Evaluating the use of biosensors for monitoring of Penicillium digitatum infection in citrus fruit

Journal Pre-proof Evaluating the use of biosensors for monitoring of Penicillium digitatum infection in citrus fruit Daniel Chalupowicz, Boris Veltman, Samir Droby, Evgeni Eltzov

PII:

S0925-4005(20)30243-4

DOI:

https://doi.org/10.1016/j.snb.2020.127896

Reference:

SNB 127896

To appear in:

Sensors and Actuators: B. Chemical

Received Date:

24 November 2019

Revised Date:

17 February 2020

Accepted Date:

18 February 2020

Please cite this article as: Chalupowicz D, Veltman B, Droby S, Eltzov E, Evaluating the use of biosensors for monitoring of Penicillium digitatum infection in citrus fruit, Sensors and Actuators: B. Chemical (2020), doi: https://doi.org/10.1016/j.snb.2020.127896

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Evaluating the use of biosensors for monitoring of Penicillium digitatum infection in citrus fruit

Daniel Chalupowicz a, Boris Veltman a,b, Samir Drobya, Evgeni Eltzova,c *

a

Department of Postharvest Science, Institute of Postharvest and Food

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Sciences, The Volcani Center, Agricultural Research Organization, Bet Dagan 50250, Israel

b

Institute of Biochemistry, Food science and Nutrition, Faculty of Agriculture,

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Food and Environment, The Hebrew University of Jerusalem, Rehovot 76100,

c

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Israel

Agro-Nanotechnology Research Center, Agriculture Research Organization,

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* Corresponding author.

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The Volcani Center, Rishon LeZion 7505101, Israel

E-mail address: [email protected] Telephone: +972-3-9683607

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Fax: +972-3-9683622

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Highlights This work describes the evaluation of a methodology for real-time and continuously monitoring infections in stored agriculture produce.

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Genetically modified bacteria allow real-time determination of the VOC changes in the air due to pathogens activity in harvested crops.

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Proposed system detected pathogens on the third day of infection before the appearance of visible fungal symptoms on the fruit surface.

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The ability to rapidly, continuously and noninvasively monitor crops health status and quality is highly desirable in all post-harvest stages (storage or transportation) and will reduce food loses.

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

ABSTRACT

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Penicillium digitatum is a major postharvest pathogen in citrus fruit that causes

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losses in all citrus-growing countries. In this study, a new whole-cell-based biosensor was developed to detect the fungus’ presence in oranges. The approach was based on bacteria's luminescent responses to changes in volatile

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organic compounds (VOCs) following infection by a pathogenic microorganism in oranges. Differences between VOC patterns in the infected and noninfected

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fruit were monitored by GC–MS and then with four different genetically modified

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bioluminescent bacterial strains. GC–MS detected the changes in emitted VOC patterns during the first infection steps. Bioreporter strains allowed pathogen detection on the third day of infection, before the appearance of visible signs of fungal infection on the surface of the orange. Thanks to their increased sensitivity, portability and ease of use, whole-cell biosensors may serve as a new tool for monitoring rot. In the future, such technology will reduce food

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losses by enabling more efficient crop management throughout postharvest treatment, storage and transport.

Keywords: Bioluminescent bacteria; Volatile organic chemicals; Penicillium digitatum; Realtime monitoring; Biosensors

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1. Introduction

Despite the extraordinary progress made in increasing food production

at the global level, the problem of food losses postharvest, along the supply

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chain, remains a major challenge in the fresh produce industry. Worldwide, postharvest losses have been estimated at 40%–50% of the harvested crop,

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mostly due to rot caused by microorganisms [1]. To mitigate these losses, there

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is an increasing demand for efficient early warning and real-time detection tools to monitor decay development in fruit and vegetables after harvest and

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throughout the supply chain.

Volatile organic compounds (VOCs) are characterized as biomolecules

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and metabolites with high vapor pressure, low boiling point, and low molecular weight. Plants emit various VOCs into their immediate surroundings, which

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serve essential functions in growth, survival, communication, defense, and interactive signaling with insect pests and microbial pathogens. VOC profiles of healthy crops may be altered after infestation with insect pests [2], or infection with pathogenic microorganisms including fungi, bacteria and viruses [3][4]. Such changes might then be used as markers of pathogen attack in fresh agricultural produce at the early stages of infection and disease development. 3

Traditional gas chromatography–mass spectrometry (GC–MS)-based approaches have been used to detect volatile compounds emitted following infection by pathogens [5-7]. However, such methods are complicated, timeconsuming, bulky, and insufficiently robust for continuous monitoring of VOCs in the atmosphere of storage or shipment facilities, or in the headspace of packaged commodities. Moreover, requirements for separate sample collection and further handling, as well as accessibility of the measurement device, make

