dataesr/scientific-paragraphs-categorization huggingface dataset:
Fifteen transcriptome libraries from M. incognita egg, J2, J3, J4, and female stages were generated by Choi et al. 39  using the Truseq RNA Sample Prep Kit (Illumina), and mRNAs were paired-end sequenced (2×101 bp) using Illumina HiSeq. 2000 technology (Supplemental Table  2 ). The libraries were downloaded and trimmed, and the transcripts were mapped using the genome reference retrieved from the WormBase Parasite database (BioProject PRJNA 340324)  73. The number of reads mapped in interest target genes was normalized in reads per million (RPM). Additionally, the expression profile of MiDaf16-like1 to 19 and MiSkn1-like1 to 4 in different nematode life stages was estimated from these same transcriptome libraries (Supplemental Fig. 1 ). Features of the MiDaf16-like and MiSkn1-like genes from M.
incognita, such as conserved domains in the gene sequences were identified using CDD Database from NCBI  74  , and PFAM Database from EMBL-EBI  75. NES motifs were predicted using NetNES 1. 1 Server 76 , while NLS motifs were predicted using the NLStradamus online tool  77. Meloidogyne incognita J2 inoculum. M. incognita J2 race 3 and M. incognita J2 strain Morelos were obtained from tomato plants (Solanum lycopersicum cv. Santa Clara) inoculated and maintained for eight weeks under greenhouse conditions. Infected roots were washed and macerated using a blender after treatment with 0. 5% sodium hypochlorite. Eggs were harvested, rinsed with tap water and subsequently separated from root debris using 100 to 550-μm sieves  78. Then, the eggs were hatched under aerobic conditions at 28 °C, and J2 were harvested every two days, decanted and quantified under a microscope using counting chambers.
2 µM of each gene-specific primer (Supplemental Table  3 ) and GoTaq ® qPCR Master Mix (Promega, Madison, Wisconsin, USA). The conditions for qPCR included an initial 95 °C for 10 min, then 40 cycles of 95 °C for 15 s and 59 °C for 50 s, followed by a final melting curve analysis. The expression of the MiDaf16-like1 (Minc3s02528g30466) and MiSkn1-like1 (Minc3s02028g27861) genes was normalized using the Mi18S (GenBank accession U81578)  79  and MiACT (Minc3s00730g16611) endogenous reference genes in A. thaliana and N. tabacum, respectively. In addition, MiSod-3like1, MiGst1-like1, and MiTTL-5-like1 gene expression were also evaluated after J2 exposure to H 2 O 2 and normalized using the Mi18S gene. Newly hatched J2 in Milli-Q water were used as a control for relative expression levels for both J2 exposed to H 2 O 2 and J2 during plant parasitism.
thaliana and (D) N. tabacum. (E) Expression profile of MiSod3-like1, MiTTL5-like1, and MiGst1-like1 during exposure of M. incognita J2 race 3 to H 2 O 2. Newly hatched J2 were used as controls for all assays of gene expression level. Error bars represent confidence intervals corresponding to three biological replicates. (F) Expression profiles of MiDaf16-like1 and MiSkn1-like1 in different life stages of M. incognita using transcriptome datasets (BioProject number: PRJNA390559 39 ); retrieved from the BioSample database (NCBI). Error bars represent confidence intervals corresponding to three libraries per life stage of the nematode. Expression profile measured.
Acknowledgements We are grateful to EMBRAPA,  UCB ,  CAPES ,  CNPq ,  INCT PlantStress Biotech , and  FAP-DF  for the financial support to scientific research. We also thank  Dr. Bum-Soo Hahn  for providing access to the transcriptome datasets of different M. incognita life stages. In addition, we thank  Bruna Medeiros Pereira  for kindly providing the initial inoculum of M. incognita race 3. M. F. B. is grateful to  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  for a postdoctoral research fellowship ( PDJ 150936/2018-4 ). I. T. L. T. and C. M. P. ( CAPES-COFECUB SV. 922/18 ) are grateful to the  CAPES/Cofecub  project for financial support in the researcher and students exchange programs between institutions.
incognita parasitism (5 to 30 days post-inoculation; dpi) in (C) A. thaliana and (D) N. tabacum. (E) Expression profile of MiSod3-like1, MiTTL5-like1, and MiGst1-like1 during exposure of M. incognita J2 race 3 to H 2 O 2. Newly hatched J2 were used as controls for all assays of gene expression level. Error bars represent confidence intervals corresponding to three biological replicates. (F) Expression profiles of MiDaf16-like1 and MiSkn1-like1 in different life stages of M. incognita using transcriptome datasets (BioProject number: PRJNA390559 39 ); retrieved from the BioSample database (NCBI). Error bars represent confidence intervals corresponding to three libraries per life stage of the nematode. Expression profile measured.
