Discrimination of Different Foodborne Pathogens onto Carbohydrate

Microarrays Using Surface Plasmon Resonance Imaging

Emilie Bulard

1,2,3,4,5

, Aurélie Bouchet-Spinelli

1,2,3

, Patricia Chaud

4,5

, André Roget

1,2,3

Roberto Calemczuk

1,2,3

, Sébastien Fort

4,5

and Thierry Livache

1,2,3

Univ. Grenoble Alpes, INAC-SPrAM-CREAB, F-3800 Grenoble, France

CNRS, SPrAM-CREAB, F-3800 Grenoble, France

CEA, INAC-SPrAM-CREAB, F-3800 Grenoble, France

Univ. Grenoble Alpes, CERMAV, F-3800 Grenoble, France

CNRS, CERMAV, F-3800 Grenoble, France

Keywords: Biosensor, Spri, Bacterial Detection, Carbohydrate, Escherichia Coli, Listeria Monocytogenes, Salmonella

Enteritidis.

Abstract: Food safety is a public health challenge. Devices allowing early, fast, label-free and in situ detection of

bacteria are of great interest to prevent outbreaks. Listeria monocytogenes, Salmonella spp. and Escherichia

coli O157:H7 are foodborne pathogens which were responsible of 60% of the hospitalizations in the USA in

2011. In this study, we conceived a carbohydrate microarray in order to detect and discriminate these three

food pathogenic bacteria. In less than 10 hours, from an initial bacterial suspension of 100 bacteria per mL,

Surface Plasmon Resonance imaging allowed the detection and the discrimination of these bacteria while

they were growing and interacting specifically with the carbohydrate microarray. Moreover, this device is

easily regenerable and can be re-used: it is probably a promising tool to early detect bacteria in food.

1 INTRODUCTION

Foodborne illness is one of the major public health

problems. The estimation of hospitalizations and

deaths caused by pathogenic food pathogens is a

challenge. However, Centers for Disease Control

and Prevention estimate that 9.4 million episodes of

foodborne illnesses occurred in the USA in 2011.

For example, 35% of hospitalizations (i.e 19,336

hospitalizations) and 28% of deaths were caused by

Salmonella spp., 19% of deaths were caused by

Listeria monocytogenes and Escherichia coli O157

outbreaks contribute to 4% of hospitalizations in

2011 (Scallan, 2011). Then these three pathogenic

strains are part of the top five pathogens contributing

to foodborne diseases resulting in hospitalizations

and/or deaths. Conventional methods to detect

bacteria in food are reliable but time-consuming.

The need for bacterial cultures is one of the major

disadvantages of these techniques allowing the

spread of epidemics. For example, detection of L.

monocytogenes require one week with standards

methods (Velusamy, 2010). During this period,

consumers are not warned.

Since the last decade, the development of fast,

sensitive and reliable techniques has emerged. To

this aim, nanotechnology has already improved early

nanodiagnostics based on selective capture of

bacteria using nanoparticles (Tallury, 2010),

fluorescent nanoparticles, quantum dots (Gilmartin,

2012), bioconjugated magnetic particles (Chu,

2013), etc) or microengines modified with lectins

(Campuzano, 2012). However, these different

methods require complex functionalized probes and

a robust chemistry for surface functionalization.

Moreover, they consist at least in a two-step process,

the first one being the capture of bacteria and the

second one being the detection of bacteria-

nanoparticles interactions. For example, bacteria-

magnetic nanoparticles are easy to remove from the

sample by magnetic separation techniques but they

still need to be identified using other classical

techniques such as MALDI-MS spectrometry, flow

cytometry (Cellular analysis and detection using

surface plasmon resonance techniques, 2013),

fluorescence or scanning electron microscopy (Gu,

2006). The disadvantage of these techniques is that

they do not allow a real-time and in situ monitoring

of the bacteria-probe interactions. To solve this

121

Bulard E., Bouchet-Spinelli A., Chaud P., Roget A., Calemczuk R., Fort S. and Livache T..