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these methods ineffective for real-time VOC measurements [3]. Another platform for VOC profiling is the electronic nose (e-nose), used for rapid and

real-time air monitoring [8]. In the last decade, the advantages and

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disadvantages of e-nose-based systems have been thoroughly discussed in

numerous reviews and original research papers [9]. For the agriculture use, e-

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nose based methods for fruits quality [10, 11] have been started to test few

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years after the first e-noses concept introduction and during the next 20 years, many more agriculture applications have been proposed [12, 13]. E-noses are a fantastic instrument that theoretically may solve any problem. But, problems

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in the extrapolation of the result from one sample set to another, gas sensor's sensitivity to humidity, price issues and incapability to provide volatiles

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compositions and concentartions are limiting their use. Thus, despite the

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sustained efforts, the use of e-nose technology in agriculture field still happens at the laboratory level and for research purposes mostly [13]. Whole-cell bacterial biosensors are another type of monitoring tool that

is based on the integration of microorganisms with an analytical format based on physics, e.g., optical or acoustic waveguide electrodes. Whole-cell opticalbased systems may be used to test the toxic effects of air contaminants, such

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as genotoxicity, cytotoxicity and oxidation, as well as damage to membranes or proteins, often providing results within 1 or 2 h [14, 15]. Genetically modified to produce light in the presence of specific chemicals [16, 17] or general stresses [18-20], these microorganisms are usually immobilized in an open-lattice structure that provides a gentle environment for the cells and allows high diffusion rates of molecules from the water or air into the matrix. Calcium alginate hydrogels are one of the most common immobilization matrices used

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in whole-cell applications [21]. The ability of bioluminescent bacteria to continuously monitor air pollutants has been demonstrated by immobilizing

cells in different calcium alginate formations (e.g., add-layers [22] and pads

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[23]). Thus, this approach could be useful as a rapid and continuous monitoring tool for VOC detection.

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Citrus is one of the world's major fruit crops, with an annual production

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in 2018 of more than 47 million tons [24]. The most common and severe decay occurring postharvest, during storage and marketing of citrus fruit, is green mold caused by Penicillium digitatum [25], with rot rates of fresh citrus fruit

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sometimes leading to losses of 30%–50% [26]. To decrease such losses, sensor technologies that will allow early-stage fungal detection are needed, to

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prevent further spread of the disease among stored oranges.

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In this study, changes in VOC patterns produced by orange fruit following

infection with P. digitatum were monitored by the GC–MS approach, and by a bioluminescent bacterial panel based on four different Escherichia coli strains that are sensitive to general stresses, i.e., cytotoxicity, genotoxicity, oxidative stress and quorum-sensing stress. GC–MS analysis of different stages of decay development showed differences in the VOC patterns between healthy and

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decayed fruit. Bioluminescent bacteria allowed the detection of infection-related VOCs before any visible decay symptoms developed on the peel.

2. Materials and methods

2.1. Materials

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Analytical grade (R)-(+)-limonene and alginic acid sodium salt were purchased from Sigma (St. Louis, MO). Ampicillin sodium salt was obtained from Fisher BioReagents (Pittsburgh, PA). Calcium chloride was from Carl Roth

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GmbH (Karlsruhe, Germany). Luria-Bertani broth (LB) was from BD DifcoTM (Sparks, MD). All stock solutions were diluted with double-distilled water and

2.2. Bacterial strains

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stored at the temperatures suggested by the manufacturers.

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Four different E. coli strains were used in this study: strains DPD2794 [27-29], DPD2511 [30] and TV1061 [31] were obtained from Shimshon Belkin

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(Hebrew University, Jerusalem, Israel), and strain K806NR [13] was obtained

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from Jan Davies (University of Calgary, Calgary, Canada). Each strain harbors a plasmid-borne fusion of its promoter (recA, grpE, katG, lasI for DPD2794, TV1061, DPD2511 and K802NR, respectively) to a lux CDABE reporter operon, responsible for the synthesis of luciferase and substrate [32]. The specificity of each strain is controlled by the promoters, which are responsible for different regulatory networks in the cell: recA – DNA-repair mechanisms, grpE –

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metabolic changes, katG – prevention of damage caused by oxidative species, and lasI – monitoring of bacterial communication systems.

2.3. Fruit material

‘Valencia’ oranges (Citrus sinesis) were obtained at different stages of harvest maturity (August 2017–March 2018) from a commercial orchard in

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southern Israel. Fruit were used immediately after harvest or stored at 5 °C until use.