We have previously shown the ability of verapamil to bind to purified human MRP1  11  , indicating it regulates MRP1 by interacting with it. Using another batch of purified protein, kindly gifted by Dr Zachary Lee Johnson and Dr Jue Chen (Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, USA), we once again showed that S-verapamil binds MRP1 with a similar affinity of 130 ± 30 nM, in agreement with our previous results  11. Furthermore, we were able to show the direct binding of the CS agent 4e with a Kd of 78. 2 ± 35 nM (Supplementary data S4). We then recorded the activity of the stimulated transport of GSH mediated by MRP1, MRP2 and chimera. For that, we quantified the intracellular GSH in the presence of verapamil as previously shown in other cell types  8, 12.
This transporter was crystallized with either GSH (PDB code 4MRP), or GSSG (PDB code 4MRS) at high enough resolutions (2. 5 and 2. 35 Å, respectively) to precisely locate each substrate, and the structure was solved using selenomethionines that ascertained sequence assignment. This model of MRP1, built long before the release of the Cryo-EM structure of human MRP1  40  , remains in adequacy with the published Cryo-EM structure, validating our choice of mutants. Based on this model, we designed nine mutants (M8-01 to M8-09) (Fig. 4b ). Glycine 1228 is the sole residue responsible for the stimulation of GSH transport and associated collateral sensitivity in TM16-TM17. To characterize the effect of the original replacement within the nine identified clusters of chimera M8, we first verified that all the mutants were well expressed and addressed to the plasma membrane (Supplementary Fig.
Furthermore, if GSH efflux stimulation was abolished with the G1228D mutant, the basal GSH transport was not, suggesting also that the mechanism of stimulated GSH transport is different from the non-stimulated one. To investigate the mechanism of GSH dependent transport mediated by MRP1, we localized on the 3D structure of the transporter  40  two putative GSH binding sites deduced from the numerous mutagenesis studies done in the past two decades on MRP1 compiled in the database http://abcmutations. hegelab. org  50 , one located in the cytoplasmic face and the other located in the membrane (Fig. 7a , zones G1 and G2). The existence of these two GSH binding sites is also corroborated by a George's team study that used a radiolabeled photoactive analog of GSH (IAAGSH)  31. Additionally, a potential drug binding site can also be predicted based on previous mutagenesis studies that allowed the identification of residues implicated in the transport of doxorubicin, vincristine, and etoposide.
Measurement of binding of CS agents on MRP1. The impact of ligand binding on intrinsic fluorescence was monitored using a Xenius XC spectrofluorometer (SAFAS) in quartz cuvettes containing 200 µl of MRP1 at 0. 5 µM in 150 mM KCl, 50 mM Tris pH 8. 0, 2 mM MgCl 2 , 2 mM DTT, and 0. 06% digitonin. Titration of S-verapamil (Sigma-Aldrich) and ligand 4e (synthesized as previously reported  62  ) was carried out by 2 µl additions of ligands solubilized in 50% DMSO. This yielded a final DMSO concentration of 4%, which was previously shown to not affect MRP1's structure  40. Tryptophans were excited at 295 nm and emission was monitored between 310 and 380 nm, with a 2 nm step, 0. 3 s acquisition average, 5 nm excitation bandwidth and 3 nm for emission. Data were acquired in triplicates for MRP1, subtracted from the buffer fluorescence.
A sequence alignment with the N-and C-terminal halves of human MRP1 was performed using the AlignMe software in slow mode to take into account the trans-membrane region in the alignment. Alignments were then manually optimized with the rationale to avoid insertion/deletion within trans-membrane fragments, but rather move them into loops, and to anchor the beginning or the end of TM helices on special residues, common to the two proteins, like for instance, positively charged residues inside, proline to introduce kinks in helices, etc. The sequence of human MRP1 was then threaded on the structure of Na-Atm1 using default parameters of the program MODELLER  63. The threaded structure was then manually inspected using Coot  64  and energy-minimized using REFMAC  65. The model was validated in Coot showing that >95% of the residues reside in the most favorable region of the Ramachandran plot.
