dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene expression microarrays Exponentially growing cells (with DMSO or aphidicolin) were harvested and RNAs were extracted with RNeasy plus mini kit (Qiagen). RNAs quality and quantity were controlled using Nanodrop ND-1000 and Bioanalyzer 2100 Expert from Agilent. cDNAs were prepared according to the standard ThermoFisher protocol from 100ng total RNA (GeneChip™ WT PLUS Reagent Kit Manual Target Preparation for GeneChip™ Whole Transcript (WT) Expression Arrays User Guide). Following fragmentation, 5. 5 µg of single stranded cDNA were hybridized on Human Clariom S Arrays in GeneChip Hybridization Oven 645 for 16 hr at 45°C. The arrays were washed and stained in the Affymetrix Fluidics Station 450. Arrays were scanned using the GeneChip Scanner GC3000 7G and images were analysed using Command Console software to obtain the raw data (values of fluorescent intensity).
Tagmented DNA was then purified using the MinElute PCR purification kit (Qiagen), amplified with 10 cycles of PCR, and purified using Agencourt AMPure SPRI beads (Beckman Coulter). Resulting material was quantified using the KAPA Library Quantification Kit for Illumina platforms (KAPA Biosystems), and sequenced with PE42 sequencing on the NextSeq 500 sequencer (Illumina). Analysis of ATAC-seq data was very similar to the analysis of ChIP-Seq data. Reads were aligned using the BWA algorithm (mem mode; default settings). Duplicate reads were removed, only reads mapping as matched pairs and only uniquely mapped reads (mapping quality >= 1) were used for further analysis. Alignments were extended in silico at their 3'-ends to a length of 200 bp and assigned to 32-nt bins along the genome. The resulting histograms (genomic "signal maps") were stored in bigWig files.
Peaks were identified using the MACS 2. 1. 0 algorithm at a cutoff of p-value 1 e-7 , without control file, and with the -nomodel option. Peaks that were on the ENCODE blacklist of known false ChIP-Seq peaks were removed. Signal maps and peak locations were used as input data to Active Motifs proprietary analysis program, which creates Excel tables containing detailed information on sample comparison, peak metrics, peak locations and gene annotations. To annotate the ATAC-seq peak value and coverage within genomic regions of interest, we used Merge BedGraph and AnnotateBed bedtools functions respectively (Galaxy Version 2. 29. 2). We then normalized the three biological replicates values across all genomic regions (Early, Mid, Late, ADV and DEL) by a 2way ANOVA Sidak's multiple comparisons test.
Processing ChIP-seq data from public databases ChIP-seq, pDamID and RepOri data were download from ENCODE, GEO, 4DN project and Replication domain data base respectively (Table  S4 ). The epigenetic marks coverage of each given regions list (Early, Mid, Late, ADV, DEL) was calculated using AnnotatedBed bedtools function (Galaxy Version 2. 29. 2). The mean for each epigenetic mark coverage in given regions was calculated to generate clustering tree heatmap based on Pearson correlations with ClustVis software  (44). Fluorescent DNA halo 400,000 cells were harvested after synchronization and treated with nuclei buffer (10 mM Tris at pH 8, 3 mM MgCl2, 0. 1 M NaCl, 0. 3 M sucrose, protease inhibitors) plus 0. 5% Nonidet P40 for 5-10 min on ice (depending on cell line). Nuclei were attached to coverslips using cytospin (1500-1800 rpm for 5-10 min, depending on cell line); stained with DAPI (2 mg/mL for 4 min); and immersed in a buffer containing 25 mM Tris (pH 8), 0.
