dataesr/scientific-paragraphs-categorization huggingface dataset:
masc. neut. adj. massiliense for Massilia, the Roman name of Marseille, where strain MT5 T was first isolated). Strain MT5 T is the type strain of the species 'Brachybacterium massiliense'. published name. Sequences were aligned using CLUSTALW, and phylogenetic inferences were obtained using a maximum likelihood method within MEGA software  [12]. Numbers at nodes are percentages of bootstrap values obtained by repeating the analysis 500 times to generate a majority consensus tree. Dermabacter hominis was used as outgroup. Scale bar indicates 0. 5% nucleotide sequence divergence. The 16S rRNA gene sequence of strain mt20 T (accession number LN906632) showed a 98. 4% similarity level with Enterobacter cloacae strain ATCC 13047 T (Accession number NR_102794)  [7, 8]  so classifying strain mt20 T as a new species within the genus Enterobacter (Fig.
4 ). The rpoB gene sequence (Accession number LN906633) showed a 95. 12% similarity level with the rpoB gene of Enterobacter cloacae strain ATCC 13047 T (Accession number AJ543726), confirming the status of strain mt20 T as a putative new species  [6]. Strain mt20 T is the type strain of 'Enterobacter timonensis' sp. nov. (ti. mo. nen'sis. L. gen. masc. timonensis, of Timone, the name of the hospital where strain mt20 T was first isolated). The 16S rRNA gene sequence of strain Marseille-P2411 (accession number LT161896) presented a similarity level of 94. 2% with Anaerosinus glycerini strain DSM 5192 (GenBank Accession number NR_025297)  [11] , which is the phylogenetically closest species with a validly published name (Fig. 5 ). A new genus was created within the family Veillonellaceae named 'Massilibacillus' (mas.
MALDI-TOF MS spectra The MALDI-TOF MS spectra of 'Khelaifiella massiliensis', 'Niameybacter massiliensis', 'Enterobacter massiliensis', 'Brachybacterium massiliense' and 'Massilibacillus massiliensis' are available at http://www. mediterraneeinfection. com/article. php? laref=256&titre=urms-database.
Nucleotide sequence accession number The 16S rRNA sequences of strains Mt13 T , Mt14 T , MT5 T and Marseille-P2411 T are deposited in the GenBank database under accession numbers LN850733, LN850735, LN906631 and LT161896, respectively. The 16S rRNA and rpoB gene sequences for strain mt20 T are also deposited in the GenBank database under accession numbers LN906632 and LN906633, respectively.
The pCW-Csy4-Hygromycin lentiviral vector was produced as a means to express the ribonuclease Csy4, which was used to cleave Csy4 sites in pLX-dual-gRNA transcripts and produce a pair of gRNAs. To make pCW-Csy4-Hygromycin, a fragment of pGL4. 11 (Addgene #59744) was amplified using primers Hyg-F and Hyg-XbaI-R (PCR4) and a fragment of pCW-Cas9 (Addgene #50661) 2 was amplified using primers hPGK-SpeI-F and hPGK-R (PCR5). Equal volumes of PCR4 and PCR5 were used as templates for a second-round PCR with the primers hPGK-SpeI-F and Hyg-XbaI-R (PCR6). PCR6 and pCW-Cas9 were digested with SpeI and XbaI and ligated to produce pCW-Csy4-Cas9. To add a hygromycin resistance gene, we replaced Cas9 with hygromycin as follows. The hygromycin gene from pSQT834 (Addgene# 53371)  (16)  was amplified using the primers Csy4-Nhe-F and Csy4-BamHI-R (PCR7).
Analysis of the high-throughput CRISPR assay data Reads were aligned to the reference sequence using STAR aligner  (21) , with default settings in genome generate mode and genome SA index N bases = 4. For mapping mode we used end-to-end read alignment, maximum of 20 mismatches, minimum of 40 matched bases and disallowed multiple alignments. FastQC was used to check read quality. Samtools sort was used to sort by read name for pairedend reads. After mapping, we wrote custom scripts to ignore the following: any reads with a deletion <8 nt from the start or end of the read, since uncertainty in that region can be high; any deletions of length 1 or 2 bases, since past experience has shown that these are often likely to be sequencing errors; any reads with multiple deletions; and any deletions 30 nt or longer.
