dataesr/scientific-paragraphs-categorization huggingface dataset:
The later uses several obtained parameters from the first fit, including the affinity of the labelled peptide for the 14-3-3  74. The reported affinities and their standard deviations are calculated over 500-1000 independent fits of simulated datasets. All binding data and the obtained fits are provided in Figures  S2  and S7. 2. Quantifying the interaction of 14-3-3g with hundreds of phosphopeptides. (A) We determined the interaction with 14-4-4g for 659 phosphomotifs of the library, and could quantify or estimate a Kd for 268 of them; the 391 remaining interactions were weaker than the Kd quantification threshold (Kd > 330 µM). (B) Volcano plot of Holdup Multiplex of the phosphopeptide library against 14-3-3g. The p-values (p-value ≤ 0. 05) and fold-change (Log2(FC) ≤ 2*SD{Log2(FC)}) thresholds used and are indicated by plain lines.
(C) Binding profile of 14-3-3g. The different phosphopeptides are ranked according to their affinity for the protein. (D) Number of 14-3-3g significant binding sites (pKd ≥ 3. 50) identified per proteins, for the 114 proteins of the BioPlex database selected for this study. (E) A sequence logo 43 for 14-3-3g binding, calculated over the 659 phosphopeptides which interaction with 14-3-3g was determined, highlights the sequence differences between phosphopeptides with pKd ≥ 3. 50 vs pKd < 3. 50. The number of phosphopeptides in each category is indicated within brackets. Position 0 corresponds to the pSer/pThr residue. (F) Affinity of 14-3-3g for phosphopeptides designed according to the positive and negative sequence logos of (E). Affinities were measured by competitive fluorescent polarization.
Acknowledgement We are grateful to  Goran Bich  and  Yves Nominé  for their advises on with peptide design and data analysis, and to  Nikolai N. Sluchanko  for providing the FCA and the labeled fpB6 peptide. GG was supported by the  Post-doctorants en France program  of the Fondation ARC and by the collaborative post-doc grant of IGBMC. The Travé team was supported by the  Ligue Contre le Cancer  (équipe labellisée 2015 to GT) and the  Agence Nationale de la Recherche  (grant  UBE3A ANR-18-CE92-0017  to GT). As a member of the IGBMC institute, the team also benefited from the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , from Instruct-ERIC, from  IdEx Unistra  ( ANR-10-IDEX-0002 ), from  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ), and from  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program  as a member of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm  under the framework of the  French Investments for the Future Program.
Permeability Assays VoC were injected with 20 µL thrombin (T4393, Sigma-Aldrich chimie, L'lsle-d'Abeau Chesnes, France) at the indicated concentrations in PBS and stimulated for 4-6 h after which 30 µL of 0. 1 mg/mL Dextran 70 kDa-FITC or Texas-Red (Thermo Fisher Scientific) were injected. Diffusion was video recorded under fluorescent lighting using a Keyence BZ-X710 microscope. The diffusive permeability coefficient Pd was calculated by measuring the differences between the average fluorescence intensity in the gel and in vessel regions over time. In order to extract quantitative data from the video microcopy files, an Image J macro (FIJI software v1. 53c  [23] ) was created to separate the vessel and the gel region. The diffusive permeability coefficient P d was then calculated using the following equation: P d = d 4 × 1 I 0 ∂I(t) ∂t 0 where d is the vessel diameter, I 0 is the average intensity in the vessel region, and I(t) is the average intensity in the gel region over time.
Actin filaments were stained with BODIPY Phalloïdin 558/568 (Thermo Fisher Scientific B-3475, 1:250). Images were taken using a confocal laser scanning microscope (Laser Scanning Microscope 880, LSM 880, Carl Zeiss, Oberkochen, Germany). Images were processed using ZEN 2 blue edition software (Carl Zeiss) and 3D reconstruction images were generated using the 3D viewer plugin from Image J software.
Cycle threshold (C T ) values were calculated at the upper linear range of the logarithm -2 amplification curve using the StepOne v2. 3 software or the Thermo Fisher Connect Relative Quantification application (Available online: https://apps. thermofisher. com/apps (accessed on 23 March 2022)). Data were expressed as 2 -∆∆C T  [24]  where ∆C T = C T of transcript of interest-C T of reference (B2M) measured in the same tube, and ∆∆C T = mean ∆C T experimental samples-mean ∆C T control samples of the same experiment. Relative quantity (RQ) is 2 -∆∆C T and transforms the logarithmic -2 data into decimal values.
using the StepOne v2. 3 software or the Thermo Fisher Connect Relative Quantification application (Available online: https://apps. thermofisher. com/apps (accessed on 23 March 2022)). Data were expressed as 2 -ΔΔC T  [24]  where ΔCT = CT of transcript of interest-CT of reference (B2M) measured in the same tube, and ΔΔCT = mean ΔCT experimental samples-mean ΔCT control samples of the same experiment. Relative quantity (RQ) is 2 -ΔΔC T and transforms the logarithmic -2 data into decimal values.