5 M NaCl, 0. 2 mM MgCl2, 1 mM PMSF, and protease inhibitors for 1 min, then in Halo Buffer (10 mM Tris at pH 8, 2 M NaCl, 10 mM ethylene diamine tetra acetic acid [EDTA], 1 mM DTT, protease inhibitors) for 4 min. After two washing steps with wash buffer 1 (25 mM Tris (pH 8), 0. 2 M NaCl, and 0. 2 mM MgCl2) for 1 min, and with buffer 2 (buffer 1 without NaCl) for 1 min extracted nuclei were fixed in 2% formaldehyde for 10 min and processed for immunofluorescence. Images containing about 200 halo per condition acquired with a Nikon Ni-E microscope and a DS-Qi2 camera with 64X objective and MFHR (Maximum Fluorescence halo Radius) was measured in Image J software.
RT differential analyses were performed on biological replicate experiments using the START-R suite software  (40)  and only significant modifications between aphidicolin and control condition were retained. It has been reported that the RT profile of somatic cells is closely related to the cell type and tissue origin  (10). By performing hierarchical clustering of RT for the different cell lines, we found that, in absence of replication stress, non-tumour cells clustered together (cluster 1) and are separated from cancer cells (cluster 2) (Figure  1a ). In cluster 2, we noticed that the replication timing of RKO is closer to HCT116, consistent with the fact that they are both colon cancer cell lines with microsatellite instability due to mismatch repair deficiency (MMR-) (Table  S1 ). Upon aphidicolin treatment, the two distinct clusters of cancer and nontumour cells remain apart but distances measuring relatedness between cancer cells are altered.
ADV aRTIL occur in heterochromatin late replicated chromosomal regions. To investigate whether aRTIL are associated to specific epigenomic signatures, we next analysed histone modifications and other epigenomic features such as Lamina-Associates-Domains (LADs) and chromatin accessibility (DNase-seq). Given that the most advanced domains in RKO are shared with K562, we used public epigenomic data on K562 to characterize ADV aRTIL. First, this approach allowed us to validate our experimental RT through the expected enrichment of the typical chromatin marks of Early, Mid and Late replicated regions (Figure  2a, b ). For instance, Early replicated regions are enriched in euchromatin marks such as H3K27ac and H3K9ac, while Late replicated regions correlate with heterochromatin marks (H3K9me3) and with LaminB1 (LADs) (Figure  2a, b ).
5 µM), its enrichment is directly correlated with γ-H2AX peak coverage  (52). Using ChIP-seq data from this study, we analysed the coverage of histone variants γ-H2AX and mH2A1. 2 in K562 with or without aphidicolin treatment. As we did for epigenomic marks, we compared ADV and DEL aRTIL coverage with Early, Mid and Late S-phase regions. We confirm that, without aphidicolin, Early-S regions are the most enriched in γ-H2AX and mH2A1. 2 histone variants (Figure  3d-e )  (53). As expected, aphidicolin induces a significant increase in γ-H2AX and mH2A1. 2 in all control regions. Interestingly, aphidicolin treatment does not modify the coverage of γ-H2AX and mH2A1. 2 within ADV aRTIL whereas it induces a strong increase of these two histone modifications coverage within DEL aRTIL (Figure  3d-e ).
Altogether, ADV aRTIL appear to be heterochromatin flexible regions which might be resistant to replication stress. Under low dose of aphidicolin, the decrease in DNA replication fork velocity can be compensated by the firing of dormant origins, preventing under-replication  (77) (78) (79) (80) (81) (82). Interestingly, it was demonstrated that in cancer cells replication origin usage is more flexible  (83). Moreover, cancer cells rely on a higher rate of origin licensing protein expression  (84, 85) , allowing a more efficient usage of dormant origins in response to replication stress  (86). Taking our data into consideration, we can imagine that, in cancer cells, the higher chromatin accessibility described within ADV aRTIL favours the access of DNA replication and repair proteins to these specific heterochromatin regions, leading to activation of dormant origins and earlier DNA replication.
