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Novel homologs of the multiple resistance regulator marAin antibiotic-contaminated environments

Sara Castiglionia,*, Francesco Pomatib, Kristin Millerb, Brendan P. Burnsb,Ettore Zuccatoa, Davide Calamaric, Brett A. Neilanb

aDepartment of Environmental Health Sciences, Mario Negri Institute for Pharmacological Research,

Via La Masa 19, 20156 Milano, ItalybSchool of Biotechnology and Biomolecular Sciences, The University of New South Wales, 2052 Sydney, AustraliacDepartment of Biotechnology and Molecular Sciences, University of Insubria, Via J.H. Dunant 3, 21100 Varese, Italy

a r t i c l e i n f o

Article history:

Received 15 April 2008

Received in revised form

2 July 2008

Accepted 4 July 2008

Available online 16 July 2008

Keywords:

Antibiotic resistance

Antibiotic contamination

Aquatic environment

marA

Bacillus

E. coli

* Corresponding author. Tel.: þ39 02 3901477E-mail address: castiglioni@marionegri.it

0043-1354/$ – see front matter ª 2008 Elsevidoi:10.1016/j.watres.2008.07.004

a b s t r a c t

Antibiotics are commonly detected in the environment as contaminants. Exposure to

antibiotics may induce antimicrobial-resistance, as well as the horizontal transfer of

resistance genes in bacterial populations. We selected the resistance gene marA, mediating

resistance to multiple antibiotics, and explored its distribution in sediment and water

samples from surface and sewage treatment waters. Ciprofloxacin and ofloxacin (fluo-

roquinolones), sulphamethoxazole (sulphonamide), erythromycin, clarythromycin, and

spiramycin (macrolides), lincomycin (lincosamide), and oxytetracycline (tetracycline) were

measured in the same samples to determine antibiotic contamination. Bacterial pop-

ulations from environmental samples were challenged with antibiotics to identify resistant

isolates. The gene marA was found in almost all environmental samples and was

confirmed by PCR amplification in antibiotic-resistant colonies. 16S rDNA sequencing

revealed that the majority of resistant isolates belonged to the Gram-positive genus

Bacillus, not previously known to possess the regulator marA. We assayed the incidence of

marA in environmental bacterial populations of Escherichia coli and Bacillus by quantitative

real-time PCR in correlation with the levels of antibiotics. Phylogenetic analysis indicated

the possible lateral acquisition of marA by Bacillus from Gram-negative Enterobacteriaceae

revealing a novel marA homolog in Bacillus. Quantitative PCR assays indicate that the

frequency of this gene in antropised environments seems to be related to bacterial expo-

sure to water-borne antibiotics.

ª 2008 Elsevier Ltd. All rights reserved.

1. Introduction aquatic and soil ecosystems (Kummerer, 2004). Recently, Dan-

The aquatic and terrestrial ecosystems are the biggest known

reservoirs of antibiotic-resistant bacteria (D’Costa et al., 2006;

Biyela et al., 2004), and continuous exposure to antimicrobial

agents has the potential to enhance both the spread of resis-

tance genes and the selection for resistant bacterial strains in

6; fax: þ39 02 39014735.(S. Castiglioni).

er Ltd. All rights reserved

tas et al. (2008) showed that phylogenetically diverse soil

bacteria can grow using both natural and synthetic antibiotics

ascarbon sources,developing a veryhigh resistance level which

can be transferred also to closely related human pathogens.

Antibiotics normally used for therapeutic treatment may not be

completely metabolised and can therefore contaminate waste,

.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 2 7 1 – 4 2 8 04272

surface and ground waters (Ternes, 1998; Hirsch et al., 1999;

Sacher etal., 2001; Calamarietal., 2003; Castiglioni etal., 2005). It

appears from the literature that environmental levels of anti-

biotics pose little environmental toxic risk, the greatest risk of

their presence being their potential to select for antibiotic

resistance amongst bacteria. Although antibiotics are found in

the environment at sub-inhibitory levels, relatively low

concentrations of antimicrobial agents can still promote

bacterial resistance (Kummerer, 2003; Cabello, 2006). Indeed,

antibiotic-resistant bacteria have been detected in sewage

(Iversen et al., 2002; Bertrand et al., 2005), surface (Ash et al.,

2002) and drinking water (Schwartz et al., 2003), soil (Burgos

et al., 2005; Nikolakopoulou et al., 2005), river sediment (Pei

et al., 2006) and marine aquaculture sites (Kim et al., 2004).

