A new spike-in-based method for quantitative metabarcoding of soil fungi and bacteria.

Compositional data Internal standards Metabarcoding Soil microbiology Spike-in

Journal

International microbiology : the official journal of the Spanish Society for Microbiology

ISSN: 1618-1905

Titre abrégé: Int Microbiol

Pays: Switzerland

ID NLM: 9816585

Informations de publication

Date de publication:
06 Sep 2023

Historique:

received: 08 05 2023

accepted: 23 08 2023

revised: 17 08 2023

medline: 6 9 2023

pubmed: 6 9 2023

entrez: 6 9 2023

Statut: aheadofprint

Résumé

Metabarcoding is a powerful tool to characterize biodiversity in biological samples. The interpretation of taxonomic profiles from metabarcoding data has been hindered by their compositional nature. Several strategies have been proposed to transform compositional data into quantitative data, but they have intrinsic limitations. Here, I propose a workflow based on bacterial and fungal cellular internal standards (spike-ins) for absolute quantification of the microbiota in soil samples. These standards were added to the samples before DNA extraction in amounts estimated after qPCRs, to target around 1-2% coverage in the sequencing run. In bacteria, proportions of spike-in reads in the sequencing run were very similar (< 2-fold change) to those predicted by the qPCR assessment, and for fungi they differed up to 40-fold. The low variation among replicates highlights the reproducibility of the method. Estimates based on multiple bacterial spike-ins were highly correlated (r = 0.99). Procrustes analysis evidenced significant biological effects on the community composition when normalizing compositional data. A protocol based on qPCR estimation of input amounts of cellular spikes is proposed as a cheap and reliable strategy for quantitative metabarcoding of biological samples.

Identifiants

DOI: 10.1007/s10123-023-00422-5 PMID: 37672116

pubmed: 37672116

doi: 10.1007/s10123-023-00422-5

pii: 10.1007/s10123-023-00422-5

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Informations de copyright

Références

Bengtsson-Palme J, Ryberg M, Hartmann M, Branco S, Wang Z, Godhe A, De Wit P, Sánchez-García M, Ebersberger I, de Sousa F et al (2013) Improved software detection and extraction of ITS1 and ITS2 from ribosomal its sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol Evol 4:914–919

doi: 10.1111/2041-210X.12073

Bonk F, Popp D, Harms H, Centler F (2018) PCR-based quantification of taxa-specific abundances in microbial communities: quantifying and avoiding common pitfalls. J Microbiol Methods 153:139–147

doi: 10.1016/j.mimet.2018.09.015 pubmed: 30267718

Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583

doi: 10.1038/nmeth.3869 pubmed: 27214047 pmcid: 4927377

Chen K, Hu Z, Xia Z, Zhao D, Li W, Tyler JK (2016) The overlooked fact: fundamental need for spike-in control for virtually all genome-wide analyses. Mol Cell Biol 36:662–667

doi: 10.1128/mcb.00970-14 pmcid: 4760223

Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ (2018) Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6:226

doi: 10.1186/s40168-018-0605-2 pubmed: 30558668 pmcid: 6298009

Dopheide A, Xie D, Buckley TR, Drummond AJ, Newcomb RD (2019) Impacts of DNA extraction and PCR on DNA metabarcoding estimates of soil biodiversity. Methods Ecol Evol 10:120–133

doi: 10.1111/2041-210X.13086

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ (2017) Microbiome datasets are compositional: and this is not optional. Front Microbiol 8:2224

doi: 10.3389/fmicb.2017.02224 pubmed: 29187837 pmcid: 5695134

Hardwick SA, Chen WY, Wong T, Kanakamedala BS, Deveson IW, Ongley SE, Santini NS, Marcellin E, Smith MA, Nielsen LK, Lovelock CE, Neilan BA, Mercer TR (2018) Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis. Nat Commun 9:1–10

doi: 10.1038/s41467-018-05555-0

Haro C, Anguita-Maeso M, Metsis M, Navas-Cortés JA, Landa BB (2021) evaluation of established methods for DNA extraction and primer pairs targeting 16S rRNA gene for bacterial microbiota profiling of olive xylem sap. Front Plant Sci 12:640829

doi: 10.3389/fpls.2021.640829 pubmed: 33777075 pmcid: 7994608

Harrison JG, John Calder W, Shuman B, Alex Buerkle C (2021) The quest for absolute abundance: the use of internal standards for DNA-based community ecology. Mol Ecol Resour 21:30–43