ACKNOWLEDGMENT We are grateful to the  Technologic platform from CRCT  ( Manon Farcé  for cytometry,  Marie Tosolini  and  Juan-Pablo Cerapio-Arroyo  for the bio-informatics analysis). The authors also thank  Nathalie Marsaud  ( GeT-Biopuces, Toulouse Biochip Facility ) for her help and advice about the experimental protocol of  Microarray Transcriptomic. We thank  Marie Jeanne Pillaire ,  Guillaume Labrousse ,  Gaëlle Legube ,  Chunlong Chen  and  Frédéric Bantignies  for their helpful advice and discussions throughout this project. We also want to thank  Sarah McClelland ,  Laure Crabbe ,  Jean-Yves Masson  and  Emmanuelle Bitoun  for their precious feedback and proofreading.
FUNDING This work was supported by  "la Ligue nationale contre le cancer" (LNCC labellisation  to JSH and VB and the grants  RS16/75-108  and  RS17/75-135  to JCC), the  "Institut National du Cancer" (INCA ,  PLBIO2016-10493  to JSH, VB and JCC), the  "Centre de Recherches en Cancérologie de Toulouse  " to VB, the  "Laboratoire d'excellence Toulouse Cancer -TOUCAN " to JSH, the  "Groupement des Entreprises Françaises contre le Cancer" (GEFLUC)  to JCC, the  "IdEx Université de Paris " ( ANR-18-IDEX-0001 ) to JCC, the generous legacy from  Ms Suzanne Larzat  to JCC, and "la  Fondation pour la Recherche Médicale " to LC. MM and VP were funded by  INSERM, the" Fondation Toulouse Cancer Santé " and " Pierre Fabre Research Institute " as part of the  Chair of Bioinformatics in Oncology of the CRCT.
Using miRnome data previously published  [10] , we observed a significant decrease expression of all the members of the miR-29 family (miR-29a-3p, miR-29c-3p, miR-29b-2-5p*, miR-29b-3p, miR-29c-5p*, miR-29b-1-5p* and miR-29a-5p*) in MG thymuses compared to control thymuses (Fig. 2a ). To validate these observations, we analyzed by RT-PCR the expression of all miR-29 subtypes. We observed that miR-29a-3p was the most strongly expressed miR-29 thymic subtype (Fig. 2a, b ), as consistent with the data from the mouse thymus  [5]. We confirmed that all miR-29 miRNAs were significantly downregulated in MG thymuses except the very low expressed miR-29b-1-5p* (Fig. 2b ). Detailed results are showed for the three most highly expressed miRNAs, miR-29a-3p, miR-29b-3p, and miR-29c-5p* (Figs. 2c, e, g ).
Nevertheless, the decrease in TECs was less significant than in the whole MG thymus probably as TECs were analyzed after 7 days in culture conditions that are different than the inflammatory thymic MG context. Despite the decrease expression of the three miR-29 subtypes, no increased expression for IFNAR1 was observed (Fig. 3b ). This suggests that there might be a threshold level for miR-29 to decrease IFNAR1 mRNA expression in TECs. In addition, human IFNAR1 is not listed as targeted by miR-29a according to the usual sequence matching databases (miRanda, TargetScan, and DIANA microT), and thus species differences clearly exist. A link between miR-29 and the IFN-I pathway was assessed through the exposure of TECs from healthy donors to IFN-β, as IFN-β is known to be overexpressed in the thymus of MG patients  [3].
Through analyzing (See figure on previous page. ) Fig. 2  Decreased expression of miR-29 subtypes in human MG thymuses. a miR-29 subtypes expression was assessed using data from a thymic miRnome study  [10]. Each point corresponds to the mean of raw intensity values for 6 non-MG adult control thymuses and 12 MG thymuses. p value was assessed by the Wilcoxon test. b-h miR-29 subtypes expression was assessed by RT-PCR in 6 non-MG adult control thymuses and 12 MG thymuses. b Each point corresponds to the mean values for the controls and MG thymuses. p values were assessed by the Wilcoxon test. c, e, g Detailed expression of miR-29a-3p, miR-29b-3p, and miR-29c-5p in control and MG thymuses with a low (orange dots) and high (red dots) degree of thymic hyperplasia. Data were normalized to the 28S.
