dataesr/scientific-paragraphs-categorization huggingface dataset:
The mutants R97A/K98A and K137A/Y138A were also generated but only for the ACU-4G/ACT-4G stem loops. MD set up. All MD simulations were carried out with GROMACS software package version 2018. 2 using the ff03 Amber 'all atom' force field with associated nucleic acid parameters and periodic boundary conditions. The protonation states of the residues were adjusted to the PH used in our NMR experiments  (6. 8). The systems were centered and solvated in a triclinic box of TIP3P water model with 1. 4 nm distance between the boundary of the box and the macromolecular complex. A [KCl] of 25mM was used and counter-ions were added to neutralize the system. Each system was first energy minimized using 50000 steps of steepest descent, then heated from 0 to 298 K at constant volume for 500 ps and equilibrated in the NPT ensemble at p = 1 atm for 500 ps which was followed by 200 ns of NPT production run.
The number of dummy scans and scans was respectively set to 16 and 512. Data were acquired with 2048 points along the direct dimension and with 128 t1 increments with a relaxation delay of 0. 2 s. Shaped pulse length and power were calculated by considering an amide 1 H bandwidth of 4. 5 ppm and a chemical shift offset of 8. 25 ppm. NMR assignment: 1 H and 15 N chemical shifts of YB-1(1-180) and TDP-43 RRM1-2 residues were assigned using our previous results (12) (YB-1) or previous assignments obtained for the unbound TDP-43 RRM1 and RRM2 (BMRB Entries:  18765 and 19922, 19290).
A marked decrease in peak intensities was observed at high YB-1C concentrations (Figure  1B ). In addition, the imino protons displayed a steeper slope than nonexchangeable protons which are not specific to base pairing. These results therefore indicated a destabilization of DNA stem/loop in addition to an increase in molecular weight (Supplementary Figure  S1a, b ). The peak intensities of the imino protons were also measured in the presence of two different RNA-recognition Motifs (RRMs), the RRM of FUS and RRM-1 of TDP-43. Despite the presence of CSPs indicating the binding of the RRMs to the DNA stem/loop (data not shown), the peak intensities were not significantly affected (Figure  1B ). According to this analysis, the DNA stem/loop destabilization was more pronounced at high YB-1 concentrations (Supplementary Figure  S1b ).
Additionally, it was notable the obese Nnat +/-p group had an identical metabolic rate to the wild-types. A small cohort of chow fed mice raised at thermoneutrality were also studied in the calorimetry system at 11-12 weeks of age (Supplementary Fig. 2H, I ). Although the pattern of energy expenditure against body weight looked to follow a similar pattern as that seen in mice studied at room temperature, the number of data points was too small to support two covariates or split into "non-obese" or "obese" groups, limiting any further detailed analysis. Finally, to further study the effects of Nnat loss on appetitive behaviour, we examined food intake in a separate cohort in a second experimental paradigm, a 'non-home cage' environment to which they had been acclimatised in a fed state beforehand.
As PEAKS software analysis could not identify peptides of more than 65 amino acids, we manually searched for the longer forms of proinsulin and pre-proinsulin, but we could not detect such forms. Pseudo-quantification of the main processed peptides produced in the pancreas, including proglucagon processed peptides, IAPP, PPY and PYY, did not detect any significant difference in peptide levels between genotypes (Fig. 5F ). Overall, our results suggest that loss of Nnat does not have a major impact on the correct processing of pro-peptides produced in the pancreas.
Chow-fed studies. Mice aged 10 weeks were singly housed for 2 weeks as acclimatisation for study within the indirect calorimetry system (MetaTrace). The mice cohort used in chow-fed room temperature calorimetry studies were drawn from mice whose body weight had been recorded for Fig. 1  and from other mice in the same facility who had been housed in an identical way. All mice studied were singly housed for 2 weeks as acclimatisation prior to the data acquisition period in the calorimetry system. Body weights were recorded at start-finish of the single housed period to make sure the mice had a stable body weight at the time of the calorimetry run. Over this acclimatisation period the average weight change over this period of acclimatisation in the mice that underwent calorimetry was 1. 2 and 3.
