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(GTG)
5
MSP-PCR Fingerprinting as a Technique for
Discrimination of Wine Associated Yeasts?
Mauricio Ramı
´rez-Castrillo
´n
1,2.
, Sandra Denise Camargo Mendes
1,2,3.
, Mario Inostroza-Ponta
4
,
Patricia Valente
2
*
1Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Campus do Vale, Porto Alegre, Brazil, 2Departamento de Microbiologia, Imunologia e
Parasitologia, ICBS, Universidade Federal do Rio Grande do Sul, Rua Sarmento Leite, Porto Alegre, Brazil, 3Empresa de Pesquisa Agropecua
´ria e Extensa
˜o Rural de Santa
Catarina, Laborato
´rio de Ana
´lises de Vinhos e Derivados, Estac¸a
˜o Experimental de Videira, Campo Experimental, Videira, Brazil, 4Departamento de Ingenierı
´a Informa
´tica,
Universidad de Santiago de Chile, Santiago de Chile, Chile
Abstract
In microbiology, identification of all isolates by sequencing is still unfeasible in small research laboratories. Therefore, many
yeast diversity studies follow a screening procedure consisting of clustering the yeast isolates using MSP-PCR fingerprinting,
followed by identification of one or a few selected representatives of each cluster by sequencing. Although this procedure
has been widely applied in the literature, it has not been properly validated. We evaluated a standardized protocol using
MSP-PCR fingerprinting with the primers (GTG)
5
and M13 for the discrimination of wine associated yeasts in South Brazil.
Two datasets were used: yeasts isolated from bottled wines and vineyard environments. We compared the discriminatory
power of both primers in a subset of 16 strains, choosing the primer (GTG)
5
for further evaluation. Afterwards, we applied
this technique to 245 strains, and compared the results with the identification obtained by partial sequencing of the LSU
rRNA gene, considered as the gold standard. An array matrix was constructed for each dataset and used as input for
clustering with two methods (hierarchical dendrograms and QAPGrid layout). For both yeast datasets, unrelated species
were clustered in the same group. The sensitivity score of (GTG)
5
MSP-PCR fingerprinting was high, but specificity was low.
As a conclusion, the yeast diversity inferred in several previous studies may have been underestimated and some isolates
were probably misidentified due to the compliance to this screening procedure.
Citation: Ramı
´rez-Castrillo
´n M, Mendes SDC, Inostroza-Ponta M, Valente P (2014) (GTG)
5
MSP-PCR Fingerprinting as a Technique for Discrimination of Wine
Associated Yeasts? PLoS ONE 9(8): e105870. doi:10.1371/journal.pone.0105870
Editor: Edward J. Louis, University of Leicester, United Kingdom
Received March 5, 2014; Accepted July 28, 2014; Published August 29, 2014
Copyright: ß2014 Ramı
´rez-Castrillo
´n et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by CNPq, CAPES, EMBRAPA, Cantina Santa Augusta, FONDECYT ‘‘Iniciacio
´n11121288’’ and COLCIENCIAS. The funders had no
role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* Email: patricia.valente@ufrgs.br
.These authors contributed equally to this work.
Introduction
Yeast identification is currently based on sequencing of domains
1 and 2 (D1/D2) of the LSU rRNA gene and/or the ITS1-5.8S-
ITS2 region [1], proposed as a universal barcode for fungi in 2011
[2]. Monitoring the contribution of each species or population,
both in industrial microbiology or yeast diversity studies, involves
the isolation and analysis of a large number of isolates, which
makes the identification of all the isolates by sequencing unfeasible
in small research laboratories. In this regard, many molecular
techniques have been developed to discriminate between different
yeast species. Among them, the Microsatellite/Minisatellite
Primed (MSP)-PCR Fingerprinting technique has been widely
applied in the literature using primers as (GAC)
5
, (GACA)
4
,
(GTG)
5
and M13. For example, the primer (GTG)
5
was frequently
used to discriminate species of the genus Saccharomyces [3–8],
characterize strains of non-Saccharomyces species [9–12], analyze
yeast diversity [13–20], and describe new yeast genus and species
[21–24]. Most of these studies use MSP-PCR fingerprinting as a
preliminary clustering step for the choice of representative strains
to be sequenced. Identification is ultimately attained by sequenc-
ing, and all the strains grouped in the same cluster of the
sequenced one are assumed to belong to the same species.
