ORIGINAL PAPER

Emission of volatile compounds by Erwinia amylovora:biological activity in vitro and possible exploitationfor bacterial identification

Francesco Spinelli • Antonio Cellini • Joel L. Vanneste •

Maria T. Rodriguez-Estrada • Guglielmo Costa •

Stefano Savioli • Frans J. M. Harren • Simona M. Cristescu

Received: 7 July 2011 / Revised: 28 November 2011 / Accepted: 8 December 2011

� Springer-Verlag 2011

Abstract Several analytical techniques such as gas

chromatography–mass spectrometry, proton transfer reac-

tion–mass spectrometry and laser photoacoustic detection,

were used to characterize the volatiles emitted by Erwinia

amylovora and other plant-pathogenic bacteria. Diverse

volatiles were found to be emitted by the different bacterial

species examined. The distinct blend of volatiles produced

by bacteria allowed their identification using an electronic

nose (e-nose). The present study reports the discrimination

of E. amylovora, the fire blight pathogen, from other plant-

associated bacteria using an e-nose based on metal oxide

semiconductor sensors. Two different approaches were

used for bacterial identification. The first one was the direct

comparison of the odorous profiles of unknown bacterial

isolates with four selected reference species. The second

approach was the use of previously developed databases

representing the odorous variability among several bacte-

rial species. Using these two strategies, the e-nose suc-

cessfully identified the isolates in 87.5 and 62.5% of the

cases, respectively. Finally, the profiling of the volatiles

emitted by E. amylovora lead to identify some metabolic

markers with a potential biological activity in vitro.

Keywords Fire blight � VOCs � Erwinia amylovora �2,3-Butanediol � Electronic nose � Plant growth promotion

Introduction

Identification of bacterial species, including plant patho-

gens, is currently based on the sequencing of ribosomal

genes (Han 2006). Although molecular techniques are

highly reliable, they are time consuming and require spe-

cific equipments and trained personnel (Tothill 2001). In

addition, they do not provide information about the phys-

iological activity of microbes (Gardner et al. 1998).

Many microbial species, such as bacteria and yeasts, are

characterized by typical odour emissions, which reflect their

metabolism and interactions with the environment (Gibson

et al. 1997; McEntegart et al. 2000; Magan and Evans 2000;

Bachinger and Mandenius 2000). Several researchers

showed that the detection of volatile organic compounds

(VOCs) produced in vitro by microorganisms can be

exploited for their identification (Gibson et al. 1997; Wilson

et al. 2004; Keshri and Magan 2000; Magan and Evans 2000;

Dutta et al. 2002; Marilley et al. 2004; Momol et al. 2004;

Rock et al. 2008; Spinelli et al. 2011b). Therefore, bacterial

identification via the analysis of VOCs could also provide

key information on the biology of the species.

In addition, VOCs identification can also allow plant

disease diagnosis (Jonsson et al. 1997; Wilson and Lester

Communicated by D. Treutter.

A contribution to the Special Issue: Pome Fruit Health.

F. Spinelli (&) � A. Cellini � G. Costa

Dipartimento di Colture Arboree, University of Bologna,

Viale Fanin 46, 40127 Bologna, Italy

e-mail: francesco.spinelli3@unibo.it

J. L. Vanneste

The New Zealand Institute for Plant and Food Research Ltd.,

Ruakura Research Centre, Private Bag 3123,

Hamilton 3240, New Zealand

M. T. Rodriguez-Estrada � S. Savioli

Dipartimento di Scienze degli Alimenti, University of Bologna,

Viale Fanin 40, 40127 Bologna, Italy

F. J. M. Harren � S. M. Cristescu

Radboud University, Life Science Trace Gas Facility,

Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands

123

Trees

DOI 10.1007/s00468-011-0667-2

1998; Harper 2001; Prithiviraj et al. 2004; Vikram et al.

2005; Laothawornkitkul et al. 2008; Spinelli et al. 2010,

2011a).

Gas or liquid chromatography combined with mass

spectrometry (GC–MS, HPLC–MS) and proton transfer

reaction–mass spectrometry (PTR-MS) provide the

opportunity to identify microorganisms, however these

methods are time consuming, expensive and usually more

complex than the standard molecular methods (Saevels

et al. 2003; Turner and Magan 2004). Therefore, simpler,

faster alternative systems for the real-time monitoring of

aroma are needed (Tothill 2001). One of the candidate

instruments to achieve this challenge is the electronic-nose

(e-nose). Electronic noses analyze gaseous mixtures to

discriminate between different, but similar, mixtures, and

in the case of simple mixtures, to quantify the concentra-

tion of the constituents (Pardo and Sberveglieri 2004).

Therefore, by mimicking the mammalian smell sensor,

e-noses are able to detect the olfactory fingerprint of a

specimen (Pavlou and Turner 2000). Presence of bacteria is

detected by changes in the chemical composition of the

headspace vapours (McEntegart et al. 2000). In contrast to

GC–MS, the e-nose does not identify and quantify each

headspace component, but it records the global effect of the

volatile mixture on the sensors (Pardo and Sberveglieri

2004). To discriminate between samples, the output from

the sensor array may be interpreted via a variety of meth-

ods, such as pattern recognition algorithms, principal

component analysis (PCA), discriminant function analysis,

cluster analysis and artificial neural network.

The gram-negative bacterium Erwinia amylovora which

is the causal agent of fire blight of pomaceous plant, leads

to the most serious bacterial disease of apple and pear trees.

Under optimal conditions fire blight can kill an entire tree

in a single growing season. The early stages of the infec-

tion are not associated with obvious symptoms; but control

of the pathogen, after it has been established, is often dif-

ficult and requires radical pruning intervention. A tool for

the early detection of E. amylovora in the orchard and in

propagation material would be useful to prevent the spread

of the disease. Electronic noses based on metal oxide

semiconductor technology may allow the recognition of

bacterial species, such as E. amylovora, based on aroma

emissions profiling.

