dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization of the COMPASS Database Using MOB-Typer MOB-typer detected 9231 plasmids bearing replicons in COMPASS. Among these plasmids, 6828 plasmids carried a single replicon of which 2469 belonged to the ColRNAI family and 19 different Inc groups. Unlike replication genes, the presence of multiple relaxases within a plasmid was rare (4%) and the number of unknown relaxases was very low (n = 22). Consequently, MOB-typer was able to specifically classified 5207 plasmids into the six MOB families (MOB C-F-H-P-Q-V ). The most frequent replicon families assigned by MOB-typer were ColRNAI (n = 639), IncI (n = 362), and IncX (n = 309) while the most predominant MOB types were MOB P (n = 2303), MOB F (n = 1422), and MOB Q (n = 717). Overall, we found that half of the plasmids (51%) of the COMPASS database are predicted to be mobilizable or self-transmissible.
, 2012). However, the analysis of the COMPASS database revealed the presence of ColRNAI replicons in bacterial species belonging to five different taxonomic classes, although it was mostly found associated to Gammaproteobacteria (96%). This result suggests that ColRNAI have a broader replication range than previously reported and should be considered as putative BHR replicon. Several studies previously showed that IncP plasmids can transfer and replicate in almost all Gram-negative bacteria. These conjugative plasmids are widely present in the environment and are also present in pathogenic and opportunistic bacteria. The presence of antibiotic resistance genes and their broad distribution raises more and more concern  (Popowska and Krawczyk-Balska, 2013). The analysis of COMPASS allowed to conclude at a wide scale on the preferential associations between replicons and relaxases  (Garcillán-Barcia et al.
CONCLUSION It has been shown that highly similar bacterial strains can contain a compelling variety of different plasmids  (Lanza et al. , 2014). The analysis of the diversity and host range of circulating plasmids is therefore essential to characterize bacterial isolates. Overall, in silico typing is likely to remain an important tool for plasmid analysis, but their performance is constrained by the diverse and dynamic nature of plasmids, the plasmid dataset of interest, and the applied bioinformatics methods. We showed the advantages of using MOB-typer versus PlasmidFinder that currently does not characterize the mobility of plasmids and only includes replicons from Enterobacteriaceae and a few Gram-positive bacteria. However, more efforts need to be done to develop a general classification scheme for plasmids from all microbial lineages.
COMPASS plasmids could be easily integrated as a reference database into pipelines for reconstructing plasmids and ascertaining host range properties to new identified plasmids. The complete classification will help deciphering the molecular epidemiology of antimicrobial resistance by identifying specific types and plasmid features linked to a particular resistance gene. Health EJP. This project has received funding from the European Union's Horizon 2020 Research and Innovation Program (H2020 EJP-AMR3-RaDAR) under Grant Agreement No. 773830.
In this context, using already-approved drugs-a concept called drug repurposing-could be of interest. Indeed, deciphering the polypharmacological profile of already-approved drugs has the potential to fast-track the discovery of new efficient drugs as their toxic profiles are already known, while highlighting new drug-target interactions. An early estimation of the degree of drug polypharmacology using seven different databases identified a total of 4767 unique interactions between 802 drugs and 480 targets, suggesting an average of 6 molecular targets per approved drug  [3]. Recently, Pe on et al. [4]  updated this estimation to an average of 11. 5 molecular targets per drug in cells obtained from a more complete dataset comprising 8535 drug-target associations and 1427 targets. This high degree of polypharmacology thus provides a unique opportunity for the repurposing of already-approved drugs to target key factors involved in human diseases.
One of the main limitations of drug development is its elevated cost. Thus, the rapid identification of novel therapeutic targets for already-approved drugs provides a route to reduce development cost by expanding their indications to human conditions in which those targets have been validated  [5, 6]. Here, we applied a multipronged approach to unveil and validate new molecular targets for a well-known repurposed drug called mebendazole (MBZ). This antihelminthic agent, which belongs to the benzimidazole class, has been shown to display potent anticancer properties in various models of human cancers  [7] [8] [9] [10] [11] [12]  and thus appears as a promising candidate for drug repurposing in oncology. Although the discovery of its therapeutic potential in brain tumors was fortuitous  [11] , it already resulted in three ongoing clinical trials in high-grade gliomas in both adult and pediatric patients (NCT01729260, NCT02644291, and NCT01837862).