Acknowledgments: We thank  Sophie Salomé-Desnoulez  ( US41-UMS2014-PLBS ) and  Corentin Spriet  ( US41-UMS2014-PLBS; CNRS, UMR 8576-UGSF ) for help with confocal microscopy and image processing,  Aude Sivery  ( IEMN, Lille ) for technical help, and  Samuel Meignan  and his team at  Centre Oscar Lambret, Lille , for continuous support and help.
We studied the capacities of dissemination and persistence of pTsB26 by in vitro assays and the fitness cost associated with pTsB26 carriage. Metadata analysis was performed in order to identify common themes in bla NDM-5-harboring IncX-3 plasmids and to gain insights in IncX-3 success in E. coli population. The worldwide circulation of bla NDM-5-harboring IncX-3 plasmids in E. coli and in various environments as well as the high stability in host bacteria observed herein could explain the contribution of this type of plasmid in the global dissemination of carbapenemase genes.
of about 20 kb contains blaNDM-5, which is preceded by IS3000 and ISAba125 and foll by bleMBL (bleomycin resistance gene), trpF (N-5′phosphoribosylanthranilate isome dsbD (disulfide oxidoreductase) and umuD (encoding a protein implicated in the SO tem). BLASTn analysis showed that pTsB26 displayed nearly the same seq (45494/45495 bp) as the plasmid pEC7-NDM-5 (accession number: MH347484) foun E. coli strain isolated from dog in South Korea. A high homology (45475/45495 bp also observed with the well characterized IncX-3-blaNDM5 pEC463-NDM5 plasmi cession number: MG545911) in an E. coli clinical strain from China  [31].
In Silico Population Study of BlaNDM-5-Harboring IncX-3 Plasmids among E. Coli Spec A genomes dataset was constructed with 28 complete genomes of E. coli-car blaNDM-5 on an IncX-3 plasmid and the genomes of B26 and B28 strains. In silico seq types, phylotypes and metadata associated with the genomes are presented in Ta More than 96% of the genomes belong to A, B1 and C phylogroups. B26 and B28 we only genomes of the B2 phylogroup in the dataset. The 31 genomes correspond strains isolated from diverse origins: humans (n = 20), environment (n = 6) and anim = 4). BLASTn analysis showed that pTsB26 displayed nearly the same sequence (45494/45495 bp) as the plasmid pEC7-NDM-5 (accession number: MH347484) found in a E. coli strain isolated from dog in South Korea. A high homology (45475/45495 bp) was also observed with the well characterized IncX-3-blaNDM5 pEC463-NDM5 plasmid (accession number: MG545911) in an E.
In Silico Population Study of bla NDM-5 -Harboring IncX-3 Plasmids among E. coli Species A genomes dataset was constructed with 28 complete genomes of E. coli-carrying bla NDM-5 on an IncX-3 plasmid and the genomes of B26 and B28 strains. In silico sequence types, phylotypes and metadata associated with the genomes are presented in Table  2. More than 96% of the genomes belong to A, B1 and C phylogroups. B26 and B28 were the only genomes of the B2 phylogroup in the dataset. The 31 genomes corresponded to strains isolated from diverse origins: humans (n = 20), environment (n = 6) and animals (n = 4). In order to study the distribution of E. coli encoding bla NDM-5 on IncX-3 plasmids within the whole E. coli populations, we reconstructed genetic links by goeBURST analysis. The dataset of 30 genomes was matched with 178 776 available genomes of E.
coli (11 058 STs) in the dataset EnteroBase (13 July 2021) (Figure  2 ). The genomes of E. coli-carrying bla NDM-5 IncX-3 plasmid spread out in 16 STs with no obvious lineage specialization. However, about half of the genomes (41. 9%) belong to the CC10. This CC is a major sub-population in E. coli because it gathers 11. 85% of the STs available in EnteroBase. The other genomes are scattered in the overall E. coli population structure. B26 and B28 belonged to ST636, which forms the CC636 together with 4 related STs. CC636 was relatively isolated in the E. coli population and gathers 451 strains (0. 25%) of the 178,776 strains of EnteroBase. Each node corresponds to a Sequence Type (ST). The size of the node is scaled to the number of genomes of that ST. Nodes linked between them present one allele in common among the 7 genes considered in the MultiLocus Sequence Type scheme.
