Global Transcriptome and Physiological Responses of Acinetobacter oleivorans DR1 Exposed to Distinct Classes of Antibiotics

Abstract

The effects of antibiotics on environment-originated nonpathogenic Acinetobacter species have been poorly explored. To understand the antibiotic-resistance mechanisms that function in nonpathogenic Acinetobacter species, we used an RNA-sequencing (RNA-seq) technique to perform global gene-expression profiling of soil-borne Acinetobacter oleivorans DR1 after exposing the bacteria to 4 classes of antibiotics (ampicillin, Amp; kanamycin, Km; tetracycline, Tc; norfloxacin, Nor). Interestingly, the well-known two global regulators, the soxR and the rpoE genes are present among 41 commonly upregulated genes under all 4 antibiotic-treatment conditions. We speculate that these common genes are essential for antibiotic resistance in DR1. Treatment with the 4 antibiotics produced diverse physiological and phenotypic changes. Km treatment induced the most dramatic phenotypic changes. Examination of mutation frequency and DNA-repair capability demonstrated the induction of the SOS response in Acinetobacter especially under Nor treatment. Based on the RNA-seq analysis, the glyoxylate-bypass genes of the citrate cycle were specifically upregulated under Amp treatment. We also identified newly recognized non-coding small RNAs of the DR1 strain, which were also confirmed by Northern blot analysis. These results reveal that treatment with antibiotics of distinct classes differentially affected the gene expression and physiology of DR1 cells. This study expands our understanding of the molecular mechanisms of antibiotic-stress response of environment-originated bacteria and provides a basis for future investigations.

Full text

Global Transcriptome and Physiological Responses of
Acinetobacter oleivorans
DR1 Exposed to Distinct
Classes of Antibiotics
Aram Heo
1
, Hyun-Jin Jang
2
, Jung-Suk Sung
2
, Woojun Park
1
*
1Laboratory of Molecular Environmental Microbiology, Department of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of Korea,
2Department of Life Science, Dongguk University, Seoul, Republic of Korea
Abstract
The effects of antibiotics on environment-originated nonpathogenic Acinetobacter species have been poorly explored. To
understand the antibiotic-resistance mechanisms that function in nonpathogenic Acinetobacter species, we used an RNA-
sequencing (RNA-seq) technique to perform global gene-expression profiling of soil-borne Acinetobacter oleivorans DR1
after exposing the bacteria to 4 classes of antibiotics (ampicillin, Amp; kanamycin, Km; tetracycline, Tc; norfloxacin, Nor).
Interestingly, the well-known two global regulators, the soxR and the rpoE genes are present among 41 commonly
upregulated genes under all 4 antibiotic-treatment conditions. We speculate that these common genes are essential for
antibiotic resistance in DR1. Treatment with the 4 antibiotics produced diverse physiological and phenotypic changes. Km
treatment induced the most dramatic phenotypic changes. Examination of mutation frequency and DNA-repair capability
demonstrated the induction of the SOS response in Acinetobacter especially under Nor treatment. Based on the RNA-seq
analysis, the glyoxylate-bypass genes of the citrate cycle were specifically upregulated under Amp treatment. We also
identified newly recognized non-coding small RNAs of the DR1 strain, which were also confirmed by Northern blot analysis.
These results reveal that treatment with antibiotics of distinct classes differentially affected the gene expression and
physiology of DR1 cells. This study expands our understanding of the molecular mechanisms of antibiotic-stress response of
environment-originated bacteria and provides a basis for future investigations.
Citation: Heo A, Jang H-J, Sung J-S, Park W (2014) Global Transcriptome and Physiological Responses of Acinetobacter oleivorans DR1 Exposed to Distinct Classes
of Antibiotics. PLoS ONE 9(10): e110215. doi:10.1371/journal.pone.0110215
Editor: Nancy E. Freitag, University of Illinois at Chicago College of Medicine, United States of America
Received June 12, 2014; Accepted September 9, 2014; Published October 17, 2014
Copyright: ß2014 Heo 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.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. The RNA-seq data were deposited in the
National Center for Biotechnology Information (NCBI) GEO site under accession numbers GSE38340, GSE44428, GSE58166 and GSE58167.
Funding: This work was supported by the Mid-career Researcher Program through an NRF grant (2014R1A2A2A05007010) funded by the Ministry of Science, ICT
& Future Planning (MSIP). 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: wpark@korea.ac.kr
Introduction
Antibiotics are abundant in various environmental habitats such
as seawater, plants, sludge, and soils [1–3]. Because antibiotics
affect our ecosystem, which includes the microbial diversity and
abundance in the environment, they are widely considered to act
as key pollutants [4,5]. Although antibiotics contaminate the
environment, how antibiotics affect environment-originated bac-
teria and their evolution remains poorly understood. Because most
antibiotics used for treating infections are produced by environ-
mental microorganisms, antibiotic resistance genes and mecha-
nisms could exist in nonclinical habitats [6]. In natural environ-
ments, antibiotic production and resistance might be considered as
biochemical warfare to eliminate competing organisms because
antibiotics suppress bacterial growth and metabolism [7]. Antibi-
otics of distinct classes act on different targets through specific
mechanisms: b-lactams lead to autolysis by interfering with cell-
wall biosynthesis [8]; aminoglycosides cause mistranslation by
targeting the 30S subunit of the ribosome [9,10]; tetracycline
inhibits protein synthesis by disrupting the binding of aminoacyl-
tRNA to the mRNA-ribosome complex [11]; and fluoroquino-
lones inhibit DNA replication by binding with DNA gyrase and
topoisomerase [12]. Antibiotic resistance could be acquired
through several ways: i) the action of antimicrobial-inactivating
enzymes, ii) reduced access of antimicrobials to bacterial targets
(decreased outer-membrane permeability and overexpression of
multidrug efflux pumps), and iii) mutations that change targets or
cellular functions [13]. Many clinical and environmental bacteria
have multiple antibiotic-resistance mechanisms [13].
The diesel-degrading A. oleivorans DR1 was isolated from the
rice paddy soil and its genome was completely sequenced [14].
Our previous studies demonstrated that quorum sensing and
biofilm formation are important for diesel-degradation in DR1
cells [14]. Most antibiotic resistance studies of Acinetobacter
species have largely focused on pathogenic Acinetobacter such as
Acinetobacter baumannii owing to high level of multidrug
resistance. Transcriptional responses to various antibiotics and
their regulation have not been extensively defined with Acineto-
bacter species. Reducing access to bacterial targets by means of
decreasing permeability and using strong efflux systems has been
reported as a major cause of multidrug resistance in Acinetobacter
species [15]. Because the genome of DR1 is similar to those of the
human pathogens A. calcoaceticus and A. baumannii [16], the
PLOS ONE | www.plosone.org 1 October 2014 | Volume 9 | Issue 10 | e110215