Di-and trinucleotides can act as gauges of splicing efficiency Several genomic studies have reported that short sequence differences, even single nucleotide disparities, can aid in the identification of exons  (Amit et al. 2012; Xiong et al. 2015). We therefore examined our genetic data for such biases. We started by comparing the effects of all 16 possible dinucleotides at all positions so as to minimize contextual effects imposed by neighboring sequences. To normalize the data, we subtracted the mean LEI at each position from each LEI value and then averaged across all positions in all HMs to get a single value for each 2-mer. The result was termed the LEI difference from mean, or LEIdm. LEIdm values ranged widely for different 2-mers, from -1. 45 for TA to +1. 26 for CG (Supplemental Fig.
The fate of a single exon is governed by a large number of RNA binding proteins The availability of relative binding specificities of 200 RBP binding domains to essentially all ∼16,000 7-mer sequences  (Ray et al. 2013 ) allowed us to ask whether any of these RBPs exhibited a correlation between binding affinity and splicing efficiency. We surveyed 91 RBP specificities designated as human from the CISBP-RNA database, where each 7-mer was assigned a normalized z-score as a measure of affinity relative to the mean of all 7-mers (http ://CISBP-RNA. ccbr. utoronto. ca/). As a preliminary examination, we can consider some extreme examples where a SBS produces an extreme phenotypic change. A change from a wild-type A to T at position 39 in HMA drops the EI from 0. 187 to 0. 001; a 7-mer encompassing this change (AGAAGGG to AGTAGGG) greatly strengthens a binding site for the known silencer HNRNPA1, with a z-score change from 2.
Splicing scores for 7-mers predict splicing efficiency The use of binding affinities of 7-mers provided in the CIS-BP database allowed us to assess the behavior of our model exon with considerable accuracy. In an effort to codify the mutant k-mer sequences as predictors of splicing in general, we therefore chose to use 7-mers. For this purpose, it was necessary to normalize the splicing phenotypes across all HMs and all positions. For each of the 76 mutant 7-mers found at a given internal position, we subtracted the average LEI from each LEI. If a 7-mer was present at more than one position, its values were averaged. For all the 7mers derived from the same HM set, the values were then scaled from +1 to -1 around the median. These normalized and scaled values were then averaged across all HM sets and are termed "effective" LEI (eLEI) scores.
Scores could be assigned to 6371 7-mers, almost all that are present in the population. Note that these eLEI scores are independent of RBP binding data. The average or sum of the eLEI scores of all 7-mers in an exon correlated well with the observed LEI of that molecule (R 2 = 0. 77, N = 5560) (Supplemental Fig. S17 ). However, for general use, we were missing eLEI scores for the 10,013 7-mers not present among these molecules. To deduce these missing values, we took advantage of the correlations found between RBP binding and splicing. We used Random Forest  (Pedregosa et al. 2011 ) to train regression trees on the affinities (zscores) of the 6371 mutant 7-mers represented in our mutant sequences, using all 91 human proteins in the CISBP-RNA database and across all positions. Based on their z-scores, each of the missing 7-mers was then added to a tree individually and the value of the closest leaf bearing a known eLEI was noted.
Rather, FASTQ reads were filtered for accuracy by accepting only those that exactly matched the expected sequence of the nonmutated stretch of 6 nt at the end of the read and that also contained no changes other than those designed. This filter typically removed ∼50% of the reads. Data filtrations and barcode de-multiplexing were carried out using custom Perl scripts (see Supplemental Methods). Approximately 10 million reads were collected for the input DNA and approximately 3 million from each of the two independent transfection experiments. The average number of reads for the input DNA was 1828, and all but two mutant sequences had at least 50 reads. The average number of mutant reads for the output cDNA was 1105, but these ranged widely, as expected (0 to 21340). Nine percent of the output reads were zero, informative of a very poor splicing efficiency; these mutants were given a pseudocount of 1 when logarithms were calculated, as for LEI.
01. The model for each HM was tested for overfitting by leave-one-out cross validation. Because each HM set can also be seen as sets of mutations compared to the true wild-type exon, we also merged nine sets of HMs. We left out HMD due to its many low inclusion values. We again performed a single stepwise regression with the starting variables being the set of protein/positions representing the union of the sets of significant protein/positions for the nine HMs used. Given the large sample size, we used a 10-fold cross validation to test the resulting model. The final model included 80 protein/positions. Perl scripts for these calculations can be found in Supplemental Methods.