Although this procedure has been widely applied in the literature,
it has not been properly validated. Furthermore, some studies have
reported difficulties in discriminating species using MSP-PCR
fingerprinting with different primers [25–29]. In this context, each
study reports different DNA amplification protocols, jeopardizing
the comparison of genetic profiles, and making it impossible to
share genotype databases among laboratories.
A MSP-PCR fingerprinting protocol with (GTG)
5
primer was
useful for the description of yeast population dynamics along the
fuel-ethanol fermentation process, and for the identification of the
dominant wild strains that could be used as starter strains [7];
however, this primer has not yet been evaluated for monitoring the
yeast dynamics in wine production in Brazil. Therefore, the
objective of this study was {I} to propose and validate a
standardized protocol for the MSP-PCR Fingerprinting technique,
and {II} to assess its reliability as a tool for discrimination of
different yeast species and clustering of isolates belonging to the
same species. This protocol was intended to be applied to wine
yeasts, and was evaluated using two datasets: yeasts isolated from
bottled wines (thereafter considered a "lower diversity" sample),
and yeasts from the winery and vineyard environments ("higher
PLOS ONE | www.plosone.org 1 August 2014 | Volume 9 | Issue 8 | e105870
diversity" sample). For the validation of the technique, identifica-
tion by sequencing was selected as gold standard. We found high
intra and inter-specific variability in the fingerprint profiles, with
clusters comprising isolates belonging to different species, suggest-
ing a high probability of misidentification when MSP-PCR
fingerprinting followed by sequencing of representatives of each
profile is applied in yeast diversity studies.
Results and Discussion
Yeast identification
From the "lower diversity" group of species (isolated from
bottled wines), we obtained the genomic DNA of 102 yeast strains,
belonging to 11 species, plus 4 non-identified isolates (Table 1). All
the isolates were identified by sequencing the D1/D2 domain of
the LSU rRNA gene or the ITS1-5.8S-ITS2 region. The analysis
was initially performed with the "lower diversity" group of yeasts,
and afterwards expanded to the "higher diversity" group. From the
"higher diversity" group (isolated from the winery and vineyard
environments, see Methods S1), we obtained 101 isolates
belonging to 20 species plus 38 non-identified isolates (Table S1).
Standardization and assessment of MSP-PCR
Fingerprinting profiles
We made an initial screening of a subset of 16 isolates with the
primers M13 and (GTG)
5
to evaluate the discriminatory power of
each primer. Both primers generated discriminative and complex
fingerprints, with band sizes ranging from 200 to 2500bp for M13
and 200 to 1800bp for (GTG)
5
. Dendrograms for M13 and
(GTG)
5
primers showed four clusters with a discriminatory power
(D) of 0.66 for M13 (Figure S1A), and 0.7 for (GTG)
5
(Figure S1B).
Nevertheless, the dendrogram made with the primer (GTG)
5
grouped all the four isolates of Saccharomyces cerevisiae in the
same cluster (Figure S1B). Literature concerning the usefulness of
these primers is conflicting. For instance, the primer (GTG)
5
was
recommended to monitor populations of yeasts in ethanol
fermentation [7]. Several authors demonstrated that non-Saccha-
romyces species participating in different fermentation processes
showed similar profiles with M13 and (GACA)
4
and greater
variability using the primers (GAC)
5
and (GTG)
5
[13,16,30,31].