DR1 strain is appropriate for studying antibiotic effects in
evolutionary aspect. To identify key genes and their functions in
the antibiotic resistance of environment-originated bacteria, we
performed whole-transcriptome profiling of Acinetobacter oleivor-
ans DR1 using RNA-Seq technique. with four representative
antibiotics: ampicillin (Amp), kanamycin (Km), tetracycline (Tc),
and norfloxacin (Nor).
Bacteria could exhibit physiological changes by changing global
gene expression pattern in response to low concentration of
antibiotics [17]. To promote understanding how antibiotic
resistance develops in DR1, we also conducted several physiolog-
ical tests on DR1 under distinct antibiotic stresses. Herein we
provide both transcriptomic and experimental evidence of
antibiotic-resistance mechanisms in DR1. Elucidating transcrip-
tional and physiological responses to distinct antibiotics might
establish novel molecular basis for antibiotic-resistance mecha-
nisms of Acinetobacter species.
Results
Comparative transcriptome analysis of A. oleivorans DR1
exposed to sub-MICs of antibiotics of distinct classes
Antibiotics have been reported to affect bacterial transcription
in a concentration-dependent manner, and using antibiotics at
concentrations as high as the MIC can cause extensive cellular
stress and death [17]. To determine the appropriate concentration
of antibiotics for the antibiotics induced transcriptome, we
measured MICs of 4 classes of antibiotics in various cell densities
(10
5
–10
8
CFU/mL). When cell density increased, the MIC of
antibiotics was increased (Figure S1). This result demonstrates the
relationship between the cell density of bacteria and the MIC of
antibiotics. Because of transcript modulation decreases at high
antibiotics concentration, DR1 cells were exposed to sub-MIC of
distinct antibiotic classes. Sub-MIC of antibiotic allows susceptible
strains to grow, but induces stress responses. The highest MICs
measured were for Amp (100–200 mg/mL), and by comparison,
DR1 cells were more susceptible to other antibiotics (MICs, 1–
8mg/mL). We speculate that high number of lactamases encoded
by the DR1 genome confer high resistance to Amp (and thus the
high MIC ranges). In this study, we selected the genes that showed
a 1.5-fold change in expression after antibiotic treatment when
compared with the expression in control cells that were not
exposed to antibiotics. In response to Amp, Km, Tc, and Nor, the
expression levels of 1054 (26.6%), 1497 (37.33%), 1170 (29.52%),
and 208 (5.25%) genes were markedly upregulated, and the levels
of 1738 (43.86%), 910 (22.96%), 1254 (31.64%), and 635 (16.02%)
genes were downregulated, respectively (Figure 1A, Table S1).
The change in the expression of the same genes in response to
each antibiotic treatment suggested that common responses were
elicited by the 4 classes of antibiotics: 41 and 14 genes were
commonly upregulated and downregulated, respectively (Fig-
ure 1B, Table 1, Table S2). Several upregulated genes appear to
encode hypothetical proteins, a redox-sensing regulatory protein
(soxR), RNA polymerase sigma factor (rpoE), dehydrogenases, and
numerous transporter proteins. The commonly downregulated
genes encoded a glycosyltransferase (wcaA), a lipoprotein (rlpA),
and 3-dehydroquinate dehydratase (aroQ) (Table S2). Our RNA-
Seq results were confirmed with quantitative real-time PCR (qRT-
PCR). Commonly up- and down- regulated genes (soxR, rpoE,
lysR, wcaA) and specifically induced genes were selected based on
expression vales in 4 antibiotics conditions (Figure S2).
Effects of antibiotics on the expression of specific genes
Clusters of orthologous groups (COGs) were analyzed to
examine specific gene-expression changes (Figure S3). DR1 cells
treated with Amp exhibited altered expression of several COG
categories: translation (COG J), transcription (COG K), and
inorganic-ion transport/metabolism (COG P) categories were
mainly downregulated, whereas lipid metabolism (COG I) and
amino-acid metabolism and transport (COG E) categories were
upregulated. By contrast, Km and Tc treatments boosted the
expression of gene clusters involved in transcription (COG K),
amino-acid metabolism/transport (COG E), and inorganic-ion
transport/metabolism (COG P) categories, whereas the treatments
downregulated the expression of gene clusters involved in cell-
wall/membrane/envelop biogenesis (COG M). Under Nor
treatment, most COG categories were not changed to the same
degree as they were changed in response to other antibiotics;
certain genes involved in inorganic-ion transport/metabolism
(COG P) were upregulated and transcription genes (COG K) were
downregulated (Figure S3). COG analyses of the transcriptomes
revealed that the genes associated with amino-acid metabolism
and transport and inorganic-ion transport and metabolism are
critical for cellular-stress and cell-death responses under all
antibiotic-treatment conditions. Our data suggest that amino-acid
metabolism and transporter systems might play key roles in
antibiotic-resistance mechanisms in Acinetobacter species.
Distinct antibiotics possess specific cellular targets such as DNA,
RNA polymerase, ribosomal proteins, and cell walls [18]. The
overexpression of the antibiotic targets could enhance the survival
of bacterial cells under antibiotic treatment [19]. Our data showed
that specific antibiotic targets were strongly upregulated under
distinct antibiotic conditions. The DR1 genome contains 9
putative lactamase genes. Interestingly, not all b-lactamases were
upregulated by Amp, and class-C-type b-lactamases were primar-
ily induced by Amp (Table 2). The expression of genes encoding
penicillin-binding proteins was downregulated under Amp treat-
ment (Table 2), which might be because of the high concentration
of Amp used. Km treatment downregulated several ribosomal-
protein genes (data not shown). However, Tc induced the
expression of several ribosomal-protein genes, including the
expression of genes encoding ribosomal proteins S13 and S7,
which are recognized to interact directly with Tc [20]. Moreover,
Tc treatment induced enzymes that modify ribosomal proteins,
such as 50S ribosomal-protein methyltransferase and 30S ribo-
somal-protein methylthiotransferase. Thus, although Km and Tc
inhibit translation by binding to ribosomes, their influences on
cellular responses appeared to differ. Cells treated with Nor
exhibited 2.92- and 2.8-fold increases in the expression of gyrA
(AOLE_18380) and gyrB (AOLE_00595), respectively, which are
recognized as targets of fluoroquinolone-class antibiotics (Table 2).
Interestingly, Tc and Nor did not induce any lactamase genes, but
Km induced class-C b-lactamase (AOLE_17635) and metallo-b-
lactamases (AOLE_00775 and AOLE_03925). Acinetobacter
species appear to express numerous efflux-pump genes that are
critical for the multidrug resistance of A. baumannii [21].
However, based on our transcriptome data, it is unclear which
efflux pumps are crucial for conferring resistance against the
antibiotics tested (Table 2). Certain efflux pumps might be specific
to each antibiotic.
Interestingly, our data showed that the expression of fimbriae/
pili-related genes changed in response to treatment with Amp,
Km, and Tc (Table S3). Previously, we reported that A. oleivorans
DR1 possesses 2 major fimbrial appendages [22]. Expression of
fimbrial and pilin proteins is consistent with the cell aggregation
and biofilm formations [23], and flagellar/pili appendages can
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 2 October 2014 | Volume 9 | Issue 10 | e110215

function as transporting or adhering machines in gram-negative
bacteria [24]. Other transcriptome data demonstrated that the
flagellar/pili metabolism-related genes were induced under diverse
stress conditions such as osmotic stress, oxidative stress [25,26].
Beyond affecting target-gene expression, antibiotics are considered
to exert secondary effects that are a part of the adaptive response
to antibiotic stress.
Effect of distinct classes of antibiotics on physiology: cell
morphology and membrane permeability
We investigated the effects of antibiotics on physiological
changes such as cell death and alterations in cell morphology
and membrane permeability in an effort to link gene-expression to
physiology. Examination of the morphology of cells stained with
49,6-diamidino-2-phenylindole (DAPI) showed that cells treated
with antibiotics were longer than wild-type cells (Figure 2A, Figure
S4), and the cells treated with Amp and Nor were nearly 4-times
longer than control cells (Figure 3A, B). Previously, b-lactams were
reported to lengthen cells by inhibiting peptidoglycan biosynthesis
[27]. Cell filamentation is often associated with the SOS response
[28]. The product of the sulA gene, a key component of the SOS
response that leads to cell elongation by binding to FtsZ or DpiAB
in a two-component system, induces cell filamentation [29].
Interestingly, no sulA gene homolog is present in Acinetobacter
species, and thus it is worth identifying the roles of other genes
involved in cell elongation in Acinetobacter species. In the cell walls
of most bacteria, peptidoglycans play an essential role in
antimicrobial resistance; peptidoglycans determine cell shape,
and their biosynthesis is critical for antibiotics resistance [30].
Peptidoglycan hydrolase is a widely conserved outer-membrane
protein that modulates cell shape in E. coli and Pseudomonas
aeruginosa [31]. The expression of AOLE_00215, which encodes
peptidoglycan hydrolase, was increased 1.5- and 1.6-fold by Km
and Tc, respectively, but was not markedly affected by Amp and
Nor.
We measured the change in membrane permeability by using
ANS, a neutral, hydrophobic fluorescent probe; in membrane-
damaged cells, fluorescence is increased because the enhanced
permeability leads to ANS uptake [32]. The fluorescence-intensity
values measured were divided by the OD
600
values for normal-
izing the measurements, and the results showed that distinct
antibiotic treatments altered membrane permeability to different
degrees (Figure 2B). The membrane-permeability properties have
a major impact on the susceptibility of microorganisms to
antibiotics [33]. Membrane permeability was increased substan-
tially after Km treatment, whereas only a slight increase of
membrane permeability was induced by Amp and Tc, which
might explain the sensitive response of DR1 cells to Km. Porins
Table 1. Genes in A. oleivorans DR1 commonly upregulated by Amp, Km, Tc, and Nor.
Locus_tag DR1 Product Genes Fold-change
Amp Km Tc Nor
AOLE_02445 Enoyl-CoA hydratase caiD 4.62 1.78 2.34 1.87
AOLE_04025 Metal-dependent hydrolase 3.84 2.97 4.43 2.45
AOLE_06735 Putative short-chain dehydrogenase 4.15 2.37 3.63 1.92
AOLE_06795 Alkylhydroperoxidase 2.94 2.23 2.92 1.58
AOLE_08565 AraC-type DNA-binding domain-containing protein araC 15.35 3.55 3.11 2.10
AOLE_08595 3-Oxoadipate enol-lactonase mhpC 3.08 1.78 2.73 1.57
AOLE_08710 3-Oxoacyl-(acyl-carrier-protein) reductase fabG 4.18 4.13 3.10 2.09
AOLE_08725 NIPSNAP family protein 3.35 3.55 6.22 2.09
AOLE_08765 Shikimate dehydrogenase aroE 15.61 7.14 12.13 5.26
AOLE_09075 Transcriptional regulator lysR 5.71 4.27 2.03 1.68
AOLE_09435 DoxX family protein 2.24 7.09 5.44 1.57
AOLE_09590 Putative tonB-like protein tonB 1.57 3.90 3.42 1.67
AOLE_10175 Putative aliphatic sulfonate-binding protein tauA 2.69 2.04 5.02 1.65
AOLE_11820 Major facilitator superfamily transporter araJ 144.14 3.30 5.23 2.84
AOLE_11830 Methyltransferase domain-containing protein ubiE 157.57 3.56 4.10 1.84
AOLE_12115 DMT-family permease 3.07 2.36 3.88 2.44
AOLE_12135 Redox-sensitive transcriptional activator SoxR soxR 2.52 4.00 2.53 2.88
AOLE_12655 ECF subfamily protein RNA polymerase sigma-24 subunit rpoE 5.05 3.56 1.82 1.75
AOLE_12705 Glycine betaine ABC transporter substrate-binding protein tauA 1.71 1.77 1.75 2.49
AOLE_12875 Phenylacetic acid degradation protein paaI 17.54 3.56 4.68 2.10
AOLE_13495 Competence-damaged family protein cinA 5.05 13.36 7.42 2.63
AOLE_14540 Peptide deformylase def 2.87 4.45 1.85 2.75
AOLE_14590 3-Phenylpropionate dioxygenase ferredoxin nirB 2.44 2.85 3.75 1.58
AOLE_14800 RNA polymerase sigma factor FecI rpoE 27.98 17.79 1.87 2.94
AOLE_16560 Short-chain dehydrogenase 1.71 2.44 2.24 1.57
AOLE_18975 GNAT family acetyltransferase 5.39 6.56 2.78 1.70
doi:10.1371/journal.pone.0110215.t001
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 3 October 2014 | Volume 9 | Issue 10 | e110215

