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metabolites
Article
Profiling of Volatile Organic Compounds from Four Plant
Growth-Promoting Rhizobacteria by SPME–GC–MS:
A Metabolomics Study
Msizi I. Mhlongo
, Lizelle A. Piater
and Ian A. Dubery *
Research Centre for Plant Metabolomics, Department of Biochemistry, University of Johannesburg, Auckland
Park, P.O. Box 524, Johannesburg 2006, South Africa
* Correspondence: idubery@uj.ac.za; Tel.: +27-011-5592401
Citation: Mhlongo, M.I.; Piater, L.A.;
Dubery, I.A. Profiling of Volatile
Organic Compounds from Four Plant
Growth-Promoting Rhizobacteria by
SPME–GC–MS: A Metabolomics
Study. Metabolites 2022, 12, 763.
Abstract: The rhizosphere microbiome is a major determinant of plant health. Plant-beneficial
or plant growth-promoting rhizobacteria (PGPR) influence plant growth, plant development and
adaptive responses, such as induced resistance/priming. These new eco-friendly choices have
highlighted volatile organic compounds (biogenic VOCs) as a potentially inexpensive, effective
and efficient substitute for the use of agrochemicals. Secreted bacterial VOCs are low molecular
weight lipophilic compounds with a low boiling point and high vapor pressures. As such, they
can act as short- or long-distance signals in the rhizosphere, affecting competing microorganisms
and impacting plant health. In this study, secreted VOCs from four PGPR strains (Pseudomonas
koreensis (N19), Ps. fluorescens (N04), Lysinibacillus sphaericus (T19) and Paenibacillus alvei (T22)) were
profiled by solid-phase micro-extraction gas chromatography mass spectrometry (SPME–GC–MS)
combined with a multivariate data analysis. Metabolomic profiling with chemometric analyses
revealed novel data on the composition of the secreted VOC blends of the four PGPR strains. Of
the 121 annotated metabolites, most are known as bioactives which are able to affect metabolism
in plant hosts. These VOCs belong to the following classes: alcohols, aldehydes, ketones, alkanes,
alkenes, acids, amines, salicylic acid derivatives, pyrazines, furans, sulfides and terpenoids. The
results further demonstrated the presence of species-specific and strain-specific VOCs, characterized
by either the absence or presence of specific VOCs in the different strains. These molecules could be
further investigated as biomarkers for the classification of an organism as a PGPR and selection for
agricultural use.
https://doi.org/10.3390/
metabo12080763
Academic Editor: Young Hae Choi
Keywords: metabolomics profiling; multivariate data analysis (MVDA); plant growth promoting
rhizobacteria (PGPR); solid-phase micro-extraction gas chromatography mass spectrometry (SPME–
GC–MS); volatile organic compounds (VOCs)
Received: 24 July 2022
Accepted: 17 August 2022
Published: 19 August 2022
Publisher’s Note: MDPI stays neutral
1. Introduction
with regard to jurisdictional claims in
Plant growth-promoting rhizobacteria (PGPR), part of the plant microbiome in the
rhizosphere, is a community that is largely influenced by plant roots and soil type [1–3].
The roots secrete molecules as part of the root exudates, and these inadvertently influence
the microbial consortium in the adjacent rhizosphere [4–6]. In agroecosystems, PGPR has
been shown to influence plant growth by improving soil nutrients, inhibiting deleterious
organisms and enhancing plant health [1,3]. Numerous strains have been reported to
interact with plants (below and above ground), resulting in either antagonistic or mutualistic symbiosis [7–10]. To help plants defend against the plethora of pathogens, PGPR can
elicit a form of resistance known as induced systemic resistance (ISR) [11,12]. Symbiotic
interactions have been thought to involve physical contact between plants and PGPR.
Nonetheless, recent studies have reported that microbial signals (i.e., volatile organic compounds or VOCs) play an integral role in multi-trophic interactions with plants. These
include functions in plant growth modulation and health, as well as enhancement of the
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Metabolites 2022, 12, 763. https://doi.org/10.3390/metabo12080763
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Metabolites 2022, 12, 763
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availability of soil nutrients and interactions with other microbial communities within the
rhizosphere [13–15].
