dataesr/scientific-paragraphs-categorization huggingface dataset:
Densitometry analysis (E) was performed with the ImageJ software by plotting the gray value across a line spanning all lanes of the gel, at the migration line corresponding to full-length tRNAs. teraction with the translation regulator YB-1  (11)  and for their G quadruplex-mediated tetramerization  (13, 23). This strongly suggests that it is the structure that mediates the interaction with the protein, although the role of these tRNA halves in inhibiting translation initiation goes beyond their interaction with YB-1  (35). In support of the latter, we have demonstrated here that the capacity of tRNA Gly 5 halves to form homodimers correlates with their intracellular and in vitro stability, and may explain the remarkable extracellular stability observed for these small RNAs. The mechanism underlying the release of tRNA-derived fragments from cells leading to the non-EV extracellular fraction is still a matter of investigation.
6 ml). We repeated SL-RT-qPCR of collected fractions (in the 9. 4-12. 4 ml range, normalizing to Ve = 9. 4 ml) but avoided heating the samples before reverse transcription (C). The amplification profile (D) was shifted two fractions (0. 50 ml) to the right respect to the elution profile of full length mature tRNAs. This was assessed by running each fraction (after concentration by ethanol precipitation) in a 15% denaturing (7 M urea) polyacrylamide gel, and staining with SYBR gold (E and F). Densitometry analysis (E) was performed with the ImageJ software by plotting the gray value across a line spanning all lanes of the gel, at the migration line corresponding to full-length tRNAs.
ACKNOWLEDGEMENTS The authors want to thank  Ricardo Ehrlich ,  M ónica Marín  and  Tamara Fernández  for fruitful discussions and for supplying yeast tRNA Phe. Agustín Correa ,  Federico Carri ón ,  Gonzalo Greif  and  Carlos Robello  assisted with  SEC ,  DSC  and sequencing, respectively. The authors want to thank members of the following facilities at IPMon:  UPR ,  UBP  and  UBM. Members of the Enzymology Lab  ( Faculty of Science, Universidad de la Rep ública ) assisted with UVmelting experiments.
■ RESULTS AND DISCUSSION Ten small molecules known to be KMO inhibitors with nanomolar-to low-micromolar-range half-maximal inhibitory concentrations (IC 50 s) (Supporting Information Table  S1 ) were selected as input to molecular similarity techniques (see Experimental Section). Small-molecule similars were extracted from the DrugBank 21 database. Nine of the actives were deemed too dissimilar to anything in DrugBank to follow up experimentally. One active compound, termed "Pharmacia WO199805660 core scaffold" displayed a high degree of similarity to seven small molecules within DrugBank (Supporting Information Table  S2 ). Patent literature documents derivatives of the Pharmacia WO199805660 core scaffold as having IC 50 s of less than 100 μM against Rattus norvegicus KMO. Substructure searching of the ChEMBL database  22  (version 23) revealed a series of 16 derivatives that have activities on eight kinases based on an IC 50 value cutoff of 10 μM (see Supporting Information Table  S3 ).
The four commercially available compounds were assayed in our KMO lysate enzymatic inhibition assay. 26 Only diclofenac was shown to have inhibitory properties against the KMO enzyme, achieving a 38% inhibition at a diclofenac concentration of 10 μM. Enzymatic testing revealed an IC 50 of 13. 6 ± 1. 34 μM for diclofenac against KMO in the cell lysate and 1. 35 ± 0. 10 mM against whole cells, as shown by the dose-response curves in Figure  2. Our microdialysis binding assay 27 demonstrated a K D of 64. 84 ± 29. 47 μM for the interaction and direct binding of diclofenac to KMO (Table  1 ). After the confirmation of diclofenac as a binder and inhibitor of KMO, five Saccharomyces cerevisiae KMO crystal structures were published by Amaral et al. 28  Of key interest to the evaluation of diclofenac as a KMO inhibitor are two of these structures: first, PDB ID 4J36 containing KMO, a flavinadenine dinucleotide (FAD) molecule, and a small-molecule inhibitor and second UPF 648, a 2-(3,4-dichlorobenzoyl)cyclopropanecarboxylic acid, displaying a K i of 74 ± 14 nM against yeast KMO and 56.
