dna metabarcoding: technical aspects -...

DNA metabarcoding: technical aspects

Pierre Taberlet

Laboratoire d'Ecologie Alpine, CNRS UMR 5553 Université Grenoble Alpes, Grenoble, France

Porto, 1-5 May 2017

Need for high throughput collection of biodiversity data

•  For research •  For management

NASA Earth Observing System: Terra Satellite Platform

Difficult to use satellites for identifying taxa and

collecting biodiversity data

Why not using environmental DNA and the metabarcoding

approach?

Technical objectives

•  Set up a DNA metabarcoding method that is: – high throughput –  robust – simple – cheap


•  DNA extraction from soil or water •  Sampling design •  Which polymerase to use? •  Quantitative aspects •  How to process hundreds of samples at

once? •  How to reduce the impact of chimeras

among samples

DNA extraction from soil

•  Up to 10g of soil •  About 25€ per extraction •  Not compatible with large-scale studies

–  Because of the cost –  Because of the complexity of the protocol


•  Up to 8 kg of soil (usually 15g) •  Add the same weight of saturated

phosphate buffer –  for 1 liter:

•  Sodium Phosphate Dibasic, 14.7g •  Sodium Phosphate Monobasic, 1.97g

•  Mix 15 minutes •  Finish the extraction with the

Macherey-Nagel Nucleospin® Soil kit


15 min in Phosphate buffer

Sampling in the field

within a few hours

Short communication

Extracellular DNA extraction is a fast, cheap and reliable alternative formulti-taxa surveys based on soil DNA

Lucie Zinger a, *, J!erome Chave a, Eric Coissac b, c, Amaia Iribar a, Eliane Louisanna d,Sophie Manzi a, Vincent Schilling a, Heidy Schimann d, Guilhem Sommeria-Klein a,Pierre Taberlet b, c

a Universit!e Toulouse 3 Paul Sabatier, CNRS, ENFA, UMR 5174 EDB, F-31062 Toulouse, Franceb CNRS, Laboratoire d'Ecologie Alpine (LECA), 38000 Grenoble, Francec Univ. Grenoble Alpes, Laboratoire d'Ecologie Alpine (LECA), 38000 Grenoble, Franced INRA UMR ECOFOG, F-97310 Kourou, French Guiana

a r t i c l e i n f o

Article history:Received 22 October 2015Received in revised form23 December 2015Accepted 15 January 2016Available online 2 February 2016

Keywords:DNA metabarcodingDNA extraction protocolTropical forestMulti-taxa biodiversity

a b s t r a c t

DNA metabarcoding on soil samples is increasingly used for large-scale and multi-taxa biodiversitystudies. However, DNA extraction may be a major bottleneck for such wide uses. It should be cost/timeeffective and allow dealing with large sample volumes so as to maximise the representativeness of bothmicro- and macro-organisms diversity. Here, we compared the performances of a fast and cheapextracellular DNA extraction protocol with a total DNA extraction method in retrieving bacterial,eukaryotic and plant diversity from tropical soil samples of ca. 10 g. The total DNA extraction protocolyielded more high-quality DNA. Yet, the extracellular DNA protocol provided similar diversity assess-ments although it presented some differences in clades relative abundance and undersampling biases.We argue that extracellular DNA is a good compromise between cost, labor, and accuracy for high-throughput DNA metabarcoding studies of soil biodiversity.

© 2016 Elsevier Ltd. All rights reserved.

Implementing efficiently soil diversity surveys across taxa andecosystem types is a formidable challenge. DNA metabarcoding is amost promising monitoring technique to meet this challenge (Biket al., 2012; Taberlet et al., 2012b; Orgiazzi et al., 2015; Thomsenand Willerslev, 2015), as it provides a high-throughput, standard-ized and cost-effective assessment of soil diversity (Bik et al., 2012;Taberlet et al., 2012b). The approach is useful for uncovering thediversity of a large array of microorganisms, such as bacteria, pro-tists or fungi (Lauber et al., 2009; Bates et al., 2013; Tedersoo et al.,2014) as well as macroorganisms such as arthropods, plants, ormammals (Andersen et al., 2012; Hiiesalu et al., 2012; Yoccoz et al.,2012; Yang et al., 2014). It also has limitations that are the focus ofactive research, such as a limited taxonomic resolution in sometaxonomic groups (Tang et al., 2012; Grattepanche et al., 2014) orPCR or sequencing biases (Wintzingerode et al., 1997; Huse et al.,2007; Schloss et al., 2011; Thomsen and Willerslev, 2015).

