dataesr/scientific-paragraphs-categorization huggingface dataset:
The 47 modules were merged into 29 modules using a cut-height of 0. 25. Among them, the 'magenta' and the 'white' modules, containing 204 and 62 metabolite features respectively, were highly correlated with RYGBP surgery (r ¼ 0. 87 P < 0. 001 and r ¼ 0. 87 P ¼ 0. 0052 respectively). Other associations between clustered modules and "a priori" selected animal traits are summarized in Table  1 , Fig. 7_A  and Sup. Figure  3. 111 and 12 metabolites features were identified as potential drivers in the "magenta" and the "white" modules respectively. 4. 5. Data integration (Fig. 7 ) Eigenvectors-based modules identified as highly correlated with RYGBP ('magenta' and 'white' from metabolomics WGCNA or 'lightcyan' and 'salmon' from OTUs WGCNA analysis) were not correlated with brain responses after sucrose stimulation whereas modules based on brain gene expression WGCNA were correlated with brain responses after sucrose stimulation, but not RYGBP.
The cecal Firmicutes/Bacteroidetes (F/B) ratio was lower compared to SHAM, which was mostly driven by the increased abundance of Bacteroidetes (data not shown). A previous study in the Ossabaw minipig identified no relationship between the cecal F/B ratio and the % of body fat, contrary to the F/B ratio in fecal microbiota  [58]. In the WGCNA lightcyan module generated, we identified Bifidobacterium as a key driver highly associated with RYGBP. Interestingly, Bifidobacterium was also found increased in humans after RYGBP  [59]. The differential weight loss between the two groups might be attributed to an increased microbial fermentation activity of polysaccharides into SCFAs, as already described in mice who received gut microbiota from RYGBP mice  [60]. SCFAs-producing bacteria such as Blautia  [61]  were found increased in RYGBP.
Acknowledgements We acknowledge the entire EAT team for their technical support at different steps of the study, especially  R egis Janvier  and  Isabelle Nogret. The authors want to thank all the staff from the  Rennes pig experimental facilities  ( UE3P ):  Alain Chauvin ,  Julien Georges , Micka€ el G enissel,  Francis Le Gouevec ,  Bruno Fontaine  and  Renan Delaunay  for their technical support. We acknowledge the  PRISM (Plateforme de Recherche en Imagerie et Spectroscopie Multimodales, Rennes, France) core facility  for its technical support, and especially  St ephane Quellec. We thank the  Biogenouest-Corsaire core facility  that supported this work, in particular  Sadia Ouzia  and  Lauriane Rambaud  from the MELISA platform for all the LC-HRMS analysis.
Isolated colonies were picked and grown on TSA 10% plates for 24-48 hours to obtain pure cultures. The taxonomy of the isolated strains was determined using Sanger sequencing of the gyrB gene. A total of 528 strains were obtained that represented the 4 main phyla (Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria) found in radish seeds, for a total of 11 assigned bacterial families and 17 genera. To select strains representative of the radish seed bacterial community, we used the Seed Microbiota Database  (Simonin et al. 2022)  originating from a meta-analysis that gathers reprocessed amplicon sequencing datasets from 50 plant species. We extracted from the database all the radish seed datasets using the gyrB marker gene (studies all conducted in our team). We obtained data from 7 independent studies on the Flamboyant5 genotype (n=295 seed samples).
Amplicons were purified with magnetic beads and pooled. Concentration of the pool was measured with quantitative PCR (KAPA Library Quantification Kit, Roche, Basel, Switzerland). Amplicon libraries were mixed with 5% PhiX and sequenced with MiSeq reagent kits v2 500 cycles (Illumina, San Diego, California, USA). A blank extraction kit control, a PCR-negative control and PCR-positive control (Lactococcus piscium, a fish pathogen that is not plant-associated) were included in each PCR plate. The raw amplicon sequencing data are available on the European Nucleotide Archive (ENA) with the accession number PRJEB58635. The bioinformatic processing of the amplicons was performed in R. In brief, primer sequences were removed with cutadapt 2. 7 (Martin 2011) and trimmed fastq files were processed with DADA2 version 1.
