dataesr/scientific-paragraphs-categorization huggingface dataset:
Targeted sequencing was performed using the MiSeq sequencer (Illumina). Generated raw NGS data were analyzed using the Sophia DDM software (v. 5. 10. 37) (Sophia Genetics). Briefly, the bioinformatics pipeline (v. 5. 5. 79) consists of an alignment of the fastq files to generate bam files (hg19 reference genome), followed by a variant calling for the determination of SNV and indels. All results are finally available in the Sophia DDM software as retained variants and low-confidence variants. Quality control limits were required for each sample tested by NGS: >1 M reads/sample, >95% mapped reads, >70% on target, >85% at 500× depth. All pathogenic (class 5) and likely pathogenic (class 4) mutations detected among the 51 gene panel were reported. Copy Number Variation (CNV) was also calculated using CNV module from Sophia DDM software working in the gene amplification mode, i.
e. , only whole-gene duplications with a sufficiently large average copy number were reported. The threshold for gene amplification was set at 3. 25, as recommended by the supplier of Sophia DDM (Sophia Genetics). Deletions and CNVs affecting only parts of genes were ignored. The gene amplification analysis included 24 genes (NRAS, ALK, SF3B1, RAF1, PIK3CA, FGFR3, PDGFRA, KIT, FBXW7, TERT, ROS1, EGFR, MET, BRAF, FGFR1, CDKN2A, RET, FGFR2, HRAS, MYOD1, KRAS, CDK4, TP53, ERBB2). The coverage levels used for gene amplification detection included a linear GC correction deduced from all of the regions included in the panel. MSI was determined using an algorithm module provided by Sophia DDM software based on reads analysis at 6 unique MS regions, for which 3 were split into forward (FWD) and reverse (REV) strands (BAT25_FWD, BAT25_REV, BAT26_REV, CAT25_REV, NR-21_FWD, NR-21_REV, NR-22_FWD, NR-22_REV, and NR-27_REV), as previously validated in FFPE tissues from endometrial and colon cancers  [32].
30 < d < 0. 50 (moderate effect), and d ≥ 0. 50 (large effect). Data were compared using the non-parametric Wilcoxon rank sum test. Statistical analyses were performed using R-studio software (version 2022. 07. 2+576) with rstatix and Wilcox test () functions. A p-value < 0. 05 was used as a significativity limit.
Further, one case of MSI status was detected in a HER2-negative, ISH unamplified, HR-positive tumor bearing a PIK3CA gene mutation (c. 1624G > A, p. (Glu542Lys)). All data are summarized in Table  2. Table  2. Histological and molecular characteristics of the tumors: Breat cancers specimens were routinely characterized using Immunohistochemistry (IHC) and In situ hybridization (ISH) for hormonal status, defined from estrogen receptor (ER) and Progesterone Receptor (PR) expression, Ki67 labeling index, HER2 expression and HER2 gene amplification. The tumor cell content was determined by expert pathologist to validate the macrodissection area. Next Generation Sequencing (NGS) was further used in this study for determination of gene amplifications defined by Copy number Variation (CNV ) > 3. 25, gene mutations analysis with variant allelic fraction (VAF) determination.
Author Contributions: Conceptualization, J. -L. M. , A. H. and M. H. ; Methodology, N. S. ; Validation, J. -L. M. , M. H. , P. G. and V. M. ; Formal Analysis, J. -L. M. and N. S. ; Investigation, J. -L. M. and M. H. ; Data Curation, J. -L. M. and M. H. ; Writing-Original Draft Preparation, J. -L. M. ; Writing-Review & Editing, J. -L. M. , M. H. , P. G. , A. L. , A. H. and V. M. ; Supervision, J. -L. M. ; Project Administration, J. -L. M. ; Funding Acquisition, J. -L. M. and V. M. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by a research grant from Seagen France (Rueil Malmaison France) and the private research funds of the Institut de Cancérologie de Lorraine. Institutional Review Board Statement: This study was approved by the Institutional Review Board of the Institut de Cancérologie de Lorraine (Comité Scientifique Interne) on 16 November 2021, decision 20221116-5.