Only sequences that contained at least 10 reads in the input library used for an IP were used in further analysis. Increasing this cutoff to 50 reads in a sample of the IPs increased R 2 values by, at most, 10%, justifying the use of a cutoff of ≥10 to optimize coverage. The sequencing data were analyzed by calculating the proportion of each mutant RNA molecule that was pulled down by the beads, based on 5479 to 5496 values with at least 10 reads for each IP. For each given mutant "m" and protein target "a," a PPD was calculated as follows: O m,a is the proportion of mutant m in the output reads of IP experiment a; ORNA a is the quantity of RNA pulled down in the output of IP experiment a; CO m,a is the proportion of mutant m in the output reads of the nonimmune serum control; CORNA is the quantity of RNA pulled down in the nonimmune serum control; I m,a is the proportion of mutant a in the input reads of IP experiment a; IRNA a is the quantity of RNA in the input of IP experiment a; PPD m,a is then an estimate of the proportion of molecules pulled down for mutant a, specifically.
The raw data was acquired on the mass spectrometer in a data-dependent mode (DDA). Full scan MS spectra were acquired in the Orbitrap (scan range 350-2000 m/z, resolution 70000, AGC target 3xe6, maximum injection time 100 ms). After the MS scans, the 20 most intense peaks were selected for HCD fragmentation at 30% of normalized collision energy. HCD spectra were also acquired in the Orbitrap (resolution 17500, AGC target 5xe 4 , maximum injection time 120 ms) with first fixed mass at 180 m/z. The raw data files generated were processed using MaxQuant (Version 1. 5. 0. 35), integrated with the Andromeda search engine as previously described. To identify protein groups, peak lists were searched against mouse database as well as list of common contaminants by Andromeda. Trypsin with a maximum number of missed cleavages of 2 was chosen.
4sU-seq analysis The raw fastq files of read pairs were first mapped to rRNA build, then the remaining unmapped reads were aligned to the 'N-masked' genome with STAR using the parameters '-outFilterMultimapNmax 1 -outFilterMismatchNmax 2 -alignEndsType EndToEnd' for all the 4sU-seq libraries  (Dobin et al. , 2013). Unique alignments were retained for further analysis. We employed the SNPsplit to separate the alignment into distinct alleles (Cast and 129S1) with the paired mode. The summary of the allelic split is listed in Table  S2. To call the allelic gene expression, the number of unique mapped read pairs for each annotated gene (CPM) was counted using the HT-seq  (Anders et al. , 2015)  with the parameters ''-t transcript -s reverse,'' then normalizing to million mapped read pairs (library size) with edgeR R package, and then splitting the CPM value by the G1 (Cast.
ACKNOWLEDGMENTS We would like to thank colleagues in the Brockdorff and Klose labs and  Chris Ponting  for discussion and comments;  Dr. Svenja Hester  and the  University of Oxford Biochemistry Department Advanced Proteomics Facility  for the MS analysis; and the  EMBL Gene Core ,  Dinko Pavlinic , and  Vladimir Benes  for RNA-seq. Work in the N. B. lab was funded by grants from the  European Research Council  ( 340081 ) and the  Wellcome Trust  ( 103768  and  091911 ). G. P. was funded by a  Wellcome Trust   PhD studentship  ( 099682 ), and  A. Castello  was funded by an  MRC Career Development Award  (# MR/L019434/1 ).
DATA AND SOFTWARE AVAILABILITY Original unprocessed gel images in this manuscript have been deposited to Mendeley Data and are available following these links: https://doi. org/10. 17632/p8835bsb8g. 1, https://doi. org/10. 17632/dfrbvsdcrf. 1. The high-throughput data reported in this study have been deposited in GEO under accession number GSE103370.
Reads from the corresponding counts files were randomly sampled (NoDox and Dox treatment) and assigned to the Cast or 129S1 alleles on the basis of a binomial distribution. Repression Score (RS null ) was calculated based on the permutated reads counts, and permutated N (N = 10000) times. The p value was defined as the fraction of RS null which was no less than RS obs (i. e. sum(ifelse(RS null > = RS obs ,1,0))/N, N = 10000). The permutation scheme was applied chromosomewide. The p value for two biological replicates was calculated separately and then combined based on Fisher's method. q-values represent combined p values after adjustment following the Benjamini-Hochberg multiple testing in R. Definition of a ''silent gene'' is based on both the calibrated RS and q-value (q < 0. 05). All the custom scripts, and pipeline used in this study are available at https://github.
