dataesr/scientific-paragraphs-categorization huggingface dataset:
Species tree phylogeny The species tree was generated by a concatenation of twenty-nine conserved proteins selected from the phylogenetic markers proposed for bacterial genome trees  [64]. Homologs of each ribosomal protein were identified using BLASTP searches in the 126 cyanobacterial genomes as well as four outgroup genomes (Chloroflexus auranticus J-10, Rhodobacter sphaeroides 2. 4. 1, Heliobacterium modesticaldum Ice1, and Chlorobium tepidum TLS) and aligned using MAFFT v6. 882b (default parameters)  [65]. Ambiguous and saturated aligned regions were removed using the BMGE software 1. 1 (parameter gap rate set to 0. 5)  [66]. The resulting twenty-nine alignments were then concatenated. A Maximum-Likelihood phylogenetic tree was generated with the alignment using PhyML 3. 1. 0. 2  [67]  using the LG amino acid substitution model with gamma-distributed rate variation (six categories), estimation of a proportion of invariable sites and exploring tree topologies using Nearest Neighbor Interchanges.
Domain phylogeny 472 KS and 939 C domain sequences predicted in the clusters were extracted from our dataset, alignments were generated and treated as described above, and used to generate for each a Maximum-Likelihood phylogenetic tree using the JTT amino acid substitution model with gamma-distributed rate variation (four categories), estimation of a proportion of invariable sites and exploring tree topologies using Nearest Neighbor Interchanges. 100 bootstrap replicates were performed for each dataset. The sequence of 132 KS domains predicted to be involved in biosynthesis of polyunsaturated fatty acids and enediynes in our dataset was extended with 85 bacterial sequences from the secondary lipids dataset described previously  [33]. The 217 sequences were aligned and filtered as described above.
Sequence similarity search Protein sequences of known secondary metabolite pathways were extracted from the NCBI web site according to the references for each cluster. These sequences were then compared with the proteins of the 126 genomes through BLASTP searches  [62]. Protein sequences of all clusters were compared against the non-redundant database of the NCBI (April 2013) with the BLASTP in order to detect homologs (evalue ≤ 1e-20, identity ≥ 30%).
Acknowledgments We acknowledge the  LABGeM team  for data management in the MicroScope platform. Funding was provided by the  Institut Pasteur , a grant by the  EU (BlueGenics)  to JP, a grant by the  US Department of Energy  ( DE-AC02 05CH11231 ) to CAK and  Academy of Finland  grants ( 118637 ,  258827 ) to KS and  DPF  ( 259505 ) as well as  University of Helsinki  grant to DPF ( 490085 ).
Viruses and mosquitoes CHIKV belonging to novel Indian Ocean lineage (IOL) of ECSA genotype, obtained from an epidemic in India in 2006 (DRDE06) (GenBank Accession No. EF210157). Initially it was isolated in BHK-21 cells and subsequently passaged in C6/36 cells. The virus is maintained at Virology Division, DRDE, Gwalior. In the present study, it was used at passage level 10 in C6/36 cells. Titre was found to be 10 8 PFU/ml through plaque assay  [26]  in Vero cells (African green monkey kidney cells). The virus was aliquoted and stored in 280uC until use. Ae. aegypti used in this study, were collected from Gwalior district, Madhya Pradesh, India in 2005 and maintained as laboratory colony in Vector Management Division, Defence Research and Development Establishment (DRDE), Gwalior at 2762uC with 70% relative humidity and 14:10 light:dark photo period.
This work was funded by  Defence Research Development Organization, Ministry of Defence, Govt of India. The authors are thankful to the Director,  Defence Research Development Establishment (DRDE) ,  Gwalior  for his keen interest and support in this study. AA thanks  Department of Biotechnology (DBT), Govt. of India  for providing Fellowship and Contingency grants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
pneumophila strains and one strain each of L. longbeachae, L. micdadei, L. hackeliae and L. fallonii); the Legionella pan-genome increased considerably to 9,194 genes and the core genome was 1,388 genes (Figure  2A ) or 1,415 genes when comparing one strain of each sequenced species (L. pneumophila Paris as representative) (Figure  2B ). Thus, the core genome of Legionella represents only about 15% of the pan-genome, indicating that the Legionella accessory genome is large. The complete annotation of these three newly sequenced genomes is available in the LegionellaScope database  [20]  and at the Institut Pasteur, LegioList  [21]. To establish a whole genome-based phylogeny of these Legionella species we used either 29 housekeeping genes or 816 orthologous genes shared among the 11 Legionella strains analyzed.