are considered to be permanently open pores, and lowering porin
expression reduces outer-membrane permeability [33]; thus,
porin-mediated permeability is a critical aspect of antibiotic-
resistance mechanisms. The DR1 genome contains several porin-
encoding genes. The expression of ompC (AOLE_10405), which
encodes an outer-membrane porin protein, increased 1.5-fold
under Km treatment, but decreased in response to Amp (6.4-fold)
and Nor (1.5-fold) and did not change after Tc treatment.
Oxidative stress, SOS response, and DNA repair in
response to distinct antibiotics
Antibiotics have been widely reported to induce the production
of reactive oxygen species (ROS), which causes oxidative stress
damage [34]. We used the fluorescent probe DHR 123 and flow
cytometry to monitor ROS generation following treatment with
the 4 antibiotics (Figure 3): under the tested conditions, treatment
with Amp, Km, and Nor, but not Tc, potently induced ROS
generation. Interestingly, the expression profiles of oxidative stress-
related genes were distinct following treatment with these
antibiotics of different classes, based on which we suggest that
distinct mechanisms exist that are used by bacteria for coping with
disparate types and levels of oxidative stress induced by various
antibiotics (Table 2). Peroxiredoxin (ahpC) and catalase (katE1)
genes were induced by Amp and the thioredoxin (trxA) gene was
highly upregulated by Km and Tc, whereas the redox-sensing
regulatory gene soxR was induced by all antibiotics. Antibiotic-
induced oxidative stress upregulated glyoxylate-bypass genes [35].
The expression levels of isocitrate lyase (aceA) and malate synthase
(aceB) genes, which are link to glyoxylate bypass, were increased
substantially in response to Amp and Nor, but not Tc and Km
(Figure 4). These results suggest that distinct classes of antibiotics
elicit different responses to oxidative stress by dissimilarly affecting
the expression of genes associated with ROS defense and
glyoxylate bypass.
Unexpectedly, only Nor treatment substantially upregulated the
expression of these SOS response-related genes and DNA-repair
genes: recA, umuDC, dinP, uvrAC, and ssb (Table 3). The SOS
response is a global response to DNA damage in bacteria that is
induced by a variety of environmental factors such as UV
radiation, chemicals, and antimicrobial compounds [36]. The
RecA protein and LexA repressor play central roles in SOS
response [37,38], but a LexA-like transcriptional repressor has
been studied only poorly in Acinetobacter species [39]. DNA
damage increases the frequency of mutations when MMC is used,
which indirectly confirms the presence of the SOS response [40].
Previously, MMC-induced mutation frequency was monitored by
measuring the increase of colonies resistant to rifampicin [41].
MMC treatment increased the rifampicin-resistance mutation
frequency 47-fold in DR1. When E. coli GC4468 and A.
baumannii ATCC17978 were used as reference strains, the
mutation frequency was determined to be increased 22- and 37-
fold in E. coli and A. baumannii, respectively (Figure 5A). Our
results reveal that crucial features of the canonical SOS response
exist in the genome of DR1 cells. When we measured antibiotic-
induced SOS response, we determined that rifampicin-resistance
mutation frequency was strongly induced only by Nor (Figure 5B).
Agreeing with these data, our reporter strains carrying GFP fused
to the recA promoter region showed that Nor treatment induced
the SOS response (Figure 5C). The fluorescence of these reporter
cells depended on the concentration of Nor, although a high
concentration of Amp increased recA expression. We could not
rule out the possibility that recA transcription and GFP translation
differ, because the RNA-seq results showed that recA expression
increased under Km treatment. Antibiotic treatment can induce
the SOS response, which can lead to the expression of umuDC
[41]. Our transcriptome analysis revealed that the umuDC genes
were induced only by Nor (Table 3). Thus, our results demon-
strated that Nor, but not other antibiotics, strongly induced the
SOS response in DR1 cells.
Loss of DNA-repair capability in response to Km and Tc
treatment
The enzymes used in base excision repair (BER) are responsible
for repairing endogenous DNA-damage lesions caused by ROS,
environmental chemicals, and ionizing radiations [42,43]. BER is
a highly conserved cellular mechanism in bacteria and humans
[42], and the lesion in the damaged DNA is removed by a DNA
glycosylase. Endonuclease IV, UDG, and Fpg are induced in
response to oxidative stress and these molecules function in
repairing DNA damage in E. coli [44]. We measured endonucle-
ase activity after treatment with the 4 antibiotics and we used the
DNA-excision assay and oligonucleotides including THF residues
[44]. Unexpectedly, in response to Km and Tc, endonuclease IV
did not exhibit BER activity that was distinct from the activity in
control (Figure 6). We also tested the activities of the 2 other
DNA-repair enzymes, UDG and Fpg (Figure S5). Fpg activity
decreased under all antibiotic conditions, whereas UDG activity
was not changed. In these assays, enzyme reactions performed
using purified E. coli endonuclease IV, UDG, and Fpg served as
positive controls. Our results showed that the DNA-repair
capability of endonuclease IV was maintained only under Amp
and Nor treatment, which suggests that each antibiotic distinctly
Figure 1. A summary of genes upregulated and downregulated
by distinct classes of antibiotics. (A) The percentages of up- and
down-regulated genes under treatment with 4 antibiotics. (B) Venn-
diagram showing the number of overlapping genes upregulated by
antibiotics of distinct classes. Fold-changes shown are a comparison of
the RPKM values of exponentially growing control cells and of cells
treated with each antibiotic. Upregulation of gene expression is .1.5-
fold change in RPKM value, downregulation is ,1.5-fold change.
doi:10.1371/journal.pone.0110215.g001
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 4 October 2014 | Volume 9 | Issue 10 | e110215