10 (Callahan et al. 2016). Chimeric sequences were identified and removed with the removeBimeraDenovo function of DADA2. Amplicon Sequence Variant (ASV) taxonomic affiliations were performed with a naive Bayesian classifier  (Wang et al. 2007 ) with our in-house gyrB database (train_set_gyrB_v4. fa. gz) available upon request. Unassigned sequences at the phylum level and parE sequences (a gyrB paralog) were filtered. After these filtering steps, samples with less than 1000 reads were excluded from the study.
Statistics and microbial community analyses All the scripts and datasets used to conduct the study are available on GitHub (https://github. com/marie-simonin/Radish_SynCom). We assessed the effect of the inoculation and sample type on the various univariate variables (e. g. seedling bacterial cell density, strain relative abundance, ASV richness) using generalized mixed models (glm function in lme4 package) and post hoc comparisons were performed using the Tukey method (warp. emm function in package emmeans). The analyses on bacterial community structure based on the amplicon sequencing data were done after rarefaction at 14116 reads per sample to conserve sufficient replicates while having a good sampling depth. Diversity and community structure analyses were performed in R 3. 6. 2 using the phyloseq (v1.
The progress in image-based high-content screening technology has facilitated high-throughput phenotypic profiling notably the quantification of cell morphology perturbation by chemicals. However, understanding the mechanism of action of a chemical and linking it to cell morphology and phenotypes remains a challenge in drug discovery. In this study, we intended to integrate molecules that induced transcriptomic perturbations and cellular morphological changes into a biological network in order to assess chemical-phenotypic relationships in humans. Such a network was enriched with existing disease information to suggest molecular and cellular profiles leading to phenotypes. Two datasets were used for this study. Firstly, we used the "Cell Painting morphological profiling assay" dataset, composed of 30,000 compounds tested on human osteosarcoma cells (named U2OS).
Secondly, we used the "L1000 mRNA profiling assay" dataset, a collection of transcriptional expression data from cultured human cells treated with approximately 20,000 bioactive small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). Furthermore, pathways, gene ontology terms and disease enrichments were performed on the transcriptomics data. Overall, our study makes it possible to develop a biological network combining chemical-gene-pathwaymorphological perturbation and disease relationships. It contains an ensemble of 9989 chemicals, 732 significant morphological features and 12,328 genes. Through diverse examples, we demonstrated that some drugs shared similar genes, pathways and morphological profiles that, taken together, could help in deciphering chemical-phenotype observations.
, 2013)  and  Seal et al. (2021)  predicted cytotoxicity and proliferation-related assays to molecular fingerprint  (Seal et al. , 2021). These results demonstrated that the models were able to detect patterns in morphological labels related to the annotated activity even though the targets were not marked explicitly. To go one step further, combinations of drugs with bioactivity on protein targets (notably from the ChEMBL  (Mendez et al. , 2019)  and ExCAPE-DB database  (Sun et al. , 2017) ) with cell painting data were explored given the possibility to link proteins to morphological perturbations  (Hofmarcher et al. , 2019; Simm et al. , 2018; Trapotsi et al. , 2021). In a more recent approach, image-based data from a live-cell imaging assay were used to catalogue the morphological phenotypes of 1008 reference compounds, which were well annotated and their MoA was known.
The image data were profiled at four concentrations and in a panel of 15 reporter cell lines, which enables the monitoring of cell morphology and the activity of various signalling pathways. (Cox et al. , 2020). Similarly, cell painting data was combined with transcriptomics data to assess the possible relation between transcriptomic changes with the corresponding alterations in morphology. For example,  Nassiri and  McCall combined 978 landmark genes and 812 image-based features for a set of 9515 drugs and found 58 genes highly correlated to 18 morphological features  (Nassiri and McCall, 2018). In addition, using shared and complementary information from these two data sources, predictions of gene expressions and morphological changes for any compounds were proposed  (Haghighi et al. , 2022 preprint).
The same analysis was also performed for chemicals having only transcriptomics data. This network has been developed for three specific purposes. The first is to identify new drug candidates sharing similar cell painting and/or gene expression signatures with chemicals known to have beneficial effects. The second is to detect the possible toxicity of a molecule when the associated MoAs are known to lead to a toxicity endpoint. Finally, the integrated data in this network can suggest the relationship between the genes/pathway's deregulation and cell morphology perturbations for a drug that could help in the investigation of repurposing drugs.