7 ± 6. 8 nM against human KMO. Another structure with PDB ID 4J33 contains S. cerevisiae KMO bound to only the FAD molecule, allowing a valuable comparison of conformational changes upon inhibitor binding. Amaral et al. suggest, through the use of model building, that UPF 648 acts as a mimic of L-Kyn, binding into the same pocket, blocking L-Kyn turnover via NADPH into 3-HK. However, UPF 648 is larger than L-Kyn, with a molecular weight of 259 versus 208, and has a significantly more hydrophobic bulk (clog P values of 2. 27 and -1. 3, respectively). This is structurally accounted for by loop movement (Pro321-Gln325) and reorientation of a sixstranded antiparallel β-sheet. Amaral asserts the validity for the use of S. cerevisiae KMO as a template for a structure-based drug design against human KMO.
In addition, the nonhuman structure of P. fluorescens KMO has been used to develop compounds with nanomolar IC 50 s 18 as well as a clinical candidate. 19 Visual overlays of UPF 648 and diclofenac show a striking similarity, suggesting a common binding mode. Indeed, docking of diclofenac into the S. cerevisiae KMO structure with PDB ID 4J36 using Autodock Vina 1. 1. 2 (see Figure  3  and Supporting Information for docking protocol) reveals a plausible binding mode.
The ChEMBL database (version 23) reports single-digit nanomolar IC 50 s/K D s for three protein targetscyclooxygenase 1 and 2 and the interleukin-8 receptor A. See Supporting Information Table  S4  for ChEMBL-assigned targets of diclofenac. Depending on the abundance and affinities of the high number of known targets for diclofenac, the effective concentration exposed to intracellular KMO may be greatly reduced. It is important to emphasize that the presented results only highlight a starting point for a possible drug repurposing process. However, the data serve to highlight the power of molecular similarity techniques and their value in the discovery of medicinal chemistry starting points and the prediction of off-target effects. Intensive work combining structural biology and several rounds of medicinal chemistry would be needed to identify a diclofenac derivative with nanomolar activity to reach a lead compound status for KMO.
■ EXPERIMENTAL SECTION Molecular Similarity. A survey of literature followed by visual inspection and clustering identified 10 potent KMO inhibitors (Supporting Information Table  S1 ) that had their predicted lowest energy three-dimensional (3D) conformation generated using Omega2 29 (OpenEye Scientific, version 2. 3. 2) and written out in the SD file format. A data set containing approved, experimental, nutraceutical, illicit, and withdrawn drugs was obtained from the DrugBank  21  Web service (data downloaded on 30/6/2010), and the same 3D conformer generation procedure was applied with the addition of a saltstripping step using OpenBabel 30 (version 2. 2. 2), generating 4648 low-energy conformers. Programs implementing MACCS, Path, and LINGO fingerprints were implemented in the Java programming language using the Chemistry Development Kit.
IC 50 data were plotted using the GraFit v5. 0. ) , was used to fit the data to obtain IC 50 and the Hill slope value s. Input parameters were percentage of inhibition (y) and logarithm of compound concentration (x). Cellular Compound IC 50 Determination. The IC 50 values of compounds were tested in the cellular context to determine the cell permeability of test compounds. HEK293-KMO(V5-6His) cells were passaged in 96-well plates at 2 × 10 4 cells per well in Dulbecco's modified Eagle's medium with 10% fetal bovine serum, 1% L-glutamine, and 1% penicillin-streptomycin. The cells were incubated overnight at 37 °C in 5% CO 2 / 95% O 2. The assay medium utilized was opti-MEM containing 1% L-glutamine, 1% penicillin-streptomycin, and 200 μM L-Kyn. The compounds for testing were diluted in this medium with a final dimethyl sulfoxide (DMSO) concentration of 1%.
Results The GIST Enhancer Landscape. Our group and others have previously reported the use of H3K27ac ChIP-seq to define cancer-specific enhancer landscapes  (11, 15, 16). These studies have revealed tumor dependencies, implicated neoplastic cellular origins, and identified translational approaches to improving cancer diagnosis and therapy. To apply this analytic platform to GIST, we performed H3K27ac ChIP-seq on 12 fresh-frozen primary GIST tumors and 9 KIT-mutant GIST cell lines (SI Appendix, Table  S1  and Dataset S4). The primary samples included three metastatic and three localized KIT-mutant tumors, four localized PDGFRAmutant tumors, and two localized SDHC-mutant GIST tumors. The GIST cell lines consisted of six driven by activating KIT mutations, two of which were subclones containing KIT tyrosine kinase inhibitor-resistance mutations, and three cell lines which harbor activating genomic KIT mutations but express KIT at low levels and do not rely on KIT expression or signaling for growth.
