Identification of plasmids in avian-associated Escherichia coli using nanopore and illumina sequencing.
Avian pathogenic escherichia coli
Hybrid assembly
Illumina sequencing
MOB-suite
Nanopore sequencing
Plasmids
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
21 Nov 2023
21 Nov 2023
Historique:
received:
10
04
2023
accepted:
03
11
2023
medline:
23
11
2023
pubmed:
22
11
2023
entrez:
22
11
2023
Statut:
epublish
Résumé
Avian pathogenic Escherichia coli (APEC) are the causative agents of colibacillosis in chickens, a disease which has significant economic impact on the poultry industry. Large plasmids detected in APEC are known to contribute to strain diversity for pathogenicity and antimicrobial resistance, but there could be other plasmids that are missed in standard analysis. In this study, we determined the impact of sequencing and assembly factors for the detection of plasmids in an E. coli whole genome sequencing project. Hybrid assembly (Illumina and Nanopore) combined with plasmid DNA extractions allowed for detection of the greatest number of plasmids in E. coli, as detected by MOB-suite software. In total, 79 plasmids were identified in 19 E. coli isolates. Hybrid assemblies were robust and consistent in quality regardless of sequencing kit used or if long reads were filtered or not. In contrast, long read only assemblies were more variable and influenced by sequencing and assembly parameters. Plasmid DNA extractions allowed for the detection of physically smaller plasmids, but when averaged over 19 isolates did not significantly change the overall number of plasmids detected. Hybrid assembly can be reliably used to detect plasmids in E. coli, especially if researchers are focused on large plasmids containing antimicrobial resistance genes and virulence factors. If the goal is comprehensive detection of all plasmids, particularly if smaller sized vectors are desired for biotechnology applications, the addition of plasmid DNA extractions to hybrid assemblies is prudent. Long read sequencing is sufficient to detect many plasmids in E. coli, however, it is more prone to errors when expanded to analyze a large number of isolates.
Sections du résumé
BACKGROUND
BACKGROUND
Avian pathogenic Escherichia coli (APEC) are the causative agents of colibacillosis in chickens, a disease which has significant economic impact on the poultry industry. Large plasmids detected in APEC are known to contribute to strain diversity for pathogenicity and antimicrobial resistance, but there could be other plasmids that are missed in standard analysis. In this study, we determined the impact of sequencing and assembly factors for the detection of plasmids in an E. coli whole genome sequencing project.
RESULTS
RESULTS
Hybrid assembly (Illumina and Nanopore) combined with plasmid DNA extractions allowed for detection of the greatest number of plasmids in E. coli, as detected by MOB-suite software. In total, 79 plasmids were identified in 19 E. coli isolates. Hybrid assemblies were robust and consistent in quality regardless of sequencing kit used or if long reads were filtered or not. In contrast, long read only assemblies were more variable and influenced by sequencing and assembly parameters. Plasmid DNA extractions allowed for the detection of physically smaller plasmids, but when averaged over 19 isolates did not significantly change the overall number of plasmids detected.
CONCLUSIONS
CONCLUSIONS
Hybrid assembly can be reliably used to detect plasmids in E. coli, especially if researchers are focused on large plasmids containing antimicrobial resistance genes and virulence factors. If the goal is comprehensive detection of all plasmids, particularly if smaller sized vectors are desired for biotechnology applications, the addition of plasmid DNA extractions to hybrid assemblies is prudent. Long read sequencing is sufficient to detect many plasmids in E. coli, however, it is more prone to errors when expanded to analyze a large number of isolates.
Identifiants
pubmed: 37990161
doi: 10.1186/s12864-023-09784-6
pii: 10.1186/s12864-023-09784-6
pmc: PMC10664647
doi:
Substances chimiques
Anti-Infective Agents
0
DNA
9007-49-2
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
698Subventions
Organisme : Natural Sciences and Engineering Research Council of Canada
ID : CRDPJ 543702-20
Organisme : Saskatchewan Agriculture Development Fund
ID : 20190173
Informations de copyright
© 2023. The Author(s).
