dataesr/scientific-paragraphs-categorization huggingface dataset:
Two distinct (54% of AAI score) genome sequences, designated DH2bin6 and DH2bin20, have been reconstructed here, both of which contain partial 16S rRNA genes (Ͼ900 bp). An analysis of the 16S rRNA gene sequence of DH2bin6 using the online NCBI BLASTn nucleotide collection (nonredunant [nr]/nucleotide) database (https:// blast. ncbi. nlm. nih. gov) shows the best hit to an uncultured magnetotactic coccus, MDA-1 (GenBank accession number AB537162; 99. 89% identity)  (22) , while DH2bin20 has a 95. 84% BLASTn identity to uncultivated Magnetic coccus CS92 (X81182)  (23). Genomes of DH2bin6 and DH2bin20 consist of 80 and 525 scaffolds with average GC contents of 56. 76% and 52. 96%, respectively (Table  1 ). Nearly complete magnetosome gene clusters, a group of genes responsible for magnetosome biogenesis and arrangement, have been identified in both genomes, which contain genes homologous to magnetosome genes of mamD, mamH, mamI, mamE, mamK, mamF, mamL, mamM, mamN, mamO, mamP, mamA, mamQ, mamB, mamS, mamT and mmsF.
Antimicrobial susceptibility test Antimicrobial susceptibility testing was performed on meropenem-resistant isolates using the disk diffusion method on a Mueller-Hinton agar (MHE BioMérieux, France) and interpreted according to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) guidelines except for cyclines that were interpreted according to CLSI recommendations. The imipenem MIC determination was performed by the E-test method (BioMérieux, France), data were interpreted according to the criteria of EUCAST. MICs of colistin were determined by broth microdilution method and the results were interpreted according to the guidelines described by the EUCAST. DTR bacteria were defined as previously described  11  , strains were considered as DTR when having an intermediate or resistant status to three antibiotic classes (β-lactams, carbapenems, and fluoroquinolones).
Colistin resistance genes All strains were screened for the presence of plasmid-mediated colistin resistance genes represented by mcr-1, mcr-2, mcr-3, mcr-4, mcr-5 and mcr-8 by PCR using specific primers and probes (table  S_1 ). Those strains were also investigated to detect possible genetic alterations associated with colistin resistance in the mgrB genes. The mgrB genes were sequenced using the BigDye Terminator Cycle Sequencing Kit (Applied Biosystems, Foster City, California, United-States). Sequences of the colistin resistance genes were recovered and compared to those of the reference strain K. pneumoniae MGH78578 carrying wild-type genes (GenBank accession number NC_009648). The PROVEAN software (http://provean. jcvi. org/index. php) was used to check whether amino acid sequence changes could induce an alteration of the protein function  18.
The resulting protein had only 29 amino acids long instead of a non-mutated protein of 47 amino acids (Figure  4. A). MgrB gene sequence change by missense mutations. 24 K. pneumoniae colistin-resistant strains had a single nucleotide substitution in the mgrB gene (Figure  4. A). Mutations induced the replacement of phenylalanine (F35I) by isoleucine at position 35 in one isolate and glycine by serine (G37S) at position 37 in three isolates. Substitution of lysine with valine at position 2 (K2V) was the most frequently encountered mutation (20 isolates) (Table  2 ). All these substitutions have been reported deleterious by the PROVEAN software. One isolate had a four-base insertion (TGCG) between nucleotides 85 and 86, resulting in frame shift and non-functional truncated protein of 27 amino acids in length.
Acknowledgements G. D. is Research Director at the  Belgian FRS-FNRS. G. D. and B. W. benefit from a financial support from  ULB. L. C. , G. D. and B. W. benefit from a  WBI-France exchange program  ( WBI, FRS, Ministère Français de l'Enseignement supérieur et de la Recherche dans le cadre des Partenariats Hubert Curien).
FUNDING DG and CT are supported by 3 grants from the  Romanian Ministry of Research and Innovation : Postdoctoral Research Projects 2020-2022 -grant number  PN-III-P1-1. 1-PD-2019-0805 ,contract number  PD 122/2020 );  CCCDI-UEFISCDI , Project No. PN-III-P4-ID-PCCF-2016-0112  within  PNCDI III , as well as by awarded for Young Research Teams  2020-2022  (Grant No. PN-III-P1-1. 1-TE-2019-0271 ),as well as by an international collaborative grant of the European Economic Space between Romania and Iceland  2020-2022  (Grant No. 19-COP-0031 ). GG is funded by  K08 HL127269  (GG),  R03 HL145226  (GG), P01CA225618 (GG), and  P30 CA006973. LQ is funded by a grant from the  National Natural Science Foundation of China  (Grant No. 81800180 ) and the  National Young Elite Scientists Sponsorship Program  by the  China Association for Science and Technology  (Grant No.
