dataesr/scientific-paragraphs-categorization huggingface dataset:
Metatranscriptomic analysis Metatranscriptomic analysis was conducted on samples from day 21. RNA extraction and rRNA depletion were performed as previously described  (Monnet et al. , 2016). Libraries were prepared with the ScriptSeq™ RNA-Seq Library Preparation Kit (Illumina) and sequenced on an Illumina NexSeq 500 instrument. Data analysis was performed according to the workflow presented in Figure  S1. Briefly, raw sequences were preprocessed using Cutadapt v1. 12  (Martin, 2011)  and filtered with SortMeRNA v2. 0  (Kopylova, Noé, & Touzet, 2012). Non-rRNA reads were mapped using TopHat2 v2. 1. 1  (Kim et al. , 2013)  against a reference database composed of coding DNA sequences (CDS) extracted from the genome sequences of strains belonging to the same species as the microorganisms that were inoculated into the cheese.
Accession numbers of the genomes are available in Table  S1  and S2  for soft and semi-hard cheeses, respectively. In order to access the transcriptome of non-inoculated species that develop in cheese as well, unmapped reads were de novo assembled using Trinity  (Grabherr et al. , 2011) , and gene prediction was conducted on this assembly using FragGeneScan  (Rho, Tang, & Ye, 2010). Unmapped reads were mapped back to these predicted CDS using TopHat2. Each CDS of the de novo assembly was taxonomically annotated using Kaiju  (Menzel, Ng, & Krogh, 2016). All CDS from both the reference database and the de novo assembly were submitted to the GhostKoala server  (Kanehisa, Sato, & Morishima, 2016)  for uniformed functional annotation based on KEGG Orthology. The count matrix was processed using custom scripts built under the statistical environment R (http://www.
Sensory analyses were carried out in an air-conditioned room (20°C) with a hygrometry between 50 and 70% according to the NF ISO 8589 method. Samples were at approximately 15°C when they were tested. Cheeses were evaluated in a monadic way. The presentation order of samples was randomized across panelists to make sure that the run order did not introduce a bias in the results. Panelists were provided with mineral water for rinsing and flatbread for palate cleaning. The data were directly collected and recorded with FIZZ software (Biosystèmes).
Data analysis All results were reported as mean ± standard deviation. All data were subjected to one-way or two-way analysis of variance (ANOVA), and differences were considered significant when p < 0. 05. A multiple comparison test (Student-Newman-Keuls test) was then applied. Distinct letters (a, b, c) were assigned to significantly different groups, whereas a combination of the corresponding letters was attributed to those located between several groups. Statistical analysis was carried out with XLStat software (Version 2018. 4, Addinsoft, France).
No significant difference was noticed between C and S cheeses. High levels of alcohols and esters were measured in R cheeses. For example, the area of ethanol, 2-propanol, 2-butanol and ethylhexanoate were three to seven times higher in R cheeses than in control cheeses (Table  S5 ). Regarding semi-hard cheeses, no significant differences were observed in texture perception, global aroma, taste intensity and salty perception (data not shown). Salt reduction only affected bitterness, which was increasingly noticed in both R and S cheeses (medium values of the bitterness attribute were 1. 28, 1. 8 and 2. 03 for C, R and S cheeses, respectively). There was no difference in the volatile compound levels in any of the semi-hard cheeses (data not shown).
Acknowledgements This work was supported by  FranceAgriMer  ( RedSel  project, grant number  320143436 ) and received the financial support of the  CNIEL (Centre National Interprofessionnel de l'Economie Laitière). We thank  Armelle Delille  ( UMR GMPA, INRA ) for GC-MS analysis and the  INRA GeT-PlaGE Platform  (http://get. genotoul. fr)for assistance in 16S rRNA amplicon sequencing. For RNA sequencing, this work has benefitted from the facilities and expertise of the highthroughput sequencing platform of  I2BC (Centre de Recherche de Gif  -www. i2bc-saclay. fr). We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources and support, and to our undergraduate students  Maxime Guilleton  and  Marie Bogusz  for bioinformatics analysis.
e. , conflicting topologies of the trees for a same taxa) related to analytical or biological factors  [41, 49]. The analytical factors include biases associated to (i) the selection of the inference criterion, (ii) taxon sampling, and (iii) specific assumptions in the modelling of sequence evolution. Biological factors are related to (i) the biological evidence used to infer phylogeny, (ii) the presence of reticular evolution phenomena (e. g. , horizontal gene transfer, incomplete lineage sorting, gene duplication, hybridisation, recombination, among others), (iii) stochastic errors or character sampling biases related to the length of the genes, and (iv) systematic errors due to the presence of noise in the dataset  [50]. One way of addressing the biases produced by the mentioned factors is dealing with the problem using a multi objective optimisation approach  [51].