ATAC-seq data analysis The raw paired-end reads were mapped to 'N_masked' mm10 genome with STAR (2. 4. 2a)  (Dobin et al. , 2013)  using the same parameters used for 4sU-seq analysis except ''-alignIntronMax 10. '' The unique alignments were kept for the further analysis. SNPsplit was employed to assign the reads allele-specifically. The bedGraph files containing the library-size (10 million mapped reads) normalized differences between G1 (Cast. ) and G2 (129S1) for each biological replicate were generated by Bedtools  (Quinlan and Hall, 2010). The differences between Dox and NoDox treatment were defined as follows: ddscore = ½G1ðDoxÞ À G2ðDoxÞ À ½G1ðNoDoxÞ À G2ðNoDoxÞ The ddscore was stored in bedGraph format. For the functional cis-elements across the chromosomes, the pre-defined ChromHMM state (https://github.
RIPseq captures genome-wide target-RNAs directly regulated by CsrA Our proteome and transcriptome data show that CsrA influences the expression of many major regulatory proteins like FleQ and FleR, the alternative sigma factor RpoS (lpp1247), the nucleoid-associated proteins Fis2 (lpp1324), Fis3 (lpp1707) and HU-beta (lpp1826) or the transmission trait enhancer protein LetE. As it is well known, transcriptome and proteome data overlap only partially and they do not allow distinguishing between direct or indirect regulations. Thus we analysed the direct interaction between CsrA and its target-RNAs by coimmunoprecipitation experiments followed by massive sequencing (RIPseq). Five independent RIPseq libraries obtained with epitope-tagged CsrA protein were created and deep sequencing of CsrA-bound transcripts using an Illumina platform was performed.
We identified in total 479 RNAs localized in the untranslated (UTR) and/or coding regions of mRNAs or of known non-coding sRNAs of L. pneumophila (S3 Table). To identify CsrA targets, we used a script developed by Dugar and colleagues  [38] , that calculates in sliding windows the coverage enrichment of the co-IP versus the control. For the comparison, the coverage files were normalized according to the number of mapped base pairs of each sample (control and co-IP). A peak was defined as an at least five times sequence enrichment in the co-IP as compared to the control IP. The values for the enrichment of each CsrA target are recorded in S3 Table. Calculation of the enrichment of A(N)GGA motifs in the CsrA target peaks defined in the co-IP as compared to those found in the control IP revealed a 1.
CsrA regulates motility by directly targeting the FleQ and FleR mRNAs RIPseq analyses identified two RNAs directly bound to CsrA, one located in the 5'UTR of the mRNA coding for the transcriptional regulator FleQ and the other one in that coding for the two-component response regulator FleR. Both regulators are indispensible for flagella biosynthesis in L. pneumophila as the deletion of one or the other led to the down regulation of all flagellar genes and consequently to a complete loss of motility  [30]. Indeed, EMSA assays confirmed the interaction of the fleQ mRNA with CsrA in vitro, and the addition of an excess of unlabeled RsmZ as control, abolished this interaction (Fig 3A). To identify the binding sites of CsrA we predicted the RNA structure with the Mfold program  [40]. This revealed two A(N) GGA-binding motives in the fleQ mRNA that are present in loops of (Fig  3B ).
This confirmed that the eukaryotic ectonucleoside triphosphate diphosphohydrolase (ecto-NTPDase) (Lpp1033) required for optimal intracellular replication  [43] , LidA, (Lpp1002) implicated in Rab1 sequestration and development of the LCV  [44] , and YlfA (Lpp2264) that inhibits endosomal trafficking  [45, 46] , are all directly targeted as purified CsrA showed clear binding in vitro to the corresponding YlfA-, LidA-and NTPase-RNAs (S6 Fig). Interestingly, as judged from the combined approach of total protein mass spectrometry analyses and RIPseq experiments, the regulatory effect of CsrA is not only repressive. For example the Dot/Icm secreted eukaryoticlike sphingosine-1-phosphate lyase LpSpl  [37]  and others are downregulated in the csrA -strain in our proteome and transcriptome dataset (S1 and S2 Tables), indicating that the effect of CsrA allows a highly coordinated life cycle regulation in a spatial and timely manner.
