
==== Front
Microbiol Resour Announc
Microbiol Resour Announc
mra
Microbiology Resource Announcements
2576-098X
American Society for Microbiology 1752 N St., N.W., Washington, DC

39162443
mra00313-24
10.1128/mra.00313-24
mra.00313-24
Genome Sequences
antimicrobial-chemotherapyAntimicrobial ChemotherapyDetection of mobile colistin resistance genes mcr-9.1 and mcr-10.1 in Enterobacter asburiae from Ecuadorian children
Cifuentes Sara G. 1 Conceptualization Data curation Formal analysis Investigation Software Validation Visualization Writing – original draft
Graham Jay 2
https://orcid.org/0000-0003-2617-9021
Trueba Gabriel 1
https://orcid.org/0000-0001-9626-4489
Cádenas Paúl A. 1 Conceptualization Formal analysis Funding acquisition Investigation Methodology Supervision Validation Writing – review and editing pacardenas@usfq.edu.ec

1 Instituto de Microbiología, Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ , Quito, Pichincha, Ecuador
2 Berkeley School of Public Health, University of California , Berkeley, California, USA
Editor Stewart Frank J. Montana State University , Bozeman, Montana, USA

Address correspondence to Paúl A. Cádenas, pacardenas@usfq.edu.ec
The authors declare no conflict of interest.

9 2024
20 8 2024
20 8 2024
13 9 e00313-2427 3 2024
10 7 2024
Copyright © 2024 Cifuentes et al.
2024
Cifuentes et al.
https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

ABSTRACT

Colistin is one of the last-line treatments for multi-drug resistant Gram-negative bacterial infections. The emergence of mobile colistin resistance genes has driven global concern and triggered the need for surveillance. Our report reveals the identification of mcr-9.1 and mcr-10.1 in Ecuador by employing a proximity ligation technique.

KEYWORDS

enterobacter
mcr
colistin
cover-dateSeptember 2024
==== Body
pmcANNOUNCEMENT

The emergence of mobile colistin resistance genes endangers the treatment of infections caused by multi-drug resistant Gram-negative bacteria (1, 2). The mobile colistin resistance gene mcr-1 was initially discovered in China in 2015 (3), followed by detection in Ecuador in 2016 (4); however, no additional variants of the mcr gene have been reported in the country since then (5–11). During a metagenomics study (12), mcr-9.1 and mcr-10.1 genes were identified. This study received approval from the Ethics Committee for Research in Human Beings at Universidad San Francisco de Quito USFQ (IRB# 2017-178M) and the Office for Protection of Human Subjects at the University of California, Berkeley (IRB# 2019-02-11803).

Two fecal samples were collected from two healthy young boys, aged 1 and 5, as part of a repeated measures study that recruited 600 children from 2018 to 2021 (13). The collection methods and transport conditions for samples have been detailed in a previous study (12). We employed a culture-independent approach, dividing the specimens into DNA extraction and crosslinking aliquots. Genomic DNA was isolated employing the Qiagen QIAamp Fast DNA Stool Mini Kit (cat. no.51604, Qiagen) following a modified protocol (14) and stored at −80°C until further analyses. The second aliquot was crosslinked with 1% formaldehyde for 20 minutes (15). DNA and crosslinked samples were sent to Phase Genomics for ProxiMeta full-service analysis. The complete protocol has been detailed previously (16), and default parameters were applied unless otherwise specified. Briefly, the shotgun library was prepared using the Watchmaker DNA Library Prep Kit (cat. no. 7K0103-096, Watchmaker Genomics, USA), and the proximity ligation library was created using the ProxiMeta Hi-C kit (cat. no. KT5045, Phase Genomics, USA). Shotgun metagenomic and Hi-C libraries were sequenced on an Illumina NovaSeqX platform (2 × 150 bp paired-end reads), yielding 708,033,774 and 293,351,862 read pairs for the two shotgun metagenomic and Hi-C libraries, respectively. Fastp v0.20.1 was used to preprocess and control the FASTQ data quality (17). Shotgun metagenomic assemblies were generated and assessed using Megahit v1.2.9 (18) and MataQUAST v5.2.0 (19), respectively. Hi-C reads were mapped to the metagenomic assemblies using BWA-MEM (0.7.17-r1198-dirty) (20, 21). Instead of using a conventional metagenomic binning approach, contigs were clustered into genome clusters with the ProxiMeta platform (Phase Genomics, USA) (22). Mash was used to compare genome clusters with NCBI RefSeq genomes, and CheckM (23) evaluated the quality of the genomes in terms of completeness and contamination. Antimicrobial resistance genes (ARGs) were detected using AMRFinderPlus (https://www.ncbi.nlm.nih.gov/pathogens/antimicrobial-resistance/AMRFinder/) and ResFinder v4.0 (24) (https://cge.food.dtu.dk/services/ResFinder-4.1/). Both colistin resistance genes showed 100% identity and 100% template coverage. Metagenome deconvolution analysis identified these ARGs on plasmid contigs, although we could not characterize the plasmids harboring these ARGs using PlasmidFinder v.2.1 (https://cge.food.dtu.dk/services/PlasmidFinder/) with the available contigs. Table 1 presents an overview of the genome assembly statistics, colistin resistance, co-resistances, and the bacterial host. This report highlights the importance and the feasibility of community-level metagenomic surveillance to detect the spread of mobile resistance genes that pose a risk to public health.

