A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents

Abstract

Introduction
The PCR-based analysis of homologous genes has become one of the most powerful approaches for species detection and identification, particularly with the recent availability of Next Generation Sequencing platforms (NGS) making it possible to identify species composition from a broad range of environmental samples. Identifying species from these samples relies on the ability to match sequences with reference barcodes for taxonomic identification. Unfortunately, most studies of environmental samples have targeted ribosomal markers, despite the fact that the mitochondrial Cytochrome c Oxidase subunit I gene (COI) is by far the most widely available sequence region in public reference libraries. This is largely because the available versatile (“universal”) COI primers target the 658 barcoding region, whose size is considered too large for many NGS applications. Moreover, traditional barcoding primers are known to be poorly conserved across some taxonomic groups.

Results
We first design a new PCR primer within the highly variable mitochondrial COI region, the “mlCOIintF” primer. We then show that this newly designed forward primer combined with the “jgHCO2198” reverse primer to target a 313 bp fragment performs well across metazoan diversity, with higher success rates than versatile primer sets traditionally used for DNA barcoding (i.e. LCO1490/HCO2198). Finally, we demonstrate how the shorter COI fragment coupled with an efficient bioinformatics pipeline can be used to characterize species diversity from environmental samples by pyrosequencing. We examine the gut contents of three species of planktivorous and benthivorous coral reef fish (family: Apogonidae and Holocentridae). After the removal of dubious COI sequences, we obtained a total of 334 prey Operational Taxonomic Units (OTUs) belonging to 14 phyla from 16 fish guts. Of these, 52.5% matched a reference barcode (>98% sequence similarity) and an additional 32% could be assigned to a higher taxonomic level using Bayesian assignment.

Conclusions
The molecular analysis of gut contents targeting the 313 COI fragment using the newly designed mlCOIintF primer in combination with the jgHCO2198 primer offers enormous promise for metazoan metabarcoding studies. We believe that this primer set will be a valuable asset for a range of applications from large-scale biodiversity assessments to food web studies.

Full text

RES E AR C H Open Access
A new versatile primer set targeting a short
fragment of the mitochondrial COI region for
metabarcoding metazoan diversity: application
for characterizing coral reef fish gut contents
Matthieu Leray
1,2*
, Joy Y Yang
3
, Christopher P Meyer
2
, Suzanne C Mills
1
, Natalia Agudelo
2
, Vincent Ranwez
4
,
Joel T Boehm
5,6
and Ryuji J Machida
7
Abstract
Introduction: The PCR-based analysis of homologous genes has become one of the most powerful approaches for
species detection and identification, particularly with the recent availability of Next Generation Sequencing
platforms (NGS) making it possible to identify species composition from a broad range of environmental samples.
Identifying species from these samples relies on the ability to match sequences with reference barcodes for
taxonomic identification. Unfortunately, most studies of environmental samples have targeted ribosomal markers,
despite the fact that the mitochondrial Cytochrome c Oxidase subunit I gene (COI) is by far the most widely
available sequence region in public reference libraries. This is largely because the available versatile (“universal”) COI
primers target the 658 barcoding region, whose size is considered too large for many NGS applications. Moreover,
traditional barcoding primers are known to be poorly conserved across some taxonomic groups.
Results: We first design a new PCR primer within the highly variable mitochondrial COI region, the “mlCOIintF”
primer. We th en show that this newly designed forward primer combined with the “jgHCO2198” reverse primer to
target a 313 bp fragment performs well across metazoan diversity, with higher success rates than versatile primer
sets traditionally used for DNA barcoding (i.e. LCO1490/HCO2198). Finally, we demonstrate how the shorter COI
fragment coupled with an efficient bioinformatics pipeline can be used to characterize species diversity from
environmental samples by pyrosequencing. We examine the gut contents of three species of planktivorous and
benthivorous coral reef fish (family: Apogonidae and Holocentridae). After th e removal of dubious COI sequences,
we obtained a total of 334 prey Operational Taxonomic Units (OTUs) belonging to 14 phyla from 16 fish guts. Of
these, 52.5% matched a reference barcode (>98% sequence similarity) and an additional 32% could be assigned to
a higher taxonomic level using Bayesian assignment.
Conclusions: The molecular analysis of gut cont ents targeting the 313 COI fragment using the newly designed
mlCOIintF primer in combination with the jgHCO2198 primer offers enormous promise for metazoan
metabarcoding studies. We believe that this primer set will be a valuable asset for a range of applications from
large-scale biodiversity assessments to food web studies.
Keywords: Second generation sequencing, DNA barcoding, Mini-barcode, Mitochondrial marker, Trophic
interactions, Food web
* Correspondence: leray.upmc@gmail.com
1
Laboratoire d'Excellence "CORAIL", USR 3278 CRIOBE CNRS-EPHE, CBETM de
l’Université de Perpignan, 66860, Perpignan Cedex, France
2
Department of Invertebrate Zoology, National Museum of Natural History,
Smithsonian Institution, P.O. Box 37012, MRC-163, Washington, DC
20013-7012, USA
Full list of author information is available at the end of the article
© 2013 Leray et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Leray et al. Frontiers in Zoology 2013, 10:34
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Introduction
Biological diversity often poses a major challenge for
ecologists who seek to understand ecological processes or
conduct biomonitoring programs. Environmental samples
commonly contain a high taxonomic diversity of small-
sized organisms (e.g., meiofauna in marine benthic sedi-
ments [1]), with numerous specimens lacking diagnostic
morphological characters (i.e. larval stages in plankton
tows [2]) or partially digested organisms in gut or faecal
contents [3]), making it difficult to identify species within
a reasonable timeframe and with sufficient accuracy [4].
Yet, DNA-based community analyses have offered some
alternatives to traditional methods and have become even
more promising with the availability of ultrasequencing
platforms now supplanting cloning. Taxon detection from
bulk samples can be achieved using PCR amplification
followed by deep sequencing of homologous gene regions.
Sequences are then compared to libraries of reference
barcodes for taxonomic identification. This so-called
“metabarcoding” approach [5] has been used as a powerful
means to understand the diversity and distribution of
meiofauna [6]. It has also been found to be an effective
tool for assessing the diversity of insects collected from
traps [7] and characterize the diet of predators [8-11] and
herbivores [12,13] through analysis of their feces or gut
content. Nevertheless, metabarcoding is still a relatively
new approach, and both methodological and analytical
improvements are necessary to further expand its range of
applications [7,14].