KIT-independent GIST cell lines clustered more closely with human mesenchymal stem cells (hMSCs), suggesting the achievement of a dedifferentiated state when escaping KIT dependency. Ranked enhancer plots of GIST tumors identify many characteristic tumor markers and dependencies in addition to multiple unique GIST-associated genes (Fig. 1B  and Dataset S1), which show a recurrent pattern in GIST tumor subtypes (SI Appendix, Fig. S1 B-E ). Ranked enhancer plots of KIT-dependent parental cell lines (Fig. 1C ) show preservation of a large subset of tumorassociated SEs, in contrast to KIT-independent cell lines (SI Appendix, Fig. S1F ). H3K27ac ChIP-seq meta tracks for characteristic and unique GIST-associated genes show preservation of chromatin modification in vivo and in vitro (SI Appendix, Fig.
The GIST Transcription Factor Network. Master regulatory TF networks have previously been shown to establish the enhancer landscape and reinforce cellular identity and to be maintained in malignancy  (5, 15). This network forms an autoregulatory loop, with master TFs driving transcription of themselves within a core circuit. The core circuit further maintains an extended regulatory circuit, and these transcriptional networks define the cell's specific enhancer pattern (e. g. , Fig. 1F  and SI Appendix, Fig. S2 ) and gene expression program. Regulatory circuitry can be computationally inferred from SE mapping, relying on H3K27ac ChIP-seq and predicted TF DNAbinding motifs  (19). Using this approach, we defined 24 core and extended regulatory circuit TFs in GIST (Materials and Methods, Fig. 2A , and Dataset S2), which highlight ETV1  (13)  and unique TFs as central to GIST biology.
4E ), compared with ETV1 sgRNAs or control conditions. These results implicate HAND1 in the regulation of KIT gene expression and suggest its essential role, together with BARX1 loss, in the development of metastatic disease. Expression of BARX1 and HAND1 Predicts Clinical Outcome. To test the hypothesis that BARX1 and HAND1 expression can predict metastatic potential, we evaluated independent expression datasets of localized and resected primary GIST tumors from patients with longitudinal clinical follow-up  (28). By RNA-seq, HAND1 was the only TF up-regulated in localized disease that would later develop metastasis, while BARX1 was the TF most enriched in localized GIST without future metastasis (Fig. 5A ). Next, we analyzed a larger, clinically annotated dataset with gene expression from localized, untreated tumors quantified by microarray  (29).
In this dataset, BARX1 expression was again correlated with no future disease recurrence (Fig. 5B ), while HAND1 was expressed in localized tumors that subsequently developed metastasis following primary resection (Fig. 5C ). There was a significant anticorrelation between BARX1 and HAND1 expression in these primary tumors (Fig. 5D ). Based solely upon expression of BARX1 or HAND1, or combined BARX1/HAND1 expression values, metastasis-free survival from the date of initial diagnosis disclosed a significantly higher risk of metastasis with low BARX1 expression (Fig. 5E ), high HAND1 expression (Fig. 5F ), or combined low BARX1 and high HAND1 expression (Fig. 5G ). The combination of low BARX1 and high HAND1 had the greatest predictive value for the development of metastatic disease, with a hazard ratio of 10.
We further define two accessory TFs, BARX1 and HAND1, which augment this network, and their antagonistic expression is closely linked to the development of metastatic disease. Additional studies are warranted to further test these unique prognostic markers of GIST clinical outcome. This type of experimental approach generates a rich dataset urgently needed to improve the understanding of GIST, and is feasible to translate into other sarcomas that commonly lack biological understanding, diagnostic markers, prognostic indicators, or targeted therapies.
Materials and Methods Cell Lines and Virus Production. All cell lines tested negative for mycoplasma infection and are not listed in the International Cell Line Authentication Committee database. Human embryonic kidney 293FT (R70007; Invitrogen), the GIST cell lines GIST-T1, GIST-T1/816, GIST430, GIST430/654, GIST430B, GIST882, GIST48, GIST48B, and GIST226  (33) , MaMel  (34) , and EWS502  (35)  were cultured in DMEM containing 10% FBS, 2 mM L-glutamine, 100 mg/mL penicillin, and 100 mg/mL streptomycin, except GIST882, which was grown in RPMI with identical supplementation. See SI Appendix, Table  S1  for descriptions of each cell line's KIT mutation. hMSCs (SCRC-4000; ATCC) were grown according to the manufacturer's recommendations. To ensure cell line identity, KIT exons were sequenced to confirm the expected coding mutations (primers used for sequencing are listed in Dataset S3).