Exchange of resistance genes is optimal under conditions

of high nutrients, such as those found in aerobic and anaer-

obic treatments of wastewater, and high bacterial density,

such as in biofilms that are common in drinking water pipes,

soil and sediments. Hospital effluents and sewage treatment

plants (STPs) are major contributors to antibiotic resistance

spreading in the environment (Kummerer, 2004). Resistance

may be exchanged in the environment between distant

bacterial species and genera by horizontal gene transfer

(HGT) (Davidson, 1999), and eventually may reach human

pathogens through the food chain (Perreten et al., 1997) with

potential serious implications for human health. HGT can

also influence ecosystem stability changing the genetic code

of bacteria with possible alteration of their functionality and

ecological role.

The classical approach to study antibiotic resistance in the

environment is by cultivation and susceptibility testing. In

this study, we aimed to identify the presence of antibiotic-

resistant bacteria in selected environmental samples by

utilising standard cultivation methods and a molecular

approach, which represents a more direct means to search the

environment for bacteria and antimicrobial-resistance genes

(Schwartz et al., 2003). Recently, real-time PCR has been uti-

lised to quantify specific bacteria and associated genes in

environmental specimens (Volkmann et al., 2004; Smith et al.,

2004; Yu et al., 2005; Klomberg et al., 2005). As the environ-

ment is a reservoir of potential antibiotic resistance that can

be selected and transmitted to animals and humans, the

determination of sources and the quantification of occur-

rences relative to resistant bacteria and their relevant genes

are of paramount ecological and sanitary interest.

We targeted marA, a multiple antibiotic resistance regu-

lator, whose expression is sufficient to activate the marRAB

operon and produce a multidrug resistant phenotype (Gam-

bino et al., 1993; Martin et al., 1996; Alekshun and Levy, 1999).

The marA locus provides resistance to tetracycline, chloram-

phenicol, ampicillin, nalidixic acid, and ciprofloxacin, as well

as to other toxic chemicals such as household disinfectants

(Alekshun and Levy, 1999). Our objective was to investigate the

distribution of antibiotic resistance in association with this

antimicrobial-resistance regulator. The presence of a broad

array of antibiotics in the environment was measured by liquid

chromatography–tandem mass spectrometry and the possible

correlation with antibiotic resistance was investigated. We

used real-time PCR to directly ascertain a quantitative corre-

lation between high/low levels of antibiotic contamination at

selected sampling sites and the relative presence of the anti-

microbial-resistance regulator marA.

2. Materials and methods

2.1. Sampling of water and sediment

Seven samples of sediment and 10 samples of water were

collected from three rivers and a sewage treatment plant (STP)

in the north of Italy (Fig. 1). The sampling sites were chosen

along the Olona, Lambro and Po Rivers that receive wastes

from the most densely inhabited and industrialised areas of

Italy (Table 1). The Varese Olona STP was also studied in

Varese.

Influent (untreated) and effluent (treated wastewater)

samples were collected in the Varese Olona STP. Olona River

was sampled before and after the emission of discharges from

the Varese Olona STP. Site 1 was close to the river spring and

received few local urban wastes (Table 1). Sites 2 and 3, located

after the STP, were sampled to monitor the fate of antibiotics

in the river. Lambro River was sampled in Milan (site 4) and

immediately before connecting with Po River (site 5) to

monitor the contamination received from Milan and the

surrounding highly inhabited area. Po is the main Italian river,

with a length of 652 km (from the Alps to the Adriatic sea) and

an average flow rate of 1500 m3/s. It collects wastewater from

a catchment area of about 71,000 km2 in the north of Italy. The

sampling sites along the Po River (sites 6–8) were selected to

detect the contribution of the Lambro’s flow into the Po.

When sampling the Varese Olona STP, influent and effluent

were obtained by pooling water collected every 20 min by an

automatic sampler for 24 h (composite 24 h). Surface waters

were obtained by pooling samples collected every 20 min

during a limited period of 2 h (composite 2 h). Influent was

collected as a composite 24 h sample. Effluent was similarly

collected as a composite 24 h sample, starting from time 0þ RT

(retention time of the wastewater in the STP). Olona River

waters were collected as a composite 2 h sample starting from

time 0þ RTþ 12 h. Sediment samples were collected in all the

sampling sites along the rivers with the exception of site 5.

All water samples (2 L) were collected at the beginning of

September 2004, transferred to glass flasks, stored at þ4 �C,

and filtered on glass microfiber filters GF/D 1.6 mm (Whatman,

Kent, UK) prior to analysis. Sediment samples were trans-

ferred to sterile tubes (50 mL) and stored at �20 �C before

being lyophilised and analysed. Particulate and sediment

samples were lyophilised (Flexi-Dry� MP, Microprocessor

Control) and stored at �20 �C until DNA extraction.

Further details regarding the sampling procedure and sites

are reported in previous publications (Calamari et al., 2003;

Castiglioni et al., 2006).

