dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, in prostate cancer, maximum temperatures up to 55 • C were reached  [127]. Moreover, in glioblastoma, patients' overall survival was improved following MHT treatment  [129]. In addition, both of these studies highlighted the fact that only moderate side effects were observed, with no serious complications  [128, 129]. Recently, a phase 0 clinical trial (NCT02033447) investigating SPIONs-induced MHT with AMF has been completed but, as far as we know, no results have been published so far. Interestingly, another recent phase I (NCT04316091) clinical trial will study MHT in osteosarcoma with SPIONs triggered by spinning magnetic fields (SMF, a new type of magnetic field) in association with neoadjuvant chemotherapy  [118]. Despite the fact that the feasibility of SPIONs-induced hyperthermia has been demonstrated at both preclinical and clinical levels, the low number of clinical trials can be partly explained by the fact that this thermotherapy is at the interface of several disciplines (physics, chemistry, biology, medicine, pharmacology) with potential issues to in designing ad hoc SPIONs.
Funding: This research was funded by a  French Government  grant managed by the  French National Research Agency  under the program " Investissements d'Avenir " with reference  ANR-10-EQPX-05-01/IMAPPI Equipex. It was also supported by the  Institut de Chimie Moléculaire de l'Université de Bourgogne  ( ICMUB UMR CNRS 6302 ) and by the  Centre George-François Leclerc  (Preclinical imaging and radiotherapy platform, nuclear medicine department).
1D, E ). Particles in both samples showed a median size of ~130 nm (Fig. 1E ). In addition, we compared particles size and concentration when measured in fluorescence mode to analyze Lp-particles labelled with the red-lipophilic dye. Size distribution was similar (Fig. 1F , right panel), and ~85% of the particles were positive for the membrane dye, consistently with our FACS data (Supplementary Fig. 1 ) and previous studies. Possible differences in size between the analyses are probably due to the different accuracy of the various methods used here, a phenomenon described in detail by Bachurski and colleagues who have compared the different methods  44. Together these results suggest that most of the Lp-derived nanoparticles considered in our study are indeed Lp-EVs. However, we could not use the NTA to measure the percentage of Lp-EVs positive for the RNA dye as the set-up of the machine was not compatible with the fluorescent properties of the RNAdye.
1D ). L. pneumophila sRNAs contained in Lp-EVs show similarity to human microRNAs. To characterize the RNA molecules present, and to identify those enriched in Lp-EVs we performed RNAseq analyses. Four independent RNAseq libraries were constructed from RNA isolated from purified Lp-EVs and as control from the bacterial pellets from which the Lp-EVs had been shed and deep sequenced using an Illumina platform. The sequences obtained from the Lp-EV RNAseq libraries were compared to those obtained from the RNA extracted from the bacterial pellets (Supplementary Fig. 1E ). The Lp-EV-sRNA cargo was defined as RNAs for which after normalization at least 1000 reads were sequenced and that showed an enrichment of a log2FC > 5 as compared to the RNAs from the bacterial pellets. Using these parameters, the analysis identified 39 different sRNAs enriched in the Lp-EVs (Supplemental Data 1).
The 20 highest enriched sRNA that were present in all four biological replicates comprised segments of 12 tRNAs, 4 sRNAs, and 4 fragments of mRNA located either in the coding region or the untranslated region (UTR) of genes (Table  1 ). To predict possible functions in the host cells we investigated whether certain of these sRNA might show similarities to human microRNA (hsa-miR) sequences using the miRBase database (http://www. mirbase. org/). We conducted the search with a cutoff E-value <12  45. This revealed that segments of 15 of the 20 L. pneumophila RNAs showed some similarity to different hsa-miRs (Table  1 ). Most interestingly, the nucleotides between 48 and 65 of RsmY showed similarity to the hsa-miR144-3p, a microRNA predicted to interact with the UTR of mRNAs coding for proteins implicated in the host immune response to pathogens, such as ddx58 mRNA (coding for RIG-I), rel mRNA (coding for c-Rel, a NFκB subunit) or mapk8 mRNA (coding JNK1).