doi: 10.1111/1755-0998.13247 pubmed: 32889760

Herlemann DP, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF (2011) Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J 5:1571–1579

doi: 10.1038/ismej.2011.41 pubmed: 21472016 pmcid: 3176514

Jones MB, Highlander SK, Anderson EL, Li W, Dayrit M, Klitgord N, Fabani MM, Seguritan V, Green J, Pride DT, Yooseph S, Biggs W, Nelson KE, Craig Venter J (2015) Library preparation methodology can influence genomic and functional predictions in human microbiome research. Proc Natl Acad Sci U S A 112:14024–14029

doi: 10.1073/pnas.1519288112 pubmed: 26512100 pmcid: 4653211

Kõljalg U, Nilsson HR, Schigel D, Tedersoo L, Larsson KH, May TW, Taylor AFS, Jeppesen TS, Frøslev TG, Lindahl BD, Põldmaa K, Saar I, Suija A, Savchenko A, Yatsiuk I, Adojaan K, Ivanov F, Piirmann T, Pöhönen R et al (2020) The taxon hypothesis paradigm - On the unambiguous detection and communication of taxa. Microorganisms 8:1–24

doi: 10.3390/microorganisms8121910

Kong J, Liu X, Wang L, Huang H, Ou D, Guo J, Laws EA, Huang B (2021) Patterns of relative and quantitative abundances of marine bacteria in surface waters of the subtropical Northwest Pacific Ocean estimated with high-throughput quantification sequencing. Front Microbiol. https://doi.org/10.3389/fmicb.2020.599614

Lin Y, Gifford S, Ducklow H, Schofield O, Cassar N (2019) towards quantitative microbiome community profiling using internal standards. Appl Environ Microbiol 85:1–14

doi: 10.1128/AEM.02634-18

Lindner DL, Banik MT (2011) Intragenomic variation in the ITS rDNA region obscures phylogenetic relationships and inflates estimates of operational taxonomic units in genus Laetiporus. Mycologia 103:731–740

doi: 10.3852/10-331 pubmed: 21289107

Liu P, Yang S, Yang S (2022) KTU: K-mer taxonomic units improve the biological relevance of amplicon sequence variant microbiota data. Methods Ecol Evol 13:560–568

doi: 10.1111/2041-210X.13758

Lofgren LA, Uehling JK, Branco S, Bruns TD, Martin F, Kennedy PG (2019) Genome-based estimates of fungal rDNA copy number variation across phylogenetic scales and ecological lifestyles. Mol Ecol 28:721–730

doi: 10.1111/mec.14995 pubmed: 30582650

Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17:10–12

doi: 10.14806/ej.17.1.200

McMurdie PJ, Holmes S (2013) Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. https://doi.org/10.1371/journal.pone.0061217

McMurdie PJ, Holmes S (2014) Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1003531

Morton JT, Marotz C, Washburne A, Silverman J, Zaramela LS, Edlund A, Zengler K, Knight R (2019) Establishing microbial composition measurement standards with reference frames. Nat Commun. https://doi.org/10.1038/s41467-019-10656-5

Murali A, Bhargava A, Wright ES (2018) IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6:140

doi: 10.1186/s40168-018-0521-5 pubmed: 30092815 pmcid: 6085705

O’Donnell K, Cigelnik E (1997) Two divergent intragenomic rDNA ITS2 types within a monophyletic lineage of the fungus Fusarium are non-orthologous. Mol Phylogenet Evol 7:103–116

doi: 10.1006/mpev.1996.0376 pubmed: 9007025

Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, others (2020) Vegan: community ecology package. R package version 2.5-6. 2019