The success of a metabarcoding analysis is particularly
contingent upon the primer set used and the target loci,
because they will determine the efficiency and accuracy
of taxon detection and identification. In general, primers
should preferentially target hypervariable DNA regions
(for high resolution taxonomic discrimination) for which
extensive libraries of reference sequences are available
(for taxonomi c identification). Furthermore, primers
should preferentially target short DNA fragments (e.g.,
< 400 bp) to maximize richness estimates [15,16] and in-
crease the probability of re covering DNA templates that
are more degraded (sheared), such as samples preserved
for extended periods of time [17] or prey items in the
gut and faecal contents of predators [18,19]. The taxo-
nomic coverage of the primer set will then depend upon
the question addressed. For example, when the goal is to
describe the diet of specialised predators (i.e. insects
consumed by bats [20,21]) or more generally to describe
the diversity and composition of a specific functional
group (i.e. nematodes in sediment s [6]), “group-specific”
primers will be effective. Alternatively, when the goal is
to obtain a comprehensive analysis of samples containing
species from numerous phyla (as most environmental
samples do), primers should target a locus found univer-
sally across all animals or plants.
Despite the inherent difficulty of designing versatile
primers (also referred to as broad-range or universal
primers), several sets are readily available to amplify nu-
clear and mitochondrial gene fragments across animals.
For example, there are primers to amplify short fragments
of the nuclear 18S and 28S ribosomal markers [22,23], but
these regions evolve slowly and may underestimate diver-
sity [24-27]. Versatile primers have also recently become
available to target a short fragment of the mitochondrial
12S gene [28], a region with high rates of molecular evolu-
tion suitable for species delineation and identification, but
taxonomic reference databases are currently highly limited
for this marker. The mitochondrial Cytochrome c Oxidase
I gene (COI) has been adopted as the standard ‘taxon
barcode’ for most animal groups [29] and is by far
the most represented in public reference libraries. As of
January 2013, the B arcode of Life Databa se included
COI sequences from >1,800,000 specimens belonging
to >160,000 species colle cted among all phyla across all
ecosystems. However, versatile primers are only avail-
able to amplify the barcoding region of 658 bp [30,31]
and are known to be poorly conserved across nema-
todes [6,2 6], gastropods [31] and echinoderms [32]
among others. A s ingle attempt was made at designing a
versatile primer to amplify a shorter “mini-barcode”
COI region [17], but it has received limited use due to
large numbers of mismatches in the priming site that
affect s its efficiency across a broad range of taxa [33].
In the first part of this paper, we use an extensive library
of COI barcodes provided by the Moorea BIOCODE pro-
ject, an “All Taxa Biotic Inventory” (www.mooreabiocode.
org), to locate a conserved priming site internal to the
highly variable 658 bp COI region. The newly designed in-
ternal primer is combined with a modified version of the
classic reverse barcoding primer HCO2198 proposed by
Folmer et al. (1994) [30] (“jgHCO2198” -[34]totargeta
313 bp COI region. We test the effectiveness of the primer
set across 287 disparate taxa from 30 phyla and we com-
pare its performance against versatile primer sets com-
monly employed for DNA barcoding.
In the second part of this paper, we demonstrate how
the new COI primer set coupled with an effective bio-
informatics pipeline allows high throughput DNA-based
characterization of prey diversity from the gut contents of
coral reef fish species with three distinct feeding modes.
Analysis of predator’s gut or faecal contents is one of
the promising applications of the DNA metabarcoding
approach. Efficient prey detection combined with high-
resolution prey identification offers the potential for im-
proving our understanding of food webs, animal feeding
behaviour [14] and prey distribution [35,36]. Previously,
due to the large amplicon size, COI was often considered
a non-suitable marker ([8,19,37], reviewed in [14]). We
propose that this new primer set will be a powerful asset
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for understanding various ecological processes and con-
ducting biomonitoring programs.
Material and methods
COI primer design and performance test
Primer design
We aimed to design a versatile PCR primer within the
658 bp COI barcoding region which could be used in
combination with a published primer commonly used
for DNA barcoding (i.e. LCO1490 or HCO2198 [30]) to
target a short DNA fragment. The Moorea BIOCODE
project provided an alignment of 6643 COI sequenc es
belonging to ~3877 marine ta xa, mostly coral reef asso-
ciated species (up to five specimens per morphospecies)
spanning 17 animal phyla (sequences available in BOLD,
projects MBMIA, MBMIB and MBFA). The information
content [entropy h(x)] at each position of the alignment
was plotted using BioEdit [38] to locate more conserved
regions within the 658 bp COI barcoding fragment
(Figure 1). A site with limited variation was located be-
tween positions 320 and 345 of the 658 bp COI region
(Figure 1). The forward primer “mlCOIintF” and its re-
verse complement “ mlCOIintR” (Table 1) were designed
herein and used for further performance testing.
Primer performance
Genomic DNA was provided by the Moorea BIOCODE
project for 287 specimens belonging to 30 animal phyla
in order to carry out amplification tests (list of taxa in
Additional file 1). Eight phyla were represented by more
than five specimens and were the most common phyla
from BIOCODE collections. These samples were orga-
nized in three 96 well plates.
We conducted preliminary tests to determine which pri-
mer combination performs best across a wide range of
phyla to amplify a short size COI fragment. To test this, 47
specimens belonging to 11 phyla (rows 10 and 11 of each
of the three plates - Additional file 1) were selected. We
used the following primer combinations to target a 313 bp
COI fragment (Figure 1): (1) mlCOIintF with HCO2198,
(2) mlCOIintF with dgHCO2198, (3) mlCOIintF with
jgHCO2198; and the following primer combinations to tar-
get a 319 bp COI fragment: (4) LCO1490 with mlCOIintR,
(5) dgLCO1490 with mlCOIintR, (6) jgLCO1490 with
mlCOIintR. It is important to note that dgHCO2198 and
Figure 1 Design of the “mlCOIint” forward and reverse complements within the highly variable COI fragment. A total of 6643 COI
sequences, spanning 17 phyla (provided by the Moorea BIOCODE project) were aligned and the entropy h(x) plotted to visualize the level of
variability at each position. h(x) = 0 when the site is conserved across all sequences (e.g., 100% A). h(x) is at a maximum when each nucleotide
occurs at equal frequency. We also present the proportion of each nucleotide between sites 320 and 345, the region where the primers
were designed.
