ARTICLE
Pattern-Mapped Multiple Detection of 11
Pathogenic Bacteria Using a 16S rDNA-Based
Oligonucleotide Microarray
Byeong Hee Hwang, Hyung Joon Cha
National Research Laboratory of Molecular Biotechnology, Department of Chemical
Engineering, Pohang University of Science and Technology, Pohang 790-784, Korea;
telephone: þ82-54-279-2280; fax: þ82-54-279-2699; e-mail: [email protected]
Received 26 October 2009; revision received 30 December 2009; accepted 11 January 2010
Published online 20 January 2010 in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/bit.22674
Introduction
ABSTRACT: Pathogen detection is an important issue in
human health due to the threats posed by severe communicable diseases. In the present study, to achieve efficient and
accurate multiple detection of 11 selected pathogenic bacteria, we constructed a 16S rDNA oligonucleotide microarray containing doubly specific capture probes. Many target
pathogens were specifically detected by the microarray with
the aid of traditional perfect match-based analysis using our
previously proposed two-dimensional visualization plot
tool. However, some target species or subtypes were difficult
to discriminate by perfect match analysis due to nonspecific
binding of conserved 16S rDNA-derived capture probes
with high sequence similarity. We noticed that the patterns
of specific spots for each strain were somewhat different in
the two-dimensional gradation plot. Therefore, to discriminate subtle differences between phylogenetically related
pathogens, a pattern-mapping statistical model was established using an artificial neural network algorithm trained by
experimental repeats. The oligonucleotide microarray system harboring doubly specific capture probes combined
with the pattern-mapping analysis tool resulted in successful
detection of all target pathogens including even subtypes
of two closely related species showing strong nonspecific
binding. Collectively, the results indicate that our novel
combined system of a 16S rDNA-based DNA microarray
and a pattern-mapping statistical analysis tool is a simple
and effective method for detecting multiple pathogens.
Biotechnol. Bioeng. 2010;106: 183–192.
ß 2010 Wiley Periodicals, Inc.
KEYWORDS: DNA chip; oligonucleotide microarray; 16S
rDNA; pathogenic bacteria; multiple detection; pattern
mapping
Correspondence to: H.J. Cha
Contract grant sponsor: Ministry for Flood, Agriculture, Forestry and Fisheries
Contract grant number: 109187-3
Contract grant sponsor: Young-Woong Satellite Laboratory Grant
Contract grant sponsor: Ministry of Education, Science and Technology (Brain Korea
21 Program)
ß 2010 Wiley Periodicals, Inc.
To date, numerous studies have investigated the detection
and diagnosis of pathogens to prevent food-poisoning
outbreaks. The well-known conventional methods have
several limitations, such as culture dependency, long
detection time (2–7 days), labor-intensive protocols, and
difficulties in parallel strain detection (de Boer and Beumer,
1999). Therefore, molecular methods such as polymerase
chain reaction (PCR), fluorescent in situ hybridization,
immunoassay, and microarrays have been developed. In
particular, the microarray is one of the most efficient
methods owing to the possibility of parallel identification
using several detection components including DNA (Call
et al., 2003; Guschin et al., 1997; Schena et al., 1995).
Short oligonucleotide probes are useful for pathogen
detection because subtle discrimination of single-nucleotide
polymorphisms is possible (Call, 2005; Yershov et al., 1996).
Therefore, several studies have demonstrated the applicability of oligonucleotide arrays for environmental microbial detection (Eom et al., 2007; Guschin et al., 1997; Loy
et al., 2005; Rudi et al., 2000; Sergeev et al., 2004; Wu et al.,
2003). In particular, several microarrays based on 16S rDNA
information have been used in phylogenetic discrimination
(Chandler et al., 2003; Eom et al., 2007; Guschin et al., 1997;
Liu et al., 2001; Peplies et al., 2006; Rudi et al., 2000). The use
of 16S rDNA as a detection modality has several advantages
such as an abundance of complete sequence information
and the availability of reported specific capture probes
and universal primers (Woese, 1987). Previously, we also
proposed the use of a 16S rDNA-based oligonucleotide
microarray for detection of seven pathogens using a single
specific capture probe (from the first variable region) and a
two-dimensional visualization strategy (Eom et al., 2007).
However, most studies on 16S rDNA-based detection,
including our previous research, showed inevitable nonspecific binding because 16S rDNA sequences offer very low
sequence diversity; thus, difficulties arise in unambiguous
Biotechnology and Bioengineering, Vol. 106, No. 2, June 1, 2010
183
discrimination of phylogenetically close bacteria or subspecies. In addition, the relatively low sensitivity of our
previous system resulted from use of the statistical array
format involving repeated redundant spots (Eom et al.,
2007). After experimental 16S rDNA sequence analysis, we
also found that sequence dissimilarity (percentage of
mismatched sequence between capture probe and target)
was the most important factor affecting probe specificity.