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2.4. Growth conditions

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Bacterial strains were grown in 10 mL LB medium [33] amended with

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100 µg/mL ampicillin overnight at 37 oC on a rotary thermo-shaker (QSR Technologies) at 120 rpm. Cultures were diluted to approximately 107 cell/mL and regrown in the same medium at 26 oC without shaking to the early

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exponential phase (OD600 of 0.2) as determined by an Ultrospec 2100 pro

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spectrometer (Amersham Biosciences).

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2.5. Inoculation of orange fruit with P. digitatum

P. digitatum was obtained from the culture collection of the Department

of Postharvest Science, Agricultural Research Organization, Volcani Center. P. digitatum spore suspensions were prepared as described previously [34, 35]. Briefly, spore suspensions were prepared from 2- to 3-week-old cultures grown

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on potato dextrose agar (PDA; Difco, Detroit, MI) plates with 250 mg/L chloramphenicol. Spores were removed from the edges of the sporulating cultures with a sterile disposable plastic bacteriological loop (Miniplast, EinShemer, Israel), and suspended in sterile distilled water. The remaining mycelial fragments were removed by filtration through four layers of sterile cheesecloth. Spores were washed twice with sterile distilled water to remove any residual nutrients. The concentration of the suspension was adjusted to

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5 × 104 spore/mL using a hemacytometer. To inoculate the fruit, 20 µL of spore suspension was applied into each wound (3 x 3 mm) made with a sterile dissecting needle in the fruit peel (3 wounds per fruit). After inoculation, fruit

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were kept under high humidity in plastic trays at 25 oC. Fruit with wounds treated

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2.6. Immobilization procedures

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with sterile water and intact nonwounded fruit served as controls.

Calcium alginate beads were prepared by the drop method [36]. The

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harvested bacterial cells were mixed 1:1 with a filter-sterilized (0.22 µm syringe, Get Biofil, Guangzhou, China) 2% (w/v) low-viscosity sodium alginate solution

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and loaded into a 10-mL plastic syringe (Romed Holland, Herenweg,

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Netherlands). The syringe was attached to a syringe pump (SYP-01, MRC Ltd., Holon, Israel). Alginate beads, 3 mm in diameter, were prepared by dropping alginate solution into a glass beaker with 0.5 M calcium chloride using a needle with a 3 mm outlet at a flow rate of 5 mL/min. Calcium alginate was stirred into the solution at 400 rpm for 15 min and then alginate beads were removed from

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the solution using tweezers and placed in the measurement chamber (a sealed 1-L glass jar, Bormioli Rocco, Fidenza, Italy).

2.7. Measuring procedures

2.7.1. Volatile analysis using bioluminescence approach Bioluminescence activity was measured in a SynergyHTX multi-mode

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reader (BioTek Instruments Inc., Winooski, VT) with white opaque 96-well microtiter plates (Nunc, Roskilde, Denmark). The plates containing the bacteria

in calcium alginate beads were exposed to limonene, air from infected and

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noninfected fruits, and control samples (n = 3 for each tested condition). During

the measurement, the temperature of the samples was maintained at 26 °C.

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Luminescence values are presented in relative light units (RLU).

2.7.2. Analysis of volatiles using GC–MS

The procedure for the solid-phase microextraction (SPME)/GC–MS

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analysis was as reported previously [37]. Briefly, individual oranges (inoculated or controls) were placed into a 1000-mL glass jar sealed with an airtight cap

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(Fig. 1B). After 180 min of headspace equilibration, a 1-cm fused silica fiber with

divinylbenzene/carboxen/polydimethylsiloxane

50/30

µm

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coated

(DBV/CAR/PDMS; Supelco, Bellefonte, PA) was inserted into the cover of the jar via a silicone septum (Fig. 1C). The SPME fiber was exposed to the headspace to absorb volatiles for 30 min, then placed in the splitless inlet of a Model 7890A GC (Agilent, Palo Alto, CA) equipped with an HP-5 column (30 m × 0.25 mm ID, 0.25 μm film thickness) (J&W Scientific, Folsom, CA) for

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volatile desorption (5 min at 250 °C). Volatile emissions of intact, woundedinoculated and wounded control fruit were identified by GC coupled with MS (Fig. 1D). The oven was run at 50 °C for 1 min, ramped up to 150 °C at 10 °C/min, held at this temperature for 5 min, ramped up to 250 °C at 20 °C/min and held at this temperature for 3 min. The helium carrier gas flow was set at 0.8 mL/min. The effluent was transferred to a Model 5975C MS detector (Agilent) that was

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set to scan from mass 40 through 206 at 7.72 scan/s in positive-ion mode, and mass spectra in electron impact (EI) mode were generated at 70 eV. Chromatographic peaks were identified by comparing the mass spectrum of

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each component with the US National Institute of Standards and Technology (NIST) library of mass spectra (2006 version).