Sequencing Data Analysis. Computational methods used for data analysis have been described previously  (15) , and are available from the J. E. B. laboratory github page (https://github. com/BradnerLab/pipeline). Sequencing read alignment and annotation. All ChIP-seq and ATAC-seq data were aligned to the human reference genome assembly hg19, GRCh37 (https:// www. ncbi. nlm. nih. gov/assembly/GCF_000001405. 13/), using Bowtie2 (37) (version 2. 2. 1), and gene annotations by Gencode annotation release 19 (https://www. gencodegenes. org/releases/19. html). Normalized read density was calculated using the bamliquidator (version 1. 0) read density calculator. Aligned reads were extended by 200 bp, and the density of reads per base pair was calculated. In each region, the density of reads was normalized to the total number of million mapped reads, generating read density in units of reads per million mapped reads per base pair (rpm/bp).
Peak finding, enhancer classification, and clustering. Peak finding was performed using Model-based Analysis for ChIP-Seq [MACS  (38) , version 1. 4. 2]. As control, matched input chromatin was used to generate background signal for peak determination. Calling of typical enhancers and superenhancers was performed using the ROSE2 (12) software package. For SE determination, an optimized stitching region was used to cluster H3K27ac peaks into contiguous enhancers at each enhancer region. SEs from each sample were hierarchically clustered on pattern similarity of median normalized H3K27ac enhancer signal as determined using pairwise Pearson correlations. ChIP-seq data representations. Ranked enhancer plots were generated by merging H3K27ac signal across subgroup samples and ranking by H3K27ac signal.
GIST-associated TFs were determined by compiling a list of all TFs with an associated SE shared between the highestquality tumor ChIP-seq datasets (n = 8). Candidate TFs were required to be expressed in GIST tumors [n = 13, fragments per kilobase of transcript per million mapped reads (FPKM) > 12] and cell lines (n = 4, FPKM > 12). The GIST transcriptional core regulatory circuitry was determined by identifying SEassociated TFs predicted to bind cooperatively to SEs in an autoregulatory loop, as previously described  (19). TF motif analysis in ATAC peaks within each SE was performed using Find Individual Motif Occurrences (FIMO)  (40) , where motifs were known. Extended regulatory circuitry was defined as GISTassociated TFs that are outside of, but connected to, the core regulatory circuitry.
With each TF displayed as a node, edges were drawn between predicted binding partners to indicate core and extended regulatory circuitry relationships. Dataset S2 indicates whether the GIST-associated TFs have an SE, TE, or absent enhancer in different ChIP-seq samples. TF dependency and sensitivity scores were derived from Project DRIVE  (21)  shRNA screening performed in GIST-T1 cells; not all GIST-associated TFs were included in the screen. RNA-seq alignment and analysis. RNA-seq data, cell count-normalized where appropriate, were aligned using STAR  (41) , with expression quantification using Cufflinks  (42)  to generate gene expression values in FPKM units. Hierarchical clustering of RNA-seq data was performed using Cluster 3. 0  (43)  and visualized with Java Treeview  (44). Gene set enrichment analysis  (45)  was performed using CP:REACTOME in the Molecular Signatures Database (software.
broadinstitute. org/gsea/). Differential expression analysis of RNAseq data was performed using edgeR  (46) , with statistical analyses reporting corrected P values. For TF differential expression, candidate TFs were required to have >10 rpm averaged across tumors and detectable levels of expression (>1 rpm) in the lower-expressing group. Array gene expression analysis was performed on 60 untreated, localized primary GIST tumors as previously described (29) using Whole Human 44K Genome Oligo Arrays (Agilent Technologies). The mean expression level was used as the cutoff to define high and low expression values. Immunoblotting. Cells were lysed in RIPA buffer containing protease inhibitor mixture (Roche) and centrifuged at 14,000 × g for 10 min to remove genomic DNA and debris. Protein concentrations were determined using a bicinchoninic acid-based assay (Pierce Biotechnology).
Immunoblots shown are representative of at least three independent in vitro experiments. Quantitative RT-PCR. Tumors were homogenized or cells were trypsinized and washed in PBS for RNA extracted using the RNeasy Mini Kit (Qiagen). Libraries of cDNA were made using the SuperScript VILO cDNA Synthesis Kit (Invitrogen). RT-PCR was performed using SYBR Select Master Mix (Life Technologies) on a ViiA 7 Real-Time PCR System (Life Technologies). Relative mRNA levels were calculated by the ΔΔCt method using GAPDH expression as reference, and normalized to expression in the GIST-T1 cell line where indicated. All RT-PCR amplicons were verified by gel electrophoresis and DNA sequencing. Primers used for RT-PCR are detailed in Dataset S3. Cloning and CRISPR Assays. Cell lines stably expressing a human codonoptimized Streptococcus pyogenes Cas9 (73310; Addgene) were generated by viral transduction.
