FEMS Microbiology Ecology 16 (1995) 9-18
Metabolic profiling as a means of characterizing plant-associated
microbial communities
Richard J. Ellis, Ian P. Thompson,
Mark J. Bailey
Natural Environment Research Council, Institute of Virology and Enuironmental Microbiology,
*
Mansfield Road, Oxford OX1 3SR, UK
Received 29 March 1994; revision received 14 July 1994; accepted 15 July 1994
Abstract
Microbial communities
from leaf and root habitats associated with sugar beet (Beta uulgaris) were characterized
according to their capacity to metabolize a range of 95 sole carbon sources available in a commercial assay, GN-BIOLOG
MicroPlates. Metabolic profiling was assessed as a method for evaluating perturbation of microbial communities of
glasshouse-grown
sugar beet inoculated with a genetically modified microorganism. This technique has allowed microbial
communities, colonising the immature leaves of treated and untreated plants to be differentiated, although no differences
were observed when plants inoculated with genetically modified microorganisms
and unmodified inoculated plants were
compared. As plants developed and differentiated,
the carbon utilization patterns observed allowed communities to be
grouped according to the habitat from which they were isolated, irrespective of treatment. These studies demonstrate that the
genetically modified microorganism,
introduced as a seed dressing colonised developing immature tissue throughout the
231-day study but did not disrupt the natural succession of microbial communities in glasshouse-grown
sugar beet plants.
Keywords:
Metabolic
profiling;
Impact assessment;
Community
1. Introduction
Plant-associated
microbial
communities
vary spatially and temporally
[1,2]. Greater variation in community structure has been recorded between samples
collected from the leaf habitat (phyllosphere)
than
for samples collected from the root habitat (rhizosphere) of the same plant species 131. This variation
may be due to exposure of the leaf habitat to extreme
fluctuations in humidity, temperature and ultra-violet
radiation which creates a highly selective and changing environment to which colonisers must adapt [4].
* Corresponding author. Tel: 0865 512361; Fax: 0865 59962;
E-mail: [email protected]
structure; Genetically
modified
(GMM)
such conditions
the phyllosphere
is often
dominated by a limited number of bacterial taxa [5,6]
whereas the more stable conditions experienced in
the rhizosphere result in communities that are often
less variable but more diverse. In addition to physical stress, nutrients on the leaf surface are limiting.
Morris and Rouse [7,8] demonstrated changes in the
bacterial community structure following the application of a nutrient source to leaves. Although little is
known of the nutritional status of leaves of any plant
species, studies of the leachates collected from leaves
have shown that many sugars, amino acids and
organic acids can be recovered [9], and may be
available for utilization by the microbial community
[lo]. Specific information on the relative concentrations of leaf associated nutrients is lacking, hence
Under
0168~6496/95/$09.50 0 1995 Federation of European Microbiological Societies. All rights reserved
SSDI 0168-6496(94)00064-6
microorganism
10
R.J. Ellis et al. /FEMS
Microbiology
their role in microbial survival and community succession is unknown.
Studies of microbial community structures have
been limited due to the difficulties of bacterial identification [ll], and an inability to isolate populations
representative of the whole community [6]. In some
cases meaningful
results have been achieved by
studying the capability of a community
to utilize
substrates rather than by investigating
individual
members of the community [l&12]. Methods which
evaluate nutrient utilization may provide a valuable
insight into the function of bacterial communities [6]
and a better way of determining shifts in community
structure caused by changes in the habitat [13].
The diversity of microbial communities associated
with plants can be classified in terms of traits related
to survival and growth [8]. As microbes rapidly adapt
to changes in the environment,
especially when nutrient availability becomes the limiting factor, nutrient utilization may be an ideal function to study in
an attempt to define the community. Individuals that
are metabolically versatile may demonstrate a greater
potential for survival in conditions where nutrient
availability varies. Conversely, organisms optimized
for specific conditions may compete effectively with
those showing greater versatility when the conditions
remain stable. The choice between generalisation
or
specialisation of an organism may be determined by
the nature of its environment [14]. Support for these
considerations
have been provided by Lindow [15]
who noted that in complex communities
individual
members utilize a more extensive range of nutrients.