For food intake (FI) and energy expenditure (EE), ANCOVA  47  was performed to assess body weight/FI or EE interactions with body weight as a covariate; genotype as a fixed factor and FI or EE as a dependent variable on SPSS version 25 (25. 0 https:// www. ibm. com/ suppo rt/ pages/ downl oading-ibm-spss-stati stics-25 IBM). Body weight used was the average body weight taken over the time in the calorimeter. Body weight change over time in the calorimeter for the animals on standard chow was as per Fig. 2 , and we observed a maximum % body weight loss of 11%. After calorimetry, male mice continued to be singly housed and females were re-group housed. Within a week of the MetaTrace analysis, body composition was determined by the TD-NMR as above. Data from all mice that underwent indirect calorimetry machine were included in analyses, except for one mouse that reported a negative food intake measure due to technical error.
Body weight, milk and water intake were recorded after the experiment (1 h). Data are presented as single or total caloric intake measured (total = full fat milk + light milk). Laser capture microdissection. Fresh frozen brains were collected from Nnat +/+ and Nnat +/-p male mice between 13 and 14 weeks of age. Coronal sections of 20-μm thickness were prepared on a cryostat (Bright OTF5000 cryostat) and mounted on RNase-free membrane-coated glass slides (Superfrost Plus, Thermo Fisher Scientific). Brain sections were fixed in 95% ethanol (30 s) and rehydrated in an ethanol series (75% ethanol, 50% ethanol) prior to staining with cresyl violet (Ambion). Sections were then dehydrated in another ethanol series of 50%, 75%, 95%, and 100% ethanol and left to air dry prior to laser capture. PVN and ARC, were then dissected using a PALM Microbeam Laser Capture Microdissection System (Zeiss) on a × 5 objective as previously described  48.
Single end reads (SE50) with an average of 9. 02 million sequenced reads per sample were obtained. Transcriptomic analysis of PVN and Arc. Gene-level read counts were imported for expression analysis using DESeq2 (v1. 24. 0)  49  separately for male PVN, male Arc and female PVN. Three samples were removed due to very low read count. Body weight of Nnat +/-p mice was dichotomised into non-obese or obese (males, non-obese < 35 g, obese > 37 g; females: non-obese < 25 g, obese > 30 g) as we observed a comparable range of body weight among wildtype chow-fed or HFD-fed mice, respectively (Supplementary Figs. 6, 7 ). Among mice on chow diet, exploratory clusters (modules) of correlated genes were identified using WGCNA (v1. 69)  50  using rlog-transformed values among genes top-ranked for any effect of sample group (Nnat +/+ , Nnat +/-p non-obese, Nnat +/-p obese; DESeq2, Likelihood Ratio Test).
Gene-level differential expression analysis was performed using pairwise contrasts (DESeq2, Wald test, lfcShrink; counts > 1) between chow-fed groups (Nnat +/+ , Nnat +/-p nonobese, Nnat +/-p obese), between 45% high-fat diet-fed mice with different genotypes (Nnat +/-p HFD-fed vs. Nnat +/+ HFD-fed) and between wildtype mice on different diets (Nnat +/+ HFD-fed vs. Nnat +/+ chow-fed). Gene annotation was obtained from R/Bioconductor package org. Mm. eg. db v3. 8. 2 (https:// bioco nduct or. org/ packa ges/ relea se/ data/ annot ation/ html/ org. Mm. eg. db. html). Supplementary Table  1A  provides the genes used for module discovery, Pearson correlations with each module eigengene (arbitrary sign), and differential expression results (genes with nominal P > 0. 01 in all DESeq2 results are not shown).