PGPR coexist in dynamic communities and secrete VOCs that allow these organisms
to survive in their environments [10]. As low molecular weight compounds and easily vaporized molecules, they can diffuse through complex matrixes, such as cellular membranes,
water, soil and air [10,16,17]. VOCs are thus suitably equipped as info-chemicals produced
by soil microbes that mediate both short- and long-distance signaling, as well as interand intra-organismic communication below and above ground [9,10,15–19]. Plant growth
enhancement and physiological processes, such as ISR and abiotic stress tolerance after
VOC exposure (summarized in Figure S1), are dependent on systemic changes modulated
by alterations in the levels of phytohormone, such as ethylene (ET), auxin and jasmonic acid
(JA). Still, other phytohormones and concomitant cross-talk cannot be excluded [19–21].
Studies suggest that the inoculation of plant roots with PGPR or exposure to VOCs involves
several signaling pathways that lead to the stimulation of photosynthesis and plant growth,
cell wall modification, phytohormone regulation and stress responses [22,23]. In addition,
PGPR VOCs can influence rhizosphere communities in either a positive or negative manner through bacterial–bacterial and bacterial–fungi interactions [10]. For example, these
can either recruit other beneficial microbes, inhibit pathogenic microbes or attract natural
enemies for feeding on soil-borne herbivores [24,25].
Multiple signals are responsible for the successful colonization of plant roots by PGPR
in a complex process controlled by molecules from the two organisms involved. In this
study, four bacterial strains with PGPR activity (Pseudomonas koreensis (N19), Ps. fluorescens
(N04), Lysinibacillus sphaericus (T19) and Paenibacillus alvei (T22)) were investigated. Previously, these PGPRs have been reported to successfully colonize maize, wheat and tomato
roots, and to enhance the growth of these plants [26,27]. Some of the elucidated mechanisms by which these bacterial isolates stimulate plant growth include the production of
indole-acetic acid and related hormones, phosphate solubilisation and the production of
siderophores [26]. Lastly, Pa. alvei (T22) was also found to be effective as a biocontrol agent
against soil-borne diseases in sorghum and wheat [28,29].
A previous study investigating the four PGPR strains revealed strain-specific defencerelated metabolic reprogramming in tomato plants [30]. The obtained results suggested that
the differential modulation of the identified metabolites (apparent from altered metabolic
profiles in response to the four PGPR strains) was dependent on bacteria-derived stimuli
and that the plants adjusted their adaptive responses based on the perceived signal(s).
As such, the aim of this investigation was to fill the gap in our understanding of how
these strains promote growth and induce resistance in crop plants. This was achieved by
profiling the secreted VOCs and comparing them to PGPR strains that were reported to
have these effects on plants.
Due to their volatile nature, VOCs are appropriately analyzed by gas chromatography
for separation coupled to mass spectrometry for detection and identification (GC–MS).
The non-destructive headspace (HS) sampling strategy is a sensitive extraction method
for detecting and analyzing natural volatile compounds, and various methods (either
dynamic or static) have been developed. In this regard, procedures such as stir bar sorptive
extraction, in-tube extraction and solid phase micro-extraction (SPME) fibers have been
applied for HS analysis [31–33]. As a solvent-free extraction method, SPME integrates the
sample preparation stages (sampling, extraction, concentration and sample introduction)
into a single step [34,35]. This significantly decreases preparation time and provides samples ready for analysis. Different needle-coating chemicals such as polydimethylsiloxane,
carboxen, divinylbenzene, polyacrylonitrile and benzenesulfonic acid have been developed
as sorptive phases for different analyte classes [35,36]. Coatings with different thickness
and polarities also broaden fiber selection [36]. Notwithstanding all the advantages offered
by SPME, the chemical base coating of the stationary phase can limit sensitivity or favor
certain classes of analytes based on their size and/or polarity [37,38].