Gene expression analysis on patient samples Gene expression analysis was conducted using the R2 microarray analysis and visualization platform (http:// r2. amc. nl). RNA-Seq data were extracted from two independent cohorts providing open access to data acquired from various forms of cancer: the Cancer Genome Atlas (TCGA) database, and from normal tissues: the Genotype-Tissue Expression (GTeX) database. GBM TCGA dataset was used and partitioned in five subtypes according to the data available: classical (n = 17), mesenchymal (n = 27), neural (n = 17), proneural (n = 24), and not determined (n = 455). We used GTeX normal brain tissue data from the following subgroups: caudate (n = 246), cortex (n = 255), frontal cortex (n = 209), nucleus accumbens (n = 246), and putamen (n = 205). Median values were recorded using log2 transformation gene expression.
Isothermal titration calorimetry Isothermal titration calorimetry was used to determine the thermodynamics parameters of the binding between MAPK14 and the selected compounds. Titrations were carried out on a MicroCal iTC200 microcalorimeter (GE Healthcare). Each experiment was designed as normal (protein in the syringe and ligand in the cell) or reverse (protein in the syringe and ligand in the cell) titrations experiments using 13 to 17 injections at 15 °C or 25 °C. Raw data were scaled after setting the titration-saturation-heat value to zero. Integrated raw ITC data were fitted to a one-site nonlinear least-squares-fit model using the MicroCal Origin plug-in as implemented in ORIGIN 9. 1 (Origin Lab, Northampton, MA, USA). Finally, DG and TDS values were calculated from the fitted DH and KD values using the equations DG = ÀRTlnKD and DG = DH À TDS.
The most promising poses were subjected to postprocessing with the commercial SEESAR software (http://biosolveit. de). Briefly, this computer-aided design tool, which was developed for modelers and chemists, relies on the HYDE method  [26]  to evaluate affinity contributions to the binding for each ligand atom. SEESAR can then be used to (a) optimize docking poses within the binding site, (b) identify optimal and suboptimal moieties from ligands, (c) analyze SAR data from 3D complex point of view, and (d) design and evaluate new analog compounds in order to optimize the series. In this work, SEESAR was initially used to identify potential issues in prioritized poses, mainly desolvation penalties, low-quality hydrogen bonds, and suboptimal torsions. Ultimately, SEESAR was used to estimate the affinity of each compound from the MBZ series from their respective postprocessed binding mode.
In silico target prediction suggests multiple novel molecular targets for MBZ Computational methods are now being actively investigated as tools for identifying new therapeutic targets for repurposed drugs. Here, we used a data-driven predictive tool called MOLTARPRED  [27]. Briefly, MOL-TARPRED (http://moltarpred. marseille. inserm. fr/) is a freely available web tool for predicting protein targets of small organic molecules. It is powered by a large knowledge base comprising 607 659 molecules and their known targets from the ChEMBL database  [28]. MOLTARPRED returns the most similar target-annotated molecules to the user-supplied query molecule, enabling the identification of a large amount of predictable targets based on those known in similar molecules. Using the current ChEMBL database, MOLTARPRED can thus predict up to 4553 different proteins as putative molecular targets  [4].
MBZ inhibits ABL1, ERK2/MAPK1, and MAPK14/p38a in vitro, with particularly high potency against MAPK14/p38a Kinases have been among the most intensively pursued targets in oncology, leading to the approval of 46 kinase inhibitors for cancer treatment to date and over 150 kinase inhibitors currently in clinical trials  [29, 30]. Interestingly, our transcriptomic analyses using freely available databases showed that 1/3 of the putative MBZ targets significantly upregulated in GBM as compared to normal brain tissue are kinases: ABL1, MAPK14/p38a, ERK2/MAPK1, and VEGFR2/KDR (Fig. 3  and Table  S1 ). Since MBZ has been previously shown to inhibit the kinase activity of the latter in vitro  [31] , we focused our validation experiments on the other three kinases, whose expression was upregulated in GBM tissue.