Table 1 COI primers used in this study
Primer label Sequence (5' - 3') Reference
LCO1490 GGTCAACAAATCATAAAGATATTGG [30]
HCO2198 TAAACTTCAGGGTGACCAAAAAATCA [30]
dgLCO1490 GGTCAACAAATCATAAAGAYATYGG [31]
dgHCO2198 TAAACTTCAGGGTGACCAAARAAYCA [31]
jgLCO1490 TITCIACIAAYCAYAARGAYATTGG [34]
jgHCO2198 TAIACYTCIGGRTGICCRAARAAYCA [34]
Uni-MinibarF1 CAAAATCATAATGAAGGCATGAGC [17]
Uni-MinibarR1 TCCACTAATCACAARGATATTGGTAC [17]
mlCOIintF GGWACWGGWTGAACWGTWTAYCCYCC herein
mlCOIintR GGRGGRTASACSGTTCASCCSGTSCC herein
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jgHCO2198 are degenerate versions of HCO2198 with the
identical priming site, as dgLCO1490 and jgLCO1490 are
to LCO1490 (see Table 1 for primer sequences and
sources). PCR amplification was performed in a total
volume of 20 μlwith0.6μlof10μM of each universal for-
ward and reverse primers, 0.2 μl of Biolase taq polymerase
(Bioline) 5 U.μl
-1
,0.8μlof50mMMg
2+
,1μlof10μM
dNTP and 1 μl of genomic DNA. Because of the high level
of degeneracy in primer sequences, we used a “touchdown”
PCR profile to minimize the probability of non-specific
amplifications. We carried out 16 initial cycles: denatur-
ation for 10s at 95°C, annealing for 30s at 62°C (−1°C per
cycle) and extension for 60s at 72°C, followed by 25 cycles
at 46°C annealing temperature. Success of PCR amplifica-
tions was checked on 1.5% agarose gels. A clear single
band of expected length indicated success whereas the
absence of a band, the presence of multiple bands or the
presence of a single band of incorrect size meant PCR
failure. The primer set providing the best results was kept
for further tests.
Secondly, the performance at amplifying the short
COI fragment across the diversity of 285 templates was
compared to the perf ormance of existing COI primer
setstargetingthe658bpCOIregioncommonlyused
for DNA barcoding , LCO1490 with HCO2198, a s
well as their degenerate versions dgLCO1490 with
dgHCO2190 and jgLCO1490 with jgHCO2198. We also
evaluated the performance of the mini-barcode primers
Uni-MinibarF1 with Uni-MinibarR1 that were designed
to amplify a short 130 bp COI fragment. For each
primer set we used optimal reagent concentrations and
thermocycler profiles found in the literature [17,31].
PCR products of the short 313 bp COI fragment were
sequenced by Sanger sequencing.
Pyrosequencing of fish gut contents
Specimen collection and gut content extraction
Nine adult specimens of the cardinal fish species, Nectamia
savayensis (Order: Perciformes; Family: Apogonidae; to-
tal length = 59-83 mm), three specimens of soldierfish,
Myripristis berndti (Order: Beryciformes; Family: Holo-
centridae; total length = 114-143 mm), and four specimens
of the squirrelfish, Sargocentron microstoma (Order:
Beryciformes; Family: Holocentridae; total length = 148-
161 mm) were collected by spear-fishing on the 9th of
August 2010, two hours after sunset in the lagoon of the
North shore of Moorea Island, French Polynesia (17°30’S,
149°50’W). The three nocturnal fish species vary in their
feeding mode and habitat use: N. savayensis occurs in the
water column between two and three meters and is strictly
planktivorous; M. berndti was collected from near reef
crevices at four meters and consumes both planktonic and
benthic prey; S. microstoma is also a benthic predator but
preys upon larger benthic invertebrates [39,40]. Approval
was granted from our institutional animal ethics com-
mittee, le Centre National de la Re cherche Scientifique
(CNRS), f or sacrificing and subsequently dissecting fish
(Permit Number: 006725). None of the fish species are
on the endangered species list and no specific autho-
rization was required from the French Polynesian
government for collection.
Fish were preserved in cold 50% ethanol in the field.
Their digestive systems were dissected within 2 hours in
the laboratory and preserved in 80% ethanol at −20°C.
After storage for 2 months, total genomic DNA was
extracted from the total prey mixtur e contained in the
digestive track using QIAGEN® DNeasy Blood & Tissue
individual columns. Genomic DNA was purified using
the MOBIO PowerClean DNA clean-up kit to prevent
interference with PCR inhibitors.
Design of predator-specific blocking primers
Gut contents of semi-digested prey homogenate contain
highly degraded prey DNA mixed with abundant high-
quality DNA of the predator itself. Therefore, predator
DNA co-amplification may prevent or bias prey recovery
if no preventive measure is taken [41-43]. Therefore, we
included pred ator-specific annealing blocking primers at
ten times the concentration of versatile primers (tailed
mlCOIintF and jgHCO2198, see below) in all PCR am-
plifications. Blocking primers are modified primers that
overlap with one of the versatile primer binding sites
and extend into a predator specific sequence. They help
prevent predator DNA amplification but simultaneously
enable amplification of DNA from prey items. We
designed blocking primers for N. savayensis, M. berndti
and S. microstoma to minimize prey DNA blocking (see
guidelines in [43]):
5’-CAAAGAATCAGAATAGGTGTTGGTAAAGA-3’,
5’-CAAAGAATCAGAACAGGTGTTGATAAAGG-3’
and 5’-CAAAGAATCAGAATAGGTGTTGATAAAGA-
3 respectively.
Primers were modified at the 3’end with a Spacer C3
CPG (3 hydrocarbons) to prevent elongation without af-
fecting their annealing properties [41].
Sample multiplexing and library preparation for Roche 454
FLX sequencing
We used a h ierarchical tagging approach with a combin-
ation of tailed PCR primers and 454 Multiplex Identi-
fiers (MIDs) to sequenc e all samples in a single 454 run.