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2. Materials and Methods
2.1. Bacterial Growth and Experimental Design
The bacterial strains (Pseudomonas koreensis (N19), Ps. fluorescens (N04), Lysinibacillus sphaericus (T19) and Paenibacillus alvei (T22)), were obtained from the PGPR culture
collection of Professor N. Labuschagne, University of Pretoria, South Africa [26,27]. The
strains were cultivated individually in Luria-Bertani (LB) medium at 28 ◦ C and stocks were
maintained in LB medium containing 15% (v/v) glycerol at −80 ◦ C. Gas chromatography
coupled to a time-of-flight mass spectrometer (GC–TOF–MS) was used to profile volatiles
secreted by these organisms in a non-targeted manner. The experiments were designed
in such a way to be correct for non-targeted metabolomic analysis in conjunction with
multivariate data analysis (i.e., three biological replicates, with each sample analyzed in
triplicate (n = 9)).
2.2. Volatile Collection and Headspace Sampling through SPME
We inoculated 50 mL of LB broth in 250 mL Erlenmeyer flasks with a 50 µL suspension
of each PGPR strain. These samples were cultured overnight at 28 ◦ C on an orbital shaker
(Labotec, Midrand, South Africa). Subsequently, 10 mL of the overnight cultures were
transferred into 20 mL sample vials (22 cm × 7.5 cm) and were further incubated under the
same conditions until saturation (OD600 > 2) was reached. LB broth without any inoculation
was used as the control. Bacterial VOCs were extracted from the headspace of the 20 mL
vials and were concentrated via SPME before desorption in the GC injection port. All
samples were sequentially incubated at 50 ◦ C in an incubator set at 300 rpm for 30 min
before VOC extraction and were kept at this temperature throughout sampling in order to
assist the release of the VOCs from the aqueous media. The SPME fibers used were fused
silica fibers with a polydimethylsiloxane/divinylbenzene (PDMS/DVB) coating (Supelco,
Munich; Germany). These were used to pierce the polytetrafluoroethylene (PTFE) septa
and expose the fibers to the headspace of the vial for 10 min. Immediately after VOC
extraction, the SPME fibers were inserted into the GC injection port for 1 min and were
exposed to 200 ◦ C for the desorption of the adsorbed VOCs. For reconditioning, the fibers
were heated at 250 ◦ C for 10 min.
2.3. Gas Chromatography—Time-of-Flight—Mass Spectrometric Analysis
For GC–MS analyses, a Pegasus BT GC–TOF–MS (LECO Corporation, St. Joseph, MI,
USA) fitted with a Rx-5MS column (30 m, 0.25 mm ID, 0.25 µm film thickness df) (Restek,
Bellefonte, PA, USA) instrument was used. SPME fibers were desorbed at 200 ◦ C for 1 min
in the injection port. Helium was used as a carrier gas, with a total flow rate of 14 mL/min
and column flow rate of 1 mL/min. GC–MS runs were 25 min, with the fiber remaining in
the injection port for 10 min after each run. The injection port was operated in a ‘splitless’
mode, with a constant He flow of 1.0 mL/min. The initial oven temperature was 50 ◦ C
and was held at this temperature for 1 min before it was ramped up to 280 ◦ C (with an
increase of 15 ◦ C/min) and was held at this temperature for 3 min. The high-resolution
(HR) time-of-flight mass spectrometer (TOF–MS) parameters were as described below. The
data acquisition rate was 20 scans/s and the extraction frequency was 30 kHz. The ion
source temperature was 200 ◦ C and the interface temperature was 200 ◦ C. The detector
voltage was related to the tuning file or 70 eV, and the mass-to-charge range (m/z) was from
35 to 500.
2.4. Multivariate Data Analysis (MVDA)
The acquired high-resolution GC–TOF–MS datasets were converted to NetCDF files
using LECO ChromaTOF-HRT software, version 5.0. These were exported to the XCMS
online statistical package (https://xcmsonline.scripps.edu, accessed on 10 November 2019),
an automated, web-based metabolomics data processing software for peak picking and
peak alignment. The parameters for the method were selected for GC–TOF–MS specificities
and were as follows: (i) feature detection was set as the centWave method, minimum peak
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width = 0.6, maximum peak width = 1.0; (ii) Rt correction was set as the Obiwarp method,
Profstep = 1, alignment was set as m/z width = 0.015, minfraction = 0.5 and bw = 5. The
resulting peak list showed 7550 variables (in a combined list) with corrected peak retention
times (Rts, min), mass-to-charge ratios (m/z) and integrated peak areas.