In the present study, we constructed an oligonucleotide
microarray system for detecting 11 selected pathogenic
bacteria (pathogenic Escherichia coli, Escherichia coli
H7:O157, Listeria monocytogenes, Salmonella enterica serotype Choleraesuis, Salmonella enterica serotype Enteritidis,
Shigella dysenteriae, Staphylococcus aureus, Vibrio cholerae,
Vibrio vulnificus, Vibrio parahaemolyticus, and Yersinia
enterocolitica) by strategic optimal design and experimental
selection of doubly specific capture probes from characteristic regions of total 16S rDNA sequence. To improve
detection specificity, we considered sequence dissimilarity to
be the most important design factor and used an artificial
standard probe strategy, as in our previous report (Hwang
and Cha, 2008). For easy, rapid, and accurate classification
of phylogenetically close pathogens, we also developed
a pattern-mapping analysis tool, featuring a probability
estimation by an artificial neural network (ANN) algorithm,
that could distinguish the spot pattern specific to each
pathogen.
Additional 16S rDNA sequences were downloaded from the
RDP II database and the NCBI gene bank. Because 16S
rDNA sequences show high similarity between closely
related species, we employed a closest comparison method
that checks species-specific regions by alignment of the
closest two 16S rDNA sequences. To this end, we employed
BioEdit software (Ibis Biosciences, Carlsbad, CA). Next,
these regions were compared to all 16S rDNA sequences of
target and target-related species. Based on the identified
regions, capture probe candidates were designed with
sequence dissimilarities of over 10–15%, and with similar
melting temperatures, using Primer Premier 5 (Premier
Biosoft International, Palo Alto, CA). Finally, capture probe
candidates were inspected using the RDP II database and
NCBI Blast searches.
We designed 22 new specific capture probe candidates
based on the above criteria. Next, the probes were chemically
synthesized with a 50 amino linker modification to contain a
six-atom spacer composed of ethylene glycol units between
the oligonucleotide and amine (BioBasic, Ontario, Canada).
After experimental selection of probe candidates (data not
shown), we chose 16 specific capture probes from the newly
designed candidates and 4 specific capture probes from
previously designed molecules (Eom et al., 2007) (Table I).
Positive control and artificial standard capture probes were
as in our previous reports (Eom et al., 2007; Hwang and Cha,
2008) (Table I).
Materials and Methods
Format Design and Preparation of DNA Microarray
Microbial Strains and Genomic DNA Isolation
The 11 microbial strains are pathogenic E. coli (American
Type Culture Collection (ATCC; Manassas, VA) 25922),
E. coli O157:H7 (ATCC 29425), L. monocytogenes (ATCC
15313), S. enterica serotype Choleraesuis (ATCC 13311),
S. enterica serotype Enteritidis (Institute for Fermentation
(IFO; Osaka, Japan) 3313), S. dysenteriae (ATCC 13313),
S. aureus (ATCC 6538), V. cholerae (ATCC 14035),
V. vulnificus (ATCC 27562), V. parahaemolyticus (ATCC
17802), and Y. enterocolitica (ATCC 23715). All species were
cultured in nutrient broth (Difco, Kansas, MO) at 30 or
378C, except V. vulnificus in trypticase soy broth (Difco)
with 1% NaCl at 308C and two E. coli strains in LB medium
at 378C. Genomic DNA was extracted and purified using the
DNeasy1 Tissue Kit (Qiagen GmbH, Hilden, Germany).
Purified bacterial genomic DNA was used as template for
PCR, followed by fluorescence labeling of each target. DNA
concentrations and purities were determined with a UV/vis
spectrometer (Mecasys, Daejeon, Korea).
Design and Synthesis of Capture Probes
Eleven 16S rDNA sequences were directly obtained by
sequencing from the chromosome of each target strain.
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Biotechnology and Bioengineering, Vol. 106, No. 2, June 1, 2010
We designed a capture probe arrangement (Fig. 1) in a
manner similar to that of our previous report (Hwang and
Cha, 2008). Black, gray, and white spots represent artificial
standard, positive control, and specific capture probes,
respectively. Four repeat spots of each specific capture probe
were surrounded by five replicate spots of artificial standard
capture probe, in a rectangular shape. Consequently, each
oligonucleotide microarray contained 22 5 spots of the
artificial standard capture probe, 1 4 spots of the positive
control capture probe on the first line, and 1 4 spots of
each specific capture probe (to include a total of 20 probes).