Five pairs of the versatile primers, mlCOIintF and
jgHCO2198, were synthe tized each with 6 base pair tags
at their 5’end (T1: AG CACG, T2: ACG CAG, T3:
ACTATC, T4: AGAC GC, T5: ATCGAC). W e tested
these tailed primer pairs (e.g. P1: T1-mlCOIintF ×
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jgHCO2198-T1 , P2: T2-mlCOIintF × jgHCO2198-T2)
across templates from a diversity of phyla and found
that they did not affect PCR performance (data not
presented). Primer sets P1, P2, P3, P4 were used to amp-
lify three gut content samples each and P5 was used for
four samples. Five PCR replications and a negative con-
trol (no DNA template) were generated per sample to
account for PCR drift [44] and to che ck for PCR con-
taminants. P CR products of the five replicates were
pooled, run on 1.5% agarose gels , and t he fragment
excised t o ensure that all primer dimer was screened
away. PCR amplicons were purified using QIAGEN®
MiniElute columns , eluted in 12 μl elution Buffer, and
PCR product concentration measured using the Qubit®
Fluoremeter (Invitrogen). Equimolar amounts of each
sample were combined in three tubes , each tube
containing amplicons generated with each of the five
tailed primer pairs. We prepared these three mixes with
the NEBNext Quick DNA Sample Prep Reagent Set 2
(New England BioL abs), which includes end-repair and
dA-tailing chemistry and then ligated with MIDs (9, 10
and 11) using the FLX Titanium Rapid Library MID
Adaptors Kit ( Roche). Ligated PC R products w ere puri-
fied using Age ncourt AMPure (Beckma n Coulter Gen-
omics ), eluted in 40 μlofTEbuffer,andpooledpriorto
emulsion PCR and 454 sequencing. Note that these 16
gut content samples were combined with 44 other sam-
ples in this run (multiplexed using the five tailed PCR
primers and 12 MIDs).
Analysis of sequence data
A diagram of the bioinformatics pipeline is provided in
Figure 2.
Denoising
“Standard flowgram file” (.sff) is the standard output of
454 platforms. It contains bases, quality and strength of
the signal for each read. We used the program Mothur
[45] to extract the flowgram data (.flow file) and s ort
reads as follow: 1) we partitioned flowgrams per sample
based on barcodes and MIDs , 2) we discarded reads
with more than two mismatche s in t he primer sequence,
3) we discarded reads w ith less than 200 flows (includ-
ing primers and barcode), 4) we discarded or trimmed
flowgrams based on standard thresholds for signal
intensity (as suggested in [46]). Following this initial
quality filtering, we conducted additional denoising of
flowgrams u sing a mothur implementation of Pyronoise
[46] tha t uses an expectation-maximization algorithm
to adjust flowgrams and translates them to DNA se-
quences (co mmand shhh .flows).
Alignment to reference barcode database and removal of
non-functional sequences
We used amino acid translations to align sequences to
the BIOCODE reference dataset using MACSE v1.00
[47]. Quality-filtered sequences were sequentially aligned
and added to the reference dataset using the option
“enrichAlignment”. This alignment strategy is only
reasonable because the studied COI fragment is highly
conserved at the amino acid levels. To further optimize
computing time, sequences were split into subsets
containing 500 sequences that were aligned in parallel
thanks to a computer farm and then progressively
merged into a single final alignment using the option
“alignTwoProfiles” . MACSE can dete ct and quantify in-
terruptions in open reading frames due to: (1) nucleotide
substitutions that result in stop codons and (2) insertion
or deletion of nucleotides (non multiples of three) that in-
duce frameshifts. Sequences with stop codons are likely
bacterial sequences, pseudogenes or chimeric sequences.
On the other hand, frameshifts may be caused by sequen-
cing errors that are frequent with the 454 platform [48].
MACSE can also detect and quantify insertions and dele-
tions that do not lead to interruptions in open reading
frames. COI is relatively conserved and indels are re-
latively uncommon. For example, only 0.9% of the se-
quences in BIOCODE dataset (including platyhelminthes,
gastropods and isopods) display a deletion of one codon
in their COI sequence, and none of the sequences in the
BIOCODE dataset have codon insertions. As a result, we
decided to keep all sequences from the 454 dataset which
satisfied the following criteria: no stop codons, no frame
shifts, no insertions and less than four deletions. For the
final dataset we retained all sequences with a single frame-
shift when they had no stop codon, no insertions and no
deletions to account for sequencing errors. Alignme nt of
these sequences with frameshift required insertion or
Figure 2 Schematic representation of the bioinformatics
pipeline used for analysis of COI sequence 454 dataset.
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deletion of a nucleotide either at the first, second or
third codon position. However, because the correct pos-
ition could not be known, we chose to remove these
codons a ll together.
Chimera removal
We used the BIOCODE reference dataset to facilitate
chimera detection implemented in UCHIME [49].
Clustering sequences in operational taxonomic units (OTUs)
Our dataset comprised seque nces belonging to a diver-
sity of taxonomic groups that are known to have dissimi-
lar rates of COI evolution. This means that using a fixed
sequence dissimilarity cutoff (i.e. 5%) for clustering OTUs
may not result in accurate species delineations. Therefore,
rather than using a conventional hierarchical clustering
method, we ran CROP [50], a Bayesian clustering program
that delineates OTUs based on the natural distribution of
the data. The program uses user-defined lower and upper
bound variance to generate clusters with different stand-
ard deviations. The settings used in CROP for clustering
sequences in OTUs will determine our estimation of taxo-
nomic diversity in each sample. Ideally, each OTU should
represent an evolutionary distinct unit. In order to
optimize lower and upper bound values, we first use
CROP to cluster sequences from the reference
BIOCODE databa se using a variety of thresholds that in
turn correspond to sequence dissimilarities (e.g., lower
and upper values of 3 and 4 correspond to sequence
dissimilarities of between 6% and 8% respectively). The
following paired lower and upper thresholds were tested
because they are within the range of intra- and inter-
specific sequence dissimilarity reported in the literature
for marine invertebrates [2,43,51,52]: -l 1.5 -u 2.5;
-l 2.5 -u 3.5; -l 3.0 -u 3.75; - 3.0 -u 4.0; -l 3.25 -u 4.25.