It is generally admitted that plasmids cause a fitness burden on their bacterial host  [52] [53] [54] [55]  and that the plasmid could be quickly eliminated from its host. High stability of IncX-3 plasmids has already been reported in Enterobacterales transconjugant strains  [28, 56]. The absence of fitness cost of IncX-3 plasmids could be explained by their small size  [25]  and by the presence of transcriptional regulator H-NS like protein  [28, 30, [57] [58] [59] [60]. The observed high stability of pTsB26 has potential due to its high conjugation frequency, to a low rate of segregational loss and due to the fact it does not pass a fitness cost onto the bacterial host. The isolates B26 and B28 are the unique representants of ST636 in the studied dataset of E. coli-carrying bla NDM-5-encoding IncX-3 plasmid (Table  2 ).
DNAs were processed for sequencing with Illumina systems (libraries using the Nextera XT DNA Library Prep kit and sequencing with the NextSeq 500 system). Paired-end reads were submitted to pre-processing using fqCleaner and to de novo assembly using SPAdes v3. 12. 0  [76]  with k-mer lengths 21, 33, 55 and 77. The raw data and assemblies have been deposited in GenBank under the BioProject accession number PRJNA796954. 4. 6. 2. Genotyping Methods B26 and B28 strains were genotyped by Clermont typing  [77]  using the in silico tool EzClermont  [78]  and by MultiLocus Sequence Typing (MLST) using the Achtman scheme (https://pubmlst. org/data, accessed on 28 September 2021).
Plasmid Sequence and Annotation The sequence of the plasmid pTsB26 was deduced from the alignment of the genomes of TsB26 and TsB28 with that of E. coli TOP10 using the ProgressiveMauve algorithm of Mauve software  [79]. Identification of plasmid incompatibility group was done with PlasmidFinder  [80]. Plasmid annotation was performed with Prokka v1. 14. 6  [81]  with default parameters through the Galaxy platform (v4. 6. 0 + galaxy0)  [82] , and the plasmid sequence was manually curated; plasmid-specific genes were named according to Thomas et al. recommendations  [83]. For ORF annotation as hypothetical protein, functional prediction was performed with default settings using NCBI BLASTp  [84] , InterProScan  [85]  and Pfam  [86]  servers. Identification of antimicrobial resistance genes was done using ResFinder 4.
Comparative Genomics of B26 and B28 Genomes Genomes of B26 and B28 were first aligned with the ProgressiveMauve algorithm of Mauve software. Snippy v4. 6. 0 (https://github. com/tseemann/snippy, accessed on 20 November 2021) was used to detect both substitutions and insertions/deletions (indels) between B26 and B28 genomes. Snippy was run on the Galaxy platform (v4. 6. 0 + galaxy0)  [82]  with the default parameters.
Genomic data, bioinformatics and phylogenetic methods Note that 196 HPAI H5N8 viruses were isolated amongst the 484 farms that experienced an outbreak between November 2016 and March 2017 (study period). The viral genomes were sequenced (one sequence per farm) at the French national reference laboratory for avian influenza (ANSES-Ploufragan, France;  Briand et al. , 2021). Sequence metadata, including Accession numbers, are provided in Supporting Information Appendix Tables  S1  and S2. Each sequence was geocoded and associated with the département where the outbreak was located along with the collection date (Supporting information  S3 ). The départements we studied are as follows: Aveyron (n = 2), Gers (n = 57), Hautes-Pyrénées (n = 14), Landes (n = 96), Lot-et-Garonne (n = 5), Pyrénées-Atlantiques (n = 15) and Tarn (n = 7).
The whole concatenated virus genomic sequences were aligned using the MUSCLE multiple sequence alignment algorithm  (Edgar, 2004)  implemented in MegaX software with default parameters  (Kumar et al. , 2018). Influenza viral genome is particularly prone to frequent genomic reassortments  (Lycett et al. , 2019) , which, if undetected, may provide spurious genomic signals. We, therefore, checked for the absence of recombination and reassortments in our aligned sequences using five different algorithms, namely BootScan, CHIMERA, MaxCHI, RDP and SisScan, implemented in RDP v4. 1  (Martin et al. , 2015). None of the algorithms detected any recombination events. Therefore, we used all available sequences (n = 196) across the seven départements for downstream analyses.
