*Aurélien Bailly, University of Zurich, Switzerland Birgit Piechulla, University of Rostock, Germany*

> *\*Correspondence: Olaf Tyc<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Plant Biotic Interactions, a section of the journal Frontiers in Microbiology*

*Received: 26 August 2015 Accepted: 27 November 2015 Published: 18 December 2015*

#### *Citation:*

*Tyc O, Zweers H, de Boer W and Garbeva P (2015) Volatiles in Inter-Specific Bacterial Interactions. Front. Microbiol. 6:1412. doi: 10.3389/fmicb.2015.01412*

Tyc et al. Volatiles and Bacterial Interactions

Chernin et al., 2011; Effmert et al., 2012; Kim et al., 2013). Furthermore, rhizosphere bacteria emit volatiles that can promote plant growth and elicit induced systemic resistance (ISR) and induced systemic tolerance (IST) in plants (Ryu et al., 2003, 2004). However, the role of volatiles in competitive interactions between soil bacteria is so far poorly understood.

In the past few years the research on volatiles emitted by bacteria received increased attention from a more applied point of view as these compounds have intriguing properties which are of great interest for agriculture (pathogen suppression), food preparation (aroma), and cosmetics industry (perfume odors; Krings and Berger, 1998; Wheatley, 2002; Beshkova et al., 2003; Schwab et al., 2008; Deetae et al., 2009; Effmert et al., 2012; Kanchiswamy et al., 2015).

Bacterial volatiles belong to different chemical classes like alkenes, alcohols, ketones, terpenes, benzenoids, pyrazines, acids, and esters. However, the composition of emitted volatiles (volatile blend composition) may vary with cultivation conditions, in particular with respect to the substrate composition of the growth media (Cleason, 2006; Blom et al., 2011; Groenhagen et al., 2013; Garbeva et al., 2014a). Other factors known to influence volatile production are microbial physiological state, oxygen availability, moisture, temperature and pH (Bjurman, 1999; Insam and Seewald, 2010; Romoli et al., 2014).

The technical developments that have been made in recent years in the field of mass spectrometry have led to the improvement of volatile compounds detection. The details of these developments have recently been summarized by Carter (2014). However, the main challenge in volatolomics is the ability to identify and quantify the entire set of emitted volatiles. The detected volatile blends are mostly quite complex and make the identification of biologically relevant volatiles a demanding and challenging task (Farag et al., 2012; Tait et al., 2014).

To date more than over 1000 microbial volatiles are reported and described in a special database for microbial VOCs called mVOC1 (Lemfack et al., 2014). Nevertheless, this number is rather low compared to the high diversity of bacterial taxa in soil, suggesting a big underestimation of the actual real number of microbial volatiles (Kai et al., 2009; Lemfack et al., 2014). Moreover, most of the studies on microbial volatile detection have dealt with monocultures of already well-described bacterial genera. Thus, very little is known on how inter-specific interactions affect the volatile production. The investigation of volatiles production in more complex communities is of great interest since it could help to reveal the ecological role of these compounds. In the last years several independent studies reported that the production of secondary metabolites by soil bacteria can be influenced by interactions with microorganisms in their vicinity (Garbeva et al., 2011b; Traxler et al., 2013; Tyc et al., 2014). A high-throughput screening performed recently in our lab revealed that interactions between soil bacterial species have major effects in both directions: induction and suppression of antimicrobial activity (Tyc et al., 2014).

In this study we aimed to understand how inter-specific bacterial interactions affect the emission of volatiles and their activity. For this we selected four strains belonging to different bacteria species that have been isolated from the soil bacterial community associated with sand sedge (*Carex arenaria* L.) namely *Chryseobacterium* sp. AD48, *Dyella* sp. AD56, *Janthinobacterium* sp. AD80, and *Tsukamurella* sp. AD106 (Tyc et al., 2014). In an earlier screening it was observed that these bacteria showed induced antimicrobial activity during interactions but not in monocultures. In the current study, it was examined if these observations were also reflected by the volatiles emission. To this end the effects of volatiles on growth of fungal, oomycetal, and bacterial model organisms produced by the bacteria in monocultures as well in pairwise combinations were tested. Our overall hypothesis is that the blend composition volatiles produced during interactions differs from that of monocultures and consequently has different effect on model target organisms.

### MATERIALS AND METHODS

#### Bacteria and Culture Conditions

The bacterial isolates applied in this work were selected based on a previous observations of antimicrobial activity triggered by inter-specific interactions (Tyc et al., 2014). Four bacterial isolates were used: *Chryseobacterium* sp. AD48 (Class: *Flavobacteriia*) GenBank: KJ685263, *Dyella* sp. AD56 (Class: *Gammaproteobacteria*) GenBank: KJ685269, *Janthinobacterium* sp. AD80 (Class: *Betaproteobacteria*) GenBank: KJ685292, and *Tsukamurella* sp. AD106 (Class: *Actinobacteria*) GenBank: KJ685317. The bacterial isolates were pre-cultured from −80◦C glycerol stocks on 1/10th TSBA (5.0 g L−1 NaCl, 1.0 g L−1 KH2PO4;3gL−1 oxoid tryptic soy broth (TSBA); 20 g L−1 Merck Agar, pH 6.5; Garbeva and de Boer, 2009) and incubated for 3 days at 24◦C before starting the experiments.

To test the effect of bacterial volatile compounds on bacterial growth and colony morphology three indicator bacteria were used: *Escherichia coli* WA321, *Staphylococcus aureus* 533R4 (Meyer and Schleifer, 1978; Tyc et al., 2014) and *Serratia marcescens* P87 (Garbeva et al., 2014b). All three indicator bacteria were pre-cultured from −80◦C glycerol stocks either on LBA media (LB-Medium Lennox, Carl Roth GmbH + Co. KG, Netherlands, art.no. X964.2, 20 g L−1 Merck Agar; *E. coli* WA321 and *S. aureus* 533R4; Sambrook and Russell, 2001) or on 1/10th TSBA (*S. marcescens* P87). The indicator organisms *E. coli* and *S. aureus* were incubated overnight at 37◦C prior application, *S. marcescens* P87 was incubated at 24◦C for 4 days prior usage. All bacterial isolates used in this study are listed in **Table 1**.

### Cultures and Growth Conditions of Fungi and Oomycetes

The fungi *Rhizoctonia solani* AG2.2IIIB and *Fusarium culmorum* PV and the oomycete *Pythium ultimum* P17 were used in this study (Garbeva et al., 2014b). The fungi and oomycete were precultured on 1/5th potato dextrose agar (PDA; 29 g L−1 Oxoid CM 139; Fiddaman and Rossall, 1993) and incubated at 24◦C for 7 days prior usage. All fungal and oomycetal organisms are listed in **Table 1**.

1http://bioinformatics*.*charite*.*de/mvoc/

#### TABLE 1 | Bacterial, fungal, and oomycetal organisms used in this study.

#### Experimental Treatments

Ten different treatments were performed in triplicates. These treatments were: monoculture 1 (*Chryseobacterium* sp. AD48), monoculture 2 (*Tsukamurella* sp. AD106), monoculture 3 (*Dyella* sp. AD56), monoculture 4 (*Janthinobacterium* sp. AD80) and pairwise interaction of the isolates: interaction 1 (*Chryseobacterium* sp. AD48 + *Tsukamurella* AD106), interaction 2 (*Dyella* sp. AD56 + *Janthinobacterium* sp. AD80), Control 1 (glass Petri dish with TSBA media without inoculated bacteria, as background control in GC/MS measurement), Control 2 (two compartment Petri dish inoculated with model organisms without exposure to bacterial volatiles), Control 3 (top bottom Petri dish inoculated with fungal/oomycetal model organisms without exposure to bacterial volatile compounds). Control 4 (two compartment Petri dish inoculated with model organisms without exposure to the tested pure volatile compounds). The effect of the produced volatiles was tested on fungal, oomycetal, and bacterial growth via determination of hyphal biomass or growth inhibition assays. For the inoculation of the experiments a single colony of each test isolate was picked from a plate and inoculated in 20 mL 1/10th TSB (5.0 g L−1 NaCl, 1.0 g L−1 KH2PO4;3gL−1 TSBA) and incubated overnight at 24◦C, 220 rpm. On the next day the OD600 of each isolate was measured on a GENESYSTM 20 spectrophotometer (Thermoscientific, Netherlands, Cat# 4001- 000) and a inoculation suspension for each treatment was prepared in 20 mL of 10 mM P-Buffer (pH 6.5) containing bacterial cells in a concentration of ∼1 × 10∧5 CFU/mL.

#### Volatile Trapping

Next to the inhibition experiments, bacterial volatiles emitted in monocultures and pairwise combinations were trapped and analyzed. For trapping of VOCs emitted by bacteria a volume of 100 μl of inoculation suspension was spread on 1/10th TSBA (20 mL) in glass Petri dishes designed for headspace volatile trapping (Garbeva et al., 2014b). The Petri dishes were closed by a lid with an outlet connected to a steel trap containing 150 mg Tenax TA and 150 mg Carbopack B (Markes International, Ltd., Llantrisant, UK; Supplementary Figure S1). All treatments were inoculated in triplicate. The volatiles were collected after 48 and 72 h of incubation and the Tenax steel traps were stored at 4◦C until GC-Q-TOF analysis.

#### GC-Q-TOF Analysis

The trapped VOCs were desorbed from the traps using an automated thermodesorption unit (Unity TD-100, Markes International, Ltd., Llantrisant, UK) at 210◦C for 12 min (He flow 50 mL/min) and trapped on a cold trap at −10◦C. The trapped volatiles were introduced into the GC-QTOF (model Agilent 7890B GC and the Agilent 7200A QTOF, Santa Clara, CA, USA) by heating the cold trap for 3 min to 280◦C. Split ratio was set to 1:10, and the column used was a 30 mm × 0.25 mm ID RXI-5MS, film thickness 0.25 μm (Restek 13424-6850, Bellefonte, PA, USA). Temperature program used was as follows: 39◦C for 2 min, from 39 to 95◦C at 3.5◦C/min, then to 165◦C at 6◦C/min, to 250◦C at 15◦C/min and finally to 300◦C at 40◦C/min, hold 20 min. The VOCs were detected by the MS operating at 70 eV in EI mode. Mass spectra were acquired in full-scanmode (30–400 AMU, 4 scans/s). Mass-spectra's were extracted with MassHunter Qualitative Analysis Software V B.06.00 Build 6.0.633.0 (Agilent Technologies, Santa Clara, CA, USA) using the GC-Q-TOF qualitative analysis module. The obtained mass spectra's were exported as mzData files for further processing in MZmine V2.14.2. The files were imported to MZmine V2.14.2 (Copyright © 2005–2012 MZmine Development Team; Katajamaa et al., 2006; Pluskal et al., 2010) and compounds were identified via their mass spectra using deconvolution function (Local-Maximum algorithm) in combination with two mass-spectral-libraries: NIST 2014 V2.20 (National Institute of Standards and Technology, USA2 ) and Wiley 7th edition spectral libraries and by their linear retention indexes (LRIs). The LRI values were calculated using an alkane calibration mix before the measurements in combination with AMDIS 2.72 (National Institute of Standards and Technology, USA). The calculated

2http://www*.*nist*.*gov

LRI were compared with those found in the NIST and in the in-house NIOO LRI database. After deconvolution and mass identification peak lists containing the mass features of each treatment (MZ-value/Retention time and the peak intensity) were created and exported as CSV files for statistical processing. The whole volatolomic workflow is shown in Supplementary Figure S2.

### Bioassay for Testing the Effect of Bacterial Volatiles on Fungal and Oomycete Growth

To test the effect of the emitted bacterial volatiles on fungal/oomycete growth the hyphal extension and biomass were measured. The assays were performed in Petri dishes containing top and bottom growth areas (Supplementary Figure S3). At the bottom of the Petri dish, 100 μl of bacterial suspensions in 10 mM phosphate buffer (pH 6.5) containing ∼1 × 10∧5 CFU/mL were spread on 20 mL 1/10th TSBA. At the lid of the Petri dish 12.5 mL of water-agar medium (WA; 20 g L−1 MERCK agar) was added and inoculated in the middle with a 6-mm-diameter PDA agar plug containing fungal (*R. solani*, *F. culmorum*) or oomycete (*P. ultimum*) hyphae. The plates were sealed with two layers of parafilm and incubated at 24◦C for 5 days. In this way the tested fungi were exposed (without direct physical contact) to the volatiles produced by the bacteria in the bottom compartment. On the fifth day the extension of the hyphae was measured in 4 evenly spaced directions and compared to the hyphae extension in the control plates (fungi exposed to 1/10th TSBA growth medium without bacteria).

### Determination of Fungal and Oomycetal Biomass

Fungal biomass was determined as described by Garbeva et al. (2014b). The whole growth area in the lids containing water agar and fungal hyphae was cut in ∼2 cm2 pieces and transferred to a glass beaker containing 100 mL of sterile demi-water (H2O). The agar was melted for ∼2.5 min in a microwave oven (temperature increased to about 100◦C). The melted agar containing the hyphae was filtered over a tea strainer and the remaining hyphae were rinsed with about 150–200 mL of hot water (∼80◦C). The hyphae were picked with tweezers from the tea strainer and transferred to a micro centrifuge tube and stored at −20◦C until analysis. For determination of fungal/oomycete biomass the frozen hyphae were transferred to a glass tube with lids with small holes and subjected to freeze-drying for 48 h (Labconco Freezone 12 with Labconco Clear Drying Chamber nr.7867000). The samples were stored in an exsiccator with dried silica gel for 3 h (Silica Gel Orange, 2–5 mm, indicator, Roth, art.nr.P077.2) prior weighing the dry biomass.

