dataesr/scientific-paragraphs-categorization huggingface dataset:
The phylogenetic tree was built by the Neighbor-Joining (NJ) method. The reliability of the inferred trees was estimated by the bootstrap method with 1,000 replications. All accession numbers are specified in parentheses. to a lesser extent with tet3 vertebrate genes, suggesting that these 3 genes shared a common ancestor. The phylogenetic analysis showed that tet1/2/3 formed three separate groups in the tree, whereas the amphioxus and ciona tet sequences rooted as an outgroup (Supplemental Fig. S6 ; protein alignment in Supplemental Fig. S7 ). In lamprey, 3 hits were found on chromosomes scaf_00044, scaf_03335 and scaf_00172, respectively, in the Genbank assembly GCA_002833325. 1. Further research in SIMRBASE confirmed this result. However, because the tet sequences on scaf_00172 and scaf_03335 were aligned to different region of putative tet gene on scaf_00044, thus it was impossible to include 3 of them together in phylogenetic tree.
Expression pattern of DNA methylation genes during gametogenesis and embryogenesis in trout. In the Phylofish database  33  we found that the high expression of DNA methylation genes in reproduction and development-related tissues seems to have been conserved throughout chordate evolution (data not shown). Therefore, in order to explore the potential different function of duplicated DNA methylation genes after WGD, we investigated their expression patterns during gametogenesis (Fig. 4A ) and embryogenesis (Fig. 5A ) in trout, which has the maximum DNA methylation genes fixed in its genome. During oogenesis, we observed that dnmt1b, dnmt3ba1, dnmt3bba2, dnmt3bbb exhibited a similar expression pattern (Fig. 4B , mean ± SD data in Supplemental Table  S4 ), which remained steady from February to May (pre-early vitellogenesis and vitellogenesis stages), was halved in September (late vitellogenesis stage), and increased several fold in November (spawn stage).
conclusion The present study is the first to analyse the evolutionary history of DNA methylation genes (dnmt, tet and tdg) in chordates. Results showed that while dnmt1, tet1, tet2, and tet3 remain as a single copy gene in the genomes of most species of chordates, dnmt3 and tdg duplicates were preferentially fixed in genomes after successive WGD events, increasing their copy numbers during evolution. The case study for trout revealed that these duplicated genes were regulated independently during gametogenesis and embryogenesis, suggesting that they may have suffered sub-or neo-functionalisation after WDG. In silico analysis. Paralogs for DNA methylation genes (dnmt, tet and tdg) in trout were identified in in NCBI assembly database (GCF_002163495. 1) using the BLAST tool. Orthologous genes of DNA methylation genes and deduced amino acid sequences for other species were identified from the Ensembl genome browser (Ensembl release 91, December 2017, http://www.
ensembl. org) or NCBI database with BLAST tool. The corresponding genes in amphioxus, elephant shark were identified in their respective genome database (https://genome. ucsc. edu/) or NCBI database through BLAST searches. SIMRBASE (https://genomes. stowers. org/search/loc), a database which provides preliminary annotations to characterise the latest lamprey assembly (GCA_002833325. 1) was also used to verify the results in lamprey. All the BLAST searches were performed using TBLASTN method with default algorithm parameters: expect threshold was set as 10; word size was set as 6; and low complexity regions were filtered. All the identified sequences were subsequently subjected to conserved domain search in NCBI (https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi) to further confirm their identities and check if there is any alteration in conserved functional sites.
Methods Phylogenetic analyses were carried out using MEGA package version 7 software  55  with the deduced amino acid sequences of DNA methylation genes. All the protein sequences were aligned with the MUSCLE method in MEGA software before constructing phylogenetic tree. Phylogenetic trees of Dnmt1, Tet and Tdg were built using Maximum Likelihood method and the whole protein sequences, whereas the phylogenetic tree of Dnmt3 was constructed using Neighbour-Joining (NJ) method with the protein alignment containing only the conserved functional domains (PWWP, ADD and Dcm). The reliability of the inferred trees was estimated using the bootstrap method with 500 or 1000 replications in Maximum likelihood method and NJ method, respectively. New gene annotations were allocated according to ZFIN Nomenclature guidelines (http://zfin.
org/). Syntenic analyses were performed with the Genomicus software, version 01. 01 (www. genomicus. biologie. ens. fr) to confirm identities of the target genes in various vertebrates. The gene loci of DNA methylation genes in lampreys, elephant sharks and trout and their syntenic genes were identified using the BLAST tool against their corresponding assembly databases in NCBI. In order to have a preliminary idea about the prediction of sub-or neo-functionalisation among multiple paralogues, the ratios between non-synonymous and synonymous differences (dN/dS) were also calculated with the codeml program in the PAML package version 4. 9i  56. The aligned amino acids sequences of dnmt1, dnmt3, tet1, tet2, tet3 and tdg genes in selected representative species were used to guide the alignment of the nucleotides sequences by PAL2NAL to get codon alignments of these genes  57.
