphs-categorization huggingface dataset:
github. io/picard/). Reads with mapping quality < 25 (including unaligned reads) were filtered out. For higher coverage sequencing runs, this process was parallelized by splitting raw fastq files and merging after alignment, sorting, and quality filtering. Duplicates were removed using samtools rmdup (http://www. htslib. org/doc/samtools. html). Genome-wide and chromosomal coverage were assessed using depth-cover (version 1. 0. 3, https://github. com/jalvz/depth-cover). Samples were screened and selected using the following criteria: 1) >20% reads aligned to the hg19 build of the human genome; 2) a C>T mismatch rate at the 5'-end and G>A at the 3'-end of the sequencing read of 4% or above (characterized with mapDamage v2. 0. 8)  (Jónsson et al. , 2013) ; 3) library complexity estimates indicating that a minimum coverage of 0.
Calling pseudohaploid genotypes Pseudohaploid genotypes for study samples were called by randomly choosing one allele from each site where there was read coverage, following the approach and software provided by Stephan Schiffels (https://github. com/stschiff/sequenceTools). Variants were called for the 1240k SNP array, which is commonly used for capture-based sequencing of ancient samples. For the newly reported samples, a median of 685,058 SNPs (167,000 to 1,029,345) were covered per sample. Data was output in eigenstrat format. This pipeline was also used to call genotypes for two published ancient DNA datasets which at the time were only available in BAM (sequence read) format  (Clemente et al. , 2021; Žegarac et al. , 2021).
Combining new genotypes with ancient and present-day published data Newly processed pseudohaploid data was merged with several datasets. Most of the published data was retrieved from the Allen Ancient Data Resource (AADR) v44. 3 (January 2021): a compilation of pseudohaploid and diploid genotypes for 5225 ancient and 3720 present-day individuals. We also included relevant genetic data made available by authors that were not in the AADR: present-day genomes from the Balkans  (Kovacevic et al. , 2014) , present-day genomes from 4 Poles, 3 Germans, and 2 Moldavians  (Pagani et al. , 2016) , and Bronze Age Italian genomes  (Saupe et al. , 2021). Pseudohaploid genotypes for published Bronze Age Aegean genomes  (Clemente et al. , 2021)  and Bronze Age Serbian genomes  (Žegarac et al. , 2021)  were generated from BAM files using our pipeline.
All published genomes were filtered for contamination based on reported contamination levels in the original study and SNP coverage based on the genomic data. To ensure maximum overlap with present-day and ancient samples in analyses, the merged dataset was subset to SNPs in the Human Origin Panel array, resulting in a total of 481,259 SNPs. For PCA, qpWave, and qpAdm modeling, SNPs that fall in CpG sites (n = 76,678) are excluded since they may have arisen from DNA damage as opposed to true genetic variation.
Principal Component Analysis (PCA) Setting up the principal component analysis Principal component analysis was performed on genotypes from present-day and Mediterranean individuals using smartpca v16000 (https://github. com/chrchang/eigensoft/blob/master/POPGEN/README). The following parameters were used: 5 outlier iterations (numoutlieriter), 10 principal components along which to remove outliers (numoutlierevec), altnormstyle set to NO, with least squares projection turned on (lsqproject set to YES). To calculate principal components only using present-day individuals, a file (poplistname) was provided with the population names of present-day individuals subsampled per population. After outlier removal, 829 individuals and 480,712 snps were used in the initial analysis. All individuals (non "reference" present-day genomes, and all of the ancient individuals) whose population was not listed in the poplistname file were projected onto the calculated principal components.
We simulated diploid genomes made up of a single, 1e8 bp long chromosome, with recombination rate and mutation rate set to 1e-8. We used the default Wright-Fisher simulation mode, where a single population of constant size N is simulated with non-overlapping populations, i. e. each generation is made up of offspring generated from the previous generation. Spatial structure is established by associating each individual with a continuous 2D coordinate (i. e. latitude and longitude), and by using these coordinates to govern three demographic processes: mate choice, competition, and dispersal. An overview of how these processes can be set up to interact in SLiM can be found in Recipe 15. 4 of SLiM v. 3. 6 (see e. g. here: https://github. com/MesserLab/SLiM/tree/v3. 6/SLiMgui/Recipes). Briefly, for mate choice, a Gaussian interaction function with maxDistance = 0.