### Bioassay for Testing the Effect of Bacterial Volatiles on Growth and Colony Morphology of Target Bacteria

The assays were performed in two-compartment Petri dishes (Greiner bio-one B.V., Alphen a/d Rijn, The Netherlands, Cat# 635102) containing two separated compartments (Supplementary Figure S4). In such way the growth response of target bacteria to volatile producing bacteria could be determined without direct physical contacts. One compartment was supplemented with 12.5 mL TSBA and contained the volatile producing bacteria either in monoculture or in pairwise interactions. The second compartment contained the indicator bacteria and was supplemented either with 12.5 mL LBA (*E. coli* WA321, *S. aureus* 533R4) or with 12.5 mL TSBA (*S. marcescens* P87). The compartment for the volatile producing bacteria was inoculated with 100 μl bacterial suspensions master mix of monocultures or pairwise interactions prepared with 20 mL of 10 mM phosphate buffer (pH 6.5) containing ∼1 × 10∧5 CFU/mL. The compartment for the indicator organisms was inoculated with four droplets (5 μL) of each indicator bacteria. The droplets of the indicator bacteria were placed in a distance of 2 cm to each other and contained 1 × 10∧5, 1 × 10∧4, 1 × 10∧3, and 1 × 10∧2 CFU/mL of either *E. coli* WA321, *S. aureus* 533R4, or *S. marcescens* P87 (Supplementary Figure S4). As controls the first compartment of the Petri dish was kept empty. After 4 days of incubation at 24◦C the plates were examined and digital photographs were taken. The digital images were analyzed using the AXIO VISION v4.8 imaging Software (Carl Zeiss Imaging Solutions GmbH, Germany) for enumeration and surface-area determination (in pixelˆ2) of the bacterial colonies. All treatments were performed in triplicate.

### Test of Pure Volatile Compounds on Bacterial Growth and Colony Morphology

The effect on growth, colony morphology and pigmentation by pure dimethyl disulfide (DMDS; CH3S2CH3), dimethyl trisulfide (DMTS; CH3S3CH3) and the mixture of both compounds was tested on *E. coli* WA321, *S. aureus* 533R4 and *S. marcescens* P87. The assays were performed in two-compartment Petri dishes (Greiner bio-one B.V., Alphen a/d Rijn, The Netherlands, Cat# 635102). Both compartments were supplemented with either 12.5 mL LBA (assay performed with *E. coli* WA321 and *S. aureus* 533R4) or with 12.5 mL TSBA (assay performed with *S. marcescens* P87). In one compartment a filter paper with a diameter of ∼5.5 mm (WhatmanTM filter paper Cat# 1003-150, 6 μm pore size) was placed on the agar surface in the middle of the compartment. Stock solutions with a concentration of 10, 1, and 0.1 μM of the pure volatile compounds (DMDS or DMTS) and the mixture of both compounds (DMDS + DMTS) were prepared by serial dilution of the pure compounds in Methanol (LiChrosolvR , Index-No: 603-001-00-X, Merck, Darmstadt, Germany). For the test a volume of 5 μl of each of the pure volatile stock solutions was added directly onto the filter paper resulting in a final concentration of 50, 5, and 0.5 μM, respectively. The other compartment was inoculated with the target bacteria *E. coli* WA321, *S. aureus* 533R4 or *S. marcescens* P87 by inoculating four spots in a distance of 2 cm from each other containing 1 × 10∧5, 1 × 10∧4, 1 × 10∧3, and 1 × 10∧2 CFU/mL (Supplementary Figure S4). As controls bacteria exposed to filter papers with no added volatile compounds were applied. The Petri dishes were sealed with a double layer of parafilm and incubated for 4 days at 24◦C. After incubation digital photographs were taken and the effect on colony growth, colony morphology and pigment production (prodigiosin) in *S. marcescens* P87 was examined. All digital images were analyzed using the AXIO VISION v4.8 imaging Software (Carl Zeiss Imaging Solutions GmbH, Germany) for enumeration and surface-area determination (in pixelˆ2) of the bacterial colonies. All treatments were performed in triplicate.

#### Statistical Analysis

Statistical analysis on volatolomic data was performed using the statistical analysis module of MetaboAnalyst V3.0, www*.*metaboanalyst*.*ca (Xia et al., 2012, 2015). Prior to statistical analysis data normalization was performed via log-transformation. To identify significant abundant mass features one-way-ANOVA with *post hoc* Tukey test (HSDtest) was performed between the data sets. To identify important mass features in the samples PLS-D analysis was performed. Mass features were considered to be statistical relevant if *p*-values were ≤0.05. Statistical relevant mass features were further used for the compound identification. Statistical analyses on fungal dry biomass and bacterial colony sizes were performed with IBM SPSS Statistics 23 (IBM, Somers, NY, USA) using one-way ANOVA and *post hoc* Tukey test between the data sets. The 5% level was taken as threshold for significance between control and volatile treatments.

### Determination of HCN, NH3 Emission, and pH Values in the Agar

All bacterial strains used in this study were tested for the emission of ammonia and HCN as well as for the ability to change the pH- value of the growth medium where the target organisms were inoculated. For these tests the bacteria were inoculated in two-compartment Petri dishes (start density ∼1 × 10∧5 CFU/mL) on 12.5 mL 1/10th TSBA. The second compartment was supplemented with 12.5 mL WA. After 4 days of growth the HCN and ammonia emission as well the pH-value of the target organism growth medium (WA) was determined. To test for the presence of Hydrocyanic acid the gaseous content of the Petri dish headspace was sucked through a Hydrocyanic acid test tube (Dräger Safety AG and CO. KGaA, Lübeck, Germany, order number: CH25701) using the Dräger accuroR gas detection pump (Dräger Safety AG and CO. KGaA, Lübeck, Germany). Presence of Hydrocyanic acid was determined by color change of the test tube (formation of a red reaction product; Supplementary Figure S5). The pH of the target organism growth medium (WA) exposed to bacterial volatiles was determined by slightly pressing a pH test-strip VWR PROLABO dosatestR (VWR international, Cat# 35309.606UK) for 30 s into the agar surface. The pH values were determined by color change of the test strip and compared to the color scale on the package (Supplementary Figure S6). The ammonia concentration was determined using the MQuantTM ammonium test kit (Merck, Darmstadt, Germany, Cat# 110024) by placing a reaction activated test-strip on the lid of the Petri dish directly opposite to the bacterial culture and fixed with tape. The Petridish were closed and sealed with parafilm and incubated for 2 h at 24◦C. After incubation the presence of ammonium was determined by color change of the test strip (Supplementary Figure S7).

### RESULTS

### Detected Headspace Volatile Compounds and GC/MS-Q-TOF Analysis

GC/MS-Q-TOF based volatolomic analysis revealed a total number of 35 compounds that were not detected in the noninoculated controls (**Table 2**). 27 compounds were obtained from the monocultures of *Chryseobacterium* sp. AD48, 15 compounds were obtained from the monocultures of *Tsukamurella* sp. AD106 and 26 compounds were detected in the interactions between these two bacteria (**Table 2**; **Figure 1A**). For the combinations of *Dyella* sp. AD56 and *Janthinobacterium* sp. AD80 we obtained a total number of 18 compounds, whereas 16 compounds were detected in the monoculture of *Janthinobacterium* sp. AD80 and only 13 compounds in the monoculture of *Dyella* sp. AD56 (**Table 2**; **Figure 1B**). We were able to tentatively identify 19 VOCs belonging to seven different chemical classes including alcohols, amines, esters, indole, thiocyanates, thioesters, and sulfides. However, a vast number of the detected compounds (*n* = 16) could not be assigned with certainty to a VOC and remained unknown. The most prominent detected headspace VOCs were sulfur containing compounds (such as sulfordioxide, methyl thioacetate, dimethyl sulfoxide, etc.). Two sulfur compounds DMDS (C2H6S2) and DMTS (C2H6S3) were produced by all bacteria (except DMTS which was not detected for *Janthinobacterium* sp. AD80).

### Effect of Inter-specific Interactions on Bacterial Volatile Blend Composition

Volatolomic analysis on monocultures and pairwise combinations of *Chryseobacterium* sp. AD48 with *Tsukamurella* sp. AD106 revealed that the volatile composition of the monocultures differed from that of the mixtures (**Figure 1A**; **Table 2**). Clear separations between controls, monocultures and pairwise combinations of *Chryseobacterium* sp. AD48 with *Tsukamurella* sp. AD106 were obtained in PCA score plots (**Figure 1A**). The volatile composition of the pairwise combinations resembled that of the monocultures of *Chryseobacterium* sp. AD48 (**Figure 1A**; **Table 2**). The indole produced by the monoculture of *Chryseobacterium* sp. AD48 was not detected in the interactions (**Table 2**).

The analysis on the volatiles emitted by monocultures and pairwise combinations of *Dyella* sp. AD56 and *Janthinobacterium* sp. AD80 revealed that the volatile profiles of the monocultures differed from that of the mixtures (**Figure 1B**; **Table 2**). Different PCA score plots

#### TABLE 2 | Tentatively identified volatile organic compounds emitted by four bacterial strains cultivated either in monoculture or in pairwise combination.

*#* = *Compound number, Chry* = *Chryseobacterium, Dye* = *Dyella, Jant* = *Janthinobacterium, Tsuk* = *Tsukamurella, MIX Chry* + *Tsuk* = *pairwise combination of Chryseobacterium* + *Tsukamurella. MIX Jant* + *Dye* = *pairwise combination of Dyella* + *Janthinobacterium.*

*X* = *detected.*

*RT*∗ = *Retention time, the RT value stated is the average.*

*ERI*∗∗ = *Experimental retention index value, the RI value stated is the average.*

were obtained between controls, monocultures and pairwise combinations of *Dyella* sp. AD56 with *Janthinobacterium* sp. AD80 (**Figure 1B**). A higher number of volatile compounds were detected in the pairwise combinations of these two bacteria. However, the higher number of detected volatiles is most probably due to the combination of the volatile blends of these two bacterial isolates. We did not detect any novel or different volatile compounds which production was triggered during the pairwise interaction of these two bacteria. Interestingly the volatile compound cyclopentene produced by the monocultures of *Dyella* sp. AD56 and *Janthinobacterium* sp. AD80 was not detected in the interactions (**Table 2**).

### Effect of Bacterial Volatiles on Fungal and Oomycetal Growth

Volatiles produced by all treatments including monocultures and pairwise combinations of the selected bacteria revealed strong growth inhibition of the plant pathogenic fungi and oomycete. The dry biomass of fungi and oomycete exposed to bacterial volatiles was significantly reduced as compared to the controls without bacterial volatiles (**Table 3**; **Figures 2** and **3**).

### Effect of Bacterial Volatiles on the Growth and Behavior of Target Bacteria

Volatiles emitted by *Chryseobacterium* sp. AD48 and the mixture of *Chryseobacterium* sp. AD48 and *Tsukamurella* sp. AD106 inhibited the growth of *E. coli* WA321 significantly as compared to the control (**Figure 4A**). This observation is in agreement with the observed volatolomic profile (**Figure 1A**) which revealed that the volatolomic profile of the mixture is dominated by the volatiles produced by the monoculture of *Chryseobacterium* sp. AD48.

Besides growth inhibition we observed significant growth promotion of *S. aureus* 533R4 when exposed to volatiles emitted by the monocultures of *Dyella* sp. AD56 (**Figure 4B**).

Changes in colony morphology of *S. marcescens* P87 were observed when exposed to volatiles emitted by *Chryseobacterium* sp. AD48 and to volatiles emitted by the mixtures of *Dyella* sp. AD56 with *Janthinobacterium* sp. AD80. The *S. marcescens* P87 colonies were more circular and round shaped (Supplementary Figure S8). However, no significant effects of bacterial volatiles on the growth of the target bacteria were also observed (Supplementary Figure S9).

### Effect of Pure Individual Volatile Compounds on the Growth and Colony Morphology of Target Bacteria

We applied a two-compartment Petri dish testing system (Supplementary Figure S4) in which the model organisms could grow without direct physical contacts to the tested pure volatile compounds. After 4 days of growth *S. marcescens* P87 colonies were small and showed a white phenotype when exposed to 50 μM of DMTS, indicating the lack of prodigiosin production (**Figure 5A**). Furthermore we observed significant inhibition of growth of *S. marcescens* P87, *E. coli* WA321 and *S. aureus* 533R4 when exposed to 50 μM of DMTS (**Figures 5–7**).

Exposure to DMDS did not reveal any significant growth inhibiting or changes in colony morphology at all concentrations tested (500 nM, 5 and 50 μM). The mixture of DMDS and DMTS resulted in growth inhibition of *S. marcescens* P87 and *E. coli* WA321 at 50 μM concentration. However, the pigmentation in *S. marcescens* P87 was not affected by the mixture of these compounds. The two lowest applied concentrations 5 and 0.5 μM of DMTS and DMDS and

*Data represent mean and standard deviation of three replicates.*

*Asterisk indicates significant differences between the treatments and the respective control (one-way ANOVA, post hoc Tukey test p < 0.05).*

eukaryotic plant-pathogens. Bars represent the average values for fungal and oomycetal biomass dry weight and error bars represent standard deviation of the mean. (A) Dry weight of *Rhizoctonia solani;* (B) dry weight of *Pythium ultimum*; (C) dry weight of *Fusarium culmorum*. Significant differences between treatments and the control are indicated by different letters (one-way ANOVA, *post hoc* Tukey test *p <* 0.05).

deviation of the mean. (A) Dry weight of *R. solani*; (B) dry weight of *P. ultimum*; (C) dry weight of *F. culmorum.* Significant differences between treatments and the control are indicated by different letters (one-way ANOVA, *post hoc* Tukey test *p <* 0.05).

the mixture of both compounds did not reveal any effect on colony morphology or growth of the tested bacteria (**Figures 5–7**).

## DISCUSSION

Bacteria coexist with many different species in a heterogeneous and challenging soil environment (Gans et al., 2005). In this environment inter-specific interactions between microorganisms are ongoing and are a key factor for their spatial distribution (Keller and Surette, 2006). To cope with the competitive conditions, bacteria developed different survival strategies such as the production of secondary metabolites with inhibitory capacity (Hibbing et al., 2010; Cornforth and Foster, 2013). Most of the studies on bacterial secondary metabolites so far were focused on non-volatile compounds (Korpi et al., 1998; Foster and Bell, 2012). However, bacteria do also release complex blends of VOCs. Yet, the effect of inter-specific interactions on volatiles production and composition is still unknown (Garbeva et al., 2014a).

