dataesr/scientific-paragraphs-categorization huggingface dataset:
4). Lastly, the alignments of the B. pendula whole transcriptome mRNA-sequencing reads cover and validate the existence of this new putative sequence. From our experience, the reason for the presence of this type of sequence in the database is usually due to an unvalidated identification method after genome sequencing and a de novo assembly of reads, which, in the case of B. pendula, was performed without a reference genome and experimental evidence. Therefore, this new theoretical full-length protein was included in all our analyses.
Protein and Pore 2D and 3D Basic Structure Organizations of BpeAQPs The solute specificity of the AQPs is determined by a series of amino acids whose orientation of the side chain lines the pores, creating various spatial constrictions. Although predictive, the conserved positions of selectivity motifs provide the key to understanding the basic organization of the pore, and these analyses can be strengthened by 2D and 3D analyses of the pore structure. In this regard, the homology-based tertiary protein structure and the biochemical properties of pore-lining residues (i. e. , channel radius, length, polarity, charge, hydrophobicity, hydropathy, solubility, and ionizable residues) across the pore were predicted on individual BpeAQPs protomers in the membranes by using Phyre2 and MOLE2. 5 software tools (Table  2  and Figure  S5 ).
Profiling of BpeAQP Transcriptome Plants are constantly exposed to a large variety of abiotic stresses. Cold stress (CS, chilling, 0-15 • C; freezing, <0 • C) has a strong impact on plant growth and development, and directly influences the species expansion, crop distribution, and yield  [123]. Cold stress tolerance in plants is a very complex trait. Studies on chilling and freezing tolerance from woody species, including silver birch, have shown that CS directly influences various physiological and metabolic reactions which are regulated by profound changes in gene expression  [124, 125]. However, the involvement of AQPs in the acclimation and/or tolerance to chilling is poorly understood. To provide insight into the physiological roles of the various B. pendula AQP isoforms, publicly available whole-transcriptome RNA-seq datasets  [33]  were processed to study the early expression patterns of the 33 BpeAQP genes in leaves under a cold stress of 6 • C (Figure  3 ).
thaliana, O. europaea, Lycopersicon esculentum (SlAQP;  [144] ) and Populus trichocarpa (PoptrAQP,  [14] ). Each BpeAQP gene accession is derived from the identifier of the GenBank GSS contig on which it is located. When necessary, it is subscripted with an additional lowercase letter to indicate that BpeAQP genes accessions differing only by this letter were originally assembled in the same GSS contig. The Betula AQP protein names, accession numbers, and sequences (genomic, CDS, and deduced proteins) used in this work are listed in Figure  1 , Figure  S1  and Table  S1 , respectively.
Bioinformatics Analysis Every putative BpeAQP sequence was carefully scrutinized, including expected AQP motifs (NPA, ar/R selectivity filter, and the FPs) and the prediction of the transmembrane topology, with Interproscan from EMBL (http://www. ebi. ac. uk/Tools/pfa/iprscan/; accessed on 2 July 2021) and the NCBI's conserved domain database (https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi; accessed on 2 July 2021). Motifs were identified by multiple sequence alignment and performed using MUSCLE (https://www. ebi. ac. uk/ Tools/msa/muscle/; accessed on 2 July 2021)  [145]  with the default parameters. Specificitydetermining positions (SDP1-9;  [21] ), which were essential to the transport of non-aqua substrates, were identified from multiple sequence alignments as described by  [146].
Molecular weight, theoretical isoelectric point, and the grand average of hydropathicity (GRAVY) of the BpeAQPs were analyzed by the ExPaSy compute pI/Mw tool (https://web. expasy. org/ protparam/; accessed on 2 July 2021). The transmembrane regions were predicted using the TMHMM-2. 0 software tool (www. cbs. dtu. dk/services/TMHMM/; accessed on 2 July 2021)  [147] , SOSUI tools (http://harrier. nagahama-i-bio. ac. jp/sosui/sosui_submit. html; accessed on 2 July 2021)  [148] , and were, when necessary, manually corrected with heterologous AtAQP, SlAQP and OleaAQP sequences. The subcellular localizations of the BpeAQPs were predicted using the online tools WoLF PSORT (http://wolf-psort. hgc. jp; accessed on 2 July 2021) and Plant-mPLoc (http://www. csbio. sjtu. edu. cn/bioinf/plant-multi/; accessed on 2 July 2021)  [149].
