Monitoring Gene Expression in Mixed Microbial Communities by Using DNA Microarrays

Abstract

A DNA microarray to monitor the expression of bacterial metabolic genes within mixed microbial communities was designed and
tested. Total RNA was extracted from pure and mixed cultures containing the 2,4-dichlorophenoxyacetic acid (2,4-D)-degrading
bacterium Ralstonia eutropha JMP134, and the inducing agent 2,4-D. Induction of the 2,4-D catabolic genes present in this organism was readily detected
4, 7, and 24 h after the addition of 2,4-D. This strain was diluted into a constructed mixed microbial community derived from
a laboratory scale sequencing batch reactor. Induction of two of five 2,4-D catabolic genes (tfdA and tfdC) from populations of JMP134 as low as 105 cells/ml was clearly detected against a background of 108 cells/ml. Induction of two others (tfdB and tfdE) was detected from populations of 106 cells/ml in the same background; however, the last gene, tfdF, showed no significant induction due to high variability. In another experiment, the induction of resin acid degradative
genes was statistically detectable in sludge-fed pulp mill effluent exposed to dehydroabietic acid in batch experiments. We
conclude that microarrays will be useful tools for the detection of bacterial gene expression in wastewaters and other complex
systems.

Full text

APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Feb. 2003, p. 769–778 Vol. 69, No. 2
0099-2240/03/$08.00⫹0 DOI: 10.1128/AEM.69.2.769–778.2003
Copyright © 2003, American Society for Microbiology. All Rights Reserved.
Monitoring Gene Expression in Mixed Microbial Communities by
Using DNA Microarrays
Philip Dennis,
1
† Elizabeth A. Edwards,
1
Steven N. Liss,
2
and Roberta Fulthorpe
3
*
Department of Chemical Engineering and Applied Chemistry, University of Toronto,
1
and Department of
Chemistry, Biology, and Chemical Engineering, Ryerson University,
2
Toronto, and Division of
Physical and Environmental Sciences, University of Toronto, Scarborough,
3
Ontario, Canada
Received 30 April 2002/Accepted 7 November 2002
A DNA microarray to monitor the expression of bacterial metabolic genes within mixed microbial commu-
nities was designed and tested. Total RNA was extracted from pure and mixed cultures containing the
2,4-dichlorophenoxyacetic acid (2,4-D)-degrading bacterium Ralstonia eutropha JMP134, and the inducing
agent 2,4-D. Induction of the 2,4-D catabolic genes present in this organism was readily detected 4, 7, and 24 h
after the addition of 2,4-D. This strain was diluted into a constructed mixed microbial community derived from
a laboratory scale sequencing batch reactor. Induction of two of five 2,4-D catabolic genes (tfdA and tfdC) from
populations of JMP134 as low as 10
5
cells/ml was clearly detected against a background of 10
8
cells/ml.
Induction of two others (tfdB and tfdE) was detected from populations of 10
6
cells/ml in the same background;
however, the last gene, tfdF, showed no significant induction due to high variability. In another experiment, the
induction of resin acid degradative genes was statistically detectable in sludge-fed pulp mill effluent exposed
to dehydroabietic acid in batch experiments. We conclude that microarrays will be useful tools for the detection
of bacterial gene expression in wastewaters and other complex systems.
Gene probes of various designs have allowed microbial ecol-
ogists to enumerate and track individual species and specific
genes in natural communities and engineered systems. Typi-
cally, the behavior of only of few genes can be addressed by
using colony blots, most-probable-number techniques, and flu-
orescence in situ probes (8, 10–12, 14, 15, 26). Genome se-
quencing projects have stimulated radical changes in experi-
mental methods from those that focus on “one gene at a time”
to those that aim to study thousands of genes or proteins at
once. One of these new genomic technologies, which arose also
from rapid developments in both robotics and miniaturization,
is DNA microarray-based technology (27). DNA microarrays
have revolutionized our ability to simultaneously carry out
hundreds or thousands of hybridization reactions at a time. In
most configurations, a DNA microarray is a glass microscope
slide onto which many thousands of DNA samples have been
spotted in a grid. DNA or mRNA is extracted from cells or
tissues, labeled with specific fluorescent molecules, and hybrid-
ized to the spotted DNA on the glass slide. The resulting image
of fluorescent spots is visualized in a confocal scanner and
digitized for quantitative analysis.
DNA microarrays have primarily been used in medical re-
search to investigate gene expression patterns in eukaryotic
cells such as human or yeast cells for which mRNA extraction,
purification, and cDNA synthesis protocols are well estab-
lished. The majority of prokaryotic microarray studies, how-
ever, have been focused on the genome of a single organism
(17, 32), often Escherichia coli, which is of limited applicability
to the complex microbial ecosystems found in wastewater
treatment systems, soils, and groundwaters. The use of DNA
microarrays for the monitoring of prokaryotic gene expression,
especially in mixed communities, is less developed in part due
to inherent difficulties related to extracting bacterial RNA and
priming cDNA synthesis from bacterial mRNA that lacks a
polyadenylated tail. Nonetheless, the use of microarrays for
the detection of prokaryotic gene expression (4, 13, 23) and the
quantitation of bacterial DNA (3) has been demonstrated.
Arbitrary primers (7), random hexanucleotide primers (33),
and species-specific C-terminal directed primers (6) have all
been used with some success.
We describe here the manufacture and testing of a prototype
DNA microarray composed of known microbial catabolic and
metabolic genes from a variety of organisms. Our aim was to
establish the methodology, sensitivity, and applicability of this
technique for measuring gene expression in a complex envi-
ronment; in particular, our focus was the study of pulp and
paper waste water treatment systems. Maintaining good per-
formance of these biological wastewater treatment systems is
relatively difficult: contaminant removal rates can vary signifi-
cantly between and within systems (19). Recently, the impor-
tance of analytical tools to better understand biological waste-
water treatment processes, particularly for monitoring and
modeling, has been emphasized (30). Although it is well es-
tablished that different microbial species exist in different bi-
ological wastewater treatment systems, no clear correlation
exists between species identity and system performance. Anal-
ysis of gene expression patterns will allow us to tease out the
environmental factors that significantly impact the induction
and repression of metabolic functions within a meaningful
context independent of culture-based methods. This can lead
to more enlightened approaches to the optimization of biore-
actors and wastewater treatment systems. Furthermore, this
* Corresponding author. Mailing address: University of Toronto at
Scarborough, 1265 Military Trail, Scarborough, Ontario M1C 1A4,
Canada. Phone: (416) 287-7221. Fax: (416) 287-7279. E-mail: fulthorpe
@utsc.utoronto.ca.