Here, we compared the volatile blends emitted by four phylogenetically different soil-bacteria either grown in monocultures or in pairwise combinations. Our results revealed that the blend of volatiles emitted during pairwise combinations differed from the volatile blends of the respective monocultures. Yet, the volatile blend of the mixtures mostly included volatiles compounds produced by monocultures, although some compounds produced by the monocultures were not detected in mixtures. For example dimethyl sulfoxide produced by *Tsukamurella* sp. AD106 was not detected in the mixture with *Chryseobacterium* sp. AD48. Another interesting example is indole which was produced by the monocultures of *Chryseobacterium* sp. AD48 but was not detected in the

presence of *Tsukamurella* sp. AD106. Indole is a very wellstudied compound and has been reported to be produced by about 85 different bacterial species including *Chryseobacterium* sp. (Yamaguchi and Yokoe, 2000; Lee and Lee, 2010). Indole and its derivatives [quinolones and (*S*)-3-hydroxytridecan-4 one] are involved in intercellular and multispecies signaling controlling diverse bacterial physiological properties like sporulation, plasmid stability, biofilm formation, drug resistance and virulence (Wang et al., 2001; Di Martino et al., 2003; Diggle et al., 2006; Nikaido et al., 2008; Lee et al., 2009; Lee and Lee, 2010). In addition, indole has been shown to have inhibitory activities on fungal growth (*Aspergillus niger*) and plant growth stimulating properties (*Arabidopsis thaliana*; Kamath and Vaidyanathan, 1990; Blom et al., 2011). In general indole is known to be a stable compound in the producing bacteria, however, many non-indole producing bacteria are able to modify and to degrade indole (Shimada et al., 2013; Lee et al., 2015). The fact that indole was not detected during the interaction of *Chryseobacterium* sp. AD48 with *Tsukamurella* sp. AD106 suggests that the production of such signaling compounds in nature depends strongly on the inter-specific interactions. Similar result was observed for the compound cyclopentene produced by the monocultures of *Dyella* sp. AD56 and *Janthinobacterium* sp. AD80 but not produced during the interaction of these two bacteria. With the volatolomic methods applied in this study we were able to detect 35 compounds from which 19 were tentatively identified. This discrepancy between numbers of detected and identified compounds shows that the identification of bacterial volatiles is yet a challenging and time demanding task, even with the use of sophisticated programs and software for metabolomics data analysis. Hence, the produced volatile blends are very complex and consist of a mixture of many unknown and difficult to identify compounds (Tait et al., 2014). Most of the VOCs that were tentatively identified within this study (∼58%) contained sulfur (e.g., methyl thiocyanate, DMDS, DMTS, dimethyl tetrasulfide, etc.). The high abundance of sulfur containing volatiles in this study can be related to the cultivation of the tested bacteria on 1/10th TSBA growth media. Several studies indicated that the composition of the volatile blend greatly depends on the growth media composition and the growth conditions (Schulz et al., 2004; Schulz and Dickschat, 2007; Blom et al., 2011; Garbeva et al., 2014b). The high amount of dimethyl di- and trisulfide detected in both monocultures and interactions

indicate that these compounds are commonly produced. Many studies have shown that bacterial volatiles play a major role in soil fungistasis (Zou et al., 2007; Garbeva et al., 2011a, 2014b; van Agtmaal et al., 2015). Indeed our results revealed that the fungal and oomycete tested organism are sensitive to bacterial volatiles and were inhibited significantly by all monocultures and pairwise combinations. The observed fungal and oomycetal growth inhibition is most probably related to sulfur containing volatiles.

While many study tested the effect of bacterial volatiles on various fungi, little is known so far on the effect of bacterial volatiles on other bacteria. In this study *E. coli* WA321 was inhibited by the volatiles emitted by *Chryseobacterium* sp. AD48 and the mixture of *Chryseobacterium* sp. AD48 with *Tsukamurella* sp. AD106. The observed growth promotion of *S. aureus* 533R4 was caused by the volatiles emitted by *Dyella* sp. AD56. However, this growth promotion was not observed by the volatiles emitted during the interaction of *Dyella* sp. AD56 with *Janthinobacterium* sp. AD80 correlating with a shift in volatile blend composition. Interestingly volatiles emitted by the monocultures of *Chryseobacterium* sp. AD48 and the mixture of *Dyella* sp. AD56 with *Janthinobacterium* sp. AD80 induced changes in colony morphology of *S. marcescens* P87.Our previous high-throughput screening for production of nonvolatile antimicrobial compounds revealed that all four bacteria used here, showed induced antibacterial activity during pairwise interactions as compared to monocultures (Tyc et al., 2014). This was not observed in the present study, as we didn't observed novel produced volatile compounds during the pairwise interactions. Therefore, it's questionable if volatiles solely play an important role as a competitive strategy between bacteria. However, it is possible that volatiles have synergistic or additive effect to other non-volatile antibacterial compounds (Schmidt et al., 2015). Many bacteria are known to emit inorganic volatiles like CO2, NH3, HCN, which also have biological activities and can have an additive effect (Effmert et al., 2012). However, such compounds were not detected in this study as significant volatile compounds.

Here, we tested two commonly produced bacterial volatile compounds for their effect on the target bacteria. The experiments with pure DMTS revealed strong growth inhibition on all tested bacterial model organisms, when applied in a

concentration of 50 μM. Bacterial growth suppression was already reported for DMDS emitted by *Pseudomonas* strains against the crown-gall diseases causing *Agrobacterium* sp. (Dandurishvili et al., 2011; Popova et al., 2014). Dimethyl trisulfide effected colony morphology and pigmentation in *S. marcescens* P87 when applied in a concentration of 50 μM. Volatiles exposed colonies showed reduced growth and white coloration indicating the lack of prodigiosin production. It is plausible that this observation is related to the inhibition of quorum-sensing as previously reported by Morohoshi et al. (2007), Chernin et al. (2011). However, the effective concentration of 50 μM DMTS is most probably very high and far away from the concentrations in which those volatile compounds are produced in nature (Groenhagen et al., 2013) as we did not observed this effect in the experiments where *S. marcescens* P87 was exposed to the volatile blend produced by bacteria. The biological relevant concentration of volatile compounds remains to be determined in future studies.

## CONCLUSION

This work revealed that inter-specific bacterial interactions affect volatile blend composition. This observed change is most probably related to the combination of volatile compounds produced by each isolate rather than triggering the production of novel volatiles as the volatile blend was composed of the mixture of the respective interacting bacteria. Furthermore, the loss of production of certain compounds during pairwise interaction suggests that the production of volatile signaling compounds (e.g. indole) in nature is influenced by inter-specific interactions. While fungi and oomycetes showed to be very sensitive to bacterial volatiles the effect of volatiles on bacteria varied greatly between no effects, growth inhibition to growth promotion depending on the volatile blend composition.

### ACKNOWLEDGMENTS

This work is supported by the BE-Basic Foundation (http://www.bebasic.org/). PG is financed by The Netherlands Organization for Scientific Research (NWO) VIDI personal grant 864.11.015. The authors want to thank Saskia Gerards for her great help during experimentation and Dr. Kees Hordijk for his help with GC/MS data analysis. This is publication 5957of the NIOO-KNAW.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal*.*frontiersin*.*org/article/10*.*3389/fmicb*.* 2015*.*01412

### REFERENCES

*coelicolor* secreted metabolome. *Mbio* 4, e459-13. doi: 10.1128/mBio.004 59-13

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2015 Tyc, Zweers, de Boer and Garbeva. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Legacy effects of anaerobic soil disinfestation on soil bacterial community composition and production of pathogen-suppressing volatiles

Maaike van Agtmaal 1 , Gera J. van Os 2 , W.H. Gera Hol 3 , Maria P.J. Hundscheid1 , Willemien T. Runia4 , Cornelis A. Hordijk 1 and Wietse de Boer 1, 5 \*

*1 Department of Microbial Ecology, Netherlands Institute of Ecology, NIOO-KNAW, Wageningen, Netherlands, 2 Applied Plant Research, Flowerbulbs, Nursery Stock and Fruit, Wageningen University and Research Centre, Lisse, Netherlands, 3 Department of Terrestrial Ecology, Netherlands Institute of Ecology, NIOO-KNAW, Wageningen, Netherlands, 4 Applied Plant Research, Subdivision Arable Farming, Multifunctional Agriculture and Field Production of Vegetables, Wageningen University and Research Centre, Lelystad, Netherlands, 5 Department of Soil Quality, Wageningen University and Research Centre, Wageningen, Netherlands*

#### *Edited by:*

*Laure Weisskopf, Agroscope, Switzerland*

#### *Reviewed by:*

*Richard Splivallo, University of Frankfurt, Germany Birgit Piechulla, University of Rostock, Germany*

#### *\*Correspondence:*

*Wietse de Boer, Netherlands Institute of Ecology, PO Box 50, 6708 PB, Wageningen, Netherlands<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Plant Biotic Interactions, a section of the journal Frontiers in Microbiology*

*Received: 20 April 2015 Accepted: 26 June 2015 Published: 10 July 2015*

#### *Citation:*

*van Agtmaal M, van Os GJ, Hol WHG, Hundscheid MPJ, Runia WT, Hordijk CA and de Boer W (2015) Legacy effects of anaerobic soil disinfestation on soil bacterial community composition and production of pathogen-suppressing volatiles. Front. Microbiol. 6:701. doi: 10.3389/fmicb.2015.00701* There is increasing evidence that microbial volatiles (VOCs) play an important role in natural suppression of soil-borne diseases, but little is known on the factors that influence production of suppressing VOCs. In the current study we examined whether a stress-induced change in soil microbial community composition would affect the production by soils of VOCs suppressing the plant-pathogenic oomycete *Pythium*. Using pyrosequencing of 16S ribosomal gene fragments we compared the composition of bacterial communities in sandy soils that had been exposed to anaerobic disinfestation (AD), a treatment used to kill harmful soil organisms, with the composition in untreated soils. Three months after the AD treatment had been finished, there was still a clear legacy effect of the former anaerobic stress on bacterial community composition with a strong increase in relative abundance of the phylum *Bacteroidetes* and a significant decrease of the phyla *Acidobacteria, Planctomycetes, Nitrospirae, Chloroflexi,* and *Chlorobi*. This change in bacterial community composition coincided with loss of production of *Pythium* suppressing soil volatiles (VOCs) and of suppression of *Pythium* impacts on Hyacinth root development. One year later, the composition of the bacterial community in the AD soils was reflecting that of the untreated soils. In addition, both production of *Pythium*-suppressing VOCs and suppression of *Pythium* in Hyacinth bioassays had returned to the levels of the untreated soil. GC/MS analysis identified several VOCs, among which compounds known to be antifungal, that were produced in the untreated soils but not in the AD soils. These compounds were again produced 15 months after the AD treatment. Our data indicate that soils exposed to a drastic stress can temporarily lose pathogen suppressive characteristics and that both loss and return of these suppressive characteristics coincides with shifts in the soil bacterial community composition. Our data are supporting the suggested importance of microbial VOCs in the natural buffer of soils against diseases caused by soil-borne pathogens.

Keywords: volatile organic compounds (VOCs), Soil-borne plant pathogens, General disease suppression, Pythium intermedium, Fungistasis, Oomycetes

#### Introduction

In the light of sustainable agriculture and the call for reduction of pesticide use, insights in the mechanisms of natural suppression of soil-borne pathogens are essential. Therefore, understanding the interactions of plant pathogens with other members of soil microbial communities is needed to develop strategies for effective and consistent control (Chaparro et al., 2012). In general, depletion of carbon sources by indigenous microbes hampers the pre-infective growth of soil-borne pathogens resulting in lower infection rates (Hoitink and Boehm, 1999). This competition-related mechanism of pathogen control is also known as "general disease suppression" (Hoitink and Boehm, 1999). Disease suppression is closely related to soil fungistasis, the restricted ability of fungal propagules to germinate or grow in most soils (Dobbs and Hinson, 1953). As for general suppression, it has been hypothesized to be caused by microbial withdrawal of nutrients from soil or even from fungal propagules (Lockwood, 1977). However, besides substrate competition, also inhibitory compounds, released by microbes, have been indicated to contribute to fungistasis (Romine and Baker, 1972; De Boer et al., 2003). This implies that not only the carbon-withdrawing activity of the total soil microbial community is involved in fungistasis but also the secondary metabolite production of certain groups within the soil microbial community. Based on this, Garbeva et al. (2011) argued that the composition of soil microbial communities is more important in fungistasis than previously has been appreciated.

Most soil-borne pathogens are poor competitors and have limited saprotrophic capacities in their pre-infective stages. Therefore, they are sensitive to general disease suppression. Among them is the oomycete genus Pythium, which includes many plant pathogenic species (Boehm and Hoitink, 1992). They infect roots of seedlings generally resulting in damping-off and, consequently, reduced yield in a broad range of crops (Martin and Loper, 1999). In flower bulb crops, several species of Pythium cause severe root rot, leading to considerable losses in bulb yield (Van Os et al., 1998). Infection can occur by zoospores and is initiated by a chemotactic response to compounds exuded by roots. Yet, Pythium is considered to be a poor competitor for these root exudates and, therefore, natural control of Pythium infection is attributed to high competitive pressure exerted by other exudate-consuming soil microbes (Chen et al., 1988; Van Os and van Ginkel, 2001). Hence, the current view on the cause of natural buffering of soils against Pythium infection is mainly pointing at resource competition rather than at interference competition (involvement of inhibitory secondary metabolites).

Antimicrobial volatile organic compounds (VOCs), emitted by soil microbes, may be an important factor in causing fungistasis facilitated by their ability to diffuse through the porous soil matrix (Wheatley, 2002; Garbeva et al., 2011; Effmert et al., 2012). The potential role of VOCs in suppression of soil-borne plant pathogenic organisms was already reviewed in Stotzky et al. (1976) but regained interest recently (Garbeva et al., 2011; Effmert et al., 2012; Weisskopf, 2013). Production of antifungal volatiles has been shown for a broad range of bacterial phyla: it has been estimated that 30–60% of the soil bacterial species can produce fungistatic volatiles (Wheatley, 2002; Zou et al., 2007). Further support for the role of volatiles in fungistasis came from an extensive inventory by Chuankun et al. (2004), who observed a significant positive correlation between fungistatic activity (inhibition of spore germination) and production of VOCs by 146 soils. The inhibition of pathogen growth by bacterial VOCs has been shown in several studies (McCain, 1966; Alström, 2001; Wheatley, 2002; Kai et al., 2007, 2009; Zou et al., 2007; Effmert et al., 2012) indicating the potential of microbial volatiles in disease reduction. Inhibition of Pythium mycelial growth by bacterial volatiles has been shown, albeit under in vitro conditions and not in soils (Garbeva et al., 2014a; Hol et al., 2015). Hence, possible involvement of volatiles in natural soil suppression of Pythium is unknown.