On the other hand, the primer M13 was able to differentiate 16
strains of S. cerevisiae, although with different amplification
conditions [32]. M13 or both M13 and (GTG)
5
primers are widely
used for assessment of yeast communities [33], and description of
new genus, species or genotypes within species [21,34], although
Libkind [28] suggested that the primer M13 is not able to separate
fingerprinting profiles in a complex of closely related species
because it amplifies more conserved regions of DNA. Thus, as our
goal was to discriminate related and unrelated yeast species, both
primers had similar discriminatory power with our subset of 16
isolates, and the primer (GTG)
5
grouped all the isolates of S.
cerevisiae in the same cluster, we chose primer (GTG)
5
for further
evaluation.
The MSP-PCR Fingerprinting was standardized using the
(GTG)
5
primer with the strain 20E (S. cerevisiae). The number of
bands in the S. cerevisiae 20E profile was similar to other (GTG)
5
fingerprinting profiles obtained for this species in other studies
[7,35,36]. The technique proved to be repeatable when tested in
two independent PCR reactions with six repetitions using the
commercial strain CLIB 2048 (S. cerevisiae). Repeatability and
reproducibility were also evident when randomly chosen strains
were analyzed in independent experiments.
We calculated the concordance between the (GTG)
5
finger-
printing and sequencing using the kappa index for the ‘‘lower’’
and ‘‘higher diversity’’ datasets, taking into account all the bands
obtained from each isolate. The 106 isolates from the "lower
diversity" dataset and the 139 isolates from the "higher diversity"
dataset showed a kappa index of 0.177 and 0.201, respectively,
Table 1. Yeasts species from bottled wines sampled in Rio Grande do Sul and Santa Catarina, South Brazil.
Species
Number of
strains Strain code
Pichia manshurica* 36 MRC188, MRC163, MRC143, MRC130, MRC142, MRC140, MRC139, MRC141, MRC128, MRC124,
MRC106B, MRC133, MRC189, MRC109, MRC110, MRC112, MRC122, MRC123, MRC125,
MRC107, MRC114, MRC115, MRC116B, MRC127, MRC136, MRC111, MRC132, MRC113,
MRC134, MRC116A, MRC171, MRC185, MRC126, MRC186, MRC121, MRC131
Dekkera bruxellensis* 30 MRC178, MRC180, MRC177, MRC88, MRC172, MRC181, MRC117, MRC120, 66E, 67E, 75E, 59E,
60E, 62E, 65E, 68E, 69E, 70E, MRC80, 73E, 77E, 74E, MRC190, MRC78, MRC79, MRC86, MRC87,
MRC182, 22E, 71E
Zygosaccharomyces bailii* 14 MRC162, MRC161, MRC137, MRC160, MRC187, MRC144, MRC145, MRC156, MRC105, MRC118,
MRC119, MRC146, MRC173, 24E
Pichia membranifaciens* 8 MRC152A, MRC153, 16E, MRC184, MRC165, MRC152B, MRC166, MRC168,
Saccharomyces cerevisiae* 7 MRC154, 26E, 72E, 20E, 15E, MRC164, 19E
Torulaspora delbrueckii 2 MRC183, 17E
Aureubasidium pullulans 1 MRC148
Candida magnoliae 1 MRC179
Candida zeylanoides 118E
Zygosaccharomyces bisporus 1 MRC158
Hanseniaspora sp.** 1 MRC81
Non identified 4 MRC129, MRC147, 23E, 25E
Total 106
* These species were assessed for clustering analysis.
**We considered these species as not identified.
doi:10.1371/journal.pone.0105870.t001
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with a confidence interval of 95% (Table 2). This means that the
concordance between the identification by sequencing (gold
standard) and by the (GTG)
5
MSP-PCR fingerprinting was slight
for the "lower diversity" and fair for the "higher diversity" dataset
[37]. High scores of sensitivity (100%, 97.4%) and low scores of
specificity (23.3%, 33.7%) with the (GTG)
5
MSP-PCR finger-
printings were found for the "lower" and "higher" diversity
datasets, respectively (Table 2). High sensitivity scores mean that
the number of isolates correctly identified by the MSP-PCR
fingerprinting was high, but the low specificity scores mean that
there were also many misidentified isolates in comparison with the
"gold standard". The low specificity scores may explain the low
concordance between the MSP-PCR fingerprinting and the
sequencing methods in the present study. The source of the
samples seemed not to influence the quality of the results, since
isolates sampled from bottled wines ("lower diversity" dataset) and
from the vineyard environments ("higher diversity" dataset)
resulted in similar kappa indexes, sensitivities and specificities.