2.2. Antibiotic measurement

We selected ciprofloxacin and ofloxacin (fluoroquinolones),

sulphamethoxazole (sulphonamide), erythromycin, clary-

thromycin, and spiramycin (macrolides), lincomycin (lincosa-

mide), and oxytetracycline (tetracycline) for the measurement

in surface and wastewater. Water samples were extracted

Fig. 1 – Sampling sites along the Olona, Lambro and Po Rivers, as well as the Varese Olona STP in the north of Italy.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 2 7 1 – 4 2 8 0 4273

using solid phase extraction (SPE) and analysed by high-

pressure liquid chromatography–tandem mass spectrometry

(HPLC–MS–MS) using a specific multi-residue method recently

validated for the simultaneous determination of several

pharmaceuticals (Castiglioni et al., 2005, 2006). Briefly, samples

were extracted by two SPE columns, an Oasis MCX (60 mg,

Waters Corp., Milford, MA) at pH 2.0 and a Lichrolut EN (200 mg,

Table 1 – List of the sampling sites selected for thisinvestigation with the flow rate and the populationequivalents for each site

Sampling sites Flow rates (m3/s) Populationequivalents

STP (Varese Olona)

Influent and effluent 0.2 120,000

Olona River

Site 1 0.1 100

Site 2 0.5 120,000

Site 3 0.5 120,000

Lambro River

Site 4 5 1,500,000

Site 5 5 5,000,000

Po River

Site 6 932 5,400,000

Site 7 932 10,000,000

Site 8 1150 11,700,000

Merck, Darmstadt, Germany) at pH 7.0. A Luna C8 column

50 mm� 2 mm i.d., 3 mm particle size (Phenomenex, Torrance,

CA, USA) was used for chromatographic separation. An

Applied Biosystem–SCIEX API 3000 triple quadrupole (Q1q2Q3)

mass spectrometer, equipped with a turbo ion spray source

(Applied Biosystems–Sciex, Thornhill, Ontario, Canada), was

used for the analysis in the multiple reaction monitoring mode

(MRM). Deuterated internal standards were used for quantifi-

cation of the eight antibiotics investigated (Table 2).

2.3. Antibiotic sensitivity testing and isolatecharacterisation

Water samples were filtered and lyophilised together with

sediment samples according to the analytical procedures

mentioned above. The Kirby–Bauer method using Mueller–

Hinton Agar was employed to identify antibiotic-resistant

isolates (Hindler and Inderlied, 1985). Briefly, water and sedi-

ment samples were plated on Mueller–Hinton Agar before

overlaying antibiotic disks to test for bacterial strains suscep-

tibility to five antibiotics: tetracycline, chloramphenicol,

ampicillin, nalidixic acid and ciprofloxacin. Plates were incu-

bated aerobically for 24 h at 37 �C. Colonies that grew within the

zone of inhibition surrounding an antibiotic disk were

suspected resistant strains and were subcultured with that

antibiotic toconfirmresistance.Onplates, astandardMinimum

Inhibitory Concentration (MIC) was utilised for each antibiotic

(Table 3) as previously recommended (Cohen et al., 1993;

Table 2 – Antibiotic concentrations (ng/L) in water samples from rivers and wastewater

Varese Olona STP Olona River Lambro River Po River

Influent Effluent Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8

Ciprofloxacin 1674.20 626.50 nd 588.50 580.50 128.40 nd 24.50 17.40 nd

Clarythromycin nd 100.10 nd 114.80 80.50 73.51 46.64 4.30 4.50 3.04

Erythromycin nd nd nd nd nd nd nd nd nd nd

Lincomycin 3.90 3.70 nd 2.20 1.90 17.31 16.70 9.60 3.88 16.88

Ofloxacin 539.80 183.10 nd 177.40 157.30 306.10 19.31 23.11 36.91 nd

Oxytetracycline nd nd nd nd nd 105.10 nd nd nd 7.67

Spiramycin nd 118.70 nd 137.80 126.70 459.50 176.70 3.37 8.07 5.71

Sulphamethoxazole nd nd nd nd nd nd nd nd nd nd

nd¼Not detected.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 2 7 1 – 4 2 8 04274

Goldman et al., 1996; Hooper et al., 1987). Isolates were cat-

egorised as resistant or sensitive according to their growth at

the MIC of the antibiotics tested.

The taxonomic identification of resistant strains was per-

formed by sequencing the amplification products obtained by

PCR with the 16S rDNA universal primers 27F and 1492R

(Neilan et al., 1997). marA primers were multiplexed with the

16S rDNA PCR to verify the presence of the resistance gene in

each isolate. Amplified DNA fragments were sequenced and

analysed as described below.