p values were assessed by the Mann-Whitney test. d ,f ,h Analyses of the sensitivity and specificity with ROC curves for miR-29a-3p, miR-29b-3p, and miR-29c-5p* in control and MG thymuses the effect of IFN-β on miR-29a-3p, the most highly expressed miR-29 subtype, we observed that IFN-β induced a decreased expression of miR-29a-3p (Fig. 3c ). The effect of IFN-β on miR-29a-3p expression is independent of DICER as IFN-β did not affect DICER mRNA expression in TECs (data not shown). Together, these data indicate that the down-regulation of miR-29 subtypes in the thymus of MG patients could be due to a lower expression of these miRNAs in TECs, potentially mediated in response to high levels of IFN-β exposure in the MG thymus.
HET mice developed symptoms more rapidly than WT mice even before the boost immunization (Fig. 4a ). At the end of the experiment, 92% of HET mice were sick (with a global clinical score over 2) as compared to only 63% for the WT mice (Fig. 4b ). We measured the serum levels of anti-T-AChR IgG and specifically anti-AChR IgG1, IgG2b, and IgG2c (IgG2 isotypes acting via the complement in C57BL/6 mice). All mice immunized with T-AChR developed anti-AChR antibodies that were not detected in CFAinjected control mice. However, we did not observe any differences between miR-29a/b-1 HET mice compared to WT mice (Figs. 4c-f ). We also measured the levels of anti-mouse AChR IgG, IgG2b, and IgG2c by coating the ELISA plates with a mouse AChR peptide but did not detect any increased level of anti-mouse AChR antibodies in miR-29a/b-1 HET mice (data not shown).
Our approach is based on the squalenoylation concept  (Couvreur et al. , 2006)  and consists in the combination of two amphiphilic compounds: (i) the squalenoyl-gemcitabine (SQ-Gem) pro-drug, able to spontaneously form stable nanoassemblies (NAs) with a size around 120 nm  (Couvreur et al. , 2008) , and (ii) the alkyl-lysophospholipid edelfosine (EF), which belongs to a new generation of antitumor drugs (Estella-Hermoso de  Mendoza et al. , 2008; Ríos-Marco et al. , 2017). Gemcitabine (Gem) is a nucleoside analogue with proven anticancer activity in several tumors. It is given as a second-line treatment in pediatric OS and is currently in Phase II/III clinical trials for NB (NCT00407433). Its chemical conjugation to squalenic acid protects this cytotoxic drug from a rapid metabolization in vivo, one of its main drawbacks in the clinic  (Dubey et al.
Cell internalization of SQ-Gem/EF NAs In order to measure cell uptake kinetics of SQ-Gem/EF NAs, 531M cells were seeded on 12-well plates at a density of 100,000 cells per 1 mL/well for 24 h to achieve 80% confluence. The fluorescently-labeled NAs and free BChol-red 2% w/w were then added at a final concentration of 10 μM (Eq. to EF and SQ-Gem) to each well. After incubation at different time intervals (0. 5, 2, 4, 8 and 24 h), cells were washed with PBS, trypsinized and centrifuged for 5 mins at 200 g. Samples were analyzed on a BD Accuri C6 flow cytometer with an argon laser as light source of 488 nm wavelength. The mean cell fluorescence intensity was determined in a total of 12,000 cells using the BD Accuri CFlow Plus software (Accuri Cytometers, Ltd. , UK). For detection of BChol-red fluorescence the emission spectrum was measured at 540-590 nm.
Cytotoxicity studies For the in vitro cytotoxicity assays the cell lines SH-SY5Y, U-2 OS were plated on 96well plates at a density of respectively 5,000 and 2,000 cells per 100 μL/well, 24 h before the addition of different concentrations of Gem, EF, SQ-Gem NAs and SQ-Gem/EF NAs in triplicate wells (n ≥ 3). After 72 h incubation, complete medium containing 15 % v/v of MTS was added to each well. Absorbance was measured 2-4 h later in a microplate reader (iEMS reader MF, Labsystem, Helsinki, Finland) at a test wavelength of 492 nm and 690 nm for the reference wavelength. The concentration of drug required to inhibit cell proliferation by 50% (IC50) was calculated using GraphPad Prism version 5. 0 (GraphPad Software, San Diego, CA, USA). For drug synergy evaluation, Gem (nM) was combined with EF (μM) at a constant combination ratio of concentrations (1:1, 1:1.