The advantages of e-noses, compared to traditional

identification techniques, are due to the time saving (min-

utes instead of days) and an operator-friendly interface.

In the present work, two electronic noses, EOS835 and

EOS507, have been compared for their efficacy in dis-

criminating the plant pathogen E. amylovora from the other

bacterial species usually isolated from the same host plants

(i.e. Malus domestica and Pyrus communis). In addition,

the VOCs produced by in vitro culture of E. amylovora and

other plant-associated bacteria were identified by PTR-MS

and GC–MS.

The e-nose used in this study was first designed and

engineered at the Sensor Lab in Brescia (Pardo and

Sberveglieri 2004), and it is now commercialized by the

SACMI Company (Imola, Italy, www.sacmi.it) as the

Electronic Olfactory System EOS507. EOS507 uses

advanced control electronics and has a user-friendly data

analysis interface. The gas is sensed by its effect on the

electrical resistance of the SnO2 semiconductor, resulting

from combustion reactions occurring with active oxygen

species on the surface of the SnO2 particles (Pardo and

Sberveglieri 2004).

As compared to the previous versions (such as EOS835),

EOS507 does not need to be connected to a computer,

since it integrates an operating system with analysis and

recognition programs.

Electronic noses does not allow identification or quanti-

fication of single compounds in complex mixtures (Pardo

and Sberveglieri 2004). Such information, could be useful to

isolate single volatile compounds with biological effects on

plant hosts, and to provide better markers for identification,

thus leading to the improvement of e-nose sensors. There-

fore, other methods must be adopted for the identification of

the single volatiles contributing to the odorous fingerprint of

the different bacterial species. GC–MS provides a precise

identification of single compounds, despite the technique

being complicated and time consuming. PTR-MS also

requires dedicated and expensive instrumentation, and its

output consists of a qualitative profile of masses that cannot

be uniquely linked to single molecules; on the other hand, it

allows a real-time monitoring of variations in the mass

profile (Boamfa et al. 2004). Laser photoacoustic detection

(LPD) is another on-line method, which can be used for the

direct measure of specific compounds, such as the gaseous

plant stress hormone ethylene (Cristescu et al. 2008).

In the present work, bacterial isolates obtained from the

epiphytic biocoenosis harboured by apple flowers, leaves

and fruitlets, were identified using traditional methods and

divided in different groups. For each group one or more

isolates were identified using molecular methods, these

isolates were then identified with the e-nose and the data

were compared with those obtained by molecular methods.

In addition, the VOCs emitted by E. amylovora 1540, 1/79

and 8865, Pantoea agglomerans P10c, Pseudomonas flu-

orescens A506, Bacillus amyloliquefaciens IN937 and

Bacillus subtilis AM01 were characterized by GC–MS and

PTR-MS. The absence of ethylene production by E. amy-

lovora was also determined by LPD.

Finally, preliminary experiments were performed to

evaluate the possible biological effects of gaseous com-

pounds released by bacterial cultures on other bacteria and

pear plants.

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123

Materials and methods

Bacterial isolation, cultivation and standard

identification

The bacterial species used in this work were E. amylovora,

Erwinia carotovora ssp. carotovora, Erwinia pyrifoliae,

Pseudomonas syringae pv. syringae, P. syringae pv.

tomato, P. syringae pv. papulans, and P. syringae pv. pisi,

Pseudomonas chicori, Pseudomonas aeruginosa, Pseudo-

monas vart, P. fluorescens, P. agglomerans, B. amyloliq-

uefaciens and B. subtilis (Table 1). Those strains were used

to build up the database of the volatile fingerprints of the

different species. Most of the species considered in this

work are commonly found on the surface of apple plants.

Among them, some strains of P. fluorescens and P. ag-

glomerans are commercially available as biological control

agents like BlightBan� and Blossom BlessTM

, respectively.

Epiphytic bacteria were isolated from the surface of

apple flowers, leaves and fruits collected from a ‘Braeburn’

apple experimental orchard at Ruakura Research Station,

Hamilton (New Zealand). For the isolation, entire flowers,

leaves or fruitlet were washed in 10 mL of a sterile solution

of MgSO4 10 mM amended with Tween 20 (1%, v/v). The

washing solution was vortexed for 1 min and then spread

on Luria Agar (LA) Petri dishes amended with cyclohex-

imide (50 mg/L). Twenty-four putatively different isolates

were purified and stored in glycerol-stock at -20�C.

All twenty-four isolates were screened for gram color-

ation, colony morphology, production of fluorescent pig-

ments on King’s B medium and production of an

hypersensitive reaction (HR) on tobacco leaves. In addi-

tion, the semi-selective Miller and Shroth medium (Miller

and Schroth 1972) and Pseudomonas Syringae Syringae

Medium (PSSM) (Atlas 2000) were used to screen for

E. amylovora and P. syringae pv syringae, respectively. In

all screening tests, the following bacterial strains were used

as a control: E. amylovora (1540, 8865, 1/79 and DCA289/

01), P. syringae pv syringae (3523 and 4647), P. ag-

glomerans (252, P10c and DCA269/01) and P. fluorescens

(A506 and A3480-73T).

The putative identification of these isolates was con-

firmed by sequencing a 804 bp fragment of the 16SrDNA

after amplification using the PCR primers 27f and 1492r

developed by Martin-Laurent et al. (2001). DNA

sequencing was carried out at the Waikato DNA Sequence

Facility. DNA sequences were resolved using a 3130XL

Genetic Analyzer System fitted with 50 cm capillary arrays

(Applied Biosystems) loaded with POP-7 polyacrylamide

matrix (Applied Biosystems). DNA templates were pre-

pared using Big Dye v3.1 terminator chemistry (Applied

Biosystems). When required, DNA sequences were com-

pared with DNA sequences deposited in GenBank� from

the National Centre for Biotechnology Information (NCBI)

using the programme BLAST 2.2.25 (Zhang et al. 2000).