Thus, each oligonucleotide chip contained a double array of
sets, with 194 spots in all. The gap between spots within an
array set was 566 mm, sufficient to minimize cross-talk. The
total dimension of the spotted area was 3.2 mm 16.8 mm,
and the whole array was duplicated at intervals of 19.3 mm
to permit two independent experiments to be carried out
using a single chip.
Each NH2-modified oligonucleotide (20 mM) was dissolved in 3X SSC spotting buffer (450 mM NaCl, 3 mM
tri-sodium citrate, and 1.5 M N,N,N-trimethyl glycine
[betaine; Sigma, St. Louis, MO], pH 6.6, final c ¼ 1.5 M).
The capture probe solutions were spotted onto aldehydecoated slides (Super Aldehyde; Telechem International,
Sunnyvale, CA) using a Microsys 5100 microarrayer
(Cartesian Technologies, Ann Arbor, MI) with the Chip
Table I.
Oligonucleotide sequences employed as capture probes and standard target, and their thermodynamic properties.a
Probe name Antisense sequences (50 –30 , 30 -amine-spacer, spacer:C6) Length (bp) Tm (8C) Rating RSD
Species
All bacteria (positive control)
Escherichia coli
Escherichia coli O157:H7
Listeria monocytogenes
Salmonella enterica serotype Cholerasuis
Salmonella enterica serotype Enteritidis
Shigella dysenteriae
Staphylococcus aureus
Vibrio cholerae
Vibrio parahaemolyticus
Vibrio vulnificus
Yersinia enterocolitica
Artificial standard
Artificial standard target
POCO
ESCO
ESCOO1
ESCOO2
LIMO1
LIMO2
SACH1
SACH2
SAEN
SHDY1
SHDY2b
STAU1
STAU2
VICH1
VICH2
VIPA1
VIPA2b
VIVU1b
VIVU2b
YEEN1
YEEN2
ARST
ARSTT
GCCGCCAGCGTTCAATCTGA
GAAGGCACATTCTCATCTCTGAAAAC
CAGCAAAGAAGCAAGCTTCTTCCT
ACTCGTCAGCAAAGAAGCAAGCT
GCATGCGCCACACTTTATCATT
CCATCTTTCAAAAGCGTGGCAT
TGCTGCGGTTATTAACCACAACA
GACTCAAGCCTGCCAGTTTCGA
AGGCACAAATCCATCTCTGGATTC
AGGCACCCTCGTATCTCTACAAGG
CCGCCACTCGTCAGCAAAGCA
AACTAGCTAATGCAGCGCGGAT
AGATGTGCACAGTTACTTACACATATGTTCT
CCTCTACCGGGCAATTTCC
CTCTACCGGGCAATTTCCCA
CCCGAAGGTTCAGATAACTCGTTT
CGTTATCGTTCCCCGAAGTTCAGAT
AAACAAGTTTCTCTGTGCTGCCGC
TGAGCCGAAGCTATCATGCGG
GTTATTGGCCTTCCTCCTCGCT
TGCGAGTAACGTCAATCCAACAA
CCCAAGGGAACCCAAGGGAAA
TTTCCCTTGGGTTCCCTTGGG-Alexa flour 647
20
26
24
23
22
22
23
22
24
24
21
22
31
19
20
24
25
24
21
22
23
21
21
66.5
62.9
63.8
62.7
62.6
64.0
63.2
64.2
63.5
63.1
68.9
63.4
63.0
63.5
62.3
63.1
66.9
66.9
65.8
63.8
63.1
66.8
66.8
91
91
73
86
80
90
86
87
74
89
100
81
74
79
79
88
93
91
83
84
89
85
85
1.9
5.9
4.1
1.6
2.1
8.6
4.5
3.7
33.3
7.9
10.8
1.6
3.6
4.0
4.0
8.4
10.2
4.0
7.2
0.6
2.4
8.3
a
All thermodynamic properties were calculated by Primer Premier.
From the previously designed capture probes (Eom et al., 2007).
b
Figure 1.
Schematic diagram of the repeated array format. The layout for the specific capture probes of each target strain is indicated above.
Hwang and Cha: Pattern-Mapped Multiple Pathogen Detection Chip
Biotechnology and Bioengineering
185
Maker 2 pin (Telechem International) at 74% humidity in a
class 10,000 clean room. After spotting and incubation
overnight in low (30%) humidity conditions, microarray
slides were incubated in a solution containing 1.3 g NaBH4
dissolved in 375 mL phosphate-buffered saline (PBS;
pH 7.4) and 125 mL ethanol for 5 min, followed by washing
twice in 0.2% (w/v) sodium dodecyl sulfate (SDS) for 1 min
each time, and twice with ddH2O. Slides were dried by
centrifugation at 1,500 rpm for 3 min, and stored at room
temperature under vacuum until further use.