Resequencing of the dotA gene confirmed the presence of the stop codon. As this strain was not able to replicate in A. castellanii, we thought that this might be due to the mutated dotA gene leading to a nonfunctional T4SS. To verify if this mutation was specific for the sequenced strain, we analyzed the dotA gene in a second L. hackeliae strain (ATCC35999). In this strain  the dotA gene was intact. Thus, the dotA gene fragmentation in the sequenced strain probably occurred during storage. However, when testing the virulence of both L. hackeliae strains in A. castellanii using the amoeba plate test, neither were able to replicate at 30°C or at 37°C (data not shown). To analyze if the Dot/Icm secretion system was functional in the sequenced strains, we used the calmodulin-dependent adenylate cyclase (CyaA) gene fusion approach  [29]  and RalF from L.
In contrast, proteins with tubulin-Figure  6  Genome comparison of two L. micdadei strains. The complete genome sequences of the two L. micdadei strains included in this study were aligned using the software Mauve. The two strains align perfectly with the exception of three mobile genetic elements specifically present in strain L. micdadei ATCC33218 and one specifically present in the Victorian isolate. The specific regions of each genome are indicated. The 'Lvh region' is indicated, as this region is, with a high number of SNPs, quite divergent between the two isolates. tyrosine ligase domains (LLo2200), probably involved in the posttranslational modification of tubulin  [58] , are absent only from L. pneumophila. With the aim to analyze whether additional eukaryotic motifs not yet identified are present in the Legionella genomes, we developed a strategy allowing for a comprehensive scan of all genomes.
First we searched the Interpro database for all motifs, which occur in at least 85% of proteins from eukaryotic genomes and only 15% or less in proteins from prokaryotic genomes. Using this criterion, we obtained 8,329 motifs that were considered as eukaryotic (see Materials and methods). All predicted Legionella proteins were scanned for these motifs. This approach allowed us to identify 10 EMs not described before in Legionella, including thaumatin, RhoGTPase and DM9 domains (Table  4 ). Interestingly, thaumatin-like proteins accumulate in plants in response to infection by pathogens and possess antifungal activity  [59, 60]  and a Drosophila DM9-containing protein is strongly up-regulated after infection of Drosphila larvae by Pseudomonas species  [61]. Many of these new EMs are only present in the newly sequenced genomes, such as synaptobrevin, an intrinsic membrane protein of small synaptic vesicles  [62]  or the clathrin/coatomer adaptinelike domain that is associated with transport between the endoplasmic reticulum and Golgi  [63].
Using this pipeline we identified 465 proteins as putative ELPs. For each of these proteins we constructed a phylogenetic tree that was curated and analyzed manually. However, for many of the ELPs a phylogenetic reconstruction did not allow clear demonstration of eukaryotic origin. Some aligned too poorly with their eukaryotic homologues or on just a small domain. This might be due to the fact that genomes of ciliated protozoa and amoeba, the known hosts of Legionella from which these ELPs are most likely acquired, are underrepresented in current databases. However, for 40 of the 465 proteins that are suggested to be of eukaryotic origin, the phylogenetic reconstruction clearly showed that they had been acquired by Legionella through horizontal gene transfer from eukaryotes (Table  5 ; Figure  S9A -C in Additional file 14).
Bacterial strains and sequence accession numbers The strains sequenced in this study were L. hackeliae strain ATCC35250 (EMBL accession number chromosome: PRJEB7321), L. micdadei ATCC 33218 (EMBL accession number chromosome: PRJEB7312) and L. fallonii strain LLAP-10 (ATCC700992; EMBL accession number chromosome: PRJEB7322)  [25]. We obtained also the draft genome sequence of L. micdadei strain 02/42 (SRA accession number SRP047311), a clinical isolate from the Victorian Infectious Disease Research Laboratory (VIDRL). In addition, the genomes of Legionella species/strains that had been completely sequenced and published previously were included in the comparative analysis: L. pneumophila (strains Paris, Lens, Philadelphia, Corby, Lorraine and HL 0604 1035, Alcoy)  [7, 8, 12, 14]  and L. longbeachae strain NSW150  [9].