Materials and methods Experimental animals. Heterozygous Depdc5 KO mice (C57BL/6N background) were obtained from the IMPC European Consortium at the Sanger Institute (UK) within the European EMMA/Infrafrontier initiative and bred at the IIT SPF animal facility. The EUCOMM/KOMP targeting strategy was based on the "knockout-first allele" (Tm1a) designed to be a knockout by splicing the cDNA to a LacZ cassette. The cassette was then inserted into the intronic region upstream the critical exon 5 of the Depdc5 locus to create a null allele of the gene  (Skarnes et al. , 2011  J o u r n a l P r e -p r o o f Relative gene expression was determined using the ΔΔCT method, normalizing data the housekeeping transcript (Gapdh). Pentylenetetrazol-induced seizures. Depdc5 +/+ and Depdc5 +/-littermates (3-4 months of age) were repeatedly injected with unitary doses of pentylenetetrazol (PTZ; 10 mg/kg intraperitoneally in 0.
For experiments with transduced neurons, only tGFP-positive neurons were analyzed. To identify bona fide synaptic boutons, we selected colocalized puncta within an area of 0. 1-1 μm 2 , corresponding to the overlapping area of pre-and post-synaptic proteins. We counted the number of synaptic puncta present within 30 μm dendrite tracts starting from the cell body. For the analysis of postsynaptic receptors, the thresholded signal of Homer1 and Gephyrin was overlapped to GluA1 and GABA A -β2 receptors, respectively, and the fluorescence intensity was measured only within this colocalization area. Data are referred to three independent experiments carried out in duplicate with 5-10 neurons analyzed per duplicate. Electron Microscopy. Low-density cultures of cortical neurons from Depdc5 +/+ and Depdc5 +/- embryos, or from WT C57BL/6J embryos infected at 7 DIV with either shScr or shDepdc5 lentiviruses were processed for transmission electron microscopy (TEM).
ACKNOWLEDGEMENTS We thank the  IMPC European Consortium at the Sanger Institute (UK)  in the frame of the European EMMA/Infrafrontier for making available the Depdc5 KO mouse (Allele:  Depdc5 tm1a  (KOMP)Wtsi ). We thank drs. Robert A. Weinberg  ( Whitehead Institute for Biomedical Research, Cambridge, MA ) for Addgene plasmid 8454,  8455 ;  Thorsten Trimbuch  ( Charité Viral Core Facility, Berlin, Germany ) for production of the lentiviruses;  Anna Rocchi  ( Italian Institute of Technology, Genova, Italy ) for advice with the selection of shRNAs;  Pietro Baldelli  ( University of Genova, Italy ) for help and advice in the electrophysiological experiments;  Monica Morini , ( Italian Institute of Technology, Genova, Italy ) and  Michele Cilli  ( IRCCS Ospedale Policlinico San Martino, Genova, Italy ) for help in breeding the mice;  Riccardo Navone ,  Diego Moruzzo  and  Arta Mehilli  ( Center for Synaptic Neuroscience, Istituto Italiano di Tecnologia, Genova, Italy ) for assistance in maintenance of mouse colonies, genotyping assays and preparation of primary neurons, respectively.
3d ). These results indicate that PT specimens contained a relatively similar proportion of CDX mutations including a G279E TP53 mutation. All identified mutations are described in the Supplementary Data 2. Overall, these data show the mutational heterogeneity of PT specimens and high similarity of the CDX and CDX-derived cell line. Next, we examined shared copy-number alterations (CNAs) between PT specimens, CTCs, the CDX and the CDX-derived cell line. In contrast to mutations, only six CNA were detected in PT specimens of which five were conserved in all CTC samples, the CDX and the cell line (Fig. 3e, f ). These six CNAs were detected in most PT specimens. Five of six CNAs were conserved during disease evolution and found in tumorigenic CTCs. We observed that the TMPRSS2-ERG fusion and the NKX3-1 loss were present in all PT specimens, CTCs, the CDX and the cell line.