Target Preparation and Hybridization
16S rDNA sequences were amplified as targets by asymmetric PCR at a 50:1 primer ratio. The universal PCR primer
set (16S-43F: 50 -TGGCTCAGATTGAACGCTGGCGGC-30
16S-1392R: 50 -ACGGGCGGTGTGTAC-30 ; Genotech, Daejeon,
Korea) for each strain was based on the first and ninth
conserved regions. The PCR mixture was composed of 2U
Taq polymerase (Takara, Otsu, Japan), 3.5 mM forward
universal primer, 0.07 mM reverse universal primer, 0.5 mM
dATP, 0.5 mM dCTP, 0.5 mM dGTP, 0.3 mM dTTP,
0.15 mM amine-modified dUTP, and 1X Taq buffer. PCR
was performed in a DNA thermal cycler (Eppendorf,
Hamburg, Germany) under the following conditions: 958C
for 5 min, 30 cycles of 958C for 1 min, 608C for 1 min, and
728C for 1 min 24 s, followed by 728C for 5 min. The
QIAquick1 PCR kit (Qiagen GmbH) was employed for
purification of amine-modified amplicons, followed by
ethanol precipitation. Next, labeling was performed using
the ARESTM Alexa Fluor1 647 DNA labeling kit (Molecular
Probes, Eugene, OR). Labeled target DNA samples included
both conserved and variable (first to ninth) regions ranging
from 1,385 to 1,400 bp in length. For multiple detection of
two pathogens, a chromosome mixture at a 1:1 ratio was
used as PCR template. Alexa Fluor1 647-labeled artificial
standard target was the same as in our previous reports
(Eom et al., 2007; Hwang and Cha, 2008) (Table I).
The fabricated oligonucleotide microarray was prehybridized in buffer containing 3X SSC solution (450 mM
NaCl, 3 mM tri-sodium citrate, pH 7.0) with 1% (w/v)
bovine serum albumin (BSA; Sigma) and 0.1% (w/v) SDS
for 30 min at 50, 55, or 608C. After washing four times (0.2%
SDS, w/v, twice, distilled water twice), the array was dried by
centrifugation at 1,500 rpm for 3 min. The array was
hybridized with a DNA mixture of 9 mL PCR-amplified
target (50–150 mg/mL) and 1 mL artificial standard target
(1 mM) in a fresh 10 mL aliquot of 2X hybridization solution
(6X SSC, 0.2% [w/v] SDS, 0.4% [w/v] BSA) under a
supported coverslip at the appropriate temperature for 12 h.
Next, the array was washed three times, first with lowstringency buffer (1X SSC, 0.2% [w/v] SDS) preheated to the
hybridization temperature for 3 min, second with highstringency wash buffer (0.1X SSC, 0.2% [w/v] SDS) for
3 min at room temperature, and finally with 0.1X SSC for
3 min at room temperature. Next, the hybridized microarray
was dried by centrifugation at 1,500 rpm for 3 min.
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Fluorescence Intensity Scanning and Data Analysis
A commercial confocal laser scanner (ScanArray Lite; GSI
Lumonics, Wilmington, MA) and quantitative microarray
analysis software (QuantArray; GSI Lumonics) were used
for image acquisition and fluorescence intensity analysis.
Scanning parameters were set at 95% of laser intensity and
85% of photomultiplier tube gain. Fluorescence intensities
of spots in scanned images were numbered by the analysis
software. Acquired experimental raw intensity data were
converted into two-dimensional plots for standard and
specific spots, with a gray gradation, using the Matlab m-file
as in our previous work (Eom et al., 2007; Hwang and Cha,
2008).
For classification of spot patterns peculiar to each target
strain, a pattern-mapping statistical analysis program was
constructed using an ANN algorithm. ANN is consisted of
many processing elements (PEs) which can process data by
summarizing and transforming them using a series of
mathematical functions like neurons in the brain (Groth,
1999). PEs are interconnected and each connections have
the weight which means connection strength. Therefore,
iterative training continue to change the weight of
connections to fit the data until the outcome values by
ANN model match the known outcome values within a
specified accuracy level. With the acquired numerical data,
all specific spots were normalized by summation of 20
specific averages. Next, the 20 specific normalized spots were
assigned as an input set to one of 11 pre-specified classes, as
an output. The pattern-mapping analysis tool was programmed by Visual Cþþ. At least five independent
experimental repeats for each pathogen were employed to
train the pattern-mapping model. In addition, extra
experimental sets were used for verification of the model,
with an error rate under 5%.