The resultant data matrixes obtained from the XCMS statistical package were imported
into SIMCA-P software, version 15.0 (Sartorius, Umeå, Sweden) and Pareto-scaling was
applied for the multivariate statistical analysis. Firstly, to reduce the dimensionality of the
data and to summarize the information contained in the datasets, a principal component
analysis (PCA, an unsupervised method for MVDA) was used. Here, the Hotelling’s
T-squared (T2 ) test was applied to test for significant differences between the mean vectors
(multivariate means) of the multivariate data sets. Secondly, to complement the PCA, a
hierarchical cluster analysis (HCA) was used to assess the trends and patterns observed on
the PCA [39–41].
Orthogonal partial least squares discriminant analysis (OPLS-DA), a supervised
MVDA method, was used to extract the maximum amount of information regarding
the significant variables from the datasets. This procedure allows for the removal of systematic variation from the experimental data (X variables) that is not correlated to discriminant
classes (Y) and Hotelling’s region, represented by the ellipse and defined by the 95% interval of confidence. The quality of the generated models is described by diagnostic tools
for metabolomics, which included: (i) cumulative model variation in the matrix X, (ii) the
goodness-of-fit parameter [R2 X(cum)], (iii) the model variance proportion [Y2 X(cum)] and
(iv) total variation of the matrix X predicted by an extracted component [Q2 (cum)] [42].
In addition, the OPLS-DA models were statistically validated using a cross-validated
analysis of variance (CV-ANOVA), with a p ≤ 0.05 indicating a good model [43], using the
SIMCA inbuilt 7-fold (default) CV method and a permutation test (50 permutations). OPLSDA S-plots were constructed for subsequent interpretation. Based on the S-plots, significant
m/z ions with a correlation of [(p(corr)] ≥ |0.5| and a covariance of (p1) ≥ |0.05| were
selected. These ions were further validated using ‘variable importance in projection’ (VIP)
scores of >1 before selection for annotation. Statistically significant features were annotated
(tentatively identified) to level 2, as defined by the metabolomics standards initiative [44],
based on their mass spectral information using the National Institute of Standards and
Technology (NIST) Mainlib [45], and Fiehn metabolomics and Mass Spectrum libraries [46],
selecting compounds with a similarity of 75% and above.
3. Results
The composition of non-selective extracted metabolomes are complex due to their
chemo-diversity. This multidimensionality presents holdups in metabolomics studies.
However, recent high-resolution (HR) technological advancements, such as the GC–HR–
time-of-flight–(TOF)–MS, have enabled researchers to concurrently detect multiple analytes
with high sensitivity, thus providing more detailed information about the metabolic profile
of the sample. Some of the advantages of the GC–TOF–MS include: (i) the deconvolution
power of the software, allowing the detection and resolution of overlapping peaks within
seconds and (ii) high-speed data acquisition without distorting the peak height and constant
MS spectral scans across a peak, regardless of peak intensity, to assist with annotation.
Visual inspection of the generated MS chromatograms showed differential profiles of the
analyzed samples with characterized unique peak populations (absent, present and/or
differing in peak intensity), thus info-graphically reflecting variances among the sampled
VOCs from the four PGPR strains (Figure S2). Data pre-processing [47] and comparative
chemometrics [48] were used to extract the underlying features that contribute to the
observed differences.
Following data pre-processing, the three-dimensional (Rt, m/z ratio and peak area)
output that was created was exported to software (SIMCA Data Analytics software) for
further multivariate statistical analysis. PCA models were computed in order to compare
the volatile profiles of the four strains investigated. PCA, as an unsupervised modeling
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tool for data exploration, reduced the dimensionality of the data and allowed for inspection of the global distribution patterns (clustering of sample groups, trends and outliers).
The computed PCA (Figure 1A) shows sample grouping of the various strains and the
characterization of the volatile profiles. It is important to note that the two Pseudomonas
strains (Ps. koreensis (N19) and Ps. fluorescens (N04)) are clustered next to each other, which
could indicate that these have similar profiles, whereas Pa. alvei (T19) was found to cluster
nearer to the uninoculated LB media. L. sphaericus (T22) was found to cluster on its own
further away from the rest, indicating that the volatile profile of this strain is significantly
different from that of the other strains. The PCA-extracted trends and patterns were further
examined by HCA for comparative exploration. The HCA models were computed using
the linkage method of Ward, considering ‘between’ and ‘within’ cluster distances. The
trees were sorted based on the size of the clusters [39–41]. The computed HCA (Figure 1B)
shows two major clusters of L. sphaericus (T22) vs. the other samples, where the latter are
further divided into two subgroups: LB (medium control) and Pa. alvei (T19), and the two
Pseudomonas strains (N04 and N19).
