DOI: 10.5061/dryad.f1vhhmh4k
title: Data from: Vegetable phylloplane microbiomes harbour class 1 integrons in novel bacterial hosts and drive the spread of chlorite resistance

description: Bacterial hosts in vegetable phylloplanes carry mobile genetic elements
 such as plasmids and transposons that are associated with integrons. These
 mobile genetic elements and their cargo genes can enter human microbiomes
 via consumption of fresh agricultural produce, including uncooked
 vegetables. This presents a risk of acquiring antimicrobial resistance
 genes from uncooked vegetables. To better understand horizontal gene
 transfer of class 1 integrons in these compartments, we applied epicPCR, a
 single-cell fusion-PCR surveillance technique, to link the class 1
 integron integrase (intI1) gene with phylogenetic markers of their
 bacterial hosts. Ready-to-eat salads carried class 1 integrons from the
 phyla Bacteroidota and Pseudomonadota, including four novel genera that
 were previously not known to be associated with intI1. We whole-genome
 sequenced Pseudomonas and Erwinia hosts of pre-clinical class 1 integrons
 that are embedded in Tn402-like transposons. The proximal gene cassette in
 these integrons was identified as a chlorite dismutase gene cassette,
 which we showed experimentally to confer chlorite resistance.
 Chlorine-derived compounds such as acidified sodium chlorite and chloride
 dioxide are used to disinfectant raw vegetables in food processing
 facilities, suggesting selection for chlorite resistance in phylloplane
 integrons. The spread of integrons conferring chlorite resistance has the
 potential to exacerbate integron-mediated antimicrobial resistance (AMR)
 via co-selection of chlorite resistance and AMR, thus highlighting the
 importance of monitoring chlorite residues in agricultural produce. These
 results demonstrate the strength of combining epicPCR and culture-based
 isolation approaches for identifying hosts and dissecting the molecular
 ecology of class 1 integrons.
description: <strong>Emulsion, paired-isolation and concatenation PCR
 (epicPCR)</strong> Approximately 150,000
 bacterial cells were resuspended in 75 µL of PCR reagents containing GC
 buffer (1x), dNTP mix (0.4 mM), Phusion High-Fidelity DNA polymerase (0.05
 U/µL) (Thermo Scientific, United States), bovine serum albumin (1 µg/µL)
 (Promega, United States), Lucigen Ready-Lyse lysozyme (500 U/µL) (LGC
 Biosearch Technologies, United States), and three primers: R926 (2 µM),
 <em>intI1</em>_outer_2 (1 µM), and R519-HS458RC bridge primer
 (0.04 µM) with the final concentrations of each reagent in parentheses.
 For each of the six phylloplane microbiome sample, three technical
 replicates of epicPCR were performed, one of which was spiked with a class
 1 integron-free <em>Synechococcus </em>species CC9311 strain
 at a population frequency of 10% to test if false associations can be
 observed between the 16S rRNA markers of
 <em>Synechococcus</em> CC9311 and class 1 integrons in each
 sample. Each PCR suspension was added to 425 µL of ABIL
 oil and was agitated at 4 ms<sup>-1</sup> for 45 s with a
 FastPrep-24 bead beating system (MP Biomedicals, United States). Each
 emulsion was aliquoted into eight portions prior to epicPCR. The
 conditions for the first stage of epicPCR are: 37˚C for 10 min (lysozyme
 lysis); 98˚C for 5 minutes (initial denaturation); 35 cycles of 98°C for
 10 s (denaturation), 55°C for 30 s (annealing), and 72°C for 30 s
 (extension); and finally, 72°C for 5 min. The aqueous phase containing DNA
 was separated from the oil phase by using a mixture of 2-methyl-1-propanol
 and sodium chloride solution (Qi et al., 2023). The extracted DNA was
 further purified using the Monarch PCR & DNA Cleanup Kit (New
 England Biolabs, United States). In the second stage of epicPCR, the DNA
 templates from the previous step were PCR amplified in four aliquots of 25
 µL reactions containing GC buffer (1x), dNTP mix (0.4 mM), Phusion
 High-Fidelity DNA polymerase (0.04 U/µL), AP27_short primer (0.8 µM),
 nested primer HS915 (0.4 µM), forward and reverse blocking primers (0.32
 µM each) under the following conditions: 98˚C for 30 s; 30 cycles of 98°C