2.4. DNA extraction, amplification, and sequencing

Five hundred milliliters of filtered particulate or 0.3 g of the

sediment were resuspended in 500 mL of TNE buffer (50 mM

Tris–HCl, pH 8.0, 50 mM NaCl, 5 mM EDTA, pH 8.0), and

extracted for genomic DNA as reported elsewhere (Neilan et al.,

2002). DNA was ethanol precipitated, resuspended in 50 mL of

Table 3 – Antibiotic susceptibility of bacterial colonies from pla

Sampling sites

Waterparticulate

Presence ofmarA

Ampicillin(1.1 mg/mL)a

Tetracycline(2 mg/mL)a

STP influent þ R R

STP effluent þ R R

Site 1 � S R

Site 2 þ R R

Site 3 þ R R

Site 4 þ R R

Site 5 þ R R

Site 6 þ R R

Site 7 þ R R

Site 8 þ R R

Sediments

Site 1 þ R R

Site 2 þ R R

Site 3 � R R

Site 4 � R R

Site 6 þ R R

Site 7 þ R R

Site 8 þ R R

R, resistant; S, sensitive. The presence of marA (þ) in each sample is also

a Minimum Inhibitory Concentration (MIC).

sterile water, and quantified using an UV spectrophotometer

(NanoDrop, USA). The suitability of each extracted DNA for

amplification was initially checked with the universal bacterial

16S rDNA PCR (Neilan et al., 1997), and in case of polymerase

inhibition samples were diluted (1/10 and 1/100) to achieve

optimal amplification.

A set of two degenerate primers were designed, based on

sequences available from the literature, to amplify marA from

environmental samples. Sequences from strains of Escherichia

coli, Shigella, Enterobacter and Salmonella were obtained from

GenBank. The designed primers were: marAF (50-CTCCATA

CTAGAYTGGATHGARGA-30) and marAR (50-TGGTGGTACGT

CRAARTARTTYTT-30), with an expected amplification product

of 280 bp. PCR conditions were as follows: each 20 mL reaction

consisted of 1 mL of dNTPs mix (2 mM), 1 mL of MgCl2 (50 mM),

2 mL of 10� buffer, 1 mL of each primer (10 mM), 0.2 mL of Taq--

polymerase (1 U/mL) (Biotech International, Perth, Australia)

and 1 mL of the extracted DNA. Sterile filtered water was added

ted water and sediment samples

Antibiotic susceptibility

Ciprofloxacin(0.02 mg/mL)a

Chloramphenicol(7 mg/mL)a

Nalidixic acid(4.5 mg/mL)a

R R R

R R R

R R R

R R R

R R R

S R S

S R R

S R R

S S R

S R R

R R R

R R R

S R R

R R R

S R R

R R R

S R R

indicated.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 2 7 1 – 4 2 8 0 4275

to 20 mL. The thermal cycling conditions for amplification were:

94 �C for 2 min, followed by 30 cycles of 94 �C for 10 s, 50 �C for

20 s, 72 �C for 25 s, with initial hot-start for 2 min at 94 �C before

the addition of 0.2 U Taq. A successive round of additional 30

PCR cycles, using 1 mL of amplified DNA as template, was

necessary to amplify marA from environmental samples.

Amplicons were analysed by 2% agarose gel electropho-

resis. Amplified DNA fragments for marA were extracted from

an agarose gel and purified by gel extraction (Promega

Corporation, Madison, WI) according to the manufacturer’s

instructions. Sequencing was performed using the Big Dye

Terminator method (ABI PRISM system, Applied Biosystems,

Foster City, CA). Sequence alignments and phylogenetic tree

constructions were performed using the ClustalX version 1.8

(Thompson et al., 1997). Homology searches were performed

using BLAST (Altschul et al., 1997).

2.5. Real-time PCR

Specific standard real-time PCR primer sets were designed

based on sequence alignments of marA homologs and 16S

rDNA using the Primer Express software 2.0.0 (Applied Bio-

systems). Alignment of sequences obtained from this study

along with those from GenBank allowed the design of specific

primers for marA from E. coli (EmarAF 50-ACGGAAATCGCG

CAAAAG-30 and EmarAR 30-CCAGATAGAGTATCGGCTCGTT

ACTT-50) and from Bacillus sp. (BmarAF 50-GCGCAAAAGCT

GAAGGAAAG-30, and BmarAR 30-TGTTGCGACTCGAAGC

CATA-50). This primer set specific for the novel marA homo-

logs in Bacillus was tested in silico versus known marA

sequences from the databases, and this indicated there was

no unspecific annealing during real-time PCR standard

conditions of two-step amplification (60 �C annealing and

extension). Similarly, 16S rDNA-based ColiF (50-CGCGTGTAT

GAAGAAGGCCT-30) and ColiR (30-TCCTCCCCGCTGAAAGTAC

TT-50), and BacillusF (50-CCTTGACGGTACCTAACCAGAAA-30),

and BacillusR (30-GATAACGCTTGCCAC CTACGTAT-50) were

produced for E. coli and Bacillus sp. taxonomic groups,

respectively. These primer sets were highly specific and did

not show any potential for cross-reactivity either in silico

(DNA alignment and primer testing) or in the laboratory (data

not shown). 16S rDNA specific primers were tested for speci-

ficity against the Ribosomal Database Project (http://rdp.cme.

msu.edu/index.jsp) and BLAST search (http://blast.ncbi.nlm.

nih.gov). While BacillusF/R primers were highly specific for

Bacillus species, ColiF/R appeared to recognise other entero-

bacterial species such as Shigella, Enterobacter and Salmonella.