The (GTG)
5
MSP-PCR fingerprinting has not been previously
evaluated for these parameters.
In order to understand the effect of the presence/absence of
each band obtained by the (GTG)
5
MSP-PCR fingerprinting for
the clustering of the isolates, the discriminatory power (D) of each
band within each species was calculated for the 245 isolates and
three reference strains. The D value of the bands for the five most
abundant species of each dataset ranged from 0.048 to 1.000 (see
Tables S2, S3). Many species had bands with D values around
1.000, meaning that those bands were able to discriminate all the
isolates within the species, therefore making the fingerprinting
profiles dissimilar among isolates from the same species. Bands
with molecular weight lower than 900 bp were consistently present
in almost all the isolates of each species, whereas the presence of
bands with molecular weight higher than 900 bp was more
variable (Figures S2, S3). Many factors may contribute for this
variable result, and can indicate an amplification bias. Among
these factors are the annealing temperature in the PCR, the purity
of DNA, the thermocycler equipment, and the electrophoresis
conditions for gel migration [38], which interfere with other
fingerprinting techniques as well [39]. Furthermore, (GTG)
5
MSP-
PCR fingerprinting was used in many studies with different
protocols [34,40–45], and there is not a consensus in the PCR
parameters (annealing temperature within a range of 42–60uC,
etc), or electrophoresis conditions (for example, agarose concen-
tration with a range of 1.4–2% w/v). This contributes for the weak
reproducibility of the technique among laboratories, and jeopar-
dizes any posterior comparison between the fingerprinting results.
In order to rule out any influence from the variable bands higher
than 900 bp in our analysis, we recalculated the kappa index,
specificity and sensitivity scores using a range of band sizes
between 200 and 900 bp. However, the results showed that
concordance did not improve (Table 2).
In the present work, the fingerprinting profiles were analyzed
based only on the number and size of bands, although band
intensity is also considered by some authors. It has been previously
suggested that the identification of two or more strains with the
same amplification pattern (number and intensity of bands) might
indicate clonality of strains from different geographical origins [7].
In our study, isolates of the species Pichia membranifaciens gave
repeatedly the same band patterns without differences due to
missing bands, although differences in band intensity of some
fingerprints occurred. Therefore, the band intensity was not used
as a variable for grouping the isolates in our study.
The ‘‘lower diversity’’ yeast dataset
When analyzing the "lower diversity" group of yeasts (isolated
from bottled wines), and considering only the species with more
than 7 isolates, we found DNA fragments of 200 to 3500 bp, with
banding patterns containing between 4 and 11 visualized bands
(Figure S4). In this dataset, strains identified as Pichia manshurica,
S. cerevisiae,Zygosaccharomyces bailii and Dekkera bruxellensis
presented different band patterns within each species. For
example, some strains of D. bruxellensis showed higher bands
(approximately 2500 pb) that were absent in others. To exclude
error in the PCR reaction as the explanation, the experiment was
repeated three times being obtained the same pattern of
amplification. This result separated this species into at least two
clusters, which might be due to variation in DNA quality (although
some nucleic acids were extracted again, quality problem cannot
be discarded) or intraspecific variability. In fact, intraspecific
variability in S. cerevisiae was found using MSP-PCR Finger-
printing with the (GTG)
5
primer [8]. We found that an increase in
the number of isolates raised the number of different band patterns
within each species.
