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17
Empirical Testing of 16S PCR Primer
Pairs Reveals Variance in Target
Specificity and Efficacy not Suggested
by in Silico Analysis
Sergio E. Morales and William E. Holben
17.1 INTRODUCTION
Q1
Molecular (i.e., culture-independent) methods, particularly
those based on the 16S rRNA gene, have been a cornerstone in modern microbial ecology [Tringe and Hugenholtz, 2008]. However, most of these approaches have
depended on PCR, which can be affected by a number of
underlying biases [Wintzingerode et al., 1997; Polz and
Cavanaugh, 1998; Crosby and Criddle, 2003; Ashelford
et al., 2005, 2006; Sipos et al., 2007]. These considerations have been studied and mitigating meaures developed
to allow comparative, but likely not comprehensive, analyses of microbial community and population diversity to
be performed [Sipos et al., 2007]. A review of the pitffalls
of PCR-mediated 16S rRNA gene analysis is presented in
these volumes by Stackebrandt (Chapter 18, Vol. I). An
additional consideration with clear implications in quantitative studies and comprehensive community surveys that
has not been directly addressed is that of primer specificity. Methods have been developed to test specificity in
silico using freely available software such as PRIMROSE
[Ashelford et al., 2002; Cole et al., 2005], but knowledge of how well these simulations correlate to actual
PCR reactions is limited [Manz et al., 1996; Meier et al.,
1999; Overmann et al., 1999; Buckley and Schmidt, 2001;
Reilly et al., 2002; Stach et al., 2003; Fierer et al., 2005;
Bathe and Hausner, 2006]. This review provides a look
into the incongruence seen between computer simulations
and empirical testing of primer sets and the repercussions
that these discrepancies likely have in quantitative analyses of microbial populations.
17.1.1 Materials and Methods
For a full outline of the materials and methods employed,
please refer to the original publication [Morales and Holben, 2009]. What follows is an abbreviated version.
17.1.1.1 Primer Design and In Silico Testing.
Primers targeting specific taxonomic groups were
designed based on a 16S rRNA gene sequence library
(total 4889 sequences) from soil at the Kellogg Biological
Station Long-Term Ecological Research site (KBS-LTER)
[Morales et al., 2009] (GenBank accession no. EU352912
to EU357802). Primer design was conducted using the
PRIMROSE software [Ashelford et al., 2002] for several
major bacterial groups identified in the KBS-specific
library based on taxonomic assignments established by
ARB [Ludwig et al., 2004] alignments (Table 17.1)
[Morales et al., 2009]. In silico testing was carried out
using PRIMROSE and either the KBS sequence library
or an ARB-generated library with over 50,000 16S
rRNA gene sequences, including archaeal and eukaryotic
sequences not specific to the KBS site. Each primer set
was tested against all target and nontarget sequences in
the KBS library using PRIMROSE.
Handbook of Molecular Microbial Ecology, Volume I: Metagenomics and Complementary Approaches, First Edition. Edited by Frans J. de Bruijn.
 2011 Wiley-Blackwell. Published 2011 by John Wiley & Sons, Inc.
135
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Chapter 17 Empirical Testing of 16S PCR Primer Pairs Reveals Variance in Target Specificity
Table 17.1 Primer Targets and Sequence Representation
within Libraries
Number of Sequencesa
Taxonomic Level
KBS
ARB
Phylum
Acidobacteria
Actinobacteria
Bacteroidetes
CD OD1
CD OP10
Chlorobi
Chloroflexi
Gemmatimonadetes
Nitrospira
Planctomycetes
Proteobacteria
Thermomicrobia
Verrucomicrobia
955
491
453
68
43
22
27
251
59
243
1,690
323
103
516
5,360
2,683
73
55
142
66
111
169
699
20,166
361
159
374
485
348
478
5,065
4,527
1,217
8,479
Class
Alphaproteobacteria
Betaproteobacteria
Deltaproteobacteria
Gammaproteobacteria
OTUb
Genus Aeromonas
Acidobacteria grp4
Genus Lysobacter
Thermomicrobia#4
Nitrosomonadales
Acidobacteria grp6
Thermomicrobia#7
Genus Bradyrhozobium
Genus Pseudomonas
Genus Comamonas
99
81
81
63
62
61
46
39
38
38
a
Total number of sequences found in the database.