In the laboratory, ColiF/R primers amplified E. coli DNA with

the highest efficiency compared to DNA from Salmonella as

assessed by serial dilution of genomic DNA from standard

strains (data not shown). No cross-reactivity was experimen-

tally detected between ColiF/R and Bacillus DNA, or between

BacillusF/R and E. coli DNA (data not shown).

Two representative samples were chosen for an exploratory

investigation by real-time PCR: a ‘‘blank’’ sample (site 1) with

no antibiotic contamination, and a contaminated sample (STP

influent) with high levels of antibiotics (Table 2). Real-time PCR

was carried out using total genomic DNA as templates with

samples extracted and purified as described in Section 2.4.

Reactions (25 mL) were performed using different dilutions

(1/10, 1/100) of samples, combined with the Sybr-Green�

Universal PCR Master Mix (Applied Biosystems). Reactions

were cycled using standard two-step real-time PCR protocol

and analysed using the ABI PRISM 7000 detection system and

software (version 1.1). The reactions were performed in tripli-

cate and blank (no-template) samples were added for each set

of primers in order to detect DNA contamination.

Our results were expressed using the comparative Ct

method following standard protocols (Livak and Schmittgen,

2001). This approach has the advantage of comparing target

genes directly to a control DNA sequence within the same

bacterium or bacterial population in order to obtain relative

quantitation.

To normalise for background contamination from E. coli

DNA in the Taq-polymerase mix (Bach et al., 2002), Ct values

from no-template PCR reactions (blank) were used as a refer-

ence for every 16S rDNA analysis; DCt values were obtained as

(Ctsample� Ctblank). Where no DNA contamination was detected

in blank amplification runs, such as for the Bacillus ribosomal

RNA gene primers, Ct values in no-template controls were set

arbitrarily to 45. Variations in this arbitrary Ct number did not

change the final ratio for the results expressed as 2�DDCt (data

not shown). The relative quantitation of marA in each DNA

sample was obtained by utilising 16S rDNA levels as internal

calibrators for quantitative DDCt calculation. DDCt quantita-

tions (marA DCt-16S DCt) were estimated for both E. coli and

Bacillus. Final 2�DDCt values were normalised using genomic 16S

rDNA copy numbers available for E. coli and Bacillus, respec-

tively (Klappenbach et al., 2001). Comparisons between

populations of E. coli and Bacillus by 16S rDNA quantitative

analysis used 2�DCt as the discriminant value.

2.6. Phylogenetic analysis

Sequences for the marA gene from isolates in this study and

from reference strains were used to infer a phylogeny,

reconstructing the phenogram from a pairwise distance

matrix (Jukes and Cantor, 1969) using the neighbor-joining

method (Saitou and Nei, 1987). All DNA sequences obtained in

this study have been submitted to the GenBank nucleotide

sequence database and assigned to accession numbers

DQ420170–DQ420219.

3. Results and discussion

3.1. Presence of antibiotics in surface and wastewater

We measured the concentrations of ciprofloxacin and ofloxacin

(fluoroquinolones), sulphamethoxazole (sulphonamide),

erythromycin, clarythromycin, and spiramycin (macrolides),

lincomycin (lincosamide), and oxytetracycline (tetracycline), in

surface and wastewater (Table 2). At the first sampling site

along the Olona River (site 1) no antibiotics were detected, while

concentrations at site 2 were comparable with those of the STP

effluent. Antibiotic levels were loweratsite 3, 1 kmdownstream

of the STP (Table 2). The effect of an STP on reducing antibiotic

concentrations has been previously described (Ternes, 1998;

Castiglioni et al., 2006). Spiramycin and clarythromycin were

detected in higher concentrations in the STP effluent possibly

Table 4 – Taxonomic identification of resistant isolatesand presence (D) of marA; nd [ not detected

Waterparticulate

Taxonomicalidentification

using 16S DNA

BLASTidentity

(%)

marA fromresistantcolonies

STP influent Enterococcus faecium 100 þSTP effluent Bacillus subtilis 97 þSite 1 Bacillus fusiformis 97 þSite 2 Bacillus fusiformis 97 þSite 3 Bacillus subtilis 95 þSite 4 Bacillus cereus 97 þSite 5 Bacillus sphaericus 96 þSite 6 Bacillus cereus 97 þSite 7 Bacillus subtilis 98 þSite 8 Bacillus pumilus 98 þ