The expression values of unmeasured genes (called inferred genes) can be predicted from the marker genes (called landmark genes) measured via linear regression. Therefore, each chemical is represented with a transcriptional profile of 12,328 unique genes (978 landmark genes and 11,350 computationally inferred genes). Two test phases were carried out, each constituting a full-fledged dataset. Phase 1 (updated March 03, 2017, https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE92742) gathers the effect of 22,891 small molecules tested on different human cell types: 68 cell lines, 3 differentiated cells, 1 induced pluripotent stem cell and 5 primary cells  (Subramanian et al. , 2017). Phase 2, (updated March 30, 2017, http:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE70138) gathers the effect of 1936 small molecules and 353 nucleic acid reagents tested on different human cell types: 30 cell lines, 3 differentiated cells, 1 embryonic stem cell and 5 primary cells  (Golub and Subramanian, 2015).
R and python packages GO and DO enrichments are calculated using the R package clusterprofiler (version 4. 2. 0) (Wu et al. , 2021)  and the DOSE package (version 3. 20. 0) (Yu et al. , 2015). The Bonferroni method is set as the adjustment method and the p-value cutoff is set at 0. 05. The translation of "EntrezID" (unique gene ID from the Entrez Gene database at the National Center for Biotechnology Information, (htt p://www. ncbi. nlm. nih. gov/gene)) to the HUGO gene symbol (gene name) is made using the R package org. Hs. eg. db (version 3. 10. 0) (Carlson, 2019). Annotated matrices (in. gtcx format) from the L1000 datasets were parsed and manipulated using the Connectivity Map (CMap, version 0. 99. 19) R package  (Natoli, 2021). This package gathers perturbational gene expression profiles built by researchers at the Broad Institute and funded by the LINCS program.
The python package "Entrez" provides numerous functions allowing to parse the Entrez database system at the National Center for Biotechnology Information. It contains a variety of biomedical data, including nucleotide and protein sequences, gene records, three-dimensional molecular structures, and biomedical literature. In our study, we used functions converting the identifiers of genes to the "HUGO symbol" nomenclature (or vice versa) and retrieving the description of genes' actions. The network visualisation was performed using the "igraph" R package (v 1. 2. 8)  (Csardi and Nepusz, 2006. https://igraph. org).
Cell painting curation and plate normalisation The Cell Painting morphological profiling assay includes 406 plates. Among them, 39 plates contained missing values, i. e. wells for which no data was available. Plates with >20% of missing wells were excluded, representing 9 plates over the 39, and the other incomplete plates would be completed with "NA" values so the entire plate would not be lost. To enhance the quality of the analysis, a normalisation step was performed. In our case normalisation was divided into two main steps: the plate-layout effect correction and the feature transformation, using R software (v3. 5. 3). The plate-layout effect was corrected thanks to the B-Score, already implemented in the R package "platetools" (v0. 1. 5) (Warchal, 2021) , and consisting of the following steps: -For each row of a plate, calculate the row median.
Deduce the row overall effect, i. e. the median of the row medians. -For all rows, subtract the row median from the wells. -Subtract the overall effect from each row median. -Repeat the procedure for columns. -Add the column overall effect to the row overall effect. -Repeat the loop until the global overall effect becomes unchanged. Because of the use of row and column medians, B-Score requires control wells to be distributed uniformly across the plate. The B-Score correction is calculated for each plate. Regarding feature transformation, a z-score was applied for each plate. This score is a combination of median and median absolute deviation (MAD), to centre and scale the data for a population (Eq. (  1 )). The negative controls, i. e. DMSO here, are recommended to be used as the normalising population when possible  (Caicedo et al.
L1000 cleaning and filtering A cleaning step was added to the protocol to homogenise the data. First, the doses were all converted to micromolar (μM) and secondly, missing or partial data were removed (such as "restricted" molecule or "-666" values). The filtering steps consist of i) selecting molecules that correspond to treatment molecules (called "trt_cp" in the data) and ii) selecting the genes that are significantly deregulated by the molecule, i. e, that have an expression z-score >2. 00 or less than -2. 00. Overall, considering dose, time, cell types and chemicals, 279,188 transcriptional profiles will be used in this analysis.