Discrimination of Different Foodborne Pathogens onto Carbohydrate Microarrays Using Surface Plasmon Resonance Imaging.

DOI: 10.5220/0005254901210126

In Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES-2015), pages 121-126

ISBN: 978-989-758-071-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

problem, label-free and real-time cutting edge

methodologies have been proposed such as Quartz

Crystal Microbalance (Chu, 2013). However, this

detection technique requires a high number of

bacteria and a uniformly functionalized surface

difficult to obtain in general. Microcantilevers have

also been used in order to detect bacteria in a

complex sample (Mader et al., 2012).

Surface Plasmon Resonance (SPR) has emerged

as a very well-adapted method for the detection of

analytes onto microarrays (Abadian, 2014, Safina,

2012). This technique, based on refractive index

changes, monitors the interactions occurring

between biomolecules grafted on a biochip and

target molecules within the sample. SPR imaging

(SPRi) collects the reflected light with a CCD

camera. Futhermore, it allows label-free detection in

real-time of up to one hundred images and SPRi

signals simultaneously within one biochip

(Bouguelia, 2013).

Our team has developed a new method called the

Culture-Capture-Measure (CCM) (Bouguelia, 2013,

Mondani, 2014). It consists in following in situ the

bacterial growth in a medium by SPRi. It allows

detecting bacteria in one step from an initial

concentration of 20 Colony Forming Units (CFU)

per mL. In these works, antibodies were used as

probes and grafted on gold surfaces. Antibodies

microarrays are efficient to detect bacteria

(Delehanty, 2002, Gao, 2010) but their cost and

fragility (dehydration and denaturation sensitivity,

single use) make them difficult to use on an

industrial scale. In this study, antibodies were

replaced by carbohydrates. Carbohydrates provide

interesting alternatives to antibodies in order to

overcome their limitations. They are less expensive,

easily-chemically-modified than antibodies. They

usually allow regeneration of the biochip because

their interactions are known to be low-energetic.

Besides, bacteria are known to interact with

carbohydrates by different ways: through particular

lectins produced on the outer cell wall, named

adhesins (Bierne, 2007, Chessa, 2009, Cowart,

1990) and also by non-specific bindings such as

interactions with membrane transporters or

electrostatic interactions (Audfray, 2013, Mader,

2012). This underlines the great potential of the use

of carbohydrates as probes for bacteria detection and

interactions.

We chose seven simple mono- and di-

saccharides for the conception of the microarray:

glucose (Glc), galactose (Gal), mannose (Man),

fucose (Fuc), maltose (Malt), N-acetylglucosamine

(GlcNAc), and sialic acid (Neu5Ac). First, pyrrole-

carbohydrate conjugates were synthetized and then

grafted onto gold surfaces by

coelectropolymerization. The microarray

functionalization was then characterized by lectin-

carbohydrate recognitions. Finally, bacterial

detection was evaluated by SPRi onto the

carbohydrate microarray and monitored by the CCM

method.

This biochip allows the detection and the clear

discrimination of the three bacterial strains tested: E.

coli O157:H7, L. monocytogenes subserotype 1/2a

and S. enteritidis.

2 MATERIALS & METHODS

2.1 Pyrrole-Carbohydrate Conjugates

Syntheses

2.1.1 Reactants

Monosaccharides, maltose, lithium bromide, sodium

hydride, allyl bromide, dimethylformamide,

acetonitrile, cysteamine, phosphate buffer,

manganese(II) chloride tetrahydrate, sodium

chloride, and sodium hydroxide were purchased

from Sigma-Aldrich (Saint-Quentin-Fallavier,

France). Pyrrole was purchased from Acros

Organics (Geel, Belgium), calcium(II) chloride from

Merck (Darmstadt, Germany).