Loss of cancer driver and tumor suppressor genes such as TP53, MAP2K4, PTEN, RB1, FAT1, CSMD, and REST was also detected in all of these samples. Two ramifications were observed, the first one composed of all CTC samples and the second of the CDX and the cell line. WGD was identified as major event occurring in the CDX and the cell line. Loss of 17p12-tel region observed in biopsy 5 including TP53 and MAP2K4 genes was conserved in all CTC samples, the CDX and the cell line. These data indicate that tumorigenic CTCs could derive from a minor subclone arising from biopsy 5 and harboring the largest loss of 17p12-tel region while subclone(s) harboring loss of the 17p12-13 region found in spatially different PT regions (TURP 1 and 2) were not conserved in CTCs and the CDX. We then examined whether genes harboring truncal or branched alterations in CTCs, the CDX and the cell line were listed in prostate cancer databases.
8-6% of the 2604 prostate tumors (Supplementary Fig. 10A ). The nine branched driver CNAs were found in genes with a frequency varying from 1. 6% to 8% excepted for PTEN which was found at 18% (Supplementary Fig. 10B ). Genes harboring trunk CNAs were two times more frequently altered in CRPC-NE than in adenocarcinomas (28% vs 12%) and were found in 35% of genes altered in CRPC (Fig. 7c ). Genes harboring branched CNAs were found in 55%, 50%, and 37% of genes altered in CRPC, CRPC-NE, and adenocarcinomas respectively (Fig. 7d ). Overall, interrogation of cBIOPortal database indicates an initial preponderance of neuroendocrine genetic abnormalities in primary tumor and acquisition of a more pronounced neuroendocrine genotype in CTCs with tumorigenic activity. Drug response of the CDX and CDX-derived cell line.
). Isolation of genomic DNA from blood, PT, CDX and cell line. DNA from formalin-fixed paraffin-embedded tumor biopsies was purified with QIAamp DNA FFPE Tissue kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. DNA from the CDX was extracted with the AllPrep DNA/RNA kit (Qiagen) and germline DNA and cell line DNA was purified with the QIAamp DNA blood kit (Qiagen). Whole-exome sequencing. Library preparation, exome capture, sequencing, and data analysis have been done by IntegraGen SA (Evry, France). Genomic DNA is captured using Agilent in-solution enrichment methodology (SureSelect SureSelect XT Clinical Reasearch Exome, Agilent) with their biotinylated oligonucleotides probes library (SureSelect XT Clinical Reasearch Exome-54 Mb, Agilent), followed by paired-end 75 bases massively parallel sequencing on Illumina HiSeq4000.
Each eluted-enriched DNA sample is then sequenced on an Illumina HiSeq4000 as paired-end 75b reads. Image analysis and base calling is performed using Illumina Real Time Analysis (2. 7. 6) with default parameters. Sequence alignment and variant calling. Base calling was performed using the Real-Time Analysis software sequence pipeline (2. 7. 7) from Illumina with default parameters. Sequence reads from amplified DNA (CTC and CD45 pools) were trimmed for Ampli1 adapters with Cutadapt (1. 14)  46. Human reads from xenograft samples were extracted by bamcmp  47. Reads were then aligned to the human genome build hg38/GRCh38. p7 using the Burrows-Wheeler Aligner (BWA) tool  48. Duplicated reads were removed using Sambamba  49. Variant calling of single-nucleotide variants (SNVs) and small insertions/deletions (indels) was performed using the Broad Institute's GATK  50, 51  HaplotypeCaller GVCF tool (3.
7) for germline variants and MuTect2 52 tool (2. 0, --max_alt_alleles_in_normal_count = 2; --max_-alt_allele_in_normal_fraction = 0. 04) for somatic variants. To keep only reliable somatic variants, we then applied the following post-filtering steps: -Variants classified as "PASS" or "t_lod_fstar" by MuTect2 (and not flagged as PID). -Coverage ≥8 in the tumor and matched normal sample. -QSS score ≥30. -Variant allele fraction in the tumor (VAFT) ≥0. 05 with ≥5 mutated reads, variant allele fraction in the normal sample (VAFN) = 0. Additional criteria were applied to generate a high-confidence set of variants from CTCs. Variants had to be present in either the primary tissue (at least 1 PT specimen) or the CDX or at least one other CTC sample. Bam-readcount (https://github. com/genome/bam-readcount) was used to rescue reliable variants that were present in at least two tumor samples and were not detected by Mutect2 because of their low VAF.
Ensembl's Variant Effect Predictor (VEP, release 87)  53  was used to annotate variants with respect to functional consequences (type of mutation and prediction of the functional impact on the protein by SIFT. 2. 2 and PolyPhen 2. 2. 2) and frequencies in public (dbSNP147, 1000 Genomes phase 3, ExAC r3. 0, COSMIC v79) and in-house databases. We used the Cancer Genome Interpreter  54  to predict driver and passenger mutations. ADO and false-positive rate estimation. CTC and CD45 + pool DNA were amplified before sequencing. To estimate ADO, we selected all reliable variants in germline or CD45 DNA using HaplotypeCaller with the following post-filtering: coverage ≥8 in both samples, ≥5 variant reads representing ≥5% of sequenced reads at that position, genotype quality ≥30. We then compared the proportions of normal/variant reads in the germline and CD45 DNA using Fisher's exact test.