Agricultural management practices may influence the composition of soil microbial communities and, therefore, also the production of pathogen-suppressing secondary metabolites. Different management practices are in use to reduce pathogen pressure. Anaerobic soil disinfestation (AD) uses crop residues and airtight covering of the soil with plastic foil to stimulate the development of anaerobic microbes producing toxic substances that eliminate harmful nematodes and fungi (Blok et al., 2000). Although AD is used as an environmentally-friendly alternative for chemical disinfestation it is expected to have a tremendous effect on microbial community composition and functioning as aerobic soil microbes face a period of anaerobiosis. Little is known on the possible legacy that AD may have on the composition and functioning of soil microbial communities after the treatment has been finished and cultivation of new crops is started. It has been shown that stress-induced shifts in soil microbial community composition can cause a drastic reduction of fungistasis (De Boer et al., 2003). Hence, there is a potential risk that AD and other disinfestation treatments have similar effects on the pathogen-suppressing activities of soil microbial communities. The current study was aimed to address possible legacy effects of AD of sandy bulb soils on bacterial community composition and soil suppressive characteristics, with special emphasis on the production of pathogen-suppressing volatiles. To this end measurements were done at the start of the flower bulb season (planting of bulbs in autumn) in the year that AD had been applied (3 months after AD) and 1 year later. The oomycete Pythium intermedium, a notorious pathogen of flower bulbs, was used to test soil suppressiveness as Pythium species are opportunistic pathogens that can rapidly cause problems under conditions where general suppressiveness has been reduced (Postma et al., 2000). Simultaneously the production of Pythiumsuppressing volatiles by AD-treated and control soils were tested and compared with results of bioassays (root development of Hyacinth bulbs in the presence of P. intermedium) to determine the role of volatiles in natural disease suppression. Bacterial community composition was determined using 454 sequencing of 16S rDNA fragments.

The tightly linked series of analyses and experiments lend strong support to the importance of bacterial community composition and–volatile production in natural suppression.

#### Materials and Methods

#### Soil Treatments and Sampling

Experiments were performed with soil samples from the experimental fields of Applied Plant Research (Wageningen UR) in Lisse, The Netherlands (coordinates: N 52.25. 52; E 4.54. 77). At this location the alluvial sandy soil has a low organic matter content ranging between 1.0 and 1.5%, which is representative of the soil type used for cultivation of flower bulbs along the dunes of the coastal area of the North Sea. In 2010, a field trial was initiated to examine the effect of soil organic matter content and management practices on disease suppression against several soil-borne pathogens. From the current experiment, plots of four soil treatments (**Table 1**) with four replicates per treatment (60 m2 per replicate) were included. In May 2010, organic matter (OM) content was elevated by incorporating a mixture of peat (95%) and cattle manure (5%) (504 tons ha−1 , 0–30 cm deep), resulting in an increase of the soil OM content from 1.2 to 3.0%. In August 2011, anaerobic soil disinfestation (AD) was applied to a subset of the plots according to the method of Blok et al. (2000) using "Herbie 7025" (Van Overbeek et al., 2014), a defined protein-rich vegetal by-product of food processing industry (Thatchtec B.V., Wageningen, The Netherlands). Herbie was applied 24 tons ha−1 , was incorporated 0–30 cm deep and anaerobic conditions were created by watering followed by airtight covering of the soil with plastic for 6 weeks. Three and fifteen months after removal of the plastic cover (November 2011 and November 2012 respectively), soil samples were taken from each field plot (22 kg per plot, randomly collected from 0 to 20 cm depth) and kept at 4◦C until use. In between sampling dates, Gladiolus was cultivated on all field plots (April-November 2012).

#### Bioassay for Assessment of Root Rot

From each of the 16 field plots, soil samples were artificially infested with a three-week-old oatmeal culture (1% v/v) of Pythium intermedium (isolate P52, Applied Plant Research Flowerbulbs, Nursery Stock and Fruit, Lisse). Non-infested and pasteurized soils (2 h at ≥ 70◦C) were used as controls. Soil moisture content was adjusted to 20% (w/w). Five bulbs from Hyacinthus orientalis cultivar "Pink Pearl" were planted in pots (3 L) and incubated during 8 weeks at 9◦C in the dark in climate cells (Hyacinth bulbs are infected during the belowground root growing phase of the bulb). Pots were sealed with plastic foil to maintain soil moisture but allow oxygen diffusion. Impact of Pythium on Hyacinth root development was assessed by measuring root weight and by rating root-rot disease symptoms. At the end of the growing period, bulbs were removed from the soil and roots were washed with tap water. Root-rot ratings of infested treatments were related to the healthy root systems of non-infested control treatments. Roots were visually examined for root rot severity according to Van Os et al. (1998) using an arbitrary disease index ranging from 0 to 5, where 0 = no root rot, 1 = 1–20%, 2 = 21–40%, 3 = 41–60%, 4 = 61–80%, and 5 = >80% root rot, i.e., relative loss of healthy root mass induced by infection, compared to the corresponding healthy root systems. Roots were scored for each plant individually and a mean root rot index for each pot was calculated. After the scoring of the disease index, roots were removed from the bulbs and excess water was removed by blotting the roots on filter paper and total fresh root weight per pot (5 bulbs) was determined. Means of four pots per soil treatment were used in statistical analysis. Separate bioassays were performed for both years.

#### *In Vitro* Tests for Production of *Pythium*-Suppressing Volatiles from Soil

An experimental set-up was designed to enable exposure of Pythium intermedium to volatiles produced by the soils, without direct contact between Pythium and soil. Soil samples equal to 20 g dry weight [20% (w/w) soil moisture content] were spread evenly on the bottom of 90 mm Petri dishes and incubated for 1 week at 10◦C. A 4 mm layer of water yeast agar (WYA, 20 g agar, 1 g KH2O4, 0.1 g (NH4)2SO4, 0.1 g yeast extract (Difco) L−1 pH 6.5) was poured in lids of Petri dishes. Agar plugs of 6 mm potato dextrose agar (PDA 19,5 g L−1 (Oxoid, Basingstoke, UK) with CMN agar 7,5 g L−1 (Boom, Meppel, The Netherlands) colonized by P. intermedium (incubated 5–10 days at 20◦C) were transferred to WYA plates and kept at 10◦C. After 48 h, a WYA agar disk (Ø 6 mm) containing Pythium mycelium

#### TABLE 1 | Overview of soil treatments, soil properties, application-, and sampling dates.

was placed in the center of the lid. The mycelium-containing lid was carefully placed on top of the bottom compartment containing soil and sealed using Parafilm (Figure S1). Plates were incubated for 10 days at 10◦C. Petri dishes without soil and with gamma-radiated soil (untreated 2012) (>25 kGray, Isotron, Ede, the Netherlands) were used as controls for conditions without microbially produced volatiles. Before the start of the experiment the gamma-radiated soil was left for 4 days in a sterile flow cabinet to remove all residual volatiles. Mycelial biomass determination was done according to the method of Garbeva et al. (2014b) with some modifications. Briefly, Pythium mycelia were harvested by melting the colonized agar from the lids of the Petri-dishes in a beaker glass with water in a microwave oven (c. 100◦C), followed by sieving with a tea strainer and three washing steps with water (c. 90◦C) in order to remove agar residues. For measurements of dry biomass weight, mycelia were frozen at −20◦C and freeze-dried during 24 h. Pictures of Pythium hyphae were taken before harvest 1 cm from the edge of the plate with a stereo microscope (Olympus, SZX12, Tokyo, Japan) connected to a AxioCam MRC5 camera (Zeiss, Jena, Germany) under a 90x magnification.

#### Trapping and GC/MS Analysis of Microbial Volatiles

For collection and analysis of released volatiles from soil the method of Garbeva et al. (2014b) was used with some modifications. Soil from two plots per treatment was randomly selected for GC/MS analysis. Briefly, soil samples were plated in special designed glass petri dishes, with an exit to which a steel trap could be connected with as trapping material 150 mg Tenax TA and 150 mg Carbopack B (Markes International Ltd., Llantrisant, UK) which could fix VOCs released from the soil. VOCs were collected after 168 hours of incubation at 10◦C. Then, traps were removed, sealed and stored at 4 ◦C until further analysis. Volatiles were desorbed from the traps using an automated thermodesorption unit (model Unity, Markes International Ltd., Llantrisant, UK) at 200◦C for 12 min (He flow 30 ml/min). Each trap was heated for 3 min up to 270◦C to introduce the volatiles into the GC/MS (model Trace, ThermoFinnigan, Austin, TX, USA). Split ratio was set to 1:4, and the used column was a 30 × 0.32 mm ID RTX-5 Silms, film thickness 0.33µm (Restek, Bellefonte, PA, USA). The used temperature program was: from 40◦C to 95◦C at 3◦C min−1 , then to 165◦C at 2◦C min−1 , and to 250◦C at 15◦C min−1 . The VOCs were detected by the MS operating at 70 eV in EI mode. Mass spectra were acquired in full scan mode (33–300 AMU, 0.4 scans s−1 ). Compounds were identified by their mass spectra using deconvolution software (AMDIS) in combination with NIST 2008 (National Institute of Standards and Technology, USA,), Wiley 7th edition spectral libraries and by their linear retention indices (lri). The lri values were compared with those found in the NIST and the NIOO lri database. Candidate compounds possibly related to volatile inhibition of Pythium growth were identified by screening for volatiles that were absent in disinfested soils (AD and ADP) in 2011 and present 1 year later (2012) and in non-disinfested soils (U and P) 2011 and 2012.

#### Pyrosequencing of Soil Bacterial Communities

DNA was extracted directly after sampling from three randomly selected plots per treatment using Mobio 96 well Powersoil R extraction kit according to the manual. Amplicons for barcoded 16S pyrosequencing were generated using PCR reactions (5 min 95◦C followed by 25 cycles 95◦C 30 s, 53◦C 60 s, 72◦C 60 s + 1 s per cycle finishing with 10 min 72◦C and 10◦C soak) performed in triplicate for each sample using the primerset 515F (5′ -GTGCCAGCMGCCGCGGTAA-3′ ) and 806R (5′ - GGACTACVSGGGTATCTAAT-3′ ) (Caporaso et al., 2011). The 515F primer included the Roche 454-pyrosequencing adapter and a GT linker, while 806R included the Roche 454- sequencing adapter, a 12-bp barcode (unique to each sample), and a GG linker. PCR products were cleaned (Qiagen Pcr purification kit) pooled and were sequenced (Macrogen Inc. Company, South Korea) on a Roche 454 automated sequencer and GS FLX system using titanium chemistry (454 Life Sciences, Branford, CT, USA). The obtained 454 sequences were filtered and analyzed using QIIME (Caporaso et al., 2010) in the Galaxy interface. Sequences were denoised (DENOISER, Reeder and Knight, 2010) and chimeras were removed using UCHIME (Edgar et al., 2011) followed by trimming of low quality reads (<200 bp, quality score 20). The remaining high quality sequences were clustered into operational taxonomic units (OTU's) using UCLUST (Edgar, 2010) with a minimal sequence identity cut off of 97% using the most abundant unique sequence as cluster representative. Sequences were deposited in the European Nucleotide Archive under accession number PRJEB6155 (http://www.ebi.ac.uk/ena/ data/view/PRJEB6155).

#### Data Analysis

In the bioassay, mean disease indices per pot were converted to percentages. The assumption of normality was tested with Shapiro–Wilk statistics and Levene's test was used to confirm homogeneity of variances. An analysis of variances, a Three-Way ANOVA, was performed to test the effects of soil treatment, Pythium addition, organic matter level and their interactions on root weight or percentage root rot.

To test the effects of VOC produced in soil on Pythium biomass the average hyphal weight per Petri dish per soil treatment was determined in 2011 (n = 16) and 2012 (n = 8). Data were calculated as percentage of the growth of the control. Normality was tested with Shapiro-Wilk test and homogeneity of variances was assessed with Levene's test. A Two-Way ANOVA was performed to determine differences between soil treatments.

A Three-Way ANOVA was performed on the data from the pyrosequencing analysis in order to test the effects of peat addition, soil treatment and sampling year and their interactions on number of reads. Pyrosequencing data were rarefied to the lowest number of obtained reads, 2047 reads per sample. Per phylum all soil treatments in the two seasons were tested for a change in relative abundance, based on the number of reads per phylum. The average number of reads per phylum per soil treatment was calculated and tested with One-Way ANOVA. The average number of OTUs per treatment was used to express OTU richness. Although the OTU richness data did not meet the assumption of normality in the analysis these data were also analyzed with a One-Way ANOVA to determine differences between treatments. Statistical analyses were done in R3.0.2 and PAST (Hammer et al., 2001).

### Results

#### Impact of *Pythium* on Hyacinth Root Rot (Bioassays)

Management practices strongly affected root biomass and root rot severity. Addition of Pythium to the soils showed an overall effect of the pathogen: the root weight was significantly reduced in all soils in the consecutive years. However, the magnitude of the effect of Pythium on root biomass reduction was different depending on the management regime the soil had received (**Figures 1A,B**, **2C**). Pythium-induced root biomass reduction was most strong in recently (2011) anaerobic disinfested (AD) soils. This was also indicated by the significant interaction between soil disinfestation and Pythium addition in 2011 (**Table 2**). In contrast to 2011, the effect of Pythium addition to the soil in 2012 was independent of the former AD treatment and did not show differences between the differently managed

Average weight of roots extending from Hyacinth bulbs grown in differently managed soils (*U*, untreated; *P*, peat addition; *AD*, anaerobic disinfestation) with and without *Pythium* addition. (C,D) Average hyphal weight of *P. intermedium* hyphae that had been exposed to volatiles

intermedium; (F), Average hyphal weight of empty plates, gamma irradiated soil and the untreated soil in 2012. Significant results of main treatment effects and interactions are presented in Table 2, marked in bold. Error bars represent standard deviation.

