dataesr/scientific-paragraphs-categorization huggingface dataset:
AMR genes that were found within 31 kbp of a MGE was considered to be associated, meaning that it has the potential to be mobilized. The threshold corresponds to the length of the largest ComTn from Enterobacteriacea in MobileElementFinders database (Tn6108). Association between MGEs and AMR genes showed that most of the AMR genes carried on MGEs were from beta-lactam class especially bla TEM-1 and beta-lactamases were mainly carried on transposons in poultry and swine. In addition, MGEs such as IS26, ISVsa3 and Tn2 were associated with many AMR genes. IS26 has played a crucial role in the dissemination of resistance determinants in Gramnegative bacteria  10, 22. AMR genes such as bla TEM genes, including extended-spectrum beta-lactamases (ESBL)          Scatter plot between abundance of AMR genes from metagenomic fecal samples and percentage of E.
Materials and methods Selection and collection of isolates. As part of the European Union-funded EFFORT project (www. effort-again st-amr. eu), fecal samples from animal farms were collected in 2014-2015 from Denmark (10 farms), France (5 farms), Germany (17 farms), The Netherlands (15 farms), Poland (10 farms), and Spain (10 farms) and. From each farm, up to 10 E. coli isolates were isolated and whole genome sequenced from poultry (n = 239 from 25 farms), swine (n = 238 from 28 farms), and veal (n = 150 from 15 farms). Farms were selected based on a ranking of antimicrobial use (AMU). Distribution of number of E. coli isolates and a list of strains including metadata can be found in Fig. S1  and Table  S1  respectively. In addition, previously sequenced metagenomic samples (poultry 8 , swine  8  and veal calf [PRJEB39685]) from the same farm and fecal samples that were used for E.
coli isolation were included in this study to compare abundance of AMR from metagenomic and whole genome sequence analysis. DNA extraction and whole genome sequencing. Genomic DNA was extracted from E. coli isolates using PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific), Qiagen, Gentra, or Puregene bacterial kits and DNA concentrations were determined using the Qubit dsDNA BR assay kit (Thermo Fisher Scientific). Sequencing libraries were prepared with the Nextera XT DNA library preparation kit or the Nextera DNA Flex library preparation kit according to the manufacturer's protocol (Illumina, Inc. , San Diego, CA, United States). Paired-end sequencing was performed on the Illumina HiSeq, MiSeq or NextSeq sequencing platform. Raw sequence data were deposited in the European Nucleotide Archive (http:// www.
0 with MGEdb v1. 0. 2  30. With these tools we could identify plasmids, miniature inverted repeats, insertion sequences, transposons and conjugative transposons. If two complete copies of the same MGE, or one complete copy and one truncated with preserved IR region was predicted within 31 kbp from one another they was flagged as putative composite transposons. In addition was MGEs located within 31 kbp from an AMR or virulence gene was considered to be associated and to have a theoretical ability to mobilize the associated gene  30. The threshold corresponds to the longest composite transposon in Enterobacteriaceae (Tn6108). Average nucleotide identity (ANI). Average nucleotide identity (ANI) was determined from assembled genomes of E. coli strains by all-against-all alignment using FastANI 31 (https:// github.
com/ kbase apps/ FastA NI) to estimate whole-genome similarity among the strains from different countries, animal hosts and farms. Single nucleotide polymorphisms tree. Single nucleotide polymorphisms (SNPs) were determined using CSI phylogeny  32. Basically, paired-end reads were mapped to the reference genome, Escherichia coli str. K-12 (accession number; NC_000913. 3), using Burrows-Wheeler Aligner (BWA) version 0. 7. 2  33. SNPs were identified using 'mpileup' module in SAMTools version 0. 1. 18  34. Qualified SNPs were chosen using following criteria: (1) a minimum distance of 10 bps between each SNP, (2) a minimum of 10% of the average depth, (3) the mapping quality was above 25, (4) the SNP quality was more than 30, and (5) all INDELs were excluded. The qualified core SNPs were concatenated to a single alignment corresponding to the position of the reference genome.
KMA is freely available at: https:// bitbu cket. org/ genom icepi demio logy/ kma and https:// cge. cbs. dtu. dk/ servi ces/ kma. The cgMLSTFinder tool, which is based on KMA, can be found at: https:// cge. cbs. dtu. dk/ servi ces/ cgMLS TFind er-1. 0/. The pairwise dissimilarities (distances) between genomes were identified based on the allele profile using 'gower' distance method from 'cluster' package version 2. 1. 0 in R. The cgMLST trees were constructed from the distance matrix using hierarchical clustering from 'ape' package version 5. 4-1 in R.