The phylogenetic studies were conducted with the BpeAQP-deduced peptide sequences, and then with a total of 221 amino acid sequences, including BpeAQPs, AtAQPs, OleaAQPs, PoptrAQPs, and SlAQPs. Alignments were performed using the ClustalW progressive alignment method, and the phylogenetic trees were inferred using the maximum parsimony method. Maximum parsimony analyses were conducted using the subtree-pruning-regrafting (SPR) algorithm, and were bootstrapped with 5000 replicates. All analyses were performed using the MEGA X program. The graphical representation and the edition of the phylogenetic trees were performed with iTOL software (https://itol. embl. de/; accessed on 2 July 2021)  [150]. The intron and exon structures of BpeAQP genes were analyzed by online software GSDS 2. 0 (http://gsds.
Differential Expression Profile of BpeAQP Gene Family (RNA-Seq Analysis) The transcriptional expression patterns of all of the BpeAQPs from various plant organs and, subsequently, in leaves undergoing cold stress were recorded from the plant samples that were previously studied in two experimental assays on B. pendula  [33]. The transcriptomic data are available in the NCBI SRA (Sequence Read Archive) database with accession numbers PRJNA535361 and PRJNA532995, respectively. Samples were collected in triplicate from the roots, young leaves, female inflorescences, and xylem (upper stem, about 10th nodes) of healthy two-year-old birch planted in the experimental field of the Northeast Forestry University (Harbin, China). Two biological replicates per sample were sequenced, and each biological replicate consisted of a pool of three plant RNAs.
Two biological replicates for time points of 1 h, 2 h, 2. 5 h, and 3 h, control plants, and three biological replicates for time points of 0. 5 h and 1. 5 h were generated. Total RNA was extracted using the CTAB (Cetyltrimethylammonium Bromide) method [154]. The constructed cDNA libraries were subjected to paired-end sequencing using the Illumina HiSeq platform at Biomarker Technologies Corporation (Beijing, China). The clean reads of each sample were obtained by filtering out any reads of low quality, and then aligned to the B. pendula reference genome using Bowtie2  [155]. The RNA-sequencing data were analyzed using the RNA-seq by Expectation-Maximization (RSEM) pipeline  [156]. RSEM could compute transcript abundance, estimating the number of RNA-seq fragments corresponding to each gene, and normalized expression values as TMM (trimmed mean of M-values).
Acknowledgments: The authors thank those contributors who made the Betula pendula genome data accessible in the public databases of NCBI and CoGe. We are grateful to  Céline Sac  and  Caroline Savel  for their technical assistance in molecular biology, and  Norbert Frizot  for his technical computer support services. We are grateful to the  Mésocentre Clermont Auvergne University and AuBi  platform for providing computing and storage resources. The authors wish to acknowledge  Patricia Mabrut  for providing the final linguistic revision of the manuscript. We are also grateful to the anonymous reviewers for their constructive comments.
A focused analysis of non-synonymous mutations, promoter methylation and homozygous deletions of MMR pathway genes confirmed a significant association between WRN dependency and hypermethylation of the MLH1 promoter (Student's t-test, FDR = 7. 72 × 10 -3 ) or mutations in MSH6 (FDR = 3. 85 × 10 -2 ); as well as mutations in the epigenetic regulator MLL2 (also known as KMT2D) (FDR = 1. 43 × 10 -4 ) (Fig. 5a ). To further validate WRN, we performed CRISPR-based cocompetition assays in which the relative fitness of WRN-knockout versus wild-type cells was compared. WRN knockout using four individual sgRNAs decreased fitness of WRN-knockout compared to wild-type cells in six MSI cell lines from colon, ovarian, endometrial and gastric cancers (Fig. 5b , Extended Data Fig. 8a  and Supplementary Table  10 ).
Discussion New approaches are needed to effectively prioritize candidate therapeutic targets for cancer treatments. We performed CRISPR-Cas9 screens in a diverse collection of cancer cells lines and combined this with genomic and tractability data to systematically nominate new cancer targets in an unbiased way. Confirmatory studies are necessary to further evaluate the priority targets that we identified. Even a modest improvement in drug-development success rates, and an expanded repertoire of targets, through approaches such as ours could provide benefits to patients with cancer. Our CRISPR-Cas9 screening results are also a resource with diverse applications in fundamental and evolutionary biology, genome engineering and disease genetics. Results are available through the project Score database (https://score.
