dataesr/scientific-paragraphs-categorization huggingface dataset:
Drug rechallenge in DILI cases is however potentially dangerous (2,75) and associated with risk of death or requirement of liver transplant  (2, 15, 76). Despite the known risks, positive rechallenge can be considered if the patient has shown important benefits from the drug and other options are not available  (76). The definition of positive rechallenge is currently defined as alanine transaminase (ALT) levels >3-5 upper limits of normal (ULN) after re-administration of the suspect drug, in a patient with normal baseline ALT  (75). Information about positive or negative rechallenges in DI-ALH is very limited and restricted to individual cases. Therefore, information on rechallenge is lacking in DI-ALH and additional data needed from controlled clinical trials, prospective registries, and large health care databases.
The present study has been supported by grants from Instituto de Salud Carlos III (ISCIII) cofounded by Fondo Europeo de Desarrollo Regional-FEDER (contract numbers: PI21/01248; PI19/00883; Clinical research platform of the Carlos III Health Institute: PT20/00127). SCReN and CIBERehd are funded by ISCIII. This publication is based upon work from COST Action "CA17112-Prospective European Drug-Induced Liver Injury Network" supported by COST (European Cooperation in Science and Technology); www. cost. eu. The following authors are members of CA17112: RJA, ESB, MIL, GPA, GK-U, MR-D, MG-C, EA, AL, SW,AG,HD,MCL,GS,EDM J o u r n a l P r e -p r o o f Foreword The European Cooperation in Science & Technology (COST Action CA17112), 'Prospective European Drug-Induced Liver Injury Network' was established in 2018; and during a joint meeting with the International AIH Group (IAIHG) at the International Liver Congress, the annual meeting of the European Association for the Study of the Liver (EASL), it was decided to hold an International Expert Conference on Drug-Induced Autoimmune Hepatitis.
Financial support statement : statement The present study has been supported by grants from Instituto de Salud Carlos III (ISCIII) cofounded by Fondo Europeo de Desarrollo Regional-FEDER (contract numbers: PI21/01248; PI19/00883; Clinical research platform of the Carlos III Health Institute: PT20/00127). SCReN and CIBERehd are funded by ISCIII. This publication is based upon work from COST Action "CA17112-Prospective European Drug-Induced Liver Injury Network" supported by COST (European Cooperation in Science and Technology); www. cost. eu. The following authors are members of CA17112: RJA, ESB, MIL, GPA, GK-U, MR-D, MG-C, EA, AL, SW,AG,HD,MCL,GS,EDM J o u r n a l P r e -p r o o f Foreword The European Cooperation in Science & Technology (COST Action CA17112), 'Prospective European Drug-Induced Liver Injury Network' was established in 2018; and during a joint meeting with the International AIH Group (IAIHG) at the International Liver Congress, the annual meeting of the European Association for the Study of the Liver (EASL), it was decided to hold an International Expert Conference on Drug-Induced Autoimmune Hepatitis.
Gene Expression Gene expression profiles were performed at the GeT-TRiX facility (GénoToul, Génopole Toulouse Midi-Pyrénées) using Agilent Sureprint G3 Mouse GE v2 microarrays (8x60K, design 074809) following the manufacturer's instructions. Data acquisition and statistical analysis were performed as previously described  17. A correction for multiple testing was applied using Benjamini-Hochberg procedure to control the False Discovery Rate (FDR). Probes with fold change (FC) ≥ 1. 5 and FDR ≤ 0. 05 were considered to be differentially expressed between conditions. The enrichment of Gene Ontology (GO) Biological Processes was evaluated using Metascape  18. Data are available in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE228554. For real-time quantitative polymerase chain reaction (RT-qPCR), 2 µg RNA samples were reverse-transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA).
Multi-omics analyses Bidirectional correlations between plasma metabolites and hepatic transcripts were investigated using N-integration discriminant analysis with DIABLO, an algorithm that aims to identify a highly correlated multi-omics signature discriminating several experimental groups using the R package Mixomics v6. 10. 9  19. We used 2 components in the models, and for the estimation of model parameters, the cross-validation procedure (CV) method was used. For the correlation networks, only correlations with a Spearman's rank correlation coefficient > 0. 96 were plotted.
Trimethylamine-N-oxide targeted LC-MS/MS measurement For TMAO extraction method is described in details in additional methods. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 18, 2023. ; https://doi. org/10. 1101/2023. 04. 17. 537169 doi: bioRxiv preprint 10 7. Publicly available datasets and databases Four independent gene expression datasets were found on the Gene Expression Omnibus data repository accessed in September 2019. GSE149229 compared hepatic transcriptome of humanized CAR mice (hCAR) fed a control diet or a phenobarbital (PB)-enriched diet. GSE98666 compared hepatic transcriptome of hCAR mice treated with CO, TCPOBOP or 6-(  4 -Chlorophenyl)imidazo[2,1-b][1,3]thiazole-5-carbaldehyde- O-(3,4-dichlorobenzyl)oxime (CITCO).