Chemical scaffolds analysis The scaffolding of the molecules was done using the ScaffoldTree algorithm  (Schuffenhauer et al. , 2007)  and the RDKit module (http://www. rdkit. org(RDKit) Accessed 26 July 2022) of python. The idea is that if two molecules share a similar scaffold, they might share similar outcomes. This is essentially true in pharmacology where a series of analogs can be synthesised from a 'hit' compound to optimise the ADMET properties of a drug candidate  (Ertl, 2014; Zheng et al. , 2021). The decomposition is governed by precise rules that allow side chains and rings to be removed from the molecules one by one until only one ring remains and the resulting scaffolds to be hierarchically clustered. As an example of the mentioned rules, if the removal of a ring would lead to a disconnected structure, this ring cannot be removed.
S1  for the correspondence between cell types and organs and their repartition in the dataset). An exception is made for the cells MCF7 (breast), HT29 (large intestine), A549 (lung), PC3 (prostate) and A375 (skin): as a large number of tests were performed on these cell types, the similarity between the molecules of these cell types was calculated separately from other cell types referring to the same organ. The Cosine similarity (S C ) between molecule A and molecule B is calculated as follows: S C (A, B) = ∑ n i=1 AiBi ̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ ∑ n i=1 A 2 i √ ̅̅̅̅̅̅̅̅̅̅̅̅̅ ∑ n i=1 B 2 i √ where A i and B i are components of vectors (z-scores of morphological features or z-scores of genes) for molecule A and B respectively.
Tanimoto coefficient calculation To analyse the structural similarity of two molecules, we calculated the Tanimoto coefficient using the OpenBabel program  (O'Boyle et al. , 2011;  The Open Babel Package, version 2. 3. 1, n. dhttp://openbabel. org (accessed Oct 2022)). We first convert the molecule's SMILES into a structure data file (. sdf format, 2D molecule) and then the Tanimoto coefficient is calculated using the MACCS fingerprints.
Global analysis of chemical-L1000-Cell Painting collection 4. 1. 1. Compounds dataset Through this study, we have collected a set of 41,121 compounds: 29,935 from the Cell Painting dataset and 21,175 from the L1000 dataset. There are 9989 compounds in common between those two datasets which make a relatively important and interesting source of chemicals for which gene deregulation and morphological perturbation can be linked to disease. The two datasets containing the 9989 compounds in common, Cell painting and L1000 respectively are available at the following DOIs: https://doi. org/10. 6084/m9. figshare. 21878391 and https://doi. org/10. 6084/m9. figshare. 21878445. The distribution of the compounds according to their belonging to the two data sets is summarised in Fig. 1. From our compound's set, the chemical space has been explored using chemical scaffolds.
It enables us to assess if compounds sharing similar scaffolds can share similar morphological properties and gene deregulation. The Cell Painting compounds have 18,571 different scaffolds whereas L1000 compounds have 13,883 different scaffolds. Interestingly, among those scaffolds, there are 5708 that represent the 9989 compounds in common between Cell Painting and L1000 datasets. The ensemble of these scaffolds can, then, be used to suggest gene expression or morphological perturbation or both, depending on the transcriptomics and/or cell painting features measured for a new chemical.
Transcriptomic data The filtering and cleaning of the L1000 dataset allowed us to obtain a collection of gene expression profiles for the 21,175 compounds at different time points (3 h, 6 h, 24 h or 48 h), 101 different doses (from 0. 001 μM to 100 μM), and 82 cell lines. In total, there are 279,188 expression profiles (1 expression profile is 1 compound tested on 1 cell line at 1 dose for 1 time). About the 9989 chemicals, it corresponds to 90,879 expression profiles that can be matched with morphological profiles for the same compound.
0, downloadable here: https://www. proteinatlas. org/abo ut/download/proteinatlas. tsv. zip). According to these data, there are 11,055 genes expressed in U2OS. These genes will make it possible to establish connections between transcriptomic data (on any of the 98 cell types considered in L1000) and the morphological data on U2OS. For example, the cell lines, EFO21, HeLa, PC3, MCF7, and HepG2 have >80% of genes expressed also in U2OS (Table  1 ). On the other hand, 74% of genes in HL60 are also present in U2OS. Still, we considered that there is sufficient coverage between gene expression from different cell types to establish a connection between transcriptomic and morphological data. The complete table of the percentage of genes in common between U2OS and other cell types is available in section Appendix as Supplementary Table  S2.