Among the 24 isolates, only 12 were chosen for molecular

identification. Those isolates represented all the different

groups of bacteria obtained with the previous screening.

Classification using the e-nose was also carried on for those

strains.

Electronic nose equipment and analysis

The olfactory fingerprints were collected with the e-noses

EOS835 and EOS507 (SACMI Inc., Imola, Italy), based on

an array of six different sensors constituted of metal-oxide

semiconductors.

Initially, 46 previously known bacterial strains belong-

ing to nine species (standard strains, Table 1) and twenty-

four unknown bacterial strains were grown on Luria broth

(LB) for 24 h, before transferring 1 mL of each culture to

20 mL of Ceria 132 medium (Vanneste et al. 1992) in

50 mL glass vials closed by a screw cap with a PTFE

septum. These cultures were incubated at 27�C for 3 h

under moderate shaking. Eight replicates from each strain

were used for the e-nose readings. Results from the 46

known strains allowed the development of a database of

olfactory fingerprints. This database was successively used

to identify the 24 unknown isolates and for the recognition

of additional type strains (e.g., E. amylovora 1/79, 178,

1392).

A second set of experiments was performed, comparing

each unknown isolate with only two strains belonging to

the most abundant species isolated from apple and pear

phyllosphere (E. amylovora and P. syringae pv. syringae or

P. agglomerans and P. fluorescens).

Gas-chromatographic air, or air filtered by multiple

passages through active charcoal and silica gel, was used as

reference. Since this air has a very low relative humidity

(RH) (close to zero), a humidifier, consisting of a porous

stone gargling in sterile water, was used to obtain air with a

RH similar to that of samples. The e-nose readings were set

according to the following parameters: ‘Warm Up’: 00,‘Wait’: 00, ‘Before’: 0.80, ‘During’: 0.30, ‘After’: 0.80,‘Clean End’: 00. For each vials, two e-nose analyses were

performed. During the first one, the headspace accumulated

during 3 h of culturing was sampled, whereas in the second

one, the volatiles readily produced were collected and

analysed.

Gas chromatography–mass spectrometry

Headspace–solid-phase microextration–GC–MS (HS–

SPME–GC–MS) determination of VOCs emission was

performed according to Francioso et al. (2010). The bacterial

strains (E. amylovora 1540, 1/79, 8865; P. fluorescens A506,

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123

Table 1 List of bacterial species and strains used as standard for the development of a VOCs profile database

Bacterial strains Organisms Type Geographic origin Isolation

Ea 1540 Erwinia amylovora Plant pathogenic New Zealand D.W. Dye

Ea 8865 Erwinia amylovora Plant pathogenic New Zealand S.V. Thomson

Ea DCA289/01 Erwinia amylovora Plant pathogenic Italy F. Spinelli

Ea 2.58 Erwinia amylovora Plant pathogenic New Zealand R.K. Taylor

Ea 7/74 Erwinia amylovora Plant pathogenic Germany W. Zeller

Ea 13253 Erwinia pyrifoliae Plant pathogenic Japan R.K. Taylor

Ea J21 Erwinia pyrifoliae Plant pathogenic Japan R.K. Taylor

Ea 13249 Erwinia pyrifoliae Plant pathogenic Japan R.K. Taylor

EIC 5 Erwinia carotovora carotovora Plant pathogenic New Zealand P.J. Wright

EIC6 Erwinia carotovora carotovora Plant pathogenic New Zealand P.J. Wright

EIC26 Erwinia carotovora carotovora Plant pathogenic New Zealand P.J. Wright

Ecc 1524 Erwinia carotovora subsp. carotovora Plant pathogenic UK D.W. Dye

Pss 3523 Pseudomonas syringae pv. syringae Plant pathogenic Australia J.P. Wilkie

Pss 4647 Pseudomonas syringae pv. syringae Plant pathogenic New Zealand J.L. Vanneste

Pss 4355 Pseudomonas syringae pv. syringae Plant pathogenic New Zealand D.R.W. Watson

Pss 02.43 Pseudomonas syringae pv. syringae Plant pathogenic Unknown Unknown

Pst 2843 Pseudomonas syringae pv. tomato Plant pathogenic USA W.H. Burkholder

Pst 3362 Pseudomonas syringae pv. tomato Plant pathogenic New Zealand J.P. Wilkie

Pst 4049 Pseudomonas syringae pv. papulans Plant pathogenic Canada B.N. Dhanvantari

Pspi 2651 Pseudomonas syringae pv. pisi Plant pathogenic New Zealand J.M. Young

Pspi 2661-69 Pseudomonas syringae pv. pisi Plant pathogenic Unknown Unknown

Pspi 2254 sub II Pseudomonas syringae pv. pisi Plant pathogenic Unknown Unknown

Psp 4040 Pseudomonas syringae pv. papulans Plant pathogenic USA B.N. Dhanvantari

Psp 4044 Pseudomonas syringae pv. papulans Plant pathogenic Canada B.N. Dhanvantari

