dataesr/scientific-paragraphs-categorization huggingface dataset:
Escherichia coli is a ubiquitous bacterium that has been widely exposed to antibiotics over the last 70 years. It has adapted by acquiring different antibiotic-resistance genes (ARGs), the census of which we aim to characterize here. To do so, we analysed 70 301 E. coli genomes obtained from the EnteroBase database and detected 1 027 651 ARGs using the AMRFinder, Mustard and ResfinderFG ARG databases. We observed a strong phylogroup and clonal lineage specific distribution of some ARGs, supporting the argument for epistasis between ARGs and the strain genetic background. However, each phylogroup had ARGs conferring a similar antibiotic class resistance pattern, indicating phenotypic adaptive convergence. The G+C content or the type of ARG was not associated with the frequency of the ARG in the database.
With the development of NGS and genomics after 2005, the number of bacterial genomes made available has increased exponentially and the identification of ARGs was made easier via the use of in silico tools. Efforts have been made to collect and organize known ARG sequences in dedicated databases, with the first antibiotic resistance database (ARDB) released as early as 2008  [5, 6]. Since then, others such as ResFinder  [6] , CARD  [7] , ARG-ANNOT  [8]  and more recently AMRFinder  [9]  have followed. Typically, these databases include thousands of ARG nucleotide and/or amino-acid sequences previously identified in cultivable and/or pathogenic bacteria. However, their content is biased as they lack the ARGs found in bacteria that are difficult to culture and those of little relevance from a medical perspective, such as commensal, strict anaerobic bacteria from the gut microbiota.
Nonetheless, we and others have found these bacteria do harbour a vast diversity of ARGs, and the latter actually differ from those found in the ARG databases  [10, 11]. These ARGs have been made available in specific databases, namely FARMEDB  [12] , ResFinderFG  [13]  and Mustard  [11] , whose content slightly overlaps with that of conventional ARG databases (Fig. S1 , available with the online version of this article). Indeed, very few observations support occurrence of a transfer of the ARG from intestinal commensals to Proteobacteria opportunistic pathogens such as E. coli  [14]. Still, such transfer has proven to be possible. For instance, tetX, a gene encoding resistance to tetracyclines  [15] , was shown to be transferred from Bacteroidetes to Proteobacteria. Furthermore, some transfer events may have gone unseen due to the lack of genomic monitoring of a large number of strains and because of the lack of sampling.
Since December 2015, EnteroBase  [30] , a public database including thousands of genomes from E. coli/Shigella and other species (Salmonella, other Escherichia species, Clostridioides, Vibrio, Yersinia, Helicobacter and Moraxella), has been available. Beyond genomes, EnteroBase includes (with varying degrees of completeness) metadata linked to the strain itself (name, source, location, laboratory, species, serotype, disease and entry/update date) and to its sequencing process (N50, coverage). Still, no data regarding antibiotic resistance are available. In this study, we leveraged the high number of E. coli genomes in EnteroBase to characterize the acquired ARGs in E. coli and, more precisely, (i) to test for specific associations between the phylogenetic background of the strains and the presence of ARGs, and (ii) to evidence ARG transfer between non-Proteobacteria species and E.
Genomic database, species classification and E. coli phylogroup determination A total of 82 063 available genomes was downloaded from Escherichia/Shigella EnteroBase (as of 1 February 2019). The genomes were classified according to their genera and species (Shigella, Escherichia coli, Escherichia albertii, Escherichia fergusonii, Escherichia marmotae or unknown). First, Shigella and enteroinvasive E. coli (EIEC) genomes were identified using in silico PCR with primers of the ipaH3 gene  [31]. Due to their specific, obligatory intracellular pathogenic trait, they were removed from the dataset. Then, using ClermonTyper  [32] , a tool that provides information about phylogroups (A, B1, B2, C, D, E, F and G) for E. coli and identifies nearest species in conjunction with Mash  [33]  (E. fergusonii, E.