Acknowledgements This work was supported by the  European Union -funded  EFFORT  project (www. effort-again st-amr. eu) ( FP7-KBBE-2013-7, Grant Agreement 613754 ). By the EJP-one health project  Full-force  (Grant Agreement No. 773830 ) and by The  Novo Nordisk Foundation  ( NNF16OC0021856 : Global Surveillance of Antimicrobial Resistance ). In addition, whole-genome sequencing done at  National Veterinary Research Institute  was possible due to support of  Department of Omics Analyses  from the  Ministry of Science and Higher Education  (Grant No. 4477/E-180/SPUB/2018 ).
Out of these non-synonymous variants located in coding regions of genes, 4 are serious candidates because they were found among the 10 most significant variants of the QTL peaks: G446S in SLC26A4 (ranked 2nd for the QTL found on BTA4 at 48. 7Mbp for Ca), V298M in ENSBTAG00000050954 (ranked 10th for the QTL found on BTA7 at 40. 3Mbp for P), F267V in ARHGAP39 (ranked 5th for the QTL found on BTA14 at 0. 4Mbp for K and Citrate) and F257Y in GHR (ranked 1st for the QTL found on BTA20 at 31. 9Mbp for P, Ca and Mg). We also found a relatively high proportion of variants  located in non-coding RNA (7. 9% of all variants in the CIs of the QTL and 13. 6% of top 1 variants). As presented in Table  6 , these variants were mostly located in long non-coding RNA (lncRNA), less frequently in micro RNA (miRNA) and small nuclear RNA (snRNA), and very rarely in small nucleolar RNA (small Cajal body-specific RNA, scaRNA), and transfer RNA (tRNA).
We then completed annotation of the QTL (1) identifying putative regulatory SNPs (rSNPs) located within transcription factor binding sites (TFBSs) according to the procedure described in the Methods section, and (2) using transcriptomic data available on the Cattle Gene Atlas website, http:// cattl egene atlas. roslin. ed. ac. uk/. In the CIs of 40 of the 83 QTL, we identified 184 variants that overlapped with the TFBSs of 438 transcription factors (TFs), i. e. putative rSNPs (see Supplementary Table  S1 ). These 438 TFs had 67 unique target genes among the 271 positional candidates identified in the QTL. Each TF targeted from 1 to 5 positional candidate genes, and 110 TFs had binding sites (BSs) at multiple loci. One TF (ASCL2) targeted 5 genes (BHLHE23, DGKQ, RMND5B, bta-let-7a-3, and bta-mir-2443), while 2, 21, and 85 TFs targeted 4, 3, and 2 genes, respectively.
Two top 1 variants, rs108972810 (~ 45. 4 Mbp in the upstream region of SMIM20 on BTA6, for Na) and rs209051255 (~ 41. 3 Mbp in the upstream region of ENSBTAG00000053872 on BTA7, for Ca) were in the TFBSs for the TFs DOF and FOXD3, respectively. In total, for 22 QTL, top 10 variants were found in the TFBSs of 19 different target genes (including CSN2, PAEP, GPAT4, BRI3BP, and PPARA ), which are potentially regulated by 47 TFs. We then assessed the degree to which these positional candidate genes and TFs demonstrated tissue-specific expression. Of the 271 genes and 438 TFs associated with milk-mineral QTL, 203 and 160, respectively, were present in the Cattle Gene Atlas, which contains expression data from 91 different tissues or cell lines. Using the model described in the Methods section, we assessed the overexpression or tissue specificity of these genes by evaluating the t-statistic and the P value, Pt, associated with the effect of the tissue under consideration (see Supplementary Tables  S2  and S3 ).
58 and 0. 61, respectively) increased the amount of all minerals affected by this region. In the ANKH region, we identified three different top 1 variants, depending on the trait. For K, the top variant was rs134021638 (at 58,386,888 bp, imputed with R 2 = 0. 92), located in an intronic region of ANKH; for Ca, Mg and citrate, the top variants were located in an lncRNA ENSBTAG00000048498 (rs110048176 at 58,189,663 bp, HD SNP for Ca and Mg and rs109956167 at 58,204,929 bp, imputed variant with R 2 = 0. 90 for citrate). ENSBTAG00000048498 spans from 58,186,301 to 58,224,434 bp and is located around 83 kbp upstream of the ANKH gene (58,307,527-58,477,497 bp). In all cases, the alleles responsible for an increase in citrate and mineral content were not the most common, with a MAF of 0. 05 for the ANKH variant (rs134021638) and 0.