One major problem in implementing DNA metabarcoding forlarge-scale applications remains the extraction of DNA from soilsamples. Soil DNA is encapsulated within complex cell walls oradsorbed onto soil particles, and can be coextracted with variableamounts of humic substances that may inhibit PCR amplification(Wintzingerode et al., 1997). Many laboratory protocols or com-mercial kits have been developed to maximize extracted DNA pu-rity/yield and downstream PCR success, and some of them nowcomply with the ISO standard (Martin-Laurent et al., 2001;Philippot et al., 2012). However, these protocols were optimizedfor assessing microbial diversity. This might hamper PCR amplifi-cation of other components of the soil biota due to the over-whelming biomass of bacteria (Taberlet et al., 2012a,b). Also,commercial kits rely on sample sizes typically ranging from 0.25 to1 g of wet soil, which provide less consistent and representativepictures of local microbial communities than larger sample vol-umes (Ranjard et al., 2003). This sampling bias would be evenworse for targeting larger organisms, unless many replicates aretaken (Andersen et al., 2012). Finally, these kits are expensive, andthe time and facilities needed for the DNA extraction process is* Corresponding author. Laboratoire Evolution et Diversit!e Biologique, UMR

CNRS-UPS 5174, 118 route de Narbonne, 31062 Toulouse Cedex 9, France.E-mail address: [email protected] (L. Zinger).

Contents lists available at ScienceDirect

Soil Biology & Biochemistry

journal homepage: www.elsevier .com/locate/soi lbio

http://dx.doi.org/10.1016/j.soilbio.2016.01.0080038-0717/© 2016 Elsevier Ltd. All rights reserved.

Soil Biology & Biochemistry 96 (2016) 16e19

DNA extraction from water

•  Two possibilities: – DNA precipitation/Centrifugation (e.g.

Ficetola et al. 2008) – Filtration

DNA precipitation/Centrifugation

•  15 ml of water •  30 ml of ethanol •  4.5 ml of Sodium Acetate (3M, pH 8) •  Centrifugation (max speed) to

precipitate DNA and pellet cell remains

Filtration •  0.45µM glass fiber membranes •  Filter large volume when possible (the

larger the better)

Turner et al. (2014) Methods in Ecology and Evolution, 5, 676-684.

Filtration

Filtration

Civade R, Dejean T, Valentini A et al. (2016) Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system. PloS One, 11, e0157366.

Valentini A, Taberlet P, Miaud C et al. (2016) Next-

generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Molecular Ecology, 25, 929–942.




among samples

© Roland Douzet/SAJF

The Roche Noire experiment

Sampling strategy in the field

PCRs

extractions

samples

plot

10m

80 soil cores per sample

● Sampling: 4 plant communities, 3 plots per community, 2 samples of 80 cores per plot ● Extraction of extracellular DNA from kilograms of soil using a phosphate buffer ● DNA amplification of the P6 loop of the chloroplast trnL (UAA) intron ● Sequencing on the 454

Dry high alpine meadows dominated by Kobresia myosuroides

Low alpine meadows dominated by Carex sempervirens

Subalpine heath dominated by Vaccinium sp.

Subalpine grasslands dominated by Festuca paniculata

"Roche Noire" experiment: projections of a between class analysis

CarexFestucaKobresiaVaccinium

Axe 1 (18.9%) Axe 2 (15.4%)

Axe 3 (13.2%)Axe 2 (15.4%)

A B

Sampling strategy (sampling on a grid)

Plot H20 of the Nouragues Field Station (CNRS, French Guiana)

100 m

Two DNA extractions per core, using 15g of soil per extraction

Spatial distribution of Bagassa guyanensis (sampling on a grid)

Plot H20 of the Nouragues Field Station (CNRS, French Guiana)

A compromise between grid sampling, and sample pooling

Last experiment in French Guiana

100 m 16 DNA extractions per plot, using 15g of soil. Each sample composed of a pooling of five cores.

What is the sampling unit?