05 significant). Red Square, representative size distribution of particles for each sample. *p = 0. 0112 (Wilcoxon). ns: p > 0. 05 (Wilcoxon). Source data provided as Source data file. E Size distribution pre-and postfloatation was unchanged. F Absolute particle concentration (left) and median size (right) measured through light scatter (All) and fluorescence (DiD) mode. Each dot is one measurement and the (mean) SD of n = 3, ns: p > 0. 05 (Wilcoxon). Source data provided as Source data file. G THP-1 cells infected for 8 h with L. pneumophila wt or ∆rsmY. Data are presented as (mean) SD of n ≥ 3 independent, biological replicates (p < 0. 0001) H) THP-1 incubated 3 h with wt or ∆rsmY Lp-EVs. Data are presented as (mean) SD of n = 5 independent biological replicates. (p < 0. 0001). (G + H) Protein quantities of RIG-I and IRAK analyzed by western blot, intensities relative to the non-infected control (NI) and RhoGDI loading control.
A two-way ANOVA for statistical analysis was performed. Right, representative western blots. Source data provided as Source data file. I Quantification of RIG-I, IRAK1 and cRel protein levels after transfection of THP-1 with RsmY and tRNA-Phe. Relative mean intensities normalized to non-transfected cells (NT) and RhoGDI loading control. Control RNA (ctrlRNA) average of random L. pneumophila DNA, anti-sense of RsmY or tRNA-Phe. Left, representative blot. Data are presented as (mean) SD of n = 8 independent biological replicates. For statistical analysis a two-way ANOVA was performed with p < 0. 05 significant (*), p < 0. 01 very significant (**), p < 0. 001 extremely significant (***). Source data provided as Source data file. J Representative EMSA (n = 3) of in vitro transcribed RNA. mavs, ago1, and mapk9 mRNAs (coding for IRAK1, RIG-I, MAVS, AGO1, and JNK2, respectively).
Data are presented as (mean) SD with each dot representing the mean of up to 600 independently analyzed cells. For statistical analysis an unpaired t-test was performed (two-tailed; p < 0. 05 significant). Source data provided as Source data file. C U2OS cells labelled with Rab5 and Rab7 CellLight incubated with DiD-labelled Lp-EVs. Co-localization of Lp-EVs with Rab5 + early endosomes (EE) 4. 5 h post-infection and with Rab7 + late endosomes (LE) 5 h postinfection. Live cell image acquisition of multiple fields per well of infected U2OS cells was performed over the time at 37 °C (5% CO 2 ) on an automated confocal microscope. Representative images of five independent experiments. White: DiD-labelled Lp-EVs; Blue: Nucleus; Red: Rab5; Green: Rab7. D Quantification of Lp-EVs co-localizing with EE and LE were determined during 5 h of infection.
Data are presented as (mean) SD with each dot representing the mean of around 500 independently analyzed cells. Source data provided as Source data file. E Quantification of purified HiBiT-GroEL EV content delivery within LgBiT-THP-1 acceptor cells, +/-Bafilomycin A1 (BA1) or Cytochalasin D (CD) treatment. Negative control, LgBiT-THP-1 cells incubated with purified Lp-EVs (no HiBiT). The luminescence in the negative control was defined as 0%. Bafilomycin A1 (loss of endosomal acidification) and Cytochalasin D (inhibition of actin polymerization) significantly reduces the content delivery into the host cell. Error bars represent the (mean) SD of at least n = 8 biological repeats. For statistical analysis, a two-way ANOVA analysis was performed with p < 0. 0001 (****). Source data provided as Source data file.
Discussion The analyses of the RNA content of L. pneumophila extracellular vesicles (Lp-EVs) and the study of its functional role in infection provides important new insights into host-pathogen communication and trans-kingdom signalling. Our dataset demonstrates that L. pneumophila-RNAs, in particular the sRNA RsmY and the tRNA-Phe, can be transported within Lp-EVs into the host cell where they are biological active and can modulate the host immune response in a miRNA-like manner. We found that Lp-EVs show a unique RNA profile, different from the RNA content of the bacterial cells from which they derived, as 39 sRNAs are highly enriched in the Lp-EVs suggesting that RNA is not randomly incorporated. Indeed, distinct RNA content has also been reported for bacterial EVs shed from Escherichia coli or Pseudomonas aeruginosa  45, 68.
As we adjusted our MOI in all experiments to the conventional flow cytrometry data, we indicate throughout the manuscript that the MOI was estimated according to conventional flow cytometry. It also needs to be noted, that we analyzed how many cells indeed take up of Lp-EVs to estimate the amounts of Lp-EVs impacting the host cell. We used high content image analyses to analyze the number of EVs detectable in infected U2OS cells. We observed on average only about 10% of cells that contained detectable EVs. Thus, even with the relative high amount of EVs used according to ZetaView quantification the cell cultures were not saturated with Lp-EVs but a high background of noninfected cells that moderate the measurable output is present. Also, the amount of EVs we used did not lead to cell death of U2OS or hMDM cells, even after 17 h pi (Supplementary Fig.