Two clustering strategies, using quantitative data based on the
molecular weight of the bands and Euclidean distances, were used
to attempt clustering the genotypic profiles obtained with the
Table 2. Specificity, sensitivity and kappa index of MSP-PCR fingerprinting using the primer (GTG)
5
in comparison with rDNA
sequencing as the gold standard for the two datasets (‘‘lower diversity’’ and ‘‘higher diversity’’), using two ranges of band sizes:
200–3500bp or 200–900 bp.
Dataset
Range of
band size MSP-PCR fingerprinting Gold standard (sequencing) Kappa index
(GTG
5
)
‘‘Lower diversity’’ 200–3500bp Specificity 23.30% 100.00% 0.177
Sensitivity 100.00% 41.70%
200–900bp Specificity 15.20% 100.00% 0.169
Sensitivity 100.00% 60.30%
‘‘Higher diversity’’ 200–3500bp Specificity 33.70% 97.10% 0.201
Sensitivity 97.40% 35.60%
200–900bp Specificity 20.20% 95.50% 0.124
Sensitivity 97.70% 36.40%
doi:10.1371/journal.pone.0105870.t002
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MSP-PCR fingerprinting technique. First, a Hierarchical Cluster-
ing algorithm (with four methods of pairwise analysis) showed
dendrograms where the isolates did not group into well-defined
clusters (Figure 1). For example, dendrograms with UPGMA,
single linkage or complete linkage showed high variability in the
clusters with a cut-off of 30%, generating approximately 30 groups
with mixed species. The Wards method, by contrast, grouped the
isolates into seven clusters using the same cut-off, but increased the
likelihood of grouping isolates from different species (Figure 1).
The other strategy was to use QAPGrid, an unsupervised graph
clustering algorithm combined with a combinatorial optimization
layout method [46]. As a result, the algorithm found 14 clusters,
with the smallest cluster containing two isolates and the largest one
containing 15 strains (Figure S2).
In general, the most abundant species were placed in several
clusters, and 85% of the clusters were represented by two or more
species in the QAPgrid output. As this algorithm groups similar
band patterns, unrelated species with similar profiles were joined
in the same group, therefore, being the resolution of the clustering
poor. For example, we expected to find mixed clusters with the
species P. manshurica and P. membranifaciens because they are
sibling species that comprise a species complex [9], but we also
found mixed clusters for D. bruxellensis, Z. bailii and S. cerevisiae,
due to the similar genetic profiles (number and size of bands) of
some isolates. As the Hierarchical Clustering and the QAPGrid
were not capable of grouping the species, we confirmed that the
problem was the raw data (fingerprinting patterns) used to
construct the matrix analyzed by both methods.
Many yeast diversity studies apply the MSP-PCR fingerprinting
to select one or two representative isolates from each pattern for
sequencing aiming the identification at the taxonomic level of
species [47–50]. Based on our results, it might be inferred that
yeast diversity was underestimated and some isolates were
misidentified in many previous works. In an attempt to assess
the probability of misidentification and consequent underestima-
tion of the species richness, we selected two mixed species clusters
(with three and 15 isolates, respectively) from the QAPgrid output
(Figure S2). For the smaller cluster, two isolates were identified as
P. manshurica and the other isolate as D. bruxellensis. If we select
only two isolates from this cluster for sequencing, the probability of
misidentification is 33%. The largest cluster contained four
different species and, if two representative isolates were selected
for sequencing, the probability of misidentification could reach
40%. This illustrates the problem of using the MSP-PCR
Fingerprinting with the primer (GTG)
5
as a technique for
grouping isolates in order to select some of them for sequencing.