10 μl of the final 120 μl was analysed on a Thermo Fisher Ultimate 3000 Nano LC system coupled to a Q Exactive Plus Orbitrap mass spectrometer as previously described (Roberts, Larraufie et al. , Diabetes 2019). Peptide identification was performed using Peaks v. 8. 0 (BSI, Waterloo, Canada), searching against the mouse SwissProt database with fixed cysteine carbamidomethylating and variable methionine oxidation, N-terminal acetylation, and pyroglutamate and C terminal amidation modifications. Peptide quantitation and manual searches were performed using Xcalibur using m/z values and retention times corresponding to selected peptides. Peak areas were normalised with generic internal standards and biopsy weight. Data for each mouse is the mean of the two extracted samples. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD024161 and 10.
Statistical analysis. All values are expressed as mean ± SD. Statistical analysis was performed by using Graph Pad Prism software (GraphPad Prism, version 9. 0. 2, https:// www. graph pad. com/ suppo rt/ faq/ prism-902-relea se-notes/) or SPSS 27 (IBM 27. 0. 0. 0 https:// www. ibm. com/ suppo rt/ pages/ downl oading-ibm-spssstati stics-27), with details of the statistical tests used provided in the figure legends. Mclust 5, an R package for model-based-clustering, was used for the clustering, classification and density analysis of the body weight phenotype as described before  22. K-means clustering analysis of phenotypical variables: body weight, energy expenditure, respiratory exchange ratio (RER), water intake, food intake and change in body weight (dBW) was used to cluster obese and non-obese Nnat +/-p mice.
Acknowledgements We thank  William Budenberg  from  Zantiks Ltd  who kindly provided the Zantiks Unit and technical support. We thank  Keith Burling  and  Peter Barker  and other members of the  Cambridge Biochemical Assay Laboratory (CBAL). This project was supported by the  National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the  NIHR  or the  Department of Health and Social Care. We thank staff at the  West Forvie  and CBS animal facilities. Mouse studies in Cambridge were supported by  Daniel Hart  and  Sarah Grocott  from the  MRC Metabolic Diseases Unit Disease Model Core.
Expression and purification of DdrD protein The gene coding for the DR0326 protein, as indicated in Genbank, was amplified from D. radiodurans genomic DNA by PCR using primers DR0326us (GGA ACA GCA TAT GGA TAC CCT GAA AAA AGC TGG AAC GATGC) and DR0326ds (GGA ATT CTT AGG CTG CCG GGG TGT TTT CGC CGG CCT CGCTC). The resulting product was inserted into the NdeI and the EcoRI cloning sites of pET21a (Novagen) to yield construct pEAW321. The construct was transformed into the E. coli expression strains STL2669 pT7pol26 [Δ(recA-srlR)306::Tn10 xonA2(sbcB -), a gift from Susan T. Lovett (Brandeis University, Waltham, MA). pT7pol26 is described in  (Lusetti et al. 2003). The cells were grown in 10 L LB broth containing 100 µg/mL ampicillin and 40 µg/mL kanamycin at 37 °C to an OD 600 nm of 0. 5. Overexpression of DdrD was then induced with 0.
Acknowledgements We thank  Michael M. Cox  and  J. Timmins  for the gift of the purified DdrD and DdrC proteins, respectively. We thank  Michael Dubow  for polishing our English. We thank also the  Institute Curie  for the use of the 137 Cs irradiation system. This work was carried out in compliance with the current laws governing genetic experimentation in France. This work was supported by the  Centre National de la Recherche Scientique  and the  Université Paris-Saclay.
Genome Sequencing, Assembling, and Functional Annotation The genome sequencing was performed by an Illumina Miseq instrument (2 × 250 bp paired-end reads) (Illumina, San Diego, CA, USA) at the University of Copenhagen by using TruSeq DNA Library Preparation Kits v2 (Illumina, San Diego, CA, USA), according to the manufacturer's instructions. Raw reads were treated and assembled to contigs on CLC Genomic Workbench 8. 5. 1. The obtained contigs were validated using  QUAST 4. 5 [29]. Genes were predicted from the contigs and further annotated with Prokka v1. 14. 5  [30]. Predicted genes were submitted to eggNOG 5. 0 database to obtain an integrated functional annotation and classification  [31].