Therefore, for our experiments with Syto ® RNA-Select we reduced the SSC voltage and adapted the SSC threshold to reduce the background noise. Thus, mainly large vesicles and EV-agglomerates were detected, which frequently appear during the purification process. Flow cytometry data are submitted to flow-repository public database (https://flowrepository. org/) accession numbers FR-FCM-Z2XL and FR-FCM-Z2XM. Floatation assays. For the floatation assays, we proceeded as previously described  42, 43. Briefly, Lp-EVs labelled with Vybrant DiD-dye or not and processed through a size exclusion column, were centrifuged at 100,000 × g for 1 h (MLA-50 rotor). Pellets were resuspended in 1 mL 60% sucrose and deposited in the bottom of the tube. 1 mL of 30% sucrose solution and 1 mL of PBS were sequentially loaded on top.
After 3 h, the THP-1 cells were pelleted by centrifugation at 500 × g, and RNA was extracted as previously described using the miRNeasy ® Mini kit (Qiagen). Subsequently, total RNA was treated with Turbo DNase (Thermo Scientific), and RNAs were further processed according to the TruSeq stranded mRNA sample preparation guide of Illumina. Before Illumina Nextseq 500/550 (Illumina) multiplex sequencing, the quantity was determined by Qubit 2. 0 (Invitrogen) and the quality was checked by Bioanalyzer (Agilent Technologies). RNAseq data analyses. For analyzing the RNA content of Lp-EVs the single-end reads in FASTQ format generated by Illumina sequencing were processed to remove adapters using Cutadapt software version 1. 15  81  , Sickle (https:// github. com/najoshi/sickle) version 1. 33 for trimming of the reads (quality threshold Phred score 20).
After adapter removal and trimming, all sequence reads shorter than 20 nucleotides were eliminated. Bowtie 1 version 1. 2. 2  82  was used with a database containing the sequences of ribosomal RNA (rRNA) operons of L. pneumophila strain Paris (NC_006368. 1) to map all reads corresponding to the rRNA sequences. All mapped reads were removed and the unmapped reads were aligned to the L. pneumophila Paris sequence using Bowtie version 2 2. 3. 4. 3  83. Only uniquely mapped reads were kept, to avoid that a read contributes for the coverage value at different positions. We build, sorted, and indexed the BAM files for each mapping result using the Samtools suite version 1. 3 (https://github. com/samtools/ samtools). To count reads overlapping each feature, we used featureCounts version 1. 6. 3 from the Subreads package  84.
The primary option was used to be sure to count only primary alignments. To perform differential analysis of the four replicates for the two conditions (Lp-EVs vs bacterial pellet), we used the package Sartools version 1. 3. 0 including DESeq2 methods. In order to normalize the counts, we used DESeq2 with the « median » option to compute size factors  85. The resulting tables containing differentially expressed genes with counts, normalized counts, fold changes (ratio of differential expression), and P-values were used and visualized using the Artemis analyzing tool version 16. 0. 17  86. The transcriptome of THP-1 cells after uptake of Lp-EVs were analyzed as described above. mi-RNA analyses. The sRNA highly enriched in the Lp-EVs were compared using the database for human mature miRNAs (has-miR) miRBase Release 22.
All possible combinations of the vector were transfected into THP-1 cells using the SG Cell Line Kit (Lonza) and luciferase activities were measured with a Synergy2 (BioTek) after 24 h following the manufactures instructions. The resulting ratios of Firefly/Renilla activities were normalized to the corresponding values from the control (ctrlRNA) experiments. When indicated, Ago2-inhibitor (BCI-137, Sigma) was added at a final concentration of 30 µM to the transfected THP-1 cells, and the luciferases activities were measured after 24 h. The data shown in the graph are the mean of four independent biological repeats, each in duplicates, using a two-way ANOVA for statistical analysis with values of p < 0. 05 significant (*), p < 0. 01 very significant (**), and p < 0. 001 extremely statistically significant (***).