† Present address: GeoSyntec Consultants, Guelph, Ontario N1G
5G3, Canada.
769

technology could lead to advances in novel gene identification
through the detection of differential gene expression under
specific environmental conditions. Before the benefits of this
technology can be realized, however, effective methods for
mRNA extraction from complex systems, cDNA labeling, hy-
bridization, and data standardization must be demonstrated.
Moreover, the limits of our detection must also be understood.
Our first prototype microarray was composed of 64 genes
from a number of organisms. The purpose of the present study
was to demonstrate the feasibility of detecting gene expression
patterns in wastewater by using DNA microarrays and to es-
tablish current detection limits.
MATERIALS AND METHODS
Arrayed gene sequences. A total of 64 genes were obtained from our own
collections or from colleagues; these genes were obtained typically as clones.
Those for which we present data are shown in Table 1. Notably, a series of genes
involved in the degradation of chlorinated aromatic compounds were included so
that the array could be tested with a known 2,4-dichlorophenoxyacetic acid
(2,4-D) degrader, Ralstonia eutropha sp. strain JMP134 (5, 24). Clones of the
degradative genes tfdA,tfdB,tfdC,tfdE, and tfdF from JMP134 were for used the
amplification of full-length genes. Shorter tfdA fragments (ca. 300 bp) were
amplified from JMP134 and two other 2,4-D-degrading strains by using redun-
dant primers. These genes, from Burkholderia sp. strain RASC and Comomonas
sp. strain TFD41 had 70 and 100% sequence similarities with the JMP134 tfdA
cloned gene, respectively (9, 20, 31). In addition, 271-bp homologues of tfdC
were amplified from three chlorobenzoate-degrading strains also by using redun-
dant primers. These sequences, from Bulkholderia sp. strains CLAB3, HH83, and
WV71 exhibited 82% similarity to the JMP134 tfdC cloned gene (18, 31). Other
genes on the array included four resin acid degradation genes of the tdt family
isolated from Pseudomonas diterpeniphila (22); five naphthalene degradation
genes; genes for the degradation of alkanes, cellulose, toluene, styrene and
others; and 16S rRNA genes from Pseudomonas and Synechococcus spp. (Table
1). Most of the latter were included for the purposes of monitoring background
levels of hybridization signals. For this first prototype array, genes were selected
based on relevance to wastewater microbial processes and availability.
PCR to prepare DNA for arraying. PCR was performed with Taq polymerase
(0.03 U/␮l), 0.2 ␮M concentrations of each primer, and a 250 ␮M concentration
of deoxynucleotide triphosphate (dNTP) mixture in a buffer containing 2.0 mM
MgCl
2
(Roche Diagnostics, Laval, Quebec, Canada) by using a Thermolyne
Temptronic thermocycler (Barnstead/Thermolyne, Dubuque, Iowa). PCR prod-
ucts were produced by using either gene-specific or general primers (M13 or pET
15B T7) with cloned sequences or genomic DNA as a template (see Table 2 for
primer sequences and specific conditions). For plasmid templates containing
cloned genes, alkaline lysis Mini-Preps (25) were diluted 50-fold in Tris-EDTA
buffer. Genomic template DNA was produced directly from cultured cells by the
method of Ausubel et al. (1). The size of PCR products was confirmed by
electrophoresis in a 1.0% agarose gel. PCR products of expected size were
purified from primers and other components of the reaction mixture by using
QiaQuick spin columns (Qiagen, Inc., Mississauga, Ontario, Canada) and eluted
in 30.0 ␮l of water. The DNA concentration of purified samples was quantified
by fluorimetry by using an Rf-Mini 150 fluorometer (Shimadzu Scientific Instru-
ments, Inc., Columbia, Md.) and a fluorescent DNA quantification kit (Bio-Rad,
Hercules, Calif.). The target DNA concentration was 50 to 200 ng/␮l; PCR
products at concentrations above or below target were concentrated in a DNA
Speed Vac (Savant, Farmingdale, N.Y.) or diluted, respectively.
Printing of DNA microarrays. DNA microarrays were printed in the Microar-
ray Centre at the University Health Network, Toronto, Ontario, Canada. PCR
products in 3⫻SSC (1⫻SSC is 0.15 M NaCl plus 0.015 M sodium citrate) buffer
were placed in 384-well polypropylene collection plates (Whatman Polyfiltronics,
Inc., Rockland, Mass.). Microarrays were produced by using Stealth Chipmaker
3 Microspotting pins (Telechem International, Sunnyvale, Calif.) onto Corning
CMT-GAPS slides and were processed according to the protocol suggested by
the manufacturer (Corning, Acton, Mass.). A total of 64 genes were arrayed in
duplicate in two separate quadrates for a total of four replicates per gene.
RNA extraction and purification. RNA was extracted from pure and mixed
cultures by using Trizol LS Reagent (Life Technologies, Gaithersburg, Md.)
according to the manufacturer’s instructions. Liquid microbial cultures were
centrifuged in 15-ml disposable tubes at 1,700 ⫻gfor 5 min to pellet the cells.