FIGURE 2 | *Pythium* volatile exposure assays and Hyacinth bioassays. (A) Differences in *Pythium* hyphal density upon volatile exposure sterilized soil (control) and untreated and disinfested soil in 2011. (B) Detailed pictures taken from agar plates after volatile exposure. (C) Results of Hyacinth bioassays in soil from differently managed fields without (−) and with (+) *Pythium intermedium* addition. U, untreated; P, peat addition; AD, anaerobic disinfestation.

TABLE 2 | Analysis of variance for root biomass and disease indexes of hyacinth bulbs in the soil bioassays and hyphal biomass in the volatile exposure assays.

*Bold numbers indicate significant main effects and significant interactions. Df, degrees of freedom; F, F-value; p, p-value. This table supplies the results of statistical analysis of the data shown in Figure 1.*

soils (**Figure 1B**). In all pasteurized soil samples, inoculation with Pythium resulted in a severe loss of root biomass, average root weight was reduced by >60% (**Figure 1E**).

Root biomass in the bioassays was not only affected by Pythium but also by the different management practices as became apparent from the control bioassays, i.e., the pots without Pythium addition. Both peat amendment and soil disinfestation significantly affected root weight in both years of sampling (see **Figures 1A,B**, **2C**; **Table 2**). Addition of organic matter significantly increased the root weight in both years; the root biomass was significantly higher in peat-amended soils (2011, 21% for P and 43% for ADP; 2012 19% for P and 16% for ADP) than in the comparable soils without peat addition. Soils that had received a recent AD treatment had a significantly reduced root weight, 39% (AD) and 14% (ADP), as compared to the untreated (U) and peat-amended soil (P) respectively. One year later this effect was reversed. In 2012, plants in formerly anaerobic disinfested soils had a higher root biomass, 10% and 7% more, compared to the non-disinfested soils with the same organic matter level. Similar to the effects of Pythium on root weight, anaerobic disinfestation enhanced the effects of Pythium on root rot symptoms in 2011 (Figure S2A; **Table 2**). This interaction was no longer apparent in 2012 (Figure S2B; **Table 2**). Even without addition of Pythium, an increase of root rot symptoms was found for recently disinfested soils (Figure S2A; **Table 2**). Peat addition reduced the severity of rootrot symptoms significantly (Figure S2B, **Table 2**). The infective ability of the applied Pythium inoculum was confirmed, as a strong increase of root rot symptoms was seen in pasteurized soil (**Figure 1E**).

#### Emission of *Pythium*-Inhibiting Volatiles by Soils and Soil Microbes

Exposure of Pythium to volatiles released from the soils resulted in a strong reduction of Pythium biomass production (**Figures 1C,D**, **2A,B**). There were, however, differences between treatments and sampling years. Compared to the empty plate control, both the untreated soil and soil with peat amendment gave a 3-fold reduction in mycelial biomass in 2011 (p < 0.0001) (**Figures 1C,D**; **Table 2**). In contrast, exposure of Pythium to volatiles released from the anaerobic disinfested soils did not (AD) or only slightly (ADP) result in reduction of Pythium biomass or hyphal density (**Figures 1C,D**, **2A,B**; **Table 2**). In 2012, this lack of volatile suppression in disinfested soils was no longer apparent as volatiles from all soils significantly reduced Pythium growth by at least 50% compared to soils (**Figures 1D**, **2A,B**). The impact of soil-derived volatiles on Pythium growth was not significantly affected by peat addition. Volatile-suppression of Pythium growth was not seen when exposed to gamma-radiated soils (**Figure 1F**), indicating that no growth-reducing volatiles were produced in soil without microflora.

#### Trapping and GC/MS Analysis of Bacterial Volatiles

GC/MS analysis identified >700 different volatile compounds that were released from the soil of which 15 compounds were found to be absent in the anaerobic disinfested soil in 2011 (**Table 3**), mostly ketones. Some of these compounds, namely 2-octanone, 2-undecanone and 2-nonanone, are known to be inhibitors of eukaryotic pathogenic soil organisms (**Table 3**). Besides ketones the 15 potential suppressive compounds included glycol ethers, alkanes, a fatty acid and two yet unidentified compounds with retention indices of 1692 and 1743. One year later, the 15 volatile compounds were again released by the previously disinfested soils.

#### Pyrosequencing of Soil Bacterial Communities

Four hundred fifty four Pyrosequencing identified over 3000 different OTUs from 31 bacterial and 2 archaeal phyla (**Figure 3A**). There was no soil treatment effect on the number of reads obtained per sample, average numbers of reads were not different between soil treatments or sampling years (**Figure 3B**). In 2011, anaerobic soil disinfestation had resulted in reduction of OTU richness. At higher organic matter level the reduction was significantly less (**Figure 3A**). In 2012 no differences in OTU richness were seen between soil treatments. Most abundant TABLE 3 | Volatile organic compounds of which the production appeared to be negatively affected by the anaerobic disinfestation treatment in 2011.

*Bold compounds have been previously identified as potential fungus suppressing compounds. Chemical names are according IUPAC.*

\**Unknown 1692a Lri: 1692; EI: 88 (100), 121 (75), 174 (10).*

\*\**Unknown 1743a Lri: 1743; EI: 104(100), 78(11), 208(3).*

*aZou et al. (2007); inhibiting concentration not given.*

*bChen et al. (2008); inhibiting concentrations not given.*

*cWeisskopf (2013), inhibiting concentrations not given. dFernando et al. (2005), inhibiting concentration not given.*

(36–63%) in all samples were OTUs assigned to Proteobacteria. Relative abundances of classes within the Proteobacteria, i.e., Alpha- Beta- Gamma- and Deltaproteobacteria, did not change significantly between different soil treatments (Figure S3). In 2011, six phyla showed significant differences between plots with and without anaerobic soil disinfestation (**Figures 3C–H**). At the start of the bulb planting season for spring flowering bulbs anaerobic soil disinfestation had still clear effects on the bacterial community composition. Relative abundances of OTUs assigned to Acidobacteria, Chloroflexi, Nitrospirae, Chlorobi, and Planctomycetes were significantly lower in the disinfested soils, whereas relative abundance of OTUs assigned to Bacteroidetes was higher compared to untreated soils. In 2012, 15 months after the disinfestation treatment, the relative abundance of these phyla was restored to the same levels as occurred in untreated soils for Acidobacteria, Chlorobi and Planctomycetes (p < 0.05), with the same tendency for Chloroflexi and Nitrospirae (p < 0.1) (**Figure 3**, Table S1).

#### Discussion

Volatile organic compounds form an important part of the underground chemical communication network between plants, fungi and bacteria (Ryu et al., 2003; Vespermann et al., 2007; Insam and Seewald, 2010; Effmert et al., 2012; Bitas et al., 2013; Fiers et al., 2013). They can have different roles in the soil including plant growth promotion and signaling (Vespermann

et al., 2007). There are also indications that VOCs produced by soil micro-organisms can have an important contribution to the restriction of growth and germination of pathogenic fungi (fungistasis) that occurs in most soils (Garbeva et al., 2011). However, despite the ability of several soil microbial VOCs to reduce pathogenic growth; little attention has been paid to the role of these VOCs in suppression of plant diseases caused by soil-borne pathogens.

Our results provide an indication about the involvement of VOCs in natural disease suppression of a soil-borne pathogen. Our study revealed interesting co-incidences of severe Pythiuminduced root weight loss, absence of production of Pythium suppressing soil volatiles and shifts in bacterial community composition shortly after an anaerobic disinfestation treatment. One year later these effects of the disinfestation treatment had largely disappeared. The coinciding dynamics of root biomass and production of suppressing volatiles suggests that microbial volatiles can have an important contribution in the natural control of Pythium intermedium. Furthermore, our results point at the importance of microbial community composition as disinfestation-induced shifts in community composition which coincided with the loss in suppressiveness by volatiles.

Induced changes in microbial community composition can yield important information on the functioning of the original soil microbial communities (Griffiths and Philippot, 2013). Management practices can alter the abundance of microbial groups that are thought to be involved in disease suppression (Garbeva et al., 2004; Mazzola, 2004; Chaparro et al., 2012; Sipilä et al., 2012). In the current investigation we observed that anaerobic soil disinfestation had a dramatic effect on soil microbial diversity and community composition. This is not surprising as the microbes in the well-drained sandy soils were confronted with a long period of oxygen-depletion resulting in a shift from aerobic metabolism to predominant anaerobic metabolism. The impact of this period of anaerobiosis was still clearly visible in the bacterial community composition at the time that flower bulbs are usually planted i.e., 3 months after the soil disinfestation treatment had been ended (removal of cover plastic). Most striking was the high abundance of Bactereroidetes, a phylum that has been shown to strongly respond to fluctuating redox conditions (DeAngelis et al., 2010).

In contrast, the phyla Acidobacteria, Planctomycetes, Nitrospirae, Chloroflexi, and Chlorobi decreased significantly after disinfestation. So far, these groups have rarely been studied in the context of disease suppression, due to limitations to obtain cultivable representatives. Strongest reduction in relative abundance of OTUs after soil disinfestation was seen for Acidobacteria. A recent study showed that Acidobacteria were one of the groups that were most sensitive to a strongly disturbing soil treatment (fumigation) (Domínguez-Mendoza et al., 2014). Earlier studies have shown that changes in land use, fertilization and management caused shifts in relative abundance of Acidobacteria and its different subgroups (Jones et al., 2009; Barnard et al., 2013; Navarrete et al., 2013). However, the impact of such shifts in abundance of Acidobacteria on disease suppressiveness has not been examined. Yet, next to our results a study by Hunter et al. (2006) provides indications for a possible role of Acidobacteria in disease suppressiveness. In that study it was observed that Acidobacteria were present in peat suppressive to P. sylvaticum whereas they were absent in peats that were conducive for P. sylvaticum damping-off. No other documentation is available on antagonistic roles of Acidobacteria, nor on production of antimicrobial VOCs. Our data suggest a potential role in the production of suppressing volatiles by Acidobacteria. However, the actual role of Acidobacteria in volatile production and disease suppression, as well as that of the other phyla that showed similar dynamics upon disinfestations remains to be established.

Remarkably, the main classes of Proteobacteria, which contain many known potential biocontrol bacteria (Weller et al., 2002; Haas and Defago, 2005) did not change significantly as a result of the disinfestation treatment (Figure S3). However, this does not necessarily imply that Proteobacteria did not contribute to Pythium disease suppression. Mendes et al. (2011) compared a soil suppressive against the plant pathogen Rhizoctonia solani with another, similar soil that was conducive for disease caused by this fungus and found similar abundances for all classes of Proteobacteria in both soils. However, at species level, e.g., within the genus Pseudomonas, differences were observed with higher abundance of antibiotic-producing species in the suppressive soil.

Comparison of the volatiles produced by differently treated soils revealed potential Pythium-inhibiting compounds, mostly methyl ketones. These VOCs were present in the untreated and peat-amended soils that exhibited a high level of natural suppression against Pythium, but absent in the recently disinfested soils that were susceptible to infection of bulbs by Pythium. Among these VOCs, there were compounds like 2 octanone, 2-nonanone and 2-undecanone that were previously found to be suppressive against soil fungi and nematodes (Chen et al., 1988; Alström, 2001; Wheatley, 2002; Gu et al., 2007; Kai et al., 2007; Effmert et al., 2012). Thus these findings support the potential suppressive role of the VOCs identified in this study. However, the antimicrobial role of the other potential suppressive compounds identified in this study remains to be assessed. Besides the compounds highlighted in this study (**Table 3**) it is possible as well that a mixture of different compounds (Tunc et al., 2007; Veras et al., 2012), compounds that were not detected with the chosen method and/or concentration dependent effects (Wheatley, 2002) are responsible for the suppression against Pythium. The biological and ecological relevance of concentration effects and volatile mixture compositions remains to be studied for the natural soil habitat.

Anaerobic soil disinfestation is applied to kill a broad range of pathogens (Blok et al., 2000). The demonstrated reduction of disease suppression shows that such a drastic treatment of the soil has the risk of a (partial) elimination of the natural suppressive microflora. After one growing season, 15 months after application, disease suppression against Pythium was restored to the level of the non-disinfested plots. Similar loss of suppressing activity of the indigenous microflora has been found with pathogen-eliminating measures like flooding (8 weeks) and chemical soil disinfestation with cis-dichloropropene or methylisothiocyanate (Van Os et al., 1999). Postma et al. (2000) observed enhanced Pythium outbreaks in cucumber grown on rockwool after sterilization of the rockwool and recolonisation by a microbial community which lacked the suppressive properties of the original community. Hence, (temporal) changes in the suppressive community, by reducing the competition pressure or elimination of useful microbes, can enhance disease outbreaks of opportunistic pathogens such as Pythium (Van Os et al., 1999; Van Os and van Ginkel, 2001). Our study included two consecutive years of bulb planting to determine the longer term effect of the management treatments on Pythium suppression. Fifteen months after the disinfestation treatment, the bacterial community composition resembled the composition of the non-disinfested soils. This is in agreement with the results of Mowlick et al. (2013) who found a restoration of the original microbial community composition in the biological disinfestation treatment after plant growth. Anaerobic disinfestation had an impact on the taxonomic composition of the soil microbial community but also on an important function, namely disease suppression. In our study, after the cultivation of the summer crop Gladiolus the suppression of Pythium in both volatile assay and bioassay also returned to the level of that in non-disinfested soils. This recovery of the natural suppression against Pythium indicates resilience of the soil to re-establish this essential ecosystem function after a strong disturbance (Griffiths and Philippot, 2013).

In the year of application (2011), the anaerobic soil disinfestation had a negative effect on Hyacinth root development even without addition of Pythium. This may be the result of phytotoxic effects of compounds that have been produced during anaerobic decomposition and were still present. It is known that decomposition of crop residues during the period of oxygen depletion can produce phytotoxic compounds (Bonanomi et al., 2007b). Since this reduced root growth was only significant in the disinfested soil without peat addition, the increased organic matter levels in peat-amended soils may have absorbed possible phytotoxic compounds.