Psp 9986 Pseudomonas syringae pv. papulans Plant pathogenic Unknown Unknown

Pc 1258 Pseudomonas chicori Plant pathogenic Unknown Unknown

Pv 83597737 Pseudomonas vart Plant pathogenic Unknown Unknown

Eh 252 Pantoea agglomerans Epiphytic USA S.V. Beer

Eh DCA269/01 Pantoea agglomerans Epiphytic Italy F. Spinelli

Eh C9-1 Pantoea agglomerans Epiphytic USA C. Ishimaru

Eh B7 Pantoea agglomerans Epiphytic Italy J.L. Vanneste

Eh B18 Pantoea agglomerans Epiphytic Italy J.L. Vanneste

Eh B22 Pantoea agglomerans Epiphytic Italy J.L. Vanneste

Eh 265 Pantoea agglomerans Epiphytic Canada D.W. Dye

P10c Pantoea agglomerans Epiphytic New Zealand J.L. Vanneste

P10c9aRI Pantoea agglomerans Epiphytic New Zealand J.L. Vanneste

Eh RGcalyx Pantoea agglomerans Epiphytic New Zealand J.L. Vanneste

Eh G9 Pantoea agglomerans Epiphytic New Zealand J.L. Vanneste

Eh G12 Pantoea agglomerans Epiphytic New Zealand J.L. Vanneste

Eh HB6 Pantoea agglomerans Epiphytic New Zealand J.L.Vanneste

Eh 318 Pantoea agglomerans Epiphytic USA S.V. Beer

Eh 10:22 Pantoea agglomerans Epiphytic New Zealand J.L. Vanneste

Pf A506 Pseudomonas fluorescens Epiphytic USA S.E. Lindow

Pf 862 Pseudomonas fluorescens Animal Health Laboratory, Hamilton NZ

Pf A3480-73T Pseudomonas fluorescens Epiphytic Unknown Unknown

Pa 981 Pseudomonas aeruginosa Human pathogen Animal Health Laboratory, Hamilton NZ

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123

B. amyloliquefaciens IN937, B. subtilis AM01, and

P. agglomerans P10c) were grown on Luria agar at 27�C

for 24 h. Successively, 100 lL of the culture was trans-

ferred in 5 mL of Murashige and Skoog medium enriched

with acetoin (1.76 g/L) to enhance 2,3-butanediol produc-

tion. Before the GC–MS analysis, the samples were

incubated at 40�C for 20 min in a thermostatic bath. A

50/30 m divynilbenzene/carboxen/polydimethylsiloxane

(DVB/Carboxen/PDMS) Stable Flex SPME fiber (Supelco,

Bellefonte, PA, USA) was inserted through the septum into

the vial, which was kept at 40�C for 30 min. Vial pene-

tration depth was set at 20 mm and, after 30 min of

extraction, the SPME fiber was inserted into the injection

port of the Shimadzu GC–MS–QP2010 Plus (Shimadzu,

Tokyo, Japan). The injection penetration depth was set at

51 mm. The SPME fiber was desorbed at 260�C for 10 min

in the split mode. The chromatographic separation of vol-

atile compounds was performed on a ZB-5 ms fused-silica

capillary column (30 m 9 0.25 mm i.d. 9 0.25 m) coated

with 5% phenylpolysiloxane–95% poly (dimethylsiloxane)

(Phenomenex, Torrance, CA, USA). The oven was pro-

grammed to heat from 45�C (kept for 10 min) to 200�C at

3�C/min and kept at 200�C for 8 min. The injector and

transfer line temperatures were set at 260 and 210�C,

respectively. Helium was used as the carrier gas at a con-

stant flow rate of 1 mL/min and a linear velocity of

36.2 cm/s; the split ratio was 1:30. The filament emission

current was 70 eV. A mass range from 40 to 700 m/z was

scanned at a rate of 1,428 amu/s. Identification of volatile

compounds was performed by comparing their retention

time and mass spectra with data reported in the literature

(Francioso et al. 2010) and the NIST/EPA/NIH Mass

Spectral Database (NIST 05, National Institute of Stan-

dards and Technology, Gaithersburg, MD, USA).

The terpenes and terpenoids produced by E. amylovora

1/79 were also determined. These compounds were col-

lected, analysed and identified according to Rapparini et al.

(2004). E. amylovora was cultured in 500 mL Erlenmeyer

flask filled with 150 mL of Ceria 132. The cultures were

incubated at 27�C for 24 h under moderate shaking. For the

collection of volatiles, 450 mL of gas were sampled from

each vial at a flow rate of 30 mL/min onto carbonaceous

traps (Carbograph 1 and Carbograph 2, LARA, Rome,

Italy) packed in glass tubes. Two VOC samplings for each

culture were collected simultaneously. The gas sampling

was conducted on four independent bacterial cultures. The

final bacterial population was assessed by 10-fold

sequential dilutions and plating. The trapped compounds

were thermodesorbed at 250�C and cryofocused at -150�C

on a fused silica liner using a Thermal Desorption Cold

Trap Injector (Chrompack, Middleburg, the Netherlands)

connected to a Hewlett Packard 5890 gas chromatograph

(GC) using a 5970 quadrupole mass spectrometer (MS) as

detection system (Hewlett Packard, Palo Alto, CA, USA).

The desorbed sample, enriched on the cryofocusing unit,

was transferred to the capillary column (60 m 9 0.25 mm

i.d., 0.25 lm film of polymethylsiloxane; HP-1, Hewlett

Packard) by heating the fused silica liner to 230�C. The

separation of terpenes was achieved by maintaining the

oven temperature at 40�C for 10 min and programming to

220�C at 5�C/min. Mass spectra and the reconstructed

chromatogram were obtained by automatically scanning in

the mass range m/z from 20 to 200 a.m.u. Identification of

terpenes was attained by comparing the retention time and

mass spectra with those of authentic standards. Quantifi-

cation of monoterpenes was performed after calculation of

standard curves and response factors for each compound,

and using d14-cymene as internal standards (ISTD). The

comparison of the response for the m/z 93 ion was gener-

ally used for quantification. The biogenic VOC emission

rates were calculated by multiplying the concentration

difference between chamber inlet and outlet air with the air

flow through the chamber, divided by the total biomass dry

weight of the plant material.

Proton transfer reaction-mass spectrometry

Proton transfer reaction-mass spectrometry analysis was

carried out for the strain E. amylovora 1540, P. fluorescens

A506, P. agglomerans P10c, B. amyloliquefaciens IN937a

and B. subtilis AM01. The measurement was taken during

the first 2 days of bacterial growth in LB medium and kept

at room temperature (22�C). Air and non-inoculated LB

medium were used as reference. All mass fragments from

mass 21 to 96 were monitored during the analysis. Three

cultures for each bacterium were measured and five scans

were made for each culture.