2.1.2 Syntheses

According to the previously reported procedure

(Fort et al., 2005), pyrrole-carbohydrate conjugates

have been prepared from the corresponding allyle

glycoside by UV-promoted radical addition of

cysteamine followed by reaction with a

pentafluorophenyl-activated pyrrole ester. Allyl

glycosides have been prepared as reported in the

literature: allyl -D-glucopyranoside and allyl -D-

galactopyranoside (Lin, 2010), allyl -D-

mannopyranoside (Nishida, 2004), allyl -maltoside

(Takeo, 1987), allyl -D-fucopyranoside (Vermeer,

2001), allyl DN-acetyl-neuraminic acid (Roy,

1990), allyl 2-acetamido-2-deoxy-beta-D-

glucopyranoside (Vauzeilles, 2001).

The different conjugates were isolated in 30-50%

yield after purification by reverse phase Solid Phase

Extraction and characterized by

C NMR and

MS spectrometry. Final solid products were stored at

4°C.

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122

2.2 Fabrication of a Carbohydrate

Microarray

Aliquots of pyrrole-carbohydrate conjugates were

dissolved in spotting buffer (50 mM phosphate

buffer, pH 6.8, 50 mM NaCl and 10% glycerol)

containing 20 mM pyrrole. The concentration of

pyrrole-carbohydrate conjugate in the mixture was

10 mM.

SPRi biochips were purchased from Horiba

Scientific (Chilly-Mazarin, France) and are made of

a high index glass prism covered with a 2 nm-thick

chromium and a 50nm-thick gold layer used as a

working electrode (about 2 cm

Coelectropolymerization of pyrrole and pyrrole-

carbohydrate conjugates on the biochips was carried

out in an automatized pipette tip (diameter 500µm)

filled with the solution to be polymerized and

containing a platinum wire used as a counter

electrode. The pipette tip was moved at the vicinity

of the gold layer of the SPR biochip, till an electrical

contact was applied between the working (gold

surface) and counter (platinum wire) electrodes. The

polymerization on the prism gold layer was

performed with a 100 ms electric pulse at a 2.0 V

bias independently of any reference electrode. After

microarraying, biochips were copiously washed with

water, dried and stored in air at 4°C (Mercey, 2008).

Each biochip was arrayed with quadruplicates of

pyrrole-carbohydrate conjugates. Moreover, four

spots of polypyrrole (Ppy) deprived of any

carbohydrates were also deposited onto the gold

surface to assess nonspecific SPR response.

2.3 SPRi Setup

Signal measurements were performed using a SPRi-

Lab+TM system (Horiba Scientific, Chilly-Mazarin,

France). Using this commercial software, regions of

interest (ROI) corresponding to individual spots on

the biochips were defined (diameter 260 µm). The

SPRi signal was monitored with a CCD camera, and

reflectivity changes (ΔR) of each ROI were followed

and plotted upon time.

2.4 Characterization of the

Carbohydrate Microarray

Functionalized biochips were characterized by SPR

measurements after injection of different lectins

already known to interact specifically with

carbohydrates (Chang, 2011, Safina, 2012) in

phosphate buffer at 25°C.

2.4.1 Lectins

UEA I (Ulex Europaeus Agglutinin-I) from Ulex

europaeus, PNA (Peanut Agglutinin) from Arachis

hypogaea, WGA (Wheat Germ Agglutinin) from

Triticum vulgaris and concanavalin A (ConA) from

Canavalia ensiformis (jack bean) were purchased

from Sigma-Aldrich (Saint-Quentin-Fallavier,

France).

2.4.2 SPRi Monitoring of Lectin -

Carbohydrate Recognitions

The concentrations used were: 500 nM of PNA

(specific for Gal), 280 nM of UEAI (specific for

Fuc), 50 nM of WGA (specific for GlcNAc,

Neu5Ac) and 1 µM of ConA in the presence of 1

mM Ca

and Mn

(specific for Man, Malt).

SPRi signals (not shown in this study) proved

the efficient grafting and the accessibility of the

grafted sugars by revealing an increase in the

reflectivity variation corresponding to the

recognition of specific spots by lectins.

2.5 Detection of Bacterial Growth by

SPRi

2.5.1 Bacterial Suspensions

E. coli O157:H7 CIP 105917 strain, L.

monocytogenes subserotype 1/2a and S. enterica

subserotype enteritidis were purchased from the

Institut Pasteur (Paris, France).