GSE149228 and GSE57056 compared hepatic transcriptome of chimeric mice with a majority of human hepatocytes fed a control diet or a PB-enriched diet. Values for Fmo3 gene expression were calculated using the GEO2R tool for microarray data and using GREIN  20  for RNA sequencing data. A list of drugs inducing sex-biased drug-induced liver injury was derived from the study conducted by George et al. 21. For each drug, interaction with CYP2B6 was searched using DrugBank 22 accessed in February 2023.
Other statistical analyses All univariate statistical analyses were performed using GraphPad Prism version 9 (GraphPad Software, San Diego, CA). Outliers were identified using the ROUT method. The Kolmogorov-Smirnov test of normality was applied to all data. Two-way ANOVA was performed within each genotype using sex (male or female) and treatment (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted April 18, 2023. ; https://doi. org/10. 1101/2023. 04. 17. 537169 doi: bioRxiv preprint 11 (CO or TCPOBOP) as fitting factors for the models and P-value representing interactions are reported. If Psex*treatment was significant, Sidak's multiple comparisons test was used as post-hoc test to determine which group differed from its appropriate control, otherwise P-values representing the main effects from the ANOVA model (namely sex or treatment) were reported.
3b ). In Car +/+ females, cholesterol and lipid signals were changed in a similar manner than in males (Fig. 3c ). Putative assignment of these differential peaks revealed that the decreased cholesterol peak mainly reflected HDL-cholesterol, decreased lipid peaks mainly belonged to LDL and increased lipids belonged to very low-density lipoproteins (VLDL). Area under the curve for selected HDL-cholesterol, lipid-LDL and lipid-VLDL signals further illustrated this CAR-dependent impact of TCPOBOP treatment on circulating lipoproteins in both sexes (Fig. 3d ). The observed decrease in total plasma-, LDL-and HDL-cholesterol were confirmed through additional biochemical assays (Additional file 10). Plasma metabolomics and hepatic transcriptomic data statistical integration revealed a strong correlation between plasma metabolites and liver transcripts (R 2 =0.
H-NMR spectra was integrated for glucose, lactate, cholesterol, lipid CH3(CH2)n (mainly in LDL), lipid CH3CH2CH2C= (mainly in VLDL) signals. E) Multi-omic integrative analysis performed on plasma metabolomic and hepatic transcriptomic datasets. F) Correlation network between hepatic transcripts and plasma metabolites. G) Foldchange (TCPOBOP-vs. CO-treated Car.
5d ). Next, we analyzed Fmo3 hepatic expression in several publicly available gene expression datasets. In the first experiment, Car -/-mice were knocked-in with human CAR coding sequence  26  and were fed diets containing 0 (CTRL) or 1000ppm phenobarbital (PB), an indirect activator of both mCAR and hCAR  27. We found that PB-fed hCAR mice had significantly lower expression of hepatic Fmo3 mRNA compared to control mice (Fig. 5e ). The second study compared wildtype (WT) mice treated with TCPOBOP vs. CO-treated mice and hCAR mice treated with CITCO (a specific agonist of hCAR) vs. CO-treated hCAR mice  28. Both TCPOBOP and CITCO-treated mice had significantly lower Fmo3 hepatic mRNA compared to their relative controls (Fig. 5f ). Finally, the last two studies were conducted in chimeric mice with human hepatocytes treated with PB  27, 29.
Again, we found a significant decrease in Fmo3 hepatic gene expression in response to PB in both datasets (Fig. 5g  and 5h ). It is worth noting that all publicly available studies were conducted in male mice only, which could explain why the decrease in Fmo3 expression seen upon CAR activation by PB or CITCO was of lower magnitude than that seen in our own in vivo experiments in females. Finally, we took advantage of the only available ChIP-seq analysis of hCAR binding in vivo to date  30  and observed that, among the 6364 unique genes associated with high-confidence hCAR-binding genes, Fmo3 was found as a hCAR-direct binding gene with a p-value=1,  26 10 -29  (Fig. 5j ). Thus, in females, CAR activation by TCPOBOP perturbated the metabolism of TMAO from TMA by inhibiting the expression of Fmo3 (Fig.
