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Plant pathogens such as rust fungi (Pucciniales) are of global economic and ecological importance. This means there is a critical need to reliably and cost‐effectively detect, identify, and monitor these fungi at large scales. We investigated and analyzed the causes of differences between next‐generation sequencing (NGS) metabarcoding approaches and traditional DNA cloning in the detection and quantification of recognized species of rust fungi from environmental samples. We found significant differences between observed and expected numbers of shared rust fungal operational taxonomic units (OTUs) among different methods. However, there was no significant difference in relative abundance of OTUs that all methods were capable of detecting. Differences among the methods were mainly driven by the method's ability to detect specific OTUs, likely caused by mismatches with the NGS metabarcoding primers to some Puccinia species. Furthermore, detection ability did not seem to be influenced by differences in sequence lengths among methods, the most appropriate bioinformatic pipeline used for each method, or the ability to detect rare species. Our findings are important to future metabarcoding studies, because they highlight the main sources of difference among methods, and rule out several mechanisms that could drive these differences. Furthermore, strong congruity among three fundamentally different and independent methods demonstrates the promising potential of NGS metabarcoding for tracking important taxa such as rust fungi from within larger NGS metabarcoding communities. Our results support the use of NGS metabarcoding for the large‐scale detection and quantification of rust fungi, but not for confirming the absence of species.
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MicrobiologyOpen. 2019;8:e780. 
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https://doi.org/10.1002/mbo3.780
www.MicrobiologyOpen.com
1 | INTRODUCTION
Plant pathogens are a critical th reat to global food security (Bebber &
Gurr, 2015), the conservation of natural ecosystems, and the future
resilience and sustainability of ecosystem ser vices (Bever, Mangan,
& Alexander, 2015). Because of their importance, there is a huge in‐
terest to biomonitor plant pathogens cost‐effectively at large scales
without the need of culturing and before possible disease outbreaks.
Received:3October2018 
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Revised:14November2018 
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Accepted:15Novembe r2018
DOI: 10.1002/mbo3.780
ORIGINAL ARTICLE
Biases in the metabarcoding of plant pathogens using rust fungi
as a model system
Andreas Makiola1,2 | Ian A. Dickie3| Robert J. Holdaway4|
Jamie R. Wood4| Kate H. Orwin4| Charles K. Lee5| Travis R. Glare2
This is an op en access article under t he terms of the Creat ive Commons Attributio n License, which permits use, dist ribution and reproduc tion in any medium,
provide d the orig inal work is proper ly cited .
© 2018 The Aut hors. MicrobiologyOpen published by John Wiley & Sons Ltd.
1Agroécologie, AgroSup Dijon,
INRA ,Unive rsitéBourgogne ,Université
Bourgogne Franche‐Comté, Dijon, France
2Bio‐Protection Research Centre, Lincoln
University,Linco ln,NewZealand
3Bio‐Protection Research Centre, School
ofBiologicalSciences,Universi tyof
Canter bury,NewZe aland
4Manaaki Whenua – Landcare Research,
Lincoln ,NewZealand
5WaikatoDNASequen cingFacility,Schoo l
ofScience,Univer sityofWaikato,Hami lton,
NewZealand
Correspondence
Andreas Makiola, Agroécologie, AgroSup
Dijon,INRA ,Univer sitéBourgogne,
UniversitéBour gogneFran che-Comté,
Dijon, France.
Email: Andreas.Makiola@inra.fr and
Ian A. Dickie, Bio‐Protection Research
Centre, School of B iological Sciences,
UniversityofCanterbu ry,NewZealand.
Email: ian.dickie@canterbury.ac.nz
Funding information
Ministry for Business Innovation and
Employment,Grant/AwardNumber:
C09X1411;TertiaryEducationCommission
Abstract
Plant pathogens such as rust fungi (Pucciniales) are of global economic and ecological
importance. This means there is a critical need to reliably and cost‐effectively detect,
identify, and monitor these fungi at large scales. We investigated and analyzed the
causes of dif ferences between next-generation sequencing (NGS) metabarcoding
approachesandtraditionalDNAcloninginthedetectionandquantificationofrecog
nized species of rust fungi from environmental samples. We found significant differ‐
ences between observed and expected numbers of shared rust fungal operational
taxonomicunits(OTUs)amongdifferentmethods.However,therewasnosignificant
differenceinrelativeabundanceofOTUsthatallmethodswerecapableofdetecting.
Differences among the methods were mainly driven by the method's ability to detect
specificOTUs,likelycausedbymismatcheswiththeNGSmetabarcodingprimersto
some Puccinia species. Furthermore, detection ability did not seem to be influenced
bydifferences insequence lengthsamongmethods,themostappropriatebioinfor
matic pipeline used for each method, or the ability to detect rare species. Our find‐
ings are important to future metabarcoding studies, because they highlight the main
sources of difference among methods, and rule out several mechanisms that could
drive these differences. Furthermore, strong congruity among three fundamentally
differentandindependentmethods demonstratesthe promising potentialofNGS
metabarcodingfortrackingimportanttaxasuchasrustfungifromwithinlargerNGS
metabarcodingcommunities.OurresultssupporttheuseofNGSmetabarcodingfor
thelarge-scaledetectionandquantificationofrustfungi,butnotforconfirmingthe
absence of species.
KEYWORDS
cloning,Illumina,IonTorrent,next-generationsequencing,plantpathogens,Pucciniales
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Rust fungi (Pucciniales) constitute one of the largest groups
of plant pat hogens, with abo ut 7,800 d escribed spe cies (Helfer,
2014), and some rus t species c an have large econ omic and eco
logical impacts. For example, myrtle rust (Austropuccinia psidii) is
currently decimating a wide range of Myrtaceae around the world
(Fernandez Winzer, Carnegie, Pegg, & Leishman, 2018; Glen,
Alfenas,Zauza, Wingfield, & Mohammed, 20 07), suchasthe en
demic Eugenia koolauensisinHawai‘i and theendemicRhodamnia
rubescens in native forests in Australia (Carnegie et al., 2016).