We particularly examined the frequency of false positives
(splitting of a taxon in two or more clusters because of
deep intraspecific variation) and false negatives (lumping
of two or more sister taxa together because of shallow in-
terspecific divergence) in comprehensively sampled and
diverse groups (i.e. Scaridae, Trapeziidae, Cypraeidae) and
found that priors of –l 3.0 and –u 4.0 provided the best re-
sults (data not shown). Yet, because the algorithm is based
on stochastic processes, CROP can still find clusters with
dissimilarities as low as 4-5% or as high as 9-10% as long
as there are enough sequences supporting the existence of
such clusters (Hao X. pers. comm.).
Taxonomic assignment of OTUs
We performed BLAST searches [53] of representative se-
quences in the local BIOCODE database (implemented in
Geneious) and in GENBANK. We considered species level
match when sequence similarity was at least 98% [2,51,52].
Whenever sequence similarity was lower than 98%, we
used the Bayesian approach implemented in the Statistical
Assignment Package (SAP, [54]) to assign the sequence to
a higher taxonomic group. SAP retrieves GENBANK
homologues for each query sequences and builds 10,000
unrooted phylogenetic trees. It then calculates the poste-
rior probability for the query sequence to belong to a ta-
xonomic group. Here we allowed SAP to download 50
GENBANK homologues at ≥70% sequence identity and we
accepted assignments at a significance level of 95% (poster -
ior probability). We combined taxonomic information and
number of sequences per OTU and per sample into a sum-
mary table for downstream analysis.
Results
Primer design and performance
We were able to find a relatively well-conserved priming
site from an alignment of COI barcode sequences pro-
vided by the Moorea BIOCODE project. The degenerate
forward mlCOIintF and mlCOIintR (128 fold degeneracy)
were designed to be used in combination with versatile
primers commonly used for DNA barcoding (Table 1) to
target 313 bp and 319 bp fragment lengths respectively
(Figure 1). Analysis of primer-template mismatches across
the BIOCODE reference library re vealed that the max -
imum number of mismatches between sequences of six
major marine phyla and the new designed primer se-
quence ne ver exceeded six (Figure 3) with the majority
of sequences showing less than four mismatches (Cnidaria:
88%, Arthropoda: 97% , Bryozoa : 91%, Anne lida : 94%,
Mollusca:92%,Chordata:84%).
Preliminary tests showed that the forward mlCOIintF
primer used in combination with the reverse jgHCO2198
(Table 1) amplified the highest proportion of metazoan di-
versity tested herein (91% - Table 2). On the other hand,
the reverse mlCOIintR primer performed poorly whether
it was used with LCO1490, dgLCO1490 or jgLCO1490
(57%, 60% and 64% respectively – Table 2). Despite the
degenerate sites in both mlCOIintF and jgHCO2198
primer sequences, particularly at the third codon position
(Table 1), there was no evidence of non-specific binding
(see single bands on agarose gel pictures in Additional
file 2) using the touchdown PCR thermal profile. A total
of 87% (250 of 285) of templates successfully amplified,
among which 93% provided good quality sequences
(GENBANK accession numbers KC706674-KC706906).
We observed high amplification success for Arthropoda
(88%, n = 99; Table 3), Molluscs (90%, n = 52), Cnidaria
(88%, n = 28), Annelida (100%, n = 25), Chordata (83%,
n = 18), Echinodermata (100%, n = 11), Bryozoa (100%,
n = 9) and Sipuncula (100%, n = 5). In comparison, primer
sets currently used for DNA barcoding to target the 658 bp
COI fragment, LCO1490 × HCO2198, dgLCO1490 ×
dgHCO2198, and jgLCO1490 × jgHCO2198 (Table 1)
had lower amplification successes (76%, 77% and 77% of
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successful amplifications across all templates respectively;
Table 3). The mini-barcode primer set, Uni-MinibarF1 with
Uni-MinibarR1, performed very poorly across the diversity
of templates (27% amplification).
Pyrosequencing of fish gut contents
We obtained a total of 93,973 flowgrams after initial
denoising with Pyronoise. Alignments of sequences to the
reference BIOCODE dataset using MACSE revealed
38,576 sequences (41%) with anomalies in their amino
acid translation. Among them, 6407 sequences with a sin-
gle frameshift but with no stop codons and no inserted or
deleted codons were kept in the dataset, as we assumed
they were the result of minor sequencing errors. All
remaining 32,169 sequences, among which 2.4% only had
a stop codon, were discarded. UCHIME detected 522
potential chimeric sequences that were also removed to
obtain a final dataset of 54,875 high quality reads. The
number of reads per individual varied from 1219 to 8423
(mean ± SD = 3430 ± 1104), most likely as a result of dif-
ferences in ligation efficiency during addition of MIDs due
to the primer tag (Additional file 3). Individual rarefaction
curves implemented in R , package VEGAN [55] indicate
that additional sequencing would be required for further
describing the gut contents of some fishes (curves do not
reach a plateau, Additional file 4).
The Bayesian clustering program CROP revealed a total
of 337 OTUs. None were identified as bacteria or non-
COI sequences from BLAST searches. Of these, 177 OTUs
(52.5%) were identified to the species level as they showed
more than 98% sequence similarity with BIOCODE or
GENBANK sequences (Figure 4A, Additional file 5). For
the three fish species separately, 56.9%, 50.5% and 52.9%
of OTUs determined from N. savayensis, M. berndti and
S. microstoma gut contents respectively had a species-level
match. Three OTUs representing the DNA of the pre-
datory fish species themselves (N. savayensis:1012
sequences, S. microstoma: 921 sequences; M. berndti:3
sequences) were removed. Importantly, none of the 177
OTUs identified to the species level were assigned to the
reference barcode of the same morphological species.
Moreover, CROP was effective at discriminating closely re-
lated species, such as 12 species within the genus Alpheus
(among which A. obesomanus and A. malleodigitus are
sister species within the obesomanus species complex) (see
Additional file 5 for more examples). The Bayesian assign-
ment tool offered further taxonomic insights by confi-
dently assigning 108 additional OTUs (32%) to a higher
taxonomic level, and only 52 OTUs (15.4%) remained
unidentified. An alignment of all representative sequences
is provided in Additional file 6 and all unique sequences
were deposited in the Dryad Repository doi:10.5061/
dryad.6gd51).
Figure 3 Distribution of mismatches between the “mlCOIint” primer sequence and templates from the Moorea BIOCODE database.