The study of Buitenhuis et al. 9  was dedicated to mineral composition while Kemper et al. 12  attempted to identify QTL that overlapped between milk production and composition traits. Only two QTL were shared between the two studies: a region located on BTA1 that affected P, for which both studies identified SLC37A1 as the best functional candidate gene, and a region located near the DGAT1 gene on BTA14 that affected Ca and P. In our study, we also identified these two regions, together with dozens of other regions located throughout the bovine genome. By considering a very large dataset (more than 1 million test-day records of 126,873 cows), we estimated heritability values for mineral and citrate composition to be moderate to high, i. e. higher  5, 7, 8, 10  than or similar  6, 9  to those previously reported in the literature.
e. in putative regulatory regions. To further refine our results, we consulted in silico annotations of rSNPs in upstream regions of genes as well as existing knowledge regarding genomic regions that are transcribed into non-coding RNA. Moreover, to support the putative roles of these variants, we also considered the tissue specificity of candidate genes and transcription factors. Unfortunately, the expression dataset, which was based on Ensembl release 94, did not contain all of the genes located in QTL regions. Here we present some examples of QTL in which the putative causal mutation was located in a regulatory region, with particular attention to functional candidate genes previously associated with milk composition and the QTL with the most significant effects in this study. GPAT4 (glycerol-3-phosphate acyltransferase 4) was the best candidate gene for the QTL on BTA 27 with effects on Mg.
85), while the second variant was an HD SNP (R 2 = 0. 997). In the region of the ANKH gene, we found 81, 65, 69, and 21 variants in the confidence intervals of QTL for citrate, Mg, K, and Ca, respectively. These represented 82 unique variants, all located either in intronic regions of the ANKH gene (59 variants between 58,344,839 and 58,388,849 bp) or in the lncRNA ENSBTAG00000048498 (23 variants between 58,185,895 and 58,212,187 bp). The same variant was ranked first for both Mg and Ca. Two top-1 variants were located in the lncRNA ENSBTAG00000048498-rs109956167 (at 58,204,929 bp) for citrate and rs110048176 (at 58,189,663 bp) for Mg and Ca-while the top-ranked variant for K was located in ANKH (rs134021638 at 58,386,888 bp). We propose rs109956167 as the best candidate causal variant because i) it was ranked 1st for the most-affected trait (citrate), ii) it was imputed, in contrast to rs110048176 which was from the HD chip, and iii) it was the only variant present in the top 10 for all traits affected by this region (4th for Mg, 6th for Ca, and 9th for K).
We hypothesize that the lncRNA ENSBTAG00000048498, which is 83 kbp downstream of the closest gene, ANKH, may affect milk mineral content by regulating the expression of ANKH and thus the amount of protein available for ion transport. When we examined the best candidate variants in the regions of SLC37A1 (rs109459130) and ANKH (rs109956167), we found that alleles of these genes did not have significant interactions with respect to the six milk composition traits analyzed in this study and that, combined together, these two regions explained a large part of the genetic variance in milk mineral content. The allele that increased mineral content was the most frequent allele of SLC37A1 (allele A, frequency = 0. 58), while it was the least frequent allele in the ANKH region (allele C, frequency = 0.
17). Compared to previous studies, our GWAS-based investigation of milk mineral composition was conducted at the whole-genome level, after imputation with the most recent reference genome and using data from a large population of animals. This approach led to the identification of a large number of genomic regions that explain a great deal of the genetic variance of the traits studied here. Moreover, post-GWAS investigations conducted using different annotation datasets enabled the identification of candidate causal genes and the prioritization of candidate variants in these genomic regions. In most situations, exonic variants were not proposed as the best candidates, although they cannot be excluded in four QTL regions, including GHR. In contrast, the best candidate variants often were putative regulatory variants.
Association analyses were performed by including in the model the most significant variant as a fixed effect and by testing all variants in these neighboring QTL that were not in strong LD with the conditional variant (r 2 < 0. 9). Functional annotations. Genomic regions and variants were annotated with FAANGMine v1. 1 (https:// faang mine. elsik lab. misso uri. edu/), developed by the Functional Annotation of ANimal Genomes initiative  18  and which integrates the ARS-UCD1. 2 bovine reference genome with a variety of external data sources, including RefSeq from NCBI (https:// www. ncbi. nlm. nih. gov) and Ensembl (https:// www. ensem bl. org) gene sets. The ability of genetic variants to alter transcription factor binding sites (TFBSs) was predicted with a custom script that used TFBS models from the JASPAR (JASPAR CORE 2018 collection  41  ), HOCOMOCO (version v10  42  ), and TRANSFAC (version v3.