Activated sludge RNA was also extracted by using Trizol and further purified
with an RNeasy spin column (Qiagen). RNA was quantified by spectrophotom-
etry at 260 nm. Treatment of RNA with Rnase-free DNase (Roche) for 15 min
TABLE 1. Genes on prototype DNA microarray
Gene
designation
GenBank
accession no. Function Organism
Fragment
size in bp
(% GC)
amoA D37875 Propene monooxygenase epoxidase subunit Nocardia coralina 800 (65)
amoB D37875 Propene monooxygenase coupling protein Nocardia coralina 400 (65)
carAb AF060489 Small subunit of iron-sulfur protein carbozole degradation Sphingomonas sp. strain CB3 600 (61)
carAc D89064 Initial dioxygenase carbazole degradation Sphingomonas sp. strain CB3 328 (52)
manA AF132735 Glycosyl hydrolase, mannan degradation Clostridium cellulovorans 1,238 (33)
nahAc2 ns
a
Naphthalene degradation Marinobacter hydrocarbonoclasticus 950 (58)
nifH U22146 Nitrogenase reductase, nitrogen fixation Synechococcus sp. strain RF-1 890 (45)
pdhA U09865 Pyruvate dehydrogenase Ralstonia eutropha 550 (67)
16S rRNA AF447138 Ribosomal RNA Pseudomonas sp. 1,300 (58)
16S rRNA Ribosomal RNA Synechococcus sp. strain RF-1 1,300 (48)
rpoN ns Transcription factor Ralstonia eutropha 550 (60)
tdtA ns Tricyclic diterpene degradation Pseudomonas diterpeniphila 1,216 (62)
tdtB ns Tricyclic diterpene degradation Pseudomonas diterpeniphila 805 (62)
tdtL AF274704 Tricyclic diterpene degradation Pseudomonas diterpeniphila 850 (62)
tdtR ns Tricyclic diterpene degradation Pseudomonas diterpeniphila 950 (62)
tfdA 41 ns Alpha-ketoglutarate-dependent 2,4-D dioxygenase; 2,4-D degradation Ralstonia eutropha TFD41 300 (64)
tfdA JMP134 M16730 Alpha-ketoglutarate-dependent 2,4-D dioxygenase; 2,4-D degradation Ralstonia eutropha JMP134 300 (64)
tfdA RASC U25717 Alpha-ketoglutarate-dependent 2,4-D dioxygenase; 2,4-D degradation Burkholderia sp. strain RASC 300 (55)
tfdA11 M16730 Alpha-ketoglutarate-dependent 2,4-D dioxygenase; 2,4-D degradation Ralstonia eutropha JMP134 801 (64)
tfdB M35097 Dichlorophenol monoxygenase 2,4-D degradation Ralstonia eutropha JMP134 1,000 (63)
tfdC M35097 Chlorocatechol dioxygenase 2,4-D degradation Ralstonia eutropha JMP134 850 (56)
tfdC CLAB3 AF068239 Chlorocatechol dioxygenase chlorobenzoate degradation Burkholderia sp. strain CLAB3 271 (64)
tfdC HH83 ns Chlorocatechol dioxygenase chlorobenzoate degradation Burkholderia sp. strain HH44 271 (64)
tfdC WV71 ns Chlorocatechol dioxygenase chlorobenzoate degradation Burkholderia sp. strain WV71 271 (64)
tfdE M35097 Dienelactone hydrolase 2,4-D degradation Ralstonia eutropha JMP134 800 (55)
tfdF M35097 Maleylacetate reductase 2,4-D degradation Ralstonia eutrophia JMP134 600 (57)
a
ns, not submitted.
770 DENNIS ET AL. APPL.ENVIRON.MICROBIOL.

TABLE 2. PCR conditions used to amplify genes used on prototype microarray
Gene designation PCR primers Program
a
PCR primer sequences
amoA M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
amoB J002B04-F/-R A F, 5⬘-TTACATCCATATGCGGCATCACGGCATGAC-3⬘
R, 5⬘-ACAGGATCCTCTCAGTCCTTGAAGCGG-3⬘
carAb J12A07-F/-R B F, 5⬘-TACTCATCCATATGTCCGTTGAACCCGTG-3⬘
R, 5⬘-GGCGGATCCTCAGAAGAACAACGTCAGG-3⬘
carAc J13A07-F/-R A F, 5⬘-TTTACCCTCATATGCGCTGGATTGACGC-3⬘
R, 5⬘-TGTGGATCCATCACGCCTCGGCTCC-3⬘
limC limC-F/-R C F, 5⬘-ACAATGGCAAGAGTAGAAGGA C-3⬘
R, 5⬘-CGTTCTACTTCAACGTTGTTCC-3⬘
manA manA-F/-R D F, 5⬘-TTTATCAATTTTAACTGCTGCG-3⬘
R, 5⬘-GCTCCAAAATTAGTGAAATTGC-3⬘
nahAc2 M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
nifH nif H-F/-R B F, 5⬘AAGGCGGTATCGGTAAGTCTAC-3⬘
R, 5⬘-CCATGTCAGCTTCCATAACTTT-3⬘
pdhA PDHA-F/-R C F, 5⬘-CTACAAGGCCGCCAGCGAGCACCA-3⬘
R, 5⬘-GCACGTACTTCTGGCCTTCCTGGTTCC-3⬘
16S rRNA1 63f/1387r C F, 5⬘-CAGGCCTAACACATGCAAGTC-3⬘
R, 5⬘-GGGCGGWGTGTACAAGGC-3⬘
16S rRNA2 63f/1387r C F, 5⬘-CAGGCCTAACACATGCAAGTC-3⬘
R, 5⬘-GGGCGGWGTGTACAAGGC-3⬘
rpoN RPON-F/-R C F, 5⬘-TGAAGAAGGCGCTGCAGGTGGATGAAG-3⬘
R, 5⬘-GGTGGACACGTGGCTGCCGAAGAAGTA-3⬘
tdtA M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
tdtB M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
tdtL M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
tdtR M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
tfdA 41 TVU/TVL E F, 5⬘-AACGCAGCGRTTRTCCCA-3⬘
R, 5⬘-ACGGAGTTCTGYGAYATG-3⬘
tfdA JMP134 TVU/TVL E F, 5⬘-AACGCAGCGRTTRTCCCA-3⬘
R, 5⬘-ACGGAGTTCTGYGAYATG-3⬘
tfdA RASC TVU/TVL E F, 5⬘-AACGCAGCGRTTRTCCCA-3⬘
R, 5⬘-ACGGAGTTCTGYGAYATG-3⬘
tfdA11 M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
tfdB M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
tfdC M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
tfdC CIAB3 CCDb/Ccde B F, 5⬘-GTITGGCATCTCIACICCIGATCGG-3⬘
R, 5⬘-CCICCCTTCGAAGTAGTACTTCIGT-3⬘
tfdC HH83 CCDb/Ccde B F, 5⬘-GTITGGCATCTCIACICCIGATCGG-3⬘
R, 5⬘-CCICCCTTCGAAGTAGTACTTCIGT-3⬘
tfdC WV71 CCDb/Ccde B F, 5⬘-GTITGGCATCTCIACICCIGATCGG-3⬘
R, 5⬘-CCICCCTTCGAAGTAGTACTTCIGT-3⬘
tfdE M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
tfdF M13 primers A F, 5⬘-CCCAGTCACGACGTTGTAAAACGAC-3⬘
R, 5⬘-AGGAAACAGCTATGACCATGATTAC-3⬘
a
Programs: A, 95°C for 1 min, 50°C for 1 min, and 72°C for 1.5 min, with a final extension for 5 min at 72°C (30 cycles); B, 95°C for 1 min, 48°C for 1 min, and 72°C
for 1.5 min, with a final extension for 5 min at 72°C (30 cycles); C, 95°C for 1 min, 55°C for 1 min, and 72°C for 1.5 min, with a final extension for 5 min at 72°C (30
cycles); D, 95°C for 1 min, 52°C for 1 min, and 72°C for 1.5 min, with a final extension for 5 min at 72°C (30 cycles); E, 94°C for 45 s, 59°C for 30 s, and 72°C for 2.0
min, with a final extension for 6 min at 72°C (35 cycles).