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OTUs belonged to 14 phyla (Figure 4A); Arthropoda,
Chordata and Annelida were the most represented, with
175 OT Us (52.4%), 42 OTUs (12.6%) and 27 OTUs (8%)
respectively. Species level matches were more prevalent
among Chordata (88.1%) and Arthropoda (64%), two
macrofaunal groups particularly well sampled by the
Moorea BIOCODE teams [56] (Figure 4A). Moreover,
taxonomic assignments were more prevalent for OT Us
represented by a high number of sequences. For ex-
ample, only 51.8% of Arthropoda OTUs matched refer-
ence barcodes when they were represented by a single
sequence, whereas 100% of OTUs represented by more
than 1000 sequences were assigned to BIOCODE or
GenBank specimen (1: 51.8%; [2-9]: 41.8%; [10–99]: 75%;
[100–999]: 83.9%; >1000: 100%; Figure 4B). Similarly,
27.6% of sequences represented by a single sequence
could not be assigned to a phylum (unknown – Figure 4B)
whereas none of the OT Us represented by more than
1000 sequences remained unidentified (1: 27.6%; [ 2-9]:
17.9%; [10–99]: 9.9%; [ 100–999]: 4.3%; >1000: 0%;
Figure 4B).
Among the 223 OTUs detected in the gut contents of
N. savayensis, 151 (67.8%) occurred in a single individual,
38 (17%) occurred in two individuals, and 34 (15.2%) in
more than two individuals (Figure 5A). Intraspecific diet
overlap was lower for M. berndti and S. microstoma with
only 7.8% and 10.6% of prey shared by two individuals
respectively. The majority of OTUs shared by more than
two individuals belonged to the phylum Arthropoda
(82%, 100% and 75% for N. savayensis, M. berndti
and S. microstoma respectively). In contrast, there was a
significant overlap in dietary composition between fish
species (Figure 5B): 31.8% of OTUs detected in the guts of
N. savayensis were also detected in the guts of M. berndti
and S. microstoma, and 53.4% and 45.2% of OTUs in M.
berndti and S. microstoma were shared with N. savayensis/
S. microstoma and N. savayensis/M. berndti respectively.
OTUs shared among predatory fish were mostly
Arthropoda , Chordata an d Annelida, but also i n-
cluded Mollusca , Echinodermata , Cnidarian, Porifera
and Hemichordata.
Discussion
The high level of variability in the COI region is problem-
atic for designing a PCR primer internal to the 658 bp
COI barcoding region [8]. As shown in this s tudy, the
mini-barcode primer set [17], which represents the only
published attempt at designing versatile primers for a
short COI fragment to date, is not effective across taxo-
nomic groups. We present an alternative primer set and
we show how it can be used for metabarcoding analyses.
We de signed the forward and reverse primers ,
mlCOIintF and mlCOIintR, within the 658 bp COI
barcoding region using a total of seve n degenerate ba ses
to accommodate variation in t he priming region. The
forward internal primer w a s always more effe ctive when
used in combination with HCO2198 (and its degenerate
versions dgHCO2198 and jgHCO2198) than its reverse
complement used with LCO1490 (and it s degenerate
versions dgLCO1490 and jgLCO1490), which likely re-
flects higher incompatibilities in the LCO1490 p riming
site th an in the HCO2198 priming site [34]. This had
Table 2 Preliminary tests to determine the primer combination that performed best to amplify a short COI fragment
Forward primer mlCOIintF LCO1490 dgLCO1490 jgLCO1490
Reverse primer HCO2198 dgHCO2198 jgHCO2198 mlCOIintR
Fragment length (bp) 313 313 313 319 319 319
Phylum Cnidaria (6) 6 6 6 2 2 2
Arthropoda (18) 16 15 16 12 11 11
Rotifera (1) 1 1 1 0 0 0
Entoprocta (1) 0 0 0 1 1 0
Annelida (4) 4 4 4 3 4 4
Nemertea (2) 2 2 2 0 0 1
Mollusca (9) 7 7 8 7 7 7
Echiura (1) 1 1 1 1 1 1
Chordata (2) 2 2 2 1 2 1
Hemichordata (2) 2 2 2 0 0 2
Echinodermata (1) 1 1 1 0 0 1
TOTAL (47) 42 41 43 27 28 30
89% 87% 91% 57% 60% 64%
Columns show the number of taxa for which the target region was successfully amplified. The total number of taxa used for each phylum is displayed in parentheses.
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been previously reported for nematodes which display a
three ba se pair deletion in the LCO1490 priming region
[6]. The overall performance of mlCOIintF use d with
jgHCO2198 wa s superior to traditional barcoding
primers. We demonstrate it s re markable efficacy across
Arthropoda, Mollusc a, Cnidaria, Annelida, Chordata,
Echinodermata, Bryozoa and Sipuncula, although fur-
ther tests should be conducted to evaluate its performance
across less represented phyla (less than five species tested).
Nevertheless, this new primer set appears to be an excep-
tional candidate for DNA barcoding and metabarcoding.
Higher degeneracy results in better amplification when
primer-sequence mismatches are present, but a major
downside can be the higher likelihood of non-specific
primer annealing. Amplification tests conducted across
284 templates showed no evidence for amplification of
non-target loci (single PCR band of exp ected size). The
touchdown PCR profile may have helped increase the
Table 3 Performance of universal primer sets for COI across phyla
Forward primer “mlCOIintF”“LCO1490”“dgLCO1490”“jgLCO1490”“Uni-MinibarF”
Reverse primer “jgHCO2198”“HCO2198”“dgHCO2198”“jgHCO2198”“Uni-MinibarR1”
Fragment length (bp) 313 658 658 658 130
Phylum Radiolaria (1) 0 0 0 0 0
Ciliophora (1) 0 1 0 0 0
Sarcomastigophora (1) 0 0 0 0 0
Amoebozoa (1) 0 0 0 0 0
Placozoa (1) 0 0 0 0 0
Porifera (4) 4 3 3 2 2
Cnidaria (28) 26 22 23 23 11
Ctenophora (2) 1 0 0 0 1
Chaetognatha (2) 2 1 2 2 0
Nematomorpha (1) 0 0 0 0 0
Nematoda (2) 1 1 0 0 0
Tardigrada (1) 0 0 0 0 0
Arthropoda (99) 87 84 80 82 30
Platyhelminthes (4) 4 1 1 0 0
Gastrotricha (3) 2 0 0 0 0
Gnathostomulida (3) 2 1 0 0 0
Rotifera (1) 1 1 1 0 0
Entoprocta (1) 0 0 1 0 0
Bryozoa (9) 9 9 8 7 5
Annelida (25) 25 23 25 23 5
Nemertea (4) 3 3 3 1 2
Sipuncula (5) 5 5 5 5 1
Mollusca (52) 47 45 49 48 11
Echiura (1) 1 1 1 1 0
Phoronida (2) 2 2 2 2 2
Brachiopoda (1) 0 1 1 1 0
Chordata (18) 15 9 12 12 4
Acoelomorpha (1) 0 0 0 0 0
Hemichordata (2) 2 0 1 2 1
Echinodermata (11) 11 4 2 11 1
Total (287) 250 217 220 222 76
(87%) (76%) (77%) (77%) (27%)
Columns present the number of taxa for which the target region was successfully amplified. Amplification success was evaluated on agarose gels (pictures shown
in Additional file 2). The total number of taxa used for each phylum is displayed in parentheses.