Bacterial cells were grown overnight at 37 °C in

a Tryptic Soy Broth medium (TSB, Saint-Quentin-

Fallavier, France). Overnight grown bacterial

cultures – corresponding to 10

-10

CFU.mL

-1

–

were used for SPRi experiments after serial dilutions

(up to 10

CFU.mL

-1

). Bacterial counting was

carried out either by McFarland turbidity

measurements (Densimat apparatus, BioMerieux,

Marcy l’Etoile, France) or colony counting after

plating and culture on solid media Tryptic Soy Agar

(TSA, Saint-Quentin-Fallavier, France). For colony

counting, the counting of 4 plates was averaged and

standard deviation was calculated.

2.5.2 Monitoring of Bacterial Kinetics using

SPRi

The freshly bacterial suspension of 100 CFU.mL

-1

TSB medium was deposited in the thermalized

(37°C) SPRi chamber (1.6 mL) onto the

carbohydrate microarray. SPRi kinetic experiment of

DiscriminationofDifferentFoodbornePathogensontoCarbohydrateMicroarraysUsingSurfacePlasmonResonance

Imaging

123

Figure 1: SPRi signals obtained from the spots of the carbohydrate microarray, after deposition of the bacterial suspension

at 10

CFU.mL

-1

in TSB; in the case of: A. buffer (control), B. S. enteritidis, C. E. coli O157:H7, and D. L. monocytogenes.

Table 1: Detection times at the inflection point of SPRi signals obtained after deposition of the bacterial suspension at 10

CFU.mL

-1

in TSB. Data were obtained from the SPRi signal averages of three independent runs.

Bacteria E. coli O157:H7 L. monocytogenes

Interacting carbohydrate Gal Ppy, the others

carbohydrates

Sialic Acid Ppy, the others

carbohydrates

Detection Time (min) 529 ± 26

604  9 739  19 783  12

the bacterial growth was realized by Culture-

Capture-Measure (CCM) (Bouguelia, 2013,

Mondani, 2014). SPRi signals were recorded in real-

time during the culture (1100 min) over the biochip.

2.5.3 SPRi Data Treatment

For each experiment, an average of SPRi signal

coming from quadruplicates was realized.

SPRi signals (i.e ΔR) of the bacterial growth

onto the carbohydrate microarray are represented by

an inverse tangent function: after smoothing kinetic

curves, first order derivative of this function was

realized. The maximum of the derivative

corresponds to the inflection point of kinetic curve

called “detection time”.

Finally, all the results present in this paper are

the average of at least three independent runs.

3 RESULTS AND DISCUSSION

Using the CCM method developed in our laboratory

(Bouguelia, 2013, Mondani, 2014), we monitored

the kinetic SPRi signal after bacterial deposition

onto the carbohydrate microarray. The curves are

represented in Figure 1.

Experimental kinetic curves were similar to

standard bacteria growth kinetics occurring in three

different stages: first, the lag phase (bacteria adapt to

the new medium), then the exponential growth

(bacteria divide regularly) and finally the stationary

phase (growth stops because of the lack of nutrients

and overpopulation). Figure 1.A represent the SPRi

signal obtained from the control (i.e TSB medium):

the linear increase in reflectivity corresponds to the

liquid evaporation occurring during the SPRi

experiment. The kinetic SPRi curve of S. enteritidis

(Figure 1.B) presents a low SPRi signal but a shape

of bacterial growth for all the carbohydrate-bacteria

interactions. On the contrary, Figures 1.C, and 1.D

corresponding to E. coli O157:H7 and L.

monocytogenes onto the carbohydrate microarray

respectively, show different carbohydrate-bacteria

interactions. In the case of E. coli O157:H7, the

galactose-E. coli interaction (red curve) was earlier

200 400 600 800 1000

TSB

R(%)

Time (min)

200 400 600 800 1000

S. enteritidis

R(%)

Time (min)

200 400 600 800 1000

L. monocytogenes 4.12

R(%)

Time (min)