Statistical analysis. The qPCR data were presented as heatmap with mean value of each condition using ClustVis (https://biit. cs. ut. ee/clustvis/). Normality of distributions was assessed using the Shapiro-Wilk test using R (v3. 4. 0)/Rcmdr Package. Data were analysed by a Kruskal-Wallis non-parametric test followed by a Tukey test as post hoc analysis.
Acknowledgements We warmly thank  F. Valleé ,  P. Maunas  and  N. Turonnet  for animal care at  INRA Lees-Athas  facilities and for their indispensable help with sampling. We also thank  F. Terrier  for the preparation of the diets. J. L. received a doctoral fellowship from the  China Scholarship Council  (File No. 201506330063 ).
The tumor microenvironment (TME) is a heterogeneous system that contributes to breast cancer progression. The Cancer Genome Atlas (TCGA) database provides global gene expression profiling data for further analysis of various malignancies, including breast cancer. Based on the ESTIMATE algorithm, immune and stromal scores were calculated according to immune or stromal components in the TME. We divided breast cancer cases into high-and low-score groups and identified differentially expressed genes (DEGs) that were significantly associated with overall survival. We performed enrichment analysis and constructed a protein-protein interaction network and found that the DEGs were mainly involved in primary immunodeficiency, T cell receptor signaling pathway and cytokine-cytokine receptor reaction.
Aging and cancer are two highly related biological phenomena, however, the causative relationship between them remains unclear and requires more study. Although many studies have been focused on investigating the mechanism why aging promotes oncogenesis, less attention has been paid to explore the special characteristics of elderly patients with cancer. It is still unknown whether aging can induce unique molecular features and whether aging-associated features should shape treatment strategies in elderly cancer patients. Based on the global gene expression data in The Cancer Genome Atlas (TCGA) database, algorithms have been developed to calculate tumor purity, such as ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data)  (9). ESTIMATE is an algorithm that calculates immune and stromal scores of cancer to predict the infiltration of immune and stromal cells by analyzing specific gene expression profiles of immune and stromal cells  (9).
Database The gene expression data of breast cancer patients were downloaded from TCGA data portal (https://tcga-data. nci. nih. gov/tcga/). Clinical information, including age, hormone receptor status and survival, were also obtained from TCGA data portal. We calculated immune scores and stromal scores by applying the ESTIMATE algorithm to gene expression data  (9). Abbreviations: BP, biological process; CAFs, cancer-associated fibroblasts; CC, cellular component; CCL2, C-C Motif Chemokine Ligand 2; DAVID, The Database for Annotation, Visualization and Integrated Discovery; DEGs, differentially expressed genes; ER, estrogen receptor; ESTIMATE, Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data; FAPα, fibroblast activation protein alpha; GBM, glioblastoma; GO, Gene ontology; HER2, human epidermal growth factor receptor-2; KEGG, Kyoto Encyclopedia of Genes and Genomes; MCODE, the Molecular Complex Detection; MF, molecular function; NSCLC, non-small cell lung cancer; OS, Overall survival; PDGFRα, plateletderived growth factor receptor alpha; PDX, patient-derived xenograft; PFS, progression-free survival; PPI, protein-protein interaction; STRING, Search Tool for the Retrieval of Interacting Genes; TAMs, tumor-associated macrophages; TCGA, The Cancer Genome Atlas; TIL, tumor-infiltrating lymphocyte; TMB, tumor mutation burden; TME, tumor microenvironment.
Recognition of Differentially Expressed Genes (DEGs) Differentially expressed genes between the high immune/stromal score group and low immune/stromal score group were detected using the package limma  (10). To reduce the false positive rate, we used the Benjamini and Hochberg false discovery rate to adjust p-values. An adjusted p < 0. 05 and |logFC| > 1 were defined as the cutoff criteria. Additionally, we used ImageGP (http://www. ehbio. com/ImageGP/index. php/ Home/Index/index. html) to construct a visual volcano plot to display the results of the dataset analysis and Morpheus online analysis software (https://software. broadinstitute. org/ morpheus/) to construct a heat map and perform hierarchical cluster analysis.