Sediments

Site 1 Bacillus cereus 96 þSite 2 Bacillus cereus 97 þ

Bacillus fusiformis 97

Site 3 Bacillus cereus 97 þSite 4 Bacillus cereus 100 þSite 6 Bacillus cereus 96 nd

Site 7 Bacillus subtilis 96 þBacillus fusiformis 97

Site 8 Bacillus fusiformis 99 nd

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 2 7 1 – 4 2 8 04276

due to retention and successive release during the treatment

process or cleavage of the respective excretion conjugates

(Hirsch et al., 1999). Concentrations in the Lambro River were

approximately 10 times higher than in the Po River, with the

former receiving higher city loads from Milan and having

a lower dilution factor. Lincomycin was the only antibiotic that

was observed at the same concentration in all the samples.

Ciprofloxacin was the most abundant compound with

a maximum concentration of about 1.5 mg/L in the STP influent,

circa 600 ng/L in theSTP effluent and theriver Olona, 120 ng/L in

the Lambro, and 20 ng/L in the Po. This is consistent with

previous reports that identified ciprofloxacin as a ubiquitous

contaminant, detected at relevant concentrations in aquatic

habitats as well as in soil, sediment, and sludge (Golet et al.,

2002; Calamari et al., 2003; Castiglioni et al., 2005). We could not

detect erythromycin and sulphamethoxazole, possibly due to

the degradation of erythromycin to its main metabolite, dehy-

droerythromycin (Hirsch et al., 1999), and the summer decrease

in sulphamethoxazole use (Castiglioni et al., 2006). The veteri-

nary antibiotic oxytetracycline was detected only in Po and

Lambro Rivers, which receive animal farm wastes.

3.2. Antibiotic sensitivity testing

To evaluate the presence of antibiotic-resistant bacteria in our

water and sediment samples, we cultured environmental

specimens in the presence of ampicillin, tetracycline, cipro-

floxacin, chloramphenicol, and nalidixic acid. Under the

conditions tested, several isolates were obtained from each

sample and were classified as resistant or sensitive. Generally,

bacteria were resistant to at least three out of the five antibi-

otics tested at standard minimum inhibitory concentrations

(Table 3). All samples showed resistance to tetracycline,

ampicillin, chloramphenicol and nalidixic acid with few

exceptions (Table 3). Ciprofloxacin is the most recently intro-

duced antibiotic among those tested and, despite the fact that

bacteria can develop resistance to this drug in a short time

(Cunha, 2001), more sites contained isolates susceptible to this

antibiotic (8/17) compared to other compounds (Table 3).

3.3. Taxonomic identification and characterisation ofresistant bacterial colonies

Purified isolates from each sensitivity test-plate were identi-

fied by 16S rDNA amplification and sequencing. Resistant

bacterial strains obtained from antibiotic sensitivity tests

belonged to the genus Bacillus, with the exception of the

Enterococcus faecium isolates from the STP influent (Table 4,

GenBank accession numbers DQ420170–DQ420188).

The presence of marA in resistant colonies was probed by

PCR amplification and the corresponding DNA bands

sequenced. Amplicons obtained in this study, each associated

with the characterised bacterial isolates as reported in Table 4,

were revealed to be novel marA homologs (GenBank accession

numbers DQ420189–DQ420207). Homologs of the multiple

antibiotic resistance gene marA could not be found in any

known Bacillus genome present in the databases (GenBank) and

in the literature (Rasko et al., 2004). To our knowledge, this is the

first evidence of the multidrug resistance regulator marA iden-

tified in strains of the Gram-positive bacterial genus Bacillus.

Previously, the mar locus has been only identified in Gram-

negative bacteria (Cohen et al., 1993; Goldman et al., 1996), and

results from the present investigation suggest the novel

possibility of these genes being transferred to Gram-positive

microorganisms. Questions remain, however, regarding the

putative function in Gram-positive bacteria of marA, a global

regulator and not the actual protein product responsible for

drug resistance (Alekshun and Levy, 1999). In E. coli the MarA

protein can control the expression of multiple chromosomal

genes affecting resistance to antibiotics (Barbosa and Levy,

2000) and can also autoactivate marRAB operon transcription

(Martin et al., 1996). The resulting mechanism of resistance is

related to the influx decrease and efflux increase of toxic

chemicals from the cell (Alekshun and Levy, 1999).

All colonies recovered in this study after antibiotic sensi-

tivity testing possessed marA (Table 4), suggesting that this

regulator could potentially be involved in mechanisms associ-

ated with the observed resistance phenotypes. Species that

comprised our resistant bacterial colonies were mostly related

to inhabitants of soil or sediments of freshwater environments.

