dataesr/scientific-paragraphs-categorization huggingface dataset:
montana root bark, which had been phytochemically investigated by Leboeuf and co-workers in our laboratory in the 1980s  14, 37  and was further analyzed in positive polarity by UPLC-HRMS². To capture the chemical diversity of this extract, the UPLC-HRMS² data were processed using the feature-based molecular networking workflow  38  , and subsequently dereplicated against the IQAMDB, hosted by GNPS  7. This pipeline resulted in dereplicating 27 unique and structurally-diverse compounds (Table  1 ). Notably, all these hits were exclusive to the IQAMDB, indicating the limited number of former Annonaceous compounds references in GNPS spectral libraries prior to the upload of the IQAMDB. Among those hits, 7 were previously reported from this plant source: Annomontine, methoxyannomontine  14  , argentinine  39  , atherosperminine  37  , coreximine  37  , liriodenine  39  , and reticuline  37.
Among all the hits, only nandigerine was hitherto unknown from Annonaceous source. Even though this aporphine alkaloid is mainly related to Hernandiaceae  12  and Lauraceae 51 , one of its derivatives, N-methylnandigerine N-oxide, had been reported from the Annonaceous Polyalthia longifolia  52. Metadata. The MS/MS spectra of the IQAMDB library are associated to a variety of details including: LC-MS/ MS acquisition parameters, RT (see Supporting Information), instrument details, smiles and InChi codes, structures, and chemical formula. These metadata are available on the GNPS website.
acknowledgements The authors would like to dedicate this article to  Pr. Michel Leboeuf , whose lifetime work was the basis for the chemical library of isoquinoline alkaloids digitized herein, and who supported the current project, and to the memory of  Prof. André Cavé. The authors are also indebted to former researchers of the  University of Angers , mostly  Prof. Jean Bruneton  and  Prof. Hélène Guinaudeau , who contributed to the isolation of many other compounds comprised in this database.
Locomotor activity Locomotor activity was measured with the open field test. Rats were individually placed in the center of a squared arena (70 ×70 cm), surrounded by wall (45 cm) of opaque plastic without any prior habituation and left alone for 5 min. Light intensity at the center of the arena was set to 30 lux. A camera was mounted directly above the field to record the animal's behavior. Each recording was analyzed with a specific software (EthoVision®, France). The total distance moved and the mean speed of animals were measured. The arena was cleaned after each assay.
5 min The mass spectrometry analysis was performed using a Thermo Exactive benchtop Orbitrap® instrument, driven by Xcalibur® software (version 2. 2 SP1). We used a heated electrospray ionization source (HESI II). The desolvation temperature was set at 350 • C, the curtain gas rate at 40 AU (arbitrary units), auxiliary gas flow at 8 AU, the ionization voltage at 3 kV and the capillary temperature at 250 • C. Acquisition was performed in positive "fullscan" mode over a wide range of masses (m/ z): 200-800 amu at a resolution of 50,000 (FWHM) and a range of masses (m/z) 60-800 amu at a resolution of 25,000 (FWHM) for ion quantification and ion confirmation (HCD fragmentation at 30 eV) respectively. Mass extracts were taken at 10 ppm for donepezil (m/ z = 380. 22202 for quantification and m/z = 91.
Acknowledgments The authors thank  Dr. Bruno Pereira  from the  Hospital University of Clermont-Ferrand  for his wise advice for data analysis and  Sabine Beaumel  for her excellent technical assistance. Dr. David Balayssac ,  Cristelle Dalbos  and  Dr. Jérôme Busserolles  participate to the  NEURO-DERISK consortium  ( IMI2 -Call 13 , Grant agreement number:  821528 ).
Faecalibacterium prausnitzii is abundant in the healthy human intestinal microbiota, and the absence or scarcity of this bacterium has been linked with inflammatory diseases and metabolic disorders. F. prausnitzii thus shows promise as a next-generation probiotic for use in restoring the balance of the gut microbial flora and, due to its strong anti-inflammatory properties, for the treatment of certain pathological conditions. However, very little information is available about gene function and regulation in this species. Here, we utilized a systems biology approach -weighted gene co-expression network analysis (WGCNA) -to analyze gene expression in three publicly available RNAseq datasets from F. prausnitzii strain A2-165, all obtained in different laboratory conditions. The co-expression network was then subdivided into 24 co-expression gene modules.