The housekeeping gene encoding RNA polymerase subunit B (rpoB) located on the host chromosome was used as a reference, orf1, orf7, orf8 of the SNJ2 genome were used as target genes. The specific primers used for qPCR were listed in Table  S3. The reaction system was described previously  (11) , 20 l mixtures were prepared containing 5 l of template, 10 l of iTaq Universal SYBR green Supermix Bio-Rad), 1 l of primer pairs (10 M), and 4 l of distilled water. Amplification was performed as above. Finally, the qRT-PCR data were analyzed by the comparative Ct method with rpoB gene as the reference. Three independent experiments were performed, and error bars indicated the standard deviations.
Strand specific RNAseq was performed using the NEB Next Ultra Directional RNA Library Prep Kit (NEB, E7420). After the library was constructed, Qubit 2. 0 was used for preliminary quantification, and the library was diluted to 1. 5 ng/l. Then Agilent 2100 BioAnalyzer was used to detect the insert size of the library. Quantitative reverse transcription PCR (qRT-PCR) was used to accurately quantify the effective concentration of the library to ensure the quality of the library RNA-seq libraries were sequenced using the Illumina novaseq 6000 platform. Using RSEM  (27) , the bowtie2 comparison results were used for statistics, and the number of Reads compared to each transcript of each sample was obtained, and the resulting data were then analyzed by Fragments Per Kilobase Per Million bases (FPKM) analysis  (28)  to reveal the expression levels of all genes in the Natrinema sp.
qPCR to test the impact of orf4 overexpression on SNJ2 infection and replication SNJ2 was incubated with 20 ml of early-exponential-phase (OD 600 ≈ 0. 3) cultures of J7-3-F, J7-3-F/pFJ6-P phaR -orf4 or J7-3-F/pFJ6-P phaR -orf4fsm at 45 • C for about 1 h. After absorption, cells were collected by centrifugation (10000 rpm, 5 min) and washed twice in the same volume of Halo-2 medium to remove the free viruses. Cell pellets were resuspended in the same volume of Halo-2 medium and cultured at 45 • C for 6h. Samples were taken every half hour for the first 3h post infection. qPCR analysis was performed using the same primer pairs and procedure as described above in 'Quantification of virus genome, attP and transcription level of genes by Quantitative-PCR' The qPCR data were analyzed according to the 2 -CT method.
Bioinformatic analysis The in silico-translated protein sequences were used as queries to search for sequence homologs in the nonredundant protein database at the NCBI using BLAST  (29). Searches for distant homologs and functional annotation of proteins were performed using HHpred (30) against different protein databases, including PDB, Pfam, CDD and COG. The protein secondary structure was predicted using Jpred  (31) , and multiple sequence alignments were performed by PROMALS3D  (32). Phylogenetic analyses were carried out using PhyML 3. 0 with the automatic model selection  (33). To search for Orc1-encoding pleolipoproviral regions in haloarchaeal genomes, sequences of the viral hallmark spike proteins from all characterized pleolipovirus isolates  (12)  were used to create a HMM profile with HM-MER 3  (34) , and the resulting profile was used to search Halobacteria nonreduntant protein database downloaded (January 2022) from the NCBI.
To study the conservation and co-occurrence of the three genes, namely, genes encoding the repressor Orf4, Orf7 and Orc1(Orf8) that are critical for the lysogeny-induction switch of SNJ2, we searched for their homologs in the population of pleolipoproviruses. To this end, we first used the HMM-based searches against haloarchaeal protein sequences (class Halobacteria) at the non-redundant protein database at the National Center for Biotechnology Information (NCBI), using the HMM profile built from the pleolipoviral hallmark spike protein as a query  (12, 44). The genomic neighborhoods of the identified spike protein-encoding genes were analyzed for the presence of orc1 and other genes of the conserved gene cluster of pleolipoviruses (see Materials and Methods). As a result, 31 Orc1-encoding proviruses were identified in haloarchaeal strains belonging to orders Natrialbales (n = 15), Halobacteriales (n = 15) and Haloferacales (n = 1), respec- The transcription level of orf8 was significantly increased when Orf8 was overexpressed from a plasmid.