Organic amendments do influence soil physical-chemical properties as well as soil microbial activity and composition (Hoper and Alabouvette, 1996; Bonanomi et al., 2007a, 2010). Therefore, we expected to find an effect of peat addition on volatile suppression. The addition of peat increased root weight and reduced root rot symptoms in 2012 as compared to the untreated soil. However, the volatile suppression of peatamended soils was not different from that of the unamended soils. This is in line with the microbial community composition which was not strongly affected by peat amendment but does imply that other mechanisms of disease suppression, besides volatiles, contribute to disease suppression after peat addition. It is clear that organic amendments and disease control measures can have long-term effects on both the soil microflora and on disease suppression, although the effects of the amendments might depend on the nature and maturity of the organic additions (Hoitink and Boehm, 1999; Termorshuizen et al., 2006). In

#### References

order to get more understanding of time-related changes it is necessary to monitor these soil characteristics during longer periods of time. Since flower bulb production, and more general arable agriculture, are not only seriously threatened by Pythium, but also by several other soil-borne fungi and nematodes, is would be recommendable to extend these studies to include also other pathogens like Rhizoctonia solani, Pratylenchus penetrans or Meloidogyne hapla. In conclusion, our study indicates that the production of suppressing volatiles by soil microbes may be an important factor in the natural suppression of root-infection by Pythium. More general, this indicates that microbial volatiles may be an essential part of the natural buffering of soils against soilborne diseases, the so-called general disease suppression. This would open new perspectives and insights for the control of soilborne pathogens. Volatile-inhibition tests as well as the presence of certain VOCs and microbial groups could be an indicator of the susceptibility of a given soil to soil-borne pathogens. Obviously, more research is needed to find support for this. In depth studies are needed to further assess the role of volatiles in disease suppression and should also consider the dynamics of production of VOCs in soils, as well as the conditions that affect the sensitivity of the pathogens to VOCs.

#### Acknowledgments

We are grateful to Mattias de Hollander for assistance with the bioinformatics. Furthermore, we would like to thank Marjan de Boer for her role in setting up the field experiment the soils originated from. Thanks to Suzanne Breeuwsma and Jan van de Bent for technical assistance. Sequences were deposited in the European Nucleotide Archive under accession number PRJEB6155 (http://www.ebi.ac.uk/ena/data/ view/PRJEB6155). The volatile data have been archived in the Data and Information Portal of the NIOO-KNAW http://data. nioo.knaw.nl/index.php and is available on request. This research is supported by the Dutch Technology Foundation STW (Project 10716), which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs. This is publication 5887 of the Netherlands Institute of Ecology.

#### Supplementary Material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2015.00701

management of soybean in Amazon forest soils. FEMS Microbiol. Ecol. 83, 607–621. doi: 10.1111/1574-6941.12018

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 van Agtmaal, van Os, Hol, Hundscheid, Runia, Hordijk and de Boer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A fragrant neighborhood: volatile mediated bacterial interactions in soil

Kristin Schulz-Bohm1 \*, Hans Zweers 1 , Wietse de Boer 1, 2 and Paolina Garbeva1

1 Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, Netherlands, 2 Department of Soil Quality, Wageningen University, Wageningen, Netherlands

There is increasing evidence that volatile organic compounds (VOCs) play essential roles in communication and competition between soil microorganisms. Here we assessed volatile-mediated interactions of a synthetic microbial community in a model system that mimics the natural conditions in the heterogeneous soil environment along the rhizosphere. Phylogenetic different soil bacterial isolates (Burkholderia sp., Dyella sp., Janthinobacterium sp., Pseudomonas sp., and Paenibacillus sp.) were inoculated as mixtures or monoculture in organic-poor, sandy soil containing artificial root exudates (ARE) and the volatile profile and growth were analyzed. Additionally, a two-compartment system was used to test if volatiles produced by inter-specific interactions in the rhizosphere can stimulate the activity of starving bacteria in the surrounding, nutrient-depleted soil. The obtained results revealed that both microbial interactions and shifts in microbial community composition had a strong effect on the volatile emission. Interestingly, the presence of a slow-growing, low abundant Paenibacillus strain significantly affected the volatile production by the other abundant members of the bacterial community as well as the growth of the interacting strains. Furthermore, volatiles released by mixtures of root-exudates consuming bacteria stimulated the activity and growth of starved bacteria. Besides growth stimulation, also an inhibition in growth was observed for starving bacteria exposed to microbial volatiles. The current work suggests that volatiles produced during microbial interactions in the rhizosphere have a significant long distance effect on microorganisms in the surrounding, nutrient-depleted soil.

Keywords: volatiles, inter-specific interactions, rhizosphere, synthetic microbial communities, low-abundant bacteria, soil microcosms

## INTRODUCTION

Microorganisms produce a great variety of secondary metabolites including antibiotics, toxins, pigments, and others. Interestingly, small molecular mass metabolites such as volatile organic compounds (VOCs) were for a long time overlooked. Research of the last decades demonstrated that bacteria produce a large set of VOCs (Kai et al., 2009; Insam and Seewald, 2010; Effmert et al., 2012; Bitas et al., 2013; Peñuelas et al., 2014). However, the knowledge about the biological and ecological function of those volatiles is still limited. Similar to well-studied plant VOCs, it can be assumed that VOCs released by bacteria perform diverse and crucial functions (Bitas et al., 2013). Recent studies revealed that bacterial volatiles can inhibit the growth of fungi or bacteria (Wrigley, 2004; Kai et al., 2007; Vespermann et al., 2007; Zou et al., 2007; Weise et al., 2012; Garbeva et al., 2014b) and in some cases they can even function as growth-promoting agent (Wheatley, 2002; Horii and Ishii, 2006; Garbeva et al., 2014a). Additionally, volatiles emitted by bacteria can influence the

#### *Edited by:*

Martin Heil, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico

#### *Reviewed by:*

Aurélien Bailly, University of Zurich, Switzerland Birgit Piechulla, University of Rostock, Germany

#### *\*Correspondence:* Kristin Schulz-Bohm

<EMAIL_ADDRESS>
#### *Specialty section:*

This article was submitted to Plant Biotic Interactions, a section of the journal Frontiers in Microbiology

*Received:* 26 August 2015 *Accepted:* 19 October 2015 *Published:* 03 November 2015

#### *Citation:*

Schulz-Bohm K, Zweers H, de Boer W and Garbeva P (2015) A fragrant neighborhood: volatile mediated bacterial interactions in soil. Front. Microbiol. 6:1212. doi: 10.3389/fmicb.2015.01212 metabolism of other surrounding bacteria (Kai et al., 2009; Garbeva et al., 2014a). Most studies, however, are performed in vitro on semi-solid media using nutrient rich conditions and may not represent the natural conditions in the microbial environment. Culture conditions including nutrient availability (Kai et al., 2009; Insam and Seewald, 2010) and the type of incubation medium substantially affect the spectrum of released VOCs (Weise et al., 2012). Thus, bacteria produce a different set of volatiles when incubated in soil as compared to incubations on agar plates (Garbeva et al., 2014b).

Soil is a complex, nutrient-poor and highly heterogeneous environment consisting of both water- and air-filled pores (Young et al., 2008). Due to their physical properties such as low molecular weight (<300 D), lipophilicity, high vapor pressure and low boiling points (Effmert et al., 2012; Bitas et al., 2013; Lemfack et al., 2014), VOCs can diffuse through water- and gasfilled pores in soil and rhizosphere environments. Therefore, VOCs can act over a wider range of scale and may play essential roles in the communication and competition between physically separated microorganisms in soil (Kai et al., 2009; Effmert et al., 2012; Garbeva et al., 2014a). Soil microbes occur generally in multi-species communities. However, our current knowledge on the ecological role of microbial volatiles is based mostly on studies focusing on volatiles released by (non-interacting) monocultures. During inter-specific interactions, the production of various secondary metabolites can be triggered (e.g., Garbeva and De Boer, 2009; Traxler et al., 2013) and these secondary metabolites can act differently as compared to metabolites released by species in monoculture (Garbeva et al., 2014a; Tyc et al., 2014).

In the present study, we aim to reveal new insights into the ecological role of VOCs in microbial interactions by assessing multi-species interactions of a synthetic microbial community in a soil model system that reflects the natural conditions in the heterogeneous soil environment along the rhizosphere. The main research questions to address were: (1) what is the role of microbial interactions and shifts in microbial community composition on volatile emission in the rhizosphere and (2) can volatiles produced during bacterial interactions in the rhizosphere stimulate the activity of starving soil microbes in the surrounding environment?

Overall, our results demonstrated that both microbial interactions and shifts in microbial community composition had a significant effect on the volatile emission and that the presence of slow growing, non-abundant bacterial species influenced the volatile production of the bacterial community. Furthermore, our results revealed that volatiles released by microbial interactions in the rhizosphere have a long distance effect on the surrounding non-active microbial community in the nutrient-depleted soil.

#### MATERIALS AND METHODS

#### Bacterial Model Strains and Growth Media

Bacterial strains (**Table 1**) were previously isolated from the rhizosphere of sand sedge (Carex arenaria) in different sandy dune soil sites (De Ridder-Duine et al., 2005) and characterized by 16S rRNA gene sequencing (Tyc et al., 2014). All bacterial isolates were pre-cultured from frozen glycerol stocks on 0.1 strength tryptone soya broth (TSB) agar (De Boer et al., 2007). Overnight cultures in 0.1 TSB medium were prepared prior to each microcosm experiment.

#### Rhizospheric Soil Microcosms

Sandy soil of low carbon-content (Figure S1) and low amount of mineral nitrogen (0.2 mg/kg nitrite and nitrate) and phosphate (1.1 mg/kg) was collected from an old river dune site near the village Bergharen (51◦ 10′N, 05◦ 40′E) in the Netherlands. The soil was dried, sieved (ø 2 mm), and gamma-sterilized by Synergy Health Ede B.V. (Netherlands). Before each microcosm experiment, the gamma-sterilized soil was acclimatized for 5 days under sterile conditions. Artificial Root Exudates (ARE) stock solution (7.5 mg carbon per ml, C/N 20.6) containing 18.4 mM glucose, 18.4 mM fructose, 9.2 mM sucrose, 9.2 mM citric acid, 4.6 mM fumaric acid, 20.5 mM lactic acid, 6.9 mM malic acid, 13.8 mM succinic acid, 2.0 mM cysteine, 6.1 mM Lalanine, 2.0 mM L-argenine, 3.6 mM L-glutamic acid, 6.1 mM Lserine, and 10 mM KH2PO4 was freshly prepared or stored at −20◦C. The composition of the ARE stock solution was based on previously published ARE-mixes (Griffiths et al., 1999; Baudoin et al., 2003; Garbeva et al., 2014b) and adapted to root-exudate composition found for the sedge Schoenus unispiculatus (Shane et al., 2006) belonging to the same family Cyperaceae as C. arenaria. Liquid inoculums consisting of bacteria (108 cells ml−1 per strain), ARE stock solution (pH 6.5), and phosphate buffer (10 mM KH2PO4, pH 6.5) were prepared. The inoculums were thoroughly mixed with the soil to establish rhizospheric soil microcosms containing 136µg carbon per g soil and 1 × 105 CFU per g soil for each strain. The initial carbon concentration in the soil microcosms was adjusted to estimated daily carbon inputs by roots of 100–1500µg carbon per g rhizosphere soil (Trofymow et al., 1987; Cheng et al., 1996). Control microcosms consisted of sterile soil, phosphate buffer and ARE stock solution without bacterial inoculation. The moisture content was 5.7% (w/w). Microcosms were incubated at 20◦C.

### Bioassay for the Selection of Bacterial Strains

Three soil microcosms for each bacterial strain were set up in 25 ml gas tight glass bottles as described above. The head space in the bottles was filled with atmospheric gas. All microcosms were incubated for 7 days at 20◦C and the bottles were flushed with fresh air every 2 days. Gas samples were taken before and after flushing with fresh air and the CO2 production was measured with an Ultra GC gas chromatograph (Interscience, The Netherlands) equipped with a flame ionization detector (FID) and a Rt-QBond (30 m, 0.32 mm, ID; Restek, USA) capillary column. Carrier gas was helium at a flow rate of 5 ml min−1 . Injector, oven and detector temperatures were 150, 50, and 350◦C, respectively. CO2 concentrations in the headspace were calculated based on external gas standards (Westfalen AG, Germany) and the ideal gas law.

Soil samples were regularly taken to monitor the bacterial growth. For this 1 g of soil was mixed with 9 ml phosphate buffer

#### TABLE 1 | Bacterial strains used in this study.

All strains are from organic poor, sandy dune soils in the Netherlands.

(pH 6.5) in 20 ml Greiner tubes. The tubes were shaken on a rotary shaker at 180 rpm for 30 min at 20◦C and serial dilutions were plated on 0.1 TSB agar. All plates were incubated at 20◦C and colony forming units (CFU) were counted after 3 days.

### Collection of Bacterial Volatiles and Monitoring of Bacterial Growth in Rhizospheric Soil Microcosms

For the collection of bacterial VOCs, rhizospheric soil microcosms were established as indicated above in glass Petri-dishes with an exit at the top to which a steel trap filled with 150 mg Tenax TA and 150 mg Carbopack B (Markes International Ltd., Llantrisant, UK) could be fixed. In the rhizospheric soil microcosms, the bacterial strains Burkholderia sp. AD024, Dyella sp. AD056, Janthinobacterium sp. AD080, Pseudomonas sp. AD021, and Paenibacillus sp. AD087 were either incubated as monocultures or as mixtures of four (without Paenibacillus sp. AD087) or five strains. The growth of the strains was monitored by quantification of bacterial 16S rRNA genes from extracted DNA of soil samples (three per microcosm) taken at the beginning of the experiment and after 96 h incubation. Soil samples were stored at −80◦C until nucleic acid extraction (see below). VOCs produced by the monocultures, bacterial mixtures and control (see above) were trapped for 24 h after 3 days of incubation. Traps were removed, capped and stored at 4◦C until analysis.