In the current study, gene expression data were obtained for F. prausnitzii A2-165 from three public RNAseq datasets. Co-expression networks were analyzed using WGCNA and genes were clustered into modules with similar expression patterns. For each module, we investigated enrichment in biological functions. The structure of the network was also analyzed to identify hub and bottleneck genes; of the 25 we identified, the majority were located in the region of six mobile genetic elements that could promote intracellular or intercellular DNA mobility. This study represents the first time that a gene expression network has been constructed for F. prausnitzii. In addition, an R-Shiny application is available online to make the data and the analyses accessible and usable to a wide audience (https://feaprau.
Data sources In the Lebas dataset  [22] , the authors explored the effects on the transcriptomic profile of F. prausnitzii A2-165 of treatment with supernatant from Lactococcus lactis subsp. lactis CNCM I-1631, Lactococcus lactis subsp. cremoris CNCM I-3558, Lactobacillus paracasei CNCM I-3689, and Streptococcus thermophilus CNCM I-3862. The raw RNA-seq fastq files are available at ArrayExpress under project E-MTAB-9387. In the D'hoe dataset, the authors sequenced RNA from F. prausnitzii A2-165 monocultures at three different time points  [21]. The RNA-seq results were deposited in the Short Read Archive under the study identifier SRP136465. In the Kang dataset  [23] , the authors compared the transcriptomic profiles of F. prausnitzii A2-165 cultured using different carbohydrates (galactose, fructose, glucose, Nacetylglucosamine) as the sole carbon source.
Data processing The workflow for this study is presented in S1 Fig. A read count table was obtained using the raw sequencing reads. After trimming reads with fastp v. 0. 20. 0 (default parameters,  [34] ), fastqformatted reads were aligned to the genome of F. prausnitzii strain A2-165 (Genome Assembly ASM273414v1) using BWA v. 0. 7. 17  [35] , allowing a single mismatch in the read. Then, samformatted alignments were sorted and converted to bam output files using SAMtools v. 1. 10 [36]. The number of reads per transcript from each sample was counted using HTSeqCount v. 0. 12. 4  [37]  and GFF-formatted gene annotations downloaded from NCBI. We checked the distribution of raw counts and performed principal component analysis in each dataset (S2 Fig). Gene expression values were normalized using the DEseq2 package v.
1. 34. 0, in R  [38]. The count table, containing 2,950 genes (S4 Table ), was filtered to eliminate non-expressed genes. The resulting final dataset contained 2,902 genes and was further processed with WGCNA  [39]. The quality of the expression matrix was evaluated by hierarchical clustering based on the distance between different samples, measured using Spearman's correlation. No outliers were detected (S3 Fig).
Data analysis Differential expression analysis was performed with the DEseq2 package v. 1. 34. 0. We applied cutoffs of P-adj (corrected FDR) < 0. 05 and absolute log2FoldChange (log2FC) > 1. 5 for the detection of genes that were differentially expressed between the conditions considered and the negative control. Partial least squares-discriminant analysis (PLS-DA) implemented in the mixOmics package of R (v. 6. 18. 1)  [40]  was used.
Functional analysis and networks After extracting the list of genes present in each module, we used eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments  [44]. Functional enrichment analysis was performed in each module with STRING (version 9. 0; https://stringdb. org/) in March 2022. Enrichments with strength > 0. 5 were selected. The resulting interaction networks contained direct (physical) and indirect (functional) interactions, derived from numerous sources including experimental repositories and computational prediction methods. Nodes with a high degree of connectivity are called "hubs"; they interact with several other genes and may thus play a central role in the network. Genes with a degree of connectivity ≥ 210 were defined as hub genes in the present study.
Instead, "betweenness" measures the total number of shortest nonredundant paths passing through a given node or edge. By definition, most of the shortest paths in a network go through nodes with a high degree of betweenness. These nodes, named "bottlenecks", become central points controlling the communication among other nodes in the network. In this study, genes with betweenness ≥ 300 were selected as bottleneck genes. Network visualization was performed with the igrah package v1. 2. 8.
Exploration of transcriptomic data with R-Shiny We developed an R-Shiny application to facilitate deeper exploration of the transcriptomic data from each of the three studies. For example, using this approach on the Lebas dataset, we observed that treatment of F. prausnitzii with supernatant from L. lactis subsp. lactis generated more transcriptional downregulation than upregulation relative to the control (see our R-Shiny application), confirming the findings of the original study  [22]. In the Kang dataset, a comparison between bacteria raised on glucose or fructose as the sole carbon source highlighted that the most upregulated gene was CG447_08360, which encodes a putative PTS glucose transporter subunit IIBC (Fig  2A  and B ). This gene was also highly upregulated in the presence of galactose.