Anti-his antibodies were used for detection of Orf8-his. Lower panel shows Ponceau S staining of the membrane as loading controls. (D) Relative expression levels of orf7 in J7-1-F, J7-1-F 8 and 8/p-8 at 0 and 9 h after MMC treatment. The expression level of orf7 in the samples was estimated by qPCR with the obtained data normalized to the level of rpoB RNA. (E) Amylase activity test to check the regulation of the P 7 promoter by Orf8. Plasmid pFJ6-P 7 -amyH, pFJ6-orf8-P 7 -amyH and pFJ6-orf8f-P 7 -amyH with amylase gene under the control of the P 7 promoter were transformed into the indicated strains: J7-1-F (snj2 + ), J7-3-F (Δsnj2) and J7-1-F 8 (Δorf8). Strains were cultured and then treated with (MMC) or without (CK) induction and amylase activity were detected by FUWA method after 16 h.
Unfortunately, structure-based searches against the PDB database using DALI did not reveal significant matches. The survey of the pleolipoproviruses in haloarchaeal genomes revealed that only ∼20% of proviruses belonging to the genus Betapleolipovirus contain the coupled orf7-like and orc1 genes. Interestingly, these two genes were not identified in proviral members of the genus Alphapleolipovirus (no provirus from the genus Gammapleolipovirus was identified during our searches). It is clear that proviruses from the family Pleolipoviridae employ distinct mechanisms to regulate their life cycle switch. Moreover, different mechanisms could be employed by closely related betapleolipoproviruses, suggestive of a possible horizontal acquisition of the orc1-orf7-like gene module of the virus. The ultimate provenance of the orc1-orf7-like gene module (e.
g. of MGE or cellular origin) remains unclear. In addition to the Orf4-Orf7-Orf8 regulatory module, we identified a type II TA system in the SNJ2 genome. Although this TA system is functional (Supplementary Figure  S5 ), it is not involved in virus life cycle switch (Figure  5A  and B ). BLASTP searches revealed that homologs of the SNJ2 TA module are widely encoded by haloarchaea. SNJ2 likely acquired the system from the host and may use it for stable provirus maintenance. Collectively, our study reveals the molecular mechanism underlying the lysogeny-induction switch of the temperate haloarchaeal virus SNJ2, which is regulated by a virus encoded DNA damage signaling pathway (Figure  9 ). The SNJ2 Orf4 is a repressor of the int SNJ2 transcription, maintaining the virus in the lysogenic state.
Samples were collected at 0, 3, 6, 9 and 12h after MMC treatment (OD 600 was adjusted to take the same amounts of samples) and crude extracts were loaded for SDS-PAGE. Anti-his antibodies were used for detection of Orf8-his. Lower panel shows Ponceau S staining of the membrane as loading controls. (D) Relative expression levels of orf7 in J7-1-F, J7-1-F 8 and 8/p-8 at 0 and 9 h after MMC treatment. The expression level of orf7 in the samples was estimated by qPCR with the obtained data normalized to the level of rpoB RNA. (E) Amylase activity test to check the regulation of the P 7 promoter by Orf8. Plasmid pFJ6-P 7 -amyH, pFJ6-orf8-P 7 -amyH and pFJ6-orf8f-P 7 -amyH with amylase gene under the control of the P 7 promoter were transformed into the indicated strains: J7-1-F (snj2 + ), J7-3-F (Δsnj2) and J7-1-F 8 (Δorf8).