Acknowledgements We thank all members of the  EZOP (Experimental Zootechny) ,  UMR Toxalim, Toulouse, France , staff for their careful help with this project. We thank the staff from the Genotoul: Anexplo, Get-TriX and  Metatoul-AXIOM  facilities. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Financial support: MH is the recipient of a PhD grant from the  French Ministry of Research. This work was supported by  Région Occitanie (Hepatomics  to N. L. ) the  Fondation pour la Recherche Médicale  (grant number  ENV202109013962  to H. G. ) and grants from the  French National Research Agency  (Hepatomorphic  ANR-20-CE14-0035  to H. G. ; Gadget  ANR-22-CE34-0005-01  to S. E. -S. ). , FL, NL and SE-S; methodology: MH, FL, MG, BE, AP and CC; software and statistical analysis: MH, AP, YL and SE-S; investigation: MH, FL, MG, BE, JB, TF, CM, CR, AF, CN, GG, ER-B and SE-S; MH, BE, AF, ER-B, UR-K, BP, EB-R, LG-P, HG, NL and SE-S; supervision: YL, ER-B, LD, UR-K, MB, BP, NL, HG and SE-S; project administration, NL, HG and SE-S; funding acquisition: MB, BP, NL, LG-P, HG and SE-S. The author(s) read and approved the final manuscript.
Western blot analysis Briefly, BV2 cells (1 ×10 6 cells/plate) pretreated with MCC for 1 h in 60-mm plates were subjected to LPS stimulation. RIPA lysis buffer was used to extract proteins from whole cells. Proteins were electrophoresed on an equal volume of 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred onto PVDF membranes. The membranes were then incubated with the appropriate primary antibodies, followed by incubation with a horseradish peroxidase-conjugated secondary antibody. Specific protein bands were detected on an X-ray film using chemiluminescence. The band intensities were estimated using the Image Studio Lite 5. 2 Quick Start Guide program for Windows.
Levels of intracellular ROS production were measured using the H 2 DCF-DA fluorescent probe (C). Data values represent the mean ± standard deviation (SD) of three independent experiments. ##p < 0. 01, ###p < 0. 001 compared with untreated cells, * p < 0. 05, * * p < 0. 01, and * ** p < 0. 001 compared with the LPS-treated cells. AG, aminoguanidine; IMC, indomethacin; LPS, lipopolysaccharide; MCC, 4-methoxycinnamyl p-coumarate; NO, nitric oxide; PGE2, prostaglandin E 2 ; ROS, reactive oxygen species. levels in cells treated solely with LPS. MCC inhibited nitrite production, whereas SnPP reversed this effect. This led to a statistically significant increase in nitrite levels compared with those in cells exposed to LPS and MCC. To further investigate whether the upstream pathways that regulate MCC-mediated Nrf2/HO-1 activation occur through the PI3K/Akt pathway, we employed the PI3K inhibitor, wortmannin.
NO and PGE 2 are produced via the regulation of iNOS and COX-2, respectively, and are primarily expressed by activated microglia  [4, 5]. We further demonstrated that MCC alleviated the expression of iNOS Fig. 3. Inhibitory effect of MCC on LPS-induced iNOS and COX-2 expression. BV2 cells were pretreated with MCC for 1 h, followed by LPS stimulation for 24 h and 9 h for protein and mRNA, respectively. Protein expression levels of iNOS (A) and COX-2 (B) were measured using western blot analysis. Data values represent the mean ± standard deviation (SD; n = 3) of densitometric values, normalized to that of GAPDH. mRNA levels of iNOS (C) and COX-2 (D) were detected by RT-qPCR. ###p < 0. 001 compared with the untreated cells, * * p < 0. 01, and * ** p < 0. 001 compared with the LPS-treated cells.
Step 5: Data handling and analysis of the questionnaire Responses to the questionnaire were collected continuously from 16 July 2022 up to the day of the convention (28/10/2022). All responses received were pseudonymized prior to any analysis. Data were stored on a password-protected separate file and transferred into a predefined Excel file to conduct the analysis. As the rating item within the questionnaire was programmed to be mandatory, no missing data approach was required. Among the qualitative questions, those about the role and challenges of the different stakeholder groups in the future HTA system were mandatory. There was no imputation on missing data for the other qualitative questions as no statistical analyses were performed on these items. A preliminary analysis of the responses was carried out by EJ and JR to present interim results during the EAA convention.
Acknowledgments We would like to acknowledge all stakeholders who contributed by submitting responses to the EAA research survey. Further, we would like to thank  SIOP Europe  for providing valuable additional comments and input. We also thank  Abbvie ,  AstraZeneca ,  Novartis ,  Roche ,  Sanofi , and  Seagen  for an unrestricted grant for EJ & JR that partially funded this research.