771

at 37°C in 10 mM Tris (pH 7.5) and 10 mM MgCl
2
had no significant impact on
the microarray signal strength, confirming that RNA, not residual DNA, was the
predominant template in labeling reactions.
Reverse transcription and labeling. Total extracted bacterial RNA was labeled
with a cyanine dye (either Cy3 or Cy5) in an “indirect”process by a modification
of the two-step labeling method available from the University Health Network
Microarray Centre (www.uhnres.utoronto.ca/services/microarray). First, cDNA
was synthesized from RNA in a reverse transcription reaction mixture containing
modified aminoacyl-dUTPs. After purification, the cDNA was labeled in a chem-
ical reaction where monofunctional cyanine dyes binds to the aminoacyl dUTPs.
In comparison to “direct labeling”(in which cyanine-labeled dNTPs are used
directly in the reverse transcription reaction), indirect labeling avoids problems
of differential incorporation of the Cy3- and Cy5-labeled dNTPs, results in lower
background fluorescence, and is more sensitive. We observed at least a twofold
increase in fluorescence intensity with the indirect method versus the direct
method; consequently, the indirect method was adopted for all experiments. The
labeling procedure was carried out as follows. Bacterial RNA (0.5 to 10.0 ␮g) was
combined in 1⫻first strand buffer (6.0 ␮l), random hexamer (4.0 ␮l), 10.0 mM
dithiothreitol, 500 ␮M dNTP mix (dATP, dCTP, and dGTP), 150 ␮M dTTP
(Life Technologies), and 150 ␮M aminoacyl-dUTP (Sigma, St. Louis, Mo.) in a
final reaction volume of 40.0 ␮l. The mixture was incubated at 25°C for 5 min,
after which 400 U of Superscript II reverse transcriptase (Life Technologies) was
added, and incubation was then continued at 25°C for another 10 min. The
reaction was warmed slowly to 37°C in an air incubator for 5 min and then
transferred to a 42°C heating block for 2 h. After incubation, reverse transcrip-
tase was inactivated by heating to 95°C for 5 min, followed by cooling on ice.
NaOH (167 mM) was added, and the reaction was heated to 65°C for 15 min to
degrade RNA, after which the solution was neutralized with 148 mM HCl and 70
mM Tris (pH 7.5).
cDNA purification. The reverse transcription reaction volume was increased to
100 ␮l with sterile distilled water, and the cDNA was purified by using a Qia-
Quick spin column according to the manufacturer’s instructions, except that the
elution step was carried out twice with 50 ␮lofH
2
O for 5 min. cDNA was
precipitated with 0.3 M sodium acetate (pH 4.8), glycogen (0.2 g/liter; Life
Technologies), and 1 volume of 100% ethanol, followed by incubation at ⫺70°C
for 30 min and centrifugation for 10 min at top speed in a microcentrifuge
(Eppendorf 5417C) at 4°C. The pellet was washed briefly with ice-cold ethanol
(70%) centrifuged for 5 min at full speed and air dried briefly.
cDNA labeling with reactive cyanine dyes. After precipitation, cDNA was
resuspended in 5.0 ␮lofH
2
O and then heated for 1 min at 42°C to dissolve the
DNA. Then, 3.0 ␮l of dye solution was added to the cDNA solution, followed by
mixing with a pipette. The dye solution consisted of 2.0 ␮l of either the Cy3 or
the Cy5 monofunctional reactive dyes (Amersham Pharmacia, Baie d’Urfe´, Que-
bec, Canada) mixed with 2.0 ␮l of 100% dimethyl sulfoxide in 0.3 M sodium
bicarbonate (pH 9.0). Tubes with cDNA and dye solution were incubated for 1 h
in the dark at room temperature to allow the chemical labeling reaction to
proceed. In this reaction, the monofunctional dyes bind to the aminoacyl dUTP
nucleotides previously incorporated during the reverse transcription reaction.
Purification of labeled cDNA. After monofunctional dye labeling, the reaction
volumes were increased to 100 ␮l and cDNA was purified by using QiaQuick spin
columns according to the manufacturer’s instructions except that washing was
performed three times with 75% ethanol and elution was performed three
separate times with 50 ␮l of elution buffer. After elution, cDNA was precipitated
as described above, and the pellets were resuspended in 2.5 ␮l of water for use
in hybridization.
Hybridization of cDNA to DNA microarrays. A 37.0-␮l mixture containing 30
␮l of DIG Easy Hyb buffer (Roche), 1.0 ␮l of a 10.0-␮g/␮l mixture of salmon
sperm DNA, 1.0 ␮l of yeast tRNA (Sigma), 2.5 ␮l of Cy3-labeled cDNA (from
sample 1), and 2.5 ␮l of Cy5-labeled cDNA (from sample 2) was placed on a
24-by-30-mm coverslip (Corning). DNA microarrays were touched to the drop of
hybridization on the coverslip and quickly inverted. Microarrays were incubated
overnight at 37°C in sealed plastic microscope slide boxes supported over DIG
Easy Hyb buffer to maintain humidity. After hybridization, the arrays were
washed three times in 650 ml of 0.1⫻SSC–0.1% sodium dodecyl sulfate, fol-
lowed by three washes in 0.1⫻SSC at 22 to 35°C. Slides were immediately dried
by centrifugation in microscope slide boxes lined with filter paper at 46 ⫻gfor
5 min.
Scanning of arrays and data analysis. Arrays were scanned at excitation wave
lengths of 532 and 635 nm to detect the Cy3 and Cy5 dyes, respectively. Arrays
were scanned by using a GenePix 4000A microarray scanner (Axon, Foster City,
Calif.). Typically, each microarray was scanned at two photomultiplier tube gain
settings for analysis. Data were corrected for background attributed to nonspe-
cific binding of the probes to the glass slide and the arrayed genes by using
GenePix Pro 3.0 software (Axon).