Coffee leaf rust (Hemileia vastatrix) is substantially damaging
Coffee plantations worldwide (Talhinhas et al., 2017). Similarly,
wheat leaf rusts like Puccinia triticina, Puccinia recondite, and
Puccinia striiformis are causing serious production losses for one of
theworld'sbigges tfoo dcro ps(McC allum,Hiebe rt,Hu er t a- Es pi no ,
& Cloutier, 2012).
While many studies focus on rust fungi as perceived pests, they
actually constitute a vital component of natural ecosystem function‐
ing. In contrast to agroecosystems, rusts in their natural ecosystems
are less well studied, and some species are threatened by extinction
due to globalchange (Helfer,2014).Becauseofthe economic and
ecological impor tance of plant pathogens, such as rust fungi, new,
reliable, and cost‐effective tools are urgently needed to monitor
them at large scales.
Next-generation sequencing metabarcoding has the potential
to develop into an effective method for the molecular identification
of multiple plant pathogens from environmental samples (Merges,
Bálint, Schmitt, Böhning-Gaese, & Neuschulz, 2018; Taberlet,
Coissac,Hajibabaei,&Rieseberg,2012).DNAmetabarcodingseems
especially promising for the monitoring of potential plant pathogens
(hereafter pathogens), because it bypasses the need for cultivation
and isolation of species, and is able to detect plant pathogenic spe
cies when they occur asymptomatically (Malcolm, Kuldau, Gugino,
&Jiménez-Gasco,2013;Stergiopoulos&Gordon,2014)oratbarely
discerniblelevels.While DNA metabarcodingholdsgreatpotential
for detecting and monitoring fungi in their environment (Durand
etal.,2017;Miller, Hopkins,Inward, &Vogler,2016; Schmidtetal.,
2013), it has not yet been widely applied to pathogens specific ally
(Abde lfatta h, Nicosia, C acciola, D roby,& S chena, 2015; Me rges et
al., 2018). It is therefore crucial to more fully understand the poten
tial limitations of this new approach.
Two limitations that frequently arise in NGS metabarcoding
studiesare the abilitytoquantify the abundances ofdifferenttaxa
(Deiner et al., 2017; Elbrecht & Leese, 2015), and the introduction
of false positives/negatives by PCR amplification, library prepara‐
tion, an d sequencin g (Coissac, R iaz, & Puillan dre, 2012). Here, we
address thesetwopossiblelimitationsofNGSmetabarcodingusing
the group of rust fungi as a model system. We investigate possi‐
ble differencesbetweenNGS met abarcoding and more traditional
cloning approaches in the detection and abundance of rust fungal
species. We also investigate what causes these differences. We use
two primer pairs because our objective in this study was to compare
methods using the best available and most appropriate approaches
foreachmethod.FortheNGSmetabarcodingapproach,weusetwo
fundamentally different sequencing technologies (Illumina MiSeq
andIonTorrentPGM)an dfung alN GSmet abarcodi ngp rimer stode
tect rust fungi from within a larger fungal community. We compare
these results to a cloning approach, targeting the same gene region
but focusing cloning on rust fungi using a rust fungi‐specific primer
pair.
We hypothesize that the three methods (Illumina, Ion Torrent,
and cloning):
1. differ in their detection of rust species (i.e., observed from
expected number of detected rust species)
2. differintheirabilitytoquantif yrelativeabundancesofrustfungal
species.
If one or both of the hypotheses are supported, we would then test
hypotheses for the mechanisms driving differences among methods.
Specifically, we hypothesize that differences among methods are due
to:
1. dif ferences in sequence lengths among methods
2. differences in the most appropriate bioinformatic pipelines for
each method
3. taxonomic biases of the methods
4. differentabilitiesofmethodstodetectrarespecies.
2 | METHODS
2.1 | Study site and sampling
We sampled thirty 20 × 20 m grassland plots. All plots were
based on an 8 × 8 km grid that is used extensively for national
biodiversity monitoring in New Zealand (Allen, Bellingham, &
Wiser, 2003) and positioned following the standard protocol of
Hu rs tan dA ll en(20 07 ).Thepl ot swerese le c te db as edont he ou t
put of the geographic information system and stratified random
sampling (Figure S1). We limited our sampling to grassland plots
located at altitudes <1,000 m. All sampling was carried out under
dry weather conditions between November 2014 and March
2015.
At each plot, samples were collected using a sterilized leaf
puncher within 64min (4min for eachof six teen 5×5m subplots)
to ensure balanced sampling of the whole plot . Every identifiable
plant par t (e.g., healthy leaves, leaves with lesions, bryophytes,
grass stems, lichens, bark, seeds), including healthy as well as dis‐
eased plant material, was sampled to get all variants and to maximize
rust fungal diversity. Since most of these samples represent above‐
ground herbaceous material (mainly leaves), we hereafter refer to
these samples simply as “leaf samples.” The leaf samples were imme
diately pooled by plot, stored in a 50‐ml Falcon tube containing ster‐
ilized DMSO -NaCl solution (20% DMSO, 0. 25M disodium-EDTA,
andNaCltosaturation,pH7.5),sealedwithParafilmM,andkeptat
4°Cuntillaborator yprocessing.
    
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MAKIO LA et AL .
2.2 | DNA extraction
TheDNA extractionfromthepooled leaf samples ofeach plot was
car r i e dou t u sing t h eMa c h e rey - N age l N ucl e o S pin9 6Pl a n tII k i t( r o bot
extraction) following the manufac turer's protocol. We used both
provided lysis buffers separately (cetrimonium bromide [CTAB] lysis
buffer PL1 and a sodium dodecyl sulfate [SDS]‐based lysis buffer PL 2)
toenhanceth eamountofextractedD NA .F ivemicro liter sofproduct
wasquantifiedusingaQubit2.0fluorometer(LifeTechnologies)and
the broad‐range assay kit following the manufacturer's protocol be
foreequallypoolingtheextractsfromthesameplot.