2 public  43  ) databases. These databases contain curated sets of transcription factor binding models represented as Position Weight Matrices (PWM), which are derived from published collections of experimentally defined eukaryote TFBSs. Only vertebrate PWMs were downloaded for use in our study. Gene overexpression or specificity in different tissues was determined using gene expression patterns of 24,616 genes (Ensembl release 94) available in the Cattle Gene Atlas, which contains 723 RNA-seq datasets representing 91 tissues and cell types, classified into 17 biological categories (http:// cattl egene atlas. roslin. ed. ac. uk/). To assess the expression specificity of each gene in a given type of tissue (by excluding tissues in the same biological category), we applied the following linear model as described in Fang et al.
44  : where ye is the vector of expression level in the tissues, assessed by the scaled log 2 FPKM (Fragments Per Kilo- base per Million mapped reads); me is the overall mean; x t is the vector of the variable with value 1 for samples of the tested tissue and -1 for samples outside the same category; b t is the corresponding tissue effect; z is the incidence matrix related to the corresponding covariables effects c , including age, sex and study effects; ee is the residual effect. Model (3) was implemented adapting R scripts available on the Cattle Gene Atlas website. For each gene, a t-statistic was computed by dividing the tissue effect by its standard error. A gene was considered to be overexpressed in a tissue if the probability associated with the t-statistic (Pt) was lower than 10 -4.
SLC37A1-ANKH genotype interactions. We tested putative interaction effects between the genes SLC37A1 and ANKH on milk mineral and citrate content with the following mixed linear model: where yd as defined in  (2) ; mi is the overall mean; g SLC37A1 and g ANKH are the fixed effects of the genotypes of the best candidate variant in the SLC37A1 and ANKH regions, respectively; g SLC37A1 x g ANKH represents the interaction between the two genotypes;M, N , and O are incidence matrices related to the individual effects of the SLC37A1 and ANKH genotypes and their interaction, respectively; s is the vector of random sire effects and P the corresponding incidence matrix; and ei is the vector of random residual effects. All effects were tested using t-statistics computed in the MIXED procedure of SAS software.
Acknowledgements This study was funded by the  French Ministry of Agriculture, Agro-food , and Forests; the  French Dairy Interbranch Organization  (CNIEL); the  Regional Union of Protected Designation Cheeses of Franche-Comté (URFAC) ; and the  Regional Council of Bourgogne-Franche-Comté , through the  project From'MIR. Chip genotypes originated from the genomic selection program  of the breeding company UMOTEST. The authors gratefully.
The tree was constructed using the neighbor-joining method and the Tamura 3-parameter (T92) model. Topology was supported by 1,000 bootstrap replicates. The sequences reported in this study are indicated by the laboratory code. The sequences downloaded from GISAID are indicated by their accession numbers. Cluster 1, marked in purple, includes sequences presenting the D614G substitution and the lack of the amino acid substitution N501Y. Cluster 2, marked in blue, includes sequences having the N501Y, A570D, D614G, and P681H substitutions. Cluster 3, marked in red, groups sequences with the N501Y, A653V, and H655Y substitutions and lacking the amino acid substitution D614G. Unlike the sequences from cluster 1 and cluster 2, the three whole-genome sequences from cluster 3 belonged to the PANGO A lineage.
Samples were transported, under refrigeration and within 24 h, to the Pasteur Institute of Tunis where they were immediately processed for SARS-CoV-2 detection by specific real-time reverse transcription PCR (RT-PCR) according to WHO-approved protocols  (29, 30). Ethical statement. This work was performed in the context of COVID-19 diagnostic efforts, and all samples used for analysis were anonymized. This study was approved by the Bio-Medical Ethics Committee of the Pasteur Institute of Tunis under Tunisia reference no. 2020/14/I/LR16IPT/V1. Primer design. Primers were designed using PrimerDesign-M online software, available through https://www. hiv. lanl. gov/content/sequence/PRIMER_DESIGN/primer_design. html (31, 32) , based on an alignment of 13,451 SARS-CoV-2 complete-genome sequences.
Several points were considered, such as melting temperatures, G1C percentage, entropy, complexity, and nucleotide composition, in order to perfectly align with the SARS-CoV-2 sequence. The selected primer sequences were as follows: IPT_FW (positions 22964 to 22987), 59ATTTCAACTGAAATCTATCAGGCC39, and IPT_REV (positions 23666 to 23647), 59CTGCACCAAGTGACATAGTG39. The indicated positions correspond to the sequence of the Wuhan reference strain (accession number NC_045512). The designed primers allow the amplification of a 703-nucleotide-long region in the S gene holding key mutations, including E484K, N501Y, A570D, D614G, and P681H, recently identified as specific to the main VOCs and VUIs of SARS-CoV-2. PCR amplification and sequencing in the S gene. Volumes of 140 ml of nasopharyngeal samples were used for viral RNA extraction with the viral RNA minikit (Qiagen, Hilden, Germany) to give a final elution volume of 60 ml of total RNA.