Signal standardization. Variability in signal intensity within and between
arrays can result from differences in cDNA probe concentrations, RNA quality
and quantity, labeling efficiency, scanner settings, different fluorescent properties
of the dyes, hybridization conditions, and other random sources of variation. In
order to make comparisons between experimental treatments on a given array or
between different arrays, signals need to be standardized for all of these param-
eters. We compared different methods of standardization (see Results).
Experiment 1: detection of tfd genes in pure culture. Parallel overnight cul-
tures of JMP134 were grown at 30°C with shaking on minimal medium with 6.6
mM pyruvic acid as a carbon source. Total RNA was extracted from 50-ml
subsamples from both cultures at time zero. Immediately after a sample was
taken for the t⫽0 h time point, one culture received 6.6 mM pyruvic acid
(noninduced) while another received 2.8 mM 2,4-D (induced). RNA was ex-
tracted at 4, 7, and 24 h postinduction from both cultures. The control (nonin-
duced) culture at each time point was labeled with Cy5, whereas the induced
culture was labeled with Cy3. Four microarrays, one for each time point, were
analyzed.
Experiment 2: detection of tfd genes in mixed cultures. Four unidentified
isolated organisms derived from a laboratory-scale sequencing batch reactor
treating pulp mill effluent (29) were used to construct an artificial mixed micro-
bial culture. Each of the isolates grew as distinctive colonies on agar containing
glucose and could be easily distinguished from JMP134. This mixed culture was
grown in liquid culture on minimal medium supplemented with 1.4 mM glucose
overnight at 30°C with shaking. An overnight culture of JMP134 was also grown
in minimal medium with 1.4 mM glucose as the carbon source. The JMP134 was
serially diluted into four 50-ml cultures that were mixed with the constructed
culture in a ratio of 1:1 such that the final JMP134 populations were 3.7 ⫻10
6
,
3.7 ⫻10
5
, 3.7 ⫻10
4
, and 3.7 ⫻10
3
cells/ml and the constructed culture popu-
lation was 1.0 ⫻10
8
cells/ml. In addition to these four dilution cultures, the pure
culture of JMP134, and the constructed mixed culture without JMP134 were also
included in this experiment. Two parallel sets of these six cultures were prepared.
One set was amended with 2.0 mM 2,4-D (induced), whereas the other was
amended with 1.4 mM glucose (noninduced). RNA extractions from both non-
induced and induced cultures were performed at 6 h postinduction. Plate counts
with minimal medium agar supplemented with 5 mM 2,4-D as the sole carbon
source were performed at the time of RNA extraction to confirm the density of
JMP134 in each culture. Six microarrays were used for this experiment. Labeled
cDNA from 2,4-D (Cy3) and glucose-amended (Cy5) parallel cultures were
compared on the same microarray.
Experiment 3: detection of resin acid degradation (tdt) genes in mixed pulp
mill bioreactor cultures. A sample of untreated (primary) pulp mill effluent from
a Kraft mill in Cornwall, Ontario, Canada, was inoculated with a bioreactor
sludge sample and grown for 16 h with stirring and aeration. The inoculum
(sludge sample) was taken from a bench-scale sequencing batch reactor fed pulp
and paper mill wastewater described by Tripathi and Allen (29). At time zero this
activated sludge culture was divided into two cultures that were amended either
with 0.5 mM dehydroabietic acid (DHA; a resin acid) or with a 0.8 mM concen-
tration of cellobiose as carbon sources. RNA was extracted from 7.5 ml of each
culture by using Trizol or Trizol plus a Qiagen RNeasy purification step at 0, 3,
6, and 24 h after carbon source amendment. For each time point, extracted RNA
from the cellobiose-amended culture was labeled with Cy3, and that from the
DHA-amended culture was labeled with Cy5; both were hybridized onto a series
of four microarrays, one for each time point.
Experiment 4: comparison of specific primers for cDNA to random hexamer
primers for cDNA synthesis. The potential for primers directed toward specific
genes to increase the sensitivity of gene expression detection compared to non-
specific random hexamer priming was tested with an undefined mixed culture
spiked with JMP134. Minimal medium (50 ml) with 1.4 mM glucose was inoc-
ulated with the constructed mixed culture and grown overnight at 30°C with
shaking. An overnight culture of JMP134 was also grown in the same manner. At
time zero, the mixed culture was subdivided into six subcultures, which were
made up to 100 ml and amended with 2.0 mM 2,4-D (cultures 1 and 3 to 6) or
1.4 mM glucose (culture 2). Cultures 3, 4, 5, and 6 were spiked with 10 ␮l, 100
␮l, 1 ml, and 10 ml of the JMP134 culture, respectively. Plate counts of the
JMP134 spike culture and all of the cultures at the time of RNA extraction were
performed on both 2,4-D agar and plate count agar. A mix of seven specific
primers were hybridized to mRNA during the cDNA synthesis step in the
indirect labeling approach utilizing Cy5 as described above. These primers were
directed to carAB (5⬘-TCAGGATCCTTTCAGCCCGAAACGTGC-3⬘), rpoN
(5⬘-GGTGGACACGTGGCTGCCGAAGAAGTA-3⬘), pdhA (5⬘-GCACGTAC
TTCTGGCCTTCTTGGTTCC-3⬘), manA (5⬘-GCTCCAAAATTAGTGAAAT
772 DENNIS ET AL. APPL.ENVIRON.MICROBIOL.

TGC-3⬘), tfdA (5⬘-ACGGAGTTCTGYGAYATG-3⬘), tfdB (5⬘-ATAGCGGTG
RTTCATYTC-3⬘), and limC (5⬘-CGAGGATTGACAGGTTGTAGCT-3⬘). cDNA
was also produced by using random hexamer-primed reverse transcription la-
beled with Cy3. The Cy3-labeled random-primed and the Cy5-labeled specific-
primed cDNAs were then hybridized to microarrays to compare signal intensity
for the specifically primed genes compared to that of the random-primed gene.