Closest classification as determined based on ≥ 97% sequence similarity at
the 16S rRNA gene.
b
17.1.1.2 Primer Optimization and In Vitro
Testing. Genomic DNA preparations from Bradyrhizobium japonicum USDA 110d, Streptomyces griseus,
Acidovorax facilis, Pseudomonas putida, Acidobacterium
capsulatum, and Pedobacter heparinus extracted using
the method of Doi [1983] were initially used to assess
and optimize amplification from each respective set of
phylum- and class-level primers (Tables 17.2 and 17.3).
Optimization consisted of (a) temperature gradient
(±10◦ C) PCRs using primer 907r as the reverse primer
in the pair and (b) the predicted melting temperature
(Tm ) for the specific primer as the central point in the
temperature gradient. Complete reaction conditions are
described fully in Morales and Holben [2009].
In order to generate positive controls for phylogenetic groups with no cultured representative available,
plasmid DNA encoding the desired target sequence from
our library was amplified using the primer pair 536f and
907r as outlined elsewhere [Morales et al., 2009]. Since all
specific primers generated in this study are internal to this
16S region, it can be used as template to generate positive controls. Comparable amplification rates were ensured
between cloned inserts and isolated genomic DNA by
comparing amplification efficiencies. PCR products were
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17.1 Introduction
Table 17.2 Phylum- and Class-Level Primer Sequences and Target Specificities as Tested by PRIMROSE
Name
Q3
Target
688-706fAB
Acidobacteria
691-709fAP
Alphaproteobacteria
Actinobacteria
697-715fAT
715-733fBP
685-703BT
Betaproteobacteria
Bacteroidetes
554-572fCH
Chlorobi
687-705fCX
Chloroflexi
542-560fDP
706-724fNP
Deltaproteobacteria
Gammaproteobacteria
Gemmatimonadetes
Nitrospira
785-802fOD1
OD1
680-698fGP
677-695fGT
889-907fOP10 OP10
681-699fPM
767-785fProt
555-573fTM
562-580fVM
853-871fWS3
Planctomycetes
Proteobacteria
Thermomicrobia
Verrucomicrobia
WS3
Sequence
GCGGTGAAATGCGTASAT
GTGAAATDCGTAGAKATT
TGCGCAGAKATCRGGARG
AAYACCRATGGCGAAGGC
GTAGCGGTGAAATGCWTA
TCCGGAWTYACTGGGTRT
AGTGGTGAAATGCGTWGA
GTGCNARCGTTGYTCGGA
CMKGTGTAGCRGTGAAAT
TTCCSGGTGTAGCGGTGG
ATCGGGAGGAASRCCKGT
GGATTAGATACYCYWGT
ASTACGGCCGCAAGGTTG
NRGTGRAGCGGTGAAATG
AAGCGTGGGGAGCAAACA
CCGGAKTYAYTGGGCGTA
YAYTGGGCGTAAAGGGWG
GTGCCGCAGCYAASSCAT
Degenerate
Bases
KBS
a
Target
ARB Database
b
Nontarget Target
Prokaryotes
Archaea
Eucarya
1
95.29
11.42
83.10
19.40
N.D.
N.D.
2
95.19
3.76
87.20
8.70
N.D.
N.D.
3
94.30
0.09
91.00
0.10
N.D.
N.D.
2
96.70
1.83
92.60
7.60
N.D.
N.D.
1
96.25
0.02
90.50
0.20
N.D.
N.D.
3
95.45
0.08
93.00
3.00
N.D.
N.D.
1
96.30
4.19
45.50
0.40
N.D.
N.D.
3
86.21
2.59
55.90
0.60
N.D.
N.D.
3
93.31
16.85
78.20
10.80
N.D.
N.D.
1
97.21
N.D.
73.90
0.40
N.D.
N.D.
3
93.22
3.94
43.80
0.70
N.D.
N.D.
3
85.29
0.70
63.00
4.10
6.60
N.D.
1
83.72
6.22
78.20
5.60
0.10
N.D.
3
94.24
0.62
83.80
0.60
N.D.
N.D.
0
89.08
7.55
73.30
23.70
N.D.
N.D.