2.3 | Preparation of next‐generation
sequencing libraries
WepreparedNGSlibrariesinaone-stepPCR(ImmolaseMoTASPpro
tocol) to avoid the risk of contamination, following Clarke, Czechowski,
Soubrier, Stevens, and Cooper (2014). We used the fungal primers
fITS7: GTGARTCATCGAATCTTTG (Ihrmark et al., 2012) and ITS4:
TCCTCCGCT TATTGATATGC (White , Bruns, Lee, & Taylor, 1990), ampli
fying the highly variable internal transcribed spacer region 2 (ITS2) with
uni versallinkers equence satthe5'endf orfIT S7:TCGTCGG CAGCG TC
andforITS4:GTCTCGTGGGCTCGG.Illuminaadaptersequenceswith
indexsequencesandcomplementarylinkersequenceswereasfollows:
F: AATGATACGGCGACCACCGAGATCTACAG‐8nt index‐TCGT
CGGCAGCGTC,.
R: CA AGCAGAAGACGGCATACGAGAT‐8nt index‐GTCTCGTG
GGCTCGG . Ion Torrent adapter seq uences with inde x sequences
andbarcodeadaptersequenceswereasfollows:
F: CCATCTC ATCCCTGCGTGTC TCCGACTCAG‐10nt ind ex‐GAT,.
R: CCACTACGCCTCCGCTT TCCTC TCTATGGGC AGTCGGTGAT
The universal fITS7 primer has been noted to exclude cer tain
Ascomycota (Penicillium, Orbiliales) and most Mucorales (Ihrmark
et al., 2012), but was chosen because it is more fungi‐specific com
pared to other universal primers (e.g., fITS9 or gITS7, which match
some plants because they are degenerated at two positions, poten
tially over whelming any fungal signal in leaf substrates). Moreover,
the prim er pair fITS7 and ITS 4 is believed to capt ure most of the
Basidiomycetes, including rust fungi, and its amplicon lengths are well
suited to next-generation sequencing (average of 258.5±27.3bp
forAscomycotaand 309.8±35.6bp forBasidiomycot a)(Bokulich&
Mills, 2013; Ihrmark et al., 2012). Purification and size selection (280–
520 bp) were performed using a PippenPrep s ystem to exclude primer
dimersandhighmolecularweightDNA ,beforepaired-endsequenc
ingthesa mpleswiththeIllum inaMiSeqplatform(250 cyclePE)atthe
Australian Genome Research Facility Ltd, Melbourne, Australia, and
withtheIonTorrentPGMplatform (400bpSE)attheWaikatoDNA
SequencingFacility,UniversityofWaikato,Hamilton,NewZealand.
2.4 | Preparation of clone libraries
The use of a rust fungi‐specific primer was necessary to focus the
cloning procedure on Pucciniales and to get to species resolution.
Weamplifiedanapproximately1,40 0-bptargetregionwiththerust
fungi‐specific forward primer Rust2inv:
GATGAAGA ACACAGTGAA A (Aime, 2006) and reverse primer
LR6:CGCC AGTTCTGCTTACC(Vilgalys&Hester,1990), star ting in
the 5.8S subunit and spanning the highly variable ITS2 region and
the three most divergent domains (D1, D2, D3) of the large subunit
(LSU, 28S).WeperformedPCRsforthetwoDNAextracts ofeach
plot using t he TaKaRa Ex Taq DNA poly merase kit ( 25µl reacti on
volumes , containing 2. 5µl 10XE x Taq buf fer,2µl d NTP mixture
(2.5mM each), 5µl 10µg/ml rabbit serum albumin (RSA), 0.6µl
10µMof each upstream and downstream primer,0.125µl TaKaRa
ExTaq,1µlDNAtemplate,and13.175µlofsterilizeddistilledwater).
PCR conditions consisted of an initial denaturation step of 2 min at
94°C,35cyclesof30sat94°C,1minat57°C ,and1.5minat72°C,
andafinalextensionof7minat72°C,asinitiallydescribedbyAime
(2006)butusingfewercycles.Wepooled1µlofPCRproductorigi
natingfromtheCTABand1µlfromtheSDS-basedlysisbuf ferDNA
extractions per plot, and cloned using the Strataclone PCR cloning
kit (Agilent, Stratagene), following the manufacturer's protocol, with
blue‐white screening of colonies. We conducted a preliminary re
striction fragment leng th polymorphism (RFLP) to determine suf‐
ficient sampling depth. The rarest pattern observed occurred five
times out of 100 colonies within a plot. On that basis, we picked 50
colonies p er plot (1,50 0 overall), resul ting in a 91.47% probab ility
ofdetecting the rarest OTU. Weperformed colonyPCRs withthe
plasmid‐specific primer pair M13–20: GTAAAACGACGGCCAG and
M13RSP: CAGGAA ACAGCTATGACCAT (Wood et al., 2012), using
the TaKaRa Ex Taq DNA po lymerase kit (15µl rea ction volumes,
containing1.5µl 10X Ex Taq buffer,1.2µl dNTP mixture (2.5mM
each), 0.6µl 10µg /ml rabbit serum albumi n (RSA), 0.24µl 10µM
ofeach upstreamanddownstream primer,0.075µlTaKaRaExTaq,
colony DNA template, and 10.15µl of sterilized distilled water).
PCR conditions consisted of an initial denaturation step of 12 min
at94°C,35cyclesof20sat94°C ,10sat55°Cand1.5minat65°C,
and a fina l extension of 10min at 65°C , following the m ethod of
Woodet al. (2012) butdoublingthe annealing time at 65°C . After
a gel visuali zation, seq uencing of colo nyP CR produc ts in the for
ward direction was conducted with the Rust2inv primer at the Bio‐
Protection sequencing facility, Lincoln University, New Zealand.