#### VOCs Analysis

VOCs were desorbed from the traps using an automated thermodesorption unit (model UnityTD-100, Markes International Ltd., UK) at 210◦C for 12 min (He flow 50 ml/min). The desorbed VOCs were subsequently collected on a cold trap at −10◦C and introduced into the GC-QTOF (model Agilent 7890B GC and the Agilent 7200A QTOF, USA) by heating the cold trap for 3 min to 280◦C. Split ratio was set to 1:201, and the column used was a 30 × 0.25 mm ID RXI-5MS with as film thickness of 0.25µm (Restek 13424-6850, Bellefonte, PA, USA). The temperature program was as follows: 2 min at 39◦C, 3,5◦C/min to 95◦C, 6◦C/min to 165◦C, 15◦C/min to 250◦C and finally 40◦C/min to 300◦C which was hold for 20 min. VOCs were detected by the MS operating at 70 eV in EI mode. Mass spectra were acquired in full scan mode (30–400 AMU, 4 spectras/s). GC/MS data were collected and converted to mzData file using the Chemstation B.06.00 (Agilent Technologies, USA). Data were further processed with MZmine 2.14.2 (Pluskal et al., 2010) with the tools mass detection (centroid mode, noise level = 1000), chromatogram builder (Min time span = 0.04 min, Min height = 1.8E04–2.5E04, m/z tolerance of 1 m/z or 5 ppm), and chromatogram deconvolution (local minimum search, chromatographic threshold = 40%, Min in RT range = 0.1 min, Min relative height = 2.0%, Min absolute height = 1.5E04, Min ratio of peak top/edge = 2, peak duration = 0.0–0.7 min). Detected and deconvoluted peaks were identified by their mass spectra using NIST MS Search and NIST 2014 (National Institute of Standards and Technology, USA,http:// www.nist.gov) and aligned by RANSAC aligner (mz tolerance = 1 m/z or 5 ppm, RT tolerance = 0.1, RT tolerance after correction = 0.05, RANSAC iteration = 10000, Min number of points =60%, threshold value = 0.1). Processed data were exported for further statistical analysis (see below). The identification of detected compounds was further evaluated by using the software AMDIS 2.72. The retention indexes were calculated for each compound and compared with those found in NIST 2014 and in house databases. Detected compounds were named as identified compounds if the Match and Reverse-Match factor of the listed compound in the NIST and in house NIOO-library was higher than 800, the spectra of the detected compound matches the one of the listed compound, and the difference between the retention index calculated for the detected compound and of the listed compound (for a semi-standard non-polar column) was not bigger than four. The Match and Reverse match factor refer to the similarity of the mass spectrum of the sample with the library spectrum of the Hit (Watson and Sparkman, 2013). Some identified compounds were also verified by co-injection of pure compounds (**Table 2**).

#### Bioassay to Test the Effect of Microbial VOCs on Nutrient-limited Bacteria in Soil

Soil microcosms containing the bacterial strains Burkholderia sp. AD024, Dyella sp. AD056, Janthinobacterium sp. AD080, TABLE 2 | Volatile organic compounds produced by a bacterial mixture of five strains (*Burkholderia sp.* AD024, *Dyella sp.* AD056, *Janthinobacterium sp.* AD080, *Pseudomonas sp.* AD021, and *Paenibacillus sp.* AD087), referred to as 5-Mix, or four strains excluding *Paenibacillus sp.* AD087, referred to as 4-Mix.

(Continued)

Bacteria were incubated for 4 days in soil supplied by ARE.

aVolatiles also detected for monocultures of Burkholderia sp. AD024;

bVolatiles also detected for monocultures of Dyella sp. AD056;

cVolatiles also detected for monocultures of Janthinobacterium sp. AD080;

dVolatiles also detected for monocultures of Paenibacillus sp. AD087;

eVolatiles also detected for monocultures of Pseudomonas sp. AD021;

RI Linear retention Index of a 30 × 0.25 mm ID RXI-5MS column;

n.s., not specified;

\* Verified by co-injection of pure compound

Pseudomonas sp. AD021, and Paenibacillus sp. AD087 were set-up in two-compartment Petri-dishes (**Figure 2** and Figure S3). Per Petri-dish, one nutrient-limited compartment was established containing 20 g gamma-sterilized soil mixed with 1 × 105 CFU per g soil of each strain and phosphate buffer (10 mM KH2PO4, pH 6.5). The second compartment was either filled with a mix of 20 g soil, phosphate buffer and ARE stock solution (138µg carbon per g soil), referred to as control, or a mix of 20 g soil, the five bacterial strains (1 × 105 CFU per g soil of each strain), phosphate buffer and ARE stock solution, referred to as Treatment 1 or Treatment 2 (**Figure 3A** and Figure S3A). In case of Treatment 2, bacteria in the nutrient-limited compartment were pre-incubated for 2 days before the bacteria-ARE-soil-mix was added to the second compartment. It can be assumed that during the pre-incubation available carbon sources in the soil should be fully consumed so that bacteria were strongly nutrient limited before they were exposed to microbial volatiles of the second compartment. The composition of the ARE containing compartment is equivalent to the rhizospheric microcosms described above. The moisture content of all compartments was 5.8% (w/w). All samples were set-up in triplicates and incubated at 20◦C for 6 days. To monitor bacterial activity and potential growth, about 1.5 g soil were taken two times per compartment at the start of the experiment and after 2, 4, and 6 days of incubation. For nucleic acid extraction, 0.5 g of the collected soil was immediately treated with a salmon sperm DNA solution and freeze-dried at −80◦C. Leftover soil collected was stored as backup at −80◦C.

#### Nucleic Acid Extraction

A solution (∼pH 8) of 10 mg per ml low molecular weight salmon sperm DNA (Sigma-Aldrich, The Netherlands) was prepared with DNase- and RNase-free water (Qiagen, The Netherlands). According to Paulin et al. (2013), 0.5 g of soil per sample was mixed with 0.5 ml of salmon sperm DNA solution in Lysing Matrix E tubes (MP Biomedicals, The Netherlands) and freezedried overnight at −80◦C. DNA and RNA were co-extracted using the modified protocol described by Griffiths et al. (2000). Briefly, 500µl of cetyl-trimethyl ammonium bromide (CTAB) buffer (10% CTAB in 0.7 M NaCl mixed with 1 volume 240 mM phosphate buffer pH 8.0), 50µl of 2% N-lauroyl sarcosine, 50µl of 2% sodium dodecyl sulfate, and 400µl of phenol-chloroformisoamyl alcohol (25:24:1, Sigma-Aldrich, The Netherlands) were added to the freeze-dried soil. A subsequent bead beating lysis for 30 s at 5.5 ms−1 was performed three-times followed by 7 min centrifugation at 14,000 rpm and 4◦C. The resulting aqueous supernatant was mixed with 1 volume (vol.) of chloroformisoamyl alcohol (24:1, Sigma-Aldrich, The Netherlands) and centrifuged for 5 min at 14,000 rpm and 4◦C. After a second phenol extraction with 1 vol. chloroform-isoamyl alcohol the aqueous supernatant was thoroughly mixed with 2 vol. of 20% polyethylene glycol 6000 (AppliChem, Germany) in 1.6 M NaCl, 0.1 vol of 100 mM MgCl2, and 1.5µl glycogen (20 mg ml−1 ; Thermo Fisher Scientific, The Netherlands). Nucleic acids were precipitated for 2 h at 4◦C, followed by 40 min centrifugation at 14,000 rpm and 4◦C. The pellet was washed with 70% ethanol, air-dried and eluted in 37–65µl DNase- and RNase-free water. Nucleic acid extracts were stored at −80◦C or subsequently used for quantification of bacterial 16S rRNA genes (see below).

### DNase Treatment and Reverse Transcription

Nucleic acid extracts were treated according to the manufacturer's protocol wi th 1–2 U DNase I (Thermo Fisher Scientific, The Netherlands) and incubated for 45 min at 37◦C. RNA was purified by an overnight precipitation at −20◦C with 0.1 vol. ammonium acetate (2.5 mM, Sigma-Aldrich), 2.5 vol. 100% ethanol, and 1.3µl glycogen, followed by centrifugation for 40 min at 13,000 rpm and 4◦C. The RNA pellet was washed twice with 70% ethanol and eluted in 27µl DNase- and RNasefree water. RNA was stored at −80◦C or subsequently used for reverse transcription with SuperScript R III First-Strand Synthesis System (Invitrogen, The Netherlands). The reverse transcription was performed according to the manufacturer's instruction. Resulting cDNA was precipitated overnight with 0.1 vol. 2 M NaCl, 2.5 vol. 100% ethanol, and 0.7µl glycogen at −20◦C, followed by 40 min centrifugation at 13,000 rpm and 4 ◦C. After washing with 70% ethanol, the cDNA pellet was eluted in 25µl DNase- and RNase-free water. cDNA was stored at −20◦C or immediately used for the quantification of bacterial 16S rRNA.

## Quantitative PCR (qPCR) of Bacterial 16S rRNA Genes and Transcripts

All qPCRs were performed with a Rotor-Gene Q cycler (Qiagen, The Netherlands) whereas each template DNA or cDNA was quantified in triplicates. The 20µl reaction mixture consisted of 1-fold SensiFAST™ SYBR R No-ROX Kit (Bioline GmbH, The Netherlands), BSA (0.5µg µl −1 ), 375–500 nM forward and reverse primers (Table S1), and 5µl of diluted template DNA or cDNA of 2–6 ng µl −1 . Negative controls consisting of DNaseand RNase-free water instead of template DNA were included in every qPCR run. Conditions for the primer sets targeting 16S rRNA gene of Burkholderia, Dyella, and Paenibacillus (Table S1) were as follows: 5 min initial denaturation at 95◦C, 37 cycles of denaturation for 30 s at 95◦C, annealing for 30 s at 64◦C, elongation for 30 s at 72◦C, and fluorescence signal

detection for 15 s at 82◦C. The 37th PCR cycle was followed by a melting curve analysis from 64 to 95◦C with increments of 1 ◦C. QPCR conditions for the primer sets targeting 16S rRNA gene of Janthinobacterium and Pseudomonas were the following: 5 min at 95◦C, ensued by 37 cycles of denaturation for 30 s at 95◦C, annealing for 20 s at 62◦C, elongation for 20 s at 72◦C, fluorescence signal detection for 15 s at 82◦C, and a melting curve analysis from 62◦C to 95◦C immediately after the last qPCR cycle. Agarose gel electrophoresis of qPCR products displayed single bands of expected size (Table S1). Gene copy numbers were calculated according to a standard curve which was set up by serially diluting M13uni/rev PCR products of a pGEM-T vector containing a 16S rRNA gene fragment of the respected target organism (Zaprasis et al., 2010).

## Statistical Analysis

All experiments were performed in triplicates. The statistical analysis of processed GC-MS data consistent with detected mass features per sample was conducted with MetaboAnalyst 3.0 (http://www.metaboanalyst.ca/MetaboAnalyst, Xia et al., 2015). Prior One-way ANOVA and multivariate analysis (PCA), data were filtered (interquantile range), and normalized (log transformation and auto scaling).

The statistical analysis on qPCR data as well as of CFU counts of Pseudomonas sp. AD021 growing as monoculture in soil microcosms supplied with or without ARE was performed with R 3.1.1 (http://www.r-project.org/) using ANOVA followed by Tukey's HSD test (De Mendiburu, 2014). To obtain normality of errors, data were log-transformed. Significance of enhanced CO2 production by Pseudomonas sp. AD021 growing in soil supplied with ARE was assessed by student's t-test. Differences revealed by statistical tests were considered significant for P < 0.05.

## RESULTS

### Bacterial Growth and Volatile Production in Rhizospheric Soil Microcosms Containing Single or Multiple Species

For the rhizospheric soil microcosms, a sandy soil was selected with a low in situ availability of carbon. Metabolic activity and growth of bacteria were restricted without the addition of ARE to this soil (Figure S1). In the presence of the supplied carbon source, the growth and CO2 production of different rhizobacterial strains was assessed (Figure S2). Finally, five phylogenetic different bacterial strains (Burkholderia sp. AD024, Dyella sp. AD056, Janthinobacterium sp. AD080, Pseudomonas sp. AD021, and Paenibacillus sp. AD087) were selected. Those strains, except for Paenibacillus sp. AD087, showed a similar CO2 production profile. A maximal CO2production was reached after 6 days of incubation (Figure S2) which suggested that the supplied ARE as sole carbon source were fully consumed. Furthermore, the selected Gram-negative strains multiplied to a maximum of about 108 CFU per g soil in the microcosms supplied with ARE (Figure S2). A quantification of 16S rRNA gene copy numbers revealed that the growth of Burkholderia sp. AD024, Dyella sp. AD056, and Pseudomonas sp. AD021 was significantly reduced in incubations with other bacteria in the ARE supplied soil (**Figure 1**). The Gram-positive strain Paenibacillus sp. AD087 grew poorly in the rhizospheric soil microcosms, both in monoculture and mixture (**Figure 1** and Figure S2). However, in presence of Paenibacillus sp. AD087 the growth of the other bacteria in the mixture was affected. For instance, the 16S rRNA gene copy number (no.) of Dyella sp. AD056 and Pseudomonas sp. AD021 was significantly higher and for Janthinobacterium sp. AD080 significantly lower as compared to incubations without Paenibacillus sp. AD087 (**Figure 1**).

A different blend of VOCs was produced by the bacterial mixture in comparison to the monocultures (**Figure 2**). The VOCs profile was different when the bacterial mixture consisted of five or four strains without Paenibacillus sp. AD087 (**Figure 2**). Volatiles released by the bacterial mixture consisted of alcohols, ketones, and esters as well as aromatic and organosulfur compounds. Some of those were also produced by the monocultures (**Table 2**). Most VOCs only released by the mixture of four bacterial strains excluding Paenibacillus sp. AD087 were alcohols and organic acids whereas VOCs released by for the mixture of all five strains consisted mainly of ketones, ester, and aromatic compounds. However, also numerous unknown volatile compounds were detected, especially for the bacterial mixture consisting of five strains (**Table 2**).

### Effect of Microbial Volatiles on Starving Bacteria

To test if volatiles produced during microbial interactions in a rhizosphere environment can stimulate starving microbes in the nutrient-depleted surrounding soil, soil microcosm experiments in two-compartment Petri-dishes with a mixture of all five bacterial strains were performed. Bacteria without additional carbon-source (compartment C4 of Treatment 1 and compartment C6 of Treatment 2) were exposed to volatiles produced by bacteria supplied with ARE (compartment C3 and C5, respectively) (**Figure 3A** and Figure S3A).