ACKNOWLEDGEMENTS We thank members of the  Chen and Du laboratories  for comments and advice in preparing the manuscript. We thank  Prof. Yuping Huang  ( College of Life Sciences, Wuhan University ) for kindly providing the pNBK-F plasmid. We thank  Dr Hua Xiang  ( Institute of Microbiology, Chinese Academy of Sciences ) for kindly providing the pRF plasmid. Author contributions:Z. C. , X. C. and S. D. designed the research. Z. C. , Y. W. , X. D. , X. D. , J. X. , Y. W. and J. W. , performed the research. Z. C. , Y. L. , M. K. , X. C. and S. D. analyzed data and wrote the manuscript.
Numerical methods We use publicly available codes CRbeam  [8]  and CRPropa  [9, 10]  for cosmic ray propagation. All interactions and cosmological expansion of the Universe are turned off. The particles were emitted isotropically and propagated until they reached a sphere of a given radius 𝐷. For each particle, we store its initial direction, final direction, and final position on the sphere. The resulting output is processed with the package healpy  [11, 12]. The use of two codes makes it possible to model a turbulent magnetic field in two different ways. In CRbeam the magnetic field is generated as a sum of plane waves with random phases and directions  [13]  and its exact value is recalculated on the fly before each next step during particle propagation. In CRPropa, on the contrary, the field is precalculated on the grid and its value is set by interpolation between grid points.
The analyses were performed using the Eoulsan pipeline version 2. 0-alpha7  (Jourdren et al. , 2012) , including read filtering, mapping, alignment filtering, read quantification, normalization and differential analysis: Before mapping, poly N read tails were trimmed, reads ≤40 bases were removed, and reads with quality mean ≤30 were discarded. Reads were then aligned against the Mus musculus genome from Ensembl version 81 using STAR (version 2. 4. 0k) (Dobin et al. , 2013). Alignments from reads matching more than once on the reference genome were removed using Java version of samtools  (Li et al. , 2009). To compute gene expression, Mus musculus GFF genome annotation version 81 from Ensembl database was used. All overlapping regions between alignments and referenced exons were counted and aggregated by genes using the HTSeq-count algorithm  (Anders et al.
, 2015). A first analysis revealed that one of the samples (KO#2 day2) featured an abnormally high level of ribosomal transcripts; this dataset was therefore excluded from subsequent analyses. The rest of the analysis was carried out using the bioinformatics software R  (R v4. 1. 2 (2021. 11. 01) ), and open access packages, using the publicly available bioinformatics course DIYtranscriptomics. com (Berry et al. , 2021). Mapped raw counts were transformed in counts per million (cpm) using the cpm function from the EdgeR package (v3. 34. 1). We filtered the genes that had a log2(cpm) above 1 for more than 3 samples, and then normalized their cpm using the TMM method  (Robinson and Oshlack, 2010). The mean-variance relationship of the filtered normalized data was modeled by voom transformation, and a linear model was fitted to the data using the lmfit function from the limma package (v3.
48. 3). Bayesian statistics for the chosen pair-wise comparisons (average KO expression compared to average WT expression for each time point) were then calculated using the eBayes function from limma, and adjusted with the BH correction. An exhaustive list of differentially expressed genes (p. value<0. 05 and |logFC|>1. 5) was pulled-out using the decideTests function. Plots in Figures  1D  and S1A  were generated in R using ggplot2  (v. 3. 3. 5). The RNASeq gene expression data and raw fastq files are available on the GEO repository (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE218080) under accession number: GSE218080. Transfection and CRISPR/Cas9 genome editing mESCs were transfected as previously described  (Souquet et al. , 2018)   S2 , gRNAs designed using the Benchling website (https://benchling.