We then extracted the resulting genotypes of all individuals from the tree sequence for downstream analysis. We only kept sites segregating in individuals at the end of the simulation ("present day"), and filtered for minor allele frequency of at least 0. 01 across the entire dataset, to make downstream analysis of simulated genomes comparable to how the empirical data was ascertained and analyzed. To assess the relationship between F ST and spatial distance, we split geographic space into a 10-by-10 grid and calculated all pairwise F ST between inhabited grid cells using Hudson's estimator with unequal sample size correction  (Bhatia et al. , 2013) , as well as the average geographic (euclidean) distance between individuals across grid cells. We used this F ST analysis to calibrate the base dispersal sigmaDisp as well as the population size N, so that F ST at maximum distance (F ST max) would approximately match the F ST max we observed at the start of the historical period (~0.
Infection assay for each of the three phages (panels), three swapped strains (yaxis), and different genotypes (x-axis). White tiles correspond to non-productive infection, i. e. no plaque could be identified. Coloured tiles correspond to the average PFU/mL normalized by the lysate titre for productive infections (Figure  S2 ). Grey tiles correspond to non-productive infection with significant adsorption, while white tiles correspond to non-adsorptive pairs (Figure  S3 ). Values are the average of three independent replicates. Strain-specific defence systems identified by DefenseFinder as of 02/2023  [70]  are displayed on the right. Associated data are available as Supplementary Dataset S1 (Adsorption) and S2 (Infection).
Solid line at y=0 represents the conjugation efficiency of Δcps mutant. We used paired Wilcoxon tests to assess statistically significant differences. C. Same as panel B, but when conjugation takes place from K. pneumoniae donors. The shapes of the data points represent the genotype of the donor strain. Associated data are available as Supplementary Dataset S3 (Conjugation assays). *** p<0. 001; ns p>0. 05.
Conjugation is not more efficient between donor and recipient expressing the same serotype, paired Wilcoxon test, P=0. 9. C. Networks of plasmid transfer between capsule states. Data drawn from the K. pneumoniae to K. pneumoniae assays. Nodes represents distinct serotypes, and Δcps mutants. Edges thickness represents the average conjugation efficiency for all pairs. Edges are coloured according to the donor, indicating the direction of transfer. Associated data are available as Supplementary Dataset S3 (Conjugation assays). *** p<0. 001; ns p>0. 05.
For this, we used only the data of conjugation from E. coli (dataset E1) since in the other dataset (E2) we used three plasmids with a different MPF type each and one cannot distinguish between the two effects (MPF and plasmid identity). To test the hypothesis that MPF differ in conjugation efficiencies we used a linear mixed model where the MPF type was the fixed effect, and the plasmid identity was the random effect. This allows to test the effect of the MPF type while conditioning for the effect of the plasmid identity. We found that the effect of MPF is significant (Figure  S4ABC , S5, F test, p =0. 023). The comparisons of all pairs using a non-conditioned linear model shows that all pairs of MPF are significantly different and gives an order of efficiency of conjugation MPFF<MPFT<MPFI in both datasets E1 and E2 (Tukey-Kramer HSD test, p <0.
001). We then tested if the MPF type and serotypes affect the conjugation rates. We made a linear mixed model where we put together as fixed effects the MPF type of the plasmid and the serotypes of the donor and recipient, while conditioning (random effects) for the identity of the donor and recipient chassis strains. This analysis was done with dataset E2 (conjugation to and from K. pneumoniae), since in E1 there is no variation in the donor. The results showed that all three effects are significant (F tests, all p <0. 001) (Figure  S4DEF ). Interestingly all parameter estimates of the fixed effects were also significant. Hence, differences within each group of variables, notably differences between serotypes both among donors and among recipients, were significant (t-tests, all p <0. 05).
pneumoniae strains (dataset E2, since for dataset E1 the donor is never capsulated). We also added an interaction term between donor and recipient serotypes. This resulted in a significant linear model (R 2 =0. 43, p<0. 001, F test), where the tests on the three variables revealed significant effect (p<0. 001), but the interaction term was non-significant (p>0. 9, same test). Accordingly, none of the comparisons between pairs of same vs. different capsule serotype were significantly different (Figure  3C ). Thus, the capsule serotypes of the donor and recipient cells have a significant and independent impact on conjugation. Finally, one can have a coarse estimate of the relevance of the three significant variables by analysing individual ANOVA where each of the variables is fitted to the conjugation efficiency.