Ethylene detection

E. amylovora 1540 and P. agglomerans P10c were grown

in LB medium supplemented with 10 lM methionine to

enhance ethylene emission. To monitor the ethylene pro-

duction, a CO2 laser-based photoacoustic trace gas detector

(ETD-300, Sensor Sense B. V., Nijmegen, The Nether-

lands) was used in combination with a gas handling system

(type VC-6, Sensor Sense B. V., Nijmegen, The Nether-

lands) designed for measuring up to six sampling cuvettes

per experiment. Analyses were performed repetitively at

10-min intervals for each sample and a constant air flux of

2 L/h. Non-inoculated medium was used as reference.

Interorganism communication

The effects of volatile compounds emitted by actively

growing E. amylovora 1/79 and P. agglomerans P10c

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123

cultures were tested for their possible effect on E. amylo-

vora growth. The headspace of donor bacterial culture was

pumped through a culture of E. amylovora 1/79 at a flow

rate of 5 mL/min. The donor and receiving bacteria were

cultured in 50 mL glass vials containing 5 mL of LB

incubated at 27�C. To avoid contamination, the air sampled

for the headspace of the donor culture was filter sterilized

before the emission in the receiving one. The headspace of

vials containing 5 mL of sterile LB was used as control.

Furthermore, emissions of volatile by E. amylovora 1/79,

B. subtilis AM01, B. amyloliquefaciens FZB42 and P. ag-

glomerans P10c were also tested for their possible effects on

the growth and the induction of disease resistance on in vitro-

cultivated pear plants (cv. ‘Abbe Fetel’). For this purpose,

bacteria and plants were grown in the same jar avoiding

physical contact. The bacteria were grown in 5 mL of solid

Ceria 132 minimal medium amended with acetoin (1.76 g/L)

to stimulate the production of 2,3-butanediol. The plants were

grown on Murashige and Skoog medium, containing sucrose

(30 g/L), myoinositol (100 mg/L), thiamine–HCl (1 mg/L),

nicotinic acid (1 mg/L), pyridoxine (1 mg/L), glycine (2

mg/L), N6-benzylaminopurine (1 mg/L), indolebutyric acid

(0.05 mg/L), GA3 (0.1 mg/L), and agar (6.5 g/L).

The jars containing the bacteria and the plants were kept

at 22�C under artificial light (12 h/day). Each treatment

consisted of six replicates containing six plants each. After

15 days of co-culture, on the plants form three replicates,

plant growth was evaluated by measuring leaf surface and

dry biomass. To evaluate the plant surface, each plants was

scanned and the resulting images were processed by an

imaging program (GIMP 2.6.6.–GNU Image Manipulation

Program). The plants from the remaining replicates were

infected with E. amylovora 1/79. The infection was per-

formed by pinching the tip of the first three leaves with

tweezers dipped in a bacterial suspension (106 cfu/mL).

Seven days after the infection, the disease incidence and

severity were measured. Disease severity was recorded as

the total length (mm) of necrotic tissue per plant.

Statistical analysis

The olfactory profiles were constructed using the Nose

Pattern Editor Program (v. 2.6.0) developed by SACMI.

Different mathematical algorithms (Classical, Fourier,

Single Point, Many Points) were tested. The Many Points

algorithm was generally chosen for the analysis where a

limited number of thesis was compared. The transformed

data were successively processed by means of principal

component analysis (PCA) using the MATLAB 6.5 soft-

ware (MathWorks Inc.).

Statistical differences in PTR-MS profiles were tested

using Statistica 7 (StatSoft Inc.). Significance was assumed

at P \ 0.05.

PCA and Cluster analysis for PTR-MS and GC–MS,

respectively, were performed using Statistica 7 (StatSoft

Inc.).

Results

Electronic nose recognition

The e-nose allowed to cluster strains of the same species,

therefore, allowing to build up the generic database and to

identify the unknown bacterial isolates (Fig. 1).

Nonetheless, the distinction among the clusters formed

by the olfactory profiles of the different species was not

always complete and a partial overlapping was found,

notably between E. amylovora and P. syringae pv syringae

(Fig. 1). Therefore, specific subplots that included only a

limited number of species, were built to stress the differ-

ences between those species. However, a partial overlap-

ping between the P. syringae pv syringae and E. amylovora

clusters still occurred (data not shown).

The twenty-four unknown bacterial strains identified by

traditional methods were grouped in seven clusters

(Table 2). At least one bacteria for each cluster was iden-

tified by the sequencing of 16S rRNA gene (Table 2).

When the isolates were identified by molecular analysis,

the e-nose classification, using the olfactory profiles of the

type strains collected at the same date, confirmed the

molecular analysis in 91.67% of the cases. On the other

Fig. 1 2-D principal component analysis on e-nose profiling of

several bacterial species. Despite an overall homogeneous clustering,

Pseudomonas syringae and Erwinia amylovora clusters partially

overlap

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123

hand, the e-nose identification by comparison to the

olfactory profiles classified in the data-bases matched the

molecular identification in 50% of the cases (Table 2).

When the isolates were identified by biochemical char-

acteristics, the e-nose classification, using the olfactory

profiles of the type strains collected at the same date,

confirmed the molecular analysis in 83.3% of the cases.

Whereas, the e-nose identification, by comparison to the

olfactory profiles classified in the databases, matched the

biochemical identification only in 50% of the cases

(Table 2).

Overall, the e-nose confirmed the identification obtained

using other techniques in about 90% of the cases, but only

when the type strains used to build the database were

included in the same measuring session. On the other hand,

the percentage of correct identification dropped to nearby

60% when the measurements were compared to the data-

base (Table 2).