The purified amplicons were sequenced using the BigDye Terminator v3. 1 kit (Applied Biosystems) and the forward and reverse PCR primers. The resulting consensus sequences were deduced by aligning the forward and the reverse sequence of each isolate, excluding primer binding regions, and are 659 nucleotides long (positions 22988 to 23665 according to the Wuhan reference strain NC_045512). They were submitted to the NCBI database under accession numbers MZ150010 to MZ150210. Whole-genome sequencing. The QIAseq SARS-CoV-2 primer panel paired with the QIAseq FX DNA library construction kit (Qiagen GmbH, Germany) was used for enriching and sequencing the entire SARS-CoV-2 viral genome. Extracted RNA from nasopharyngeal swabs was first depleted of rRNA using the RiboZero rRNA removal kit (Illumina, USA).
bioinformatics. babraham. ac. uk/projects/fastqc/). Low-quality reads and adapters were filtered using Trimmomatic version 0. 39  (33)  with a Phred quality score of 30 as the threshold. Genome consensus sequences were assembled by mapping to the SARS-CoV-2 reference genome with GenBank accession number NC_045512 (Wuhan-Hu-1 isolate) using Spades assembler version 3. 15. 0 (34) , with thresholds of 80% for nucleotide sequence coverage and 90% for nucleotide similarity. The new SARS-CoV-2 sequences obtained were submitted to the GISAID database (https://www. gisaid. org) (35, 36)  with the following MicrobiolSpectrum. asm. org 9 accession numbers: EPI_ISL_2035560, EPI_ISL_2035563, EPI_ISL_2035720, EPI_ISL_2035734, EPI_ISL_2035752, EPI_ISL_2035753, EPI_ISL_2035940 to EPI_ISL_2035949, EPI_ISL_2035988, and EPI_ISL_2036077.
Phylogenetic analysis. The obtained partial S gene sequences and selected whole-genome sequences were aligned with representative SARS-CoV-2 reference sequences of the nine recognized GISAID clades publicly available in the GISAID database by using MUSCLE multiple sequence alignment algorithms  (37)  implemented in MEGAX  (38). Phylogenetic analyses were performed on nucleotide sequences using the maximum-likelihood method with the Tamura 3-parameter model and then on amino acid sequences, obtained from the aligned sequences, using the maximum-likelihood method and the Jones-Taylor-Thornton model. The tree topologies were supported by 1,000 bootstrap replicates. Mutation profiles in the open reading frame 1a (ORF1a), ORF1b, S, ORF3a, E, M, ORF6, ORF7a, ORF8, N, and ORF10 genomic regions of SARS-CoV-2 were assessed by comparing the nucleotide and deduced amino acid sequences of the Tunisian strains with those of the Wuhan reference strain, using the sequence alignment performed by MUSCLE multiple sequence alignment algorithms  (37)  implemented in MEGAX  (38).
Data availability. Partial S gene amplicon consensus sequences were submitted to the NCBI database under accession numbers MZ150010 to MZ150210. New SARS-CoV-2 sequences generated here were submitted to the GISAID database (https://www. gisaid. org) (35, 36)  with the following accession numbers: EPI_ISL_2035560, EPI_ISL_2035563, EPI_ISL_2035720, EPI_ISL_2035734, EPI_ISL_2035752, EPI_ISL_2035753, EPI_ISL_2035940 to EPI_ISL_2035949, EPI_ISL_2035988 and EPI_ISL_2036077.
Proteomics computational analysis MS data were analysed using MaxQuant  34, 35  software version 1. 6. 3. 4 and searched against the SwissProt subset of the H. sapiens Uniprot database (http://www. uniprot. org/) containing 20,365 entries (February 2019 release) to which 248 common laboratory contaminants were added. The enzyme specificity was set to trypsin with a maximum number of missed cleavages set to 2. Peptide identification was performed with an initial precursor mass deviation of 7 ppm and a fragment mass deviation of 20 ppm with subsequent non-linear mass recalibration. 36 xidation of methionine residues was searched as variable modification; carbamidomethylation of cysteines was searched as a fixed modification. The false discovery rate (FDR) for peptide, protein and site identification was set to 1% and was calculated using a decoy database approach.