At various time points (5', 15', 30', 1, 2, 4, 8, 24 and 48 h), around by 150 µL of blood were collected from the submandibular vein in EDTA surface-coated tubes (n = 3 at each time point). The simultaneous quantification of Gem, SQ-Gem and EF in mouse plasma was performed using a UHPLC/MS/MS method previously optimized (see SM for further details). Pharmacokinetic calculations were performed by non-compartmental analysis using PKSolver add-in program for pharmacokinetics and pharmacodynamics data analysis  (Zhang et al. , 2010).
Acknowledgements Asociación Española Contra el Cáncer (AECC) (CI14142069BLAN), Red nanoCARE ( RED2018-102469-T ) and  Fundación Caja Navarra/Obra Social La Caixa  are gratefully acknowledged. We would like to gratefully thank grants from the  Asociación de familiares y amigos de pacientes con neuroblastoma (NEN).
Patient samples Patients' samples were selected in January 2018 from the Institut de Cancerologie de Lorraine biobank (Nancy, France) (CircuLOR-1 study (NCT02751177)). Methods were performed in accordance with the relevant guidelines and regulations and approved by the ethical and scientific board of Institut de Cancerologie de Lorraine. This study has been specifically approved by the ethical and scientific board of Institut de Cancerologie de Lorraine. All samples were anonymized prior to analysis. Plasma samples were collected from patients with a mCRC with a requested KRAS, NRAS or BRAF genotyping. cfDNA was extracted from plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany). Then, BEAMing method was performed with the OncoBEAM CRC1kit (Sysmex Inostics, Hamburg, Germany).
This technique consists on a hybridization between DNA-coated beads and a sequence specific fluorescein-labeled probe which are then analyzed by flow cytometrie  [21, 22]. Data are processed by a software and results then reported as "no mutation detected" or "mutation detected". In our study, mutation detection with Idylla platform was performed on 15 samples with ctKRAS cartridge and in 3 samples with ctNRAS-BRAF cartridge. Discrepant samples were assessed using a third assay. DNA was extracted from plasma using the QIAamp1 Circulating Nucleic Acid kit (Qiagen, Hilden, Germany), libraries were then prepared with the custom STS 51-Gene kit (Sophia genetics, Saint-Sulpice, Switzerland), a capture-based target enrichment kit. Sequencing was performed on MiSeq (Illumina, San Diego, USA) and results were treated using Sophia DDM1 software (Sophia genetics).
Combined with crystal structures of the LdcI decamer (PDB ID: 3N75) and the RavA monomer (PDB ID: 3NBX), our first low resolution cryo-electron microscopy (cryo-EM) map of the LdcI-RavA cage, performed imposing the D5 symmetry of the LdcI onto the whole assembly (EMD-2679), provided initial insights into the elements involved in the complex formation  13. Specifically, rotations of the C-terminal arms of RavA with respect to the N-terminal AAA+ ATPase modules, and accompanying massive reorientation of the tip domains called LARA (LdcI Associating domain of RavA), were shown to mediate RavA binding to either LdcI or adjacent RavA monomers in the cage  13. The lateral contacts observed between neighbouring RavA hexamers in the LdcI-RavA complex are unique amongst AAA+ ATPases. Building further upon these results, we now present a higher resolution cryo-EM structure of the LdcI-RavA cage in the presence of ADP, obtained without symmetry imposition.