Annotation and genome comparison The newly sequenced genomes of L. fallonii, L. hackeliae and L. micdadei were integrated into the MicroScope platform  [81]  to perform automatic and expert annotation of the genes, and comparative analysis with the already sequenced and integrated L. pneumophila strains. Micros-Scope annotation is based on a number of integrated bioinformatic tools: Blast on UniProt and specialized genomic data, InterPro, COG, PRIAM, synteny group computation using the complete bacterial genomes available at NCBI RefSeq, and so on (for more details see  [82] ). Orthologous groups were established using the program PanOCT  [83]  with the following parameters: e-value 1e-5, percent identity ≥30, and length of match ≥65. The programs Easyfig and BRIG  [84, 85]  were used for graphical representation of genome regions compared using BLAST.
Detection of new eukaryotic motifs in Legionella proteins To better define the term 'eukaryotic motifs' we searched for the already known EMs in all proteins present in the Pfam database and calculated their occurrence in eukaryotic proteins or prokaryotic proteins. The previously described EMs in Legionella showed an occurrence of about 99% in eukaryotic proteins and only 1% in prokaryotic ones, with the ankyrin repeats being the less restricted to eukaryotic proteins (85%). The only exception is Sel-1 domains, which were considered as EMs. Sel-1 domains have now been shown to be highly present also in prokaryotes. However, since this domain is present in many substrates of the Dot/Icm system and it was shown to be implicated in host-pathogen interactions  [87] , it was taken into account.
Based on the frequencies of the typical EMs present in Legionella we searched the Interpro database for all motifs that occur in eukaryotes at least to 85%. Using this criterion we obtained 8,329 motifs that can be considered as eukaryotic. These motifs were searched in all proteins predicted in the different Legionella genomes. This approach identified 10 eukaryotic motifs previously not described in Legionella proteins.
Detection of genes transferred from eukaryotes to Legionella To detect genes with putative eukaryotic origin we developed a pipeline based on several step filters. This pipeline was applied to one protein of each of the orthologous groups of the pan-proteome of the five studied species to avoid redundancy in the detection process with proteins of the same orthologous group. The first step consisted of discarding the protein families without significant similarity to eukaryotic sequences. This was achieved by a homology search using Blastp with an evalue cutoff of ≤10e -4 and a BLOSUM62 matrix with a representative protein of each group of orthologous families of the Legionella pan-genome against a database containing 83 genomes representative of all major eukaryotic phyla and certain viruses.
In particular, members of Amoebozoa and other protist lineages that may be hosts for Legionella were included in this database. The results of the first filter led to the recovery of 2,669 proteins of the Legionella pan-genome with significant homology to eukaryotic sequences in the database. Then, among these 2,669 protein families those that have closer homologues in bacteria were discarded by searching for homologues against a database containing both eukaryotic and prokaryotic sequences using the same criteria. Only those that had at least a hit against a eukaryotic sequence among the first 25 hits were further selected. This step led to the selection of 465 protein families of the Legionella pan-genome representing ELP candidates. Finally, we carried out automatic phylogenetic reconstruction of these 465 proteins and their bacterial and eukaryotic homologues.
The different steps of the pipeline were: (1) for each selected putative ELP the corresponding orthologues in other Legionella species analyzed where added if present; (2) each group of homologous sequences was aligned with MUSCLE  [88] ; (3) unambiguously aligned positions were automatically selected using the multiple alignment trimming program BMGE with low stringency parameters  [89] ; (4) preliminary maximum likelihood trees were obtained using FastTree  [90]. We applied a strict filter to select only very likely ELPs. Then each of the 465 trees was manually inspected to select those where the Legionella sequences were branching within eukaryotes or were closer to eukaryotic sequences than to prokaryotic ones. This allowed identification of 40 Legionella proteins that aligned well with their eukaryotic homologues.
For those having a sufficient number of eukaryotic homologues and a sufficient number of positions that could be selected after trimming, we proceeded to phylogenetic analysis by maximum likelihood using LG +4 gamma as the evolutionary model. Then, we selected those trees where the Legionella sequences were branching within eukaryotes or were closer to eukaryotic sequences than to prokaryotes. Finally, in order to verify the eventual existence of closer bacterial homologues or additional eukaryotic homologues from representatives not present in our local database, we performed a Blast on the non-redundant database at the NCBI. Alignments were obtained and trimmed, and trees reconstructed as described above.