Reversesequencingwasnotconductedbecausethegeneregionsof
interest (ITS2, D1, D2, D3) lie within the first 750 bp of the forward
sequencingread.
2.5 | Bioinformatics
Wetrimmedlow-qualitybasesattheclonelibrarysequence begin
n i n g s a n de n d s , a n d r e m o v e d p r i m e r an d v e c t o r s e q u e n c e s . W ea l i g n e d
the sequ ences using t he MUSCLE ver sion 3.8. 31a lgorithm (Edg ar,
2004)andtrimmedthebeginning,sotheystartatthesamepointof
thegen eregionasth esequ ences fromIonTor re nta ndI llumi nau sing
thefITS7primer.Identicalsequenceswerede-replicatedandN-pad
dedtothesamelength.N-padding(i.e.,addingNs,whichrepresent
anynucleotide)totheendofeachsequenceuntiltheyhavethesame
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lengths was needed because the clustering algorithm used consid
ers terminal gaps to be absolute differences. However,N-padding
only was necessaryfortwo shortclone sequences. Not N-padding
ofthesetwosequenceswouldhaveresultedintwoadditionalOTUs
but would not have changed the overall results. We clustered the se
quencestoa97%similaritythresholdwithoutusingsingletonsusing
UPARSE algorithm(Edgar,2010).This threshold represents theITS
barcode gap for the over whelming majority of fungal species, includ
ing the subdivision Pucciniomycotina (Schoch et al., 2012).
The forward and reverse Illumina reads were merged using
the fastq_mergepairs command of USE ARCH version 9.0.2132,
andsequenceswithmore than one expected errorandlessthan
175bpwereremoved.IonTorrentsequenceswereonlyusedifthe
forward and the reverse primer complement could be found within
thesequenceandifthesequencewasatleast175bplong.Wedis
ca rd edIonTo rrents equ en ce sw it hm orethantw oe xp ect ed er rors
(EE). We set a higher EE threshold because the mean expected
errorrateoftheIonTorrentrunsatthesequencelengthof300bp
was two. We tri mmed non-biol ogical (prim er) sequences , allow
ing 10% bp misma tch using the Py thon tool “cutadap t” version
1.13 (Martin, 2011) if the forward primer or the reverse primer
complementcould be found at the sequenceends.Identicalse
quences were de-replicated. Illumina and Ion Torrentdata were
independentlyclusteredto97%similaritythresholdwithoutusing
singletons,usingthe UPARSEgreedyclusteringalgorithm(Edgar,
2013).
We constructed a reference database from UNITE and INSD
(accessed20.11.2016)andmatchedtherepresentativesequenceof
eachOTUtothisdatabase using BLASTversion 2.5.0+(Altschulet
al.,1997).WeconsideredanOTUtorepresenttheorderPucciniales
if it matched Pucciniales sequences in the database >80% iden
tity over atleast 150bp (Nguyenetal.,2016;Schochetal.,2012).
Extraction blanks, and positive and negative controls, were checked
for contamination. Tag jumping (false combinations of tags and
samples,which cause incorrectassignmentofsequences) (Schnell,
Bohmann, & Gilber t, 2015) was accounted for by using a regres‐
sion of the abundance of contaminants versus the maximum of
total abundances in all other samples. The coefficient estimate for
the 90thquantileregression was thenused to subtractthat many
sequencesfromallOTUs. Hence,thistag-jumping correction takes
into account thefact thatmore abundant OTUs are morelikely to
dotagjumping.WeblastedOTUsobtainedfromthethreedifferent
methods against each other and considered them to be the same
OTUiftheymatchedat>98.5%similarity,whichcorrespondstoap
proximately3%clusteringoftheNGSdatausingthedistance-based
greedyclu steringUPARS Eal gor ithm(Edga r,2013),butallowsdiffer
entsequencelengthsas opposed to matchingwithUSEARCHver
sion 9.0.2132 (Altschul et al., 1997; Edgar, 2010, 2013).
2.6 | Statistical analyses
WeusedRversion3.4.1(R CoreTeam, 2017)for conduc tinganaly
ses and creating graphs if not stated otherwise. To test whether a
method d etected more or f ewer shared/unique r ust fungal OTUs
than expected by chance, we used the “permatswap” function of the
R package “vegan” version 2.0–7 (Oksanen et al., 2017) to create a
null expectation. The simulated community matrices are based on
MonteCarloiterations,wherebythetot alnumberofOTUsperplot
andtotalabundancewithinOTUwerekeptconstant.Wetestedfor
differencesinOTUabundancesamongmethodsusingageneralized
additive model (GAM) of the package “mgcv” version 1.8–18 (Wood,
2001). A GAM was selected because: (a) it allows beta distribution
for the response variable, which in this case was the appropriate dis‐
tributionfortheproportionalabundanceofeachOTUfoundwithin
aplot(toaccount for different sequencing depths);and(b)theap
proach allowstestingforOTUand plot as random effects,and in
teractionbetweenmethodandOTU.Datawererescaledtoexclude
zeros and ones, as suggested by Smithson and Verkuilen (20 06).
Wald test was used to test the significance of each parametric and
smooth term (Wood, 2012). To see whether perceived rust fungi
communities differ among methods, we converted the obtained
communit y data into Jaccard distance matrices using Wisconsin
doublestandardization.FourplotswithzeroOTUs,aswellasunique
communities, had to be discarded because of a dissimilarity of one.
We displayed the dissimilarities with nonlinear multidimensional
scaling and tested for significance between the configurations
using Procrustes rotation and the “protest” function part of the
“vegan” package, and the “mantel.test” function of the “ape” pack‐
age(Paradis,Claude, &Strimmer,2004).We testedwhether abias
amongmethodswascausedbydifferentsequencelengthsorbioin
formaticpipelines,applyingthesamesequencelength(248bp)and/
or an identical bioinformatic pipeline to all methods. To look for a
taxonomic bias in detecting the different methods, we constructed
aneighbor-netphylogeny(Bryant&Moulton,2004)usingSplitstree
4.0(Huson, Kloepper, &Bryant,2008)andusedchi-squaretestto
test whether taxonomic clusters are independent of methods. We
tested whether a possible difference is due to the detection of rare
anddominantOTUsby rerunning all tests using thetopandlower
50%oftherank abundanceofeachmethod.Species identities are
based on the best BL AST match and were displayed as networks
using the “ig raph” package ve rsion 1.0.1 (Csard i & Nepusz, 20 06)
with edge width representing relative species abundance within
method.