Paloi S, Luangsa-ard JJ, Mhuantong W, Stadler M, Kobmoo N (2022) Intragenomic variation in nuclear ribosomal markers and its implication in species delimitation, identification and barcoding in fungi. Fungal Biol Rev 42:1–33

doi: 10.1016/j.fbr.2022.04.002

Peres-Neto PR, Jackson DA (2001) How well do multivariate data sets match? The advantages of a procrustean superimposition approach over the Mantel test. Oecologia 129:169–178

doi: 10.1007/s004420100720 pubmed: 28547594

Props R, Kerckhof FM, Rubbens P, De VJ, Sanabria EH, Waegeman W, Monsieurs P, Hammes F, Boon N (2017) Absolute quantification of microbial taxon abundances. ISME J 11:584–587

doi: 10.1038/ismej.2016.117 pubmed: 27612291

Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2012) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596

doi: 10.1093/nar/gks1219 pubmed: 23193283 pmcid: 3531112

Quinn TP, Erb I, Gloor G, Notredame C, Richardson MF, Crowley TM (2019) A field guide for the compositional analysis of any-omics data. GigaScience 8:1–14

doi: 10.1093/gigascience/giz107

R Core Team (2022) R: a language and environment for statistical computing

Rodriguez-Mena S, Camacho M, de los Santos B, Miranda L, Camacho-Sanchez M (2022) Microbiota modulation in blueberry rhizosphere by biocontrol bacteria. Microbiol Res (Pavia) 13:809–824

doi: 10.3390/microbiolres13040057

Ruppert KM, Kline RJ, Rahman MS (2019) Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: a systematic review in methods, monitoring, and applications of global eDNA. Glob Ecol Conserv 17:e00547

doi: 10.1016/j.gecco.2019.e00547

Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, Chen W, Fungal Barcoding Consortium (2012) Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc Natl Acad Sci USA 109:6241–6246

doi: 10.1073/pnas.1117018109 pubmed: 22454494 pmcid: 3341068

Shelton AO, Gold ZJ, Jensen AJ, D′Agnese E, Andruszkiewicz Allan E, Van Cise A, Gallego R, Ramón-Laca A, Garber-Yonts M, Parsons K, Kelly RP (2022) Toward quantitative metabarcoding. Ecology.: https://doi.org/10.1002/ecy.3906

Sidstedt M, Rådström P, Hedman J (2020) PCR inhibition in qPCR, dPCR and MPS-mechanisms and solutions. Anal Bioanal Chem 412:2009–2023

doi: 10.1007/s00216-020-02490-2 pubmed: 32052066 pmcid: 7072044

Smets W, Leff JW, Bradford MA, McCulley RL, Lebeer S, Fierer N (2016) A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing. Soil Biol Biochem 96:145–151

doi: 10.1016/j.soilbio.2016.02.003

Stämmler F, Gläsner J, Hiergeist A, Holler E, Weber D, Oefner PJ, Gessner A, Spang R (2016) Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4:1–13

doi: 10.1186/s40168-016-0175-0

Stoddard SF, Smith BJ, Hein R, Roller BRK, Schmidt TM (2015) rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res 43:D593–D598

doi: 10.1093/nar/gku1201 pubmed: 25414355

Taberlet P, Coissac E, Pompanon F, Brochmann C, Willerslev E (2012) Towards next-generation biodiversity assessment using DNA metabarcoding. Mol Ecol 21:2045–2050

doi: 10.1111/j.1365-294X.2012.05470.x pubmed: 22486824

Tedersoo L, Bahram M, Zinger L, Nilsson RH, Kennedy PG, Yang T, Anslan S, Mikryukov V (2022) Best practices in metabarcoding of fungi: from experimental design to results. Mol Ecol 31:2769–2795

doi: 10.1111/mec.16460 pubmed: 35395127

Tsilimigras MCB, Fodor AA (2016) Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann Epidemiol 26:330–335

doi: 10.1016/j.annepidem.2016.03.002 pubmed: 27255738

White TJ, Bruns T, Lee S, Taylor J (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR Protoc a Guid to methods Appl 18:315–322

Wright ES (2016) Using DECIPHER v2. 0 to analyze big biological sequence data in R. R J. 8:352–359

doi: 10.32614/RJ-2016-025

Yang L, Lou J, Wang H, Wu L, Xu J (2018a) Use of an improved high-throughput absolute abundance quantification method to characterize soil bacterial community and dynamics. Sci Total Environ 633:360–371

doi: 10.1016/j.scitotenv.2018.03.201 pubmed: 29574379

Yang R-H, Su J-H, Shang J-J, Wu Y-Y, Li Y, Bao D-P, Yao Y-J (2018b) Evaluation of the ribosomal DNA internal transcribed spacer (ITS), specifically ITS1 and ITS2, for the analysis of fungal diversity by deep sequencing. PLoS One 13:e0206428

doi: 10.1371/journal.pone.0206428 pubmed: 30359454 pmcid: 6201957