Results
Design and Assessment of Oligonucleotide Probes
In the present study, we used sequence dissimilarity greater
than 10–15% as main considering factor for probe design to
ensure specificity. Full 16S rDNA sequences were compared
only between the two phylogenetically closest species.
By such direct sequence comparison, we easily obtained
specific sequences for any two species, with two or more
mismatches. In addition, the specific sequences were verified
to be more then 10–15% dissimilar to all aligned sequences
of the 11 target pathogens. Next, the thermodynamic
properties of several probe candidates with the unique
signature sequences were calculated (Table I). A similar
melting temperature was regarded as the most important
thermodynamic property to ensure similar stringencies
during hybridization. The second probe design principle
was match analysis using the RDP II and NCBI databases.
Next, two or three candidate probes for each target
bacterium were selected, in order of specificity. Finally,
after experimental selection of probe candidates from the
first to the ninth variable regions of the 16S rDNA sequence,
20 specific capture probes with high specificity were selected
for 11 target pathogens (Table I). While we obtained two
specific capture probes for nine pathogens, only one capture
probe was chosen for each of E. coli and S. enterica serotype
Enteritidis due to the limited specific region of 16S rDNA.
The positive control capture probe was based on the first
conserved region sequence, to check for successful PCR
labeling (Eom et al., 2007). The artificial standard capture
probe and target were designed to have no significant
similarity with any bacterial or eukaryote sequence in the
NCBI database for verification of regional and chip-to-chip
variations as well as quantitative analysis (Hwang and Cha,
2008).
Analyses of Artificial Standard and Control Spots
In the present work, we used asymmetric PCR and an
artificial standard as the reference control (Hwang and Cha,
2008). Asymmetric PCR amplifies more sense target
sequence than antisense, and each product length was close
to 1,400 bp and contained variable sequences from the first
to the ninth regions of 16S rDNA. After assessment of
the concentration effect (0- to 10,000-fold dilution) of the
artificial standard target (final concentrations from 5 mM to
0.5 nM were used), we found that the fluorescence intensity
of artificial standard spots was saturated when the artificial
target was 0- to 10-fold diluted (thus, at 5 and 0.5 mM) (data
not shown). Therefore, the artificial standard target was used
at a 100-fold (50 nM) dilution in subsequent experiments.
The hybridization temperature was set at 508C owing to low
sensitivity at 55 and 608C (data not shown). At first glance,
all labeled positive control and artificial standard spots were
clearly detected after hybridization (Fig. 2A). The uniformly
strong fluorescence was observed on the positive control
spots (first four spots in Fig. 2A). Because it was difficult to
distinguish intensity differences between spots on scanned
raw images, the intensities of artificial standard spots were
represented by a two-dimensional visualization (Eom et al.,
2007; Hwang and Cha, 2008) using a gray gradation plot
(Fig. 2B). In this system, white indicates the highest spot
intensity and black the lowest. With this technique, almost
all artificial standard spot intensities appeared uniform
except for those of S. enterica serotype Enteritidis and
V. vulnificus (Fig. 2B-e and B-j).
gradation (Fig. 2C). Generally, analysis of pathogen
detection is based on a perfect match between the target
and specific capture probes (Keramas et al., 2003; Warsen
et al., 2004). We found that our microarray system clearly
discriminated seven strains among 11 target pathogens
(Fig. 2C), showing perfect matches on specific spots.
However, it was impossible to discriminate between S.
enterica serotypes Choleraesuis and Enteritidis (Fig. 2C-d vs.
C-e). In addition, with the two E. coli pathogens, strong
nonspecific hybridization to Shigella spots was observed,
even though specific spots for each E. coli subspecies were
also seen (Fig. 2C-a vs. C-b). Nonspecific binding to Shigella
spots was also noted when the two Salmonella serotypes were
tested, although the nonspecific intensities were not strong
(Fig. 2C-d vs. C-e).
We tested the sensitivity of our microarray system using
S. aureus as a representative strain. In the range of 1–10
cell numbers, noticeable specific spots were not shown
(Fig. 3A-a and A-b) while double specific spots (STAU1 and
STAU2) were clearly observed for 102–104 cells (Fig. 3A-c to
A-e). When we plotted fluorescence intensity values of two
specific spots with cell numbers, we confirmed that low cell
numbers (1–10 cells) had almost similar intensity values
with negative control spots, but the intensity values of the
high cell number samples (102–104 cells) were all clearly
distinguishable (Fig. 3B). Interestingly, almost linear
correlations were revealed for both specific spots of
STAU1 and STAU2 in the range of 102–104 cell numbers.