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Three different orange groups were used for each experiment: wounded-

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inoculated, wounded-noninoculated and nonwounded-noninoculated (n = 3 for each group). In addition, additional controls (i.e., P. digitatum culture, PDA

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growth medium, and empty jars) were tested.

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2.8. Limonene detection

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Calcium alginate-immobilized bioluminescent bacteria (section 2.2) were

exposed to different limonene concentrations (0.37, 1.67 and 3.74 ppb) at room temperature for 2 h in the hermetically closed 1-L jars. The bacterial beads were placed at the bottom of the jar and 15-µL aliquots of the different limonene concentrations were placed nearby. There was no direct contact between the limonene liquid and beads; bacterial exposure to the VOCs was occurred by

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chemical evaporation and then diffusion from the headspace to the immobilization matrix. Then, the beads were removed from the 1-L jar to a 96well titer plate and measured as explained in section 2.7.1.

2.9.

Determination of the effect of P. digitatum infection on bioluminescent

responses

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To determine the bacterium–alginate beads’ ability to sense differences in the VOCs emitted by infected and noninfected orange fruit, they were placed in a sealed container (1-L jar) and incubated with the fruit at room temperature

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for 2 h (Fig. 1E). Then the beads were removed from the container to a 96-well titer plate (Fig. 1F) and bioluminescence was measured as explained in section

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2.7.1 (Fig. 1G).

2.10. Effect of decay development on bacterial bioluminescence

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To determine the effect of the VOCs, emitted at different stages of the infection, on bacterial responses, infected fruit were incubated with the

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immobilized beads and bioluminescence was measured as described in section

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2.7.1. Measurements were taken after different incubation periods displaying different stages of decay development.

2.11. Kinetic measurements

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Kinetic measurements were obtained by daily monitoring of VOC changes and bacterial responses in the wounded-inoculated, woundednoninoculated and nonwounded-noninoculated fruit during a 4-day incubation period. Each measurement day, oranges were sealed in 1-L jar and VOCs were monitored by GC–MS (section 2.7.2) and the bioluminescence approach (section 2.7.1).

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2.12. Data analysis

The bioluminescence signal indicating the bacterial response to the

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different toxic compounds was expressed as the induction factor, which was

calculated using the formula: induction factor = Bi/BC, where Bi is the maximum

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bioluminescence signal for the tested toxicant and BC is the maximum signal

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for the control. The temperature ratio was calculated as B37/BRT, where B37 is the maximum bioluminescent signal of the tested bacteria at 37 oC and BRT is the maximum signal at room temperature.

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2.13. Reproducibility of the system and statistics For each experimental stage, a minimum of thirty separate and different

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setups was exposed to the examined parameters. Paired t-tests were employed

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to compare between the P. digitatum-inoculated and noninoculated oranges.

3. Results and discussion

3.1. Effect of P. digitatum infection of oranges on VOC emissions

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The first step in this study was to determine the effect of P. digitatum infection on VOC-emission profiles of orange fruit. VOC release by nonwounded-noninoculated, wounded-inoculated and wounded-noninoculated fruit was monitored by GC–MS. Figure 2 demonstrates the changes in VOC profile (composition and concentrations) in infected vs. control fruit as the infection progresses. The observed differences can be divided into three groups. The first contains VOCs with higher concentrations in infected fruit

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compared to controls (e.g., limonene and ethyl octylate). The most marked changes between infected and noninfected fruit were observed in the emitted limonene concentrations, with an over 15-fold increase in infected samples (Fig.

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2). Limonene is naturally occurring and the most abundant (86.8% of total

volatiles) monoterpene of the VOCs emitted by orange [38]. While many studies

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have reported limonene as part of the fruit's natural defense mechanism against

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fungal pathogens [39, 40], it may also facilitate infection by promoting P. digitatum spore germination and germ-tube elongation [38]. Figure 2 shows a clear effect of the wounding step on the VOC composition in the headspace. A

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possible reason for this increase is the mechanical wounding procedure itself, which releases limonene from the destroyed orange peel oil glands. In the

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control wounded-noninoculated fruit, limonene levels decreased on day 2 to

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those in the nonwounded-noninoculated samples and remained constant thereafter. In the wounded-infected fruit, there was also a decrease in limonene level on day 2, but this was followed by an increase (Fig. 3). A possible reason for these changes is the effect of P. digitatum on the orange peel during the infection processes, where it continues to rupture the oil glands in the wounded tissue and increases the limonene concentration in the headspace. It is

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important to mention that differences in VOC concentrations between infectedwounded and noninfected-wounded oranges were observed for each identified volatile, even on the first day of infection. The second group included all VOCs whose expression was not affected by infection or wounding processes, being found in equal amounts under all tested conditions. In this case, the VOCs might be produced by different ripening and metabolic processes in the fruit that are not associated with the