3 | RESULTS
3.1 | Differences among methods in detection of
OTUs
There weresevenrust fungalOTUs shared amongthethreemeth
ods, which was much less than would be expec ted by random sam‐
pling (17.05±0.33). The dif ference was driven b y OTUs uniquely
detectedby singlemethods (Figure1),thatis,Illumina (one unique
OTU) and Ion Torrent (t wo unique OTUs), and cl oning (10 unique
OTUs). The thre e methods (i.e ., cloning, Il lumina, and I on Torrent)
hencedifferedindetectionofrustfungalOTUs.
    
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3.2 | No differences among methods in relative
abundances of shared OTUs and in perceived
community composition
Therewasnoevidenceofdifferencesinquantificationofrelative
abundances among the three methods (i.e., cloning, Illumina, and
Ion Torrent) amo ng OTUs that all met hods were c apable of de
tecting. A likelihood ratio test between models with and without
aninteract ionterm(method×OTU)wasnotsignificant(χ2 = 7.6 2 ,
df = 12, p = 0.81). In general, rust communities perceived by the
three methods did not result in largely different community pat
terns, as visualized by theoverlap of thecommunities in NMDS
(Figure 2). Mantel test and Procrustes analysis confirmed simi
larity (p < 0.05) for Ion Torrent/cloning (abundance data), and
Ion Torrent/cloning and Illumina/Ion Torrent (presence/absence
data).
3.3 | Mechanisms driving OTU detection
differences among methods
Differences in detection among methods seemed not to be due
to sequen ce length dif ferences a mong method s. After tri mming
all sequences to thesame length (248bp), which is the shortest
common sequence of all methods, and rerunning the analysis,
the number of observed (seven) compared to randomly expected
(17) shared rus t OTUs stayed unchan ged. Differe nces in detec
tion among methods also seemed not to be due to differences in
the most appropriate bioinformatic pipelines for each method.
Usinganidenticalbioinformaticpipelineforallmethodsmadedif
ferenceseven moreextreme,withonlyfourOTUs shared among
methods, compared to seven (with the most appropriate pipelines)
or 17 (expected). Differences in detection among methods were
dueto ataxonomicbiasof themethods.Neighbor-netphylogeny
FIGURE 1 (a) Observed and (b)
expected number of rust fungal
operationaltaxonomicunits(OTUs)per
method.OTUswereconsideredtobe
identicalamongmethodswhen>98.5%
BLAST similarity. Expectations were
based on Monte Carlo random sampling
(100iterations)anddisplayedwith95%
confidence intervals
Observed OTUs Expected OTUs
(a) (b)
FIGURE 2 Multidimensional scaling of rust communities (using abundance and presence/absence data) as perceived by three different
methods:Illumina(green,squares),IonTorrent(blue,circles),cloning(orange,triangles).Fourplotsweredroppedbecauseoflackofany
detected rust communities in these plots
NMDS (Abundances) NMDS (Presence/Absence)
(a) (b)
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(Figure 3) indicates three taxonomic clusters. Cluster 1 could
equally bedetectedbyall methods;cluster2 wasonly detected
using Illumina; cluster 3 was only detected using cloning. The chi‐
square test for independencewassignificant( χ2 = 17.536, df=4,
p<0.01)andconfirmedthatclusters werenotequallyformedby
the different methods.
Speciesidentitiesofcluster3(i.e.,uniquelydetectedbycloning)
andcluster 2(i.e., uniquelydetectedby Illumina)weredisplayedin
a co-occurrence network (Figure 4). While Illumina'suniquely de
tected species is from the genus Kuehneola,uniquelydetectedspe
cies from cloning and Ion Torrent are from the genus Puccinia. The
taxonomic bias seemed not to be driven by poor detection of rare
FIGURE 3 Neighbor-netphylogenyof
rust fungal operational taxonomic units
(OTUs)detectedbythedif ferentmethods:
Illumina(squares),IonTorrent(circles),
cloning (triangles)
FIGURE 4 Networkrepresentingsharedanduniquerustfungaloperationaltaxonomicunits(OTUs)amongmethods.Edgewidth
representsproportionalabundanceofanOTUwithinmethod.SpeciesidentitiesarebasedontheirbestBL ASTmatch.OTUsfoundineach
methodareconsideredtobeidenticalwhenshowing>98.5%sequencesimilarit y
    
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OTUs. The sa me clusters occ ur when only consid ering the uppe r
50% of rank a bundance, h ereafter c alled domin ant OTUs (Figures
S2andS3),andwhenonlyconsideringthelower50%ofrankabun
dance, hereafter rare OTUs (Figures S4 and S5). The number of
observedshareddominant(six)andrare(two)OTUsstilldifferssig
nificantlyfromrandomly expected(11.08±0.36OTUs)sharedrust
OTUs.Thisdifferenceinobservedfromexpectedisstillmainlydue
totheuniquelydetectedOTUsfromcloning (cluster2ofFigureS2
andcluster3ofFigureS 4).
Differences in detection among methods seemed to be caused
by base pair mismatches of the NGS metabarcoding primer pair.
Table 1 shows selec ted species that were detected by cloning but
notbyNGSmetabarcodingandhadatleastonebasepairmismatch
totheNGSmetabarcodingprimers.