Acknowledgements We thank  Dr. Zachary Lee Johnson  and  Dr. Jue Chen  ( Laboratory of Membrane Biology and Biophysics , The  Rockefeller University  and the  Howard Hughes Medical Institute, New York, USA)  for the production and purification of human MRP1. We thank the  French Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche  and the  Ligue Nationale contre le Cancer  for the doctoral fellowship accorded to  Lauriane Dury ,  Rachad Nasr  and  Doriane Lorendeau. Financial support of this work was provided by grants from the  French ANR and Hungarian NIH  ( 2010-INT-1101-01 ) and from the  Ligue Nationale contre le Cancer  (Team certified Ligue 2012 and Comité du Rhône  2016-2017 ).
Metabolite Profiles of Meat Products In vitro digests of 33 different meat samples from 10 processed meat types (Table  1 ) were analyzed by high resolution mass spectrometry coupled with ultra-high performance liquid chromatography (UHPLC-HRMS) and 5503 metabolite features present in at least four samples were selected for further analysis. When plotting the scores of the first and second principal components (PC1 and PC2) of a principal components analysis (PCA), it was found that meat products of the same type tend to cluster together (Figure  1 ). PC1 which accounts for 16% of the metabolite variations separates bacon from the other products. Some metabolite features that contribute to scores with negative loadings had exact masses matching with some lipids, and others with high positive loadings had exact masses matching with lipids and peptides in metabolite databases (Supplementary Materials Table  S1 ).
Metabolites Specific for Fermented Meat Products A separate analysis was performed to identify compounds that were significantly elevated in fermented products (n = 12) compared to non-processed pork (n = 18). A Welch's t-test revealed several compounds that were elevated in fermented sausage and not found in all other processed meat products and non-processed meat. Among these are histamine, tryptamine and tyramine, which are all biogenic amines produced from amino acids by microbial metabolism (Table  3 ). In addition to these compounds, we specifically screened the data set for the presence of the biogenic amine γ-aminobutyric acid (GABA) previously reported in processed meat  [15]. We extracted its intensities from the raw data and found it to be significantly elevated in fermented meat compared to non-processed pork.
Data Processing The raw data were processed using the MassHunter software package (Mass Profiler Professional version B14. 9. 1, Qualitative Analysis version B06. 00 and DA Reprocessor version B. 05. 00; Agilent technologies, Santa Clara, CA, USA) with Agilent's recursive feature finding method. For this, metabolite features were extracted from the raw data and aligned over all samples of the experiment based on mass (+/-15 ppm) and retention time (+/-0. 1 min). In a second, targeted recursive search, all features that were present in more than 3 samples in the experiment were extracted from the raw data. The resulting data were aligned with the same criteria to obtain the final feature table. If no signal was found for a metabolite feature in a sample, the intensity was imputed as 1. Detailed parameters can be found in Supplementary Materials Table  S4.
Metabolite Annotation Metabolite features were annotated by searching metabolites with matching mass in the Human Metabolome Database (HMDB) and METLIN as [M+H] + and [M+Na] + ions with a mass tolerance of 8 ppm  [36, 37]. For the compounds specific of meat processing, targeted fragmentation (MS/MS) spectra were acquired at 10 and 20 eV and compared to those of commercially available standards, in-house synthesised standards or to compounds extracted from liquid smoke or black pepper. Levels of confidence given to each annotated peak were those defined by Sumner et al. [22]  where level 1 corresponds to matching exact mass, retention time and MS/MS fragmentation spectrum with those of an authentic chemical standard. Level 2 corresponds to very probable annotation based on matching exact mass, similar physico-chemical properties and MS/MS spectrum with those curated in HMDB and METLIN or the references cited in Supplementary Materials Table  S3.
05. Only compounds that showed at least a twofold increase in the intensity of processed meat compared to non-processed meat after log transformations were retained for annotation. A second Welch's t-test was performed to identify all features that were significantly elevated in salami digests compared to digests of lean and fatty pork. The biogenic amine γ-aminobutyric acid (GABA) has been reported to be a microbial metabolite in fermented meat but was not included in the first feature table. Its intensities were manually extracted from the raw data using the Agilent Profinder software (+/-8 ppm) and added to the feature table for the comparison of fermented sausage vs. pork. All statistical analyses and graphical presentations were carried out using the R open-source statistical computing software version 3.