ACKNOWLEDGMENT This work was supported by  ANR   JCJC grant   CycloSyn  ( ANR-18-CE07-0008 ) and  ANR PRC  grant  CYCADA  ( ANR-22-CE07-0016 ). A. S. thanks  Labex CHARMMMAT  ( ANR-11-LABX-0039 ) for a  M2 grant  and  ANR  for a  PhD fellowship.
S1. 1 H NMR data of entries 2, 3, 5-8 in the Table 1 The 1 H NMR spectra of entries 2, 3, 5, 6, 7, and 9 in the Table  1  were recorded using a 400 MHz NMR spectrometer (JMTC-400/54/JJ/YH, JEOL Ltd. , Tokyo, Japan) at 400 MHz. The 1 H NMR data are shown in below: Entry 2: 1 H NMR (400 MHz, CDCl3, TMS, 1,4-dioxane) δ 0. 82-0. 91 (m, 2. 48H), 1. 24-1. 27 (m, 5. 77H), 1. 47-1. 53 (m, 1. 41H), 3. 16-3. 28 (m, 1. 42H), 3. 43 (s, 0. 18H). Entry 3: 1 H NMR (400 MHz, CDCl3, TMS, 1,4-dioxane) δ 0. 84-0. 90 (m, 3. 97H), 1. 24-1. 27 (m, 9. 05H), 1. 47-1. 52 (m, 2. 20H), 3. 25-3. 33 (m, 2. 48H), 3. 43 (s, 1. 06H). Entry 5: 1 H NMR (400 MHz, CDCl3, TMS, 1,4-dioxane) δ 0. 78-0. 88 (m, 8. 27H), 1. 22-1. 29 (m, 17. 7H), 1. 41-1. 55 (m, 4. 05H), 3. 12-3. 27 (m, 5. 56H), 3. 48 (br, 0. 26H). Entry 6: 1 H NMR (400 MHz, CDCl3, TMS, 1,4-dioxane) δ 0.
Introduction In February 2023, hundreds of thousands of molecules have been reported in the main natural product (NP) databases such as the naturaL prOducTs occUrrence databaSe (LOTUS, 276,518 NPs, https://lotus. naturalproducts. net/), the Dictionary of Natural Products  (DNP 31. 1, 324, 967  NPs, https://dnp. chemnetbase. com/), the COlleCtion of Open Natural ProdUcTs (COCONUT, 407,270 NPs, https://coconut. naturalproducts. net/) or The Natural Products Atlas (33,372 NPs, https://www. npatlas. org/)  (Rutz et al. , 2021; Sorokina et al. , 2021; van Santen et al. , 2019). Entries in these databases are increasing over the time while new molecules are being discovered. Regarding Natural Products Atlas (NP Atlas), which describes molecules belonging to both fungal and bacterial reigns  (with 20,304 (60.
Fungal strains Different fungal strains have been used for this experimentation. Three strains are coming from the MMS fungal collection: Penicillium expansum (MMS 42) isolated from marine sediments, Le Croisic, France; Trichoderma sp. (MMS 1255) isolated from marine sediments, Port-du-Bec, France and Aspergillus pseudoglaucus (MMS 1589) isolated from a culture of the microalgae Prorocentrum lima PL4V isolated in Galicia, Spain. One strain belongs to the UBO culture collection (UBOCC): Hortaea werneckii (UBOCC-A-208029, Mo34, Hw) isolated from hydrothermal vents in the mid-Atlantic Ridge  (Burgaud et al. , 2010). Accession numbers for identification sequences for MMS 1255 available on GenBank are JQ653081. 1,  KU758964. 1, MN450663. 1, and MN428075. 1 (van Bohemen et al. , 2021). The ITS and β-tubulin sequences for MMS 1589 strain are available on GenBank under accession numbers MN134000.
Halogenating enzyme The Hortaea werneckii vanadium chloroperoxidase (HwvCPO) is a recombinant enzyme produced by E. Coli transformed with the plasmid pQE81L + vHPO_Hw and pKJE7 (for chaperones expression) (GenBank ID: OP555106). The recombinant protein was purified using a Ni-NTA column followed by a gel filtration purification as previously described  (Cochereau et al. , 2023). The purified protein is composed of a mix of HwvCPO and chaperone proteins and its concentration was estimated at 5 mg/mL. The enzyme was stored at -20 °C before use. In addition to the biochemical characteristics and enzymatic constants already described  (Cochereau et al. , 2023) , the enzyme was found stable for at least 79 h when stored at 4 °C or 20 °C (Fig. S1 ).