During epidemics in livestock, humans can spread pathogens between farms, either as fomites directly or through sharing of equipment  (Mansley, 2004; van Andel et al. , 2020). Hence, we also considered the human population per département as a separate predictor. These time-independent variables were computed from the French Directorate-General for Food (DGAl) and CIFOG databases  (Guinat et al. , 2019). We also considered the total number of infected farms in a département to be a potential predictor of viral spread as départements with high number of cases could represent a major source of virus for other departments. This variable is strongly positively correlated with sample sizes, that is, the numbers of sequences obtained in each département (S4), which can be a potential predictor as well  (Müller et al.
However, we did not consider the duck movements as a time-varying predictor, which could have impacted our ability to detect duck movements as a predictor of viral transmission between départements. This hypothesis could be tested in the future with duck movement data collected over time (time-dependent variable). Alternatively, our results could also mean that the virus did not spread in the long-distance through duck movements, but by some other mechanisms. Two major contenders for alternative mechanisms of viral spread are humans and farm equipment as fomites and wild birds as carriers. We did not find any significant association between human population density-which acted as a proxy for human activity-and viral spread. This suggested that human populations, which were not specifically involved in the duck-producing industry, did not play a major role in spreading the virus.
ACKNOWLEDGEMENTS This work was financially supported by the  FEDER/Région Occitanie Recherche et Sociétés 2018-AI-TRACK  and the  PREDYT  project  (Fonds pour la Recherche sur l'Influenza Aviaire ). This study was also supported by the " Chair for Avian Biosecurity ", hosted by the  National Veterinary College of Toulouse  and funded by the  Direction Generale de l'Alimentation, Ministère de l'Agriculture et de l'Alimentation, France. NFM is funded by the  Swiss National Science Foundation  ( P2EZP3_191891 ). CG is funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 842621. DC gratefully acknowledges the contribution of the  INRAE MaIAGE unit  in the form of free access to their MIGALE computer cluster. We also thank all the members of the EPIDESA team for their support and help.
RESULTS The phototrophic species Rhodovastum atsumiense G2-11 acquired MGCs from an unknown alphaproteobacterial MTB by recent HGT In a systematic database search for novel MGCs, we identified several orthologs of known magnetosome genes in the recently released draft genome sequence of the culturable anoxygenic phototroph Rhodovastum atsumiense G2-11  [25]. This finding was unexpected as, after isolation of G2-11 from a paddy field more than 20 years ago, no magnetosome formation has been reported  [26]. Furthermore, no MTB has been identified so far among phototrophs or within the Acetobacteraceae family to which G2-11 belongs  [26]  (Fig. 1a ). Since the available genome version (NZ_VWPK00000000) was highly fragmented (226 contigs), and the magnetosome genes were distributed over several contigs with gaps, we first resequenced and assembled the complete genome using long reads generated by Illumina and Nanopore technologies.
RNAseq reveals poor expression levels and antisense transcription in the MGCs of G2-11 We set on to determine whether the magnetosome genes are transcribed in G2-11. To this end, we analyzed its whole transcriptome for the photoheterotrophic conditions, under which the best growth was observed, in two biological replicates. The expression levels of all the encoded genes calculated as TPM (transcripts per million) demonstrated a high correlation between the two replicates (Pearson's r = 0. 98). Most genes of the (mms6-like1)(mmsF-like1) mamH1IEKLMOH2 cluster were only poorly or not transcribed at all (Fig. 2a , Supplementary dataset). Transcription of mms6-like1, mamF-like1, mamL, mamH1, mamI, and mamK, for example, did not pass the noise background threshold (TPM ≤ 2) in both replicates and were unlikely to be expressed, whereas mamE, mamM, mamH2, feoAm, and feoBm slightly exceeded the threshold in at least one replicate and might be weakly transcribed (Fig.
2a ). Although the TPM of mamO (TPM = 5. 67-6. 10, Supplementary dataset) exceeded the background threshold, the coverage plot reveals that the number of mapped reads sharply rises at its 3'-end, whereas the 5'-end has low read coverage (Fig. 2b ). This indicates the presence of an internal transcription start site (TSS) and its associated promoter within the coding sequence of mamO instead of the full transcription of the gene. Localization of an active promoter within mamO was recently described in MSR-1, suggesting that the transcriptional organization of MGCs may be more broadly conserved across MTB than assumed previously  [39]. Transcription of genes within the mag123, (mms6-like2)(mmsF-like2), and mamAPQRBST clusters significantly exceeded the threshold, with the expression levels of mag1, mamT, and mamS being above the overall median.