[22]cted compounds significantly elevated in digests of processed meat compared to digests of non-processed meat. Abbreviations: retention time (RT); parts per million (ppm). ap-Values of Welch's t-test comparing intensities of metabolites in processed meat digests vs. non-processed meat digests adjusted for multiple testing using the FDR-method. bMetaboliteclusterscorrespond to the hierarchical clustering shown in the heat map of Figure2. cLevel 1 identification corresponds to matching exact mass, retention time and MS/MS fragmentation pattern with an authentic chemical standard, level 2 corresponds to matching exact mass and MS/MS fragmentation pattern to spectral databases[22].
Histological determinations Oil Red O staining was performed on 16 mm cryostat sections of liver tissue snap frozen on dry ice. Liver sections were fixed by incubation in 4% paraformaldehyde for 20 min before staining with 0. 3% Oil Red O dissolved in 60% isopropanol for 10 min. Hematoxylin counterstaining was performed before mounting. Oil Red O staining was visualized under a 20X objective with an Olympus BÂ612 microscope and a DP71 CCD camera (Olympus, Rungis, France). Quantification was performed by measuring the stained area with NIH ImageJ software (NIH, Bethesda, MD, USA). Glomerulus density was determined as the number of glomeruli per mm 2 in the kidneys of the adult IUGR and control mice on 7-mm-thick paraffin sections stained with hematoxylin and eosin.
Sequence data were filtered and trimmed to obtain good-quality reads. The preprocessed reads were then mapped to the reference genome mm9 from the University of California at Santa Cruz (UCSC, https://genome. ucsc. edu/) with Bowtie 1. 0. 0. The distribution of uniquely mapped reads was analyzed. The read enrichment region (peak) was scanned from the whole genome with MACS (model-based analysis of ChIPSeq). After peak scanning, the distribution of peaks was analyzed with CEAS (the cis-regulatory element annotation system), and differential binding sites between the control and IUGR groups were analyzed with the DiffBind package of R/Bioconductor. The pipeline for bioinformatics analysis is shown in Supplementary Figure  S2. A differential analysis of ChIPS sequencing data was performed with edgeR, Trended_edgeR, DESeq, and DESeq 2 approaches.
A comparison of these four approaches revealed that outputs overlapped only partially, with significant differences detected in many peaks in the Trended_edgeR and DESeq 2 analyses. Differences in histone enrichment sites were considered significant only for adjusted p values < 0. 05 in both Trended_edgeR and DESeq 2 analyses. Some genes were then excluded from the analysis because there were fewer than 100 reads for these genes, they were more than 4 kbp from the transcription start site (TSS, consistent with the H3K4me3 distribution profile), or the log2-fold change in enrichment was between À0. 33 and þ 0. 33. Pathway and gene ontology (GO) function analyses were performed for the genes with differential binding sites with DAVID  [28]. The STRING database (https://string-db. org) was used for protein interactions as illustrated for the subset of genes displaying > 1.
We analyzed pathway enrichment with the Database for Annotation, Visualization, and Integrated Discovery (DA-VID v6. 8)  [28] , which identified genes involved in transcriptional regulation, particularly in relation to polymerase II promoters, chromatin modifications, and protein phosphorylation (Supplementary Figure  S6 , top panel). These gene products were evenly distributed between the three cellular compartments (cytoplasm, nucleus, and membrane) (Supplementary Figure  S6 , middle panel). A particular enrichment in genes encoding proteins with transferase activity and nucleotide (DNA/RNA) binding capacity was observed. For these proteins, we observed an enrichment in several gene ontology (GO) terms relating to epigenetics (nucleotide binding, poly(A) RNA binding, DNA binding, chromatin binding, and histone-lysine N-methyltransferase activity) and signaling pathways (protein kinase activity, kinase activity, and protein serine/threonine kinase activity) for biological processes (Supplementary Figure  S6 , bottom panel), confirming the involvement of epigenetic modifications in the programming of adult metabolism by the perinatal nutritional environment.