Figure 1. Exploratory data analysis of volatile organic compounds produced by the four PGPR
strains (N19, N04, T19, T22) with unsupervised chemometric methods. LB = control comprising
of uninoculated Luria-Bertani medium. (A): A PCA scores scatter plot of all the samples colored
according to PGPR strain. The ellipse represents the Hotelling’s T2 distribution with 95% confidence.
The PCA model presented here was a 5-component model, with R2 X of 93.6% and Q2 (cum) of 91.3%.
(B): The HCA dendrogram corresponding to the PC analysis. Unsupervised statistical analysis is
used to generate the subgrouping of samples based on similar observations in (A), whereas the HCA
dendrogram shows the hierarchical relationship between samples (B).
Although the models generated from unsupervised PCA and HCA revealed the
structures within/between the datasets, the modeling lacks predictive power. Hence, this
modeling was followed by an alternative method, namely orthogonal projection to latent
discriminant analysis (OPLS-DA). OPLS-DA models were calculated using two predefined
conditions (LB vs. each bacterial strain) in order to extract differences in the samples under
investigation so as to assist with the identification of features that were responsible for the
observed dissimilarities [48,49]. As an example, the OPLS-DA score plot for L. sphaericus
(T22) (Figure 2A) shows distinct separation of the inoculated sample from the uninoculated
sample. The corresponding S-plot (Figure 2B) was used to identify features that positively
correlated to the various inoculations.
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−
Figure 2. OPLS-DA modeling and variable/feature selection of Lysinibacillus sphaericus (T22) data
acquired on GC–TOF–MS. (A): A typical OPLS-DA score separating uninoculated media (LB) vs. inoculated media (T22) (1 + 1 + 0 components, R2 X = 0.895, Q2 = 0.999, CV-ANOVA p-value = 1.3 × 10−13 ).
(B): An OPLS-DA loadings S-plot for the same model in (A); only variables with a correlation [p(corr)]
≥
≥
of ≥|0.5| and a covariance of (p1) ≥ |0.05| were chosen as discriminating variables and were
identified using the m/z to generate elemental composition. (C): A variable importance for the projection (VIP) plot for the same model, pointing mathematically to the importance of each variable in
contributing to group separation in the OPLS-DA model. −(D): The response permutation test plot
(n = 50) for the same OPLS-DA model: R2 (0.0, 0.207) and Q2 (0.0−0.739) values of the permuted
models are represented on the left-hand side of the plot, corresponding to y-axis intercepts. Similar
figures for VOCs secreted by N04, N19 and T19 are presented in Figures S3–S5.
To prevent bias in selecting significant features, variable importance in projection (VIP)
plots were constructed to rank the importance of individual variables to the models [48,49].
As such, VIP plots (Figure 2C) were used to validate the variable selection based on the
S-plots, and only variables with a VIP score of >1 were selected for annotation—these are
summarized in Table S1. Furthermore, to validate the predictive capability of the computed
OPLS-DA models, a response permutation test (with n = 50) (Figure 2D) was used. In
this statistical test, the R2 and Q2 values of the true model are compared with that of the
permutated model. The test was carried out by random assignment of the dataset rows
to two different groups, after which the OPLS-DA models were fitted to each permutated
class variable. The R2 and Q2 values were then computed for the permutated models and
compared to the values of the true models. The model validity was supported by having all
Q2 values of the permuted dataset (to the left) lower than the Q2 value on the actual data
set (to the right). Furthermore, the regression line (the line joining the point of observed
Q2 to the centroid of a cluster of permuted Q2 values) has a negative intercept value on
the Y-axis [50,51]. The y-axis displays R2 and Q2 , whereas the x-axis shows the correlation
coefficient of the permuted and observed data. The two points on the right represent the
observed R2 and Q2 . The green and blue dots represent R2 and Q2 values, respectively.