As the transcription PTS genes is usually regulated in response to substrate availability  [48] , this suggests that CG447_08360 encodes a galactose/glucose transporter. Finally, the D'hoe dataset provided the opportunity to track F. prausnitzii gene expression across three growth time-points. In the R-Shiny application, it was possible to compare, for example, expression at time 15h with that at time 3h and to highlight the most upregulated and downregulated genes between these time-points (Fig  2C.
were preprocessed using a similar workflow (S1 Fig). Details on these experiments and the samples used are presented in Table 1. To investigate differences in the transcriptomic profiles among the different conditions analyzed, we conducted a PLS-DA. This revealed a clear separation along the gene expression datasets from the different publications along the first two principal components, which explained 40% and 9% of the observed covariance (Fig 1A). While component 1 was sufficient to distinguish the Lebas dataset, the second component was necessary for separation of the D'hoe and Kang datasets. In total, we identified 50 genes that could be used to discriminate between the Lebas and Kang samples (component 1) and 50 genes that could be used to distinguish the D'hoe and Kang datasets (component 2) (Fig 1B).
Lebas, D'hoe and Kang) by calculating values of Module Significance (MS) for module-trait correlations (Fig 4A). The expression levels for each module across the 24 samples were then visualized (Fig 4B). Several large modules represent expression changes that clearly delineate between Lebas on one side and D'hoe and Kang onthe other. Indeed, the blue, cyan, pink, brown, and purple modules (Pearson correlation coefficient ≥ 0. 75) correspond to genes that were upregulated in the D'hoe and Kang datasets, while the green, salmon, red, and yellow modules (Pearson correlation coefficient ≤ -0. 75) represent genes that were upregulated in the Lebas dataset. This suggests that the expression patterns of the genes in these modules reflect the ways in which the growth of F. prausnitzii differed among the conditions used in the different studies.
ERR4363144Fig 1. 1 Fig 1. Partial least-square discriminant analysis of RNA-seq data. (A) PLS-DA plot of RNA-seq data showing clear transcriptome-based discrimination among the three publication datasets. Each point represents the transcriptome signature of one sample, with ellipses representing 95% confidence level. (B) Genes with high discriminatory ability were identified from PLS-DA. (C) Histogram showing the distribution of COG categories (Clusters of Orthologous Genes) associated with the genes with the highest discriminatory power in the two components generated by PLS-DA. See S5Table for the full list of.
Imaging and Quantification: Representative images were captured from a one airy unit pinhole setting using a LSM710 or LSM800 laser-scanning confocal microscope (Carl Zeiss, Jena, Germany) with ZEN2012 software (Zeiss). Emission spectra for each dye were limited as follows: DAPI (<480 nm), Fluorescein (505-540 nm), Cy3 (560-610 nm), PI (590-680 nm). In selected cases (as indicated in the figure legend), projection images were merged from orthogonal scanning (z-stack) using step-intervals of 1. 0 μm. Images were processed using ZEN2012 software. For quantification of α2δ1, ATX, LPA1, GFAP, P2X3 and Galanin whole DRGs were tile scanned from 3 to 5 randomly selected sections with a minimum of 50 μm in between sections. Pixel intensity and the % area that was immunoreactive were quantified using ImageJ.
Conditioned cell culture media analysis SGC cultures were stimulated for 24 h with 10 μM 18:1 LPA and conditioned culture media was then collected, flash frozen and stored at -80 • C until use. CXCL1, TNF and IL-1β levels in the media were evaluated using an MSD Proinflammatory Panel 1 kit (Mesoscale Discovery Systems, catalogue # K15048D). The assay was performed according to the manufacturer's instructions. Cytokine levels of undiluted media samples were measured in technical duplicates. The plate was read using a MSD QuickPlex SQ 120 (Mesoscale Discovery Systems) and the data were analyzed in Mesoscale Workbench (Mesoscale Discovery Systems).