ARG identification, plasmid incompatibility group, chromosomal multilocus sequence type (MLST) determination and G+C content Diamond  [35]  was used to identify all the ARGs in EnteroBase by aligning all genomes against the AMRFinder  [9]  (29 04 2019 version), Mustard  [11]  (30 09 2017 version) and the ResFind-erFG  [13]  (21 12 2016 version) databases (with a minimum coverage × identity value greater than 0. 64 for nucleotidic sequences corresponding to 80 % identity and 80 % coverage). All redundancies (sequences sharing 100 % identity in nucleotides) between the databases were removed. The ARG families were selected according to the Mustard website (http:// mgps. eu/ Mustard). All ResFinderFG and Mustard ARGs originatng from non-Proteobacteria were further investigated. When a genome was found to include an ARG putatively originating from a non-Proteobacteria, contamination (i.
Impact Statement We analysed a large set of Escherichia coli genomes and searched for antibiotic-resistance genes (ARGs) using various databases. We observed that ARGs distributed according to the phylogenetic background of the strains, supporting the observation that constraints were at play within E. coli. Moreover, we identified four instances of putative transfers of ARGs from a phylum other than that of E. coli, stressing the strong inter-phyla barrier for ARG exchange. However, the capacity of the acquired ARGs to provide resistance against the most used antibiotic families did not differ according to the phylogenetic background, stressing that the different lineages of E. coli adapted to the antibiotic pressure with the acquisition of ARGs their genetic background could accommodate.
This research is to our knowledge the first of its kind to study the acquired resistome of E. coli, an intestinal, ubiquitous bacterium that has been exposed to antibiotics from their earliest use. In this connection, the results could help in better understanding how bacteria adapt to antibiotics by acquiring ARGs. the EnteroBase database (98 % identity and a minimum of 95 % coverage). MLST was determined using mlst software based on the Warwick University or Pasteur Institute MLST schemes available from the pubMLST database (https:// github. com/ tseemann/ mlst)  [38]. G+C content (%) deviation between E. coli and the acquired ARG was measured using the E. coli G+C defined previously  [39]. ARG sub-family classification was obtained by clustering all the ARGs from the three databases (AMRFinder, Mustard and ResfinderFG) using cd-hit-est based on a 90 % identity threshold  [40, 41].
Statistical analysis and normalization To circumvent the sequencing biases for the richness and diversity estimation, the data were normalized so that each phylogroup would include the same number of genomes (n=10 000) by re-sampling (for C, D and F phylogroups) or sub-sampling (for A, B1, B2 and E phylogroups) while maintaining the proportionality. The same protocol was applied to the STs. For all other statistical analysis, the complete dataset was used (without normalization). We tested the correlations between phylogroup, ST, plasmid incompatibility group and ARG using the corrplot package of R v3. 4. 2 and the corrmat function. The preferential distribution of some ARGs within phylogroups was tested using the Kruskal-Wallis test and Benjamini-Hochberg correction. The diversity of the ARGs in some STs was measured using the Shannon index in R (v3.
Distribution of the species and E. coli phylogroups among the E. coli/Shigella EnteroBase During the curation step, 5144 genomes (6. 3 % of genomes) were re-assigned to another genus/species. After curation, we identified 70 301 E. coli, 221 Escherichia clade I, 5 Escherichia clade II, 26 Escherichia clade III, 17 Escherichia clade IV, 102 Escherichia clade V (E. marmotae), 216 E. albertii, 36 E. fergusonii and 11 139 Shigella and EIEC (enteroinvasive E. coli). Among E. coli, the phylogroups were distributed as follows: B1 (25 265), A (12 469), B2  (12 414 ), E (11 965), D (4139), F (2403) and C (1646) (Fig. 1 ). The three major STs were ST11 (n=10 059, phylogroup E), ST131 (n=5143, phylogroup B2) and ST10 (n=3959, phylogroup A), making up 27 % of the E. coli in the database. Of note, ST11 and ST131 include the O157:H7 Shiga-toxin-producing E.
Most frequent ARGs found in E. coli First, we used the AMRFinder database and identified a total of 314 091 ARGs in E. coli genomes. The mean number of ARGs by genome was around 4. 5 and the median was 2, and we observed a minimum of 0 ARGs and a maximum of 47 ARGs. The first part (n=164 519) included genes matching to known genes with 100 % of identity and coverage. This corresponded to 381 ARGs out of the 4955 genes included in the AMRFinder database. The second part (n=149 572) was made of variants sharing a coverage × identity value greater than 0. 64 for nucleotide sequences (corresponding to 80 % identity and 80 % coverage). This comprised variants for 328 genes (including 169 genes not previously detected when the 100 % identity and coverage parameters applied) in the AMRFinder database.