RNA libraries of total RNA, Ago2-IP, and IgG-IP were prepared following the NEBNext Small RNA Library Prep Set for Illumina and sequenced with the NextSeq 500/550 v2 (Illumina). Analyses of Ago2 CLIP-Seq data. Single end reads in FASTQ format generated by Illumina sequencing were processed to remove adapters using Cutadapt software version 1. 15  73  and Sickle (https://github. com/najoshi/sickle) for trimming of the reads (quality threshold Phred score 20). For each remaining read, the 3'end nucleotide was removed as it is always mismatching during the alignment to the reference sequence. After adapter removal and trimming, all sequence reads shorter than 12 nucleotides were eliminated. We also removed reads longer than 36 nucleotides, to focus on miRNA enrichment. Good quality reads were aligned to a database containing the human genome sequence build GRCh38 plus the L.
Images were transferred to the Columbus Image Data Storage and Analysis System version 2. 9. 1 (Perkin Elmer) for evaluation. High-Content Analyses (HCA) were performed by using in-house developed HCA scripts in Columbus, shared upon request. Briefly, Hoechst 33342 signal was used to find the nuclei of cells and then segment nuclei and cytoplasm in each cell. Then, DiD signal was used to find Lp-EVs in the cytoplasm of hMDMs, and the fluorescence of FISH probes was used to analyze which Lp-EVs were positive for RsmY RNA. Lp-EV content release characterization: HiBiT/LgBiT secretion assay. Lp-EVs of bacteria expressing GroEL-HiBiT (pMMB) were purified as described before. The Luciferase activity of the total input was quantified using the Nano-Glo (R) HiBiT Lytic detection System (Promega) with a Synergy2 (BioTek) following the manufactures instructions.
For the analysis, the Columbus Image Data Storage and Analysis System (Perkin Elmer) was used. HCA was performed as described above. The DiD signal was used to detect Lp-EVs in the cytoplasm of U2OS-Sec61β and the fluorescence of Mitotracker, Lysotracker, Sec61β-GFP, Rab5, and Rab7-CellLight were measured inside each Lp-EV in order to analyze whether they co-localized with mitochondria, lysosomes, ER, Rab5, or Rab7 endosomes, respectively. For Z-stack time-lapse 3D videos, U2OS-Sec61β cells were plated in IBIDI dishes and imaged in an Ultraview-VOX spinning-disk confocal microscope (Perkin Elmer) using a 63X objective, excitation lasers at 405, 488, 561, and 640 nm, and emission filters at 450, 540, 600, and 690 nm. Time-lapse 3D videos were analyzed and edited using Volocity software version 6.
Acknowledgements Work in the C. B. laboratory was financed by the grant  n°ANR-20-CE15-0021-01-BIOEV , the  Institut Pasteur , the  Fondation pour la Recherche Médicale (FRM)  grant  N°E QU201903007847  and the grant n°ANR-10-LABX-62-IBEID. We thank  Monica Rolando  for critically reading of the manuscript and helpful discussions and  Nathalie Aulner  and  Anne Danckaert ,  Photonic BioImaging (PBI) UTechS  for support. For electron microscopy experiments  M. Nilges  and the  Equipex CACSICE  ( n°ANR-11-EQPX-0008 ) provided funding for the Falcon II direct detector. G. L. lab is funded by  ANR-20-CE15-0021-02 ,  ANR-19-CE18-0020-03 , and  ANR-18-IDEX-0001 ,  IdEx Université de Paris. S. B. is a recipient of a PhD fellowship from  PSL University. The funders, other than the authors, did not play any role in the study or in the preparation of the article or decision to publish.
Data availability The sequence reads as well as the coverage files of the RNAseq libraries of the Lp-EV analyses have been deposited in the NCBI Gene Expression Omnibus (GEO) database  90. Accession number GSE159109. The RNAseq data from THP-1 cells have been deposited in the NCBI Gene Expression Omnibus (GEO) database. Accession number GSE190376. Flow cytometry data are submitted to flow-repository public database (https:// flowrepository. org/) accession numbers FR-FCM-Z2XL and FR-FCM-Z2XM. Source data are provided with this paper.
Recently, paradigm shifting strides have been made regarding the discovery and mode of action of carbapenemase inhibitors  [12] , including the recent discovery that MBL inhibitors do not necessarily require direct interaction with zinc for them to perform their action  [13]. Traditionally, the approaches used for the discovery of new molecules involve several methods. One strategy is to screen crude extracts from natural substances for biological activity against organisms of interest, without knowledge of the mechanism of action. Another method, called chemical screening, is based on selecting substances from biological sources and performing exhaustive biochemical analyses without taking into account their potential to be used as treatments. These identified substances would then be deposited in large databases and screened for potential therapeutic properties at a later stage.