Whole-Genome Phylogenetic Analysis To determine the phylogenetic origin of Chryseobacterium sp. KMC2 in the Chryseobacterium genus, the whole-genome sequences of 11 publicly available Chryseobacterium species were downloaded from NCBI database to construct a phylogenetic tree. The whole-genome sequence-based phylogenetic tree was inferred by using an online pipeline: The Reference sequence Alignment based Phylogeny builder (REAL-PHY 1. 12)  [32] , based on the merge reference alignments. All genomes were merged to generate the reference sequences. Sequence reads from each query genome were chopped into 50 bp fragments and mapped to the reference via Bowtie 2  [33]. Multiple sequence alignments were reconstructed, and a phylogenetic tree was inferred by PhyML 3. 0  [34]. Visualization of the obtained phylogenetic tree was generated by iTOL v5  [35].
Moreover, gene presence-absence patterns among these genomes were analyzed with M1CR0B1AL1Z3R server  [36] , which was further used to calculate homology using GLOOME with fixed gene gain/loss ratio  [37]. The phylogenetic tree was visualized by FigTree v1. 4. 4 (tree. bio. ed. ac. uk/software/figtree/) (accessed on 15 April 2021), using the branches to display the gene gain and loss rates. Average Nucleotide Identity (ANI) was calculated using OrthoANI  [38].
Secondary Metabolite Gene Cluster Detection and Annotation Assembled contigs of four Chryseobacterium species were uploaded to antiSMASH 5. 0 secondary metabolite genome mining web platform  [21]. Predicted secondary metabolites gene clusters from Chryseobacterium sp. KMC2 were compared with other keratinolytic Chryseobacterium species. Gene annotation of each cluster from Chryseobacterium sp. KMC2 was performed by Prokka v1. 14. 5  [30]  and BLASTP with the NCBI database. The best match sequencing ID was recorded for the annotated genes. Synteny and features of conservative secondary metabolite gene clusters were analyzed by using Easyfig 2. 2. 2, showing the similarity of gene sequences  [39]. Feature comparison of amino acid sequences and motifs from core synthetic genes were analyzed by using Clustal Omega  [40]  to get the multiple sequence alignment and using Seq2logo to generate sequence logo  [41].
Metabolic Networks Construction and Protease Families Prediction The genomes of Chryseobacterium sp. KMC2 and other three Chryseobacterium species were submitted to GhostKOALA  [42]  to obtain the KO number for each gene, then genes were assigned to different metabolic pathways and functional categories. Following the metabolic networks construction of Chryseobacterium sp. KMC2 was achieved through mapping the annotated enzyme genes to KEGG  [43]  reference pathway and Biocyc database  [44]  manually. Protease families' prediction was performed based on CDSs sequence alignment against the MEROPS peptidase database according to the peptide-based functional annotation principle of Peptide Pattern Recognition (PPR)  [45]  implemented by the Homology to Peptide Pattern method  [46]. The signal peptides of putative proteases were predicted by the SignalP 5.
Metabolic Potential Comparison of Four Keratinolytic Chryseobacterium Genomes About 40% of the genes from the four genomes were annotated and classified into various functional categories based on the KEGG database. The vast majority of annotated genes belonged to metabolism, genetic information processing, environmental information processing, and cellular processes (Figure  2 ). The functional categories of the genomes were overall highly similar, with ~85% of annotated genes assigned to "metabolism" (category A) which included ~1. 000 genes into the sub-category "global and overview maps". Additionally, about 8% and 4% annotated genes from each genome were assigned to "genetic information processing" (category B) and "environmental information processing" (category C), respectively. The remaining annotated genes belonged to "cellular processes" (category D), which occupied 3% of the annotated genomes approximately.