Table 2. Antimicrobial resistance-associated genes and target genes in A. oleivorans DR1.
Locus_tag DR1 Product Description Genes Fold-change
Amp Km Tc Nor
b-Lactamases
AOLE_05220 b-Lactamase Class C b-lactamase ampC 1.05 22.06 23.45 21.31
AOLE_06930 b-Lactamase class C Class C b-lactamase ampC 5.20 1.93 1.03 1.00
AOLE_12585 b-Lactamase Class C b-lactamase ampC 1.41 1.19 1.72 21.09
AOLE_17635 b-Lactamase class C Class C b-lactamase ampC 4.20 3.44 1.53 1.25
AOLE_11070 b-Lactamase class D Class D b-lactamase bla
oxa-66
1.28 22.14 21.43 21.65
AOLE_00775 Metallo-b-lactamase superfamily protein Metallo-b-lactamase superfamily fpaA 22.31 2.51 1.51 22.19
AOLE_03925 Putative metallo-b-lactamase Metallo-b-lactamase superfamily gloB 3.17 2.56 22.55 21.06
AOLE_10040 b-Lactamase Metallo-b-lactamase superfamily gloB 21.32 1.72 23.28 21.11
AOLE_17515 Metallo-b-lactamase superfamily protein Metallo-b-lactamase superfamily gloB 21.17 21.05 21.33 21.05
AOLE_01440 Penicillin binding protein transpeptidase domain protein ftsI 21.03 21.20 21.62 21.03
AOLE_01470 Putative penicillin-binding protein (PonA) mrcA 22.22 24.35 23.34 21.11
AOLE_05610 Penicillin-binding protein 1B mrcB 21.80 23.78 23.30 21.04
AOLE_14240 Penicillin-binding protein 2 ftsI 24.09 21.47 21.68 21.16
Aminoglycosides
AOLE_08490 Predicted aminoglycoside phosphotransferase aminoglycoside 69-acetyltransferase aacA4 3.93 1.30 21.65 21.07
AOLE_18475 Aminoglycoside2’-N-acetyltransferase(AAC(29)-Ib) aminoglycoside 29-acetyltransferase aacB 28.13 2.15 1.99 21.03
Fluoroquinolones
AOLE_00020 DNA gyrase subunit B GyrB mutation gyrB 21.82 21.65 21.48 1.24
AOLE_00595 DNA topoisomerase IV subunit B GyrB mutation gyrB 21.18 1.61 21.62 2.80
AOLE_04195 DNA gyrase subunit A His-78RAsn gyrA 22.17 21.51 23.26 21.01
AOLE_18380 DNA topoisomerase IV subunit A GyrA mutation gyrA 21.22 21.56 22.00 2.92
Efflux pumps
AOLE_00955 MFS transporter, metabolite:H+symporter (MHS) family protein MFS-family efflux pump uhpC 23.37 1.97 1.31 1.00
AOLE_00175 MFS-family transporter MFS-family efflux pump araJ 21.63 4.02 2.99 1.38
AOLE_01040 MFS-family transporter MFS-family efflux pump araJ 24.27 23.45 1.14 21.32
AOLE_12350 MFS-family transporter MFS-family efflux pump araJ 24.03 22.09 1.02 1.13
AOLE_00050 RND-type efflux pump RND-family efflux pump dctP 9.53 21.03 1.52 1.02
AOLE_04230 Putative RND-family drug transporter RND-family efflux pump emrA 2.16 215.53 22.57 21.21
AOLE_09410 RND-type efflux pump RND-family efflux pump 6.59 1.19 3.25 1.58
AOLE_18750 RND-superfamily exporter RND-family efflux pump 77.60 19.11 1.34 21.13
AOLE_00035 ABC transporter ATP-binding protein ABC-family efflux pump uup 23.32 21.18 22.10 1.22
AOLE_01345 Putative ABC transporter ATP-binding protein ABC-family efflux pump uup 23.46 1.23 23.34 1.06
AOLE_17260 ABC transporter ATP-binding protein ABC-family efflux pump uup 23.46 22.54 23.51 21.01
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 5 October 2014 | Volume 9 | Issue 10 | e110215

affects the genes encoding DNA-repair enzymes. The expression
of endonuclease IV (AOLE_14840) was upregulated by Km but
not the other 3 antibiotics, and the expression of Fpg
(AOLE_03065) was decreased 2.3-fold and increased 1.7-fold in
response to Amp and Km, respectively, but was unaffected by Tc
and Nor. Our data reveal that the activity of DNA-repair enzymes
was not correlated with the expression of the genes encoding these
enzymes.
Discussion
In this study, we conducted a comparative transcriptome
analysis and examined the physiological changes in soil-borne A.
oleivorans DR1 exposed to antibiotics of distinct classes. Although
the antibiotic resistance of A. baumannii has been widely studied
[45], the transcriptional response elicited by various antibiotics in
other Acinetobacter species remains poorly documented. The
effects of antibiotics and the antibiotic-resistance mechanism in
DR1 have been described previously [22,46,47], but this is first
study in which the transcriptional changes induced in DR1 cells by
4 antibiotics have comparatively analyzed. Our results revealed
that the MIC of Amp exhibited extremely high ranges, which
could be due to high number of lactamases encoded by the DR1
genome. Amp was hydrolyzed by various b-lactamases present in
the periplasm before Amp can reach its targets [48]. Moreover,
Amp induced the genes involved in glyoxylate bypass (Figure 4).
Glyoxylate bypass is induced in numerous bacteria when carbon
Table 2. Cont.
Locus_tag DR1 Product Description Genes Fold-change
Amp Km Tc Nor
AOLE_00290 Multidrug-resistance protein norM MATE-family efflux pump norM 23.58 21.34 1.92 21.03
AOLE_00530 Na+-driven multidrug efflux pump MATE-family efflux pump norM 2.40 1.14 1.73 1.16
AOLE_05880 MATE efflux family protein MATE-family efflux pump norM -2.29 21.69 2.25 1.28
AOLE_17460 Multidrug ABC transporter MATE-family efflux pump norM 23.07 1.57 21.22 21.20
AOLE_05535 Quaternary ammonium compound-resistance protein QacE SMR-family efflux pump qacE 22.23 2.48 1.71 1.47
AOLE_16200 Quaternary ammonium compound-resistance protein SugE SMR-family efflux pump sugE 21.59 4.62 1.97 1.15
doi:10.1371/journal.pone.0110215.t002
Figure 2. Influence of distinct classes of antibiotics on cell
morphology, and membrane permeability in DR1. (A) The
average cell size was measured from 50 cells treated with antibiotics.
(B) Membrane permeability was measured using ANS. The error bars
indicate standard deviation from triplicate experiments.
doi:10.1371/journal.pone.0110215.g002
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 6 October 2014 | Volume 9 | Issue 10 | e110215

and energy sources are scarce or when oxidative stress is generated
[49,50]. Copper stress, which causes oxidative stress, induced
glyoxylate bypass in Pseudomonas [51]. Glyoxylate bypass was
particularly induced under Amp and Nor conditions (Figure 4).
Km strongly induced oxidative stress and caused growth defects,
but could not induce glyoxylate bypass. Therefore, we speculated
that there are other factors that induce glyoxylate bypass in DR1
under antibiotic conditions.
In E. coli, sublethal concentrations of aminoglycosides increased
the expression of several genes involved in heat-shock response,
such as htpG,ibpA,groES, and asrA [52]. Aminoglycosides also
induced the Lon protease in P. aeruginosa [53]. Our data showed
that genes encoding chaperones and proteases (DnaK,
AOLE_19360; GroEL, AOLE_03915; GroES, AOLE_03910)
exhibit high RPKM values under Km treatment. These results
suggest that chaperones and proteases might play a key role in
mistranslation under Km condition in DR1 cells. Our data
showed that endonucleases did not exhibit DNA-repair capabil-
ities in DR1 cells treated with Km and Tc. Intriguingly, only
ribosome-targeting antibiotics caused a loss of DNA-repair
capability; this is probably because of the long protein-maturation
times required for DNA-repair enzymes. Antibiotics can interfere
with the metabolic pathways of bacteria, and this can cause
structural alterations in the bacterial cell wall and surface
appendages including flagella, fimbriae, and pili [54]. Bacteria
employ extracellular structures such as pili and fimbriae in
attachment and invasion, biofilm formation, cell motility, and
transport across membranes [55]. Km and Tc have similar target
regions, and they inhibit protein synthesis by binding to the 30S
subunit of the ribosome [11,13]. Our transcriptomic data showed
that Km and Tc markedly induced fimbriae/pili-related genes.
Interestingly, these antibiotics also upregulated the natural
Figure 3. Measurement of oxidative stress induced by antibiotics. Intracellular superoxide-anion generation was measured using DHR 123.
Fluorescence intensity was determined using flow cytometry and is represented as a histogram. FITC-A indicates the intensity of green fluorescence
and the number of cells exhibiting the corresponding fluorescence intensity (amount of ROS production). The fluorescence histograms are of the
samples before and after antibiotic treatment; solid and dotted lines are untreated cells and antibiotic-treated cells, respectively. (A) Amp, (B) Km, (C)
Tc, (D) Nor. A shift to stronger fluorescence indicates a greater generation of oxidative stress.
doi:10.1371/journal.pone.0110215.g003
Figure 4. Expression of citrate-cycle genes in
A. oleivorans
DR1
treated with distinct antibiotics. Gene-expression changes are
represented by a color gradient that is based on the fold-changes of
gene expression in response to antibiotic treatments.
doi:10.1371/journal.pone.0110215.g004
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 7 October 2014 | Volume 9 | Issue 10 | e110215