All tested compounds showed differential potency against the three kinases, with nocodozale being the most potent at inhibiting ABL1 activity (IC 50 = 78 AE 34 nM), MBZ at inhibiting MAPK14 activity (IC 50 = 104 AE 46 nM), and albendazole at inhibiting ERK2 activity (IC 50 = 3. 4 AE 1. 5 µM). The Fig. 2. MOLTARPRED ligand-centric target prediction workflow. The most similar database molecules to the query molecule (mebendazole) are identified as previously detailed  [27]. Known targets for these molecules are retrieved from the ChEMBL database. A reliability score for each query-target association prediction is calculated based on the proportion of the query's top hits binding to the predicted target. The proteinprotein interaction network of the 21 human proteins predicted as targets of MBZ is provided (stringdb.
Prediction of the binding mode of MBZ within MAPK14 using molecular modeling More than 200 structures of MAPK14 (UniProt ID Q16539) are available from the Protein Data Bank (PDB) structural database. Kinases are known to be highly flexible proteins, and large structural rearrangements can be observed after the superimposition of all structures in the same referential. Docking simulations were performed on several PDB structures. The 3FLY one resulted in a coherent predicted binding mode for MBZ and its analog compounds. The postprocessed binding mode using SEESAR is displayed in Fig. 6  using both 3D and 2D imageries. As anticipated from the measured enthalpy data by ITC, several hydrogen bond interactions are predicted by the model. More precisely, the carbamate-benzimidazole moiety is expected to make two hydrogen bonds with backbone atoms from M109, a crucial hot spot of the ATP-binding pocket.
In this context, drug repurposing holds great promise to develop safe, effective, and inexpensive therapeutic options that can be made readily available to cancer patients regardless of their socioeconomic status  [5, 33]. Over the last decade, MBZ has been recognized as an attractive candidate for drug repurposing in various models of human cancers  [7] [8] [9] [10] [11] [12]. Its anticancer properties were notably demonstrated in gliomas  [11, 14, 34] , and there are currently three ongoing clinical trials in high-grade gliomas in both adult and pediatric patients (NCT01729260, NCT02644291, and NCT01837862). Furthermore, a recent phase I trial demonstrated that administration of MBZ concomitantly with radiotherapy and temozolomide chemotherapy was safe in highgrade glioma patients  [35].
Interestingly, it has been demonstrated that the binding mode of selective MAPK14 inhibitors is characterized by a compound-induced peptide flip between M109 and G110  [48]. Besides, some MBZ-urea inhibitors are  known to bind the hinge region from other kinases such as VEGFR2  [49] , and one reference structure is publicly available from the PDB database (PDB ID 2OH4). Interestingly, the carbamate-benzimidazole moiety adopts the same interaction pattern with the homologous hot spot from VEGFR2 protein. These structural data strengthen the reliability of the predicted binding mode of the MBZ series within the MAPK14-binding site. Furthermore, additional molecular modeling studies supported MBZ selectivity profile, that is, a similar potency between MAPK14 and ABL1 but almost no activity for MAPK1.
The MAPK14/p38a signaling pathway has been classically considered a tumor suppressor. However, several studies have also demonstrated the protumorigenic activities of MAPK14, which facilitates the Fig. 6. Proposed binding mode of mebendazole to MAPK14. The binding mode of MBZ to MAPK14 protein is depicted as 2D (A) and 3D diagrams (B), which were generated using the 'Ligand interaction' feature from MOE and PYMOL software, respectively. The benzimidazole core is predicted to make hydrogen bond interaction with the hot spot M109, and the other aromatic moiety is deeply buried in a hydrophobic pocket of the binding site. MBZ and the side chain from M109 are displayed in sticks, and surrounding protein residues are depicted in lines. The backbone of the protein is shown as a cartoon representation, and key amino acids involved in MBZ binding are indicated.
7%) but not for BrdUTP (7% vs. 11%, respectively, Additional file 2: Fig. S1c ), confirming that BrdU affects the current in the pore. In conclusion, current alterations due to BrdU reduce Metrichor basecalling accuracy but to an extent that does not strongly affect read alignment to the reference sequence. Python scripts were then developed to realign current steps to the reference DNA sequence. To allow comparisons between multiple experiments, we normalized each profile by subtracting its mean current intensity. As shown on Fig. 1 (c  and d ), thymidine (T) was associated in most cases with the highest current values in native DNA, and its substitution by BrdU further increased the current at these sites but not at other bases. This probably explains why BrdU did not cause systematic basecalling errors at T sites (Additional file 2: Fig.