Data Availability Statement: Reference genomes were downloaded from the NCBI database: Chryseobacterium camelliae Dolsongi-HT1 (GenBank: GCA_002770595. 1), Chryseobacterium gallinarum strain DSM 27622 (GenBank: GCA_001021975. 1), Chryseobacterium sp. P1-3 (GenBank: GCA_000738495. 1), Chryseobacterium gleum NCTC11432 (GenBank: GCA_900636535. 1), Chryseobacterium bernardetii H4638 (GenBank: GCA_003815955. 1), Chryseobacterium arthrosphaerae FDAAGOS 519 (GenBank: GCA_003812705. 1), Chryseobacterium indologenes FDAARGOS 337 (GenBank: GCA_002208925. 2), Chryseobacterium joostei DSM 16927 (GenBank: GCA_003815775. 1), Chryseobacterium glaciei IHBB 10212 (GenBank: GCA_001648155. 1), Chryseobacterium carnipullorum F9942 (GenBank: GCA_003815855. 1), and Chryseobacterium sp. SNU WT5 (GenBank: GCA_007362475.
1). Raw sequencing data were deposited in the Sequence Read Archive (SRA) database under the BioProject number PRJNA686768 with an accession number SRR13278108. The assembled genome sequence of Chryseobacterium sp. KMC2 has been deposited at DDBJ/ENA/GenBank under the accession JAESIT000000000. Sequences of predicted proteases in Chryseobacterium sp. KMC2 genome are available in the figshare repository: doi. org/10. 6084/m9. figshare. 14453043.
Sub G1 and cell cycle analysis by flow cytometer After 24 h of treatment, SH-SY5Y cells were trypsinized and dispersed into single cells. All treated cells were collected and washed with ice cold 1X PBS. Washed cells were fixed and immobilized in ice cold 70 % ethanol until required. For staining, fixed cells were washed twice with ice cold 1X PBS and then subsequently incubated with a propidium iodide (PI) staining solution containing 100 μg/mL RNase A, 0. 1 % Triton-X 100 and 10 μg/mL PI for 30 min. Stained cells were analysed using a FACS Calibur flow cytometer (BD Biosciences, Franklin Lakes, NJ) and CELLQuest Pro software (BD Biosciences).
Acknowledgements We would like to thank  Dr Narisorn Kitiyanant  ( Institute of Molecular Biosciences, Mahidol University, Thailand ) for providing us with the SH-SY5Y cell line,  Prof. Saovaros Svasti  ( Institute of Molecular Biosciences, Mahidol University, Thailand ) for kindly providing cisplatin and  Dr Jean-Francois Hernandez  ( Institut des Biomolecules Max Mousseron, University of Montpellier, France ) for kindly providing the fluorimetric substrate JMV2770. This work was supported by  Mahidol University and The Thailand Research Fund  ( BRG6180002 ). SM was supported by a  Mahidol University  postdoctoral research sponsorship.
coli K12 regulons described in the Regulon Database  13. Among the upregulated regulons, those associated with the response to acidic pH are largely overrepresented (Supplementary Fig. 1c ), and genes involved in the response to acidic pH are highly overexpressed as early as 1 h of P. I. (Supplementary Fig. 1d ). Members of the second family of upregulated regulons govern the bacterial growth mode (cell division, LPS, capsule, or biofilm determinants) (Supplementary Fig. 1c ). Among the genes controlled by these TFs, we observed, upon entry into the macrophage, a general upregulation of the genes annotated for biofilms and adhesion (Fig. 2d ). Consistent with this change in the LF82 growth, upon entry into the macrophage, we observed a severe downregulation of the FlhDC regulon, which contains motility genes (Supplementary Fig.