Enrichment Analysis of DEGs Gene ontology (GO) analysis is an approach to annotate genes and gene products through analyzing high-throughput genome or transcriptome data  (11). Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database collection that can help in analysis of genomes, biological pathways, diseases, and drugs  (12). The Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david. ncifcrf. gov/) is an online bioinformatics tool providing a means for functional analysis of a number of genes and proteins  (13). Significance levels were set at p < 0. 05. We used DAVID to visualize the core biological process (BP), molecular function (MF), cellular component (CC), and pathways among the DEGs. Furthermore, we utilized ImageGP (http://www. ehbio. com/ImageGP/index.
Construction of a PPI Network Search Tool for the Retrieval of Interacting Genes (STRING) is an online tool that helps evaluate protein-protein interaction (PPI) networks  (14). We used the STRING app in Cytoscape to analyze DEGs to identify potential relationships among the DEGs. We set the cutoff criteria as a confidence score ≥ 0. 4 and maximum number of interactors = 0. Moreover, we utilized the Molecular Complex Detection (MCODE) app to screen modules in the PPI network in Cytoscape with the following criteria: degree cut-off = 2, node score cut-off = 0. 2, k-core = 2, and max. depth = 100  (15). DAVID was used to perform pathway analysis of genes in these modules. To explore the potential information, we carried out GO and KEGG pathway analyses.
Statistical Analysis Overall survival (OS) curves of individual DEGs that were significantly associated with OS were downloaded from the website http://gepia. cancer-pku. cn/. Kaplan-Meier survival analyses of immune/stromal scores and age were performed using GraphPad Prism 8. 0. 1 software, p-values were calculated with a log-rank test, and p < 0. 05 was set as the cut-off. All relevant statistical analyses were performed with SPSS 22. 0 software (IBM Corporation, Armonk, NY, USA), and significance levels were set at p < 0. 05.
Immune Scores and Stromal Scores Are Significantly Associated With Breast Cancer Subtypes We downloaded gene expression data and clinical information of breast cancer patients from TCGA database. After exclusion of patients with incomplete clinical information and the normal control group, 1,089 breast cancer patients were included in our study. Based on the pathological diagnosis given in TCGA database, breast cancer was classified into five clear subtypes: infiltrating ductal carcinoma, medullary carcinoma, metaplastic carcinoma, infiltrating lobular carcinoma and mucinous carcinoma. The 1,089 cases included 779 (71. 5%) infiltrating ductal carcinoma, 203 (18. 6%) infiltrating lobular carcinoma, 5 (0. 5%) medullary carcinoma, 9 (0. 8%) metaplastic carcinoma, 17 (1. 6%) mucinous carcinoma, 29 (2.
Association Between DEGs and Overall Survival To investigate the potential effects of individual DEGs on OS, we downloaded overall survival curves from the website http://gepia. cancer-pku. cn/. Among all the 403 DEGs, only 129 upregulated genes were significantly associated with OS and predicted better prognosis of patients (Figure  3 ; p < 0. 05). What's more, we validated those selected DEGs in the Breast Cancer Metabric data downloaded from cBioPortal (http://download. cbioportal. org/brca_metabric. tar. gz). We drew survival curves using the Graphpad software and found that higher expression of 102 genes was significantly associated with longer overall survival (Figure  6 ). To explore the crosstalk between these DEGs, we performed protein-protein interactions network analysis using the online tool STRING.
Above all, the microenvironment changes with age, which may reduce the effects of immunotherapy. This study still has some shortcomings. The data were only derived from TCGA database and lacked verification with real-world data. We only analyzed transcriptome data, without assessing data associated with non-coding transcriptomes, epigenetic differences or proteomics. We only used bioinformatics analysis, and the study lacked evidence obtained through other methods, such as immunohistochemistry and proteomics, and lacked further validation with a patient-derived xenograft (PDX) mouse model. In conclusion, based on ESTIMATE algorithmderived immune/stromal scores, we identified immune microenvironment-related genes that are associated with better patient prognosis. Aging was found to be a risk factor for tumorigenesis and progression and influenced the immune microenvironment and immunotherapy efficacy by altering the number of TILs and checkpoint expression levels.