Different sampling strategies




among samples

Tests with different polymerases •  Non proof-reading polymerases

– Amplitaq Gold – Platinum® PCR SuperMix – TaqMan® Environmental Master Mix 2.0 – Taq DNA polymerase QIAGEN

•  Proof-reading polymerases – Q5 – AccuPrime Pfx – Pfu Turbo polymerase – PfuUltra II Fusion Hs DNA polymerase

PCR errors •  Results

– More artifacts with proof-reading polymerases when using complex templates

– Must have "phosphorothioate" primers when using proof-reading polymerase

phosphodiesterbind

phosphorothioatebind

PCR errors

GGGCAATCCTGAGCCAACATTACCCGTTAGGACTCGGCCATCCCCAATAGCTAT

Taqpolymerase


Proof-readingpolymerase

PCR errors


Taqpolymerase

GGGCAATCCTGAGCCCATTACCCGTTAGGACTCGGCCATCCCCAATAGCTAT


PCR errors


Taqpolymerase

GGGCAATCCTGAGCCGGTAGGGGTTATCGATACATTACCCGTTAGGACTCGGCCATCCCCAATAGCTAT


PCR errors


Taqpolymerase



Results

•  Each polymerase has its own characteristics and produce results relatively different from others

•  The use of the Taq polymerase (and not a proof-reading enzyme) seems to be a good compromise




among samples

Experiments with a known template Species Dilution Sequence

Taxus baccata 1.000000 atccgtattataggaacaataattttattttctagaaaagg

Salvia pratensis 0.500000 atcctgttttctcaaaacaaaggttcaaaaaacgaaaaaaaaaag

Populus tremula 0.250000 atcctatttttcgaaaacaaacaaaaaaacaaacaaaggttcataaagacagaataagaatacaaaag

Rumex acetosa 0.125000 ctcctcctttccaaaaggaagaataaaaaag

Carpinus betulus 0.062500 atcctgttttcccaaaacaaataaaacaaatttaagggttcataaagcgagaataaaaaag

Fraxinus excelsior 0.031250 atcctgttttcccaaaacaaaggttcagaaagaaaaaag

Picea abies 0.015625 atccggttcatggagacaatagtttcttcttttattctcctaagataggaaggg

Lonicera xylosteum 0.007813 atccagttttccgaaaacaagggtttagaaagcaaaaatcaaaaag

Abies alba 0.003906 atccggttcatagagaaaagggtttctctccttctcctaaggaaagg

Acer campestre 0.001953 atcctgttttacgagaataaaacaaagcaaacaagggttcagaaagcgagaaaggg

Briza media 0.000977 atccgtgttttgagaaaacaagggggttctcgaactagaatacaaaggaaaag

Rosa canina 0.000488 atcccgttttatgaaaacaaacaaggtttcagaaagcgagaataaataaag

Capsella bursa-pastoris 0.000244 atcctggtttacgcgaacacaccggagtttacaaagcgagaaaaaagg

Geranium robertianum 0.000122 atccttttttacgaaaataaagaggggctcacaaagcgagaatagaaaaaaag

Rhododendron ferrugineum 0.000061 atccttttttcgcaaacaaacaaagattccgaaagctaaaaaaaag

Lotus corniculatus 0.000031 atcctgctttacgaaaacaagggaaagttcagttaagaaagcgacgagaaaaatg

1 2 3 4 5

23

45

GWM−337

log concentration in template

log

num

ber o

f seq

uenc

e re

ads

Quantitative aspects: results

1 2 3 4 5

12

34

5

GWM−334


log

num

ber o

f seq

uenc

e re

ads

Amplitaq Gold

without elongation time r = 0.88

with 1 min elongation time r = 0.96

1 2 3 4 5

23

45

GWM−337


log

num

ber o

f seq

uenc

e re

ads

1 2 3 4 5

01

23

45

6

GWM−343


log

num

ber o

f seq

uenc

e re

ads

Quantitative aspects: results

Amplitaq Gold

phosphorothioate primers r = 0.86

with 1 min elongation time r = 0.96

AccuPrime Pfx

Quantitative aspects: reproducibility Amplitaq Gold with 1 min elongation time

1 2 3 4 5

12

34

PCR_1


log

num

ber o

f seq

uenc

e re

ads

1 2 3 4 5

01

23

4

PCR_2


log

num

ber o

f seq

uenc

e re

ads

1 2 3 4 5

12

34

PCR_3


log

num

ber o

f seq

uenc

e re

ads

1 2 3 4 5

12

34

5

PCR_4


log

num

ber o

f seq

uenc

e re

ads

1 2 3 4 5

12

34

PCR_5


log

num

ber o

f seq

uenc

e re

ads

1 2 3 4 5

12

34

PCR_6


log

num

ber o

f seq

uenc

e re

ads

1 2 3 4 5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

PCR_7


log

num

ber o

f seq

uenc

e re

ads

1 2 3 4 5

01

23

4

PCR_8


log

num

ber o

f seq

uenc

e re

ads




among samples

The Illumina/Solexa

technology (1)