The dashed lines indicate the corresponding fitted regression lines for the observed and
permutated R2 and Q2 .
PGPR produce a large spectrum of VOCs, contributing to their ability to interact
with and influence neighboring organisms or plants. The ‘volatilome’ of a specific strain
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consists of wide and diverse VOCs with regards to their chemical and physical properties.
The composition and relative abundance of the VOC blend is highly dependent on the
specific strain, but also on the growth phase and growth conditions [52,53]. Moreover,
the emission of specific VOCs can be induced or influenced by interacting organisms
present in the bacterial environment. In general, closely related species emit a similar
blend of VOCs (with some species-specific metabolites) compared to distantly related
species [52]. Using SPME–GC–TOF–MS to profile the VOCs produced by the four PGPR
strains (Ps. fluorescens (N04), Ps. koreensis (N19), Pa. alvei (T19) and L. sphaericus (T22)), a
total of 121 VOCs (Table S1) were identified. The annotated VOCs generally belong to the
following classes: aldehydes, alcohols, ketones, acids, alkanes, alkenes, amines, derivatives
of salicylic acid, pyrazines, furans, sulfides and terpenoids (Table S1), and most have been
previously reported. The two Pseudomonads had 28 VOCs in common (Figure 3), whereas
the four strains were found to share only a small number of overlapping molecular patterns
among a large pool of different VOCs (Figure 4). These included 2-tridecanol, 2-decanone,
2-dodecanone, nonane, decanal, dodecanal, tetradecanal, isoamyl salicylate, benzene (2methyloctyl), pyrazine (2,5-dimethyl), pyrazine (trimethyl), pyrazine (3-butyl-2,5-dimethyl)
and ß-ocimene. The observed variation in VOC profiles could suggest that these strains
promote plant growth and inhibit pathogen growth in different ways.
Figure 3. Venn diagram comparing the volatile organic compounds of the two Pseudomonas strains.
The diagram shows shared and distinct volatile organic compounds from Pseudomonas koreensis (N19)
and Pseudomonas fluorescens (N04), as indicated by the numbers in the intersections and ellipses,
respectively.
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Figure 4. Venn diagram of volatile organic compounds produced by Pseudomonas strains (Pseudomonas
koreensis (N19) and Pseudomonas fluorescens (N04)), vs. Paenibacillus alvei (T22) and Lysinibacillus
sphaericus (T19). The diagram shows overlapping and distinct VOCs, indicated by the numbers in the
intersections and ellipses, respectively.
4. Discussion
4.1. Comparison and Evaluation of Volatile Organic Compounds Secreted by the Two PGPR
Pseudomonas Strains
Pseudomonas spp. are one of the most dominating groups in the rhizosphere for which
both the promotion of plant growth and antagonistic properties towards pathogens have
been documented. Interestingly, studies have shown that bacteria of the same species can
exhibit different VOC profiles and have differential perturbation effects on the physiology
of plants and microorganisms [15,53]. These have been found to lead to enhanced plant
growth and induce resistance and inhibit pathogen growth [26]. In this study, we first
compared the profile of two strains of plant protective Pseudomonas spp. in order to identify
common/unique VOCs (Figure 3). The two strains had 28 VOCs in common, 34 VOCs
specific to Ps. fluorescens (N04) and 18 VOCs specific to Ps. koreensis (N19) (Figure 3).
The shared VOCs included methyl salicylate (MeSA), dimethyl trisulfide, nonadecane
and 11-hexadecen-1-ol, amongst others. Interestingly, Ps. fluorescens (N04) had hexyl
salicylate among its unique VOCs, whereas Ps. koreensis had isoamyl salicylate as a unique
VOC (Figure 3). SA and MeSA are known plant hormones that play different roles in
plant growth and development [54]. Accordingly, the ability of these strains to secrete SA
derivatives could be one of the mechanisms they employ to establish mutual relationships,
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enhance plant growth and induce resistance. An interesting molecule worth mentioning
is the dimethyl disulfide specific to Ps. koreensis (N19), which has been commercialized
as a soil fumigant against soil-borne pathogens and nematodes due to its antimicrobial
properties and its ability to prevent nematodes from feeding on roots [25].