3
91.02
8.30
74.50
8.20
N.D.
N.D.
3
92.23
4.38
80.50
7.50
N.D.
N.D.
3
94.12
5.42
67.90
5.50
N.D.
N.D.
c
a Target
sequences are those belonging to the phylogenetic group intended for amplification.
Nontarget sequences are sequences not belonging to the phylogenetic group intended for amplification.
c
N.D., not detected.
b
Q4
thereafter used for positive controls for all amplification
reactions. Minimal DNA concentrations (determined using
10-fold serial dilutions down to 1 pg of control DNA)
and optimal empirically determined annealing temperature from prior temperature gradient analyses were used in
all reactions. Each optimized primer set was subjected to
specificity screening by PCR using negative control DNAs
(nontarget sequences) as template as described [Morales
et al., 2009].
17.1.1.3 Real-Time PCR Assays. Quantitative
real-time PCR (qPCR) was performed on an iCycler iQ
thermocycler (Bio-Rad, Hercules, CA) using conditions
described in Morales and Holben [2009]. Two independent rounds of triplicate reactions were performed
for each target, and the results of at least three qPCRs
were analyzed. Abundances for all replicate reactions
were related to a standard curve by their respective
fluorescence intensity values, giving values of relative
concentration.
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Chapter 17 Empirical Testing of 16S PCR Primer Pairs Reveals Variance in Target Specificity
Table 17.3 Primer Sequences and Target Specificities to the KBS Library as Indicated by PRIMROSE for the Top 10
Most Abundant OTUs (Based on 97% Sequence Similarity)
Name
Coma851-869f
Pseudo573-591f
Aero851-869f
Acido(#4)599-617f
Lyso726-744f
Thermo(#4)735-753f
Nitro813-831f
Acido(#6)654-672f
Thermo(#7)658-676f
Brady850-868f
a
b
Target
Sequence
Degenerate Bases
Genus Comamonas
Genus Pseudomonas
Genus Aeromonas
Acidobacteria grp4
Genus Lysobacter
Thermomicrobia#4
Nitrosomonadales
Acidobacteria grp6
Thermomicrobia#7
Genus Bradyrhozobium
YCAGTRMCGAAGCTAACG
AAGSGCKCGTAGGYGGTT
GGSTKCCGGMGCTAACGC
CGAYTGTGAAATCTCCGG
CGAAGGCGGYTSYCTGGA
CTTCCTGGCCTGTTCTTG
TAAACGATGTCGACTGGT
GAGDTYGGGAGAGGGATG
GCAGGAGAGGGTAGTGGA
CTWGTGGCGMAGCTAACG
3
3
3
1
3
0
0
2
0
2
a
Target
97.37
94.74
86.87
96.30
92.59
95.24
95.16
93.44
95.65
94.87
b
Nontarget
4.99
2.47
1.94
0.04
1.83
0.14
0.10
0.50
0.08
0.19
Target sequences are those belonging to the phylogenetic group intended for amplification.
Nontarget sequences are sequences not belonging to the phylogenetic group intended for amplification.
17.1.1.4 Confirmation of Primer Specificity in
a Complex Environment Using Clone Libraries.
The specificity of two primer sets was tested by generation of clone libraries that were generated from amplicons
produced using total soil DNA from the same KBS-LTER
soil and subjected to DNA sequence analysis. PCR reactions were performed in triplicate using primer pairs 688706fAB plus 907r and Acido (#6)654–672 plus 907r,
purified using the Qiaquick PCR purification kit (Qiagen, Valencia, CA) and cloned as previously described
[Morales and Holben, 2009]. Taxonomic assignments of
the cloned sequences were obtained using the RDP Classifier program [Wang et al., 2007].
17.2
RESULTS AND DISCUSSION
17.2.1 In Silico Versus In Vitro
Validation
Of 28 phylogenetic group-specific forward primers
targeting the 16S rRNA gene (Tables 17.2 and 17.3),
only three (Acido(#4)599-617f, Acido(#6)654-672f, and
Nitro813-831f) had sufficient specificity to support their
subsequent use in qPCR-based population studies. This
was surprising given that all higher-order primers (targeting phylum- and class-level groups) (Table 17.2) were
predicted by Primrose to detect about 92.7% (standard
error [SE], 1%) of their specific targets, while conversely
only predicted to amplify 4.6% (SE, 1.1%) of nontarget
sequences. This was also the case with primers targeting
the 10 most abundant OTUs (genus-level phylotypes
based on 97% sequence similarity) identified from the
KBS-LTER dataset (Table 17.3). On average, these
primers were predicted to detect 94.2% (SE, 0.9%) of
their specific targets, while detecting only 1.2% (SE,
0.5%) of nontarget sequences in the KBS database.