A total of 550 genes out of 4955 (11. 1 %) of the AMRFinder database were, thus, found at least once in E. coli genomes. The 20 most frequent ARGs sharing 100 % identity with genes from AMRFinder are depicted in Fig. 2. We predominantly found genes encoding β-lactamases and aminoglycoside-modifying enzymes (AMEs), the three most abundant genes being bla TEM-1 (n=16 766), aph(3′′)-Ib (n=15 481) and aph(6)-Id (n=12 845). Among the 234 β-lactamases found in the AMRFinder database, 27 sub-families were identified (with 100 % identity or in variants). The five β-lactamases bla TEM (n=18 364), bla CTX-M (n=10 640), bla OXA-1-like (n=4075), bla CMY-2-like (n=1994) and bla SHV (n=518) were the most frequent (Table  S1 ). We also observed a high frequency of ARGs conferring resistance to antibiotics that are used to treat infections not caused by E.
g. β-lactamase and AME) would have a minimal impact on the fitness of the recipient strain even if originating from a G+C-divergent background. Conversely, genes encoding proteins interacting with the cellular content (e. g. efflux and target modification) would impact the fitness of the recipient, especially when originating from a G+C-divergent background  [44]. However, in our dataset, we did not observe a distinct pattern according to the type of resistance (Mann-Whitney test, P=0. 7, mean for drug-modifying enzyme equal to 3. 969, and 2. 582 for ARGs with cellular interactions; Fig. 6 ).
Non-Proteobacteria ARGs can be exceptionally found in E. coli Lastly, we assessed whether some ARGs found in non-Proteobacteria bacteria were present in the E. coli genomes of EnteroBase using the specific databases ResFinderFG and Mustard. We observed a high number of hits (n=833 814) in the Mustard and ResFinderFG databases, mostly corresponding to genes predicted to originate from Proteobacteria. The first part (n=271 458) included genes matching to known genes with 100 % of identity and coverage. This corresponded to 37 different genes identified in the 6095 genes of the Mustard database (Table  S5 ) and 44 different genes identified in the 2282 genes of the ResFinderFG database (Table  S6 ). The second part (n=562 356) was made of variants sharing a coverage × identity value greater than 0.
64 for nucleotide sequences (corresponding to 80 % identity and 80 % coverage) with known genes. This made up 385 673 variants for 23 additional genes in Mustard (Table  S5 ) and 176 683 variants for 61 additional genes in ResFinderFG (Table  S6 ). Interestingly, we identified a putative β-lactamase encoding gene from ResFinderFG (beta_lactamase, KU546399. 1, faeces, AMX) and Mustard (MC3. MG60. AS1. GP1. C4251. G1) databases also found in the strain Bacteroides uniformis NBRC 113350 (NCBI GenBank accession no. NZ_AP019724. 1). The E. coli bearing this gene was from phylogroup A, ST744 (Table  1 ), and had been isolated in Germany  [45]  from a patient screened for multidrug-resistant bacteria at the  The G+C content deviation corresponds to the difference between the mean G+C content of E.
DISCUSSION Using a large number of genomes, we were able to assess the diversity and distribution of ARGs in E. coli. From a global perspective, we assumed that the richness of acquired ARGs in E. coli was somewhat limited with regards to the high number of ARGs in the literature and the closeness of E. coli to human activities (the ARGs found in E. coli representing 11. 1 % of the AMRFinder database). This suggested constraints in the path for ARG exchanges and sustainability between species (phylogenetic origin of the gene, interaction within the cell of the gene product  [44, 46] ), but also within the E. coli species. We did not find any evidence of a link between the G+C content or the functional class of the transferred genes and their frequency in the database. However, some ARGs had specific associations with the genomic background (phylogroups and STs), other ARGs and plasmid incompatibility groups.
coli. In a previous work, we observed that the vast majority of ARGs found in the intestinal microbiota were very distinct from those found in cultivable bacteria (including E. coli) and that few arguments were found to support their mobility  [11]. Taking advantage of the largest E. coli database to date, we could observe that ARGs could actually be exchanged between E. coli and intestinal commensals, such as was observed for tetX  [50]. Even if anecdotal in this dataset, with four observations, the very fact that they were detected suggests that they are not that uncommon, but unlike tetX which has met with success, their spread is very limited to date as they were only found in one genome each. The donor bacteria were strict anaerobic bacteria (Bacteroides uniformis, Bifidobacterium breve and C.