Acknowledgements: We are grateful to  Vedrana Andric ,  Alessandro Berto ,  Marta Boira  and  Salomé Neuvendel  for help in mESCs culture, differentiation, cell line establishment, or western blot analyses. We thank  Charlène Boumendil  and  Pierre Therizols  for helpful discussions, and  Benoit Palancade ,  Roger Karess ,  Charlène Boumendil  and the  Doye lab members  for critical reading of the manuscript. We also acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , for help with cell sorting, FACS analyses, and spinning disk imaging.
Funding Work in the laboratory of VD was supported by the  Centre national de la recherche scientifique (CNRS) , the  "Fondation pour la Recherche Médicale" (FRM, Foundation for Medical Research)  under grants No  DEQ20150734355 , "Equipe FRM 2015" and  EQU202003010205 , " Equipe FRM 2020 " to VD, and by the  Labex Who Am I?  ( ANR-11-LABX-0071 ; Idex  ANR-11-IDEX-0005-02 ). CO received PhD fellowships from  Ecole Doctorale BioSPC ,  Université Paris Cité  and from the  "Fondation pour la Recherche Médicale " (fourth year), AV received a post-doc grant from the  Labex Who Am  I? (2019 post-doc call) and B. S. was supported by ''la  Fondation ARC pour la Recherche sur le Cancer '' (PDF 20130606747). The ImagoSeine core facility was supported by founds from  IBISA  and the  France-Bioimaging  ( ANR-10-INBS-04 ) infrastructures.
During this second exposure, the grating period in the photoresist was fixed at 770 nm by the laser wavelength and the angle of incidence of the beams (controlled by a Labview program), giving rise to a fringe pattern where the two beams overlap. This period was chosen based on simulations with the software MC Grating  [28]  by considering two criteria: (i) maximizing the lateral and central extrema of the optical switch pattern to gain sensitivity and dynamic range (as discussed further below), (ii) while maintaining a high enough angular spread between working points to easily reach each of them without obstructing the incoming beam with the photodiodes (as shown in Figure  1a ). Indeed, with a large period, the working points become too close to each other to be distinguished or even reached with the setup illustrated in Figure  1a.
These responses were computed using the software MC Grating based on Chandezon's method  [31] , by modelling one period of the ideal grating bounded by water. Experimental switch patterns were obtained using the set-up sketched in Figure  1a  for water injected into the flow cell. The experimental switch patterns presented in Figure  4  as the black, blue, and purple curves were normalized by dividing the respective measurements (photodiode output voltage of the transimpedance circuit) by the maximum value achieved for each. The normalized experimental switch patterns plotted in Figure  4  were measured at three different areas on the sample. They are quite similar, demonstrating that the sample is homogeneous in its fabrication. The theoretical response, plotted as the yellow curves, was also obtained with the software MC Grating, by modelling one period of the fabricated grating.
Toward this aim, we first developed a strategy to analyze in a high-throughput manner the efficiency of pre-mRNA 3 0 -end cleavage by RNAsequencing (RNA-Seq). This strategy is based on the evaluation of the number of reads located in 500 nt-long windows either upstream (total RNA) or downstream of the PAS (PAS-uncleaved RNA; Fig  5A ). An increase in the ratio of downstream reads to upstream reads indicates inhibition of 3 0 -end cleavage, leading to read-through transcription  (Vilborg et al, 2015). Focusing on 4,208 expressed genes (Dataset EV1) with detectable reads downstream of the PAS and using a cut-off of P < 0. 05, this analysis identified 378 pre-mRNAs with UV-repressed 3 0 -end processing and 108 pre-mRNAs with a more efficient 3 0 -end processing in UV-treated compared with untreated cells (UV-resistant 3 0 -end processing; Fig  5B ).