competence-associated type-IV pilus-assembly proteins encoded
by AOLE_15230 (3.5-fold) and AOLE_17785 (3.69-fold).
Fluoroquinolones can induce the SOS response [56], key
regulators of which are the proteins LexA and RecA [35,36].
However, the lack of a LexA homolog indicates a critical role of
other regulators for SOS response in Acinetobacter species [57].
Here, transcriptome analysis demonstrated that in DR1 cells, Nor
strongly induced genes involved in the typical SOS response and
DNA-repair genes. The relative amounts of SOS gene expression
are determined primarily through by transcriptional regulation.
Our previous study showed that, Nor treatment caused target-gene
mutation in gyrA (AOLE_04195) and persister formation in DR1
[46]. Our data additionally validated the SOS response of
Acinetobacter species by showing that DNA damage enhanced
mutation frequency. This characteristic of DR1 might be helpful
for having resistance to antibiotics stress.
Noncoding RNAs are commonly referred to as small RNAs
because they are 50–500 nucleotides in size [58]. Small RNAs are
potent regulatory molecules that function at the transcriptional or
posttranscriptional level [59]. Interestingly, RNA-seq mapping
data revealed that the noncoding regions of DR1 contain
sequences of small-RNA candidates (Table S4). Three small-
RNA candidates are conserved in certain Acinetobacter species.
The Northern blot analysis confirmed the expression of small-
RNA candidates (Figure S6).
In a recent study on A. baumannii, 31 putative small RNAs
were identified using computational approaches [60]. Two of these
small RNAs display sequence similarities with those of the DR1
strain and other Acinetobacter species. However, these 2 small
RNAs were not induced under our tested conditions. Small RNAs
play key roles in efflux-pump regulation and antimicrobial-agent
resistance in A. baumannii [60], and efflux pumps are widely
accepted to bestow clinically relevant resistance to antibiotics [61].
How small RNAs involved in expression of efflux pumps remains
to be investigated in DR1 cells. Our study will serve as a baseline
for understanding the effects of antibiotics on Acinetobacter
Table 3. Expression change of functional gene clusters.
Locus_tag DR1 Product Genes Fold change
Amp Km Tc Nor
SOS-response genes
AOLE_07085 Nucleotidyltransferase/DNA polymerase dinP 22.67 1.48 2.34 1.57
AOLE_07375 Recombinase A recA 2.05 3.04 1.02 5.17
AOLE_07965 DNA-directed DNA polymerase UmuC umuC 21.43 22.26 2.24 4.20
AOLE_07970 DNA polymerase V component 21.43 21.69 21.10 5.41
AOLE_11745 SOS-response transcriptional repressor (RecA-mediated autopeptidases) umuD 21.18 1.82 21.57 3.15
AOLE_14875 DNA polymerase V component 1.18 3.20 2.18 6.51
AOLE_14880 DNA-directed DNA polymerase UmuC umuC 24.42 22.49 1.51 2.13
AOLE_18420 DNA polymerase IV dinP 21.67 1.34 21.01 21.53
DNA repair-related genes
AOLE_05830 Putative DNA-binding/iron metalloprotein/AP endonuclease 28.18 2.00 21.26 21.33
AOLE_13505 Metalloendopeptidase-like membrane protein nlpD 2.96 1.35 22.53 1.06
AOLE_14215 Endonuclease III nth 23.09 1.81 21.31 21.19
AOLE_14840 HNH endonuclease 21.04 5.00 21.12 21.43
AOLE_18425 Endoribonuclease L2PSP family protein tdcF 21.31 21.42 27.04 1.11
AOLE_18840 Endoribonuclease L-PSP family protein tdcF 5.89 22.73 1.24 21.16
AOLE_03065 Formamidopyrimidine-DNA glycosylase mutM 22.32 1.72 21.24 21.03
AOLE_10805 Uracil-DNA glycosylase ung 22.90 1.89 21.41 21.02
Oxidative stress-related genes
AOLE_01750 Cu/Zn superoxide dismutase sodC 2.99 21.24 1.10 21.07
AOLE_02915 Peroxiredoxin ahpC 2.72 21.38 23.47 21.98
AOLE_05305 Superoxide dismutase sodA 21.57 21.27 21.54 21.16
AOLE_07635 Thioredoxin trxA 21.77 5.36 8.61 2.10
AOLE_11770 Catalase katE 3.98 1.22 1.56 21.17
AOLE_12135 Redox-sensitive transcriptional activator SoxR soxR 2.52 4.00 2.53 2.88
AOLE_12755 Catalase katE 1.41 1.54 2.09 21.75
AOLE_13380 Peroxiredoxin ahpC 1.23 1.24 21.87 21.23
AOLE_14380 Hydrogen peroxide-inducible genes activator oxyR 1.11 1.40 21.79 1.08
AOLE_16430 Thioredoxin trxA 1.09 21.24 22.07 21.09
AOLE_17390 Catalase katG 21.19 23.11 22.88 1.34
AOLE_18445 SoxR-family transcriptional regulator soxR 1.17 1.38 1.47 21.32
doi:10.1371/journal.pone.0110215.t003
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 8 October 2014 | Volume 9 | Issue 10 | e110215

species, and it should help in developing a new strategy for
predicting novel antibiotic-resistance mechanisms, as well as for
preventing multidrug resistance across multiple species of bacteria
by using this soil-borne bacterium.
Materials and Methods
Bacterial strains, growth conditions, and antibiotics
The bacterial strains used in this study are listed in Table S5.
Environment-originated nonpathogenic A. oleivorans DR1 was
grown in nutrient broth at 30uC with rotational shaking at
220 rpm. Bacteria harboring plasmids and wild-type bacteria were
cultured under the same conditions. Escherichia coli GC 4468 and
A. baumannii ATCC17978 were grown at 37uC in LB and
aerated by means of shaking. In bacterial antibiotic-treatment
experiments, we used commercially available Rifampicin (Sigma-
Aldrich, USA), Amp (Bioshop, Canada), Km (Bioshop, Canada),
Tc (Sigma-Aldrich, USA), and Nor (Sigma-Aldrich, USA).
Determination of antibiotic minimum inhibitory
concentrations (MICs) of A. oleivorans DR1
MICs were determined in liquid nutrient medium by using 96-
well polystyrene microtiter plates (Costar, USA). DR1 cells were
grown overnight in nutrient broth at 30uC with shaking at
220 rpm. The cells were washed twice with phosphate-buffered
saline (PBS) and inoculated at a cell density of 10
5
,10
8
CFU/mL
in 200 mL of nutrient broth containing 0–256 mg/mL of each
antibiotic (Amp, Km, Tc, Nor), and then grown in 96-well
polystyrene plates at 30uC for 24 h without shaking. MICs were
determined by measuring the optical density at 600 nm (OD
600
)
by using a microtiter-plate reader (PowerWaveXS, Bio-Tek,
USA); the MICs were the lowest concentrations of the 4 antibiotics
at which OD
600
was ,0.04.
RNA extraction, sequencing, and analysis
Total RNA of DR1 cells grown in nutrient media was isolated
from exponential-phase cells (OD
600
,0.4). Cells were grown at
30uC with shaking at 220 rpm and when they reached the
exponential phase, they were treated without or with each
antibiotic at the sub-MIC (Amp,100 g/mL, Km, 4 g/mL, Tc:
1 g/mL, Nor: 4 g/mL) for 15 min. Total RNA was extracted
using RNeasy Mini kits (Qiagen, USA) by following the
manufacturer’s instructions. The isolated RNA was stored at
280uC until use. All RNA-sequencing and alignment procedures
were conducted by Chunlab (Seoul, South Korea). The RNA was
subjected to a subtractive Hyb-based rRNA-removal process by
using the MICROBExpress Bacterial mRNA Enrichment Kit
(Ambion, USA), and subsequent processes, including library
construction, were performed as described previously (Table S1)
[62]. RNA sequencing was performed using 2 runs of the Illumina
Figure 5. SOS-response induction in
Acinetobacter oleivorans
DR1. The mutation frequency, which corresponds to the rifampicin-
resistance CFU count divided by the total CFU count, was measured and
is represented on the Y-axis in the case of each antibiotic. (A) MMC-
induced mutagenesis frequency. (B) Mutagenesis frequency induced by
antibiotics of distinct classes. (C) Effect of antibiotics on recA expression
was confirmed using a GFP fusion protein.
doi:10.1371/journal.pone.0110215.g005
Figure 6. Verification of endonuclease IV activity by using the
base-excision DNA-repair assay. DNA-repair capability of endonu-
clease IV was measured in DR1 exposed to distinct classes antibiotics.
(A) Schematic representation of DNA substrate containing a site-specific
THF residue. (B) A representative autoradiograph of gel electrophoresis
to measure in vitro BER products. (C) Quantification of endonuclease IV
BER activity. S, substrate; P, product; C, positive control; U, untreated
negative control. Error bars indicate the S.D. calculated for each data
point (n = 2).
doi:10.1371/journal.pone.0110215.g006
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 9 October 2014 | Volume 9 | Issue 10 | e110215