Gene Selection and Genomic Information Obtention We selected 22 genes involved in nitrogen associated phenotypes during the fermentation process, identified by QTL mapping and validated by reciprocal hemizygosity analyses (i. e. , with a demonstrated effect over the phenotype)  [26, 35, [38] [39] [40] 52]. Importantly, most of the selected genes were mapped using yeast populations where the founder strains are the DBVPG6765 (WE), DBVPG6044 (WA), YPS128 (NA) and Y12 (SA) strains  [22, 23]. The list of genes selected, including their functional information, QTL detected and bibliographic reference, is shown in Supplementary Table  S1. The genomic information for the 22 selected genes was obtained from the "1002 yeast genome project"  [21]. A VCF (Variant Calling Format) file containing the genotype information of the 22 selected genes was transformed into linear sequences utilising a custom script (please see next section).
Data Processing and Phylogenetic Inference A custom script based on the vcfR package v1. 8. 0  [54]  was used to process the 22 gene sequence variations for 1011 strains stored as a VCF file. For each gene, its sequence was compared to the reference genome and produced a multiple sequence alignment using the information of the 1011 strains, which was stored as an individual FASTA file. Then, an integrated phylogenetic tree (Total Evidence tree, TE) was inferred using the direct concatenation of the sequences for these 22 genes stored as different files, according to the total evidence paradigm  [55]. This paradigm, also called "combined analysis" or "congruence approach", combines all the data sets associated to diverse biological evidence before the inference of a phylogenetic tree, for example, by concatenating sequences of multiple genes  [56].
This is considered a good alternative when the data sets are statistically congruent (i. e. , similar evolutionary rates or non-conflicting phylogenetic trees)  [49]. The TE tree was constructed using the Neighbor-joining strategy, following the same procedure described by  [21] , by applying the bionj algorithm available in the phangorn R package v2. 5. 5  [57]. This strategy was used to infer 22 phylogenetic trees which represent the individual evolutionary history of each gene. In addition, the congruence of these individual trees was calculated by using the Congruence Among Distance Matrix (CADM) metric  [49]. This metric computes a coefficient of concordance W (0 ≤ W ≤ 1), where 0 represents a complete disagreement between the input trees and 1 means complete agreement.
Comparison of the Phylogenetic Tree Topologies A quantitative comparison between phylogenetic trees was performed. To do this, a similarity matrix of trees was built by calculating the percentage of clades in common (PC)  [58] , PC = 100 × 1 -RF (2m-6)) , where the RF is the topological Robinson-Foulds distance between trees and m is the number of strains. The RF distance corresponds to the minimum number of editions (merging or splitting nodes) necessary to transform one unrooted tree into a second tree  [59]. Then, the Multidimensional Scaling method, available in the treespace R package v1. 1. 3. 1  [60] , was used to graphically compare the tree topologies by exploring the landscape of phylogenetic trees. Two types of comparisons were performed; first, the topologies of all the trees produced were compared: the TE tree, the REF described by  [21] , the individual trees related to the 22 selected genes, and a randomly created topology (OUT).
The OUT tree was inferred to be contrasted with the REF tree, in order to assure that the differences (or similarities) between trees are produced by the feature of the data instead of noise or bias associated to the inference methods. Then, all trees generated (TE, REF, OUT and individual trees for each gene) were pruned using the phangorn R package to perform specific comparisons between (i) the subtrees (cluster/subcluster) associated to the clades defined by  [21]  and (ii) the representative strains of the clean lineages WE, WA, NA and SA  [19]. The RF metric compares trees inferred using the same set of elements (strains), thus, the tree topologies coming from the DBVPG6765 (WE) strain were not compared respect to the REF tree, since this strain was not included by  [21].