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probability of primer-template specificity with high
annealing temperatures during the first PCR cycles.
Nevertheless, we also experimented with PCR conditions
such as 35 cycles at 48°C for selected samples without
observing any evidence for non-selective amplification
(data not presented). Amplification and pyrosequencing
of the 313 bp COI fragment from fish gut contents
represents a better test of the likelihood of this primer
set to co-amplify contaminants. Bacteria are particularly
preponderant in gut and faecal samples [43] and can
become problematic when misconstrued as prey items
[57]. We used a sequence analysis pipeline that takes
advantage of the coding properties of the COI region to
exclude dubious DNA fragments. As a result, 34.2% of
Figure 4 Diversity, identity and sequence abundance of Operational Taxonomic Units (OTUs) recovered from fish gut contents. A) The
number of OTUs per phylum is presented for all fish guts pooled together. OTUs were identified from BLASTn searches performed in the Moorea
BIOCODE database and GENBANK. We considered a match to be at the species level when sequence similarity to a reference barcode was >98%.
When sequence similarity was < 98%, we used the Bayesian assignment tool implemented in SAP to assign each OTU to a higher taxonomic
group, accepting assignments at a significance level of 95% (posterior probability). B) The proportion of OTUs presented per abundance classes.
Abundance corresponds to the number of sequences per OTUs.
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sequences were removed from the dataset, among which
2.4% which had a stop codon were potential bacteria.
Most anomalies were not attributable to co-amplification
of contaminants but rather base insertions causing frame-
shifts. Pyronoise (used as initial denoising) removes errors
caused by incorrect interpretation of signal intensity dur-
ing 454 pyrosequencing, therefore, numerous frameshifts
may in fact be the result of nucleotide mis-incorporation
during PCR amplification. Therefore, we highly recom-
mend using a proofreading taq polymerase to generate
amplicons with fewer errors in future metabarcoding ana-
lysis. DNA may also get damaged during digestion [14].
Other types of environmental samples where animals are
collected alive (i.e. plankton tows) may be less susceptible
to this type of error and should be tested.
Diversity analysis was conducted with a high-quality
sequence dataset free of non-coding dubious sequences
to ensure the exclusion of artefacts. A total of 344 OTUs
spanning 14 different phyla were identified which further
confirms the remarkable versatility of the primer set.
Arthropoda, Chordata and Annelida were the most rep-
resented in terms of number of OTUs. This is in accord-
ance with our morphological observations of prey
remains, as well as with previous studies that des cribed
these three groups as the main food source of these
generalist fish species [40,58-60]. Among all prey OTUs,
Figure 5 Intra- (A) and inter-specific (B) dietary content. The proportion of OTUs in each of the three most diverse phyla is presented.
n = number of OTUs in each category. Note that the number of OTUs in A is larger than 334, the total number of OTUs found in fish gut contents,
because some OTUs are shared between individuals of the three species. The list of phyla contained in the category “Others” is presented in Figure 4.
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52.5% had a direct match with a reference barcode,
mostly from the Moorea BIOCODE sequence library.
Although remarkable, the proportion of species-level as-
signments is lower than in a previous dietary study
conducted in Moorea, where 94% of undigested prey
found in the guts of common generalist predatory fish
could be identified using DNA barcoding of individual
prey items [56]. The metabarcoding analysis conducted
in the present study is not restricted to large prey
(>2 mm) with hard parts, such as decapods and molluscs
that received in-depth sampling by the Moorea
BIOCODE teams [56]. Most unassigned OTUs belong to
under-represented phyla (i.e. Porifera, Sipuncula and
Rhodophyta), possibly pelagic (i.e. Maxillipoda) or small
sized species (< 2 mm adult size). Interestingly, we found
that OTUs represented by fewer sequences or OTUs
detected in the guts of a single fish were more likely to
remain unidentified. It is well known that primer bias
(the number and position of mismatches with the primer
sequence) and biological factors (i.e. le vel of digestion
[61], variation in the amount of DNA target between tis-
sue types and genome size [62], or differences in DNA
survival rates during digestion [63]) affect quantitative
estimates. Yet, assuming that BIOCODE was able to
inventory and barcode most common fish and macro-
invertebrates of the Moorea ecosystem, this suggests
that species represented by a single seque nce are either
mostly rare or belong to small sized organisms. Due to
the relatively lower sampling effort dedicated to the
pelagic environment relative to the benthic environment
by BIOCODE, we expected the frequency of species
assignments to be lower for the strictly planktivorous
species N. savayensis than for the strictly benthic feeder
S. microstoma. However, our analysis revealed that N.
savayensis had consumed eggs or larvae of numerous
benthic species, whilst S. microstoma appeared to be
very effective at sampling juvenile and adult stages of
coral reef associated fish and motile invertebrates. We
found 55 OTUs in the guts of S. microstoma, among
which were ten arthropods and two fish OTUs that were
never collected during the 6 years of the BIOCODE pro-
ject. This shows that fish are great integrators of their
immediate environment as they consume species that
are not easily accessible to traditional sampling methods
(see [36]). Metabarcoding analysis also detected unex-
pected species, including the terrestrial crab Cardinosa
carniflex (which has planktonic larvae) and the crown-
of-thorn seastar Acanthaster planci (a voracious preda-
tor responsible for dramatic reductions in coral cover
and changes in benthic communities in Moorea between
2009 and 2011 [64,65]).