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orange's defense mechanisms [41]. The third group of VOCs (e.g., α-selinene, α-terpineol, ethyl caproate,

linalool, β-myrcene) were observed only in wounded-infected fruit (Fig. 2). Such

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differences between infected and noninfected fruit are a well-known phenomenon, not only in oranges [42, 43], but also in many other postharvest

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fresh produce [3, 44]. These VOCs may serve as signaling elements in plant–

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microbe interactions and contribute to the plant's survival and defense mechanisms [45]. Some of them were observed at the beginning of the monitoring period, and others at the end (Fig. 3). Similar changes in

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monoterpenes, monoterpene alcohols, monoterpene aldehydes, and esters in response to P. digitatum have been reported previously [42, 46]. Overall, in this

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third group, the levels of the VOCs seemed to change after inoculation,

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suggesting the existence of substantial metabolic reprogramming in response to P. digitatum infection [42]. One member of the third group of VOCs, linalool, is usually expressed in orange fruit and has been shown to have high antifungal activity [47]. A possible reason for the delay in linalool detection is a belowthreshold concentration on the first few days of measurement. It is only at the latest time point, with infection progress and intensive peel damage, that it

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increased to measurable values. The finding of ethyl caproate only the headspace of infected fruit samples also suggests peel-damaging processes as its source. However, its higher presence in the oil glands allowed earlier detection than for linalool, supported by the increase in ethyl caproate concentration as infection progressed. Following P. digitatum infection, some of the volatile metabolites underwent a significant and continuous increase throughout

the

measurement

period.

For

example,

ethyl

octylate

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concentrations continuously increased in the infected orange fruit, whereas its value remained constant in noninfected samples (Fig. 3).

Of all represented VOCs, only cyclohexane and caryophyllene were

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found at detectable concentrations in the P. digitatum fungal cultures as well

(i.e., controls without oranges) (Supplementary Table 1). The role of these

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chemicals is unknown, but both have been reported as VOCs that may be

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produced by different fungi [48-50] and by different citrus species [51, 52]. In addition, the absence of all listed VOCs in the air samples collected from the clear plastic/glass holders and jars used as the test chambers further suggests

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their biological source (Supplementary Table 1). These results showed that orange VOC-emission patterns not only

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change after fungus infection, but that these changes become more

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pronounced as the infection progresses. Thus, real-time monitoring of these changes may be used as a marker to detect pathogen presence in oranges, even at the early infection steps. The VOC limonene showed the most pronounced changes in the headspace, suggesting its use as an indicator for P. digitatum activity in infected fruit. Therefore, limonene was chosen as a

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candidate marker to evaluate its detection by bioluminescent bacteria in the headspace.

3.2. Effect of limonene in the headspace on bacterial responses

To determine the ability of the proposed bioreporter microorganisms to

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sense the presence of VOCs, four bacterial strains were exposed to different limonene concentrations in the headspace. Figure 4 demonstrates the

responses of the four bioreporter strains to different limonene concentrations.

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While all strains were triggered by limonene, the highest output signals were

observed with strain TV1061, which is sensitive to cytotoxic stress (Fig. 4). The

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recombinant bacterial strain TV1061 has been modified to produce light

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responses triggered by cytotoxic-based damage, such as protein damage or changes in membrane integrity [31]. The toxic effects of limonene against different microorganisms, including fungi and algae, have been reported

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previously [53-56]. The mechanism governing the antibacterial and antifungal properties of limonene is connected with the monoterpene hydrocarbons' ability

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to incorporate into microorganism's membranes [57]. This alters membrane leading

to

interference

with

various

cell-membrane-related

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integrity,

mechanisms (e.g., respiration, enzymatic reactions of wall synthesis, transport, and maintenance of ion gradients) [58]. Scanning electron microscope analysis of different bacteria treated with limonene revealed alterations in the composition of the membrane's fatty acids and in the morphology of the cell [59, 60]. Thus, the observed pronounced induction of strain TV1061 might be

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explained by limonene's triggering of bacterial responses. Another possible reason for such a high response might be intracellular reactive oxygen species (ROS) accumulation in the bacteria [61, 62] during the membrane-damaging processes; ROS are cytotoxic to cells, damaging lipids, proteins and nucleic acids [63]. Another source of ROS in the testing chamber might be radicals produced by limonene light-oxidation processes [64]. Accumulation of these ROS chemicals to concentrations that may affect bacteria is time-consuming

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possesses which may delay cell responses. Indeed, not only positive responses with oxidation- and genotoxicity-sensitive strains DPD2794 and DPD2511,

respectively, indicating about ROS presence in the air. But, delay in DPD2511

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strain responses enforces such an assumption (Fig. 4). Changes in quorumsensing responses also indicate limonene's toxic effects on the whole bacterial

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population (strain K802NR, Fig. 4). All strains responded in a dose-dependent

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manner, i.e., an increase in the headspace concentration of limonene induced a more pronounced bacterial response.