Tn2006 was associated with the AbaR25 resistance island, as has been previously described for Tn2006  (23, 24). AbaR25 had the same genetic environment as AbaR25-I type  (24). The Tn2006-linked clone (ACM-2-6, ACM-8, ACM-9, ACM-34, ACM-36, and ACM-37) also harbored a circulating cryptic plasmid showing 100% BLAST sequence similarity to pA85-2 (GenBank accession number CP021786) and to pAB0057 (GenBank accession number CP001183)  (8). The plasmid was also 99. 99% similar to pAb-G7-1 (GenBank accession number KJ586856)  (25) , with only one base pair substitution difference (A¡C). pA85-2 carried no resistance determinants but harbored a BrnT/BrnA toxinantitoxin system, TonB-dependent receptor, and several hypothetical proteins, including septicolysin. Interestingly, ACM-7 and ACM-28 carried both a chromosomal Tn2006 mobilizing bla OXA-23 and a pMAL-1 plasmid carrying bla OXA-72 , with no evidence of bla OXA-72 exchange between the plasmid and the chromosome (Table  S2 ).
pMAL-1-linked clone. Nineteen ST-502/2059 CRAB (48. 7%) isolates were recovered from 15 patients and harbored the intrinsically carried bla OXA-66 in addition to bla OXA-72. Genome analysis of ACM-17, a representative of OXA-72-producing isolates, revealed that bla OXA-72 was carried on a small 9,810-bp plasmid that was identical to a previously identified GR2 plasmid in A. baumannii isolated from Serbia in 2016 and designated pMAL-1 (GenBank accession no. KX230793. 1). Subsequently, BLAST analysis and genome alignments showed that the remaining ST-502 isolates also carried the same pMAL-1 plasmid. This plasmid was conjugative and had a genetic backbone identical to that of pMAL-1, being in the same order and orientation as repAci1-repAci2 (replicase genes), two XerC/XerD sites (tyrosine recombinase sites) bracketing bla OXA-72 , as well as ISAba31, tonB, sep (septicolysin-encoding gene), and a few hypothetical proteins.
Other plasmids were also closely related to the one detected in this study, including pA105-2 (GenBank accession number KR535993. 1), isolated from A. baumannii in 2015, p2ABST25 (GenBank accession number AEPA01000396. 1; negative for ISAba31), and pAB0057 (GenBank accession number NC_011585. 1; differing in the region flanking XerC/XerD; replaced by bla OXA-72 and ISAba31 in pMAL-1) (Fig. 3 ). ACM-11, ACM-17, ACM-25, and ACM-35, which were part of the pMAL-1 clone, harbored other plasmids, such as pTG22653 (GenBank accession number CP039519. 1) and ABAY04001 (GenBank accession number MK386680. 1) (Table  S2 ). pMAL-2 circulating plasmid. In ACM-4, ACM-7, and ACM-29, representing the Tn2006-linked clone, and in ACM-12, ACM-15, ACM-25, ACM-31, ACM-35, and ACM-41, representing the pMAL-1-linked clone, we detected a common 70,499-bp plasmid.
The plasmid was 100% identical to pAB-MAL-2 (GenBank accession number KX230794. 1) (26) but had additional hypothetical proteins and a toxin-antitoxin (TA) higAB system. pAB-MAL-2 carried the tellurite resistance gene in addition to a Zeta toxin family protein, an ornithine cyclodeaminase, and many hypothetical proteins with unknown functions while being negative for antibiotic resistance determinants (Table  S2  and Fig. S1 to S3 ). In order to better characterize the higAB system found on pAB-MAL-2, we compared it with a recently described plasmid, pAB120, having the higBA2 Ab TA module  (27). Alignment of the TA components revealed 60. 3% and 58. 8% similarity to higB2 and higA of pAB120, respectively. The higAB system was preceded by ISAba125 (Fig. S1 ). BLAST analysis showed 100% identity to higB2 of A.
baumannii ACICU plasmid pACICU2 (GenBank accession number CP031382) and to the type II toxin-antitoxin system RelE/ParE in many other A. baumannii plasmids. higA was 100% identical to antitoxin higA1, linked to A. baumannii ACICU plasmid pACICU2 and to transcriptional regulators in other A. baumannii plasmids. Whole-genome SNP phylogenetic analysis. In total, 71,864 SNP sites were identified. Isolates were separated into two major clusters, of which two representative genomes were chosen to be additionally sequenced using PacBio long-read sequencing technology, ACM-2 (representing the isolates carrying bla OXA-23 and forming the Tn2006-mediated clone) and ACM-17 (representing the isolates carrying bla OXA-72 and forming the pMAL-1-mediated clones). The results obtained confirmed the presence of two circulating clones.