In addition, the ‘volatilome’ of these strains (common or unique) (Figure 3) consisted
of the following classes of compounds: alcohols, ketones, aldehydes, alkanes and sulfides.
These classes of VOCs have been reported in various PGPR strains and form part of the
complex communication in the rhizosphere that leads to growth stimulation, induced resistance or pathogen growth inhibition. For example, Pseudomonas strains that are associated
with potato roots produce VOCs with the capacity to greatly inhibit Phytophthora infestans,
and SPME–GC–MS has shown that these strains secrete similar and unique VOCs that are
associated with such inhibition [55]. This report further demonstrated that these strains had
varying effects on the mycelial growth of P. infestans, thus, indicating that the VOC blend
(with regards to its composition and concentration) plays an important role in pathogen
inhibition. The VOCs identified in [55] included alcohols, sulfides, alkanes, ketones and
aldehydes. Dimethyl disulfide was the only VOC that was found to be secreted in high
concentrations. Moreover, when the inhibitory ability of these strains was tested against
other plant pathogens, such as Rhizoctonia solani, Helminthosporium solani, Dickeya dianthicola
and Fusarium oxysporum, varying inhibitory effects on the growth of the different phytopathogens were observed [55]. Similarly, VOCs from Ps. chlororaphis promoted growth
and salt tolerance in Arabidopsis, and 2,3-butanediol from Ps. chlororaphis was identified as
a VOC that contributed to the induced resistance of tobacco against Erwinia carotovora, but
not against Pseudomonas syringae pv. tabaci [21,56].
Analogous to the cited examples, the uniqueness of the VOCs observed in this study
(Figure 3) might further explain the different metabolic perturbations caused by these
strains in tomato seedlings [57]. In this study, we observed that root inoculation with the
four strains induced specific differential metabolic perturbations that were characterized
by metabolites from the hydroxycinnamate, benzoate, flavonoid and glycoalkaloid classes.
Furthermore, the targeted analysis of aromatic amino acids indicated differential quantitative increases or decreases over two days in response to the four PGPR strains. The
observed differences could be a reflection of how effectively the PGPR strains interacted
with the tomato roots, as well as the relative activities of specific metabolic pathways that
contribute to the annotated VOCs.
4.2. Comparison and Evaluation of Volatile Organic Compounds Secreted by the Two Pseudomonas
Strains, Paenibacillus alvei (T22) and Lysinibacillus sphaericus (T19)
The VOC profiles of the two Pseudomonas strains (Ps. fluorescens (N04) and Ps. koreensis
(N19)) were subsequently analyzed and compared with two PGPR strains from the Lysinibacillus (Ly. Sphaericus (T19)) and Paenibacillus (Pa. alvei (T22)) genera in order to evaluate
the chemical compositions of the volatile blends and to establish whether these strains
secrete similar or unique compounds (Figure 4). An assessment of the annotated VOCs of
these different strains showed that they have only 13 metabolites in common (Figure 4),
with Pa. alvei (T22) having the highest number of unique secreted VOCs under experimental
conditions (Figure 4). Moreover, the VOC profiles of the different PGPR strains showed
that these species are diverse, as seen by the different combinations of VOCs that were
shared by the species and those that were species-specific. The observed differences in the
PGPR strains could be an indication that these organisms employ unique mechanisms in
order to interact with their neighbors and affect physiological process.
Previous studies have shown that PGPR strains secrete a wide range of VOCs that act as
info-chemicals within the bacterial community (among the same species or among different
species) or with surrounding organisms [15,58,59]. For example, ref. [59] showed that VOCs
produced by four strains of Bacillus spp. and Paenibacillus spp. had varying antagonistic
effects against soil-borne plant pathogens, including Ascochyta citrullina, Alternaria brassicae
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and Al. solani. Through headspace sampling and GC–MS analyses, an overlapping and
unique volatile pattern could be found among the different species.
Pa. alvei (T22) has been studied by us for growth promotion and induced resistance
against phytopathogens. Treatment of Sorghum bicolor seedlings with this strain prior to
inoculation with F. pseudograminearum [28] and Colletotrichum sublineolum [29] significantly
lowered the progression of disease by the phytopathogens. In addition, this strain was
also effective in inducing a primed state against Phytophthora capsici infection in tomato
seedlings [60]. As indicated in Figure 4, Pa. alvei (T22) secreted the highest number (26) of
unique VOCs that possibly contribute to its mechanism of action to induce/enhance the
aforementioned resistance.