MEthodS CRISPR-Cas9 screening. Plasmids. All plasmids have previously been described  10  and are available through Addgene (Cas9 vector, 68343; gRNA vector, 67974). Plasmids were packaged using the ViraPower Lentiviral Expression System (Invitrogen, K4975-00) as per the manufacturer's instructions. Cell culture. Cell lines used in this study (Supplementary Table  1 ) were selected from 1,000 cell line panel  7  of the Genomics of Drug Sensitivity in Cancer study, had been annotated in the Cell Model Passports database (https://cellmodelpassports. sanger. ac. uk/) and were maintained as previously described  7. To control for cross-contamination and sample swaps a panel of 92 single-nucleotide polymorphisms was profiled for each cell line before and following completion of the CRISPR-Cas9 screening pipeline.
This study includes commonly misidentified cell lines: Ca9-22, short tandem repeat (STR) analysis confirmed that the identity matched the Japanese Collection of Research Bioresources Cell Bank (JCRB) reference (JCRB0625) and RIKEN (RCB1976); MKN28, noted as derivative of MKN74 in Cell Model Passports and clinical information matches MKN74; KP-1N, known misidentification issue, Cell Model Passports data for both KP-1N & Panc-1 are identical; OVMIU, known misidentification issue, Cell Model Passports data for both OVMIU and OVSAYO are identical; SK-MG-1, STR profile matches JCRB profile, which internally matches Marcus, Cell Model Passport data for both SK-MG-1 and Marcus are identical. Commonly misidentified lines have been noted in Supplementary Table  1  and on the Cell Model Passport. All commonly misidentified cell lines were retained, because the misidentification does not impact tissue or cancer type of origin, and all datasets used were generated in-house from the same matched cell line.
The HT-29 cell line was screened with both libraries and resulting datasets were kept separated for comparative analyses (results are summarized in Extended Data Fig. 2j ). A total of 3. 3 × 10 7 cells were transduced with an appropriate volume of the lentiviral-packaged whole-genome sgRNA library to achieve 30% transduction efficiency (100× library coverage). The volume was determined for each cell line using a titration of the packaged library and assessing the percentage of blue fluorescent protein (BFP)-positive cells by flow cytometry. Transductions were performed in technical triplicate (or duplicate for cell lines with a large cell size such as glioblastoma). Owing to the large number of screens performed, multiple batches of packaged library virus were prepared. Each batch was tested in HT-29 cells to ensure consistency between batch preparations.
In addition, the HT-29 cell line was screened every 3 months to ensure the quality of data generated by the pipeline was consistent. Transduction efficiency was assessed 72 h after transduction. Samples with a transduction efficiency between 15 and 60% were used for puromycin selection. The appropriate concentration of puromycin for each individual cell line was determined from a dose-response curve (puromycin range, 1-5 μg ml -1 ) and cell viability was assessed using a CellTiter-Glo 2. 0 Assay (Promega, G9241). The percentage BFP-positive cells was reassessed after a minimum of 96 h of puromycin selection. For samples with less than 80% BFP-positive cells, puromycin selection was extended for an additional 3 days and the percentage of BFP-positive cells was assessed again. Cells were maintained until day 14 after transduction with a minimum of 5.
d. lower than the average. On the basis of our low-level quality control and screening performance, the final analysis set was composed of 324 cell lines (Supplementary Table  1 ). Further details on these analyses are included in the Supplementary Information. sgRNA count preprocessing and CRISPR-bias correction. The analysis set of 324 cell lines was further processed using CRISPRcleanR 32 (https://github. com/francescojm/CRISPRcleanR). sgRNAs with less than 30 reads in the plasmid counts and sgRNAs belonging to only the Library v1. 1 were first removed. The remaining sgRNAs were assembled into one file per cell line, including the read counts from the matching library plasmid and all replicates and then normalized using a median-ratio method to adjust for the effect of library sizes and read count distributions  33.