In the previous publications, the same whole-cell based bioreporters

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have been shown similar positive response patterns to the different tested compounds [20, 65, 66]. Based on these findings, figure 1S was created to

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demonstrate such a typical response pattern, and this scheme may be used to

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explain all changes in bacterial responses to the tested limonene concentrations in this study. For example, both DPD2794 and K802NR light activity were first induced and then decreased with increasing in the limonene concentrations (Fig. 4). In this case, the first tested limonene concentration (0.37 ppb) was too low to produce a visible effect on the cells. Thus, such bacterial responses may be located in section 1a. At the next used

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concentration (1.87 ppb), bacteria were induced by limonene and therefore, such response may be placed in section 2 (Fig. 4). Finally, at the last tested concentration (3.74 ppb), the light activity of the cells decreased again, indicating about its possible higher damage effect and these results may be located in section 1a. These results demonstrate the ability of bacterial bioreporters to detect the presence of VOCs in the headspace, justifying their use as sensing

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3.3. Effect of fruit infection on bacterial responses

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elements in future experiments.

To determine the ability of the bioreporter bacteria to respond to the

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changes in VOC emission generated by P. digitatum infection, the

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bioluminescent microorganisms, enclosed in calcium alginate beads, were exposed to wounded-noninfected, wounded-infected, and nonwoundednoninfected (control) oranges in sealed glass jars (Fig. 5). Similar to the

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responses obtained with pure limonene, the highest difference between infected and control groups was observed with strain TV1061, which is sensitive

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to cytotoxic stresses. Nevertheless, limonene is only one, albeit large

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component of the mixture of many different aromatic hydrocarbons, fatty acids, and alcohols released from damaged oil glands during peel wounding and infection processes [67]. As such, we cannot conclude that it is responsible for the bacterial induction. Similar to limonene, other aromatic hydrocarbons or alcohols have cytotoxic effects, and strain TV1061's responses may be explained by the general cytotoxicity produced by all of the main VOC

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components (Fig. 5). Differences between bacterial responses in the wounded vs. nonwounded samples enforce the assumption that the peel-wounding process (mechanical or infection-sourced) causes the bioreporter responses. As in the case with limonene, all bacterial strains responded differently to the tested samples (Fig. 5). Strains TV1061, DPD2794, and DPD2511 showed a stronger reaction to wounded-infected vs. wounded-noninfected samples, suggesting their ability to detect changes in VOC composition due to

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the wounding and infection processes. In contrast, strain K802NR's bioluminescent activity was only slightly inhibited by the infected oranges. The

light activity in this strain is regulated by the expression of specific signaling

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molecules (e.g., N-acyl homoserine lactone) that are regulated by a bacterial density-dependent cell–cell communication process [13]. It is clear that some

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VOCs are involved in microbial interactions, acting as signaling and quorum-

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sensing compounds [68, 69]. Infected plants may produce structural analogs or mimics of N-acyl homoserine lactones, or anti-quorum-sensing compounds [70]. Chemicals with anti-quorum-sensing effects have been found in pea

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(Pisum sativum) seedlings [71], strawberries [72], and leguminous plants [73]. As some of the VOCs (e.g., linalool, terpinen-4-ol, α-selinene) were only

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detected in infected fruit (Fig. 2), their sources are suggested to be the

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pathogens or fruit-defense mechanisms [74]. For example, a quorum-sensinginhibition effect of linalool, one of the volatiles found only in infected samples, has been reported [75]. Thus, the inhibitory effect on strain K802NR's bioluminescent activity may be explained by the presence of these VOCs in the headspace.

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The main conclusion from this section is that differences in the bacterial responses to the infected and noninfected samples enable detection of infection in the fruit.

3.4. Effect of the infection process in fruit on bacterial responses

The main purpose of this research was to develop a bioluminescent

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bacteria-based biosensor that would allow determination of VOC changes in the air due to infection in harvested crops. The monitoring of VOC profiles is an emerging field with the potential for immediate application in crop management

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at all postharvest stages (storage and transportation). Our GC–MS system

detected changes in VOC patterns at the first infection steps. To determine the

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ability of the proposed bacterial sensor system to detect such changes, the air

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around infected and noninfected fruit was monitored with four bacterial strains (Fig. 6). As with limonene, each strain responded differently to the infected fruit, with the proposed biosensors detecting changes on the third day of infection,

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before the appearance of visible fungal symptoms on the fruit surface. Furthermore, increasing bacterial response with disease progression indicated

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a correlation between infection state and cell luminescence (Fig. 6). The

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increasing VOC concentrations as infection progresses are a result of the peeldamage process, which destroys oil glands on the surface of the fruit and releases chemicals into the air. Thus, at longer infection times, more oil glands will be destroyed, and higher released VOC concentrations will have a stronger effect on the bioreporter bacteria.