Thus, we determined that the sensitivity of the specific
capture probes is around 10–102 cells, equivalent to 50–
500 fg of pathogen chromosome, based on the presence of
4.5 1015 g of DNA in a single bacterial cell (Call et al.,
2003).
The relative standard deviation (RSD) of each specific
capture probe was calculated as the standard deviation for
the average number of perfect matches multiplied by 100 in
at least five repeated independent experiments. The factors
affecting RSD values are spot-to-spot or chip-to-chip
variation during chip making, labeling, hybridization, and
washing steps. In bioanalytical assays, the acceptable upper
limit for the RSD is generally 10–25 and a low RSD indicates
that each experiment yielded accurate and reliable data
(Braggio et al., 1996; Findlay et al., 2000). Among the
specific capture probes, that for S. enterica serotype
Enteritidis had a high RSD of 33.3 resulting from very
low and fluctuated intensity of specific matching (Table I).
All other probes showed RSD values less than 11%,
demonstrating appropriate precision and reliability.
Analysis of Specific Spots Based on Perfect Matches
With the specific spots, each line of four spots represents
specific capturing of individual pathogen DNA and perfect
match positions are indicated by white boxes in the scanned
raw images (Fig. 2A). As with the artificial standard spots
discussed above, it was difficult to distinguish intensity
differences between spots on the simple scanned raw images,
so the intensities of specific spots were represented by a gray
Statistical Analysis of Detection Results Based on
Pattern Mapping
We noticed that the patterns of fluorescent spots varied
somewhat according to the target pathogen even with the
two Salmonella serotypes and the two E. coli subspecies
(Fig. 2C). Therefore, to classify a unique spot pattern for
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Figure 2.
A: Raw hybridization data obtained when each asymmetric amplified target was exposed to the probe array in hybridization buffer. Each rectangular box indicates a
perfect match. Two-dimensional visualization plots for (B) the artificial standard and (C) specific spots. Intensities are represented by gray gradations. The maximum intensity is
white, whereas the minimum intensity is black. (a) E. coli, (b) E. coli O157:H7, (c) L. monocytogenes, (d) S. enterica serotype Choleraesuis, (e) S. enterica serotype Enteritidis,
(f) S. dysenteriae, (g) S. aureus, (h) V. cholerae, (i) V. vulnificus, (j) V. parahaemolyticus, and (k) Y. enterocolitica.
Figure 3.
A: Two-dimensional visualization plots for specific spots according to S. aureus cell numbers. (a) 1 cell, (b) 10 cells, (c) 102 cells, (d) 103 cells, and (e) 104
cells. B: Semi-log scattered plot for fluorescence intensity values of two specific spots according to cell numbers.
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Biotechnology and Bioengineering, Vol. 106, No. 2, June 1, 2010
Figure 4. Predicted detection of each target pathogen using the pattern-mapping statistical analysis program with an artificial neural network algorithm. The upper bar graph
is the average of the 20 spot intensities, with standard deviations, for each pathogen, and the lower bar graph shows the detection probabilities for the 11 pathogens.
each target strain, we used statistical analysis involving
a pattern-mapping strategy. We constructed the patternmapping model using an ANN algorithm (Fig. 4). The
pattern-mapping analysis model was developed using
experimental datasets from more than five independent
repeat tests for each pathogen. The graphic user interface for
the pattern-mapping analysis program was designed to show
detection probability (Fig. 4). Numerical data on the
20 specific spot intensities from scanned raw images were
directly inserted into the model as inputs and the analyzed
outputs were plotted as two two-dimensional bar graphs;
20-spot intensity averages with standard deviations in the
upper plot and the detection probabilities for the 11 species
in the lower plot. By this method, all pathogens were
clearly discriminated by signature pattern analysis (Fig. 4).
Importantly, the two Salmonella serotypes were clearly
distinguished. The two E. coli subspecies were also
clearly discriminated by pattern mapping. The probability
of specific pathogen detection was almost 100% for all
11 pathogens, and the probability of nonspecific detection
was less than 10%. We checked interference of our
microarray system using nontarget bacterium (data not
shown). In results, Bacillus cereus, different species with
11 target pathogens, was not detected in any specific spots
and pattern-mapping analysis result. Next, to evaluate the
possibility of simultaneous pathogen detection using
pattern-mapping analysis, we performed detection experiments employing two-chromosome mixtures. As shown
in Figure 5, the mixed species pairs of E. coli and E. coli
O157:H7, S. aureus and V. parahaemolyticus, and V. cholerae
and V. vulnificus were clearly detected. Detection probabilities for E. coli and E. coli O157:H7, S. aureus and
V. parahaemolyticus, and V. cholerae and V. vulnificus were 1
and 0.999 (Fig. 5A-d), 1 and 0.523 (Fig. 5B-d), and 1 and
0.60 (Fig. 5C-d), respectively.