Primer-target mismatch (theoretical number of mismatched base pairs allowed during annealing [Figs. 17.1
and 17.2]) is suggested as a potential source of the
discrepancy between in silico and in vitro specificity.
Single mismatches were sufficient to increase nonspecific
target detection six- to ninefold in silico, suggesting that
during PCR amplification, primers with slight internal
mismatches may bind sufficiently well to enable an
initial elongation event (as opposed to 3# mismatches,
which are more effective at preventing nonspecific
priming). This initial amplification subsequently provides
a perfectly matched template allowing for high efficiency
amplification of nontargets in subsequent rounds of PCR.
We concluded that mismatching between primer and
DNA template lends high specificity only where there
is virtually no initial elongation taking place, which
is unlikely unless multiple, consecutive, or 3# prime
mismatches are present, or when both primers in a
reaction have a high degree of specificity.
17.2.2 Effect of Nonspecific
Amplification on Real-Time qPCR
and Clone Libraries
Unless properly validated, primers used in PCR-based
analyses are likely to adversely affect the results and
interpretation of an experiment, particularly where quantitative data are desired (e.g., for population studies). The
target detection and PCR efficiencies of two primer sets,
one that passed validation [Acido (#6)654–672 plus 907r,
specific for Acidobacteria group 6 targets] and one that
did not (688-706fAB plus 907r, putatively specific for the
phylum Acidobacteria), were compared using a real-time
qPCR assay to assess the effect of using faulty primers.
A 10% difference in PCR efficiency was observed
between the primer pairs, with Acido (#6)654–672 plus
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100
90
80
70
60
50
40
30
20
10
0
(A)
100
90
80
70
60
50
40
30
20
10
0
(B)
100
(A)
90
80
70
KBS target
KBS non-target
ARB target
ARB non-target
60
50
Percent of sequences detected
Percent of sequences detected
17.3 Conclusion
40
30
20
KBS target
KBS non-target
10
0
100
(B)
90
80
70
60
0
1
2
Number of mismatches
3
Figure 17.1 Effect of mismatched bases on the recovery of target
and nontarget sequences using phylum-level primers. Primers were
tested in silico against 4889 sequences from the KBS-LTER library
and >50,000 sequences from the ARB database. (A) Thermomicrobia-specific primer 555-573fTM; (B) Gemmatimonadetesspecific primer 677-695fGT.
50
40
30
20
10
0
0
1
2
Number of mismatches
3
Figure 17.2 Effect of mismatched bases on the recovery of target
907r exhibiting 103.05% efficiency while 688-706fAB
plus 907r showed 92.9% efficiency. Although both
primer sets were able to detect and quantify different
target concentrations, both alone and in the presence of
total community DNA from soil (Fig. 17.3), the higher
PCR efficiency of the primer pair containing Acido
(#6)654–672 resulted in threefold more signal for a given
target concentration compared to that with the primer
pair containing 688-706fAB. Interestingly, despite the
lower efficiency of amplification with the phylum-level
primer set, quantification of target sequences from an
unspiked soil sample from the KBS-LTER (treatment 1,
replicate plot 1) indicated a higher abundance (4 pg, or
107 copies) of phylum-level acidobacterial targets than
of genus-level Acidobacteria group 6 targets (0.23 pg, or
105 copies) per 10 ng of soil extracted DNA, indicating
a low proportion of Acidobacteria group 6 within the
total Acidobacteria phylum representatives present in
that soil. However, any conclusion arising from the
primer that did not pass validation would be erroneous
due to the high degree of nonspecific amplification as
determined by cloning PCR products from the same
reactions (Table 17.4). Indeed, as expected from the
results of the validation experiments, the phylum-level
and nontarget sequences using OTU-level (97% sequence similarity)
primers. Primers were tested in silico against 4889 sequences from
the KBS-LTER library. (A) OTU-specific primer Coma851-869f; (B)
OTU-specific primer Pseudo573-591f.