2e ). As previously shown  [14] , initiation regions detected as ascending RFD segments coincided with the position of known yeast origins (from OriDB  [23] , Fig. 2f ). These results demonstrate that our BrdU detection methods (CNN and TM) and track orientation procedures are robust  and precise and that nanopore sequencing of pulse-chase labelled BrdU replication tracks, hereafter referred to as FORK-seq, is a valid alternative to OK-seq to produce genome-wide RFD profiles. For comparison purposes, we attempted to use the D-NAscent software  [11]  to detect BrdU tracks on the same reads as CNN and TM. Although significant BrdU incorporation was detected at similar positions to CNN and TM, the signal was weaker (Additional file 2: Fig. S5 ). Using the same algorithm as for CNN or TM signals, most D-NAscent tracks could be neither detected nor oriented even after trying to optimize the detection parameters (Additional file 2: Fig.
5d ). Thus, the presence of the consensus motif at sporadic initiation events was much rarer than in clusters, and barely more frequent than in random sequences (p-value = 0. 03, non-parametric test). The ACS provides a binding site for ORC. Other required elements specify a nucleosome-free gap that facilitates ORC binding and subsequent loading of the Mcm2-7 complex, a core component of the replicative helicase required for initiation  [25, 26]. Using published datasets, we computed the distance between our initiation events and ORC  [25]  (Fig. 5e ) or Mcm2-7  [27]  (Fig. 5f ) binding sites. As expected, the clustered initation events were found much closer to known ORC and Mcm2-7 binding sites than random genomic positions. On the contrary, the sporadic events behaved very similarly to random sequences (Fig.
The BrdU signal obtained by D-NAscent software detection was located at similar positions to RepNano but was weaker, precluding automatic BrdU track detection and orientation and assembly of a valid RFD profile. The number of B calls was about 4 times lower in D-NAscent than in TM: in the highly substituted sample, TM and D-Nascent called 10. 3% and 2. 6% of all T sites, respectively. This represents a significant difference between the D-NAscent and FORK-seq computational methodologies. The other significant difference between the D-NAscent and FORK-seq experiments is the replication labeling strategy. Müller et al labelled replication by synchronizing thymidine-prototroph cells in G1 and releasing them in the presence of limiting BrdU concentrations. They found that the level of BrdU incorporation decreased as S phase progressed, suggesting that the intracellular ratio of BrdUTP to dTTP declined due to increasing activation of endogenous thymidine synthesis and/or declining rate of BrdU import.
FORK-seq relies on the incorporation of BrdU in replicating DNA, which is achievable in many experimental systems. All the sequencing reads used in this study were obtained with only six runs on the MinION sequencing apparatus, and we now routinely obtain the same number of reads with one sequencing run. When applied to mammalian cells, with larger genomes and wider spaced origins, both the length and number of sequenced DNA molecules may become limiting. However, read lengths of up to 2. 3 Mb have been reported  [6]  so there is no absolute technological limitation to improve read length. As to throughput, the recent availability of the PromethION, which delivers times more data per single flow cell and can run 48 flow cells in parallel, should allow high coverage studies of human genome replication in the near future.
6 µm C18 100 Å, LC Column 150 x 2. 1 mm; Phenomenex) and employing a flow rate of 100 µL/min. Samples were analyzed in technical duplicates and MS data were analyzed using the software TraceFinder (Thermo Scientific). A defined mixture of BrdU and dT was used for measuring the combined relative ionization and fragmentation efficiencies of these ions used for calculating the percentage of BrdU incorporation at native thymidine sites.
Nanopore sequencing and data preprocessing MinION sequencing libraries were prepared according to the manufacturer protocols using the 2D low input kit with R9 chemistry for the plasmid experiments and the 1D ligation kit with R9. 5 or R9. 4. 1 chemistry for the yeast experiments. ONT protocol and software versions used for the different samples are listed in Additional file 1: Table  T1 , T3 and T4. For the plasmid experiments, raw reads were basecalled using Metrichor (ONT) with default parameters and aligned on the reference using BWA MEM  [19]  with the -x ont2d option (-k14 -W20 -r10 -A1 -B1 -O1 -E1 -L0). The event sequences were extracted using poretools  [49]  and aligned on the reference using the sam file information and custom Python scripts. Current values were normalized by subtracting the whole read average current.