Figure 1. Dendrograms of the clustering of the strains from the "lower diversity" dataset by Hierarchical Clustering using: (a)
average linkage (b) complete linkage, (c) single linkage, and (d) Wards method. The distance was computed using the Euclidean distance
between the genetic profiles based on the MSP-PCR fingerprinting with the primer GTG
5
.
doi:10.1371/journal.pone.0105870.g001
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Materials and Methods
Strains and growth conditions
The yeast strains isolated in this study are listed in Table 1 and
Table S1. Two sets of samples were included. The first group of
yeasts (n = 106) was isolated from South Brazilian bottled wines
("lower diversity" dataset, Table 1), and the second group (n = 139)
was isolated from environments surrounding the wineries (vine-
yard soil, effluent, leaves, fruits, cellars – "higher diversity" dataset,
Table S1). Details concerning the isolation of yeast strains can be
seen in Methods S1. Field collections were conducted according to
EPAGRI diversity rules, and all necessary permits were obtained
for the field studies (Codes 1414, 13214). Reference strains used in
this study were: Saccharomyces cerevisiae CLIB 2048, Saccharo-
myces bayanus CLIB 2033 and Saccharomyces uvarum CLIB 2028
(Collection de Levures d’Interet Biotechnologique, Paris-Grignon,
France).
DNA extraction
Two protocols were used in this study. DNA of yeasts isolated
from bottled wine was extracted with the potassium acetate-based
protocol proposed by [51] with some modifications. Pure colonies
of each strain were grown in GYP broth at 30uC for 18 hours.
After centrifugation and washing with distilled water, the biomass
of each culture was re-suspended in 400mL of lysis buffer (0.5 M
NaCl, 10 mM EDTA, 2% SDS, 50 mM Tris-HCl, pH 8) and
incubated for 60 min at 65uC. The other steps were done as
described in [51]. Genomic DNA of samples collected in the
second group was extracted using the classic protocol with phenol/
chloroform [52]. The quality of the extracted DNA was analyzed
on agarose gels (1% w/v) and assessing the A260/A280 ratio.
MSP-PCR Fingerprinting
MSP-PCR Fingerprinting using the primers (GTG)
5
or M13
was optimized from [7] using strain 20E (S. cerevisiae). Different
concentrations of each reagent used in PCR were tested: MgCl
2
(1.5–4.5 mM), primer (0.2–1.4 pmol/mL), dNTPs (10–70mM) and
DNA (110–0.1 ng/mL). The optimized reaction mix for a volume
of 25mL was: 1 U of Taq polimerase (Invitrogen), 1X buffer
reaction, 3 mM MgCl
2
, 1 pmol/mL primer, 60mM dNTPs Mix
and 5mL of DNA (1 ng/mL). The program started at 94uC for
5 min followed by 35 cycles at 94uC for 15 s, 55uC for 45 s, and
72uC for 90 s, with final extension at 72uC for 6 min.
The PCR products were separated in 1.8% (w/v) agarose gels
(Bioron, Ludwigshafen, Germany; 12.5 cm width; 8.5 cm height)
made in 1X TAE buffer (40 mM Tris–Acetate, 1 mM EDTA,
pH 8.0) using electrophoresis with stacking: initial migration at
110 V for 5 min followed by 70 V for 180 min. Gels were stained
with GelRed (Biotium, Hayward, USA) for visualization under
UV light and digital image capturing was done using the Geni2
gelDoc System (Syngene, Cambridge, UK). The resulting
fingerprints were analyzed using the software GeneTools. The
1 Kb plus or 1 Kb (Invitrogen) molecular weight marker was used
to compare the sizes of the bands.
Yeast molecular identification
The divergent D1/D2 domain of the LSU rRNA gene was
amplified and sequenced with NL1 and NL4 primers [53]. The
ITS1-5.8S-ITS2 region was amplified and sequenced with ITS1
and ITS4 primers [54]. Amplification conditions were as follows:
one initial cycle at 94uC for 5 min, 35 cycles at 94uC for 15 s,
55uC for 45 s, 72uC for 90 s, and a final extension cycle at 72uC
for 6 min. The PCR products were examined by electrophoresis
on a 1.5% agarose gel at 100 V for 45 min and stained with
GelRed for visualization under UV light. Digital image capturing
was done using the Geni2 gelDoc System (Syngene, Cambridge,
UK).