Statistics Mechanical and heat hypersensitivity behavioral data, and quantification data for immunohistochemistry, qPCR and western blotting is presented as mean ± standard error of mean (SEM) and cold hypersensitivity data are presented as the median with the interquartile range. To compare mechanical hypersensitivity a two-way ANOVA for repeated measures with the Geisser-Greenhouse correction in order to not assume sphericity and Tukey's post-hoc test was used. Heat and acetone cold sensitivity were assessed by a one-way ANOVA with Tukey's post hoc test and SDs were assumed to be equal. Immunohistochemistry and gene expressed data were analyzed by a one-way ANOVA with Tukey's post hoc test and SDs were assumed to be equal. Arthritis clinical scores were assessed at the group level with Kruskal-Wallis test followed by Dunn's post hoc test.
7C-F ). Critically, LPA 1 immunoreactivity was absent in Lpar1-/-mice (Fig. 7C ). Thus, both in situ hybridization and immunofluorescence staining profiles suggest that LPA 1 is expressed by satellite glial cells (SGCs) in the DRG. To determine the cellular localization of LPA 1 , we examined if LPA 1 colocalizes with glutamine synthase (GS) and/or Iba1, markers for SGCs and DRG resident macrophages, respectively. LPA 1 immunoreactive cells co-localized with GS (Fig. 7G ) but not Iba1 immunoreactivity (Fig. 7H ). Taken together, these data demonstrate that SGCs express LPA 1 in the DRG.
Thus, the main conclusion from this study is that LPA signalling regulates CAIA-induced pain-like behavior by LPA activation of SGCs via LPA 1. A humanized antibody reported to be against LPA was well tolerated in a phase 1a clinical trial (NCT02341508) completed in 2016, but further development was not pursued. Regardless, this suggests that LPA signalling could be further developed as a therapeutic target for chronic pain. As persistent pain remains a problem for RA patients, new therapeutic strategies are needed. Interfering with LPA signalling or SGC activation could provide new strategies to manage RA-associated pain that is refractory to inflammation targeting therapies as well as pain in other autoimmune diseases.
Acknowledgements Funding: This work was supported by the  Swedish Research Council  (grant number  542-2013-8373 ) and the  Knut and Alice Wallenberg Foundation  (grant number  2012. 0206  and  2018. 0161 ), European Research Council (ERC)  under the  Horizon 2020 research and innovation programme  (grant agreement number  866075 ),  Ragnar Söderberg Foundation  (grant number  M138/12 ) and a donation from the  Lundblad family (CIS) ; the Konung Gustaf V:s 80-year foundation, the  International Association for the Study of Pain John J. Bonica Fellowship  and a  Canadian Institutes of Health Research  fellowship (grant number  MFE-171299 ) (EK);  Swedish Brain Foundation (JS) ;  Region Uppsala  ( ALF-grant  and  R&D  funds), strategic funds  Uppsala University and Magnus Bergvall Foundation (KK) ; and a  United States Department of Defence  (grant number  W81XWH-17-1-0455  (to JC).
The more similar the MS/MS spectra are, the closer to one the cosine score is, and the stronger they are connected in a cluster. MN building is performed using online platforms such as GNPS or MetGem software  [7, 10]. The resulting MN considerably facilitates and increases the reliability of compound identification based on the use of standards and online database querying  [11]. LC-HRMS/MS untargeted metabolomic analyses using MNs have been successfully applied in clinical toxicology to identify toxicants and their metabolites, including new psychoactive substances (NPS), belonging to the xenometabolome  [12] [13] [14] [15] [16] [17]. Indeed, in clinical toxicology laboratories, the identification of exogenous metabolites is based on the comparison of the MS data to a list of known compounds using a targeted approach.
However, this approach limits the identification of metabolites to those contained in the used database and is therefore not suitable for the identification of new metabolites. In contrast, untargeted approaches with dedicated data processing allows for the identification of the involved toxicants and their metabolites in a comprehensive manner, especially in poisoned patients  [14]. Identification of previously undescribed xenometabolites of commonly used drugs has been successfully performed using MNs  [18]. Using this approach, new metabolites of quetiapine have been identified both from in vitro and human samples  [18]. The usefulness of MNs in the identification of toxicants and their metabolites, namely the xenometabolome, was thus recently questioned  [14, 18]. Nevertheless, the reliability of MNs in the identification of the metabolites of a given xenobiotic as exhaustively as possible remains to be established.