Variants with a significant difference (P < 0. 05), a variant allele fraction between 0. 2 and 0. 8 in germline DNA and <0. 1 or >0. 9 in the CD45 DNA were considered to have undergone ADO. To estimate the false-positive rates in CTC samples, we divided the number of potentially false-positive events by the number of target bases covered ≥8X in the same sample. We adopted a conservative approach and considered as false positive all events not found in the primary tumor and the CDX. Copy-number analysis. To identify CNAs, we identified germline singlenucleotide polymorphisms (SNPs) in each sample and we calculated the coverage log-ratio (LRR) and B allele frequency (BAF) at each SNP site. Genomic profiles were divided into homogeneous segments by applying the circular binary segmentation algorithm, as implemented in the Bioconductor package DNAcopy  55  , to both LRR and BAF values.
This method allows regrouping samples sharing the same mutational profile. In practice, the matrix of VAF was used as input, and the PCA (resp. hierarchical clustering) was performed on this matrix using prcomp (resp. hclust) function from Bioconductor stats package. Phylogenetic tree. All non-silent somatic mutations present in at least two samples were considered for the purpose of determining phylogenetic trees. Trees were built using a binary presence/absence matrix built from the VAF of each sample (present = VAF > 0). The R Bioconductor package phangorn v2. 3. 1  57  was used to perform the parsimony ratchet method  58  , generating unrooted trees. The number of mutations and driver mutations on each branch were then determined by selecting mutations present in all samples downstream the branch.
Fixation and permeabilization steps were performed for pan-cytokeratins and E-cadherin antibodies using the Fix&Perm kit (Thermo Fisher) according to the manufacturer's instructions. 2 × 10 5 cells were incubated with each antibody or corresponding negative control isotype antibodies (Supplementary Table  2 ) at RT for 20 min. Acquisition was performed with a LSR Fortessa cytometer (BD Biosciences) equipped with BD FACS Diva software. Data were analyzed using the Kaluza software (Beckman Coulter). RNA extraction and sequencing. RNA was extracted from the LNCaP cell line, the CDX and the CDX-derived cell line for RNA sequencing using the AllPrep DNA/ RNA Mini Kit according to the manufacturer's instructions (Qiagen). Library preparation, sequencing, and data analysis have been done by IntegraGen SA (Evry, France).
Quality of reads was assessed for each sample using FastQC. Fastq files were aligned to the reference Human genome hg38 with STAR1  59. Reads mapping to multiple locations were removed. Gene expression was quantified using the full Gencode v26 annotation. STAR was used to obtain the number of reads associated to each gene in the Gencode v26 database (restricted to protein-coding genes, antisense and lincR-NAs). Bioconductor DESeq package  60  was used to import raw HTSeq counts for each sample into R statistical software and extract the count matrix. After normalizing for library size, the count matrix was normalized by the coding length of genes to compute FPKM scores (number of fragments per kilobase of exon model and millions of mapped reads). Bigwig visualization files were generated using STAR and the bam2wig python script  61.
Unsupervised analysis. Bioconductor edgeR package  62  was used to import raw HTSeq counts into R statistical software, and compute normalized log2 CPM (counts per millions of mapped reads) using the TMM, weighted trimmed mean of M-values, as normalization procedure. The normalized expression matrix from the 1000 most variant genes (based on standard deviation) or from a custom 250 gene list was used to classify the samples according to their gene expression patterns using PCA, hierarchical clustering and consensus clustering. Standard R functions were used to perform the PCA and hierarchical clustering (with Euclidean distance and Ward method). Consensus clustering (Bioconductor ConsensusClusterPlus package) was used to examine the stability of the clusters. Consensus partitions of the data set were established in K clusters (for K = 2, 3, …, 8), on the basis of 1000 resampling iterations (80% of genes, 80% of sample) of hierarchical clustering, with Pearson's dissimilarity as the distance metric and Ward's method for linkage analysis.