 for 10 s, 55°C for 30 s and 72°C for 15 s; followed by 72°C for 2 min. Gel
 electrophoresis was performed to confirm that the epicPCR products were
 approximately 880 bp in size. epicPCR products were
 treated with the NEBNext Ultra II End Repair/dA-Tailing Module (New
 England Biolabs, United States). Native barcodes from the SQK-NBD114.96
 Native Barcoding Kit (ONT, United Kingdom) were ligated to end-repaired
 DNA using the Blunt/TA Ligase Master Mix (New England Biolabs, United
 States). Individually barcoded DNA samples were pooled and ligated to
 unique native adaptors using NEBNext Quick T4 DNA Ligase (New England
 Biolabs, United States). The multiplexed sequencing libraries were loaded
 into an R10.4.1 MinION flow cell for Nanopore sequencing on a MinION Mk1B
 sequencer (ONT, United Kingdom). Simplex basecalling, trimming of adaptors
 and barcodes, and demultiplexing of sequencing data were performed using
 Dorado (v0.5.2) with the super accurate basecalling model
 dna_r10.4.1_e8.2_400bps_sup@v4.3.0 (Oxford Nanopore, United
 Kingdom). <strong>Filtering and taxonomic
 profiling of epicPCR Nanopore reads</strong> We
 first filtered epicPCR Nanopore reads ensuring that they were flanked by
 the nested forward and reverse primers in the correct orientation using
 pychopper (v2.7.9) (https://github.com/epi2me-labs/pychopper) [parameters: -t 48 -m edlib -p -b]. These full-length epicPCR amplicon reads were then filtered using chopper v0.7.0 (De Coster and Rademakers, 2023), ensuring a minimum average base-level accuracy of Q10, and a length between 850 and 950 bp [parameters: -q 10 -l 850 --maxlength 950 --threads 48]. Pychopper was then re-run on the filtered full-length amplicons to separately identify and trim the 16S rRNA gene and <em>intI1</em> regions from the full length epicPCR products, respectively [parameters: -t 48 -m edlib -b]. The 16S rRNA gene region was extracted by pychopper using the reverse complement of the bridging primer (used as the forward primer) and the reverse nested primer (AP27_short). Conversely, the <em>intI1</em> gene region was extracted by pychopper using the forward nested primer (HS915) together with the bridging primer. Taxonomic profiling of the trimmed 16S rRNA regions of the epicPCR amplicons was performed using Emu v3.4.5 (Curry et al., 2022) against the Greengenes2 database v2022.10 (McDonald et al., 2023). Emu is optimised for taxonomic profiling amplicons from long-read sequencing and estimates relative abundance by mapping the reads to a taxonomic database. It employs an expectation-maximisation algorithm for accurate error-correction by iteratively refining taxon relative abundances based on total read mapping counts. We employed Emu by setting a minimum relative abundance cut-off of 0.5% [parameters: abundance --min-abundance 0.005 --threads 48], and then using emu <em>collapse-taxonomy</em> to collapse taxonomic profiles at the genus level [parameter: rank="genus"]. Analyses with these parameters showed that no spiked <em>Synechococcus</em> 16S rRNA sequence was detected in our set. We further filtered genera, retaining those only observed in at least two independent replicates (n ≥ 2) within each salad sample type. To assess if <em>intI1-</em>carraige had been previously reported in the genera we observed, the nucleotide sequence of <em>intI1 </em>from the IncW R388 plasmid was queried against the NCBI Nucleotide database by blastN using default parameters. The database searches were carried out individually for each of the thirteen genera observed in this study. <strong>References</strong> Curry KD, Wang Q, Nute MG, Tyshaieva A, Reeves E, Soriano S, et al. Emu: Species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data. Nat Methods 2022; 19: 845-853. De Coster W, Rademakers R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics 2023; 39. McDonald D, Jiang Y, Balaban M, Cantrell K, Zhu Q, Gonzalez A, et al. Greengenes2 unifies microbial data in a single reference tree. Nat Biotechnol 2023. Qi Q, Ghaly TM, Penesyan A, Rajabal V, Stacey JA, Tetu SG, et al. Uncovering bacterial hosts of class 1 integrons in an urban coastal aquatic environment with a single-cell fusion-polymerase chain reaction technology. Environ Sci Technol 2023; 57: 4870-4879.