HiSeq to generate single-ended 100-bp reads. The genome
sequence of A. oleivorans DR1 was retrieved from the NCBI
database (accession number NC_014259.1). Quality-filtered reads
were aligned to the reference-genome sequence by using the CLC
Genomics Workbench 6.5.1 tool (CLC bio, Denmark). Mapping
was based on a minimal length of 100 bp, with an allowance of up
to 2 mismatches. The relative transcript abundance was measured
in reads per kilobase of exon sequence per million mapped
sequence rea20kds (RPKM) [63]. The mapping results were
visualized using the CLRNAseq program (Chunlab, South Korea).
The RNA-seq data were deposited in the National Center for
Biotechnology Information (NCBI) GEO site under accession
numbers GSE38340, GSE44428, GSE58166 and GSE58167.
Quantitative real-time PCR (qRT-PCR) analysis
cDNA was synthesized from 1 mg each RNA extract by using
gene specific primers. (Table S5) and the primers for genes were
used as templates for quantitative real-time PCR (qRT-PCR). The
25 ml PCR mixture included 12.5 ml iQ SYBR Green Supermix
(Bio-Rad, USA), 1 ml of each primer (0.5 mM), 2 ml cDNA, and
8.5 ml distilled water. The PCR reactions were conducted at 95uC
for 3 min, followed by 40 cycles consisting of 30 s at 95uC, 30 s at
60uC, and 30 s at 72uC. The expression level of each gene was
normalized to the 16S rRNA expression level that was quantified
with 16s rRNA-341F/16s rRNA-534R primers. Relative quanti-
fications were performed in triplicate.
Cell membrane permeability assays
The fluorescent probe 8-anilino-1-naphthylenesulfonic acid
(ANS; Sigma-Aldrich, USA) was used for assessing the integrity
of bacterial cell membranes. Overnight cultures were diluted 100-
fold in 5 mL of fresh medium and grown to the logarithmic-
growth phase at 30uC and 220 rpm. After the cells were treated
with or without each antibiotic at the exponential phase (OD
600
,0.4) for 15 min, 1 mL of the cell cultures was harvested by
centrifugation (13,0006g, 1 min) and washed twice with PBS.
The resuspended solutions were supplemented with ANS (1 mL,
3 mM) and maintained at room temperature for 10 min in the
dark. The fluorescence intensity of cells was measured using a
microplate reader. The filter set used for fluorescence measure-
ments included a 555-nm excitation filter and 590-nm emission
filter. The possibility that distinct growth rates were measured
under various experimental conditions was excluded by normal-
izing protein amounts (in mg). Cell membrane permeability assays
were performed 3 times independently.
Microscopic observation
Antibiotics (used at the sub-MICs) was added to the cells at the
exponential phase (OD600nm = 0.4), and the cells were then
incubated for 30 min at 30uC. 1 mL of the cell cultures was
harvested by centrifugation (13,0006g, 1 min) and washed twice
with PBS. The resuspended solutions were supplemented with
49,6-diamidino- 2-phenylindole (DAPI) (1 mL, 2 mg/mL), and
maintained at room temperature for 10 min in the dark. DAPI -
treated cells was washed and resuspended using PBS. Then, 5 mL
of cells was placed on a glass slide and observed. Bacteria treated
with antibiotics were viewed with a Carl ZeissAxio Imager
microscope (ZEISS, Germany).
Measurement of oxidative stress
Intracellular superoxide-anion generation was measured using
dihydrorhodamine (DHR) 123 (Sigma-Aldrich, USA). The cells
were grown to exponential phase (OD
600
,0.4) and treated for
15 min with the antibiotics (used at the sub-MICs). The cells were
washed twice and resuspended using PBS and then treated with
DHR 123 (2.5 mg/mL) for 1 h in the dark at 30uC. DHR-123-
treated cells were washed and resuspended using PBS, and the
intracellular superoxide anion-mediated oxidation of DHR 123
was assayed be means of FACSverse flow cytometry (BD
Biosciences, San Jose, CA, USA). The samples were analyzed by
using a fluorescein isothiocyanate (FITC) argon-ion laser for
excitation, and fluorescence intensity was determined and
analyzed by measuring 10,000 cell counts. BD FACSuite software
was used for data analysis.
DNA damage-induced mutagenesis frequency
Cells were grown to the exponential phase (OD
600
,0.4) and
treated without (control) or with 1 g/mL MMC (sub-MIC) and
antibiotics (used at the sub-MICs) for 1 h. After the treatment,
cells were washed twice and resuspended using PBS and
inoculated at a cell density of 5610
8
CFU/mL in 5 mL of fresh
nutrient broth and grown with shaking at the appropriate
temperature for 24 h. The cultures were collected and diluted in
PBS and then plated on nutrient or LB agar media containing
either 100 mg/mL rifampicin or no rifampicin to calculate the
frequencies of rifampicin-resistance mutations. Colonies were
counted after incubation for 24 h at the appropriate temperatures.
Mutation frequency was determined from the relative percentage
of CFU/mL ((CFU at 100 g/mL rifampicin/CFU at no
rifampicin) 6100).
Construction of transcriptional-fusion green fluorescent
protein (GFP) and quantification of GFP fluorescence
The broad-host-range expression vector pRK415 was used for
constructing transcriptional-fusion GFP. A fragment of the recA
promoter region was amplified by means of Polymerase chain
reaction (PCR) performed using pRKprecA-gfp-F/pRKprecA-
gfp-R primer pairs (Table S5). A 190-bp fragment of the promoter
region of recA was cloned into the KpnI/BamHI cloning site of the
multi cloning site of the pRK415 vector. The amplicon (715 bp)
obtained using pRKgfp-F/pRKgfp-R was cloned into the
BamHI/EcoRI cloning site of the pRK415 vector to generate
transcriptional-fusion GFP. The plasmid was extracted using a
Dyne Plasmid Miniprep Kit (DYNEBIO, Korea). The constructed
plasmid was then introduced into E. coli Top10 and A. oleivorans
DR1 by electroporation. Competent cells (50 ml) were transformed
with 2.5 ml plasmid DNA samples using a Micropulser (Bio-Rad,
USA) with a time constant range of 3.0–3.5 ms and a constant
voltage of 4.5–5 kV. PCR was conducted to confirm insertion of
the gfp gene using the GFP-F/GFP-R primer set (Table S5).
Overnight cultures of the DR1 harboring constructing transcrip-
tional-fusion GFP grown in nutrient broth were diluted 100-fold in
5 mL of fresh medium and then incubated with shaking. At the
exponential-growth phase (OD
600
,0.4), the antibiotics were
added and the cells were incubated for 1 h. A 1-mL aliquot of
each GFP fusion-strain culture was harvested and centrifuged at
13,0006gfor 1 min and then washed twice with PBS. The
resuspended cells were transferred to polystyrene 48-well micro-
titer plates (BD Biosciences, USA) and the GFP fluorescence
intensity of the cells was quantified using a Multi-Detection
Microplate Reader (Sense, HIDEX, Finland). The GFP fusion-
strain expressed a stable GFP variant that has an excitation
wavelength of 488 nm and emission wavelengths of 507–510 nm.
The OD
600
of each culture was measured using a microtiter-plate
reader (PowerWaveXS; Bio-Tek, USA). The possibility of distinct
growth rates being measured under various experimental condi-
tions was excluded by normalizing the measured fluorescence
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 10 October 2014 | Volume 9 | Issue 10 | e110215