Phylogenetic reconstruction For phylogenetic reconstruction two different data sets were created: one based on the concatenated alignment of 29 housekeeping genes (lpp0086 (uvrB), lpp0152 (pgk), lpp0419 (rpoA), lpp0467 (ffh), lpp0575 (serS), lpp0749 (pros), lpp0791 (glyA), lpp1020 (lig), lpp1271 (cysS), lpp1399 (trpS), lpp1434 (aspD), lpp1534 (ruvB), lpp1738 (nrdA), lpp1765 (recA), lpp1830 (tig), lpp1837 (lepA), lpp2004 (metK), lpp2006 (dnaJ), lpp2013 (argS), lpp2020 (eno), lpp2662 (ftsZ), lpp2698 (uvrC), lpp2802 (dnaX), lpp2877 (recN), lpp2941 (metG), lpp3002 (rho), lpp3053 (atpD), lpp3055 (atpA), lpp3073 (thdF)) and another one based on all ortholgous genes among the studied species and C. burnetii as outgroup (816 genes). With these data sets the alignment of amino acids and the alignment of nucleotides based on the amino acid alignment were carried out.
Acknowledgements We would like to acknowledge  P Glaser  for critical reading of the manuscript and help from  J Bernal  with the calcoflour assay. The work in CB's laboratory received financial support from the  Institut Pasteur ,  the Centre National de la Recherche (CNRS) , the  Fondation pour la Recherche Médicale (FRM)  grant number  DEQ20120323697 , the  French Region Ile de France (DIM Malinf ) and from the  ANR-10-PATH-004  project, in the frame of  ERA-Net PathoGenoMics. The MicroScope platform received financial support from  GIS IBiSA. MS acknowledges support from the  Deutsche Forschungsgemeinschaft (DFG). ELH was supported by the  Australian Research Council.
Full length K. pneumoniae PLD1 and its closest homologs all presented the conserved motif HXK(X) 4 D and a serine or threonine approximately eight residues after asparagine (Figure  3 ), but no other conserved domain was described in each family, thus making it difficult to infer protein function only by sequence analysis. In order to obtain evidence that the product of these three genes coding either for full length or partial PLD-family proteins are important for bacterial survival in vivo, we checked by RT-PCR for their mRNA expression. We observed that these genes are expressed both in bacteria grown for four hours in Trypto Casein Soy broth (GTCS) medium, as well as in the lungs of mice infected for 24 hours (data not shown). These results prompted us to further check for a putative involvement of the full length PLD gene, called pld1, on K.
Involvement of the phospholipase D family protein gene pld1 in KP 52. 145 virulence As PLD-family proteins have been shown to be involved in virulence  [75] [76] [77] , we decided to characterize the role of the full length PLD1 protein. We first tested a pld mutant strain in a K. pneumoniae murine model. Mice were infected intranasally with 10 8 of either the wild-type bacteria, a pld1 mutant, or the mutant strain complemented with a plasmid expressing the putative PLD1 protein (pPLD), and their survival was monitored for seven days. Interestingly, the mutant strain appeared avirulent in a mouse model of acute pneumonia while mice infected with the wild-type and the complemented strain succumbed in less than one week (Figure  4 ). However, the wild-type, the mutant and the complemented mutant strains grew equally well in Luria-Bertani broth (LB) broth (data not shown).
SD9 wild-type strain also presented an extra lipid spot in comparison to the PLD1-expressing strain, possibly representing the PLD1 substrate (Figure  5A ). Densitometric analysis of iodine-stained lipids on TLC plates revealed that this lipid spot corresponded to 21% of the total amount of lipids in SD9 strain, but only 6% of SD9 complemented with a plasmid expressing pld1. Mass spectrometry (MS) analysis of total lipid extract was carried out to identify such lipid. Comparing lipid profiles by MS, we found a lipid of mass 788. 4 present only in the PLD1-deficient strain (Figure  5B ) and identified it as phosphatidyl glycerol (PG) using the LipidMaps database. As mentioned above, the bacterial PLD-family proteins can be classified in four subfamilies. One of them, the cardiolipin synthase is able to convert two PG molecules into glycerol and cardiolipin, or to catalyze the opposite reaction, leading to PG formation  [80].