Nevertheless, other complementary approaches (e. g. , analysis of non-synonymous SNPs across the "1002 yeast genomes project" strains) could help to better support this idea. Since we replaced in tree topologies comparison the DBVPG6765 strain by the DBVPG1106 strain as representative of the Wine/European cluster, we included the DBVPG6765 strain in the TE tree using the information from the SGRP database (Figure  3A ). The DBVPG6765 strain is grouped with the Wine/European clade but seems to have a greater phylogenetic distance: this is probably due to the inclusion of the RIM15 gene within the analysis, since the DBVPG6765 strain carries a unique allele with polymorphisms not present in other strains of its clade  [26, 53]. Afterwards, we localised the representative strains in the 2D tree space plot, observing that DBVPG6765, DBVPG6044, K12 and YPS128 strains were grouped together, being separated from the DBVPG1106 strain and distant to other strains (Figure  3B  and Supplementary Figure  S1 ).
Funding: This work was funded by  Universidad de Santiago de Chile  [grant  USA1799-VRIDEI 081998SS-PAP] to E. I. K. -P. ;  CONICYT/PCI  [grant  REDI170239 ],  CONICYT/FONDECYT  [grant  11170158 ] and  Instituto Milenio iBio -Iniciativa Científica Milenio MINECON  to F. S. ;  CONICYT/FONDECYT  [grant  1150522 ] to C. M. ; and  DICYT-VRIDEI  [grant  USACH 061819IP ] to M. V. -C. and M. I. -P. Acknowledgments: We thank  Angela Contreras  for her initial work on VCF files.
A presence positive staining of α-SG was only detected in with mCh signal (Fig. 1B , bottom panels), confirming that the membrane protein revealed by IF was produced by the exogenous SGCA sequence. The R77C-α-SGmCh fusion protein was characterized after validation of the WT-α-SGmCh construct. The choice of this particular mutation was based on the number of occurrences of patients affected with this mutation that were reported in the Leiden Muscular Dystrophy database (http://www. dmd. nl) and on previous studies demonstrating the rescue of the R77C protein using pharmacological treatments  [13] [14] [15] [16] 18. Con ocal analysis of α-SG IF revealed intracellular staining in the permeabilized condition (Fig. 1C , top panels) and no detectable membrane localization of the R77C mutant protein in the non-permeabilized condition (Fig.
Pictures were acquired with a 10x objective in high-resolution camera mode and were analyzed using the colocalization bioapplication quantifying positive cells for membrane α-SG staining and mCherry tag expression. Chemical library. The chemical libraries were obtained from Prestwick Chemical and Sigma-Aldrich for the LOPAC and distributed in 384 well plate format. The Prestwick Chemical library contains 1280 FDA approved drugs and the LOPAC library contains a collection of 1280 pharmacologically active compounds. Both libraries were tested at 5 μM. Data analysis. Data analysis of the screening was performed using a customized Hiscreen application (Discngine) connected to Spotfire software (Tibco Software Inc. ). The robustness of the assay was evaluated by calculating for each plate the Zʹ factor on the percentage of mCherry/α-SG positive cells parameter as follows Zʹ = 1 -[3(SDP + SDN)/(MP -MN)] where MP and MN correspond to the means of the positive (30 nM bortezomib) and negative (0.
The sequencing reads (FASTQ files) were imported into the RNA-seq pipeline of Partek Flow software (v6 Partek Inc). Briefly, the pipeline consists of trimming of adapters and bases followed by a two-step alignment method. The sequencing reads were mapped on the Homo sapiens reference genome (hg19) with Tophat2 (v2. 0. 8). Then, the unaligned reads were realigned using Bowtie2 (v2. 1. 0). The resulting aligned reads from both the Tophat and the Bowtie2 steps were combined into one data node and annotated on RefSeq Release 84 using the Partek E/M algorithm. Gene abundances were quantified and expression levels were normalized on reads per million values (RPM). Differentially expressed genes were identified using Partek Gene Specific Analysis (GSA) when their FDR (False discovery rate) are lower than 0.
01, the fold change are superior to 1. 5 or inferior to -1. 5 and the expression value in all sample are superior to 100 RPM. Biological interpretations of the filtered list of differentially expressed genes were performed using Enrichr software http://amp. pharm. mssm. edu/Enrichr/  36, 37. The heat map was generated by average linkage hierarchical clustering of the differentially expressed genes between THSP 3 µM and DMSO 0. 1% (cutoff FDR ≤ 0. 01 RPM ≥ 100), using Euclidean distance as the distance metric. Scaled expression values are color-coded according to the legend.