intensity relative to the OD
600
value. One fluorescence unit was
defined as [(fluorescence intensity of cells/fluorescence intensity of
PBS buffer)/OD
600
of cells], and a relative fluorescence unit (fold)
was defined as [fluorescence unit of treated cells/fluorescence unit
of control (untreated) cells].
In vitro base-excision repair (BER) assay
Overnight cultures were diluted 100-fold in 500 mL of fresh
nutrient medium and grown to the exponential phase
(OD
600
,0.4) at 30uC and 180 rpm. After the cells were treated
with or without antibiotics for 15 min, a cell cultures was
harvested by centrifugation (10,0006g, 30 min) and washed twice
with PBS. The cell pellet was resuspended in ,5 ml of sonication
buffer (50 mM Tris-HCl (pH 8.0), 1 mM EDTA, and 0.1 mM
DTT), and cells were lysed by sonification. After cell debris was
removed by centrifugation (13,0006g, 20 min) at 4uC, superna-
tant was collected and placed on ice. Cell-free extract was
transferred to Eppendorf tubes (0.5 mL) and stored in 100 ml
aliquots at 280uC. The radionucleotide [c-
32
P] ATP was obtained
from PerkinElmer Life Sciences (Wellesley, USA). We purchased
E. coli uracil-DNA glycosylase (UDG), formamidopyrimidine-
DNA glycosylase (Fpg), endonuclease IV, and T4 polynucleotide
kinase (New England Biolabs, UK). Micro Bio-Spin 30 Chroma-
tography Columns were from Bio-Rad. DNA oligonucleotides
containing uracil, tetrahydrofuran (THF), or 8-oxoguanine (8-
oxoG) residues were provided by Dr. B. Demple, SUNY-Stony
Brook (Stony Brook, USA), and these were amplified by means of
PCR performed using 30F-F/30F-R, U30-F/U30-R, OxoG-F/
OxoG-R primer pairs (Table S5). The endonuclease-IV-activity
assay was performed in a reaction mixture 10 mL containing
50 mM Hepes-KOH (pH 7.5), 8 mM MgCl
2
, 5% glycerol,
0.5 mM DTT, 0.1 mg/mL BSA, and 1 nM 59-end-labeled
duplex-DNA substrate containing THF residues. The reactions
were initiated by adding 10, 20, or 50 ng of cell-free extracts and
were incubated at 37uC. Aliquots of each reaction were withdrawn
at 30 min, and the reactions were terminated by adding
formamide loading buffer. The reaction products were separated
by performing electrophoresis; we used 15% denaturing poly-
acrylamide gels containing 7 M urea in 90 mM Tris, 90 mM
boric acid, and 2 mM EDTA. Gels were dried using a gel dryer
(Model 583, Bio-Rad, USA), and products were visualized by
means of autoradiography and quantified using ImageQuant
software v5.2. The percentage of cleaved AP sites was calculated
from amount of products divided by the sum of total products and
substrates.
Northern blot assay
Total RNA (5 mg) were run on denaturing agarose gels
containing 0.25 M formaldehyde, and the gels were stained with
ethidium bromide (EtBr) to visualize 23S and 16S rRNA. The
fractionated RNA was transferred to nylon membranes (Schleicher
& Schuell, Germany) using a Turboblotter (Schleicher & Schuell,
Germany). The mRNA levels were determined by hybridizing the
membrane with a gene specific,
32
P-labeled probe (Takara, Japan)
prepared by PCR amplification with their respective primer pair
as indicated in Table S5. Autoradiography was conducted using
an IP plate (Fujifilm, Japan) and a Multiplex Bio-Imaging system
(FLA-7000; Fujifilm, Japan).
Supporting Information
Figure S1 Determination of MIC under different cell
density in A. oleivorans DR1.
(TIF)
Figure S2 Confirmation of RNA-Seq results with qRT-
PCR. (A) Commonly up- and down- regulated genes were
confirmed the gene expression on 4 antibiotics conditions. (B)
Three genes were selected based on expression value on each
antibiotics condition.
(TIF)
Figure S3 COG assignments of differently expressed
genes under distinct antibiotics conditions. The percent-
age of up-regulated and down-regulated genes was sorted by
general COG categories. Colors of the bars indicate the changes of
gene expression. Red, gene expression is .1.5-fold change in
RPKM value, Brown, gene expression is ,1.5-fold change in
RPKM value, Gray, gene expression of between a 21.5 and 1.5-
fold change in value. COG abbreviations for the functional
categories: A, RNA processing and modification; B, chromatin
structure and dynamics; C, energy production and conversion; D,
cell cycle control and mitosis; E, amino acid metabolism and
transport; F, nucleotide metabolism and transport; G, carbohy-
drate metabolism and transport; H, coenzyme metabolism; I, lipid
metabolism; J, translation, including ribosome structure and
biogenesis; K, transcription; L, replication, recombination, and
repair; M, cell wall structure and biogenesis and outer membrane;
N, secretion, motility and chemotaxis; O, molecular chaperones
and related functions; P, inorganic ion transport and metabolism;
Q, secondary metabolite biosynthesis, transport, and catabolism;
T, signal transduction; U, intracellular trafficking, secretion, and
vesicular transport; V, defense mechanisms.
(TIF)
Figure S4 Microscopic observation of antibiotics treat-
ed and untreated DR1 cells. Morphology observation of cell
treated with antibiotics. Phage contrast and staining with DAPI
are shown. The scale bar represents 20 mm.
(TIF)
Figure S5 Verification of UDG and Fpg activity by using
the base-excision DNA-repair assay.
(TIF)
Figure S6 Confirmation of expression of small RNA
candidates using Northern blot. The expression of small
RNA candidate was determined under antibiotics conditions using
Northern blot. The ethidium bromide (EtBr) staining demonstrat-
ed consistent loading in all lanes.
(TIF)
Table S1 Total number of reads aligning with the
regions of interest (coverage) of the five libraries
constructed from the RNA samples.
(DOCX)
Table S2 Fimbriae/pili related gene expression profiles
by different class antibiotics.
(DOCX)
Table S3 Bacterial strains, plasmids, and primers used
in this study.
(DOCX)
Table S4 The feature of small RNA genes in A.
oleivorans DR1.
(DOCX)
Table S5 Bacterial strains, plasmid and oligonucleo-
tides sequence used in this study.
(DOCX)
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 11 October 2014 | Volume 9 | Issue 10 | e110215