Nonetheless, our results demonstrate that the presence of magnetosome genes does not necessarily correlate with magnetosome biosynthesis, urging researchers to be cautious when interpreting genomic data from public databases in the lack of phenotype or at least the evidence of magnetic enrichment, like in the study of  Uzun et al. (2020)   [50]. Our analyses suggest a foreign origin of the MGCs in G2-11, deriving them from an unknown alphaproteobacterial MTB likely belonging or related to Azospirillaceae. The compact organization, tetranucleotide usage bias and the fact that no MTBs have been revealed among Acetobacteraceae to date support a rather recent HGT of the magnetosome genes to G2-11. Despite the lack of other known representatives of the genus Rhodovastum, it can be estimated that the HGT event took place at least after the delineation of the contemporary extant Acetobacteraceae genera.
6 × AMPure XP. After library profile analysis conducted by an Agilent 2100 Bioanalyzer (Agilent Technologies) and qPCR quantification (MxPro, Agilent Technologies), the library was sequenced on an MiSeq with a MiSeq Reagent Kit v. 2 (2 × 250 bp; Illumina Inc. ). A total of 3. 27 × 106 paired-end reads were obtained. The Illumina reads were trimmed by removing lowquality nucleotides (Q < 20), sequencing adapters, and primer sequences using an internal software based on the FastX package (http:// hannonlab. cshl. edu/fastx_toolkit/index. htmL). Reads shorter than 30 nucleotides after trimming were also discarded. For Nanopore sequencing, library preparation was done with 1 μg of the same input DNA following the 1D Native barcoding genomic DNA protocol with EXP-NBD104 and the SQK-LSK109 ligation kit (Oxford Nanopore).
The library was sequenced using a Nanopore R9. 4. 1. revD flow cell (Oxford Nanopore) and the PromethION device with the MinKNOW v. 4. 0. 5 and Guppy v. 4. 0. 11 software. A total of 309 928 reads were obtained with a N50 of 8. 07 Kb. Two hybrid assemblies were launched in parallel with Unicycler v. 0. 4. 6 (default options) and Unicycler v. 0. 4. 6 (-sc option for SPAdes) and compared to circularize chromosomes and plasmids  [53]. The final assembly resulted in a single chromosome 6. 48 Mb in length with a GC content of 69. 0% and 8 plasmids (from 220 Kb to 10. 69 Kb) with an average GC content of 66. 1%. Assembly completeness and contamination were estimated at 99. 5 and 0. 54%, respectively, using checkM v. 1. 0. 11 [54]  with a set of 336 Rhodospirillales-specific markers. The automatic annotation was performed with the MicroScope platform (https:// mage.
3. 3. 2) programs set  [59]  available on the Galaxy server (https://usegalaxy. eu) [60]. For this, the number of reads that overlap 50 nt bin fragments in the genome was counted and normalized to the number of mapped reads per million. The resulting binned counts per million (CPM) were processed as bedgraph or bigwig format files. The coverage histograms were visualized using R package Sushi v. 1. 32. 0 (Phanstiel, D. H. Sushi: Tools for visualizing genomics data. R package version 1. 16. 0. https://www. bioconductor. org/packages/ release/bioc/htmL/Sushi. htmL (2019)). The fraction of reads indicating reversion to the wildtype locus by excision of the MAG cassette was calculated after mapping reads to the wildtype and the mutant reference genomes by variant calling implemented in Geneious (8.
Molecular phylogeny Since the previously built tree based on the 16 S rRNA gene affiliated Rhodovastum atsumiense to the phylum Alphaproteobacteria, order Rhodospirillales, family Acetobacteraceae  [26] , we investigated the evolutionary relationships between strain G2-11 and its Acetobacteraceae relatives using whole genome sequences. First the proteome of all nonredundant (1 representative species per genus) closed genomes of Acetobacteraceae (LPSN nomenclature, ranked at the order level in GTDB) were downloaded from the public repository RefSeq database at NCBI in April 2022. This database was enriched with the draft genome of the closest relatives of R. atsumiense: Acidisphaera rubrifaciens strain HS-AP3 and Rhodopila globiformis strain DSM 161. The dataset was also completed with a set of genomes from the Rhodospirillaceae family and the Azospirilllaceae family that was then used as an external outgroup based on the latest Alphaproteobacteria phylogeny  [29].