difficile) commonly found in the gut microbiota at high abundances  [51]  alongside E. coli. Considering that humans have been using antibiotics for more than 70 years, very favourable conditions have been met for ARG transfers between anaerobic bacteria and E. coli. That such transfers have been observed so rarely supports the hypothesis that anaerobic bacteria may indeed provide ARGs to E. coli, but that the contribution to the worldwide AMR issue seems to be minor. Indeed, the most successful ARGs found in E. coli originate from other Proteobacteria species  [14] , e. g. bla CTX-M progenitors are Kluyvera spp. belonging to the Enterobacterales  [52]. Besides, the observations of the ARG transfer would not have been possible if not for the use of multiple databases to cover the broadest range of ARGs.
While databases such as AMRFinder may be found suitable to identify ARGs from clinically relevant bacteria, they may not be appropriate when it comes to looking for ARGs originating from other environments, such as the gut microbiota. We acknowledge some limitations of the present study. Despite including a large number of genomes, EnteroBase suffers from inclusion biases in that strains of interest (e. g. the resistant and/or the pathogenic ones) are the most sequenced. Indeed, EnteroBase includes a large number of STEC (Shiga-toxin-producing E. coli), mainly the O157:H7 serotype, ExPEC (extra-intestinal pathogenic E. coli) with the emerging ST131 and many strains producing extended spectrum β-lactamases. This may have led to an overestimation of the associations between ARGs, phylogenetic traits and replicons.
Also, many more ARGs may have been found in E. coli, perhaps including some from intestinal commensals that were not found of interest to be cultured and sequenced. Hence, we assume we did not cover the global picture of the acquired ARGs always found in E. coli, but only a part. Nonetheless, we believe our findings are sound with regards to the very high number of strains included in this study. We also know that some genomes with contamination can be found in the database, which warrants the use of specific tools upstream of genome analysis. In all, we observed that ARGs were distributed in the E. coli phylogroups/STs with a preferential fashion. In the meantime, they provided resistance to the same antibiotic families. Furthermore, we observed that the transfer of ARGs between non-Proteobacteria and E.
Acknowledgements We are grateful to  Professor Alexander Mellmann  ( University of Münster, Germany ) for providing the E. coli strain containing the Bacteroides uniformis β-lactamase-encoding gene, and to  James R. Johnson  ( University of Minnesota, MN, USA ),  Lance B. Price  ( University of Washington, DC, USA ) and  Maliha Aziz  ( University of Washington, DC, USA ) for helping in understanding the S. aureus/E. coli blaZ finding. We are grateful to  Olivier Clermont  for his help in the database curation. We are grateful to  Lucile Vigué  for the assistance with statistics analysis. Finally, we thank  Olivier Tenaillon  for his proofreading of the manuscript. We are also grateful to the reviewers whose comments allowed us to go further in the interpretation of the data.
sporopollenin. However, we also reveal substantial chemical differences between extant and fossil sporopollenin, which can be tied to both early diagenetic changes acting on the sporomorphs and chemical derivates of sample processing. Our results demonstrate that directly integrating extant and late Quaternary chemical data should be tractable as long as comparable sample processing routines are maintained. Consistent differences between extant and deeper time sporomorphs, however, suggests that classifying fossil specimens using extant training sets will be challenging. Further work is therefore required to understand and simulate the effects of diagenetic processes on sporopollenin chemistry. chemical signature. This has yielded information in three key areas: (1) the chemical composition of sporopollenin, the biopolymer that makes up the outer wall (exine) of sporomorphs and gives them an exceptionally high preservation potential in the geological record  (Blokker et al.
, 2006;; Watson et al. 2007; Lomax et al. 2008 Lomax et al. , 2012;; Fraser et al. 2011 Fraser et al. , 2014b;; Willis et al. 2011; Lomax & Fraser 2015; Jardine et al. 2016 Jardine et al. , 2017 Jardine et al. , 2020a;; Seddon et al. 2017 Seddon et al. , 2019;; Bell et al. 2018). Despite the high potential of these methods, there are a series of unresolved issues linked to the feasibility of integrating chemical datasets derived from extant and fossil sporomorphs. (Note that here we use the term 'fossil' to denote sporomorphs that have been recovered from the geological record, rather than inferring any fossilization processes per se. ) These issues relate mostly to how sporopollenin chemistry changes as a function of diagenesis within natural sedimentary systems, and also to the laboratory methods used to isolate sporopollenin from fresh sporomorphs.