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Indeed, the strongest VOC effect on bacteria was observed at the last measurement point, when the highest VOC differences were observed between infected and noninfected samples (Fig. 5). Similar to previous results, the highest response was observed with strain TV1061, indicating a cytotoxic effect of VOCs on the immobilized cells. As reported in section 3.3, the response of strain K802NR on the third day of infection was lower in the infected than in the noninfected samples. However, the next day, this strain's response to the

ro of

headspace of the contaminated fruit increased. This might be due to the presence of new VOCs in the air, i.e., of chemicals that were detected only on

the fourth day of measurements (e.g., linalool). The bacterial responses were

-p

affected by the overall VOC changes in headspace, but it is still not possible to point to a specific chemical producing this effect. However, the important role

re

of limonene in this induction effect can be seen in the similarity between its 10-

lP

fold increase in the air (on day 4) (Fig. 3) and the 10-fold induction in strain TV1061's luminescence response (Fig. 6). These results suggest that the proposed biosensor can detect the infection process in crops at early stages.

na

Furthermore, the difference in the bacterial responses suggests the potential to

ur

create specific footprints for the different pathogens in fruit in future studies.

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4. Conclusions

This work presents the ability of bioluminescent bacteria to detect

pathogen presence in oranges. The responses of four different bioluminescent strains to the infected fruit enabled detection of fungal activity in the oranges on the third day of infection, before the appearance of visible signs on the fruit

21

surface. The main purpose of this study was the determination capability of the bioreporter bacteria to sense changes in VOC profiles. At the next research steps, these bioreporters will be incorporate with portable optical systems (e.g., CMOS based sensors) to create a proprietary field-operable real-time device, that will provide more efficient postharvest orange management in the future. All this will enhance our ability to obtain valuable information on decay development along the supply chain and facilitate rational decisions for the

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management of fresh agricultural produce after harvest.

Author contribution statements

-p

Eltzov Evgeni (E.E.) developed the theory and performed a proof of concept studies. E.E. encouraged and Daniel Chalupowicz (D.C.) and Boris Veltman

re

(B.V.) to investigate the use of this concept for the determination capability of

lP

the system to detect changes in the VOC patterns during infections in oranges. Samir Droby (S.D.) contributes his expertise in the field of the fungus-based postharvest diseases.

R.A. and D.C. carried out the experiments and

na

contributed equally to the project findings. All authors discussed the results and commented on the manuscript and contributed to the design and

ur

implementation of the research, to the analysis of the results and the writing of

Jo

the manuscript.

Declaration of conflicting interests The authors claim that there are no conflicting interests either from the funding source or their institutional affiliations. We thus state that there are no conflict of interest from the authors.

22

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Author Biographies Evgeni Eltzov is a researcher in the Department of Postharvest and Food Sciences, Volcani Center, Israel. He completed his Ph.D. in Environmental Engineering at BenGurion University. Dr. Eltzov's research interests include the development of bioluminescent bacterial panels for toxicity detection, point of care devices for healthcare, applications for volatile organic chemicals in the agricultural field, nanobased biosensors, and real-time monitoring systems. In the last 2 years, he has been developing solutions for real-time decay monitoring during postharvest stages based on specific infection source VOC detection in the air. He has more than 12 years'

ro of

experience in the microbiology and biosensor fields and is the author of over 35 peer-

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reviewed scientific publications, conference papers, and patents.

28

A

B

Glass 1L jar

Calcium alginate immobilized bacteria

SPME fiber

E G

C

ro of

Plate reader

F

-p

96-well plate HPLC

lP

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D

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Fig. 1. Schematic presentation of the measurement processes. One liter glass jar (A) which may close hermetically will be used as a measuring sealed chamber for future experiments. Orange fruit will be placed into measurement chamber (B) and SPME fiber will be inserted into the cover of the jar via a silicone septum (C) The SPME fiber will be exposed to the headspace to absorb volatiles for 30 min and removed to the HPLC machine for analyzing (D). To determine the bacterium–alginate beads’ ability to sense differences in the VOCs emitted by infected and noninfected orange fruit, they were placed in a sealed container and incubated with the fruit at room temperature for 2 h (E). Then the beads were removed from the container to a 96-well titer plate (F) and bioluminescence was measured in a plate reader (G).