5. Conclusions
Cues originating from mutualistic ecological interactions often contain highly specific
information. Recent studies have demonstrated that VOCs emitted by PGPR can be used as
a novel agroecological strategy to improve plant growth and yields, as well as to improve
resistance to abiotic and biotic stresses. PGPR are members of diverse bacterial genera, and
differences in plant protective capabilities can be linked to intrinsic biochemical activities.
Advances made in analytical instrumentation have made it possible to resolve analytes
present in a complex mixture in a short period of time. Here, SMPE–GC–MS, in combination
with chemometrics, were successfully used to profile the VOCs secreted from strains of
Ps. koreensis, Ps. fluorescens, L. sphaericus and Pa. alvei. Our results indicate that the VOC
profiles of rhizobacteria can be quite specific and that even strains of the same species may
present unique profiles. These observable differences in the VOC blends can be due to the
relative activities of specific metabolic pathways active in these strains. The main groups of
VOCs secreted by PGPR include ketones, alkanes, alkenes and alcohols, and these were all
identified in the present study. Of the investigated strains, the ‘volatilome’ of Pa. alvei (T22)
exhibited more unique features in comparison to the others. These annotated molecules
could be further investigated as biomarkers for the classification of an organism as a PGPR
and their selection for agricultural use.
Currently, efforts to elucidate the mode of action of a specific compound or mixture
regulating the plant physiological processes leading to growth, higher yield and inducible
protection are underway. Thus, these results support the view that diverse PGPR may
affect plant biochemical pathways and processes with different mechanism(s) of action.
This might even be applicable if the PGPR belong to the same species, emphasizing the
importance of characteristic strain-specific features. In addition, the number of annotated
VOCs indicates that there could be more bioactives/volatiles than have been reported thus
far. Furthermore, this research highlights the power of high-resolution mass spectrometry
for metabolomic application in order to gain deeper insight into the chemical signaling
pathways in the rhizosphere and the mechanism(s) of action of plant-protective bacteria.
Metabolomic profiling thus offers another tool to functionally dissect the role(s) of VOCs in
PGPR priming.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/metabo12080763/s1, Figure S1: Action mechanisms of PGPRderived VOCs; Figure S2: Representative GC–TOF–MS chromatograms of PGPR volatiles sampled by
headspace SPME. Figure S3: OPLS-DA modeling and variable/feature selection of Pseudomonas fluorescens (N04) data acquired using GC–TOF–MS. Figure S4: OPLS-DA modeling and variable/feature
selection of Pseudomonas koreensis (N19) data acquired using GC–TOF–MS. Figure S5: OPLS-DA
modeling and variable/feature selection of Paenibacillus alvei (T19) data acquired using GC–TOF–MS.
Table S1: VOC profiles of PGPR strains: Pseudomonas fluorescens (N04), Pseudomonas koreensis (N19),
Paenibacillus alvei (T19) and Lysinibacillus sphaericus (T22).
Author Contributions: Conceptualization, M.I.M. and I.A.D.; formal analysis, M.I.M.; investigation,
M.I.M.; methodology, M.I.M.; resources, I.A.D.; supervision, L.A.P. and I.A.D.; data curation, M.I.M.;
validation, M.I.M.; writing—original draft, M.I.M.; writing—review and editing, L.A.P. and I.A.D.
All authors have read and agreed to the published version of the manuscript.
Metabolites 2022, 12, 763
11 of 13
Funding: Funding from the South African National Research Foundation (NRF) to I.A.D. (grant
number 95818) supported the research.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data needed to evaluate the conclusions in the paper are present in the
paper and the Supplementary Materials. Additional data related to this paper may be requested from
the corresponding author.
Acknowledgments: N. Labuschagne is thanked for supplying the bacterial cultures used in this
study. LECO (SA) and M. Pieterse are thanked for supplying the analytical infrastructure and for
their assistance with the volatile analyses. The University of Johannesburg is thanked for providing
fellowship support to M.I.M.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
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