Promoters were particularly enriched in the H3K4me3 histone mark targeted by ChIPSeq. We therefore checked for any potential overrepresentation of transcription factor binding sites. A particular enrichment in the EGR1 (24 times, p < 10 À4 ), EGR1-pwm (25 times, p < 0. 05), and Zic3 (15 times, p < 0. 01) sites was observed in the total datasets of the IUGR-N and control groups. Zic 3 is best known for its role in the initial establishment of left-right symmetry during early development, and we thus did not further pursue studies of its role in metabolic alterations during adulthood associated with IUGR. EGR1, also known as NGF-1a, is a transcription factor that is strongly expressed in the liver. It is expressed with circadian rhythms and plays a crucial role in regulating liver metabolism.
It has also been implicated in cell cycle regulation and fibrogenesis via the TNF-a and IL6 pathways  [45, 46]. However, a large proportion of the known EGR1 target genes were not found in our ChIPSeq dataset and did not display differential enrichment between the IUGR-N and control groups at the thresholds applied. Moreover, Sirius red staining was not consistent with higher levels of liver fibrosis in the 12-month-old IUGR mice than in the age-matched control mice (data not shown). Taken together, these data suggest that, despite its crucial role in the liver, EGR1 does not play a key role in metabolic dysregulation associated with IUGR. We then used the STRING database to identify potential interactions (https://string-db. org). Interactions for genes with significantly differentially enriched H3K4me3 histone marks, with a >1.
5-fold difference between the IUGR-N and control groups and filtered with a high The four statistical analysis approaches indicated overlapping significant differential H3K4me3 peaks (adjusted p value < 0. 05) between the 3-month-old male IUGR-N and control mice (n ¼ 5e6 per group) as revealed by ChIP sequencing. Statistically significant differential peaks common to Trended_edgeR and DESeq 2 (5490 peaks) were conserved for further analysis. (B) The heatmap of differential H3K4me3 peaks showed that the male controls (blue group) and IUGR-N (pink group) mice were segregated. (C) Protein interactions with high confidence illustrated with the STRING database for the subset of genes displaying differential enrichment (>1. 5-fold) between the IUGR-N and control groups. Each node corresponds to one protein, and relevant connections are illustrated with connecting lines.
The red core indicates proteins related to pathways in cancer, the blue core denotes insulin signaling pathway proteins, the yellow core represents axon guidance proteins, and the green core demonstrates Fc epsilon RI-mediated signaling pathway proteins according to the KEGG pathway analysis with the STRING database. The green circles indicate greater enrichment in the H3K4me3 mark in the IUGR-N group and the red circles indicate higher enrichment in the control group. confidence setting, are illustrated. There were significantly more interactions than expected (enrichment in proteineprotein interactions: p ¼ 3. 31 Â 10 À6 ) involving many genes associated with cancer biology, axon guidance, and metabolism according to STRING's Reactome and KEGG pathways (Figure  3C ). Interestingly, the Akt1 gene was identified as the main network's principal node.
This suggests that any increase in the food supply during lactation, after a restriction, may be sensed as overfeeding, thereby increasing susceptibility to cardiometabolic diseases, but the absolute amount of food may also be important, as these effects occurred later in the IUGR-N mice than in the IUGR-O mice. This delay in the IUGR-N mice enabled to detect molecular modifications before the appearance of obvious physiological alterations, concerning glucose homeostasis in particular. These modifications, which appeared before changes to the DAG and ceramide levels, may be associated with epigenetic changes in the liver, highlighting the crucial role of this particular mechanism. The ChIPSeq dataset highlighted a few hundred genes displaying differential enrichment in the H3K4me3 histone mark between the young adult control and male IUGR-N mice due to the stringency of the postbioinformatic analysis.
This analysis revealed alterations in the expression of genes involved in various pathways in cancer, coagulation, axon guidance, metabolism, and the insulin signaling pathway as suggested by the STRING database. The potential influence of perinatal nutrition in cancers was illustrated, in particular, by the Reactome pathway, suggesting that challenges in mice born with IUGR could lead to differences in sensitivity to liver cancer development. We focused on modifications associated with metabolic alterations. The STRING network centered on the Akt gene suggested an important role for this gene, and, more globally, for early changes to the insulin signaling pathway in the pre-symptomatic 3-month-old IUGR-N mice. Consistent with this finding, we observed highly coherent modifications affecting various levels of this pathway (Figure  6 ).