It is possible that our novel marA homologs could be involved in

the activation of certain endogenous drug or chemical export

systems, such as those that have been already described for

Bacillus (Grkovic et al., 2002). In any case, the present report of

potentially toxigenic and antibiotic-resistant Bacillus cereus

strains bearing the marA regulator may be of concern, as this

species is commonly responsible for food poisoning.

3.4. Detection of marA in environmental samples

The presence of marA was observed in all water samples except

site 1, and in all sediment samples except sites 3 and 4 (Table 3).

Every environmental sample appeared to be characterised by

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 2 7 1 – 4 2 8 0 4277

a single nucleotide sequence, each showing a homology of 97–

100% with other database entries for this multiple antibiotic

resistance regulator (accession numbers DQ420208–DQ420219).

Antibiotics were not detected in the surface water at site 1,

which is close to the river spring and receives little urban waste.

Organic compounds such as antibiotics and other contami-

nants can be adsorbed and concentrated in sediments (Golet

et al., 2002). It is possible that the occurrence of marA in

sediments at site 1 was associated with the presence of

accumulated antimicrobials or other local pollutants in this

matrix (Alekshun and Levy, 1999), which have not been ana-

lysed in this study.

3.5. Quantitative analysis of marA in environmentalsamples

The possible linkage between antibiotic environmental

contamination and the insurgence of bacterial resistance is

currently a topic of debate. Antibiotic susceptibility tests and

a marA specific PCR showed that the occurrence of resistance

genes or resistant phenotypes could not be clearly correlated

to the pattern of antibiotic contamination (Table 3). We

hypothesised that a quantitative analysis of resistance genes

could be useful to study the possible correlation between the

occurrence of antibiotics and the development of resistance in

the environment. Water-borne antibiotics may select for

a higher proportion of cells carrying resistance genes in

a bacterial population, rather than determining the presence

or absence of these genes in the community. Thus, real-time

quantitative PCR (q-PCR) was performed on two samples with

different antibiotic contamination profiles: site 1 and the STP

influent (Table 2).

We focused first on E. coli specific marA, and then on the

novel homolog found in our antibiotic-resistant Bacillus colo-

nies. E. coli specific marA was detected in both samples,

although the gene was observed at higher levels in the STP

influents relative to 16S rDNA (Table 5). E. coli and entero-

bacterial cells were more prevalent in the STP influent

compared to site 1 as assessed by 16S rDNA-based quantita-

tive analysis. marA specific for Bacillus was confirmed to be

present only in the STP influent and not at site 1 (Table 5),

although Bacillus strains were found at both sampling sites by

16S rDNA-based quantitative analysis (with a higher preva-

lence in the STP influent). These data suggested that the level

of bacterial resistance could be related to local pollutants

exposure among which antibiotics may represent a significant

Table 5 – Real-time PCR comparison for site 1 and the STP infl

E. coli

Raw valuea Norma

Site 1 16S rDNA 1.50� 0.16

marA 16.22� 5.49

STP influent 16S rDNA 1033.70� 73.46 1

marA 65.27� 4.96

a Values (average� standard error) correspond to 2�DCt (n¼ 3) and 2�DDCt

b Real-time PCR average raw-values adjusted for the 16S rDNA copy n

(Klappenbach et al., 2001).

selective pressure. Our initial data also revealed that marA

was borne mainly by Enterobacteriaceae in antibiotic-

contaminated environments. We observed that only 0.6% of

Bacillus cells in sewage water had genes that encoded for

multiple antibiotic resistance (Table 5). Enterobacteria do not

survive long in surface waters and may represent a health

hazard only in regions with local sewage discharge. On the

other hand, Bacillus strains bearing marA can be a long-term

vehicle for the spread of antibiotic resistance genes in the

environment.

3.6. Phylogeny of marA

With the aim of exploring the ancestor/descendant relation-

ships among our novel marA homologs, we constructed

a phylogenetic tree using sequences obtained from environ-

mental samples and antibiotic-resistant bacterial isolates,

together with known marA genes collected from databases

(Fig. 2). Sequences obtained from bacterial isolates and envi-

ronmental samples clustered into two distinct clades. The first

major clade contained marA sequences from the databases,

which branched into four sub-clades:Enterobacter (A), Salmonella

(B), E. coli and Shigella (C), and the DNA sequences obtained from

antibiotic-resistant colonies (D). marA sequences obtained from

environmental samples formed the second lineage of the

phylogenetic tree (E) (Fig. 2). According to this phylogenetic

analysis, marA sequences amplified from bacterial isolates

were different from those detected in the environment, and

more closely related to the marA from E. coli and Shigella (Fig. 2C

and D). Our phenogram suggested that both E. coli and Bacillus

acquired marA from an ancestral sequence found in Shigella.

