High-throughput qPCR and 16S rRNA gene amplicon sequencing as complementary methods for the investigation of the cheese microbiota.
Bacteria
/ classification
Cheese
/ microbiology
Computational Biology
DNA, Bacterial
/ genetics
High-Throughput Nucleotide Sequencing
/ methods
High-Throughput Screening Assays
/ methods
Microbiota
/ genetics
RNA, Ribosomal, 16S
/ genetics
Real-Time Polymerase Chain Reaction
/ methods
Sequence Analysis, DNA
Journal
BMC microbiology
ISSN: 1471-2180
Titre abrégé: BMC Microbiol
Pays: England
ID NLM: 100966981
Informations de publication
Date de publication:
07 02 2022
07 02 2022
Historique:
received:
23
09
2021
accepted:
17
01
2022
entrez:
8
2
2022
pubmed:
9
2
2022
medline:
8
3
2022
Statut:
epublish
Résumé
Next-generation sequencing (NGS) methods and especially 16S rRNA gene amplicon sequencing have become indispensable tools in microbial ecology. While they have opened up new possibilities for studying microbial communities, they also have one drawback, namely providing only relative abundances and thus compositional data. Quantitative PCR (qPCR) has been used for years for the quantification of bacteria. However, this method requires the development of specific primers and has a low throughput. The constraint of low throughput has recently been overcome by the development of high-throughput qPCR (HT-qPCR), which allows for the simultaneous detection of the most prevalent bacteria in moderately complex systems, such as cheese and other fermented dairy foods. In the present study, the performance of the two approaches, NGS and HT-qPCR, was compared by analyzing the same DNA samples from 21 Raclette du Valais protected designation of origin (PDO) cheeses. Based on the results obtained, the differences, accuracy, and usefulness of the two approaches were studied in detail. The results obtained using NGS (non-targeted) and HT-qPCR (targeted) show considerable agreement in determining the microbial composition of the cheese DNA samples studied, albeit the fundamentally different nature of these two approaches. A few inconsistencies in species detection were observed, particularly for less abundant ones. The detailed comparison of the results for 15 bacterial species/groups measured by both methods revealed a considerable bias for certain bacterial species in the measurements of the amplicon sequencing approach. We identified as probable origin to this PCR bias due to primer mismatches, variations in the number of copies for the 16S rRNA gene, and bias introduced in the bioinformatics analysis. As the normalized microbial composition results of NGS and HT-qPCR agreed for most of the 21 cheese samples analyzed, both methods can be considered as complementary and reliable for studying the microbial composition of cheese. Their combined application proved to be very helpful in identifying potential biases and overcoming methodological limitations in the quantitative analysis of the cheese microbiota.
Sections du résumé
BACKGROUND
Next-generation sequencing (NGS) methods and especially 16S rRNA gene amplicon sequencing have become indispensable tools in microbial ecology. While they have opened up new possibilities for studying microbial communities, they also have one drawback, namely providing only relative abundances and thus compositional data. Quantitative PCR (qPCR) has been used for years for the quantification of bacteria. However, this method requires the development of specific primers and has a low throughput. The constraint of low throughput has recently been overcome by the development of high-throughput qPCR (HT-qPCR), which allows for the simultaneous detection of the most prevalent bacteria in moderately complex systems, such as cheese and other fermented dairy foods. In the present study, the performance of the two approaches, NGS and HT-qPCR, was compared by analyzing the same DNA samples from 21 Raclette du Valais protected designation of origin (PDO) cheeses. Based on the results obtained, the differences, accuracy, and usefulness of the two approaches were studied in detail.
RESULTS
The results obtained using NGS (non-targeted) and HT-qPCR (targeted) show considerable agreement in determining the microbial composition of the cheese DNA samples studied, albeit the fundamentally different nature of these two approaches. A few inconsistencies in species detection were observed, particularly for less abundant ones. The detailed comparison of the results for 15 bacterial species/groups measured by both methods revealed a considerable bias for certain bacterial species in the measurements of the amplicon sequencing approach. We identified as probable origin to this PCR bias due to primer mismatches, variations in the number of copies for the 16S rRNA gene, and bias introduced in the bioinformatics analysis.
CONCLUSION
As the normalized microbial composition results of NGS and HT-qPCR agreed for most of the 21 cheese samples analyzed, both methods can be considered as complementary and reliable for studying the microbial composition of cheese. Their combined application proved to be very helpful in identifying potential biases and overcoming methodological limitations in the quantitative analysis of the cheese microbiota.
Identifiants
pubmed: 35130830
doi: 10.1186/s12866-022-02451-y
pii: 10.1186/s12866-022-02451-y
pmc: PMC8819918
doi:
Substances chimiques
DNA, Bacterial
0
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
48Informations de copyright
© 2022. The Author(s).