Using PFGE, MLST, and PCR typing methods in combination with WGS data and SNP-based Bayesian transmission network analysis, we were able to characterize at high-scale resolution a hospital CRAB outbreak caused by a Tn2006-linked clone carrying bla OXA-23 and a pMAL-1-linked clone carrying bla OXA-72. An outbreak driven by chromosomally encoded resistance determinants had a more consistent evolution and was easier to retrace using core genome sequence data from short genome reads. Alternatively, a plasmid-driven outbreak followed less of a straight evolutionary path during the sampling. Both clones additionally harbored circulating plasmids, ensuring the survival of the fittest and contributing to virulence.
MATERIALS AND METHODS Ethical approval. Ethical approval was not required, as the isolates were collected as part of routine clinical care and patient data collection followed patient discharge from the hospital and/or death. No additional isolates were collected beyond those obtained from routine clinical care, and no diagnostic or treatment decisions were affected by the outcomes of this study. Bacterial isolates. A total of 41 A. baumannii isolates were collected between April and December 2016 from hospitalized patients at a 544-bed hospital in Lebanon and were designated ACM-1 to -37 based on the date of their isolation and ACM-38 to -41 for the isolates with no trackable records. The initial identification of Acinetobacter species was carried out using automated microbial identification systems (Vitek and BD Phoenix).
(47). Briefly, plugs were digested using ApaI (Thermo Fisher Scientific, MA, USA) for 2 h at 37°C. DNA fragments were then separated on 1% Seakem Gold gel using a CHEF DR-III system (Bio-Rad Laboratories, Inc. , CA, USA) for 16 h with initial and final switch time of 7 s and 20 s, respectively. To compare the patterns on different gels, Salmonella enterica subsp. enterica serovar Braenderup (ATCC BAA664TM) was used as the reference for band size using XbaI restriction digestion (Thermo Fisher Scientific, MA, USA). The gel then was stained with ethidium bromide and viewed under UV light. Fingerprints were analyzed using BioNumerics software, version 7. 6. 1 (Applied Maths, St-Martens-Latem, Belgium). Bands not detected automatically were manually assigned. Fingerprints were clustered according to the band-based coefficient, which measures similarity based upon common and different bands with 0.
Resulting chromosomal contigs of ACM-2 and ACM-17 were corrected by Illumina data with Pilon, v1. 23  (53) , and the overlapping ends of chromosomes were trimmed after manual inspection of reads mapped by BWA-MEM algorithm as implemented in BWA, v0. 7. 17  (54) , and Bowtie, v2. 3. 4. 2  (55). Genome assembly was done using HGAP4 (56) with minimum seed coverage of 30. PCR-based amplification was used to fill the sequence gaps. Genome analysis. Resfinder (57), MLST 1. 8 server, ISfinder database (58), BLAST  (59) , and the open reading frame (ORF) finder tool (59) were utilized to identify resistance genes, sequence types (STs), ISs, and plasmid identification and annotation, respectively. Mauve (version 2. 3. 1) was used for comparative genome alignments  (60). The core genome alignments of each clone were used for outbreak analysis.
To detect recombinant regions, core genomes were aligned using Gubbins  (65). Recombinations were masked using maskrc (https://github. com/kwongj/maskrc-svg). Phylogenetic analysis. For the purposes of phylogenetic analysis of all the recovered isolates, along with ACM-14, which was used as an outgroup, core genome sequences were extracted from the contigs using an in-house script. The script was used to extract all nonrepetitive homologous sequences that are longer than 399 bp using NUCmer, version 3. 1  (66) , output as generated by QUAST, version 5. 0. 0  (67). Core genome sequences were aligned by MAFFT, version 7. 407 (68), using default gap penalties and the memsave parameter, FFT-NS-2 strategy, and iterative refinement with a maximum of two iterations. SNP sites then were extracted from aligned sequences using the SNP-sites tool, version 2.
4. 0  (69). In order to infer the phylogeny of the two clones, quality-trimmed Illumina reads were mapped to the chromosomal sequences (ACM-2 or ACM-17) using Bowtie, version 2. 3. 4. 2  (55). SNPs were consequently called using VarScan, version 2. 4. 3  (70) , with minimum read depth set to 8, minimum base quality of 20, and variant allele frequency of Ն0. 8. All sites where at least one isolate had a read depth of Ͻ8 were removed from the final data sets. All three data sets were analyzed by jModelTest, version 2. 1. 10, to determine the most appropriate models of nucleotide substitution  (71)  Plasmid analysis. Sequence gaps resulting from short Illumina reads were closed using PCR and Sanger sequencing. Seven primer pairs were designed using SeqBuilder software (Lasergene, Madison, WI).