CRISPR guide RNAs were cloned into Lenti-sgRNA-EFS-GFP (65656; Addgene)  (27). All sgRNAs were designed using the CRISPR Design Tool (crispr. mit. edu/) and detailed in Dataset S3. Selected sgRNAs were designed to target the upstream coding region or DNA-binding domain of TFs, and named according to the targeted exon. For sgRNA-GFP competition assays, flow cytometry to quantify GFP-expressing cells was performed in 96-well plates using a Guava easyCyte Flow Cytometer. Statistical Analysis. Center values, error bars, P-value cutoffs, number of replicates, and statistical tests are identified in the corresponding figure legends. Replicates represent separate tumors or unique cell lines where indicated, or in experiments using the same cell line the replicates represent separate treatments of cultures from the original stock unless otherwise specified.
Error bars are shown for all data points, with replicates as a measure of variation within each group. Samples sizes were not predetermined, and the investigators were not blinded to allocation during experiment or outcome assessment. Kaplan-Meier analysis of metastasis-free survival was calculated from the date of initial diagnosis to the date of first metastasis, relapse, last follow-up, or death. Hazard ratios and P values were calculated using the Cox proportional hazard model. Data and Materials Availability. Novel ChIP-seq, ATAC-seq, and RNA-seq data presented in this publication are available online through Gene Expression Omnibus accession no. GSE95864. RNA-seq data of primary GIST tumors (Fig. 5A ) are available through the Sequence Read Archive under accession no. SRP057793. Microarray data are available under ArrayExpress accession no.
ACKNOWLEDGMENTS. We thank  Nacef Bahri  for assistance with cell lines, the  Dana-Farber Cancer Institute Molecular Biology Core Facilities , and the  Brigham and Women's Hospital tissue bank. Rick Kaitz ,  Charles Lin ,  Andrew J. Wagner ,  Suzanne George ,  Jennifer Perry ,  Leonora Linden ,  Richard Wale ,  Nancy Louise , and  Al Lou  contributed essential discussion. We thank  J. E. B. and  S. A. A. laboratory members  and the  Dana-Farber Cancer Institute Hematology/Oncology  Fellowship Program for support. We are indebted to the patients and families who donated clinical samples that enabled this research. Support for this work was provided by the following sources: Erica's  Entourage Sarcoma Epigenome Project Fund  (J. E. B. and G. D. D. ), the  Dana-Farber Cancer Institute Mittelman Fellowship  (to M.
In France, Salmonella Derby is one of the most prevalent serotypes in pork and poultry meat. Since 2006, it has ranked among the 10 most frequent Salmonella serotypes isolated in humans. In previous publications, Salmonella Derby isolates have been characterized by pulsed field gel electrophoresis (PFGE) and antimicrobial resistance (AMR) profiles revealing the existence of different pulsotypes and AMR phenotypic groups. However, these results suffer from the low discriminatory power of these typing methods. In the present study, we built a collection of 140 strains of S. Derby collected in France from 2014 to 2015 representative of the pork and poultry food sectors. The whole collection was characterized using whole genome sequencing (WGS), providing a significant contribution to the knowledge of this underrepresented serotype, with few genomes available in public databases.
Considering the prevalence of S. Derby in the pork and poultry food sectors, we decided to thoroughly investigate the genetic diversity of this serotype using a WGS approach on a large dataset (n = 140) representative of the geographical and source origins in France. In contrast to conventional molecular typing methods, WGS has the potential to compare whole genomes at a single-nucleotide resolution. Methods based on single-nucleotide polymorphisms (SNPs) allow for a detailed, targeted analysis of variations among related bacterial isolates. WGS has recently been postulated to be an ultimate subtyping technique  (Gilchrist et al. , 2015; Dunn, 2016) , and SNP-based cluster analysis was already successfully used to explore the genomic diversity of Salmonella isolates across serotypes as well as among and within specific food sources  (Wilson et al.
Phylogenetic analyses on the dataset were computed using RaXml software  (Stamatakis, 2014). The phylogenetic trees were constructed under the maximum likelihood criterion using the GTR-gamma model of nucleotide evolution. The phylogenetic analyses were based on the pseudogenome obtained using the GATK. The phylogenetic data were visualized using interactive Tree Of Life (iTOL 3  )  (Letunic and Bork, 2016).