Acknowledgements I-Stem  and Genethon are part of the  Biotherapies Institute for Rare Diseases (BIRD). Both institutes are funded with the continuous support of  Association Française contre les Myopathies  ( AFM-Telethon ),  INSERM  and  Genopole. This work was supported by grants from the  National Infrastructure Engineering for Pluripotent and differentiated Stem cells INGESTEM  ( Investissement d' Avenir - ANR-11-INBS-0009 ), the  Strategic Research Initiatives (IRS)  of  Université Paris-Saclay BioTherAlliance, the laboratoire d'Excellence Revive (Investissement d' Avenir ;  ANR-10-LABX-73 ), the  domaine d'intéret majeur (DIM) Biothérapies  and the  Translational Research Infrastructure for Biotherapies in Neurosciences NeurATRIS  ( Investissement d' Avenir - ANR-11-INBS-0011 ). SH was supported by a  PhD fellowship  from the  University of Paris-Saclay.
(D) Functional enrichment analysis of up-regulated genes in fibroblasts overexpressing R77C-α-SGmCh after 24 h of treatment with 30 nM bortezomib, 3 µM thiostrepton or 1 µM MG132 vs 0. 1% DMSO. Analysis has been performed with the Panther pathway database. Values are expressed as combined scores and p-values are indicated; thiostrepton = THSP; bortezomib = BTZ.
Acknowledgements We thank  Véronique Pautot  and  Patrick Laufs  for critical comments. NB is funded by an allocation from  École Normale Supérieure de Lyon. NA is supported by the  Agence Nationale de la Recherche  grant  LEAFNET  ( ANR-12-PDOC-0003 ). The IJPB benefits from the support of the  Labex Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ).
Genetic and genomic characterization Real-time and standard PCR were performed to screen for the presence of carbapenem resistance genes, including bla OXA-48 , bla KPC , bla NDM and bla VIM  [12]. Whole genome sequencing of the CP strains was performed using the MinION (Oxford Nanopore Technologies Inc. , UK) and the MiSeq (Illumina Inc. , San Diego, CA, USA) technologies in order to determine the carbapenemase genes variants, the genetic environment, and the genetic support of these genes. The long-read sequencing data generated by Nanopore and short-read data produced by Illumina sequencing were assembled using SPAdes genome assembler  [13]. ARG-ANNOT database available on the ABRicate pipeline and Prokka software were used to identify the antibiotic resistances genes and to annotate genomes, respectively  [14, 15].
Clonal relationship SNPs analysis (available at https://cge. cbs. dtu. dk/services/ CSIPhylogeny/) was conducted to study the genomic difference between the three strains and to determine the possible clonal relationship. In order to determine the sequence type (ST) of isolated strains, Multi Locus Sequence Typing (MLST) analysis was performed in silico using the MLST database (available at https://cge. cbs. dtu. dk/services/MLST/).
Conjugation experiments and plasmid analysis Conjugation was conducted on the three C. freundii isolates using azide-resistant Escherichia coli J53 as a recipient, as previously described  [16]. The transconjugants were selected on Luria Bertani (LB) agar (Beckton Dickinson, Le Pont de Claix, France) supplemented with sodium azide (120 μg/ml) and Ertapenem (2 μg/ml). Plasmids analysis was performed in silico. Plasmid incompatibility type was identified using PlasmidFinder database (available at https://cge. cbs. dtu. dk/services/PlasmidFinder/) and Jspecies Web Server was used to calculate the extent of identity between the plasmids  [17].
Funding This work was supported by the  ANR   FloraStopInfectMRE project ,  InfecERA-ERANET-Acciones complementarias  ( PCIN-2015-094 ) to CU and by  IHU Méditerranée Infection, Marseille, France  and by the  French Government  under the «Investissements d'avenir» (Investments for the Future) proGram managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Provence Alpes Côte d'Azur and European funding FEDER PRIMI.