description: # Vegetable phylloplane microbiomes harbour class 1 integrons in novel
 bacterial hosts and a chlorite-resistance-conferring gene cassette
 [https://doi.org/10.5061/dryad.f1vhhmh4k](https://doi.org/10.5061/dryad.f1vhhmh4k) The eighteen FASTQ files that are available for download are as follows: S1_1, S1_2, S1_3, S2_1, S2_2, S2_3, S3_1, S3_2, S3_3, ML1_1, ML1_2, ML1_3, ML2_1, ML2_2, ML2_3, ML3_1, ML3_2, ML3_3 ## Abbreviations S = spinach leaf salad; ML = mixed leaf salad The first digit after the letters denotes biological replicates. Each biological replicate represents a packaged salad product purchased from the supermarket on a different day and was processed for microbiome extraction independently. The second digit after the underscore denotes the technical replicates. ## Code/Software The epicPCR Nanopore reads in the FASTQ files were filtered to ensure that they were flanked by the nested forward and reverse primers in the correct orientation using pychopper (v2.7.9) ([https://github.com/epi2me-labs/pychopper](https://github.com/epi2me-labs/pychopper) [parameters: -t 48 -m edlib -p -b]. Next, full-length epicPCR amplicon reads were filtered using chopper (v0.7.0), ensuring a minimum average base-level accuracy of Q10, and a length between 850 and 950 bp [parameters: -q 10 -l 850 --maxlength 950 --threads 48]. Pychopper was then re-run on the filtered full-length amplicons to identify and trim the 16S rRNA gene and *intI1* regions from the full length epicPCR products [parameters: -t 48 -m edlib -b]. For the 16S region, the reverse complement of the bridging primer (used as the forward primer) and the reverse nested primer (AP27_short) were used by pychopper. Conversely, the *intI1* region was extracted by pychopper using the forward nested primer (HS915) together with the bridging primer.  Taxonomic profiling of the trimmed 16S rRNA regions of the epicPCR amplicons was performed using Emu (v3.4.5) against the Greengenes2 database (v2022.10). Emu is optimised for full-length, error-prone 16S rRNA reads and estimates relative abundance by mapping the reads to the Greengenes2 taxonomic database, employing an expectation-maximisation algorithm for accurate error-correction by iteratively refining taxon relative abundances based on total read mapping counts. We employed Emu by setting a minimum relative abundance cut-off of 0.5% [parameters: abundance --min-abundance 0.005 --threads 48], and then using emu collapse-taxonomy to collapse taxonomic profiles at the genus level [parameter: rank="genus"]. The genera observed in the analysis output must be observed in at least two independent replicates (n ≥ 2) within each salad sample type.   **References** De Coster, W.; Rademakers, R. NanoPack2: population-scale evaluation of long-read sequencing data. *Bioinformatics ***2023**, *39* (5). Curry, K. D.; Wang, Q.; Nute, M. G.; Tyshaieva, A.; Reeves, E.; Soriano, S.; Wu, Q.; Graeber, E.; Finzer, P.; Mendling, W.; et al. Emu: Species-level microbial community profiling of full-length 16S rRNA Oxford Nanopore sequencing data. *Nat Methods ***2022**, *19* (7), 845-853. McDonald, D.; Jiang, Y.; Balaban, M.; Cantrell, K.; Zhu, Q.; Gonzalez, A.; Morton, J. T.; Nicolaou, G.; Parks, D. H.; Karst, S. M.; et al. Greengenes2 unifies microbial data in a single reference tree. *Nat Biotechnol ***2023**.