A third approach, called target-oriented screening, relies on first selecting the biological target and then screening compounds for their activity based on their predicted interactions with the target. These discovery pipelines have yielded the vast majority of the drugs being used at the present time, including antibiotics. Although historically effective, these approaches are resource-and manpower-intensive, and are prone to "re-discovering" the same molecules due to the use of similar algorithms and databases, which is financially and intellectually inefficient and can impede progress. This has opened the way for the exploration of alternative approaches to drug discovery. One such approach is applying machine learning and complex algorithms for the virtual screening of possible compounds that can have the desired effects.
Virtual screening of carbapenemase inhibitors, using molecular docking techniques based on different sampling and filtering algorithms, together with molecular dynamicsbased applications to study specific inhibitor/enzyme interaction systems, is a successful approach that has previously demonstrated its utility  [14]. One of the many advantages of using traditional approaches to drug discovery in recent decades is the generation of extensive databases of molecules. The strides taken in computational analyses have opened the way to new methods and approaches that have a wide range of applications. These applications include the description of the dynamical properties of macromolecules (including proteins), providing structural interpretation of experimental data, and simulating interactions between molecules.
In addition, the myriad of databases, potential targets, and search parameters to choose from can prove to be quite challenging. Moreover, the potential toxic effects of the molecules and unfavorable PK/PD parameters might result in a loss of several years of searching and experimentation, as would be the case with any other method. In this context, it is important to count on the joint efforts of research groups with expertise in different areas of knowledge, capable of collaborating not only to obtain new carbapenemase inhibitors but, above all, establishing a useful framework and strategy for working on this discovery. For this reason, the "EPIC Alliance" network (Alliance for the Exploration of Pipelines for Inhibitors of Carbapenemases) (Joint Programming Initiative on Antimicrobial Resistance https://www.
jpiamr. eu/projects/aepic; Date: 1 May 2020) was created. In brief, the aim of the EPIC Alliance is to discuss and plan optimization strategies for the computational approach that would allow for an efficient search for promising molecules that can be taken beyond the hypothetical level and tested through lab experiments, animal models, and clinical trials. The action route of the "EPIC Alliance" network is used to create a discussion forum for the exchange of ideas and knowledge to bridge the gaps between different research groups and develop the best strategy for discovering carbapenemase inhibitors. This forum will attempt to discover the best biochemical, molecular, and physical parameters to be used for the data mining of carbapenemase inhibitors; define which databases are most suitable for data mining; determine how to translate the computational findings into experiments; and determine the cost-efficiency of this strategy.
Uniting experts from these different fields is expected to result in a synergistic collaboration that can tackle the discovery of carbapenemase inhibitors from different angles and increase the chance of bringing the potentially discovered molecules to the market. From this forum, we propose that the scientific community think up new strategies to be followed for the discovery of new carbapenemase inhibitors, so that this process is efficient and capable of providing results in the shortest possible time and within acceptable time and economic costs. The discussion is to be established in five phases: (i) information exchange: sharing their expertise amongst different fields, both theoretical and experimental; (ii) choosing biochemical, molecular, and physical parameters, in addition to the algorithms, pipelines, and databases that are most suitable for data mining of carbapenemase inhibitors; (iii) designing in vitro and in vivo experiments, and clinical trials; (iv) preparing a feasibility study to determine whether the new approach is better than existing ones, as well as preparing a proof of concept; and (v) evaluating the preliminary results obtained in order to optimize and fine-tune the strategy.
L) Tregs as a percent of total CD4+ T cells of nontreated side tumor. Statistical significance was calculated using two-tailed Student's t-test. M) Successful depletion was verified by flow cytometry analysis using splenocytes collected eight days after the first i. p. injection. N) Tumor growth curves of LKR13 two-tumor model under the context of CD4 + or CD8 + T cell depletion (n = 6-7). Antibodies were i. p. injected on one day before and one day after tumor inoculation and repeated once per week. Statistical significance was calculated using two-way analysis of variance. Data are shown as the mean ± SE, * P <0. 05, * * P < 0. 01, * * * P < 0. 001, and * * * * P < 0. 0001. immune response including an increase in cytotoxic CD4 + T cells that results in lung tumor regression; the decline in immunosuppressive G-MDSCs and Tregs in the tumors of Mito-ATO-treated mice may have allowed for the expansion of CD4 + T cells.