Depletion/enrichment fold changes for individual sgRNAs were quantified between post library-transduction read counts and library plasmid read counts at the individual replicate level. This was performed using the ccr. NormfoldChanges function of CRISPRcleanR. Next we performed a correction of gene-independent responses to CRISPR-Cas9 targeting 34 using the ccr. GWclean function of CRISPRcleanR with default parameters. Calling CRISPR-Cas9 gene knockout fitness effects. The CRISPRcleanR-corrected sgRNAs-level values (corrected fold change values) were used as input into an in-house-generated R implementation of the BAGEL method  30  to call significantly depleted genes (code publicly available at https://github. com/francescojm/ BAGELR). Our BAGEL implementation computes gene-level Bayesian factors by the sgRNAs on a targeted-gene basis, by averaging instead of summing them.
Additionally, it uses reference sets of predefined essential and non-essential genes  30. However, in order to avoid their status (essential or non-essential) being defined a priori, we removed any high-confidence cancer driver genes as defined previously 7 from these sets. The resulting curated reference gene sets are available as built-in data objects in the R implementation of BAGEL (curated_BAGEL_essential. rdata and curated_BAGEL_nonEssential. rdata, both available at https://github. com/ francescojm/BAGELR/tree/master/data). A statistical significance threshold for gene-level Bayesian factors was determined for each cell line as described previously  8. Each gene was assigned a scaled Bayesian factor computed by subtracting the Bayesian factor at the 5% FDR threshold defined for each cell line from the original Bayesian factor, and a binary fitness score equal to 1 if the resulting scaled Bayesian factor was greater than 0.
Further details on these analyses are included in the Supplementary Information. In addition, CRISPRcleanR-corrected sgRNA treatment counts were derived from the corrected sgRNA-level count fold changes (using the ccr. correctCounts function of CRISPRcleanR) and used as input into MAGeCK  35  to compute the depletion significance using mean-variance modelling. This was performed using the MAGeCK Python package (version 0. 5. 3), specifying in the command line call that no normalization was required (as this was already performed by CRISPRcleanR). At the end of this stage, the following gene-level depletion score matrices were produced for each cell line: raw count fold changes, copy number bias-corrected count fold changes, Bayesian factors, scaled Bayesian factors, binary fitness scores and MAGeCK depletion FDRs.
All scores are summarized for each cell line and available at https://cog. sanger. ac. uk/cmp/download/essenti-ality_matrices. zip, together with all the sgRNAs raw count files (available at https:// cog. sanger. ac. uk/cmp/download/raw_sgrnas_counts. zip). High-level CRISPR screen data analyses. Adaptive daisy model (ADaM) to identify core fitness genes. We designed the adaptive daisy model (ADaM), an heuristic algorithm for the identification of core fitness genes, implemented it in an R package and made it publicly available at https://github. com/francescojm/ADaM. ADaM is based on the daisy model  8  , but it adaptively determines the minimal number of cell lines m from a given cancer type in which a gene should exert a significant fitness effect for that gene to be considered a core fitness gene for that cancer type.
ADaM is described further in the Supplementary Information. In order to identify pan-cancer core fitness genes, we applied the same method to determine the minimal number k of cancer types for which a gene should be predicted as a pan-cancer core fitness gene. Characterization of ADaM pan-cancer core fitness genes. Reference sets of essential and non-essential genes were extracted from a previously published study  30. Other reference gene sets (used while characterizing the ADaM pan-cancer core fitness genes, described below) were derived from the Molecular Signature Database (MSigDB  36  ) and post-processed as described previously  32. A more recent set of a priori known essential genes was derived from a previously published study  9. The pan-cancer core fitness genes that did not belong to any of the aforementioned gene sets were tested for gene family enrichments (using a hypergeometric test) by deriving gene annotations using the BioMart R package  37  and biological pathway enrichments using a comprehensive collection of pathways gene sets from Pathway Commons 38 (post-processed to reduce redundancies across different sets as described previously  39  ).