The development of commercial systems for identifying bacterial isolates by means of sole-carbonsource utilization patterns [16] provides a method by
which assessment of the nutritional potential of whole
communities
can be made. BIOLOG
GN MicroPlates (BIOLOG Inc., Hayward, CA> assess the
ability of inocula to utilize 95 different carbon sources
(including carbohydrates, organic acids, amino acids,
polymers, esters, alcohols, amides, amines, aromatics, and phosphorylated and brominated compounds)
in a single step. The potential of this method for
differentiating
communities
from soil and aquatic
habitats has already been demonstrated [13].
To realize the potential and hazards of genetically
modified inoculants in agricultural systems, a better
understanding
of the microbiology
of plants is re-
Ecology 16 (1995) 9-18
quired. The introduction
of a genetically modified
microorganism (GMM) to a natural habitat may disrupt the ecology of the existing microbial community
and produce an undesired effect. A knowledge of the
indigenous microbial community is a prerequisite for
the release of GMMs. Investigations
of the microflora of sugar beet indicated that fluorescent pseudomonads
were the most abundant
colonizers
throughout the growing season [2,17]. An isolate
from sugar beet leaves, Pseudomonas SBW25 [18],
was selected and chromosomally
marked by site
directed homologous recombination
with two gene
cassettes. The recombinant
bacteria, SBW25EeZY
6KX (Bailey, unpublished),
differed from the wildtype only by the expression
of P-galactosidase
(lucZY), kanamycin resistance (kun’) and catechol
2,3 dioxygenase (xyZE). The impact of the release of
SBW25EeZY6KX
on microbial succession in the
by comparing
the
phytosphere
was assessed
metabolic
profiles of microbial
communities
extracted from GMM-treated and -untreated sugar beet
plants.
2. Materials
and methods
2.1. Plants and bacteria
Sugar beet (Beta vulgaris var. Amethyst, Germains, UK) plants were grown in an air conditioned
glasshouse, in 20 X 20 cm pots of unmodified soil
obtained directly from a field site at the University
Field Station, Wytham, Oxford, UK. All plants were
watered by drip irrigation to the soil surface to
maintain moisture content. Plants were grown under
conditions
of normal day length (August 1992March 1993) with air temperature maintained between 11°C and 34°C.
Bacteria (fluorescent pseudomonad
SBW25 and
its genetically
modified
form SBW25EeZY6KX
(ZucZY, kun’, xyZE)) were grown overnight at 28°C
in Tryptic soy broth (Difco, UK), washed and resuspended in an equal volume of quarter strength Ringer
solution (Oxoid, UK). Pelleted commercial
sugar
beet seeds (Germains, Kings Lynn, UK) were soaked
in suspensions of SBW25 or SBW25EeZY6KX
or
sterile Ringer solution for 10 min, dried on filter
paper and planted immediately
in soil. Each seed
R.J. Ellis et aL/FEMS
carried approximately
(cfu) of the inoculated
2 X 10’ colony
strain.
forming
Microbiology
units
2.2. Sample preparation
Three leaf types (senescing, mature and immature) and root peel (rhizoplane) were sampled from
fully differentiated plants on four occasions, 64, 103,
190 and 231 days after planting. On each sampling
occasion three replicates from each treatment were
collected, each replicate containing pooled material
from three plants grown in the same pot. Pooled
samples of whole leaf or 1 mm thick root peel were
suspended 1 part in 10 (w/v) in i strength Ringer
solution (Oxoid, UK) and homogenized in a Waring
Commercial Blendor at full speed for 1 min. The
resulting homogenate was serially diluted (lo-fold).
2.3. Metabolic profiling
The pellet from 1 ml of undiluted homogenate
was washed twice in 30 ml 0.85% saline by centrifugation at 7740 X g at 4°C for 15 min (Beckman,
UK) and then resuspended in 20 ml 0.85% saline.
I
II
11
Ecology 16‘(1995) 9-18
Commercially
prepared microtitre plates (BIOLOG,
Hayward, CA) with a total of 95 different solecarbon-sources
in individual wells, together with a
redox dye, tetrazolium violet, which indicates respiration via production of NADH [16] were inoculated
with 150 ~1 aliquots of the suspension in each well.