4 | DISCUSSION
This stu dy demonstrat es that NGS metab arcoding is an eff ective
techniquefor large-scale detection ofrustfungus plant pathogens,
but that taxonomic biases due to primer selection are a potential
limitation. To the best of our knowledge, this is the first study with
areal-worldapplicationandcomparisonofcloningandNGSmeta
barcoding to survey Pucciniales. We found dif ferences in the detec‐
tion of rust fungus species among Illumina and Ion Torrent platforms,
andcloningfollowedbySangersequencing.However,wefoundno
significant difference in the relative abundances of the rust fungus
species that all methods were capable of detecting. The mechanism
driving detection differences among methods seemed to be due to
a taxonomic bias, which was very likely caused by base pair mis‐
matches of th e NGS metabarc oding primer pai r to some Puccinia
species. Otherwise, the consistency among fundamentally different
andindependentmolecularmethodsshowsthatNGSmetabarcoding
and cloning are on a par. Altogether, the results support the applica‐
tion of NGS m etabarcodin g for the large-sc ale detectio n of plant
pathogens (presences) and contradict it s application for inferring ab‐
sence of species, depending on the primer pairs. These findings are
important to future metabarcoding studies because they highlight
the main source of difference among methods and rule out several
mechanisms that could drive differences.
Themaindifferencebetweenthemethods(NGSmetabarcod
ing and cloning) was due to their biases in species detection, not
quantification.This suggests thatprevious problems whenusing
quantitativenext-generationsequencing data (Elbrecht& Leese,
2015; Piñol, Mir, Gomez‐Polo, & Agustí, 2015) were probably
induced by PCR, and not by the methodorsequencing platform
per se. Furthermore, this is in line with the finding that the dif
ference in detection between NGS metabarcoding and cloning
shows a ta xonomic bias. Both t he NGS metabarc oding and the
cloning primers have either a perfect match or only a maximum of
two base pair mismatches to all detected rust fungi in this study.
Moreover,theNGSmet abarcodingprimers were thoughttocap
ture most of the Basidiomycetes (Ihrmark et al., 2012; White et al.,
1990),inclu di ngrustfungi .Consequent ly,theNG Sm et ab arcodin g
and the cloning primers would be expected to detec t a similar as
semblageofrustfungi.However,thebasepairmismatchesofthe
NG Smet abarcodingpri me ro cc ur insp ec ie st hata re on lydetec te d
by cloning, and the cloning primer had no mismatches in these spe
cies. The lower specificityof the “universal”NGSmetabarcoding
primers is therefore more likely to discriminate against the ampli
ficationofthosespecieswhenexposedto 100%matching other
fungalsequencetemplates(Bellemainetal.,2010).Loweringthe
annealing temperatures might help remedy these mismatch biases
for this group in the future, par ticularly as none are very close
to the 3' end of primers (Table 1). Although taxonomically clus
tered, the Pucciniaspe ciesw it hthebas epairmi smatc hofth eNGS
Species
5’‐fITS7 (forward primer)
GTGARTCATCGA ATCTTTG
3’‐ITS4 (rever se primer)
GCATATCAATAAGCGGAGGA
Puccinia calcitrapaea
GTGA ATCATTGA ATCTTTG GCATATCA ATAAG CAGAGGA
Puccinia nishidanab
....A T C A T TGAATCTTTG G CATATCA ATAAGC AGAGGA
Puccinia balsamorrhizaec
......C A T TGA ATCTTTG GCATATC AATA AGCAGAGGA
Puccinia komaroviid
GTGA ATCATTGA ATCTTTG GCATATCA ATAAG CAGAGGA
Puccinia hieraciie
......C A T C G A A T C T T T G GCATATCA ATAAG CAGAGGA
Note. Mismatches are highlighted (bold and underlined).
Sequenceswereselected fromtheNational CenterforBiotechnology Information(NCBI) tocover
the gene region of cloning and metabarcoding primers when possible.
Dot indic ates no entry of base pair in the database.
Accession numbers are given as footnotes. Accession numbers:
aJN2 04183 .1 bHM 02 2141.1cJ N204182.1dKC466553.1eHQ 317515.1
TABLE 1 Metabarcoding primer
mismatches to selected species that were
detected by cloning but not by
metabarcoding
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metabarcoding primer seemed not to fall into a known taxonomic
cluster,likeasubgenus(VanderMerwe,Ericson,Walker,Thrall,&
Burdon, 2007).
Numerous NGS metabarcoding studie s have pointed out that
NGS metabarcoding primers can discriminate against certain ta xa
(Bellemainet al.,2010; Clarke et al., 2014;Elbrecht&Leese,2015;
Schmidt e t al., 2013). Some stud ies have tried to limit t his bias to some
extent by us ing quantit ative PCR and cor rection f actors ( Thomas,
Deagle, Eveson,Harsch, &Trites,2016),primermixes( Tedersoo et
al.,2015),orblockingoligonucleotidestonon-targetDNA(Piñolet
al., 2015). Ficetola et al. (2010) proposed an “electronic PCR” ap‐
plication to measure barcode coverage and specificit y. This in silico
approach has proven useful to identify the appropriate barcode gene
regions and when comparing different primers for fungi (Bellemain
etal.,2010)andvertebrates(Valentinietal.,2016).Theresultsfrom
this study and from the literature, taken together, highlight the im‐
portance of primer choice for NGS metabarcoding studies. NGS
metabarcoding studies should therefore carefully examine in silico
what taxa their primers might discriminate against in order to select
appropriateNGSmetabarcodingmarkersandaidtheinterpretation
of results.