TABLE 1 Summary of data for two metagenome-assembled genomes of Enterobacter asburiae strains carrying mobile colistin resistance and other mobile and genomic ARGs

Bin ID/sample name	Taxonomy	Genome size	Genome completion (%)	Contig N50	Number of contigs	GC (%)	Mobile mcr gene	Other mobile antimicrobial resistance genes	Genomic antimicrobial resistance genes	SRA accession no./assembly accession no.	
bin_3/HC45	Enterobacter asburiae L1	4,081,523	91.26	19,822	273	56.13	mcr-10.1	qnrB19
tet(A)	blaACT-4
blaACT-7
oqxA oqxB	SRS20620200
JBEOKR000000000.1	
bin_4/HC32	Enterobacter asburiae B	4,677,763	94.85	20,261	145	55.65	mcr-9.1	N/A	blaACT-6
fosA oqxA oqxB	SRS20620198
JBEOKS000000000.1	

ACKNOWLEDGMENTS

Funding for specimen collection, demographic data, and sample storage was partly supported by the National Institutes of Health NIH (grant number R01AI135118). Global Health Equity Scholars NIH FIC TW010540 funded Sara Cifuentes. We appreciate the collaboration of PhaseGenomics to prepare the libraries, shotgun, and Hi-C sequencing (Illumina NovaSeqX), the deconvolution process, and the use of data available in the ProxiMeta Metagenome Deconvolution Platform.

DATA AVAILABILITY

This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank

as BioProject PRJNA1082298 (see Table 1 for more accession numbers).
==== Refs
REFERENCES