2 ). The complete LdcI-RavA cage is formed by two parallel LdcI decamers surrounded by five hexameric RavA spirals. Therefore, to illustrate the overall architecture of the LdcI-RavA cage, we constructed a C5-symmetrised map of Class 1 (Methods). Each RavA hexamer harbours six binding interfaces: two lateral interactions with neighbouring RavA monomers mediated by the triple helical domain of RavA (Fig. 1b ,), and two interactions per LdcI decamer mediated by the LARA domains at the end of the four other RavA monomers (Fig. 1 ). The resulting map differs dramatically from the previously published one  13  (EMD-2679, PDB ID: 4UPB) that was calculated with a D5 symmetry inherent to LdcI, thereby leading to a distortion of the RavA spirals into a C2-symmetrical assembly. A pseudo-atomic model of the cage was then created by flexibly fitting crystal structures of LdcI and RavA into the maps of Class 1 and 2 using iMODFIT  27  (Methods).
The positions of the LARA domains of RavA contacting LdcI were inferred from a cryo-EM map of the LdcI-LARA complex (EMD-3206)  28. In contrast to what was anticipated from the LdcI-LARA cryo-EM map, the crystal structure of the LdcI decamers (PDB ID: 3N75)  12  remained virtually unchanged upon fitting into the map of the LdcI-RavA complex, indicating that RavA binding does not affect the LdcI conformation. As for the atomic model of RavA to be used for flexible fitting into the spiral RavA density inside the cage, we decided to reexamine the crystal packing in the RavA structure (PDB ID: 3NBX)  8. Indeed, while RavA was reported to crystallise as a monomer, analysis of the crystal packing in space group P6 5 reveals a continuous left-handed RavA helix (Fig. 2a ) with ADP molecules bound at the intersubunit interface (Supplementary Fig.
Free RavA has a spiral and a flat C2-symmetric conformation. Initial processing of the RavA-ADP dataset indicated a strongly preferred top-view orientation of the particles on the cryo-EM grid, resulting in a nonuniform distribution of angular projections. A second dataset was therefore collected at a 30°tilt (see "Methods"). 2D class averages from the combined dataset revealed the presence of two distinct conformational states of the RavA hexamer, containing either one (Fig. 3a , b, blue squares) or two gaps (Fig. 3a, b , red squares). 3D classification and refinement (see "Methods", Supplementary Figs. 4  and 5 , Supplementary Table  1 ) resulted in two maps (at overall resolution of 7 and 6 Å, respectively), corresponding to an open spiral with a single nucleotide-free seam (Fig. 3c , Supplementary Figs.
All of these residues directly interact with ADP in the intact active site interface (Supplementary Fig. 3 , Supplementary Fig. 6c, d ). In an attempt to mimic the active ATP-bound state of the RavA hexamer, a cryo-EM dataset was collected on free RavA in the presence of ATPγS, a slowly-hydrolysable ATP analogue often used to stabilise the ATP-bound state of ATPases. However, 2D class averages indicated that RavA-ATPγS displays even more conformational heterogeneity than RavA-ADP (Supplementary Fig. 7 ). Both the C2-symmetric and spiral conformations are present, as well as a seemingly C6-symmetric ring and even a C7-symmetric oligomer. The biological relevance of these two additional states is uncertain (see for instance Sysoeva, 2017  36  for review), and this heterogeneity, coupled with a strongly preferential orientation, hampers successful 3D separation.
However, a comparison of 2D class averages displaying the asymmetric spiral and C2-symmetric closed ring conformations of RavA-ADP and RavA-ATPγS datasets does not reveal any noticeable differences. Most importantly, this observation suggests that, similarly to ADP, ATPγS binding does not fix RavA in one particular structural state. Structural insights into the ATPase cycle of RavA. The crystal structure of the RavA monomer 8 corroborated the former phylogeny-based classification of the MoxR family as a member of the AAA+ clade 7  [24] [25] [26] 37. Thi  clade harbours in particular an additional linker (termed the pre-Sensor 2 insertion) that repositions the C-terminal helical lid of the AAA+ module relative to the N-terminal αβα core domain  25, 26. In such a spatial configuration, different from all other AAA+ clades and unique to clade 7, the Sensor 2 motif cannot contribute to ATP binding and hydrolysis in the same monomer (Supplementary Fig.