The Illumina/Solexa technology (2)

Library preparation for Illumina sequencing

•  Making blunt ends (polishing) •  Adding a A on the 3' end •  Adaptor ligation •  A few PCR cycles (up to 15)

HiSeq 2500

•  Company: Illumina® •  Website: www.illumina.com •  Fragment length: 125 bases (2x125

paired-ends) •  Number of reads per run: 8 109 •  Total output per run: 1 Tb •  Time per run: 6 days •  Random distribution of the clusters

HiSeq 4000

•  Company: Illumina® •  Website: www.illumina.com •  Fragment length: 150 bases

(2x150 paired-ends) •  Number of reads per run: 8.6-10 billions •  Total output per run: 1.3-1.5 Tb •  Time per run: 3.5 days •  Regular distribution of the clusters

Traditional versus next generation sequencing

samplingandDNAextrac:on

DNAamplifica:on

sequencing

results

ACGTTA

ACGTTG

ACGTTA

ACATTA

ACGCTA

tradi:onalsequencing nextgenera:onsequencing

bioinforma:cs

ACGTTA

ACGTTG

ACGTTA

ACATTA

ACGCTA

Two strategies •  Sequencing adaptors included on the

5'-end of the amplification primers – but difficult to use with highly diluted

template, and number of samples limited; expensive at the primer level

•  Amplification without the sequencing adaptors and subsequent library preparation as for genomic DNA – expensive at the library preparation level;

some technical difficulties for avoiding "tag-jumping"

A simple tagging system to identify the different samples

•  By adding a specific sequence tag on the 5' end of the primers •  5’-NNacagcacaGGGCAATCCTGAGCCAA-3’•  5'-NNNacagcacaGGGCAATCCTGAGCCAA-3'•  5'-NNNNacagcacaGGGCAATCCTGAGCCAA-3’•  In order to limit the cost of the primers, we suggest using a different tag on

each side of the PCR product •  But problem of chimeras •  Rare taxa difficult to identify




among samples

Leaking among samples during library preparation Library with 5 PCR cycles

acacacac

acagcaca

gtgtacat

tatgtcag

tagtcgca

tactatac

actagatc

gatcgcga

acacacac 113482

acagcaca 126335

gtgtacat 93809

tatgtcag 160242

tagtcgca 184574

tactatac 132409

actagatc 86878

gatcgcga 105527

(positive control with 16 plant species, 8 different PCRs)

Leaking among samples during library preparation Library with 5 PCR cycles (chimeras: 9.11%)

acacacac

acagcaca

gtgtacat

tatgtcag

tagtcgca

tactatac

actagatc

gatcgcga

acacacac 113482 2018 1113 1713 2241 1874 1375 1662

acagcaca 1737 126335 1540 2014 2900 1689 1335 2030

gtgtacat 1174 1689 93809 1676 1890 1292 1001 1514

tatgtcag 1429 1724 1427 160242 2443 1451 1164 1698

tagtcgca 2071 2950 1859 2851 184574 2104 1669 3224

tactatac 2148 1868 1263 1927 2395 132409 1755 1924

actagatc 1694 1674 1201 1565 2035 1754 86878 1913

gatcgcga 1358 1928 1132 1809 2773 1545 1392 105527


Leaking among samples during library preparation Library without PCR cycle (chimeras: 0.18%)

acacacac

acagcaca

gtgtacat

tatgtcag

tagtcgca

tactatac

actagatc

gatcgcga

acacacac 105624 44 21 62 26 35 21 50

acagcaca 16 59338 11 28 16 20 6 19

gtgtacat 13 8 45835 19 7 17 7 11

tatgtcag 29 18 7 219741 57 30 10 31

tagtcgca 24 19 8 38 87859 20 17 27

tactatac 26 16 13 49 30 73452 51 29

actagatc 13 8 8 20 12 17 41547 25

gatcgcga 18 16 8 21 11 13 5 50994


To reduce chimeras among samples

•  No PCR cycle during library preparation •  Use the same tag on each side of the

PCR products (but high cost for primers): very low level of chimeras

•  Use a different tag on each side of the PCR products: low level of chimeras

Librairie without PCR (MetaFast protocol)

Librairie without PCR (standard protocol)

Thousands of PCR

Plate 1

Plate 2

Thank you for your attention

dna metabarcoding: technical aspects -...

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