The only marA sequence recovered in this study from E. faecium

(Table 4) also clustered with the Bacillus orthologs (Fig. 2D),

indicating the possibility of HGT between Enterococcus and

Bacillus.

In more general terms, our phylogenetic tree suggested

a delineation of marA sequences in Bacillus strains and

Enterobacteriaceae originating from an Enterobacter (Fig. 2).

Additionally, divergent bacterial and environmental DNA

sequences share a single early common ancestor. This indi-

cated that multiple antibiotic resistance and global regulators

such as marA could be more widespread among diverse

environmental bacterial populations than expected from

previous studies. D’Costa et al. (2006) and Dantas et al. (2008)

have recently highlighted the wealth of resistance strategies

that can be found in soil bacteria.

uent

Bacillus

lised valueb Raw valuea Normalised valueb

0.21 72.28� 47.3 8.12

2.32 Not detected –

47.67 2453.99� 403.31 275.73

9.32 0.0548� 0.0112 0.0062

(n¼ 6) for the 16S rDNA and marA analyses, respectively.

umber per genome (7 and 8.9 for E. coli and Bacillus, respectively)

Fig. 2 – Phylogenetic affiliations of marA homologs from bacterial isolates and environmental samples. The Bacillus

thuringiensis transcription regulator gene araC was included as a reference outgroup. The scale represents one substitution

per 100 base pair positions. Significant local bootstrapping values (1000 resampling cycles) are shown. (A) Enterobacter

strains; (B) Salmonella strains; (C) E. coli and Shigella strains; (D) antibiotic-resistant isolates from this study; (E)

environmental samples from this study. The number in the sequence designation refers to the different sampling sites,

while W and S indicate water and sediment, respectively.

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 2 7 1 – 4 2 8 04278

3.7. Methodological considerations

At the present stage, we can only speculate about the actual

diversity and distribution of marA and its associated resistance

in the environment, since the approach employed in this study

has several limitations. Our investigation on the bacterial

community present in aqueous samples was based on

membrane filtration, which may have selected only bacteria

with cell dimensions greater than 1.6 mm, both for total envi-

ronmental DNA extraction and plate culturing. The fact that

marA was borne mainly by Enterobacteriaceae in contami-

nated environments may explain the reason why we could

amplify only one dominant marA nucleotide sequence from

each of our water and sediment samples, which appeared

phylogenetically different from our cultured strains. The use of

nested PCR may also have increased this bias. Lyophilisation of

samples similarly could have biased our cultivation analyses

favouring bacteria of the Bacillus group, which can produce

endospores. Another bias could be introduced by the culture

media itself that would not be suitable for all soil organisms.

This signifies that data obtained from our extracted total DNA

and our cultivated strains may not be representative for the

total environmental bacterial community. The aim of our

q-PCR analysis was exploratory and suffered from challenges

associated with the quantitation of environmental bacterial

samples and the lack of more test samples. Future studies that

w a t e r r e s e a r c h 4 2 ( 2 0 0 8 ) 4 2 7 1 – 4 2 8 0 4279

will build on this present work include a larger area of study

and additional well-suited locations to conclusively support

a correlation between environmental levels of antibiotics and

multidrug resistance.

4. Conclusions

Our study shows that the multidrug resistance regulator marA

was widespread in environmental samples, but the presence/

absence of marA or associated resistant phenotypes was not

clearly correlated with the occurrence of antibiotic contami-

nation as we expected. Instead, preliminary q-PCR analysis of

marA in environmental samples suggested a possible relation

between antibiotic exposure and the level of bacterial resis-

tance, and suggested that the primary cause for the spread of

antibiotic resistance is anthropogenic (WWTP effluents).

Using a combination of different techniques we revealed

the presence of the multidrug resistance regulator marA in the

genus Bacillus, suggesting the acquisition of this novel

homolog by HGT and its putative origin in Enterobacteriaceae.

The approach employed was novel and the information pre-

sented is of topical environmental relevance. Most studies

investigating antibiotic-resistant bacteria have focused

strictly on faecally derived organisms and this is one of the

few studies that identify environmental bacteria as a target for

anthropogenic-induced antibiotic resistance. The identifica-

tion of multiple resistance genes in the toxic species B. cereus

may have implications for human health. Our results are

encouraging for further investigations to identify the origin

and distribution of antimicrobial-resistance in the environ-

ment. Antibiotic prescription and/or wastewater treatment

must adapt to face this increasing concern.

Acknowledgements

This work was partially supported by the Italian MIUR project

2002098317 (2002). The authors are grateful to C. Rossetti,

F. Goh and R. Cavaliere for the support in the experimental

work, and to T. Salmon for assistance in designing degenerate

primers. A special thanks to A. Infantino for assistance with

real-time PCR data analysis.

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