Data treatment First, the files (*. raw) obtained from the HPLC-HRMS Exactive Orbitrap MS were converted in *. mzXML fromat in centroid mode by msconvert software to be further treated  (Chambers et al. , 2012). The output format was *. mzXML with a binary encoding precision of 64-bits. Peak Picking window was used to convert profile mode to centroid mode with the CWT algorithm (snr = 0. 1 peak-Space = 0. 1 msLevel = 1-). This operation was carried out separately for the two scan polarities: positive and negative. Then, the software Mzmine v2. 53 was used to obtain the peak lists of each sample with specific parameters (Tables  S1-S2 , supplementary information). For each peak list, adducts, complexes and peaks coming from the culture media were deleted using a custom database created with the peaks from medium control samples triplicates.
Annotation of compounds Monoisotopic masses of compounds were calculated for all features of interest (halogenated and non-halogenated precursors) and compared with the "Natural Products Atlas" database to annotate already known features  (van Santen et al. , 2019). To identify putative non-halogenated precursors, the bromine atom (monoisotopic mass: 78. 91834) was replaced by a hydrogen atom (monoisotopic mass: 1. 00783) in the mass calculations.
werneckii extracts was not as straightforward as for P. expansum MMS 42 or P. pseudoglaucus MMS 1589, either. Here, it is more difficult to raise similar hypotheses as for Trichoderma extracts as very few specialized metabolites are described from this yeast, with only seven compounds described in NP Atlas database in the Teratosphaeriaceae family and only one (hortein, not detected in our samples) in this species (Fig. S10 ). Nonetheless, in the negative mode, even though the percentage increase in halogenated features is weaker (up to 1. 4 times more between RMTE and ETE) compared to MMS 42 (3. 91 times more) and MMS 1589 (2 times more), in these three cases, the number of detected brominated features remains significantly higher compared to RMTE group (in ESI -, Mo34 p-value: 0. 027, MMS 42 p-value: 1.
, 2016; Saikia et al. , 2016). This hypothesis can then explain the detection of brominated features in these extracts. A total of 658 characteristic features are listed in Table  S5  and represent the ions highlighted by the metabolomic study as being more significantly expressed in the ETE group for MMS 42 and MMS 1589 strains (p-value < 0. 05). Among these features, 107 could be annotated thanks to the NP Atlas database using the filter of molecules from the Aspergillaceae family. It only represents 16 % of the listed compounds, highlighting potential new molecules to isolate and describe in future studies. Communesin A and B are the main compounds produced by the P. expansum marine derived strain MMS 42 as well as the terrestrial derived strain  (Andersen et al. , 2004). Detection of these compounds is no more observable after the enzymatic reaction with HwvCPO (Fig.
However, this strategy allowed modifying the chemodiversity in most fungal extracts with the production of new compounds never described in databases so far. While this study more specifically focused on the formation of 12-bromo-communesin as a proof of concept, it paves the way to the production of many other potentially new halogenated natural compounds. In fact, further work is in progress to describe other new brominated compounds identified in this study. Interestingly, while the production of brominated metabolites is clearly dependent on the reactivity of the compounds present in fungal extracts, as weak bromination rate was observed when few electron-rich compounds were present in the extract, some spontaneous bromination were also observed with the reaction medium only (without the enzyme), which would need more study.
The concentration of the DNA in the pooled fragments was assessed by fluorometric quantification (Qubit, Thermo Fisher). The pooled libraries were denatured with NaOH to a final concentration of 0. 1 N and diluted with a hybridization buffer before sequencing. Sequences were performed with MiSeq v2 reagents using the 300-cycle kit (Illumina) as recommended by the manufacturer. The raw sequences were demultiplexed and quality trimmed with a phred score of 30 to avoid primer bias. Primer sequences were trimmed from 5′ ends of sequences to avoid primer bias in sequenced fragments. Reads were compared to a custom database consisting of the 3D7 reference sequence for base calling. The bioinformatics analysis was carried out using the CLC Genomics Workbench 22 software package (Qiagen). Laboratory reference parasite strains (Dd2, 7G8, HB3, and Cambodian culture-adapted) with known alleles in each gene served as controls.
Mutations associated with resistance to anti-malarial drugs Of the 377 samples analysed, 346 (91. 7%), 369 (97. 9%), 368 (97. 6%), and 374 (99. 2%) were successfully sequenced for Pfcrt, Pfmdr-1, dhfr, and dhps. There was no significant difference between the sites, except for Pfcrt, where the proportion of missing data was lower in Gaoua (3. 7% in Gaoua vs. 18. 6% and 16. 0% in Banfora and Orodara, respectively, p < 0. 0001).
PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i. v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.