Références
Methods Mol Biol. 2014;1079:155-70
pubmed: 24170401
Nucleic Acids Res. 2019 Jul 2;47(W1):W256-W259
pubmed: 30931475
Nat Commun. 2019 Nov 6;10(1):5029
pubmed: 31695033
Nat Commun. 2021 Jun 11;12(1):3562
pubmed: 34117246
Appl Environ Microbiol. 2012 Aug;78(16):5717-23
pubmed: 22685131
Appl Environ Microbiol. 2007 Dec;73(23):7697-702
pubmed: 17921264
Int J Food Microbiol. 2012 Feb 1;153(1-2):192-202
pubmed: 22154239
ISME J. 2017 Feb;11(2):584-587
pubmed: 27612291
Commun Biol. 2019 Aug 5;2:291
pubmed: 31396571
J Bacteriol. 2004 May;186(9):2629-35
pubmed: 15090503
PeerJ. 2020 Feb 18;8:e8544
pubmed: 32110486
BMC Genomics. 2015 Dec 12;16:1056
pubmed: 26651617
J Microbiol Methods. 2018 Oct;153:139-147
pubmed: 30267718
Nature. 2017 Nov 23;551(7681):457-463
pubmed: 29088705
Nucleic Acids Res. 2015 Jan;43(Database issue):D593-8
pubmed: 25414355
PLoS One. 2020 Jan 16;15(1):e0227434
pubmed: 31945086
BMC Genomics. 2019 Jul 8;20(1):560
pubmed: 31286860
Front Microbiol. 2018 Aug 20;9:1920
pubmed: 30177922
PLoS One. 2014 Feb 24;9(2):e89680
pubmed: 24586960
Nat Methods. 2016 Jul;13(7):581-3
pubmed: 27214047
FEMS Microbiol Rev. 2013 Sep;37(5):664-98
pubmed: 23808865
Appl Microbiol Biotechnol. 2021 Mar;105(6):2307-2318
pubmed: 33661344
Food Res Int. 2020 Mar;129:108860
pubmed: 32036924
PLoS One. 2013 Apr 22;8(4):e61217
pubmed: 23630581
Microbiome. 2018 Aug 9;6(1):140
pubmed: 30092815
Eur J Clin Microbiol Infect Dis. 2019 Jun;38(6):1059-1070
pubmed: 30834996
Food Microbiol. 2017 Sep;66:64-71
pubmed: 28576374
Nature. 2010 Mar 4;464(7285):59-65
pubmed: 20203603
Food Microbiol. 2013 Dec;36(2):207-15
pubmed: 24010599
Nucleic Acids Res. 2010 Dec;38(22):e200
pubmed: 20880993
Front Microbiol. 2017 Nov 15;8:2224
pubmed: 29187837
Nature. 2020 Sep;585(7825):357-362
pubmed: 32939066
Microbiome. 2018 Feb 26;6(1):41
pubmed: 29482646
Appl Environ Microbiol. 2001 Aug;67(8):3450-4
pubmed: 11472918
Nat Methods. 2020 Mar;17(3):261-272
pubmed: 32015543
Sci Data. 2020 Mar 16;7(1):92
pubmed: 32179734
Appl Environ Microbiol. 2003 Jan;69(1):444-51
pubmed: 12514026
BMC Genomics. 2013 Nov 14;14:788
pubmed: 24225361
Front Microbiol. 2016 Apr 20;7:459
pubmed: 27148170
Front Microbiol. 2017 Oct 25;8:2017
pubmed: 29118739
Biochem Biophys Res Commun. 2016 Jan 22;469(4):967-77
pubmed: 26718401
PeerJ. 2018 Apr 18;6:e4652
pubmed: 29682424
Appl Environ Microbiol. 2012 Mar;78(6):1890-8
pubmed: 22247135
Int J Syst Evol Microbiol. 2009 Apr;59(Pt 4):754-60
pubmed: 19329601
J Dairy Sci. 2017 Oct;100(10):7812-7824
pubmed: 28822547
Elife. 2019 Sep 10;8:
pubmed: 31502536
Int J Food Microbiol. 2015 Dec 23;215:124-30
pubmed: 26432602
Appl Environ Microbiol. 2016 Jun 13;82(13):3988-3995
pubmed: 27107125
Int J Syst Evol Microbiol. 2009 Jan;59(Pt 1):7-12
pubmed: 19126714
BMC Genomics. 2016 Jan 14;17:55
pubmed: 26763898
BMC Genomics. 2020 Oct 22;21(1):733
pubmed: 33092529
FEMS Microbiol Rev. 2004 May;28(2):251-60
pubmed: 15109787
mSphere. 2018 Oct 17;3(5):
pubmed: 30333179
Front Microbiol. 2017 Sep 21;8:1829
pubmed: 29033905
mSystems. 2021 Apr 6;6(2):
pubmed: 33824193
Appl Environ Microbiol. 2013 May;79(9):2891-8
pubmed: 23435884
Bioinformatics. 2006 Nov 1;22(21):2688-90
pubmed: 16928733
mSystems. 2016 Jun 14;1(3):
pubmed: 27822529
PLoS One. 2014 Apr 08;9(4):e93827
pubmed: 24714158
Front Microbiol. 2021 Jan 07;11:619166
pubmed: 33488561
Appl Environ Microbiol. 2011 Apr;77(7):2264-74
pubmed: 21317261
Antonie Van Leeuwenhoek. 2018 Dec;111(12):2361-2370
pubmed: 30043188
Brief Bioinform. 2021 Jan 18;22(1):178-193
pubmed: 31848574
PLoS One. 2017 Mar 24;12(3):e0174223
pubmed: 28339484
Sci Data. 2019 Feb 05;6:190007
pubmed: 30720800
J Appl Microbiol. 2016 Feb;120(2):329-45
pubmed: 26551888
Microb Ecol. 2021 Feb;81(2):535-539
pubmed: 32862246
Food Microbiol. 2021 Feb;93:103613
pubmed: 32912585
Food Chem. 2021 Mar 15;340:128154
pubmed: 33010641
Int J Food Microbiol. 2014 Aug 18;185:127-35
pubmed: 24960294
Food Microbiol. 2011 Aug;28(5):848-61
pubmed: 21569926
Sci Rep. 2017 Jul 31;7(1):6589
pubmed: 28761145
Int J Food Microbiol. 2017 Aug 16;255:7-16
pubmed: 28558331
mBio. 2020 Sep 22;11(5):
pubmed: 32963001