The remaining cancer types were collapsed on a tissue basis (annotation in Supplementary Table  1 ) and the resulting tissues with at least 10 cell lines were included in the analysis (bone, central nervous system, oesophagus, haematopoietic and lymphoid). A total of 14 analyses (referred for simplicity as cancer-type-specific ANOVAs in the main text and below) plus a pan-cancer analysis including all screened cell lines were performed. Each ANOVA was performed using the analytical framework described previously  7  and implemented in a Python package  41  (https://github. com/CancerRxGene/gdsctools). Only genes that did not belong to any set of prior known essential genes (defined in the previous sections) and not predicted by ADaM to be core fitness genes were included in the analyses. For all tested gene fitness-CDE associations, effect size estimations versus pooled s.
Further details are provided in the Supplementary Information. Characterization of target protein families and enrichment analysis. To characterize protein families and compute statistical enrichment, we made use of the Panther online tool  43. GPX4 differential expression analysis. RNA-sequencing gene expression measurements transformed using voom  44  were obtained from a previously published study  45. For GPX4 analysis, cell lines were divided into two groups according to their loss-of-fitness response to GPX4 knockout (using BAGEL FDR < 5% as significance threshold for gene depletion) and gene expression fold changes were calculated between the GPX4 non-dependent and dependent cell lines (log 2 values of the mean difference). Differential gene expression was statistically assessed using the R package Limma  46.
CellTiter-Glo data were read on an Envision Multiplate Reader and data analysis was performed using GraphPad Prism 7 software. Student's t-test was performed using the multiple t-test module in Prism 7. The sgRNA sequences that were used are listed in Supplementary Table  10. RNA interference. A pool of four siRNAs that target WRN were used (Dharmacon, L-010378-00-0005). HCT116 cells were grown and transfected with siRNA using the RNAiMAX (Invitrogen) transfection reagent following the manufacturer's instructions. Each experiment included: mock control (transfection lipid only), ON-TARGETplus Non-targeting Control Pool (Dharmacon, D-001810-10-05) as a negative control, and polo-like kinase 1 (PLK1) (Dharmacon, L-003290-00-0010), which served as a positive control. siRNA sequences are listed in Supplementary Table  10.
The average object-summed intensity was calculated using the IncuCyte software (Essen Instruments). Mouse xenograft studies. Female non-obese diabetic/severe combined immunodeficiency (NOD/SCID) mice (Charles River Laboratories) were used in all in vivo studies. All animal procedures were approved by the Ethical Committee of the Institute and by the Italian Ministry of Health (authorization 806/2016-PR). The methods were carried out in accordance with the approved guidelines. Mice were purchased from Charles River Laboratories, maintained in hyperventilated cages and manipulated under pathogen-free conditions. In particular, mice were housed in individually sterilized cages; each cage contained a maximum of seven mice and optimal amounts of sterilized food, water and bedding. HCT116 xenografts were established by subcutaneous inoculation of 2 × 10 6 cells into the right posterior flank of 5-to 6-week-old mice.
In vivo procedures and related biobank data were managed using the Laboratory Assistant Suite, a web-based proprietary data management system for automated data tracking  47. Immunohistochemistry. Formalin-fixed, paraffin-embedded tissues explanted from cell xenografts were partially sectioned (10-μm thick) using a microtome. Then, 4-μm paraffin tissue sections were dried in a 37 °C oven overnight. Slides were deparaffinized in xylene and rehydrated through graded alcohol to water. Endogenous peroxidase was blocked in 3% hydrogen peroxide for 30 min. Microwave antigen retrieval was carried out using a microwave oven (750 W for 10 min) in 10 mmol l -1 citrate buffer, pH 6. 0. Slides were incubated with monoclonal mouse anti-human KI-67 (1:100; DAKO) overnight at 4 °C inside a moist chamber.
d, Data quality control threshold based on the distributions of Pearson's correlation values of sgRNA fold change values between replicates of the same cell line (in green) and all possible pairwise comparisons (in grey), considering the 838 highly informative sgRNAs (described in the Methods). e, Percentage of experiments passing the quality control filter defined in d. f, Pearson's correlation values as described in d for the cell lines in the final analysis set. g, ROC and precision/recall curves were obtained after classifying predefined essential (n = 354) and non-essential (n = 747) genes based on gene-level rank positions calculated using depletion fold changes. The median areas under the curve across all cell lines are reported. h, Glass's Δ scores quantifying the depletion effect size for genes that encode ribosomal proteins (n = 61) and a priori known essential (n = 354) genes for all cell lines.