29

Abundance

5x109

4x109

3x109

2x109

1x109 0 Day 1

Day 2

Day 3

Day 4

Day 1

Day 2

Day 3

Day 4

Day 1

Wounded-noninfected

Nonwounded-noninfected

Day 2

Day 3

Day 4

Wounded-Infected

Abundance

concentrations with progression of P. digitatum infection. 5.00E+09

4.50E+09 4.00E+09

N.S.

*

-p

3.50E+09

3.00E+09 2.50E+09 2.00E+09

1.00E+09 0.00E+00 -

**

N.S.

lP

N.S.

5.00E+08

re

N.S.

1.50E+09

+

-

* *

+

Ethyl octylate

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Limonene

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Fig. 2. GC–MS monitoring of the kinetic changes in VOC composition and

*

* * *

-

+

Ethyl caproate

Day 1

Day 2

-

+

Linalool

Day 3

ur

Fig. 3. GC–MS monitoring of the kinetic changes in the concentrations of major citrus VOCs along P. digitatum infection and decay development. N.S.- not significant. *P <

Jo

0.001, **P < 0.01 by pair t-test between the P. digitatum-infected and noninfected oranges.

30

Day 4

2.5

5 55

7 77

2

1 11

0.5

-1-1 -10 00

1 11

0 0

2 22

1

3 33

2

6 66

5

1.87 ppb

7 77

6

-1-1 -10 00

2 22 1

Time (h.)

DPD2794

10

9 99

8

7 77

6

5 55

3 33

4

3 33

1 11

2

5 55

0

Time (hr.) Time Time(hr.) (hr.) Time (hr.)

3.74 ppb

1 11

0

7

Induction Factor Induction Factor

7 77

5 55

4

0.5 3 33 2

4 44 3

0.37 ppb

5 55 4

6 66 5

1.87 ppb

-1-1 -10 00

16

1 11

2 22

3 33

4 44

5 55

6 66

7 77

12 10

4

0

1

2

3

1.87 ppb

4

5

6(hr.) Time (hr.) Time Time (hr.)

7

2

1 11

0 0

2 22

1

3 33

2

0.37 ppb

Time (hr.) 3.74 ppb Time (h.)

4 44

3

1.87 ppb

5 55

4

6 66

5

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Fig. 4. Kinetic responses of the different bioreporter strains (K802NR, DPD2511, DPD2794, TV1061) to various concentrations of limonene in the a headspace.

lP

Induction factor values represent the ratio between the responses of bacteria exposed to

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limonene and bacteria incubated with clean air.

31

7 77

6 (hr.) Time (hr.) Time Time (hr.)

7

Time (hr.) 3.74 ppb Time (h.)

-p

0.37 ppb

-1-1 -10 00

7

14

6

0

Time (hr.) Time Time(hr.) (hr.)

TV1016

18

8

1 11

7 77 6

Time (hr.) 3.74 ppb Time (h.)

1111 11

factor Induction Factor Induction

Induction Factor Induction Factor

9 99

4 44

3

0.37 ppb 1111 11

1

3 33

1

1 11

2

1.5

5 55

1.5

3 33

DPD2511

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7 77

9 99

2.5

factor Induction Factor Induction

K802NR

3

factor Induction Factor Induction

9 99

1111 11

Induction Factor Induction Factor

factor Induction Factor Induction

Induction Factor Induction Factor

1111 11

Factor Induction factor Induction

60 50

*

40 30

20

*

*

**

0

K802NR Wounded Wounded-Infected inoculated

DPD2511

Wounded Wounded- Uninfected noninoculated

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10

DPD2794

Nonwounded Unwounded noninoculated

P. digitatum P. Digitatum culture

TV1061

PTA agarplates plate PDA

Clear chamber Empty jar

-p

Fig. 5. Responses of the bioluminescent strains (K802NR, DPD2511, DPD2794,

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TV1061) to P. digitatum-inoculated wounded oranges and noninoculated-wounded and nonwounded oranges. Induction factor values represent the ratio between the responses

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0.01, **P < 0.001 by t-test.

lP

of bacteria exposed to the tested parameters and those incubated with clean air. *P <

32

Induction Factor

40

*

35 30 *

25 20 15 10

* N.S. N.S. N.S.

N.S. N.S.

**

N.S.

0 -

+

-

K802NR

+

-

DPD2511

**

N.S.

N.S.

+

-

DPD2794

day 1

day 2

day 3

-p

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TV1061) to P. digitatum-infected (+) and noninfected (-) oranges. Induction factor values represent the ratio between the responses of bacteria exposed to infected and

lP

noninfected oranges and those incubated with clean air. N.S.- not significant. *P < 0.001, **P < 0.01 by pair t-test between the P. digitatum-infected and noninfected

na ur Jo 33

+

TV1061

Fig. 6. Kinetic responses of the bioluminescent strains (K802NR, DPD2511, DPD2794,

oranges.

*

N.S.

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5

*

day 4