Discussion
Most microarrays using the 16S rDNA detection principle
suffer from nonspecific binding caused by low sequence
diversity (Chandler et al., 2003; Eom et al., 2007; Guschin
et al., 1997; Liu et al., 2001; Peplies et al., 2006; Rudi et al.,
2000). Thus, several specific genes have been used as
Hwang and Cha: Pattern-Mapped Multiple Pathogen Detection Chip
Biotechnology and Bioengineering
189
Figure 5. Simultaneous detection of (A) E. coli and E. coli O157:H7, (B) S. aureus and V. parahaemolyticus, and (C) V. cholerae and V. vulnificus. (a) Raw hybridization data.
Two-dimensional visualization plots for (b) the artificial standard and (c) specific spots. (d) Predicted detection of the two target pathogens using the pattern-mapping program.
detection principles for multiple target pathogens to achieve
accuracy in the absence of nonspecific hybridization
(Bodrossy et al., 2003; Volokhov et al., 2003; Wilson
et al., 2002). However, multiple detection principles require
complicated preparation (such as multiplex PCR) of
multiple target DNAs, limiting the number of targets
available for efficient simultaneous detection, and are
associated with difficulties in linear amplification of targets
and subsequent quantitative analysis. On the contrary, a
single detection principle, such as that based on 16S rDNA,
can yield simple and easy detection of multiple target
pathogens. 16S rDNA also has further advantages such as
resolution of species discrimination level, involvement of
phylogenetic information, and detection feasibility of
unknown phylogenetically close species.
To reduce nonspecific binding in 16S rDNA-based
microarray systems, various strategies have been used for
the design of specific capture probes (Loy et al., 2005; Peplies
et al., 2003; Reyes-Lopez et al., 2003). Reyes-Lopez et al.
(2003) used multiple alignment for probe design from
within the two conserved regions flanking a highly variable
region. Peplies et al. (2003) achieved specific probe design by
considering secondary structures in 16S rRNA that could
affect hybridization between capture probe and target. Loy
et al. (2005) designed 16S rDNA probes using melting
temperature (Tm) and DG values as probe design criteria.
Previously, we have used multiple alignment, Tm, DG values,
and secondary structure when considering the design of
specific capture probes (Eom et al., 2007). In the present
study, we were of the view that sequence dissimilarity greater
than 10–15% was the most important probe characteristic
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Biotechnology and Bioengineering, Vol. 106, No. 2, June 1, 2010
ensuring specificity as described in previous studies (Call
et al., 2003; Eom et al., 2007). After the present experimental
analyses, we also confirmed that sequence dissimilarity was
the most important factor affecting probe specificity. To
greatly reduce the time and effort traditionally required for
the design of specific capture probes for target species
sharing similar sequences, we directly compared full 16S
rDNA sequences between the two phylogenetically closest
species. By such design strategy, we easily obtained specific
sequences for any two species, with two or more
mismatches. Thus, our design strategy efficiently found
specific capture probes for target microorganisms. However,
although we attempted to design two or more specific
capture probes for each target pathogen to improve
detection specificity and precision, we chose only one
specific capture probe for each of E. coli and S. enterica
serotype Enteritidis because, except for one variable region,
these strains share very similar sequences with their close
relatives (Table I).
Uniformity of artificial standard spot intensity indicates
reproducibility of the hybridization and washing steps.
Intensity differences in artificial standard spots between
chips can result from chip-to-chip variation. In addition,
differences can be due to higher fluorescence intensity at
initial loading of the hybridization mixture onto the slide,
heterogeneous hybridization, and/or irregular spot size.
Therefore, using this artificial standard probe strategy
(Hwang and Cha, 2008), regional and chip-to-chip
variations can be verified, and quantitative analysis may
also be possible. From the uniformly bright intensities of the
four positive control spots, it was clear that PCR labeling of
the target probe was successful and that hybridization data
would be reliable. Usually, analysis based on the scanned
raw images is used to distinguish intensity differences.
However, because it was difficult to distinguish subtle
intensity differences between spots on scanned raw images,
here, we used a two-dimensional visualization analysis with
gray gradation (Eom et al., 2007, Hwang and Cha, 2008).
The gray gradation visualization tool enables facile and
minute discrimination between spot intensities, through
enlargement of the intensity scale.