RESULTS AND DISCUSSION
Standardization of signals. Effective interpretation of mi-
croarray readings requires standardization of the signals from
each fluorophore within and among arrays. The most logical
way to do this is to standardize all signals to a parameter which
should be equal between treatments. We tested the use of four
such parameters. For each array and for each fluorophore, we
calculated the sum of the signal intensities of all genes on the
array (T), the sum of intensities of 16S rRNA genes (R), and
the sum of the intensities of all of the control genes, i.e., those
not expected to be induced (C). (In each case we used mean
intensity corrected for background.) We compared four meth-
ods for standardizing the data: dividing the intensity of a spe-
cific gene by T, by R, by T-R, or by C-R. Standardizing on the
basis of R gave significantly different intensity ratios of induced
to noninduced for the same genes on the same arrays scanned
at different gains, which was clearly unsatisfactory. The same
problem occurred with T, since the 16S rRNA genes contrib-
uted strongly to the total signal (frequently accounting for
⬎80% of total fluorescence). The intensities of the spots cor-
responding to the 16S rRNA genes were often at or near
saturation; hence, they were unsuitable for standardization.
Data standardized on the basis of T-R eliminated differences
between replicate scans; however, control (noninduced) genes
showed various ratios over the course of an experiment when
they should have been equivalent. Finally, standardization on
the basis of C-R, i.e., the sum of all noninduced, nonribosomal
genes, produced the same data for replicates and constant data
for control genes over time. This method was therefore
adopted for all analyses.
Distribution of signal data. Fluorescence levels from all
DNA spots after hybridization with cDNA derived from con-
trol (noninduced) cultures were not normally distributed, nor
were the ratios of data from each fluorophore (Fig. 1). How-
ever, a log
2
transformation of the ratios was sufficient to nor-
FIG. 1. Frequency distribution of data from a typical micrarray experiment. The number of DNA spots giving signals within the intervals
indicated on the xaxes—raw fluorescence for each fluor, the ratio of Cy3 to Cy5 fluorescence, and the log
2
(ratio ⫹1)—are shown. There is a
progressively better fit to normal distribution, as indicated by the Shapiro-Wilk statistic. The probability that a distribution differs from normality
is indicated on each of the plots, only the log-tranformed ratio did not differ significantly from normal.
VOL. 69, 2003 MICROBIAL GENE EXPRESSION IN DNA MICROARRAYS 773

malize the ratio data. Because a significant number of ratios
were at or close to zero, we used a log
2
(x⫹1) transformation
[the log(x⫹1) transformation is commonly used to normalize
data that exhibits a positively skewed distribution and is nec-
essary when zero values of xare common] (2, 35). Log
10
trans-
formations are normally used, but we use log
2
here so that
transformed values largely reflect the degree of expression
increase of a treated culture over control. Thus, a ratio of 1
gives a value of 1, whereas a ratio of 2 gives a value of 1.6, and
a ratio of 3 gives a value of 2. After this the log
2
(x⫹1) value
becomes increasingly similar to log
2
(x), and the yaxis can be
read as the fold increase.
Experiment 1: detection of tfd genes and homologues in pure
culture. Four microarrays, one for each time point (0, 4, 7, and
24 h postinduction), were analyzed. For each of the tfd genes
and their homologues, the normalized Cy3/Cy5 (i.e., 2,4-D
grown/pyruvate grown) signal ratios were calculated, trans-
formed, and averaged for the four replicates on each array.
Induction of the JMP134 genes was clearly detected after 4 and
7 h when three- to fivefold increases in signal intensity were
detected in tfdA,tfdC, and tfdE (Fig. 2A). Increases were lower
for tfdB and tfdF but still significant. Increases in the expres-
sion of all five genes at 4, 7, and 24 h were all significant at P
⬍0.00001, except for tfdC at 24 h, which was significant at P⫽
0.00162 (Fig. 2A). Different variants of the tfdA gene were
included on the array: the full 800 bp of tfdA gene cloned from
JMP134, a 300-bp fragment amplified from JMP134, a 300-bp
fragment amplified from TFD41 (a 2,4-D degrader harboring
exact homologues of the tfd genes of JMP134), a 300-bp frag-
ment amplified from Burkholderia sp. strain RASC, and a tfdA
variant that is 70% similar to tfdA. The data obtained from
these variants of the tfdA illustrated the specificity of the hy-
FIG. 2. Gene expression in a pure culture of R. eutropha JMP134
measured at 0, 4, 7, and 24 h after induction with 2,4-D. The yaxis
shows the log
2
(ratio ⫹1), where the ratio is equal to the standardized
fluorescence for induced culture divided by the standardized fluores-
cence for the control culture. Data were standardized on the basis of
total microarray fluorescence, excluding ribosomal and tfd genes prior
to averaging of replicate data. (A) Induction of JMP134 tfd genes;
(B) induction of tfdA genes of different lengths and similarities to
JMP134; (C) induction of tfdC-like genes; (D) expression patterns of
non-tfd genes. The induction signal drops for all genes at 24 h as 2,4-D
is depleted from the media.
774 DENNIS ET AL. APPL.ENVIRON.MICROBIOL.

bridization reactions. Induction was detectable (P⬍0.00001 at
4 and 7 h) for all but the PCR product from RASC (Fig. 2B).
The data indicate that fragment size was not very important
between 300 and 800 bp, although the smaller pieces do have
significantly lower signals at 4 and7h(P⬍0.02 and P⬍0.002
for the JMP134 fragment and P⬍0.00002 and P⬍0.000001
for the TFD41 fragment, respectively). Signals from tfdA from
RASC, only 70% similar to the probe, were statistically higher
at 4, 7, and 24 h than at time zero (P⬍0.05, P⬍0.0007, and
P⬍0.00016, respectively), although the increase was much
smaller than for the 100% similar targets. Similar results were
obtained for the various tfdC genes on the array. Induction was
clearly detected for all tfdC genes at hours 4 and 7 (P⬍0.0001;
Fig. 2C). Ratio increases indicative of induction were less
marked for the 82% similar tfdC gene homologues amplified
from CLAB3, HH83, and WV71 (Fig. 2C). This is to be ex-
pected since the mRNA derived probe will hybridize less
strongly to these less-similar targets. Lack of control and ribo-
somal genes is shown in Fig. 2D.
We are confident that RNA was not significantly contami-
nated by DNA. The lack of significant differences between
RNA samples treated with RNase-free DNase and those left
untreated confirms that RNA and not DNA was the template
from which labeled cDNA was produced. This is important if
we are to be sure that gene expression and not simply gene
presence (i.e., changes in the microbial community structure
etc.) is being monitored. We did not treat samples with RNase
in order to see whether the signal could be eliminated. How-
ever, we would not expect to see significant differences in
induction patterns between the genes in this experiment if
genomic DNA were being detected, since the genes are all
carried together on one plasmid.