The determinants of conjugation shape the distribution of natural plasmids Our analysis revealed that conjugation efficiency in the laboratory was strongly impacted by capsule expression and the serotypes of both donor and recipient cells, as well as by the MPF type. Do these results contribute to a better understanding of the distribution of plasmids in populations? We retrieved the non-redundant dataset of 623 complete genomes of K. pneumoniae from RefSeq, which contained 2,386 plasmids. We used these genomes to build a pangenome including 29,043 gene families. We extracted the pangenome gene families present in single copy in more than 90% of the strains and used these 3,940 gene families to build the species phylogenetic tree and used the genomic data to predict the capsule locus type of each strain (serotype).
Finally, each plasmid was characterised in terms of the presence of the conjugation machinery, virulence factors, and antibiotic resistance genes. We found that 21% of the plasmids carried all the key genes for conjugation: 23% were of type MPFI, 27% MPFT, and 50% MPFF. Some plasmids of all types carried antibiotic resistance genes (56% of all plasmids) but only Type F plasmids carried virulence factors (8%, half of which carried resistance genes too) (Figure  5A ). Type F plasmids seem to be the least susceptible to the presence of a capsule and they also seem to be the most abundant across the species. To assess if the rates of plasmid acquisition vary with the serotype, we traced the history of acquisition of each plasmid on the species tree. We found that 68% of the conjugative plasmids were acquired in terminal branches, implying that conjugative plasmids in this dataset were acquired very recently.
Mutants and transconjugants validation We performed DNA extraction with the guanidium thiocyanate method [120], with modifications. DNA was extracted from pelleted cells grown overnight in LB supplemented with 0. 7 mM EDTA and appropriate antibiotics for plasmid maintenance. Additionally, RNAse A treatment (37°C, 30 minutes) was performed before DNA precipitation. Each clone was sequenced by Illumina with 150pb paired-end reads from NextSeq 550, yielding approximately 1 GB of data per clone. The raw data were deposited in the BioProject PRJNA952961. All mutants generated in this study were verified by whole-genome sequencing and comparison to the reference wildtype genome with Breseq v0. 37. 0 with default options [111] (Supplementary File S6). We also assembled the genomes de novo with Spades v3.
Finally, we extracted and circularized the plasmid contig in DH10B with SnapGene (Supplementary File S3), according to i) the paired-end read mapped with bwa-mem v0. 7. 17  [124]  and visualized in IGV  [125]  ii) the assembly of the plasmid contig in the original donor strain and iii) long-reads obtained via lowcoverage (10x) Pacbio sequencing of the original donor strain that were mapped onto the assembly with bwa-mem v0. 7. 17 (BioProject: PRJNA952961).
We also compared this quantity with the widely used, and simpler formula T/R, and found a Pearson correlation coefficient of 0. 97 (p<0. 001) between the two quantities after log-transformation. Hence, these two values are highly correlated and may be interchangeable under our conditions. All analyses were performed with the conjugation efficiency formula above, since it was shown to be less susceptible to experimental parameters  [72, 73]. The data are available in Supplementary Dataset S3.
Effective volume measurement We inoculated a 15 µL drop from an overnight culture for each strain onto LB agar plates and incubated the plates at 37°C. After 24h of growth, we resuspended each colony into 1. 5mL Eppendorf tubes containing 50µL of PBS. Tubes were vortexed for 1min, then left for 15min at room temperature, and vortexed again for 1min. This allowed for complete dissolution of colonies. We then used a 0. 5-10µL micropipette to measure the excess volume. In parallel, we performed serial dilutions followed by plating on LB agar to estimate the total number of CFU in the resuspended colony. The effective volume was then computed as the ratio of CFU by the volume of the colony. This quantity represents the average volume occupied by a cell in a colony. The data are available in Supplementary Dataset S4, S5 and S6.
Genome data We retrieved all the K. pneumoniae complete genomes available in the NCBI non-redundant RefSeq database, accessed in March 2021, along with their gene annotations. This resulted in a set of 730 genomes containing 2386 associated plasmids. The pairwise genetic distances between all genomes of the species was calculated using MASH  [129]. Strains that were too divergent (MASH distance >6%) to the reference strain or too similar (<0. 0001) to any other strains were removed from further analysis. A total of 623 genomes were analysed. The information on the genomes (including accession numbers) is available in Supplementary Dataset S7.