Our microarray system clearly discriminated 7 strains
among 11 target pathogens (Fig. 2C). Thus, the specificities
of the capture probes used in the present work were
significantly better than those achieved with previous probes
(Eom et al., 2007) due to the advanced probe design and
selection strategies. However, because S. enterica serotypes
have very conservative 16S rDNA sequences, it was
impossible to discriminate between two serotypes using
simple observation of specific spots (Fig. 2C). With the
two E. coli pathogens, strong nonspecific hybridization to
Shigella spots was also observed. We surmise that the
nonspecific fluorescence of Shigella spots may be attributed
to the low (4.8%) level of sequence dissimilarity afforded by
a single base pair mismatch between S. dysenteriae and E. coli
or Salmonella serotypes, and/or because the Shigella-specific
probe had the highest melting temperature of 68.98C (GC
content of 61.9%) among the 20 specific capture probes
(Table I). Thus, we determined that some pathogens could
not be clearly discriminated by perfect match analysis
when a conserved sequence such as 16S rDNA was used
for detection. Therefore, to provide clear discrimination
between very closely related pathogens, multiple detection
principles or other analytical strategies may be required.
Based on the observation of somewhat different
fluorescent spot patterns according to the target pathogen
even with the two S. enterica serotypes and the two E. coli
subspecies (Fig. 2C), we constructed the statistical patternmapping model to classify a unique spot pattern for each
target strain. As a well-known statistical decision-making
tool, the ANN has been used to model nonlinear relationships between inputs and outputs or to discover patterns in
data (Jain et al., 1996, 2000). This algorithm has mainly been
applied to cancer diagnosis (Khan et al., 2001; Wei et al.,
2004); variable spots are detected in a two-color comparison
using a cDNA microarray and several cancer genotypes have
been discriminated by clear differences in spot patterns.
Importantly, the two Salmonella serotypes and the two
E. coli subspecies were clearly distinguished by pattern
mapping (Fig. 4); this had not been possible by perfect
match analysis (Fig. 2). Thus, our pattern-mapping
statistical analysis strategy successfully provided clear
discrimination between very close species or subspecies,
as detected by a microarray system using conservative 16S
rDNA information as the sole detection principle. From the
evaluation of simultaneous pathogen detection possibility
using pattern-mapping tool, we found that detection
probabilities of second target strains were relatively lower
than first target strains (Fig. 5). This result showed that the
pattern-mapping model developed using only single
pathogen detection data might not be fully sufficient for
more reliable detection of mixed species. Therefore, to
achieve more accurate mixed detection with a high
probability, it might be necessary to use multiple pathogen
detection data for training of the pattern-mapping model.
The sensitivity value of our microarray system was close
to the 10 cells reported to be the lower detection limit of a
similar method using a PCR-amplified fluorescence-labeled
microarray (Call, 2005) and was much better than the 103–
104 cells acquired in our previous study (Eom et al., 2007).
The sensitivity improvement might result from the lower
hybridization temperature and/or greatly reduced repeat
levels in specific capture spots. The detectable cell number is
close to the infectious dose (10 cells) of certain pathogens
such as S. dysenteriae and V. vulnificus (Kothary and Babu,
2001).
In conclusion, we designed and generated a 16S rDNA
information-based oligonucleotide microarray system using
doubly specific capture probes for efficient and accurate
detection of 11 selected pathogenic bacteria. Many target
pathogens were clearly differentiated by perfect match
analysis using two-dimensional visualization plots for
standard and specific spots. However, some phylogenetically
close strains, including E. coli subspecies and Salmonella
serotypes, showed high nonspecific binding due to
conserved 16S rDNA sequences, resulting in difficulties in
detection based on only perfect match analysis. As we
observed that spot patterns were somewhat different
according to the target pathogen used (even between
phylogenetically closely related strains), we employed a
pattern-mapping statistical analysis strategy using an ANN
model. After development of the pattern-mapping model
using at least five sets of independently obtained data for
each pathogen, all target pathogens were easily and clearly
discriminated. Thus, the proposed novel system combining
a 16S rDNA-based microarray and pattern-mapping
analysis can reliably classify species and even some subtypes
of target pathogens easily, rapidly, accurately, and simultaneously. In addition, this total solution system can be
successfully expanded using the easy ‘‘add-up’’ concept to
detect many other microorganisms in foods or clinical
environments.
This work was supported by Technology Development program for
Agriculture and Forestry (#109187-3) from the Ministry for Flood,
Agriculture, Forestry and Fisheries, Young-Woong Satellite Laboratory grant, and the Brain Korea 21 program from the Ministry of
Education, Science and Technology, Korea. We thank Ms. Jae-Won
Lee (POSTECH) and Mr. Jung-Soo Lee (Young-Woong) for help with
pathogenic bacteria preparation and Dr. Jin-Hyun Park (P&I Consulting) for help with microarray data analysis.
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