The more recent version of the e-nose (EOS507)

implemented several modifications allowing a better sta-

bility and, therefore, a higher reliability of data obtained in

different measure sessions. Preliminary data support the

feasibility of a data-base for the identification of species.

GC–MS analysis

The composition of VOCs, expressed as percent of the total

ion count (TIC), emitted by the different bacterial strains is

presented in Table 3. Nineteen compounds were found

in the headspace of E. amylovora cultures, which were

not detected in the sterile growing medium. Among them,

2,3-butanedione, 3-methyl-1-butanol, 2,3-butanediol, phenyl-

ethyl alcohol, 3-hydroxy-2-butanone and 2-methyl-1-propanol

were produced by all the E. amylovora strains examined.

2-methyl-1-propanol was the only compound exclusively

emitted by E. amylovora. The percentage of some of

the released volatiles varied from different strains of

E. amylovora, but 3-methyl-1-butanol, 3-hydroxy-2-buta-

none, and phenylethyl alcohol were always the most abundant.

In the VOC profile of P. agglomerans, 3-methyl-1-butanol

acetate was the characteristic of this species representing

*87% of total VOC. Regarding the VOC profiles of both

B. amyloliquefaciens and B. subtilis, 5 compounds: 2-buta-

none, 3-hydroxy-2-butanone, acetic acid, 2,3-butanediol

and 3-methyl-butanoic acid were common to both strains.

However, ethyl acetate and acetic acid butyl ester were

characteristic of B. amyloliquefaciens, whereas isopropyl

alcohol was only found in B. subtilis. The main VOC present

in P. fluorescens was 1-undecene (*71% of total VOC),

which was only detected in this strain.

The differences found in the headspace composition of

the diverse bacterial cultures allowed to discriminate the

species via cluster analysis (Fig. 2). The distinction among

the bacterial species was based on the presence or absence

of VOCs in the headspace and not on the abundance of the

single VOCs. The E. amylovora strains were all linked

together by the cluster analysis, P. agglomerans (formerly

Erwinia herbicola) was, due to their taxonomic proximity,

the closest bacterium to E. amylovora followed by P. flu-

orescens. The two Bacillus species clustered together and

they were clearly separated from the gram-negative bac-

teria and they were the most distant from E. amylovora.

E. amylovora strain 1/79 was also used for a charac-

terization of terpene emissions. Isoprene and limonene

were the most intensely emitted terpenes (Fig. 3). In

addition, isoprene, limonene, b-pinene and 3-carene,

Table 2 Comparison of the identification of different bacterial species based on the sequencing of 16S rRNA genes (molecular identification), or

on biochemical characteristics (putative identification) and e-nose profiling

Isolate nr. Molecular identification e-nose 1* e-nose 2* Isolate nr. Putative identification e-nose 1* e-nose 2*

4 Pseudomonas graminis ? ? 1 Pantoea agglomerans ? ?

6 Pantoea agglomerans ? ? 3 Pantoea agglomerans - -

16 Pseudomonas syringae ? - 5 Pseudomonas graminis - -

20 Pseudomonas sp. ? ? 7 Pantoea agglomerans ? ?

23 Pseudomonas graminis ? - 10 Pantoea agglomerans ? ?

27 Pseudomonas syringae ? - 11 Pantoea agglomerans ? ?

28 Pseudomonas syringae - - 19 Pseudomonas fluorescens ? ?

29 Pantoea agglomerans ? - 22 Pseudomonas fluorescens ? ?

34 Pseudomonas syringae ? - 24 Pantoea agglomerans ? -

35 Staphylococcus haemolyticus ? ? 25 Pseudomonas graminis ? ?

37 Staphylococcus haemolyticus ? ? 30 Pantoea agglomerans ? ?

38 Bacillus sp. ? ? 36 Erwinia amylovora ? ?

Electronic nose recognition was tested either along with standard bacterial strains (1*), or in a different session (2*). ? means that the

identification obtained with the e-nose was in agreement with either the identification based on 16SrDNA sequence or on the identification based

on biochemical characteristics

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123

appeared to correlate with bacterial growth, showing a

continuous growing trend as the bacterial population dou-

bled from 4 9 108 U/mL to 8 9 108 U/mL, whereas, the

opposite trend was found for 1,8-cineol, b-myrcene and

camphor.

PTR-MS analysis

The bacterial strains included in PTR-MS analysis showed

different emission levels for the selected masses (Fig. 4).

Significant m/z can be putatively identified as acetic acid

and/or propanol (m/z 61), dimethyl sulfide (m/z 63), iso-

prene (m/z 69), butyric and isobutyric fragment (m/z 71),

2-butanone (m/z 73) and 2,3-butanedione (m/z 87). Masses

61, 69 and 71 distinctively characterized VOCs emissions

from E. amylovora compared to the other bacterial species,

whereas masses 73 and 87 were particularly abundant in

both E. amylovora and B. amyloliquefaciens. Mass 61 may

be ascribed to acetic acid or propanol; according to GC–

MS data, isopropanol emission was actually found in one

E. amylovora strain, therefore the former identification

appears more unlikely.