Experiment 2: detection of tfd genes in mixed cultures. The
induction of the various full-length tfd genes was detected at
JMP134 populations from 10
3
to 10
7
cells/ml in a mixed culture
of 10
8
cells/ml (Fig. 3A). Induction of the tfdA gene was sta-
tistically detectable in JMP134 populations of as low as 3.7 ⫻
10
3
cells/ml (Student ttest compared to control, P⬍0.0197)
(Fig. 3A, top panel). However, the signals for tfdA genes in the
control cultures were unusually low (close to or below back-
ground) in this experiment, so this result was perhaps atypical.
Detection of the tfdA genes was more significant above 3.7 ⫻
10
4
cells/ml (P⬍0.0002). The lowest significant detection of
the tfdC gene occurred at 3.7 ⫻10
5
cells/ml (P⬍0.01782). For
tfdB and tfdE genes, the lowest detection levels were 3.7 ⫻10
6
cells/ml (P⬍0.00004 and ⬍0.00003, respectively). Results for
tfdF are obscured because of high variability. Detection limits
with different tfdA gene fragments and homologues were sim-
ilar (Fig. 3B). Detection of the tfdA 300-bp PCR fragments
from JMP134 and TFD41 was significant at 10
6
(P⬍0.0239)
and 10
5
cells/ml (P⬍0.0357), respectively (Fig. 3B, middle
panels). High variation in signals from the low similarity tfdA
from RASC allowed no induction detection at any population
(Fig. 3B, bottom panel). Detection of the tfdC homologues was
significant only for the gene amplified from the chlorobenzoate
degrader HH83 at 10
6
cells/ml (P⬍0.001) but not for CLAB3
and WV71 (Fig. 3C). No induction was observed for control
genes (not induced by 2,4-D [data not shown]). Overall, we can
say that detection of tfd gene induction at populations of 10
5
cells/ml in a background of 10
8
cells/ml of other bacteria is
certainly achievable. Lower detection limits may be possible
with longer gene fragments that are 100% similar, if back-
grounds are low. Since a great deal of sequence variation
typically exists within catabolic gene families, it will be impor-
tant for researchers to include as many sequence variants as
possible for a given function.
This level of detection is encouraging, especially compared
to recent reports for detection of genes in environmental sam-
ples by using DNA microarrays (3). Mohn and Stewart (21)
found levels of DHA-degrading bacteria to be ca. 1.1 ⫻10
6
CFU/ml, which is within the detection limit observed in our
experiments for the introduced 2,4-D degrader.
Experiment 3: detection of resin acid degradation genes in
mixed pulp mill bioreactor cultures. The resin acid DHA is a
substance commonly found in pulp mill effluents (21, 34). The
addition of DHA to pulp mill effluent bioreactor cultures led to
slight but highly statistically significant increases in the expres-
sion of resin acid degradation genes tdtA (P⬍0.0003), tdtB
(P⬍0.00014), and tdtL (P⬍0.000014) (Fig. 4). There was no
increase (after 1 day) of the regulatory tdtR gene, and there
were no significant differences between time points in the lev-
els of rpoN,amoB, or the overall average spot signal intensity
ratios for the array.
Experiment 4: comparison of random hexamer primers to
specific primers for cDNA synthesis. When primers specific for
the 3⬘end of a particular gene sequence were used instead of
random primers for the reverse transcription and subsequent
labeling of mRNA, much higher signal intensities were ob-
served for some genes. As shown in Table 3, when we used
FIG. 2—Continued.
VOL. 69, 2003 MICROBIAL GENE EXPRESSION IN DNA MICROARRAYS 775

specific primers targeting tfdA,tfdB,carAb,limC,manA,pdhA,
and rpoN, the hybridization signals from limC,pdhA, and rpoN
were much higher than those achieved by using a random
labeling approach. However, no improvement was seen for the
other four genes. The ratio of signal intensity obtained with the
specific primer mix to that obtained with the random primer
mix was generally lower than 1 for nontargeted genes (i.e.,
amoA,amoB,carAc,nahAc2, and ribosomal genes). This is to
be expected since nontargeted genes should not be reverse
transcribed by the specific primer mix. This experiment con-
firmed that specific primers may lead to significant signal en-
hancement, but the results were inconsistent. An inherent dan-
ger is the formation of primer dimers through complementary
pairing between two different primers, a danger that increases
as the number of different types of primers is added to the
mixture. For carefully chosen primers, for a limited number of
genes, this approach is worth pursuing.
For detection of a very high number of transcripts, however,
two other approaches will likely prove fruitful. Further work on
optimizing reverse transcription, labeling, and hybridization
methodologies with random primers might also improve de-
tection limits. In particular, reverse transcription reactions fol-
lowed by labeling often lead to very poor labeling for unknown
reasons, so some optimization is warranted. Another approach
is the selective enrichment of mRNA from total RNA. Reverse
transcription of mRNA from prokaryotes into cDNA is com-
plicated by the fact that ca. 95% of prokaryotic transcripts are
rRNA (28). Furthermore, bacterial mRNA lacks a poly(A) tail
(32) and therefore cannot be selectively labeled with oligo(dT)
primers. Labeling total RNA with random primers was effec-
tive and sensitive enough for detecting expression of some
genes from relatively abundant populations in a mixed com-
FIG. 3. Gene expression in a bioreactor community spiked with
JMP134 at different concentrations and induced with 2,4-D. The yaxes
are as described for Fig. 2. RNA was sampled at 6 h postinduction
from induced and noninduced cultures. The control sample (bars la-
beled C) was the total bioreactor community population (10
8
cells/ml)
with no JMP134 added. The populations of JMP134 were 3.7 times the
amounts shown on xaxis, where the highest population was from a
pure culture. (A) Induction of JMP134 tfd genes; (B) induction of tfdA
genes of different lengths and similarities to JMP134; (C) induction of
tfdC-like genes.
776 DENNIS ET AL. APPL.ENVIRON.MICROBIOL.

munity. However, the fact that a high percentage of prokary-
otic transcript is rRNA was reflected in the high spot intensi-
ties of the two 16S rRNA genes present on the microarray.
These always exhibited the highest reading of any gene
present, i.e., the reduction of rRNA. We are currently testing
removal of rRNA by using oligonucleotide-linked magnetic
beads or RNase H treatment after priming cDNA manufacture
with ribosome-specific oligonucleotides.