Author Contributions
Conceived and designed the experiments: AH HJ JS WP. Performed the
experiments: AH HJ. Analyzed the data: AH WP. Contributed reagents/
materials/analysis tools: AH HJ JS WP. Contributed to the writing of the
manuscript: HJ WP.
References
1. Hartzell JD, Kim AS, Kortepet er MG, Moran KA (2007) Acinetobacter
pneumonia: a review. Med Gen Med 9: 4.
2. Forster DH, Daschner FD (1998) Acinetobacter species as nosocomial pathogens.
Eur J Clin Microbiol Infect Dis 17: 73–77.
3. Towner KJ, Bergogne-Be´re´zin E, Fewson CA (1991) Acinetobacter: Portrait of a
Genus. In Towner KJ, Bergogne-Be´re´zin E, Fewson CA, editors. The biology of
Acinetobacter: taxonomy, clinical importance, molecular biology, physiology,
industrial relevance. New York, NY: Plenum Press pp. 1–24.
4. Martinez JL (2009) Environmental pollution by antibiotics and by antibiotic
resistance determinants. Environ Pollut 157: 2893–2902.
5. Ku¨mmerer K (2003) Significance of antibiotics in the environment. J Antimi-
crob Chemother 52: 5–7.
6. Alonso A, Sa´nchez P, Martı
´nez JL (2001) Environmental selection of antibiotic
resistance genes. Environ Microbiol 3: 1–9.
7. Martı
´nez JL (2008) Antibiotics and antibiotic resistance genes in natural
environments. Science 321: 365–367.
8. Pitout JD, Sanders CC, Sanders WE Jr (1997) Antimicrobial resistance with
focus on beta-lactam resistance in gram-negative bacilli. Am J Med 103: 51–59.
9. Davis BD (1987) Mechanism of bactericidal action of amino glycosides.
Microbiol Rev 51: 341–350.
10. Kohanski MA, Dwyer DJ, Wierzbowski J, Cottarel G, Collins JJ (2008)
Mistranslation of membrane proteins and two-component system activation
trigger antibiotic-mediated cell death. Cell 135: 679–690.
11. Chopra I, Roberts M (2001) Tetracycline antibiotics: mode of action,
applications, molecular biology, and epidemiology of bacterial resistance.
Microbiol Mol Biol Rev 65: 232–260.
12. Hooper DC (2001) Mechanisms of action of antimicrobials: focus on
fluoroquinolones. Clin Infect Dis 32 Suppl 1: S9–S15.
13. Manchanda V, Sanchaita S, Singh N (2010) Multidrug resistant Acinetobacter.
J Glob Infect Dis 2: 291–304.
14. Jung J, Baek JH, Park W (2010) Complete genome sequence of the diesel-
degrading Acinetobacter sp. strain DR1. J Bacteriol 192: 4794–4795.
15. Poirel L, Bonnin RA, Nordmann P (2011) Genetic basis of antibiotic resistance
in pathogenic Acinetobacter species. IUBMB Life 63: 1061–1067.
16. Jung J, Madsen EL, Jeon CO, Park W (2011) Comparative genomic analysis of
Acinetobacter oleivorans DR1 to determine strain-specific genomic regions and
gentisate biodegradation. Appl Environ Microbiol 77: 7418–7424.
17. Chopra I, Roberts M (2001) Tetracycline antibiotics: mode of action,
applications, molecular biology, and epidemiology of bacterial resistance.
Microbiol Mol Biol Rev 65: 232–260.
18. Fajardo A, Martı
´nez JL (2008) Antibiotics as signals that trigger specific bacterial
responses. Curr Opin Microbiol 112: 161–167.
19. Kohanski MA, Dwyer DJ, Collins JJ (2010) How antibiotics kill bacteria: from
targets to networks. Nat Rev Microbiol 8: 423–435.
20. Tenover FC (2006) Mechanisms of antim icrobial resistanc e in bacteria.
Am J Med 119: S3-S10.
21. Yonath A, Bashan A (2004) Ribosomal crystallography: initiation, peptide bond
formation, and amino acid polymerization are hampered by antibiotics. Annu
Rev Microbiol 58: 233–251.
22. Adams MD, Goglin K, Molyneaux N, Hujer KM, Lavender H (2008)
Comparative genome sequence analysis of multidrug-resistant Acinetobacter
baumannii. J Bacteriol 190: 8053–8064.
23. Kang YS, Park W (2010) Trade-off between antibiotic resistance and biological
fitness in Acinetobacter sp. strain DR1. Environ Microbiol 12: 1304–1318.
24. Prigent-Combaret C, Sanguin H, Champier L, Bertrand C, Monnez C, et al.
(2012) The bacterial thiopurine methyltransferase tellurite resistance process is
highly dependent upon aggregation properties and oxidative stress response.
Environ Microbiol 14: 2645–260.
25. Fronzes R, Remaut H, Waksman G (2008) Architectures and biogenesis of non-
flagellar protein appendages in gram-negative bacteria. EMBO J 27: 2271–
2280.
26. Chen SH, Chen RY, Xu XL, Xiao WB (2012) Microarray analysis and
phenotypic response of Pseudomonas aeruginosa PAO1 under hyperbaric
oxyhelium conditions. Can J Microbiol 58: 158–169.
27. Fle´chard M, Cortes MA, Re´pe´rant M, Germon P (2012) New role for the ibeA
gene in H2O2 stress resistance of Escherichia coli. J Bacteriol 194: 4550–4560.
28. Yao Z, Kahne D, Kishony R (2012) Distinct single-cell morphological dynamics
under beta-lactam antibiotics. Mol Cell 48: 705–712.
29. Bi E, Lutkenhaus J (1993) Cell division inhibitors SulA and MinCD prevent
formation of the FtsZ ring. J Bacteriol 175: 1118–1125.
30. Jones C, Holland IB (1985) Role of the SulB (FtsZ) protein in division inhibition
during the SOS response in Escherichia coli: FtsZ stabilizes the inhibitor SulA in
maxicells. Proc Natl Acad Sci U S A 82: 6045–6049.
31. Turner RD, Vollmer W, Foster SJ (2014) Different walls for rods and balls: the
diversity of peptidoglycan. Mol Microbiol 91: 862–874.
32. Frirdich E, Gaynor EC (2013) Peptidoglycan hydrolases, bacterial shape , and
pathogenesis. Curr Opin Microbiol 16: 767–778.
33. Braun V, Oldmixo n E (1979) Relationship between steps in 8-anilino-1-
naphthalene sulfonate (ANS) fluorescence an d changes in the energized
membrane state and in intracellular and extracellular adenosine 59-triphosphate
(ATP) levels following bacteriophage T5 infection of Escherichia coli.
J Supramol Struct 10: 329–347.
34. Nikaido H, Vaara M (1985) Molecular basis of bacterial outer membrane
permeability. Microbiol Rev 49: 1–32.
35. Kohanski MA, Dwyer DJ, Hayete B, Lawrence CA, Collins JJ (2007) A common
mechanism of cellular death induced by bactericidal antibiotics. Cell 130: 797–
810.
36. Rui B, Shen T, Zhou H, Liu J, Chen J, et al. (2010) A systematic investigation of
Escherichia coli central carbon metabolism in response to superoxide stress.
BMC Syst Biol 4: 122.
37. Eisenstadt E, Carlton BC, Brown BJ (1994) Gene mutation. In Gerhardt P,
Murray RGE, Wood WA, and Krieg NR editors. Methods for general and
molecular bacteriology. American Society for Microbiology. Washington, D.C:
Plenum Press pp. 297–316.
38. Aranda J, Bardina C, Beceiro A, Rumbo S, Cabral MP, et al. (2010)
Acinetobacter baumannii RecA protein in repair of DNA damage, antimicrobial
resistance, general stress response, and virulence. J Bacteriol 193: 3740–3747.
39. Kelley WL (2006) Lex marks the spot: the virulent side of SOS and a closer look
at the LexA regulon. Mol Microbiol 62: 1228–1238.
40. Hare JM, Bradley JA, Lin CL, Elam TJ (2012) Diverse responses to UV light
exposure in Acinetobacter include the capacity for DNA damage-induced
mutagenesis in the opportunistic pathogens Acinetobacter baumannii and
Acinetobacter ursingii. Microbiology 158: 601–611.
41. Baharoglu Z, Mazel D (2011) Vibrio cholerae triggers SOS and mutagenesis in
response to a wide range of antibiotics: a route towards multiresistance.
Antimicrob Agents Chemother 55: 2438–2441.
42. Hare JM, Perkins SN, Gregg-J olly LA (2006) A constitutively expressed,
truncated umuDC operon regulates the recA-dependent DNA damage induction
of a gene in Acinetobacter baylyi Strain ADP1. Appl Environ Microbiol 72:
4036–4043.
43. Zharkov DO (2008) Base excision DNA repair. Cell Mol Life Sci 65: 1544–1165.
44. Blainey PC, van Oijen AM, Banerjee A, Verdine GL, Xie XS (2006) A base-
excision DNA-repair protein finds intra helical lesion bases by fast sliding in
contact with DNA. Proc Natl Acad Sci U S A 103: 5752–5757.
45. Ide H, Tedzuka K, Shimzu H, Kimura Y, Purmal AA, et al. (1994) Alpha-
deoxyadenosine, a major anoxic radiolysis product of adenine in DNA, is a
substrate for Escherichia coli endonuclease IV. Biochemistry 33: 7842–7847.
46. Poirel L, Bonnin RA, Nordmann P (2011) Genetic basis of antibiotic resistance
in pathogenic Acinetobacter species. IUBMB Life 63: 1061–1067.
47. Kim J, Noh J, Park W (2013) Insight into norfloxacin resistance of Acinetobacter
oleivorans DR1: target gene mutation, persister, and RNA-Seq analyses.
J Microbiol Biotechnol 23: 1293–1303.
48. Hong H, Ko HJ, Choi IG, Park W (2014) Previously undescribed plasmids
recovered from activated sludge confer tetracycline resistance and phenotypic
changes to Acinetobacter oleivorans DR1. Microb Ecol 67: 369–379.
49. Gupta V, Garg R, Garg S, Chander J, Attri AK (2010) Coexistence of extended
spectrum beta-lactamases, AmpC beta-lactamases and metallo-beta-lactamases
in Acinetobacter baumannii from burns patients: a report from a tertiary care
centre of India. Ann Burns Fire Disasters 26: 189–192.
50. Lemuth K, Hardiman T, Winter S, Pfeiffer D, Keller MA (2008) Global
transcription and metabolic flux analysis of Escherichia coli in glucose-limited
fed-batch cultivations. Appl Environ Microbiol 74: 7002–7015.
51. Schroeter R, Voigt B, Ju¨rgen B, Methling K, Po¨ther DC (2011) The peroxide
stress response of Bacillus licheniformis. Proteomics 11: 2851–2866.
52. Li K, Pidatala RR, Ramakrishna W (2012) Mutational, proteomic and
metabolomic analysis of a plant growth promoting copper-resistant Pseudomo-
nas spp. FEMS Microbiol Lett 335: 140–148.
53. Kindrachuk K, Ferna´ndez L, Bains M, Hancock R (2011) Involvement of an
ATP-dependent protease, PA0779/AsrA, in inducing heat shock in response to
tobramycin in Pseudomonas aeruginosa. Antimicrob Agents Chemother 55:
1874–1882.
54. Marr AK, Overhage J, Bains M, Hancock RE (2007) The Lon protease of
Pseudomonas aeruginosa is induced by aminoglycosides and is involved in
biofilm formation and motility. Microbiology 153: 474–482.
55. Braga PC, Ricci D (2011) Imaging bacterial shape, surface, and appendages
before and after treatment with antibiotics. Methods Mol Biol 736: 391–399.
56. Fronzes R, Remaut H, Waksman G (2008) Architectures and biogenesis of non-
flagellar protein appendages in gram-negative bacteria. EMBO J 27: 2271–
2280.
57. Phillips I, Culebras E, Moreno F, Baquero F (1987) Induction of the SOS
response by new 4-quinolones. J Antimicrob Chemother 20: 631–638.
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 12 October 2014 | Volume 9 | Issue 10 | e110215

58. Aranda J, Bardina C, Beceiro A, Rumbo S, Cabral MP, et al. (2011)
Acinetobacter baumannii RecA protein in repair of DNA damage, antimicrobial
resistance, general stress response, and virulence. J Bacteriol 193: 3740–3747.
59. Waters LS, Storz G (2009) Regulatory RNAs in bacteria. Cell 136: 615–628.
60. Gottesman S (2005) Micros for microbes: non-coding regulatory RNAs in
bacteria. Trends Genet 21: 399–404.
61. Sharma R, Arya S, Patil SD, Sharma A, Jain PK, et al. (2014) Identification of
novel regulatory small RNAs in Acinetobacter baumannii. PLoS One 9: e93833.
62. Li XZ, Nikaido H (2004) Efflux-mediated drug resistance in bacteria. Drugs 64:
159–204.
63. Yi H, Cho YJ, Won S, Lee JE, Jin YH, et al. (2011) Duplex-specific nuclease
efficiently removes rRNA for prokaryotic RNA-Seq. Nucleic Acids Res. 39:
e140.
Antibiotic-Induced Transcriptomes in Acinetobacter oleivorans
PLOS ONE | www.plosone.org 13 October 2014 | Volume 9 | Issue 10 | e110215