Table 3 GC–MS identification of volatile compounds emitted by three Erwinia amylovora strains (Ea 1540, 1/79 and 8865), Pantoea ag-glomerans (Pa P10c), Bacillus amyloliquefaciens (Ba IN937), Bacillus subtilis (Bs AM01) and Pseudomonas fluorescens (Pf A506)

Compound Retention

time (min)

Ea 1540

(% area)

Ea 1/79

(% area)

Ea 8865

(% area)

Pa P10c

(% area)

Ba IN937

(% area)

Bs AM01

(% area)

Pf A506

(% area)

Propane, 2-ethoxy-2-methyl- 1.60 0.79 2.42

Heptane, 4-methyl- 1.78 1.51 0.91 4.93

1-Propen-2-ol, acetate 1.90 3.34

2,4-Dimethyl-1-heptene 2.40 3.49 1.71 1.65 10.97

Ethyl acetate 2.50 13.54

2-Butanone 2.50 0.57 0.33 1.90 2.38

Butanal, 3-methyl- 2.70 1.92

2-Butanone, 3-methyl- 2.80 4.01

Isopropyl alcohol 2.80 0.32

Ethyl alcohol 2.87 41.06 3.16 1.94

2,3-Butanedione 3.49 1.57 0.71 1.95 2.74 9.85

Heptane, 3,3,4-trimethyl- 3.90 1.66

Disulfide, dimethyl 5.60 5.99

Acetic acid, butyl ester 5.90 1.69

1-Propanol, 2-methyl- 6.50 0.76 12.95 1.03

1-Butanol, 3-methyl-, acetate 8.00 87.29 2.46

1-Butanol 9.12 0.46

1-Undecene 9.40 71.53

2-Heptanone 11.30 3.33

1-Butanol, 3-methyl- 13.19 64.35 26.69 73.10 2.77 5.05

1-Pentanol 15.76 0.27

2-Butanone, 3-hydroxy- 16.87 24.29 9.62 14.03 3.14 43.25 46.23

1-Pentanol, 2-methyl 18.67 0.20

Acetic acid 25.60 1.05 1.61

Pyrazine, tetramethyl- 26.50 26.48

Benzaldehyde 28.40 2.28

2-Heptanol, 5-methyl- 29.30 0.68

2,3-Butanediol, [R-(R*,R*)]- 29.60 0.70 Trace Trace 31.23 Trace

2,5-Hexanedione, 3,4-

dihydroxy-3,4-dimethyl-

32.80 1.20

Butanoic acid, 3-methyl 34.90 1.52 1.94

Pentanoic acid, 3-methyl- 34.90 1.82 0.98

1-Nonanol 34.90 0.77

Phenylethyl alcohol 43.40 3.06 0.23 1.65 0.96

The estimated peak area and the retention time are reported. Each data is the average of three replicates

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123

The data obtained by PTR-MS analyzed via PCA con-

firmed that the bacterial species group the in different

clusters (Fig. 5).

Ethylene detection

Ethylene produced by E. amylovora strains 1540, 1/79 and

8865, P. agglomerans strain P10c and B. amyloliquefaciens

IN937a was monitored in real-time or with an accumula-

tion system. The ethylene concentration in the bacterial

cultures did not change during time and was similar to

levels found in sterile LB medium. This indicates that those

bacteria did not release ethylene (Fig. 6).

Biological effects of VOCs

To verify whether the produced volatiles compounds were

affected by intra-specific (E. amylovora 1/79) or inter-

specific competitors (P. agglomerans P10c), the VOCs

emitted by actively growing Ea1/79 and P10c cultures were

transferred to a newly inoculated Ea1/79 culture. Neither

the VOCs present in P10c, nor in Ea1/79 culture affected

the pathogen growth (Fig. 7).

The GC–MS and PTR-MS data suggested that E. amy-

lovora is able to produce 2,3-butanediol. In addition, 2,3-

butanediol production was confirmed in E. amylovora,

P. agglomerans, and B. amyloliquefaciens by a positive

reaction with Methyl Red-Voges Proskauer (MRVP) test

(Bergey et al. 1984). Since 2,3-butanediol has been sug-

gested to promote plant growth and resistance against

pathogens (Ryu et al. 2003), the effect of the VOCs

released by a E. amylovora cultures was tested in vitro on

pear plants.

Fig. 2 Cluster analysis of the GC–MS data obtained from the

headspace analysis of Erwinia amylovora Ea 1540, Ea 1/79, Ea 8865,

Pantoea agglomernas P10c, Pseudomonas fluorescens PfA506,

Bacillus subtilis AM01 and Bacillus amyloliquefaciens IN937. The

data used for the cluster analysis are reported in Table 3

Fig. 3 Terpene emitted by Erwinia amylovora strain 1/79. The data,

normalized for bacterial population, are expressed as mean ± stan-

dard error of three independent replicates

Fig. 4 PTR-MS profiles of Erwinia amylovora (Ea 1540), Bacillussubtilis (Bs AM01), Bacillus amyloliquefaciens (Ba IN937), Pantoeaagglomerans (Pa P10c) and Pseudomonas fluorescens (Pf A506).

Columns, inside each mass fragment, labelled with the same letter do

not differ according to SNK test (P B 0.05)

Fig. 5 Cluster analysis of the PTR-MS data obtained from the

headspace analysis of Erwinia amylovora (Ea), Pantoea agglomerans(Pa), Pseudomonas fluorescens (Pf), Bacillus subtilis (Bs) and

Bacillus amyloliquefaciens (Ba). The data used for the cluster

analysis are reported in Fig. 4

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Plants showed marked increases in dry weight and leaf

area in co-culture with 2,3-butanediol-producing bacteria,

including E. amylovora (Fig. 8). These increases were

mainly due to a higher number of later shoots characterized

by smaller leaves, which modified the plant habitus.

Therefore, the volatiles emitted by the bacterial culture

produced a branching effect on plant. Although the pre-

liminary data could not be statistically validated, the dis-

ease incidence appears to be slightly reduced in the plants

grown with 2,3-butanediol-producing bacteria (namely,

E. amylovora 1/79, P. agglomerans P10c, and B. amylo-

liquefaciens FZB42) compared to the negative control

(B. subtilis AM01).

Discussion

The power and accuracy of e-nose technology were already

demonstrated in many fields, such as medical diagnosis,

foods and drinks industries, and environmental assessment

of pollutants. In this work, the e-nose was successfully used

for the recognition of different plant associated bacteria.