1 Nation RL, Li J. 2009. Colistin in the 21st century. Curr Opin Infect Dis 22 :535–543. doi:10.1097/QCO.0b013e328332e672 19797945
2 Falagas ME, Kasiakou SK, Saravolatz LD. 2005. Colistin: the revival of polymyxins for the management of multidrug-resistant Gram-negative bacterial infections. Clin Infect Dis 40 :1333–1341. doi:10.1086/429323 15825037
3 Liu Y-Y, Wang Y, Walsh TR, Yi L-X, Zhang R, Spencer J, Doi Y, Tian G, Dong B, Huang X, Yu L-F, Gu D, Ren H, Chen X, Lv L, He D, Zhou H, Liang Z, Liu J-H, Shen J. 2016. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological and molecular biological study. Lancet Infect Dis 16 :161–168. doi:10.1016/S1473-3099(15)00424-7 26603172
4 Ortega-Paredes D, Barba P, Zurita J. 2016. Colistin-resistant Escherichia coli clinical isolate harbouring the mcr-1 gene in ecuador. Epidemiol Infect 144 :2967–2970. doi:10.1017/S0950268816001369 27586373
5 Loayza-Villa F, Salinas L, Tijet N, Villavicencio F, Tamayo R, Salas S, Rivera R, Villacis J, Satan C, Ushiña L, Muñoz O, Zurita J, Melano R, Reyes J, Trueba GA. 2020. Diverse Escherichia coli lineages from domestic animals carrying colistin resistance gene mcr-1 in an ecuadorian household. J Glob Antimicrob Resist 22 :63–67. doi:10.1016/j.jgar.2019.12.002 31841712
6 Bastidas-Caldes C, de Waard JH, Salgado MS, Villacís MJ, Coral-Almeida M, Yamamoto Y, Calvopiña M. 2022. Worldwide prevalence of mcr-mediated colistin-resistance Escherichia coli in isolates of clinical samples, healthy humans, and livestock-a systematic review and meta-analysis. Pathogens 11 :659. doi:10.3390/pathogens11060659 35745513
7 Li Y, Dai X, Zeng J, Gao Y, Zhang Z, Zhang L. 2020. Characterization of the global distribution and diversified plasmid reservoirs of the colistin resistance gene mcr-9. Sci Rep 10 :8113. doi:10.1038/s41598-020-65106-w 32415232
8 Ling Z, Yin W, Shen Z, Wang Y, Shen J, Walsh TR. 2020. Epidemiology of mobile colistin resistance genes mcr-1 to mcr-9. J Antimicrob Chemother 75 :3087–3095. doi:10.1093/jac/dkaa205 32514524
9 Martiny H-M, Munk P, Brinch C, Szarvas J, Aarestrup FM, Petersen TN. 2022. Global distribution of mcr gene variants in 214K metagenomic samples. mSystems 7 :e0010522. doi:10.1128/msystems.00105-22 35343801
10 Thanh Hoang HT, Yamamoto M, Calvopina M, Bastidas-Caldes C, Khong DT, Nguyen TN, Kawahara R, Yamaguchi T, Yamamoto Y. 2023. Comparative genome analysis of colistin-resistant Escherichia coli harboring mcr isolated from rural community residents in ecuador and vietnam. PLoS ONE 18 :e0293940. doi:10.1371/journal.pone.0293940 37917755
11 Wang R, van Dorp L, Shaw LP, Bradley P, Wang Q, Wang X, Jin L, Zhang Q, Liu Y, Rieux A, Dorai-Schneiders T, Weinert LA, Iqbal Z, Didelot X, Wang H, Balloux F. 2018. The global distribution and spread of the mobilized colistin resistance gene mcr-1. Nat Commun 9 :1179. doi:10.1038/s41467-018-03205-z 29563494
12 Cifuentes SG, Graham J, Loayza F, Saraiva C, Salinas L, Trueba G, Cárdenas PA. 2022. Evaluation of changes in the faecal resistome associated with children’s exposure to domestic animals and food animal production. J Glob Antimicrob Resist 31 :212–215. doi:10.1016/j.jgar.2022.09.009 36202201
13 Amato HK, Loayza F, Salinas L, Paredes D, Garcia D, Sarzosa S, Saraiva-Garcia C, Johnson TJ, Pickering AJ, Riley LW, Trueba G, Graham JP. 2023. Risk factors for extended-spectrum beta-lactamase (ESBL)-producing E. coli carriage among children in a food animal-producing region of Ecuador: a repeated measures observational study. PLoS Med 20 :e1004299. doi:10.1371/journal.pmed.1004299 37831716
14 Knudsen BE, Bergmark L, Munk P, Lukjancenko O, Priemé A, Aarestrup FM, Pamp SJ. 2016. Impact of sample type and DNA isolation procedure on genomic inference of microbiome composition. mSystems 1 :e00095-16. doi:10.1128/mSystems.00095-16 27822556
15 Burton JN, Liachko I, Dunham MJ, Shendure J. 2014. Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps. G3 (Bethesda) 4 :1339–1346. doi:10.1534/g3.114.011825 24855317
16 Stalder T, Press MO, Sullivan S, Liachko I, Top EM. 2019. Linking the resistome and plasmidome to the microbiome. ISME J 13 :2437–2446. doi:10.1038/s41396-019-0446-4 31147603
17 Chen S. 2023. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta 2 :e107. doi:10.1002/imt2.107 38868435
18 Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31 :1674–1676. doi:10.1093/bioinformatics/btv033 25609793
19 Mikheenko A, Saveliev V, Gurevich A. 2016. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics 32 :1088–1090. doi:10.1093/bioinformatics/btv697 26614127
20 Li H. 2013. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. doi:10.48550/arxiv.1303.3997
21 Li H, Durbin R. 2009. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25 :1754–1760. doi:10.1093/bioinformatics/btp324 19451168
22 Press MO, Wiser AH, Kronenberg ZN, Langford KW, Shakya M, Lo C-C, Mueller KA, Sullivan ST, Chain PSG, Liachko I. 2017. Hi-C deconvolution of a human gut microbiome yields high-quality draft genomes and reveals plasmid-genome interactions. Genomics. Genomics, Genomics. doi:10.1101/198713
23 Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. 2015. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25 :1043–1055. doi:10.1101/gr.186072.114 25977477
24 Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, Philippon A, Allesoe RL, Rebelo AR, Florensa AF, et al. . 2020. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 75 :3491–3500. doi:10.1093/jac/dkaa345 32780112