Mfold analysis of the secondary structure of the RNA region containing the CsrA binding site predicted two energetically favored conformations: a) the A(N)GGA motif was buried in a double-stranded sequence followed by a strong hairpin and b) the CsrA regulatory motif was present in a loop and the subsequent hairpin was destabilized (Fig  7A ). Furthermore, adjacent to the hairpin we identified a short sequence closely related to an auxiliary element assisting in Rho-dependent termination of transcription  [47]. To test our hypothesis that this region might be involved in preliminary transcription termination of the gap operon, we performed in vitro transcriptional assays in presence of NusG, an additional factor known to facilitate recognition of the termination signal  [47, 48]. The full-length transcript appeared in absence of the Rho factor whereas premature termination was observed when purified Rho was added to the reaction (Fig  7B ).
g. acnA, icd, sdh/suc, sfcA) were present (Table  2  and S3 Table). Thus, results from our combined datasets indicate that CsrA is a critical regulator of the carbon flux from the nutrient source-in L. pneumophila mainly amino acids like serine and threonine, but also glucose  [49, 50] -to obtain energy via oxidative phosphorylation. CsrA seems to play a pivotal role in the production of metabolic intermediates and cell components including the interconversion of amino acids or the biosynthesis of nucleotides and fatty acids/lipids essential for efficient replication and cell proliferation (Table  1  and S3 Table). Metabolite flux analyses are under way to further substantiate our results obtained from the RIPseq, transcriptome and proteome data. In contrast, CsrA is hindering the synthesis of storage molecules and short-chain fatty acids according to our microarray and proteomic data.
The right one, shows the A(N)GGA-motif located in an open loop with high CsrAinteraction affinity and the hairpin structure is disrupted. Below, the nucleic acid sequence is shown that was used for Mfold modeling and the transcription termination assays. Red, CsrA-binding site A(N)GGA; green, the potential transcription terminator hairpin; blue, the putative auxiliary element of Rho-dependent termination. B) Left panel: In vitro transcription termination assay in presence of 1 μM of purified NusG-protein and varying concentration of Rho-and CsrA-protein (+ 0. 5 μM, ++ 1μM; Lane 1: no Rho, no CsrA, lane 2: 1 μM Rho, no CsrA, lane 3: 1 μM Rho, 0. 5 μM CsrA, lane 4: 1 μM Rho, 1 μM CsrA, lane 5: 1 μM Rho, no CsrA, lane 6: 1 μM Rho, 1 μM CsrA). A representative 10% urea-PAGE gel shows the formation of the truncated transcript from the Rho-dependent termination without CsrA and the full-length transcript with CsrA.
Analysis of deep sequencing data The reads in FASTQ format generated by Illumina sequencing were filtered using FastXclipper from the FastX toolkit (http://hannonlab. cshl. edu/fastx_toolkit/) and Tagdust  [67]  for adapter removal. Trimming of reads was performed using Sickle (https://github. com/najoshi/sickle). To assure high sequence quality, we used a cutoff phred score of 20. After trimming, all the sequence reads shorter than 18nt were eliminated. Reads were aligned to the L. pneumophila strain Paris chromosome sequence (NCBI Acc. -No: NC_006368. 1) using Bowtie 2 software  [68]. Only the uniquely mapped reads are kept, so, a read cannot contribute for the coverage value at different positions. Duplicate reads were removed from mapping results (with samtools rmdup) and BAM files were built using the Samtools software  [69].
RIPseq data and enrichment of CsrA target analyses For sample and control, coverage files (wiggle files) were generated for the forward and the reverse strand and normalized according to the number of mapped base pairs. For each couple sample/control, a scale factor was applied to scale the small sample up to the bigger sample. To define CsrA-bound RNA regions, enrichment regions (peaks) were detected using a python script "sliding_window_peak_calling_script. py" previously described  [38]  and available at Zenodo (https://zenodo. org/record/49292). Peak detection was performed separately for the forward and reverse strand of each replicon, according to the author description and with a minimum required fold change (FC) of 5 for the enrichment. To identify CsrA targets, overlaps between gene annotations and enrichment regions were performed with bedtools suite  [74].
Slides were scanned on a GenePix 4000A scanner (Axon Instruments). Laser power and/or PMT were adjusted to balance the two channels and the resulting files were analyzed using Genepix Pro 4. 0 software. Spots were excluded from analysis in case of high local background fluorescence, slide abnormalities, or weak intensity. Data normalization and differential analysis were conducted using the R software (http:// www. R-project. org). No background subtraction was performed, but a careful graphical examination of all the slides was conducted to ensure a homogeneous, low-level background in both channels. A loess normalization  [78]  was performed on a slide-by-slide basis (BioConductor package marray; http://www. bioconductor. org/packages/bioc/stable/src/contrib/html/marray. html). Differential analysis was carried out separately for each comparison between two time points, using the VM method (VarMixt package  [79] ), together with the Benjamini and Yekutieli  [80]  p-value adjustment method.