Pan and persistent genomes The pangenome is the full repertoire of homologous gene families in a species. The pangenome of K. pneumoniae was identified using the module pangenome of the software PanACoTa [130] v1. 3. 1. Briefly, gene families were built with MMseqs2 v13. 45111, with an identity and bi-directional coverage threshold of 80%. This analysis resulted in 29,043 gene families among the 623 genomes (Supplementary Dataset S8). We then computed the persistent genome with a persistence threshold of 90%, meaning that a gene family must be present in single copy in at least 90% of the genomes to be considered persistent, and found 3,940 gene families.
Capsule locus typing We used Kaptive [122,134] with default options and the "K locus primary reference" to identify the capsule locus type (CLT) of strains. The predicted CLT is assigned a confidence level, which relies on the overall alignment to the reference CLT, the allelic composition of the locus, and its fragmentation level. We assigned the CLT to "unknown" when the confidence level of Kaptive was indicated as "none" or "low," as suggested by the authors of the software. The annotation is available in Supplementary Dataset S9. Reconstruction of the scenario of plasmid gain. We used inference of ancestral states to identify the branches in the tree where the plasmid was acquired. For this, we inferred the ancestral state of each plasmid pangenome family with PastML (v1. 9. 23) [135] using the MAP algorithm and the F81 model.
For each of these gene families, we obtained a list of branches where they were acquired. Genes from the same plasmid can come out of this analysis has having been acquired at different branches if there were changes in the plasmids after acquisition (gene gains or gene losses). To identify the plasmids most likely acquired in the terminal branches of the of the species tree, we counted how many genes of the plasmid were acquired in these branches. We defined that a plasmid was acquired in the terminal branch if at least 75% of its genes were inferred to be absent in the first parental node (i. e. acquired since then). Those are referred as recent gains. The other plasmids were not considered in the analysis presented on Figure  5. The data are available in Supplementary Dataset S10.
Acknowledgements We thank  Olivier Tenaillon  and  Nienke Buddelmeijer  for fruitful discussions. We thank  Sylvain Brisse  for providing us with the Klebsiella strains. We thank  Jean-Marc Ghigo  and  Christophe Beloin  for the gift of plasmid R1-dr19. We thank  Julia Bos  for the gift of phage phK1. We are grateful to  Maxime Policarpo ,  Grégoire Haouy ,  Léone Debarge  and the members of the  Microbial Evolutionary.
Funding This work was supported by an  ANR JCJC (Agence national de recherche)  grant [ ANR 18 CE12 0001 01 ENCAPSULATION ] awarded to O. R. and by the grant [ ANR 16 CE15 0022 03 PREDIRES ] awarded to EPCR. The laboratory is funded by a  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases ' grant [ ANR-10-LABX-62-IBEID ], the  INCEPTION program [ PIA/ANR-16-CONV-0005 ], and the FRM [ EQU201903007835 ]. M. H. has received funding from the  FIRE Doctoral School (Centre de Recherche Interdisciplinaire , programme Bettencourt) to attend conferences. P. D. -C. was financially supported by a  Ramón y Cajal  contract  RYC2019-028015- I funded by  MCIN/AEI/10. 13039/501100011033 ,  ESF Invest  in your future. The funders had no role in the study design, data collection and interpretation, or the decision to submit the work for publication.
coli DH10B donors. Points represent the average of independent triplicates, with colours and shapes corresponding respectively to plasmids (panel A) and strains. Solid line at y=0 represents the conjugation efficiency of Δcps mutant. We used paired Wilcoxon tests to assess statistically significant differences. C. Same as panel B, but when conjugation takes place from K. pneumoniae donors. The shapes of the data points represent the genotype of the donor strain. Associated data are available as Supplementary Dataset S3 (Conjugation assays). *** p<0. 001; ns p>0. 05.
3 [138], phangorn v2. 5. 5 [133], and treeio v1. 10 [139]  for the phylogenetic analyses. Genomics lab for helpful discussions. This work used the computational and storage services (TARS & MAESTRO cluster) provided by the IT department at Institut Pasteur, Paris, and the media preparation services proved by the Plateforme Milieux. The sequencing work was performed by the Biomics Platform, C2RT, Institut Pasteur, Paris, France, supported by France Génomique (ANR-10-INBS-09) and IBISA.