We thus hypothesized that SCFAs concentration in the supernatants of these bacteria could explain the activation of the AhR pathway as previously reported  23. We therefore quantified the concentration of SCFAs in some bacterial supernatants (Supplementary Table  S1  and Fig. 2A ). A principal component analysis (PCA) of the complete dataset (SCFA concentrations and AhR activity) revealed that AhR activity and butyrate concentration were major contributors of total variability (Fig. 2B ). Further Spearman correlation analysis showed a strong positive correlation between AhR activation and the production of butyrate quantified in commensal bacteria supernatants (rho = 0. 4966, Fig. 2C ). To reinforce experimentally the observed correlations, we tested pure SCFAs at different physiological concentrations found in the intestine (ranging from 0.
We first generated a homology model of human AhR ligand binding domain (AhR PAS-B) using HIF-2α PAS-B domain as template similarly to Bisson et al. 45, 46. The AhR PAS-B homology domain was obtained with RaptorX modelling software and the binding cavity was defined using HOLLOW and Carver softwares (Fig. 8A ). Then, the model was minimized to avoid clashes between side chains. From the best scoring solutions cluster using HADDOCK, we observed different orientations of the butyrate inside the AhR PAS-B cavity. Based on four different docking experiments, two main orientations were found for butyrate in the pocket of human AhR PAS-B domain. Interactions between butyrate and AhR are dominated by polar contacts with the side chains of Q383 and S365, for the first orientation (Fig. 8B ) and with side chains of Q383 and H291, for the second one (Fig.
Lamp1 and GAPDH were used as purification controls for the cytoplasmic proteins. Modelling of butyrate binding to AhR. For modelling the structure of the complex between AhR and butyrate or propionate, HADDOCK software was used  65, 66. HADDOCK is a highly successful modelling approach that makes use of structural knowledge when available to drive the docking procedure. In this case the crystal structure of the heterodimeric HIF-2α:ARNT complex (PDB code 4ZP4)  67  was used as template in order to dock butyrate to human AhR similarly to Bisson et al. , using RaptorX  45, 46, 68, 69. The cavity of AhR PAS-B was defined by CARVER and HOLLOW  70, 71. The figures were generated with PyMOL Molecular Graphic System, version 1. 8 Schrödinger, LLC.
Statistical analysis. Presented results are representative of at least three independent experiments. The PCA analysis and Spearman correlation were performed using R and RStudio software. Graphics were produced using Prism GraphPad software. The data distribution was tested using D' Agostino-Person omnibus normality test. Normally distributed data was checked using two-sided t test, otherwise, non parametric Wilcoxon signed-rank or Mann-Whitney tests were performed according to the data set. In all tests, ns: P > 0. 05, *P ≤ 0. 05, **P ≤ 0. 01, ***P ≤ 0. 001, ****P < 0. 0001.
Acknowledgements The authors are grateful to  Dr Marion Espeli  ( INSERM UMR-S996 ) and to the member of the team Functionality of the  Intestinal Ecosystem (Micalis-INRA Jouy-en-Josas ) for helpful discussions. authors are grateful to  Agnès David  and  Catherine Michel  for the quantification of SCFA in bacterial cultures. This work was supported by the  Institut National de la Recherche Agronomique (INRA) , by grants funded by  EU-FP7   METACARDIS  ( HEALTH-F4-2012-305312 ).
TRP channels therefore serve as communication hubs for the cells that express them, and their regulation is crucial for their function. Several partner proteins have been identified for TRPs over the last decades, among which some are common for different members of this channel family. Currently 700 of the protein-protein interactions of TRP channels are gathered and visualized in network database  [48]. Among these interactions, we will focus here on the relatively recent field of nongenomic action of steroids on TRP channels. In this regard we will describe how protein-protein interaction defines the hormonal action on TRP channel activity via the Sigma-1 (Sig-1R) and the androgen receptor as depicted in Figure  2. Molecules targeting these interactions are represented by triangles: red triangles indicate an inhibitory effect, green triangles an activating effect.
TRP channels therefore serve as communication hubs for the cells that express them, and their regulation is crucial for their function. Several partner proteins have been identified for TRPs over the last decades, among which some are common for different members of this channel family. Currently 700 of the protein-protein interactions of TRP channels are gathered and visualized in network database  [48]. Among these interactions, we will focus here on the relatively recent field of nongenomic action of steroids on TRP channels. In this regard we will describe how protein-protein interaction defines the hormonal action on TRP channel activity via the Sigma-1 (Sig-1R) and the androgen receptor as depicted in Figure  2. The Sig-1R is a protein mainly localized in the endoplasmic reticulum, where it functions as a ligand-operated chaperone  [49].