Clearly, a number of challenges still remain to be overcome
to most effectively apply microarray technology to monitoring
gene expression in complex microbial ecosystems. These in-
clude increasing the sensitivity (limit of detection) of the pro-
cedure, decreasing the amount of time required for performing
each analysis (now ⬃4 days), and increasing the number of
microbial genes per array. Increasing the ease with which the
number of genes per array could be approached through the
use of oligonucleotide probes instead of PCR products as in
the present study. Oligonucleotide probes would obviate the
need to obtain genetic material in the form of cloned genes or
cultured isolates; instead short regions of published gene se-
quences could simply be synthesized. Kane et al. (16) demon-
strated that 50-mer oligonucleotides were useful as probes on
DNA microarrays. An alternate approach to the use of known
genes would be the production of libraries of genes derived
from genomic DNA or cDNA from a specific environment (for
example, a wastewater treatment system). Such an array could
then be used in the identification of previously unknown genes
active under various conditions; in addition, correlation of
gene expression patterns to phenotypic properties might be
possible that could be useful in monitoring the efficiency and
health of treatment systems. Despite the challenges, the po-
tential for monitoring in situ microbial gene expression in
wastewater systems appears feasible; future studies will focus
on addressing and optimizing approaches and more thoroughly
assessing the capabilities of this promising technology.
ACKNOWLEDGMENTS
Financial support was provided by the Minimizing the Impact of
Pulp and Paper Effluents Research Consortium at the University of
Toronto Pulp and Paper Centre (supported by Aracruz SA, Domtar,
Ltd., Eka Chemicals, Georgia Pacific, Irving Pulp and Paper, Japan
Carlit, Sterling Pulp Chemicals, Carter Holt Harvey, and Tembec
Ltd.), by a CHIR grant to Aled Edwards, and by an NSERC strategic
grant.
We thank Pascale Macgregor, Ronit Andorn-Broza, Fred Better-
man, and Jing Sung for technical assistance. We especiallly thank Neil
Winegarden and others at the Microarray Centre at the University
Health Network. We also thank the numerous researchers who sent
genetic material used in the present study, including P. Barbieri, B. J.
Berger, A. Chakrabarty, A. Cook, E. Diaz, R. Eaton, H. Engesser, D.
FIG. 4. Detection of resin acid degradation genes in pulp and pa-
per treatment community spiked with DHA. tdtA,tdtB, and tdtL are
functional resin acid degradation genes; tdtR is a regulatory gene. rpoN
is a sigma factor protein gene (not expected to change), and amoB is
the gene for ammonia oxidase small subunit (not expected to change).
The average data for the whole microarray are also indicated. The y
axis is as described for Fig. 2. Shown are bars giving the log
2
ratio of
DHA- to glucose-grown culture signals plus the standard deviations of
the ratio (n⫽4). The first (solid) bar of each pair indicates the time
zero level; the second (shaded) bar is the culture sampled after 25 h.
An asterisk indicates significant changes in expression at Pⱕ0.0003.
TABLE 3. Ratios of specifically primed signals to randomly primed signals
Gene Avg ratio (SD)
a
in culture:
23456
Genes targeted by specific primers
tfdA (300 bp) 3.1 (1.6) 0.78 (0.21) 1.7 (0.3) 0.97 (0.22) 0.43 (0.04)
tfdA (800 bp) 0.92 (0.36) 0.26 (0.00) 0.31 (0.07) 0.19 (0.05) 0.16 (0.01)
tfdB 0.41 (0.03) 0.09 (0.01) 0.17 (0.03) 0.14 (0.06) 0.19 (0.02)
carAb 1.7 (1.2) 0.24 (0.13) 0.17 (0.06) 0.19 (0.09) 0.23 (0.08)
limC 43 (20) 9.7 (2.3) 12 (1.5) 9.6 (0.68) 13 (1.8)
manA 5.4 (5.9) 0.86 (0.16) 0.53 (0.01) 0.30 (0.02) 0.41 (0.19)
pdhA 30 (14) 9.7 (0.45) 11.7 (0.85) 11.2 (0.44) 12.8 (0.06)
rpoN 29 (10) 24.1 (6.0) 19.5 (1.7) 14.3 (3.7) 16.0 (1.8)
Genes not targeted by specific primers
amoA 0.42 (0.03) 0.09 (0.01) 0.15 (0.02) 0.11 (0.00) 0.18 (0.02)
amoB 0.58 (0.35) 0.35 (0.2) 0.37 (0.15) 0.25 (0.09) 0.40 (0.03)
carAc 1.05 (0.50) 0.36 (0.44) 0.65 (0.14) 0.31 (0.17) 0.64 (0.41)
nahAc2 0.93 (0.64) 0.17 (0.06) 0.24 (0.12) 0.19 (0.08) 0.74 (0.53)
rRNA1 0.64 (0.11) 0.40 (0.04) 0.29 (0.01) 0.27 (0.02) 0.38 (0.01)
rRNA2 0.49 (0.03) 0.20 (0.01) 0.19 (0.01) 0.15 (0.01) 0.23 (0.01)
a
Data are averages from four replicates. Cultures: 2, amended with 0.25 g of glucose/liter no JMP134, plate counts ⫽1.1 ⫻10
8
; 3, amended with 2 mM 2,4-D,
JMP134 ⫽1.0 ⫻10
4
, plate counts ⫽1.01 ⫻10
8
; 4, amended with 2 mM 2,4-D, JMP134 ⫽3.0 ⫻10
4
, plate counts ⫽8.2 ⫻10
7
; 5, amended with 2 mM 2,4-D, JMP134
⫽3.0 ⫻10
5
, plate counts not determined; 6, amended with 2 mM 2,4-D, JMP134 ⫽1.8 ⫻10
6
, plate counts ⫽7.6 ⫻10
7
cells/ml.
VOL. 69, 2003 MICROBIAL GENE EXPRESSION IN DNA MICROARRAYS 777

Gibson, P. Hallenbeck, C. Harwood, B. Hedlund, J. Heider, W. Hillen,
P. Hoffman, T. C. Huang, Y. Katayama, F. Kunst, A. Kolsto, K.
Inatomi, G. Lloyd-Jones, S. Kaplan, A. Kulakov, T. M. Louie, H.
Matusaki, H. Mori, C. Murrell, Y. Nagata, H. Nojiri, T. Omata, R.
Parales, D. H. Pieper, A. Sorokin, H. Saeki, B. Witholt, R. Wittich, T.
Wood, S. Vuilleumier, I. Yamamoto, G. Zylstra, and the late R. Cam
Wyndham.
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