Références
PLoS One. 2012;7(1):e29481
pubmed: 22238616
Commun Biol. 2021 Apr 12;4(1):457
pubmed: 33846529
Crit Rev Biotechnol. 2004;24(4):155-208
pubmed: 15707158
F1000Res. 2019 Dec 23;8:2138
pubmed: 31984131
Bioinformatics. 2018 Sep 1;34(17):i884-i890
pubmed: 30423086
PLoS Comput Biol. 2017 Jun 8;13(6):e1005595
pubmed: 28594827
Nat Biotechnol. 2019 May;37(5):540-546
pubmed: 30936562
Gene. 2002 Jan 9;282(1-2):33-41
pubmed: 11814675
J Antimicrob Chemother. 2015 Oct;70(10):2763-9
pubmed: 26142410
Nat Commun. 2021 Feb 3;12(1):765
pubmed: 33536414
Microb Genom. 2021 Aug;7(8):
pubmed: 34431763
FEMS Microbiol Rev. 2012 Nov;36(6):1083-104
pubmed: 22393901
J Antimicrob Chemother. 2017 Mar 1;72(3):696-699
pubmed: 27999050
Trends Ecol Evol. 2012 Jun;27(6):346-52
pubmed: 22459247
Trends Biotechnol. 2000 Sep;18(9):380-8
pubmed: 10942962
Microb Genom. 2017 Jun 9;3(8):e000118
pubmed: 29026658
J Appl Microbiol. 2007 Feb;102(2):548-54
pubmed: 17241361
Ann N Y Acad Sci. 2017 Jan;1388(1):78-91
pubmed: 27875856
Avian Dis. 1996 Oct-Dec;40(4):927-30
pubmed: 8980827
Vet Microbiol. 2012 Aug 17;158(3-4):384-93
pubmed: 22464157
mSphere. 2021 Apr 14;6(2):
pubmed: 33853876
Am J Trop Med Hyg. 2019 Feb;100(2):227-228
pubmed: 30608047
Microbiol Mol Biol Rev. 2009 Dec;73(4):750-74
pubmed: 19946140
Nat Methods. 2022 Jul;19(7):823-826
pubmed: 35789207
Access Microbiol. 2020 Jun 19;2(9):acmi000143
pubmed: 33195978
EcoSal Plus. 2021 Feb;9(2):
pubmed: 33634776
Microbiol Resour Announc. 2023 May 17;12(5):e0011023
pubmed: 37098978
Proc Natl Acad Sci U S A. 2009 Oct 20;106(42):17939-44
pubmed: 19815525
PeerJ. 2019 May 31;7:e6995
pubmed: 31183253
J Bacteriol. 2006 Jan;188(2):745-58
pubmed: 16385064
J Clin Microbiol. 2012 May;50(5):1673-8
pubmed: 22378905
Genome Res. 2017 May;27(5):722-736
pubmed: 28298431
Microorganisms. 2021 Dec 10;9(12):
pubmed: 34946161
Infect Immun. 2010 Apr;78(4):1528-41
pubmed: 20086082
Sci Rep. 2020 Mar 9;10(1):4310
pubmed: 32152350
J Bacteriol. 1950 Aug;60(2):119-27
pubmed: 14774362
Bioinformatics. 2018 Aug 1;34(15):2666-2669
pubmed: 29547981
FEMS Microbiol Lett. 2015 Aug;362(15):fnv118
pubmed: 26204893
Microb Genom. 2019 Sep;5(9):
pubmed: 31483244
Microb Genom. 2018 Aug;4(8):
pubmed: 30052170
Bioinformatics. 2013 Apr 15;29(8):1072-5
pubmed: 23422339
PeerJ. 2021 Mar 4;9:e11025
pubmed: 33717713
Microb Genom. 2022 Sep;8(9):
pubmed: 36129737
PLoS One. 2014 Nov 19;9(11):e112963
pubmed: 25409509
Antimicrob Agents Chemother. 2009 Feb;53(2):716-27
pubmed: 19015365