The sequences were obtained with ABI-PRISM 3100 Genetic
Analyzer (Life Technologies Corp., USA) using standard protocols
at the ‘‘Ludwig Biotecnologia’’ facility in Alvorada-RS, Brazil, and
were compared with the sequences of type strains published in the
GenBank database using the software YeastIP [55]. A cut-off of
99% similarity was used to identify the isolates.
Clustering analysis
Two clustering algorithms were used to group the (GTG)
5
MSP-PCR Fingerprinting profiles: (a) a Hierarchical Clustering
algorithm with four versions for pairwise analysis: average linkage,
complete linkage, single linkage and Wards method; (b) QAPGrid,
an unsupervised graph clustering algorithm combined with a
combinatorial optimization layout method [46].
For both clustering algorithms, a matrix was constructed
considering each isolate and the total number of bands (n = 23),
with the size of each band for each isolate. The size of the bands
took into consideration a deviation of 50 bp for the smallest bands,
and 200 bp for the largest ones, due to the agarose gel resolution.
Thus, each isolate was represented by 23 integer numbers
corresponding to the size of the bands found by the MSP-PCR
Fingerprinting method. If a band were not present for an isolate,
we considered a value of zero for that band. We used a Euclidean
distance between each pair of isolates to compute the distance of
the genetic profiles of isolates. The matrix is available in Dataset
S1.
The second method incorporates the use of a graph-based
clustering algorithm that automatically finds the number of
clusters based on the distance between the genetic profiles of the
isolates. After the clustering is performed, the QAPGrid algorithm
produces a layout representative of the clusters. Details of the
clustering and layout algorithms can be found in Inostroza-Ponta
et al. [46,56]. This combination has been successfully applied in
the analysis of other type of genetic data [57–58].
Discriminatory power
In order to compare the discriminatory power (D) of the primers
M13 and (GTG)
5
in the MSP-PCR fingerprinting, we used the
index of discrimination proposed by [59–60], which is based on
the Simpson’s index of diversity. The discriminatory power was
calculated based on a subset of 16 strains from the "lower
diversity" dataset. Dendrograms were constructed based on the
Wards method and Euclidean distances, and grouped with a cut-
off of 50%.
The equation used for the calculation of the discriminatory
power is as follows:
D~1{
1
N(N{1) X
s
j{1
xj(xj{1)
where D is the index of discriminatory power, N, the number of
unrelated strains tested, S, the number of different types, and x
j
,
the number of strains belonging to the j
th
type, assuming that
strains will be classified into mutually exclusive categories. A D
value of 1.0 indicates that the primer was able to distinguish each
isolate of a community from all other members of that community.
Conversely, an index of 0.0 indicates that all isolates of a
community were of an identical type [59–60].
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Afterwards, the discriminatory power of each band of the MSP-
PCR fingerprinting profile with the primer (GTG)
5
was calculated
for 208 isolates and the three reference strains according to the
equation described above. The discriminatory power (D) of each
band obtained in the (GTG)
5
MSP-PCR fingerprinting was
calculated as the measurement of the variation of ‘‘alleles’’
(presence or absence of bands) by each ‘‘locus’’ (band position),
with a range between zero (homogeneity) and one (heterogeneity).
A low D indicates a ‘‘locus’’ with similar "alleles" (presence or
absence of bands), while a high D indicates a ‘‘locus’’ with an
irregular presence of bands among the isolates.
Concordance between the (GTG)
5
MSP-PCR
fingerprinting and sequencing, sensitivity and specificity
assessments
We evaluated the concordance between the identification by
(GTG)
5
MSP-PCR fingerprinting and by sequencing using the
whole ‘‘lower diversity’’ (n = 106) and ‘‘higher diversity’’ (n = 139)
datasets, and the Kappa index [61]. The sensitivity and specificity
indexes were assessed using the McNemar test for comparison of
the results obtained by sequencing (considered as the gold
standard) and the ones obtained by the (GTG)
5
MSP-PCR
fingerprinting [62]. The sensitivity indicates the percentage of
isolates identified by sequencing that were identified as the same
species by the MSP-PCR fingerprinting (true positive isolates), and
is a measure of the probability that an isolate belonging to a
certain species will be correctly identified at that species by the
(GTG)
5
MSP-PCR fingerprinting. The specificity indicates the
percentage of isolates that were not identified in a certain species
by the sequencing methodology which were not identified in that
species by the MSP-PCR fingerprinting (true negatives) either. We
considered the isolates not identified by sequencing as true
negative results. All the tests were estimated with a confidence
interval of 95%.