Cell Painting data inclusion From the 9989 compounds that are in the L1000 and Cell painting datasets, on average, 256 genes are deregulated (the median value is genes) and 17 morphological features are perturbed (the median value is 1 feature) by this set of compounds. The compound with the higher number of perturbed morphological features (675 over 732) is named "BRD-K15935639-001-02-0" in the Broad nomenclature and is a proteasome inhibitor. In the L1000 dataset, this molecule has been tested for 7 conditions (4 different cell types at 10 μM and for 6 or 24 h) and deregulates between 309 and 3781 genes according to the condition. Surprisingly, we observed that there is a large set of molecules (4517) that do not disturb significantly any morphological features (with a |z-score| < 2. 00) although some of them deregulate many genes.
Application with the chemical-L1000-Cell Painting collection Once the integration and cleaning of the datasets were realised, the chemical-L1000-Cell Painting collection can be used for several purposes such as providing mechanisms of action of a chemical that could explain morphological perturbations observed in phenotypic screening, identifying morphological features and gene deregulations associated with diseases and phenotypes or predicting gene expression and/or morphological profiles by chemical similarity. We will present below some examples of possible applications using this data collection.
Results from one chemical (Resveratrol) As a first example, the resveratrol molecule was investigated. Found in CP and L1000 datasets, gene deregulations and morphological features were collected. Resveratrol is a non-flavonoid polyphenol compound that exists in grapes, berries, peanuts and red wine. It has been demonstrated to have multiple pharmacological properties such as oxidative stress, inflammation regulation, apoptosis, antithrombotic and to slow-down atherosclerosis  (Fan et al. , 2022). It has also been tested as chemopreventive therapy  (Shankar et al. , 2022)  for different cancer types including oral, colorectal, prostate and breast cancer. As well, it has been used as a plant-based nutrient in the treatment of memory problems leading to dementia  (de Vries et al. , 2022). Its multiple benefits and the fact that it operates in many signalling pathways make it a prime candidate for investigation.
Although resveratrol was not tested on U2OS cells in the L1000 experiments, transcriptomics data on CD34, another bone-related cell, was available. Using a 10 μM dose and a 24 h exposure time, the CD34 disturbs 59 genes including 42 which are also expressed in the U2OS cells. From these 42 genes, the resveratrol specifically deregulates 5 bone-related genes: BMP4, FHL2, JUND, LYN, and SMAD3. They are expressed in the cytoplasm, the endomembrane system and the nucleus. Interestingly, 3 genes BMP4, FHL2, and SMAD3 in combination with two others (LCN2 and CXCL13) are significantly involved in the osteoarthritis pathway (DOID:8398). Also, it has been reported that FHL2 deficiency results in osteopenia and thus an increase in FHL2 activity could be beneficial to limit osteoporosis  (Günther et al.
, 2005). The results of this analysis could suggest that resveratrol could be beneficial against osteoarthritis. Looking at the Cell Painting dataset, 118 morphological features are significatively perturbed by the resveratrol. 37 features concern the cytoplasm, 55 concern the cell and 26 concern the nucleus. This information taken together with transcriptomics data suggests that there is a link between the compartment concerned by the morphological features and the location of the deregulated genes, e. g. FHL2, BMP4, JUND, LYN and SMAD3 expressed in the cytoplasm in U2OS. So, the combination of this gene signature and morphological cell signature could be of potential interest in the investigation of some bone disorders.
Results from two chemicals (amiodarone and prochlorperazine) with the same toxicological endpoint (Drug-Induced Liver Injury) As a second example, the subnetwork of amiodarone and prochlorperazine compounds was studied. Amiodarone is an antiarrhythmic drug that is indicated in the treatment of supraventricular and ventricular tachyarrhythmia. Although this drug is very effective in the treatment of cardiac arrhythmia and is widely prescribed, it has many side effects hitting several major organs (lung, thyroid, eye, and liver)  (Biancatelli et al. , 2019). Prochlorperazine is commonly used as an antiemetic drug  (Glare et al. , 2011)  and also as an antipsychotic agent  (Chen et al. , 2022). It has been reported that prochlorperazine can cause liver injuries and is classified as "uncommon but likely cause of clinically apparent liver injury" according to the LiverTox® database (LiverTox 2012 https://www.