E RT-PCR analysis of p53 3 0 flanking region using the same primers employed in the data panel above (n = 3). Lane 1 is a control PCR amplification of cDNA derived from the reverse transcription of a control mRNA using oligo (dT). Lanes 2-7 are PCR amplification of reverse transcribed p53 chromatin-associated pre-mRNA using oligo oligo (dT). F RT-PCR analysis of p53 3 0 flanking region (n = 3). cDNAs were derived from nucleoplasmic-associated RNA reverse transcribed using random primer. Source data are available online for this figure. candidate pre-mRNAs of the UV-resistant group were resistant to 3 0end processing inhibition by UV (Fig 6A, blue bars). These include several DDR-related genes, namely XRCC5 and HMGB1. Because our RNA-seq approach is limited by the sensitivity of read detection downstream of the PAS, we also analyzed six genes from the p53 pathway by RT-qPCR.
One of them (i. e. , NOXA) exhibited 3 0 -end processing inhibition by UV, while four genes escaped repression (i. e. , MDM2, PUMA, PTEN, and FAS; Fig 6A  , right ). Altogether, our RT-qPCR analyses identified 15 pre-mRNAs that were resistant to 3 0 -end processing inhibition by UV, and 12 pre-mRNAs that underwent inhibition. Then, we measured by RT-qPCR the nucleoplasm/chromatin ratio of PAS-uncleaved pre-mRNA. Remarkably, all 15 UV-resistant pre-mRNAs had a high nucleoplasm/chromatin ratio, indicating that PAS cleavage occurs at least in part post-transcriptionally (Fig 6B ). By contrast, none of the 12 UV-repressed pre-mRNAs were abundant in the nucleoplasm  (Fig 6B). These data show that resistance of PAS cleavage to UV correlates with its occurrence in the nucleoplasm, thus extending our findings on p53.
▸ Figure  6. The 3 0 -end processing of diverse pre-mRNAs undergoing a CoTC cleavage event is maintained in response to UV-induced DNA damage. A RT-qPCR (uncleaved/total RNA) on nuclear RNA extracted from UV-treated or -untreated A549 cells (n = 3), to assess the regulation of 3 0 -end processing of 20 pre-mRNAs randomly selected from the previous RNA-sequencing data. B RT-qPCR (Released uncleaved pre-mRNA nucleoplasm/chromatin) on RNA extracted from the nucleoplasm and the chromatin fractions of A549 UV-treated cells (n = 3). Data information: "n" indicates the number of biological replicates for each experiment. All data are presented as the mean AE s. e. m. Source data are available online for this figure. processing that is tightly coordinated to transcriptional termination  (Luna et al, 2005; Hamperl et al, 2017; Nilsson et al, 2018; Teloni et al, 2019; Reimer et al, 2021).
Propidium iodide staining Cells were harvested in ice and washed with PBS. They were fixed in 70% ethanol for 30 min at À20°C. They were washed twice in PBS pelleted by centrifugation at 850 g for 5 min at 4°C. The cells were then resuspended in a solution containing 3. 5 mM Tris-HCl pH 7. 6 (Thermo Scientific), 10 mM NaCl (Thermo Scientific), 50 lg/ml propidium iodide (Sigma P4170), 0. 1% IGEPAL (Thermo Scientific), 20 lg/ml RnaseA (Sigma), and water. The acquisition of stained cells was performed using an LSRII flow cytometer (BD Biosciences). The acquired data were analyzed using FlowJo software.
Libraries were equimolarly pooled. Sequencing was carried out on a HiSeq instrument (Illumina) to obtain around 40 million raw singleend reads of 100 nucleotides per sample. Fastq files were generated using bcl2fastq. RNA-seq reads of good quality were trimmed in their 5 0 -and 3 0 -ends with the cutadapt software to remove uninformative nucleotides due to primer sequences. Trimmed reads of 100 bp or more were aligned on the Human genome (hg19) using Tophat2. Only reads with a mapping quality score of 20 or more were retained (samtools) for downstream analysis. Gene coordinates were obtained on the basis of overlapping Refseq transcripts with the same gene symbol. For each gene, two 500 bp regions located upstream and downstream of the PAS at the end of the gene were defined (genes with a downstream region overlapping another gene were discarded), and reads located in the upstream and downstream regions were counted in each sample.