Coverage profiles showed a distinct distribution of H3K27me3, H2AK119Ub, and H3K9me3 on the X chromosome in naïve compared to primed hESCs (Figure  3B ). Notably, H3K27me3 and H2AK119Ub covered around 60% of the X chromosome in naïve hESCs and 40 to 50% in primed hESCs while most of the X chromosome (>60%) was covered by H3K9me3 in primed hESCs vs 33% in naïve hESCs (Figure  3C ). H3K27me3 and H2AK119Ub patterns were highly correlated together and anticorrelated with H3K9me3 in both primed and naïve hESCs (Supplementary Figure  3A ). H3K27me3 and H2AK119Ub signals accumulated into several smaller domains which spanned most of the XIST-coated dampened X chromosome in naïve hESCs (Figure  3B ). We observed similar profiles of H3K27me3 and H2AK119Ub using other published datasets of naïve H9 hESCs reprogrammed using PXGL or T2iLGo (Supplementary Figure  3B, C ).
Raw data processing RNA-seq, fastGRO-seq, CUT&RUN, and RAP-seq reads were trimmed using trim_galore (0. 6. 5) (https://github. com/FelixKrueger/TrimGalore) with a minimum length of 50 bp. Reads were then mapped to the human genome (hg38) and mouse genome (mm10) using bowtie2 (2. 4. 4)  (Langmead and Salzberg, 2012)  with the following parameters: --local --very-sensitive-local --no-unal --no-mixed --no-discordant --phred33 -L 10 -X 700. Reads were then deduplicated using MarkDuplicates from picard (2. 23. 5) (http://broadinstitute. github. io/picard/) with the following options: --CREATE_INDEX=true --VALIDATION_STRINGENCY=SILENT --REMOVE_DUPLICATES=true --ASSUME_SORTED=true. Bam files were sorted, filtered (minimum mapping quality=10) and indexed with samtools (1. 13)  (Danecek et al. , 2021).
Single cell expression data analysis Fastq files from  (Petropoulos et al. , 2016)  and  (Zhou et al. , 2019)  were obtained from ENA (European Nucleotide Archive), under accession numbers PRJEB11202 and PRJNA431392, respectively. STAR (2. 7. 9a) was used for read alignment to GRCh38 reference human genome. STAR index was generated using the following options: STAR --runMode genomeGenerate --runThreadN 30 --genomeFastaFiles HS. GRCh38. fa --limitGenomeGenerateRAM 168632718037 --outFileNamePrefix STAR_INDEX. --sjdbGTFfile Homo_sapiens. GRCh38. 90. gtf. --genomeDir STAR_INDEX_petropoulos_2016 and --sjdbOverhang 42 were used for Petropoulos data while --genomeDir STAR_INDEX and --sjdbOverhang 149 were used for Zhou data. For Petropoulos data: Reads (single-end) were aligned using the following options: STAR --soloType SmartSeq --soloFeatures Gene --runThreadN 12 --genomeDir STAR_INDEX_petropoulos_2016 --readFilesCommand zcat --readFilesIn sample_A.
Acknowledgments We thank lab members for critical evaluation of the work leading to this publication, and  Céline Morey  and  Slimane Ait Si Ali  for critical reading of the manuscript. We thank the Epigenomic, the Microscopy, the Vectorology and the  BIBS Platforms , all hosted in UMR7216 Epigenetic and Cell Fate, for technical advice and access to instruments. We thank the  ICGex NGS  platform of the  Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan   Cancer III ) and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOSInserm_12554 ) for the high-throughput sequencing. We thank the Bioinformatics platform of the  Institut Curie  for data management, quality control and primary analysis.
This project was funded by  Agence Nationale pour la Recherche  [ ANR-14-CE10-0017  to C. R. ];  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  [ 101020423 ];  LabEx 'Who Am I ?' [ ANR-11-LABX-0071 ];  Université de Paris IdEx  [ ANR-18-IDEX-0001 ] funded by the  French Government  through its ' Investments for the Future' program. E. C. was supported by fellowships from the  French Ministry of Education and Research  and from the  French Medical Research Foundation (FRM).
Data availability All RNA-seq, RAP-seq, ATAC-seq, CUT&RUN, MeD-seq, and fastGRO-seq data have been deposited at GEO (XXX) and are publicly available as of the date of publication. Microscopy data reported in this paper will be shared by the lead contact upon request. This paper analyzes existing, publicly available data. scRNA-seq data from human embryos are available at ENA (European Nucleotide Archive), under accession numbers PRJEB11202 and PRJNA431392. Hi-C data for H9 hESCs available at GEO GSE133126. CUT&RUN and Minute ChIP data for naïve H9 hESCs are available at GSE176175 and GSE181244, respectively.