2007 ). While such changes to the sporopollenin chemical signature would hamper the direct integration of extant and fossil chemical datasets, relating rates and thresholds of change in experimental settings to those in natural geological systems is difficult, and so it is not known at which combination of temperature, pressure and time repolymerization occurs  (Watson et al. 2012; Fraser et al. 2014a; Bernard et al. 2015). Laboratory processing methods may also influence sporopollenin chemistry. Sporomorphs collected from living plants or herbarium specimens require processing to remove the external labile compounds (proteins, lipids and carbohydrates) and isolate the sporopollenin wall  (Traverse 2007; Jardine et al. 2015). It is currently not well understood how processing procedures may influence sporopollenin chemistry, and therefore whether 'pure' sporopollenin is left over or some modified form of it  (Dom ınguez et al.
These studies have provided a wealth of information on the controls on the chemical signature of sporopollenin, but the restricted nature of their scope makes wider generalizations difficult. This is a particular problem for chemotaxonomic analysis of fossil datasets, since most research in this area to date has focused exclusively on freshly harvested or herbarium material, without additional isolation of the sporopollenin  (Pappas et al. 2003; Schulte et al. 2008; Dell'Anna et al. 2009; Zimmermann 2010 ; Zimmermann taxonomic signature in their chemical make-up, it is still not clear how much of this signal is driven by the external labile components and how much taxonomic information is recorded within the sporopollenin biopolymer. If there is only a weak taxonomic signal present in sporopollenin itself, attempts to apply chemotaxonomy to the fossil record will have limited success, even if diagenetic changes in sporopollenin are subtle enough for extant and fossil datasets to be integrated successfully.
Here, we explore these issues with a large dataset incorporating a range of taxa from different time periods, using Fourier transform infrared (FTIR) microspectroscopy to track changes in pollen chemistry. These datasets have been combined to tackle key research questions in the development of chemopalynology. The first question focuses on testing whether chemotaxonomic information is recorded within sporopollenin. In testing this concept, we have focused on the grasses (Poaceae) as they present a classic problem in palynology: despite being highly diverse (> 12 000 species) and ecologically dominant in savannah and steppe environments, grass pollen is morphologically highly similar and challenging to classify below the family level  (Bush 2002; Str€ omberg 2011; Mander et al. 2013; Julier et al.
2016; Jardine et al. 2019). Valuable palaeoecological and palaeoenvironmental information, such as relative contributions from aquatic versus terrestrial grasses, or domesticated versus wild, is therefore often lost in palynological studies  (Bush 2002; Jardine et al. 2019). Our main dataset comprises extant grass pollen collected from 50 South American species in 9 subfamilies, which we use to quantify classification potential in acetolysed sporopollenin. Additionally, we use this dataset to test whether the photosynthetic pathways in grasses can be determined in the broader FTIR fingerprint. Being able to reliably chemotype photosynthetic pathways of fossil pollen (C 3 vs C 4 ) would provide much needed information on the evolution of C 3 and C 4 grasses, and their relative dominance across different habitats  (Nelson et al.
2006 (Nelson et al. , 2008;; Str€ omberg 2011). The second question explores whether extant and fossil chemical datasets can be integrated to enable chemotaxonomic classification, by determining the impact of diagenetic processes on sporopollenin preservation within natural sedimentary settings. To answer this question, we assembled a dataset of Miocene to Pleistocene (~18 to ~0. 5 Ma) grass pollen from western Amazonia  (Hoorn 1994; Vink 2012; Berm udez et al. 2017 ) and the Amazon Fan  (Hoorn et al. 2017) , to provide a direct comparison with the extant grasses examined to deliver on the first question. We also use late Pleistocene (~500 to 13 ka) grass pollen data from Lake Bosumtwi in Ghana  (Miller & Gosling 2014; Jardine et al. 2016)  to examine chemical changes on a 10 4 to 10 5 year timescale, providing a temporal bridge between the extant and fossil grass datasets.