The 16S rRNA copy no. per g soil increased over time for bacteria supplied with ARE as well as for bacteria in the nutrientdepleted soil that were exposed to microbial VOCs (**Figure 3B** and Figure S3B). For the control microcosms, in which the ARE supplied compartment did not contain bacteria (**Figure 3A** and Figure S3A), the bacterial 16S rRNA copy no. per g soil did not significantly change over time. After 6 days of incubation, the 16S rRNA copy no. per g soil was significantly higher for all five bacterial strains in the nutrient-depleted soil exposed to microbial VOCs as compared to the control without exposure to microbial VOCs (**Figure 3B**). In case of Dyella sp. AD056 and Paenibacillus sp. AD087, the increase in 16S rRNA copy no. per g soil after 6 days of incubation was 1.5 and 12 times higher, respectively, when starving bacteria were exposed to microbial VOCs (compartment C4) as compared to bacteria growing in the ARE containing compartment (compartment C3) (**Figure 3B**). A similar trend was observed when bacteria were pre-incubated for 2 days in the soil before exposure to microbial VOCs (Figure S3B). Hence, volatiles released by mixtures of ARE-consuming bacteria significantly affected the activity of starving bacteria without ARE, based on an increase in 16S rRNA.

The 16S rRNA gene copy no. per g soil was measured for Burkholderia sp. AD024, Dyella sp. AD056, and Paenibacillus sp. AD087 to determine potential growth stimulation by exposure to bacterial VOCs under starving conditions. In case of Burkholderia sp. AD024 and Dyella sp. AD056, a significantly higher increase in 16S rRNA gene copy no. per g soil was observed for starving bacteria exposed to bacterial VOCs in comparison to the control (**Figure 3C**). In contrast, the 16S rRNA gene copy no. per g soil decreased over time for the strain Paenibacillus sp. AD087, even when nutrients were available or bacteria were exposed to VOCs (**Figure 3C**). Thus, the increase in activity by exposure to bacterial VOCs is coinciding with growth for the strains Burkholderia sp. AD024 and Dyella sp. AD056 but not for the Gram-positive Paenibacillus sp. AD087.

### DISCUSSION

Most of the current knowledge about the possible functioning of microbial volatiles is based on in vitro studies under nutrient rich conditions (e.g., Kai et al., 2007; Vespermann et al., 2007; Zou et al., 2007). This is different from the nutrientlimiting conditions with which microbes have to deal in most soil environments. Here, we developed a soil model system mimicking more closely the natural situation occurring in and around the rhizosphere to reveal new insights into the ecological role of volatiles in microbial interactions in soil.

For bacteria incubated in rhizospheric soil microcosms, it was observed that the volatiles produced by the mixture differed from those produced by each strain. Some volatiles were only emitted by the bacterial mixture and not by the monocultures. The shift in the volatile blend can be due to competitive interactions (Garbeva et al., 2014a). This was indicated by a significant decrease in 16S rRNA gene copy number of Burkholderia sp., Dyella sp., and Pseudomonas sp. when they were growing in mixture with other bacterial strains. Besides inter-specific competitive interactions it was also observed that shifts in the bacterial community composition, i.e., bacterial mixture consistent of four or five different strains, influenced the volatile production. Therefore, as proposed by Mc Neal and Herbert (2009) changes in the VOCs profile can be a potential indicator of microbial community composition shifts. Indeed, recent studies pointed at a relationship between the composition of soil bacterial communities and that of VOCs (Hol et al., 2015; Van Agtmaal et al., 2015). Interestingly, the volatile emission by the bacterial mixture was strongly affected by the presence of the poorlygrowing, non-abundant Paenibacillus sp. This is in line with recent studies revealing that low abundant microbes can play an important role in ecosystem functioning (Hol et al., 2010, 2015; Lynch and Neufeld, 2015). Furthermore, we observed that in the presence of the slow-growing Paenibacillus sp., the 16S rRNA gene copy number of Dyella sp. and Pseudomonas sp. was significantly higher. Hence, non-abundant species can promote the growth of dominant species. On the other hand, they may also trigger the production of various growth-suppressing secondary metabolites such as antimicrobial volatiles (Jousset et al., 2014; Hol et al., 2015). In the presence of Paenibacillus sp., several additional volatiles were released by the bacterial mixture which might be involved in the growth suppression of Janthinobacterium sp. besides non-vol atile antimicrobial compounds released by the interacting bacteria. Among those volatiles 2,5-bis(1-methylethy)-pyrazine was produced. It was recently reported that Paenibacillus can produce pyrazines with broad spectrum antimicrobial activity against bacteria, fungi and yeast (Rybakova et al., 2015). Here, the pyrazine-derivate was only detected for the bacterial mixture but not for Paenibacillus sp. in monoculture. However, it is possible that due to competitive interactions in the mixture the Gram-positive strain became active and started to produce the pyrazine compound.

Microorganisms are thought to use VOCs to influence other organisms living at a distance in the same soil environment (Stahl and Parkin, 1996). In the present study, we tested if volatiles produced by bacterial mixtures growing on root exudates can affect the activity of starving bacteria unable to access those root exudates. While microbes in the rhizosphere benefit from a constant flow of organic substrates (Effmert et al., 2012) volatile compounds released from the rhizosphere can represent an important carbon source to microorganisms in the nutrient-poor surrounding soil (Owen et al., 2007; Gramss and Bergmann, 2008; Ramirez et al., 2009). Besides, it has been suggested that volatiles produced by rhizosphere microorganisms could act as chemoattractants to the nutrient-rich environment around the roots (Garbeva et al., 2014a). Our results revealed that after 2 days of incubation, the activity of the starving

FIGURE 3 | Influence of bacterial volatiles on bacteria in nutrient-depleted soil. Increase in 16S rRNA copy number (B) and 16S rRNA gene copy number per g soil (C) were determined for a bacterial community consisting of Burkholderia sp. AD024, Dyella sp. AD056, Janthinobacterium sp. AD080, Pseudomonas sp. AD021, and Paenibacillus sp. AD087 which was incubated in nutrient-poor soil. (A) In Treatment 1, bacteria in nutrient-depleted soil in compartment C4 (dark gray) were exposed to volatiles produced by bacteria supplied with artificial root exudates (ARE) in compartment C3 (light gray). The control compartment C2 (black) represents bacteria in nutrient-depleted soil not exposed to bacterial volatiles. Data represent mean (n = 6) and standard error corrected for the starting time point t0. Different letters indicate significant differences (P < 0.05) between values resulted from One-way ANOVA and Tukey's HSD test.

bacteria Burkholderia sp., Dyella sp., and Pseudomonas sp. was already stimulated by exposure to microbial VOCs. After 6 days of incubation, the activity of all five bacterial strains in the nutrient-depleted soil was significant increased by the exposure to microbial VOCs. Thus, volatiles released by rhizosphereinhabiting bacterial communities can stimulate the activity of the surrounding starving bacteria in nutrient-depleted soil. The mechanism behind the activation of bacteria by volatiles, however, remains unclear. It was reported that volatiles can function as growth-promoting agents (Wheatley, 2002; Horii and Ishii, 2006; Garbeva et al., 2014a) which might explain the increase in bacterial activity by exposure to microbial VOCs. Quantification of 16S rRNA gene copy number revealed that the growth of the starving Burkholderia sp. and Dyella sp. was induced by exposure to microbial VOCs. Hence, the growth of starving bacteria in soil can be promoted by volatiles released from the rhizosphere. Bacteria are able to detoxify VOCs and/or use them as carbon and energy source. For instance, it was shown that Pseudomonas fluorescens is able to degrade the volatile compound alpha-pinene and to use it as a sole carbon source (Best et al., 1987; Kleinheinz et al., 1999). Furthermore, Burkholderia was reported as a toluene-degrader (Chen et al., 2015) and Dyella was abundant on a biofilter exposed to terpenes (Moe et al., 2013). In the current study, various terpenes and butylated hydroxytoluene were released by the bacterial mixture supplied with ARE in soil. It can be assumed that some of these volatiles stimulated the growth of Burkholderia sp. and Dyella sp. in the nutrient-depleted soil. This, however, needs to be verified in future studies.

Interestingly, while the activity of Paenibacillus sp. was strongly induced by exposure to microbial VOCs the growth was suppressed. As previously reported some VOCs can act as growth-inhibiting, toxic agents (Peñuelas et al., 2014) and induce stress response (Kim et al., 2013; Garbeva et al., 2014a). This might explain the strong induction in the activity and at the same time growth-suppression of Paenibacillus sp. by exposure to microbial VOCs. Another possible scenario for the strong increase in activity of Paenibacillus sp. is the induction of motility to escape from the toxic environment. A stimulation of motility by exposure to microbial VOCs was already reported for several bacterial species (Kim et al., 2013; Hagai et al., 2014;

#### REFERENCES

Garbeva et al., 2014a). For microbes living in the heterogeneous soil environment, an activation of motility by volatiles may be important to move toward nutrient-rich regions or to escape from hostile areas. Hence, volatiles in soil can provide important information on the quality of the nearby surroundings.

Besides the role as growth-promoting or growth-suppressing agents (Peñuelas et al., 2014), volatiles can also induce the production of non-volatile secondary metabolites. For instance, it was recently reported that microbial volatiles induce the production of inhibiting secondary metabolites in Pseudomonas fluorescence against Gram-positive bacteria (Garbeva et al., 2014a). Thus, the growth suppression of Paenibacillus sp. may be also due to non-volatile secondary metabolites which production was activated by microbial volatiles.

In conclusion, this study revealed that the blend of released volatiles from the rhizosphere is affected by inter-specific competitive interactions and shifts in the microbial community composition. Moreover, the presence of non-abundant, slowgrowing species can strongly influence the volatile production by other dominant species. Based on our results and other recent studies it is evident that microbial volatiles in soil can serve multiple roles as C-source, defense metabolites, chemoattractant, repellants or other unknown so far. Hence, volatiles released by rhizosphere-inhabiting microbial communities can have a significant long distance effect on starving microorganisms in the surrounding, nutrient-depleted soil.

#### ACKNOWLEDGMENTS

We thank Saskia Gerards, Olaf Tyc, Cornelis Hordijk (Netherlands Institute of Ecology), and Marjolein M. Meulblok for technical assistance and helpful discussions. Support for this study was provided by The Netherlands Organization for Scientific Research (NWO), VIDI personal grant (864.11.015). This is publication 5946 of the NIOO-KNAW.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2015.01212

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Schulz-Bohm, Zweers, de Boer and Garbeva. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A meta-analysis approach for assessing the diversity and specificity of belowground root and microbial volatiles

*Denis Schenkel1,2, Marie C. Lemfack3, Birgit Piechulla3 and Richard Splivallo1,2\**

*1 Institute for Molecular Biosciences, Goethe University Frankfurt, Frankfurt, Germany, 2 Integrative Fungal Research Cluster, Frankfurt, Germany, 3 Institute for Biological Sciences, University of Rostock, Rostock, Germany*

#### *Edited by:*

*Choong-Min Ryu, Korea Research Institute of Bioscience and Biotechnology, South Korea*

#### *Reviewed by:*

*Huiming Zhang, Shanghai Center for Plant Stress Biology – Chinese Academy of Sciences, China Mohamed Farag, Cairo University, Egypt*

#### *\*Correspondence:*

*Richard Splivallo, Institute for Molecular Biosciences, Goethe University Frankfurt, Max-von-Laue Strasse 9, 60438 Frankfurt, Germany<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Plant Biotic Interactions, a section of the journal Frontiers in Plant Science*

*Received: 20 July 2015 Accepted: 24 August 2015 Published: 14 September 2015*

#### *Citation:*

*Schenkel D, Lemfack MC, Piechulla B and Splivallo R (2015) A meta-analysis approach for assessing the diversity and specificity of belowground root and microbial volatiles. Front. Plant Sci. 6:707. doi: 10.3389/fpls.2015.00707* Volatile organic compounds are secondary metabolites emitted by all organisms, especially by plants and microbes. Their role as aboveground signals has been established for decades. Recent evidence suggests that they might have a nonnegligible role belowground and might be involved in root–root and root–microbial/pest interactions. Our aim here was to make a comprehensive review of belowground volatile diversity using a meta-analysis approach. At first we synthesized current literature knowledge on plant root volatiles and classified them in terms of chemical diversity. In a second step, relying on the mVOC database of microbial volatiles, we classified volatiles based on their emitters (bacteria vs. fungi) and their specific ecological niche (i.e., rhizosphere, soil). Our results highlight similarities and differences among root and microbial volatiles and also suggest that some might be niche specific. We further explored the possibility that volatiles might be involved in intra- and inter-specific root–root communication and discuss the ecological implications of such scenario. Overall this work synthesizes current knowledge on the belowground volatilome and the potential signaling role of its constituents. It also highlights that the total diversity of belowground volatiles might be orders of magnitude larger that the few hundreds of compounds described to date.

 Unlike primary metabolites which are directly involved in regular growth and development, secondary metabolites might be produced only at specific developmental stages or under certain circumstances; hence they might provide a functional readout of cellular state (Patti et al., 2012). Tens of 1000s of secondary metabolites derived from plants and microbes are known to humans as drugs, food additives or flavors, and fragrances; yet, their ecological functions remain poorly understood.

Secondary metabolites indeed play a central role in inter-organismic interactions. In numerous cases volatile and non-volatile secondary metabolites have been implicated in defense and communication among organisms. Recently, volatiles have attracted sustained attention, especially in belowground communication, due to their ability to travel further distances than non-volatile metabolites (Rasmann et al., 2005; Wenke et al., 2010; Peñuelas et al., 2014). Because of their potent biological activities on plants, the use of volatiles in agriculture have been suggested as a possible alternative to pesticides (Bitas et al., 2013; Kanchiswamy et al., 2015). A search through literature and databases allows estimating the known structural diversity of volatiles derived from plant flowers – about 1700 volatiles from 991 species (Knudsen et al., 2006; Dunkel et al., 2009) – and from microbes, including fungi and bacteria – 1093 volatiles from 491 microbes at the time of this study (Lemfack et al., 2014). Yet considering that 107–109 bacterial species (Schloss and Handelsman, 2004), 1.5 million fungal species (Hawksworth, 2001) and 2,98,000 of plant species (Mora et al., 2011) might exist on earth, the number of volatiles will increase as new species are being characterized and discovered.