This study also ruled out several mechanisms that could possibly
drivedetectiondifferencesbetweenNGSmet abarcodingandclon
ing.We found noevidence thatsequencelength, most appropriate
bioinformatic pipeline, or ability to detect rare species caused any
differencesamongmethods.Wefoundthatshorteningallsequences
tothelengthoftheshortest sequence(248bp)didnot change the
interpretation of the overall results and resulting phylogeny. Min and
Hickey(2007)andHanetal.(2013)showedthatreducingsequence
lengthcan haveeffectsonthe accuracyof phylogenieswhenDNA
barcoding fungi. They also showed that despite some loss of phylo‐
geneticsignal,shortersequencescanstillresolvetheterminalnodes
ofthephylogeny quite efficiently in most cases.Currentnex t-gen
eration sequencing technologies still require the amplification of
short sequences,andsomebarcoderegions(e.g.,the ITSregionfor
fungi) can lack the necessar y resolution for particular fungal taxa
(Gazis, Rehner & Chaverri, 2011). Despite these challenges, short
sequencesprovideenoughresolutionatagenusandof tenawithin-
genus level for the majority of fungi (Blaalid et al., 2013). While short
sequenceshavebeenrepeatedlyshowntobesufficientforgenus-
or even species‐level identifications (Blaalid et al., 2013; Bokulich &
Mills,2013),futurenext-generationsequencingtechnologiesshould
be able to overcome the current leng th limitations and provide the
field ofNGS metabarcodingwithevenbetter speciesdelimitations
(Goodwin, McPherson, & McCombie, 2016).
Bioinformatic pipelines can have profound effects on the outcome
of NGS metabarcoding studies (Flynn, Brown, Chain, MacIsaac, &
Cristes cu, 2015). In this study, the error rate s trongly differ ed between
Illumina,IonTorrent,andSanger sequencing runs. Using an identical
bioinformaticpipeline,suchasidenticalqualityfilteringandclustering,
resultedinamu chl owe rnumberofsharedOTUsamo ngt hem ethod s.
These results justify using the most appropriate bioinformatic pipeline
for each method. Moreover, we did not find any effec t of rare species
on detection ability among methods. The same t axonomic bias among
the methods occurred when only looking at the dominant or only look
ingattherareOTUs.RareOTUsinNGSmetabarcodingdataaregen
erally more prone to errors due to the accumulation of errors (Dickie,
2010), tag jumping (Schnell et al., 2015), chimera formation (Edgar,
Haas, Clemente,Quince, & Knight,2011),or falsepositive/negatives
(Ficetola etal., 2010). However, previous studies have shown thatif
these problems associated with rareOTUsareovercome,theability
ofNGS metabarcodingtodetect rarespeciesisequal to or exceeds
non-molecularmethods(Valentinietal.,2016;Zhanetal.,2013).
Next-generationsequencingmetabarcodingseems appropriate
for the large‐scale detection of rust fungi and less appropriate for
inferring absence of species. For example, the species Puccinia sorghi
wasinitiallypresent intheraw data ofall threemethods.However,
onlytwosequencesofthisspecieswerepresentintheIlluminaraw
data. Th ese two seq uences exh ibited a point m utation or a p ossi
blesequencingerrorintheirreversesequencereadandgottreated
as unique sequences (singletons) after merging. Hence, although
initially present in the Illumina raw data, these two sequences
could not fo rm an OTU. This phen omenon of speci es getting lo st
duringmergingofpaired-endsequencinghasbeennotedearlierby
Nguyen,Smith,Peay,andKennedy(2015)andwasgenerallycaused
by the usual ly poorer qualit y of reverse sequen cing reads of the
Illumina MiSeq platform. The problem of missing extremely rare
species, however, is not method specific, as the case of Kuehneola
uredinisdemonstrates.Thisrarespecieshadatotalof47sequences
intheIlluminadataandwasinitiallypresentasasinglesequencein
the raw data of the clone libraries. Because singletons got discarded
regardless of the method, Kuehneola uredinis got discarded from the
clone data. The fact that the cloning primer pair had a perfect match
to Kuehneola uredinis and that this species got picked up once clearly
shows that the detection of rare species does not rely on the applied
method butratheronsequencingdepthand bioinformaticassump
tions. Picking a greater number of clones would probably have re‐
sultedinatleastanothersequenceofKuehneola uredines, and hence
detection of this species. Despite failing to detect two rare species
by some methods, other rare species, such as Uromyces dactylidis
and Puccinia hordei, could be detected regardless of the method.
Another way of easily missing species when merging paired‐
endsequencing readsistolose“toolong”sequences,sincethese
would not overlap. This can be simply tested by not merging the
reads and using forward and reverse reads separately. In this
study, we found no rust fungus species getting lost during merging
asares ultof“toolong”s eq uence s. Theac tualIllum inase quencin g
process, however, is known for discriminating against longer ampl
icons (Allen et al., 2016). Although less likely than, for instance, a
primer mismatch, the Puccinia species that could not be detected
byNGS metabarcoding butcould by cloningcould possiblyhave
beenmissedduringthenext-generationsequencingprocessdue
to slightly longer amplicons. We did not compare abundance data
to a field survey or biomass, but found no significant difference in
relativeabundancesofOTUson plotlevelamongNGSmetabar
codingandcloning.Thissuggeststhatanybiasesinquantification
    
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 9 of 11
MAKIO LA et AL .
using molecular techniques are not method dependent. Despite
issues arising from PCR (yet common for most molecular methods)
suchasthedif ferencein rRNA copy numbers,several studiesdo
show NGS me tabarcoding t o be successful f or semiquantit ative
abundance estimation of, for example, feather mite communi
ties in birds (Diaz‐Real, Serrano, Piriz, & Jovani, 2015), fish and
amphibians in freshwater ecosystems (Evans et al., 2016), plant–
pollinator interactions (Pornon et al., 2016), the biomass of mac
roinvertebrates (Elbrecht & Leese, 2015), and fungi (Taylor et al.,
2016). These studies suggest that if obstacles associated with PCR
biasescanbeovercome,NGSmetabarcodingholdspromisingpo
tentialnotonlyforthedetectionbutalsoforthequantificationof
species.Moreover,PCR-freetechniquesmayremedyprimerand
amplification biases (including chimera formation) in the near fu
ture. Dif ferent gene copy numbers still pose a significant challenge
for biomass estimates but could be overcome with the growing
number of whole genome databases.