2  Transcriptomic analysis of macrophage infection by LF82. a Expression data for the LF82 genes during infection of THP1 macrophages. Selected genes with highly significant expression changes at 6 h of P. I. are noted. b Expression data for the human genes from RNA-seq experiments at 1 and 6 h of P. I. Gene markers of the M1 pro inflammatory macrophage phenotype are noted. c Representation of the main E. coli pathways presenting large transcriptomic responses at 6 h of P. I. of THP1 macrophages (top 500 genes ranked on DE-seq). These networks were constructed with cytoscape and were based on the data presented on Supplementary Fig. 1. Upregulated genes (green) and downregulated genes (orange). d Annotation of the biofilm formation pathways adapted from KEGG ko2026 according to RNA-seq data at 6 h of P.
Metamorph Software (Universal Imaging) was used to collect data. STED imaging. Infected macrophage were fixed and immunostained as described above. Imaging was performed with a STED expert Line Abberior Instruments GmbH coupled to a SliceScope Scientifica microscope. Images were acquired in two steps, first the confocal configuration for the mCherry bacteria (561 nm) and the Lamp1 staining (485 nm) and second with the STED configuration for the WGA staining (excitation at 640 nm, depletion at 775 nm). Detection was performed with avalanche photodiodes with filter cubes for green (500-550 nm), red (605-625 nm), and far red (650-720 nm) fluorescence. RNA-seq and Tn-seq. Dual RNA-seq  12  and Tn-seq  19  experiments are described in the Supplementary information. The data are available at GEO # GSE154648.
Statistics and reproducibility. All data were expressed as mean and Standard Error of the Mean (SEM). P values were determined by the Student's unpaired t-test. The reproducibility was determined by using biological replicates and repeating the experiment 3-7 times as described in the figure legends. e Expression data from RNA-seq experiments at 1 and 6 h of P. I. of THP1 macrophages for the genes annotated for their participation in iron homeostasis in LF82. Genes were organized according to their position on the genome. Clusters of genes are delineated by a vertical line. f Expression data from RNA-seq experiments for the genes annotated for their participation in iron homeostasis in humans. g Summary of our results unveiling LF82's strategy to survive within macrophage. Non-replicating AIEC (red), Replicating AIEC expressing HPI and the biofilm pathway (green), biofilm matix (blue waves), Nucleus (dark blue).
Acknowledgements We thank the high throughput sequencing core facilities of  I2BC (Centre de Recherche de Gif -http://www. i2bc. paris-saclay. fr/) and  Curie Institute  (supported by the  ANR-10-EQPX-03  and  ANR10-INBS-09-08  grants from the  Agence Nationale de la Recherche  and by the  Canceropôle Ile-de-France). We are very grateful to  Ugo Szachnowski ,  Nicolas Lapaque ,  Eric Allemand  and the members of the CIRB imaging facility. This work has received support from  ANR  (https://anr. fr)with the references  ANR-10-LABX-54  MEMOLIFE (O. E. ) and  ANR-11-IDEX-0001-02   PSL* Research University  (O. E. ), from the  ANR  with the reference  ANR-18-CE35-0007  (O. E. ),  ANR-15-CE12-0007  (A. M. ), from the  European Research Council "EpincRNA" (starting)  and  "DARK" (consolidator) grants  (A.
1C  and Supplementary Table  2 ). These proteins belong to six significant enriched KEGG pathways cluster (p < 0. 05): Spliceosome, RNA transport, RNA degradation, mRNA surveillance, DNA replication and Homologous Recombination (Fig. 2A ). To go further, enriched GO Biological Processes and Molecular Functions terms were also determined. This analysis revealed that proteins enriched on contrained-G4 structures are mainly associated with DNA and RNA transactions (Fig. 2B-C ), in agreement with current knowledge on genomic localization and biological function of G4. In line with these data, 214 out of 425 proteins enriched on G4 structures have been described as nucleic acid interacting factors, as indicated by terms from GO Molecular functions data analyses (Fig. 1C  and Supplementary Table  3 ).