Supporting Information
Figure S1 Dendrograms of the MSP-PCR fingerprinting
profiles with the primers M13 (a) and (GTG)
5
(b) of a
subset of 16 strains from the "lower diversity" dataset
for the analysis of the discriminatory power of the
primers. The dendrograms were constructed by the Hierarchical
Clustering using the Wards method, and the distance was
computed using the Euclidean distance between the genetic
profiles. We used a cut-off of 50% for the calculation of the
discriminatory power.
(TIF)
Figure S2 QAPGrid layout for the clustering of the
strains from the "lower diversity" dataset. The distance
was computed using the Euclidean distance between the genetic
profiles based on the MSP-PCR fingerprinting with the primer
GTG
5
. Each strain is represented as a bar chart. The colors
represent the different species based on the molecular identifica-
tion, and the legend is the same as in Figs. 1A and 1B. Each bar
represents one band in the fingerprinting profile of each strain, the
horizontal axis shows the band position in the fingerprinting, and
the vertical axis represents the size of the band (bp). The dashed
lines indicate the two clusters (smaller and bigger) used for the
calculation of the probability of misidentification and consequent
underestimation of the species richness.
(TIF)
Figure S3 Layout of the MSP-PCR fingerprinting pro-
files with the primer (GTG)
5
of the most abundant
species within the "higher diversity" dataset. Each symbol
represents one band in the fingerprinting profile. S. cerevisiae (a),
H. uvarum (b), P. kudriavzevii (c) and P. occidentalis (d). The
profiles of the reference strains S. bayanus CLIB 2033 S. uvarum
CLIB 2028 and S. cerevisiae CLIB 2048 are shown in Fig. S2a.
Each symbol represents one band in the fingerprinting profile of
each strain, and the vertical axis shows the size of the band (bp).
(TIF)
Figure S4 Representative agarose gel of MSP-PCR
fingerprinting using the primer (GTG)
5
.01: Dekkera
bruxellensis MRC181; 02: Pichia manshurica MRC163; 03: D.
bruxellensis MRC172; 04: Pichia membranifaciens MRC152A;
05: D. bruxellensis MRC177; 06: Torulaspora delbrueckii
MRC183; 07: Zygosaccharomyces bailii MRC162; 08: D. brux-
ellensis MRC178; 10: D. bruxellensis MRC180; 11: D. bruxellensis
MRC88; 12: P. manshurica MRC188. 1Kb Plus was used as
Molecular Weight Marker (MPM).
(TIF)
Table S1 Yeast species from the vineyard and winery
environments collected in Santa Catarina, Brazil.
(DOC)
Table S2 Discriminatory power of each band obtained
by the MSP-PCR fingerprinting with (GTG)
5
for the five
most abundant species from the "lower diversity"
dataset.
(DOC)
Table S3 Discriminatory power of each band obtained
by the MSP-PCR fingerprinting with (GTG)
5
for the five
most abundant species from the "higher diversity"
dataset.
(DOC)
Methods S1 Detailed methods for yeast isolation exper-
iments.
(DOC)
Dataset S1 Data matrix.
(XLS)
Acknowledgments
We thank Marilene H. Vainstein for carefully reading the manuscript.
Author Contributions
Conceived and designed the experiments: MRC SDCM PV. Performed
the experiments: MRC SDCM. Analyzed the data: MRC SDCM MIP.
Contributed reagents/materials/analysis tools: PV. Wrote the paper:
MRC SDCM MIP PV.
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