Next-generation sequencing metabarcoding has been increas
ingly recognized as a promising tool for biomonitoring species and
complex communities atlarge scales(Holdaway etal.,2017).Inre
cent cases, it has been applied to plant pathogenic fungi (Merges et
al., 2018) and oomycetes (Burgess et al., 2017). It is impor tant to un
derstandtheadvantages anddisadvantagesofusingNGSmetabar
coding for detecting and monitoring important functional groups
at the ecosystems scale. Our study suggests that rust fungi can be
tracked from within a larger NGS metabarcoding dataset, which
should facilitate the future monitoring of this critically import ant
group of fungi.
ACKNOWLEDGEMENTS
We thank landowners and the Department of Conservation for allow
ing access and sampling, and Manaaki Whenua – Landcare Research
staff, especially Larry Burrows and Karen Boot, for their extensive
support in the field and laboratory. We acknowledge support pro‐
vided by John Longmore of theWaikatoDNA Sequencing Facility.
ThisresearchwasfundedbytheNewZealandMinistryofBusiness,
Innovation and Employment (MBIE Contrac t No. C09X1411) via
Manaaki Whenua – Landcare Research, and by Centre of Research
Excellence funding to the Bio‐Protection Research Centre.
CONFLICT OF INTEREST
The authors declare that the research was conducted in the ab
sence of any commercial or financial relationships that could be
construed as a potential conflic t of interest.
AUTHORS CONTRIBUTION
The samp les used in t his paper were p art of a stu dy led by RH,
IAD,JRW,and KHO. JRW,IAD,RH,andKHOconceived primary
funding, with additional funding from the Bio‐Protection Research
Centre, led by TRG. IAD and TRG advised AM. AM collected the
sampleswithhelpfromRHandothers.AM,CKL,andJRWdevel
oped the methods, and AM and CKL carried out molecular char
acterization. AM and IAD conducted data analysis. AM produced
the figures and wrote the first draft . All other authors provided
editorial input.
ETHICS STATEMENT
This article does not contain any studies with human participants
or animals performed by any of the authors.
DATA ACCESSIBILITY
The data associated with the paper are available from the Manaaki
Whenuadatarepositoryatht tps://doi.org/10.25898/KK41-CY40.
ORCID
Andreas Makiola https://orcid.org/0000‐0002‐9611‐9238
Ian A. Dickie https://orcid.org/0000-0002-2740-2128
Jamie R. Wood https://orcid.org/0000‐0001‐8008‐6083
Travis R. Glare https://orcid.org/0000‐00017795‐8709
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SUPPORTING INFORMATION
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Suppor ting Information section at the end of the article.
How to cite this article:MakiolaA ,DickieIA,HoldawayRJ,
et al. Biases in the metabarcoding of plant pathogens using
rust fungi as a model system. MicrobiologyOpen.
2019;8:e78 0. https://doi.org/10.1002/mbo3.780
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... The accuracy of these identifications was confirmed through comprehensive multiple sequence alignments and phylogenetic tree reconstruction ( Figure S2). By contrast, Makiola et al. (2019) used the fITS7/ITS4 primer pair and identified a total of 15 rust fungus ...
... However, it is difficult to directly compare the rust fungus diversity reported in Makiola et al. (2019) with our study due to variations in sampled substrates and primer choices between the two studies. ...
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... Other databases for the barcoding of pest and pathogens are listed in Table 16.1. (BOLD) Phytopathogens and pests are a major threat to global food security, conservation and resilience/sustainability of ecosystems; hence their extensive cost-efficient biomonitoring before disease outbreak is important (Choudhary et al. 2021;Makiola et al. 2019;Bebber and Gurr 2015;Bever et al. 2015). Sustainable food production requires accurate/precise/confident and on time monitoring, identification and species discrimination of various pests and pathogens of crop plants to establish appropriate disease/pest management strategies to limit economic losses. ...
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... Therefore, implementing harmonized harvesting practices, DNA barcoding validation, chemical profiling, adulteration detection tests, thirdparty certification, traceability systems, storage and packaging standards, and regular audits and inspections can collectively establish robust quality control measures for Grubs-based herbal products in the industry. One notable limitation of metabarcoding, as observed in our study, lies in the challenges associated with quantifying species abundance due to primer and sequencing preferences (Allan et al., 2021;Makiola et al., 2019;Thomas et al., 2016). This hinders the ability of this method to provide clear insights into the extent of contamination in terms of quantity (Bagley et al., 2019;Lamb et al., 2019). ...
... These issues can have important impacts on the final result of the analyses, e.g., when the relationship between read counts and abundances/ biomass is not straightforward, which is often the case Piñol et al., 2019;Takahara et al., 2012;Thomas et al., 2016). Third, the bioinformatic stage, from raw sequences to well-defined OTU/ASV or assigned taxa, also presents technical difficulties and results from this stage may depend on arbitrary choices (reads filtering, clustering methods, blast methods, reference database, etc. -Deiner et al., 2015, Knight et al., 2018, Porter and Hajibabaei, 2018, Bush et al., 2019, Makiola et al., 2019, Pauvert et al., 2019, Zinger et al., 2019. Fourth, with the contingency table in hand comes the reconstruction of networks. ...
... These issues can have important impacts on the final result of the analyses, e.g., when the relationship between read counts and abundances/ biomass is not straightforward, which is often the case Piñol et al., 2019;Takahara et al., 2012;Thomas et al., 2016). Third, the bioinformatic stage, from raw sequences to well-defined OTU/ASV or assigned taxa, also presents technical difficulties and results from this stage may depend on arbitrary choices (reads filtering, clustering methods, blast methods, reference database, etc. -Deiner et al., 2015, Knight et al., 2018, Porter and Hajibabaei, 2018, Bush et al., 2019, Makiola et al., 2019, Pauvert et al., 2019, Zinger et al., 2019. Fourth, with the contingency table in hand comes the reconstruction of networks. ...
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This book has 15 chapters focusing on the resistance of wheat to major diseases. Information on the various major wheat diseases and their economic importance are also presented.
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