200 400 600 800 1000

R(%)

E.coli O157:H7

Time (min)

A. B.

C. D.

ΔR%

Time(min)

ΔR%

Time(min)

ΔR%

Time(min)

ΔR%

Time(min)

E.coliO157:H7

TSB

L.monocytogenes

200 400 600 800 1000

S. enteritidis

zoom

S.enteritidis

Glc

Gal

Man

Fuc

Malt

GlcNAc

Sialicacid

Ppy

BIODEVICES2015-InternationalConferenceonBiomedicalElectronicsandDevices

124

detected by SPRi than the others interactions. In the

case of L. monocytogenes, the SPRi signal earlier

detected is the sialic acid-L. monocytogenes

interaction (dark blue curve). Moreover, the SPRi

curve presents two plateaux: the first one may

correspond to the classical stationary phase and the

second may correspond to the development of the

bacterial biofilm.

In this study, the amplitude of the SPRi signal

could not be relevant to discriminate the different

strains. In the case of S. enteritidis and E. coli

O157:H7, ΔR is less than 20-30%. On the contrary,

in the case of L. monocytogenes, ΔR is around 50-

60%. Indeed, theSPRisignalalsodependsonthe

contributionofonebacteriumtothisSPRisignal.

Thus, the ΔR value can not be used as a criterion

because of the different shapes and sizes of the three

strains. However, it is possible to rationalize the

results by defining for each bacterium a “detection

time” (Table 1). In the case of E. coli O157:H7, the

“detection time” of the galactose-E. coli interaction

is 529 ± 26 min whereas the average “detection

time” of the {control, others carbohydrates}-E. coli

interaction is 604  9 min. Then the time difference

between these two “detection times” is 76  32 min.

That significates that E. coli interact 76  32

minutes earlier with the galactose spots than with the

others. In the case of L. monocytogenes, the time

scale is rather different from the one of E. coli

O157:H7. The “detection time” of the sialic acid-L.

monocytogenes interaction is 739  19 min and thus

the time difference between this time and the

“detection time” of the {control, others

carbohydrates}-L. monocytogenes interaction is 44

 9 minutes.

The observation of the SPRi signals and the

quantitative analysis is promising for the further

identification and differentiation of the three

bacterial strains onto our carbohydrate microarray.

This microarray is also regenerable through soft

washing (SDS 2% and sodium hydroxide 0.02M)

and can be stored at room temperature in air up to 6

months.

4 CONCLUSIONS

This work in progress presents an innovative

concept to detect specifically food pathogenic

bacteria. Thanks to the Culture-Capture-Measure-

ment methodology developed in our team, bacteria

are detected in less than 10 hours, from an initial

suspension of 10

CFU.mL

-1

, onto a carbohydrate

microarray by SPRi.

We have demonstrated that S. enteritidis, E. coli

O157:H7 and L. monocytogenes bacteria interact

differently on the carbohydrate microarray (i.e

different SPRi signals) that is promising to identify

these three pathogenic strains. Furthermore, E. coli

O157:H7 has showed particular interaction with the

galactose spots whereas L. monocytogenes interacted

preferentially with the sialic acid spots.

Then, this cheap, regenerable carbohydrate

microarray seems to be a promising tool to detect

bacteria in food and a good alternative to the use of

expensive, and fragile antibodies. The identification

and the differentiation of different strains inside one

bacteria specie, in particular E. coli and L.

monocytogenes, are ongoing in our team.

ACKNOWLEDGEMENTS

This work has been partially supported by the Labex

ARCANE (ANR-11-LABX-0003-01). We thank the

Chimie NanoBio-ICMG platforms (Université

Joseph Fourier, Grenoble, France) for mass

spectrometry and NMR analyses. We akcnowledge

the French Agency for Food, Environmental and

Occupational Health and Safety (ANSES), Food

Safety Laboratory (Maisons-Alfort, France) for

providing us bacterial strains and the Centre de

Ressources Biologiques de l’Institut Pasteur (Paris,

France) for providing the E. coli O157:H7 strain

(CIP105917).

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