Molecular Network Built from Data Acquired Using LC-HRMS/MS in Positive Ion Mode To identify additional tramadol metabolites in an untargeted manner, LC-HRMS/MS data acquired in positive ion mode were used to generate a MN. An overview of the MN is displayed in Figure  3. On this MN, the node corresponding to the MS/MS spectra of the [M + H] + ion of tramadol was located and allowed to delimit the cluster (C1) of structurally related tramadol compounds, i. e. , its metabolites. (Figure  3B ). The fact that the node corresponding to M1 was included in this cluster is indicative that networking is based on the product ion at m/z 58. 0660 and was supported by the lack of a node corresponding to M2, M4, and M5 (Figure  3B ). The systematic annotation of the different nodes of the tramadol cluster in accordance to mass spectrometry fragmentation rules associated to tramadol, allowed for the identification of 15 phase I/II metabolites.
MS/MS spectra displayed other product ions corresponding to a subsequent fragmentation of trihydroxydihydropyrane carboxylate ion, supporting the presence of the glucuronide. Besides trihydroxydihydropyrane carboxylate, the use of SIRIUS software allowed for the identification of the main product ions characteristic of the glucuronide radical: dihydrofurane carboxylate, oxobutanoate, hydroxyacrylate, and cycloprane carboxylate, detected at m/z 113. 0232, m/z 99. 0075, m/z 87. 0076, and m/z 85. 0283, respectively. Based on the MS/MS spectra obtained using negative ion mode and displaying these product ions, a cluster (C2) was generated. C2 included all glucurono-conjugated tramadol metabolites (Figure  6 ). However, based on glucuronide radical fragmentation, C2 also included glucurono-conjugated metabolites of other xenobiotics.
This approach is also useful to improve the knowledge of metabolism of a given molecule. Finally, it is also helpful for the determination of chemical structures such as those of new psychoactive substances, allowing for the characterization of the corresponding in vivo metabolites, a prior step in determining pharmacokinetic properties. The use of MNs is thus a valuable approach facilitating a comprehensive systematic identification without a priori knowledge of such unknown compounds and their metabolites. The identification of metabolites using MNs in untargeted metabolomic dataset may be performed through different approaches. Identification may be achieved by database querying, a highly efficient method for the dereplication of described metabolites, allowing for the anchorage of the MN prior to the propagation of annotation.
Due to the non-interventional design of the study, written consent was waived. Since the patient was unconscious during her management in the intensive care unit before her death, her next of kin was fully informed why the research was being conducted and how the data will be used. All of the urine and plasma samples were collected in the toxicological critical care of the university hospital. The case involved a Caucasian 32-year-old women who had ingested 9000 mg sustained-release tramadol  [21]. The patient was found with hypoglycemia, bradypnea, and hypotension  [21]. The electroencephalogram displayed a non-reactive encephalopathy. Despite optimal management in the intensive care unit, brain death occurred on day 12. Regarding the plasma and the urine samples analyzed in this study, they were collected at day 2, day 3, and day 11 following the tramadol ingestion.
0 kV for positive ion mode and 2. 2 kV for negative ion mode; sheath gas and auxiliary gas were 35 and 15 arbitrary units, respectively; S-lens RF level 60; and vaporizer temperature and capillary temperature were both set at 320 °C. Nitrogen was used for spray stabilization, for collision induced dissociation experiments in the HCD cell, and as the damping gas in the C-trap. AGC target was fixed to 1E5 for MS/MS experiments. Transient time was fixed to 120 and 50 ms for full MS and MS/MS scans, respectively. The mass spectrometer was calibrated in the positive and negative modes prior to analyses. Data were acquired in a data-dependent MS2 (ddMS2) mode. In this mode, a full-scan acquisition was performed at a resolution of 70,000 between m/z 100 to 1000, followed by the acquisition of MS/MS spectra of the three most intense ions, at a resolution of 17,500 with an isolation window of 2 Th.
The acquisition of MS and MS/MS data was managed by Thermo Scientific TraceFinder ® software. Data processing parameters. Raw data files acquired in positive and negative ion modes were converted into open source mzXML files under MSConvert 3. 0™. The data processing was performed under MZmine 2. 53  [29]. Briefly, MS and MS/MS spectral data were extracted using a mass detection noise level set at 1E5 and 5E3, respectively. Extracted ion chromatograms were then systematically built using the ADAP algorithms (minimum group size of 4 scans, a group intensity threshold of 500,000, and an m/z tolerance of 10 ppm)  [30]. The ADAP wavelets chromatogram deconvolution algorithm was then applied and set at the following parameters: signal to noise ratio = 0, coefficient/area ratio = 20, peak duration range = 0.