4 X using MP titanium + 20 X using SR titanium. Following the primary assembly of the genomes, an in silico finishing approach, based on the methods described by Pop et al. [84] , was performed in order to identify small and identical repeats on the genomes. In such cases and without high quality bases discrepancies, the contigs containing the small repeats were manually duplicated and added to the assembly. Scaffolds were aligned to experimentally determined BglII optical maps generated by OpGen company from purified chromosomal DNA, using MapSolver version 3. 1 software. Such alignments were used to check for the quality of the assemblies. Additionally, specific pairs of primers were designed in order to close all remaining gaps. PCR products were purified in NucleoFast 96 plates (Macherey Nagel, Düren, Germany) and aliquots were used for sequencing reactions with the BigDye Terminator Cycle Sequencing Ready reaction Kit (Applied Biosystems, Foster City, CA, USA) on a ABI Prism 3730XL DNA Analyzer (Applied Biosystems).
The resulting sequences were added to the previous assembly using Phred/Phrap/Consed. The sequences of K. pneumoniae genomes have been deposited to the European Nucleotide Archive and are accessible under the accession numbers: FO834904, FO834905 and FO834906 (strain Kp52. 145), CCBO00000000 (strain SB2390) and CCCQ000000000 (strain SB3193).
Functional annotation of genomes In order to gain functional insights about the genome sequences, protein-coding genes were predicted and annotated using the CAAT-box genome browser  [85] , using a combination of GeneMark predictions and Blastx results against the Uniprot database. All the putative open reading frames (ORF) longer than 120 nucleotides presenting similarity to sequences of the Uniprot database or positive GeneMark result were considered for further analysis. The final set of CDSs underwent a manual annotation process based on description of similarity. Pfam and COG database searches, as well as SignalP, TMHMM and PredTMBB predictions were performed to improve the degree of annotation confidence, if necessary. CDSs were described as 'highly similar to', 'similar to' or 'weakly similar to' if they presented more than 70%, between 50% and 69% or less than 50% similarity to the protein hit sequence.
Thin-layer chromatography and mass spectrometry of lipids Bacterial lipids were extracted by the method of Bligh and Dyer  [87]. Briefly, bacterial stationary-phase cultures were concentrated 10 times and mixed with chloroform: methanol. After centrifugation at 1,000 rpm for five minutes, the organic phase was recovered. Lipid profiles were analyzed by two-dimensional TLC using TLC Silica gel 60 F 254 plates (Merck, Whitehouse Station, NJ, USA). as the stationary phase and a chloroform 9:1 methanol mixture as the mobile phase in both dimensions. Staining was performed by iodine vapor. TLC calibrated images were aquired in ImageScanner using LabScan v5. 0 software. The relative intensity of each spot was calculated in ImageMaster two-dimensional Platinum v7. 0 software. Alternatively, lipid extracts were analyzed by MS and MS/MS in an ESI-Q-Tof Micro (Waters), in positive ion mode.
Acknowledgements LMSL, AT and this work were supported by an  ERA-Net Pathogenomic 2008  grant allocated to both  P. Sansonetti  and  J. Bengoechea. ASA is a recipient of a fellowship from the  Fundação para a Ciência e a Tecnologia from Portugal. The authors thank  Stephane Salamone  and  David Touboul  for their help with lipid analysis.
For this purpose, we measured the transcriptional response of all protein coding genes and long non-coding RNAs upon pharmacological Notch inhibition in the human T-cell acute lymphoblastic leukemia cell line CUTLL1 using RNA-sequencing. Similar Notch dependent profiles were established for normal human CD34 + thymic T-cell progenitors exposed to Notch signaling activity in vivo. In addition, we generated long non-coding RNA expression profiles (array data) from ex vivo isolated Notch active CD34 + and Notch inactive CD4 + CD8 + thymocytes and from a primary cohort of 15 T-cell acute lymphoblastic leukemia patients with known NOTCH1 mutation status. Integration of these expression datasets with publicly available Notch1 ChIP-sequencing data resulted in the identification of long non-coding RNAs directly regulated by Notch activity in normal and malignant T cells.
4] [45]  Despite this initial progress, mechanisms of upstream regulation of lncRNAs have so far remained largely unexplored. In this study, we investigate the role of Notch in the control of lncRNA transcription in the context of normal T-cell development and T-ALL. To this end, lncRNA expression was measured following modulation of Notch signaling in the T-ALL cell line CUTLL1 as well as in normal human thymocytes, and the recently published data on genome-wide Notch1 binding sites was used to reveal the potential for direct regulation. 34 Using this approach, we identified a total of 40 Notch-driven lncRNAs, thereby revealing a novel layer in the molecular machinery that mediates Notch signaling.