The PCA plot was generated with R pcaMethods library using as inputs 1kb long current sequences (positions 100-1100 on the reference plasmid sequence) from 1000 reads of each sample. For the yeast experiments, raw reads were basecalled using Albacore (ONT). We used Tombo (ONT) preprocess annotate_raw_with_fastqs to add the DNA sequence obtained from Albacore into the raw fast5 file. The sequence was then mapped and corrected using the reference genome and the current signal was segmented and assigned to each base using Tombo resquiggle command (which calls minimap2). These two steps output a sequence of bases associated with its average current (N i , m i ). Next steps are integrated in the RepNano software (available at https://github. com/organic-chemistry/RepNano [21] ). RepNano first computed the current shifts δ i = m i -m i+1 and used (N i , δ i ) as the input of further processing steps.
Reads where the quadratic optimized distance was greater than 0. 25 were discarded. Finally, the dataset was updated with the drift normalized current shift signals obtained for each read: δ normalized i = α 0 δ i + β 0 (step 2). We applied this procedure twice on the two thymidine samples (Thy_R1 and Thy_R2), and the two BrdU rich samples (BrdU_R1 and BrdU_R2), removing 4% of the thymidine sample reads and 12% of the BrdU sample reads. We then averaged the 2 resulting thymidine (resp. BrdU) transition matrices to determine the matrix T M T (resp. T M B ) containing the expected normalized current shift for transition in the thymidine and BrdU context (δ T N (6) , δ B N (6) ), respectively. In the same manner, we applied the second step of the normalization procedure using T M T as the reference transition matrix to all samples resulting in homogeneously normalized data used in all subsequent analysis steps.
CNN learning We used 4000 reads of each of the 7 samples described above for CNN learning. The learning was done splitting the mapped reads into sequences of length 96, and the error between the prediction and the correct sequence was minimised using a stochastic gradient descent optimizer implemented in Keras  [50]  using default parameters, a learning rate of 0. 1, a decay rate of 10 -6 and a momentum of 0. 9. The learning dataset was randomly split in 90% training and 10% validation sequences. After each epoch the loss was evaluated on the validation set and after 5 consecutive steps with no improvement of the validation loss, the learning was stopped. We then computed the average BrdU content for the reads coming from the BrdU-rich samples, which allowed us to separate BrdU-substituted reads from parental thymidine reads.
D-NAscent calling We downloaded D-Nascent from https://github. com/MBoemo/DNAscent (on 2019/11/20). After compilation, we used the detect function to call BrdU from our fast5 files. As described in  [11] , we called a BrdU when the log-likelihood was > 2. 5 and we used as input for subsequent track detection the number of B per 100 bp windows.
BrdU track calling and orientation A home-made Python script was used to detect and orient replication tracks. It is included in the RepNano package. Reads < 5 kb were not used for this analysis. For CNN and TM, BrdU content along the reads was smoothed over 30 consecutive positions corresponding to a B or a T after TM basecalling (present on average every 18 bases). For D-NAscent, we smoothed on two 100 bp-windows. We discarded reads with no tracks by filtering on both a minimal standard deviation of the BrdU proportion on the whole read (0. 115 for CNN, 0. 3 for TM, 0. 02 for D-Nascent), and a minimal signal amplitude (0. 4 for CNN, 0. 5 for TM, 0. 08 for D-Nascent). The signal was rescaled between 0 and 1 and subjected to piecewise linear segmentation using the Ramer-Douglas-Peucker algorithm implemented in the Python module simplification.
RFD profiles All following procedures are presented in the script RepNanoScript. r. CNN and TM fork data were imported into R as GenomicRanges aligned to the SacCer3 version of the yeast genome. OK-seq data from our lab were imported in R as GenomicAlignments and paired-end reads were converted into GenomicRanges. Duplicates were removed at this step. OK-seq data from the ligase mutant  [14]  were downloaded from NCBI GEO website (GSE33786) and also converted into GenomicRanges. RFD was computed using R (see RepNanoFunction. r script) as the difference between rightward-and leftward-fork coverage normalised by total coverage. The data were smoothed using a running mean with a 1 kb window. RFD profiles were exported into bigwig files using the export function from the rtracklayer package. Spearman correlations between RFD profiles were computed with the base R cor function and a heatmap was produced with ggcorplot package.