Acidobacteria primer set (which had failed validation)
recovered target-specific phylotypes but also resulted in
recovery of a large number of nontarget phylotypes from
soil community DNA (35% versus 65%, respectively).
In contrast, the Acidobacteria group 6-specific primer set
Acido (#6)654–672 plus 907r (which passed validation)
displayed an extremely high level of specificity, resulting
in 96% of all sequences recovered from the soil community being classified as Acidobacteria group 6 and
the remaining 4% not being highly associated with any
particular phylum (Table 17.4).
17.3 CONCLUSION
Primer design and validation is key to accurate assessment
of bacterial community structure and population response.
Other studies have highlighted problems in primer design
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Chapter 17 Empirical Testing of 16S PCR Primer Pairs Reveals Variance in Target Specificity
Fold change in target detection
140
c17.tex
100000
AB
AB#6
10000
1000
100
10
1
0.1
1 pg
1 ng
Target
10 ng
Soil
+1 pg +1 ng +10 ng
Soil + Target
Figure 17.3 Specific detection of Acidobacterium target DNA (Acidobacteria group 6 clone 302.F22 DNA) using the Acido (#6)654–672
plus 907r and 688-706fAB plus 907r primer sets. The given amounts of target DNA were tested alone or after addition to 9 ng of total
community DNA isolated from KBS-LTER treatment 1, replicate plot 1. Values indicate the fold change in detection of the target group as a
function of the amount of target added. Values for each primer set were normalized to 1 pg of specific target to show fold change in detection.
Error bars are one SE of the mean for two rounds of triplicate qPCRs (final n = 3). Target, Acidobacteria group 6 clone 302.F22; Soil, 9 ng of
total community DNA extracted from soils at the KBS-LTER treatment 1, replicate plot 1.
Table 17.4 Phylogenetic Distribution of Soil Clones
Generated Using Primers 688-706fAB and Acido
(#6)654–672
a
Taxonomic Classification
Targetb
Nontarget
Actinobacteria
Bacteroidetes
Proteobacteria
Chloroflexi
Unclassified
AB
(27)c
35%
65% (50)
13% (10)
3% (2)
9% (7)
1% (1)
39% (30)
AB & 6
96% (96)c
4% (3)
INTERNET RESOURCES
Primrose (http://www.cardiff.ac.uk/biosi/research/
biosoft/)
The KBS LTER Site (http://lter.kbs.msu.edu/)
ARB (http://www.arb-home.de/)
Acknowledgments
4% (3)
a
As determined using RDP Classifier with 80% confidence threshhold.
Target for AB is the phylum Acidobacteria, AB & 6 is Acidobacteria
Group 6.
c
Number of sequences in each group is shown in parentheses.
b
[Baker et al., 2003; Wang and Qian, 2009], but, as demonstrated in this study, empirical testing is indispensable for
accurate assessment of efficacy and validation of specificity. This study provides a general strategy for others
interested in developing and rigorously testing 16S rRNA
gene-based primers for quantitative analysis of specific
phyla, classes, or OTUs in environmental samples. Our
approach allows accurate validation of primer sets without
the need to specifically test each set against all potential
targets in a community, an unreasonable feat given the
depth of diversity in the studied sample [Morales et al.,
2009]. This study also highlights the pitfalls of solely in
silico primer design and testing when dealing with complex mixtures of DNA as generally encountered when
studying microbial communities.
The authors would like to thank the Applied and
Environmental Microbiology Journal for permission to
reproduce figures and tables from [Morales and Holben,
2009]. Funding for this project was provided by the U.S.
Department of Agriculture National Research Initiative
(USDA-CSREES grant 2004–03501). Soil samples for
this project were graciously provided by the Kellogg
Biological Station Long Term Ecological Research
project (KBS-LTER).
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Q5
Q6
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Queries in Chapter 17
Q1. Confirm chap. no.
Q2. We have shortened the running head, since it exceeds the textwidth. Please confirm
Q3. Kindly check data in final column
Q4. Where is ND in table?
Q5. Microbiol. ok? or Microbial?
Q6. Supply initial(s)
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