Electronic nose was initially used to confirm the identifi-

cation of a number of known standard strains. Subse-

quently, the variability of the olfactory profiles of the

different strains belonging to the same bacterial species

was determined and exploited to build up a database of

bacterial olfactory profiles. Later, these data were tested for

the recognition of unknown bacterial cultures isolated from

a New Zealand apple orchard.

Overall, the discrimination power of the e-nose for

plant-associated bacteria appears promising. This is espe-

cially true when standards species are used in the same

measuring session: 21 out of 24 (87.5%) samples were

identified accordingly to molecular or phenotypical char-

acterization (Table 3, e-nose 1 columns). On the other

hand, the score (62.5%) is poor when unknown samples are

referred to a standard data-base obtained in a different

measuring session (Table 3, e-nose 2 columns). This may

be due to inherent limits in the e-nose construction

(resulting in measure drifts), its sensitivity to external

conditions (such as humidity of the sample and the envi-

ronmental air), and intra-specific variability in bacterial

species. These issues undermined the efforts to constitute a

reliable database of bacterial olfactive profiles. Nonethe-

less, technological advances improved the measuring sta-

bility, as shown by the recent e-nose model EOS507;

moreover, the data-base can in principle be indefinitely

refined by including olfactive profiles from more and more

diverse bacterial species. E. amylovora is considered a

relatively homogeneous species, in contrast with highly

diversified species such as P. syringae. Thus, although all

the E. amylovora strains would probably cluster together,

partial overlapping or inclusion within the distinctive fea-

tures of other species may occur.

In this sense, GC–MS and, to some extent, PTR-MS,

would help identify marker components in the odour blend

of a specific bacterial species. Among the most interesting,

putative markers, a special interest is represented by

compounds with a biological effect. For instance, terpenes

are well-known modulators of plant pathogenic responses

(Theis and Lerdau 2003), and their precursor, isoprene,

Fig. 6 CO2 laser-based ethylene detection: the ethylene release by

the axenic culture medium (LB) and different bacterial culture. The

graph shows ethylene production at 2 h of accumulation in the

headspace. None of the bacteria produce detectable amount of

ethylene

Fig. 7 Growth of Erwiniaamylovora Ea1/79 treated with

VOCs produced by Pantoeaagglomerans P10c (a) and

E. amylovora Ea1/79

(b) cultures. VOCs emitted

by the axenic growing medium

were used as a control.

Standard error is shown

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123

could be detected by GC–MS and, putatively, by PTR-MS

in E. amylovora.

Isoamyl alcohol (3-methyl-butanol) and 2-methyl-pro-

panol were found in plant growth-promoting bacteria, such

as B. subtilis strain GB03 and B. amyloliquefaciens IN937a

(Farag et al. 2006). Their synthesis proceeds from valine,

leucine and isoleucine. These products were not detected

for the same species in the present work, probably due to

the different growing conditions and experimental settings.

However, 3-methyl-butanol and 2-methyl-propanol were

found in E. amylovora, along with other compounds from

related pathways, such as 3-methyl-pentanoic acid. Inter-

estingly, Farag and colleagues found that GB03 and

IN937a induced systemic resistance by the ethylene–

jasmonate pathway and other mechanisms, respectively.

The ethylene–jasmonate independent resistance triggered

by IN937a may be primed by specifically emitted com-

pounds, such as isoamyl alcohol, acetic acid butyl ester and

butanol-3-methyl acetate.

Acetoin (3-hydroxy-2-butanone) and its derivative 2,3-

butanediol are known to promote plant growth (Ryu et al.

2003; Farag et al. 2006; Zhang et al. 2007). The bio-

chemical pathway starts from pyruvate, and prevails over

pyruvate fermentation, yielding lactate or ethanol, and

respiration in acidic and anoxic conditions (Ryu et al.

2003). A possible physiological effect of 2,3-butanediol

affects auxin metabolism (Zhang et al. 2007), leading to

increased plant growth and lateral root emission. In this

work, acetoin, 2,3-butanediol, and the related compound

2,3-butanedione were found in E. amylovora; furthermore,

pear plants exposed to 2,3-butanediol-emitting bacteria

were promoted in dry mass and branching, thus supporting

the involvement of auxin regulation.

In Arabidopsis, 2,3-butanediol not only acts as growth

promoting compound (Ryu et al. 2003), but it is also able to

trigger induced system resistance (Ryu et al. 2004). How-

ever, in our experiments, only a modest, non significant,

reduction of plant susceptibility to E. amylovora was found

on the plant grown alongside 2,3-butanediol producing

bacteria. These differences may be linked to the dissimilar

physiological and ecological interactions occurring in the

two pathosystems. Nonetheless, further experiments are

needed to confirm this trend and to verify whether the

VOCs produced by E. amylovora can prime plant defences.

Finally, since neither IN937a, or E. amylovora produce

ethylene the induction of plant resistance might linked with

an ethylene-independent mechanism.

In conclusion, the feasibility of bacterial isolates dis-

crimination on the basis of volatile emissions was dem-

onstrated. The knowledge of volatiles contributing to the

biological effects and odorous profiles of plant pathogenic

bacteria may represent the first and crucial step to develop

highly sensitive, specific sensors for their identification and

for the early diagnosis of plant diseases.

New information on bacterial metabolism and activity,

arising from the identification of the emitted VOCs, may

shed light on the interactions between bacterial pathogens

and plant hosts, and provide useful technological tools for

their modulation.

Acknowledgments The authors gratefully thank Elena Rondelli,

Deirdre Cornish and Janet Yu for their active participation to the

research and for their excellent work. The project was partially fun-

ded by Q-Detect: FP7-KBBE Project ‘‘Developing quarantine pest

detection methods for use by national plant protection organizations

(NPPO) and inspection services’’. We also thank the COST-Action

864: PomeFruitHealth for coordinating the European research groups

on fire blight and proving the opportunity of research exchanges and

collaborations.

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