The cumulative distribution functions (CDFs) of the consensus matrices were used to determine the optimal number of clusters (K = 3 for instance), considering both the shape of the functions and the area under the CDF curves. Differential expression analysis. The Bioconductor limma package  63  was used to test for differential expression using the voom transformation. Only genes expressed in at least one sample (FPKM ≥ 0. 1) were tested to improve the statistical power of the analysis. A q value threshold of ≤0. 1 and a minimum log2 fold change of 1 were used to define differentially expressed genes. Pathway enrichment analysis. Hypergeometric tests were used to identify gene sets from the custom gene list, overrepresented of up-or downregulated genes, correcting for multiple testing with the Benjamini-Hochberg procedure.
Acknowledgements We thank  Dr. Patrycja Pawlikowska  and  Tala Tayoun  for very helpful discussions and critical reading of the manuscript and  Integragen GeCo  ( Evry, France ) and  Dr. Marc Deloger  for bioinformatics analysis. We are thankful to the members of the  CTCTrap consortium. We are very grateful to the patients and their families. The study was supported by  ANR-15-CE17-0006-01 ,  CTCTrap FP7 HEALTH #305341 , and the  Fondation ARC pour la Recherche sur le Cancer  (no. PJA20171206530 ). V. F. and E. P. were supported by the  Fondation pour la Recherche Médicale  (no. FDT20160435543 FDT20150532072 ).
Acknowledgments: The  University of Udine (Italy)  and  INRAE (Jouy en Josas, France ) are acknowledged for experimental facilities. The  Campus France  is gratefully acknowledged for the travel support through the  Galilée Grant   37184VG  to MM and JV. We thank  Slavica Stankic  ( INSP, France ) for the gift of nanoparticles used in this work and  Aurore Vidy  ( Institute Pasteur, France ) for technical expertise in FACS measurements. We thank  Seav-Ly Tran  ( INRAE, France ) for critical reading of the manuscript.
10. 1. 1 (Agilent Technologies, Les Ulis, France). Raw data (median signal intensity) were filtered; log2 transformed and normalized using quantile method  [11]. A model was fitted using the limma lmFit function  [12]. Microarray data and experimental details are available in NCBI's Gene Expression Omnibus  [13]  and are accessible through GEO Series accession number GSE145620 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE145620).
The final cDNA libraries were checked for quality and quantified using capillary electrophoresis. Libraries were loaded in the flow cell at 2. 8 nM and clusters were generated using Cbot and sequenced on HiSeq 4000 (Illumina) as single-end 50 base reads, according to manufacturer's instructions. Adapters were removed using FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/index. html). The trimmed sequencing reads of each library were then mapped and annotated with ncPRO-seq  [14]  onto the mus musculus genome GRCm38. p6 genome (mm9). Briefly, read alignment was performed using Bowtie v0. 12. 8, allowing a sum of qualities of mismatching bases lower than 50 (denoted -e 50). Aligned reads were then annotated accordingly against ortholog miRNAs in mice (miRBase release 22. 1). MiRNA data were Log2 transformed and normalized using the quantile method  [11].
Application of MicroRNA Master Regulator Analysis (MMRA) The four steps of the MicroRNA Master Regulator Analysis (MMRA) pipeline  [17]  was applied to identify miRNA-gene interactions possibly involved in the differential biological regulations in each comparison (HFepa vs. reference and HFoleic vs. reference). MMRA consists of four sequential steps, each aimed at progressively reducing the number of candidate miRNAs: (i) differential expression analysis to highlight miRNAs with subtype-specific expression; (ii) target gene transcript enrichment analysis, to further select those miRNAs whose predicted targets are enriched in the associated subtype gene signature; (iii) network analysis, in which an mRNA network is constructed around each miRNA and tested for enrichment in signature genes; and (iv) identification of miRNAs whose expression "explains" the expression of subtype signature genes.
• MMRA step1: MiRNA differential expression analysis Pair-wise comparisons between biological conditions in miRNA expression data were performed using the Bioconductor package DEGseq  [18]  to capture the differentially expressed miRNAs. miRNAs with FC > 0. 25, p < 0. 01 (corresponding to an FDR < 0. 05)  [15]  were defined as differentially expressed between biological conditions and retained for further analysis.
• MMRA step2: target genes enrichment analysis For each miRNA differentially expressed in a biological condition, miRNA target genes were predicted by combining the results of three miRNA-target databases (miRTarBase  [19] , miRDB  [20] , and TargetScan  [21] ). Only miRNA-target interactions predicted by at least two databases were included as putative targets in our analysis. We performed a target enrichment analysis in the "up" and "down" gene signature of the biological condition in which the miRNA was found differentially expressed. The enrichment analysis was performed with a Fisher's exact test. The miRNAs whose targets were enriched with a Fisher's p < 0. 05 were retained for the following steps of the pipeline.