All adult fish were collected on the same night at the
same site within a short period of time, enabling some
preliminary insights on food partitioning among coral
reef fishes. The extent of dietary overlap for species
coexisting on coral reefs has long been debated [66], but
overlap has often been estimated using dietary data with
low taxonomic resolution [67]. We found limited evi-
dence for dietary partitioning between species despite
different feeding modes while intra-specific overlap in
prey composition was more limited. Such intraspecific
partitioning may be due to intraspecific competition or
individual specialization, with all three species having
access to a large pool of shared prey [68]. We also
observed large variation in prey diversity between indi-
vidual fish that could either be caused by differences in
feeding intensity or efficacy. Together these preliminary
results further highlight the importance of using high-
resolution dietary information and conside r individual
level variation in prey consumption for understanding
the role of food partitioning for the coexistence of coral
reef fishes.
Conclusions
The molecular analysis of gut contents targeting the
313 COI fragment using the newly designed mlCOIintF
primer in combination with the jgHCO2198 primer offers
enormous promise for metazoan metabarcoding studies.
This primer set performs exceptionally well across meta-
zoan phylogenetic diversity. We believe that this primer
set will be a valuable asset for a range of applications from
large-scale biodiversity assessments to food web studies.
In particular, it could be used to rapidly assess anthropo-
genic impacts on biodiversity and ecosystem function,
especially in highly diverse and fragile environments such
as coral reefs or tropical forests.
Additional files
Additional file 1: List of taxa used for comparing the performance
of primer sets. Genomic DNA for both terrestrial and marine species
was provided by the Moorea Biocode project. Photographs and
additional information about each specimen can be obtained at http://
biocode.berkeley.edu.
Additional file 2: Agarose gel image showing the amplification
success of a 313 bp COI fragment across taxa belonging to 30
animal phyla. The forward primer mlCOIintF and reverse primer
jgHCO2198 were used. List of taxa is shown in Additional file 1. Summary
of results is shown in Table 3 of the main text.
Additional file 3: Differences in sequence recovery due to a bias in
ligation efficiency during addition of multiplex identifiers (MIDs).
We used a hierarchical tagging approach: following PCR ampl ification
with versatile primers synthetized with a 6 bp barcode (T1 through T5) at
the 5’ end, samples were pooled resulting in 12 pools of five samples
each. A different MID identifier was ligated to each pool. The mean (± SD)
proportion of sequences per sample is represented on the y axis. Twelve
MID tags were used to multiplex 60 samples in this 454 sequencing run.
Additional file 4: Individual rarefaction curves illustrating the
accumulation of prey diversity with sequencing. Each curve
represents the gut contents of an individual fish.
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Additional file 5: List of taxa recovered from fish gut contents by
targeting the 313 bp COI region. A representative sequence per OTU
was used for taxonomic identification. BIOCODE reference specimen
number or GENBANK accession number are indicated when sequence
similarity with reference barcode sequence was >98% (using BLASTn
search). Photographs and additional information about BIOCODE
reference specimens can be obtained at http://biocode.berkeley.edu.
When sequence similarity was < 98%, we used the Bayesian assignment
tool implemented in SAP to assign each OTU to a higher taxonomic
group. # indiv.: number of individual fish. # seq.: number of sequences for
each OTU.
Additional file 6: Fasta formatted alignment of OTU representative
sequences. See Additional file 5 for taxonomic identification.
Competing interests
The authors declare that they have no competing interests.
Author’ contributions
ML, SCM, CPM, JTB and RJM designed the study. ML designed the versatile
primers and blocking primers, collected the fish, performed the laboratory
work for metabarcoding analysis of gut contents, performed data analysis
and wrote the manuscript. CM provided the Moorea BIOCODE sequence
library and genomic DNA samples. NA conducted primers tests in the
laboratory. JTB helped collect the fish and tested the blocking primers in the
laboratory. RJM helped computing primer-template mismatches. JYY and VR
provided critical help for analysing 454 sequence data. SCM supervised the
project and helped writing the manuscript. All authors read and approved
the final manu script.
Acknowledgements
We thank Gustav Paulay, Arthur Anker and the BIOCODE teams who
collected and identified marine and terrestrial specimens, the “ Centre de
Recherche Insulaire et Observatoire de l’Environnement (CRIOBE) de Moorea”
and the Richard B. Gump field station in Moorea for logistical support. We
also greatly acknowledge the Gordon and Betty Moore Foundat ion,
Smithsonian Institution Fellowship Program, France American Cultural
Exchange program (FACE - Partner University Fund) and ANR-11-JSV7-012-01
Live and Let Die for financial support. Ehsan Kayal and Yvonne Linton
provided constructive comments on an early draft of the manuscript. We are
also grateful for insightful comments provided by Nancy Knowlton. This
study was part of M. Leray PhD research program at Université Pierre et
Marie Curie (Paris VI) and Ecole Pratique des Hautes Etudes (EPHE) under the
supervision of S.C. Mills.
Author details
1
Laboratoire d'Excellence "CORAIL", USR 3278 CRIOBE CNRS-EPHE, CBETM de
l’Université de Perpignan, 66860, Perpignan Cedex, France.
2
Department of
Invertebrate Zoology, National Museum of Natural History, Smithsonian
Institution, P.O. Box 37012, MRC-163, Washington, DC 20013-7012, USA.
3
National Human Genome Research Institute, National Institutes of Health,
Bethesda, Maryland, USA.
4
Montpellier SupAgro (UMR AGAP), Montpellier,
France.
5
Biology Department, City College of New York, New York, NY 10031,
USA.
6
The Graduate Center, City University of New York, New York, NY 10016,
USA.
7
Biodiversity Research Center, Academia Sinica, Taipei, Taiwan.
Received: 14 March 2013 Accepted: 23 May 2013
Published: 14 June 2013
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doi:10.1186/1742-9994-10-34
Cite this article as: Leray et al.: A new versatile primer set targeting a
short fragment of the mitochondrial COI region for metabarcoding
metazoan diversity: application for characterizing coral reef fish gut
contents. Frontiers in Zoology 2013 10:34.
Leray et al. Frontiers in Zoology 2013, 10:34 Page 14 of 14
http://www.frontiersinzoology.com/content/10/1/34