The plates were incubated at 28°C for 60 h. Each
well was scored on a 4-point scale (0,1,2,3) according to the extent of colour production observed and
entered in a data base for statistical analysis.
2.4. Bacterial
enumeration
Diluted suspensions
of plant tissue homogenate
were spread onto 3% Tryptic soy broth agar (TSBA,
Difco, UK) amended with 50 mg 1-l cyclohexamide
and Pseudomonas
agar base supplemented
with 10
mg 1-l cetrimide, 10 mg 1-r fucidin, and 50 mg 1-l
cephaloridine
(PSA-CFC, Oxoid, UK). Agar plates
were incubated at 15°C for 4 days and those containing between 20 and 200 colonies counted. The numbers of bacteria and pseudomonads
present were
expressed as cfu per gram (dry weight) of tissue. Dry
Ill
Iv
Fig. 1. Dendrogram showing the relationships between the metabolic profiles of microbial communities isolated from glasshouse-grown
sugar beet. Samples were taken from treated and untreated plants from different leaf types throughout the growing season. The relationship
between samples is measured in Euclids and clustered by the ‘average between groups’ method. (Key: Treatment; W-wild-type SBW25,
R-recombinant
SBW25EeZY-6KX,
U-untreated; 64, 103, 190 and 231 days after planting; Tissue type; S-immature leaf, 3’-mature leaf,
r-senescent leaf; replicate; - 1, - 2, - 3). Cophenetic correlation coefficient = 0.887.
R.J. Ellis et al. /FEMS Microbiology Ecology 16 (1995) 9-18
12
Data analysis and statistics
The relationship between metabolic profiles was
determined by calculation of Euclidean distance, a
measure of dissimilarity, and clustered by the ‘average between groups’ method (UNISTAT, UK). The
actual distances calculated and the distances shown
on the dendrogram were compared by Pearson product-moment
correlation (cophenetic
correlation)
to
ensure that the dendrogram gave an accurate representation of the data [19]. The relationships between
samples taken over the season and those taken on the
same day from different habitats were investigated.
An estimation of the metabolic diversity was made
by calculating the percentage of positive reactions in
each plate. The variance in the values obtained was
assessed by one way analysis of variance and the
least significant difference (LSD) determined to test
the significance
of the differences between group
means [20]. The degree of association between BIOLOG metabolic diversity estimations and the plate
18
,
count data was determined by Pearson product-moment correlation and significance tested by the one
tailed t-test. All statistical analysis was undertaken
using the UNISTAT statistical package (Unistat Ltd.,
London, UK).
The data for bacterial enumeration
of triplicates
from four tissue types derived from three treatments
(untreated, wild-type SBW25 treated and recombinant SBW25 treated) of sugar beet plants 231 days
after planting (3 X 4 X 3 = 36 samples) were log
transformed and were analysed by two-way analysis
of variance [20]. Differences between variances of
groupings
generated
by cluster analysis
of the
metabolic profiles were assessed by the variance
ratio test (F-test) [21].
3. Results
3.1. Metabolic profding
Fig. 1 shows the relationship
metabolic profiles obtained from leaf
on four occasions (64, 103, 190 and
sowing) during development of sugar
between
the
samples taken
231 days after
beet plants in
I
17 16 15 14 13 g
12-
d
a
'llo-
5
fg
Q0-
4
7-
UJ
65432lo-
Fig. 2. Dendrogram indicating the relationships between metabolic profiles of leaf and root microbial communities from glasshouse-grown
sugar beet plants 231 days after planting. The relationship between each replicated sample is measured in Euclids and clustered by the
‘average between groups’ method. (Key: Treatment; W-wild-type SBW 25, R-recombinant
SBW 25, U-untreated; Replicate 1, 2 and 3;
Tissue type; 5’-immature leaf, 3’-mature leaf, l’-senescent leaf, RT-root peel). Cophenetic correlation coefficient = 0.922.
R.J. Ellis et al. / FEMS Microbiology
the glasshouse. Cluster analysis separated the samples into four distinct groups. Group I consisted of
the immature (expanding) leaf samples together with
samples of maturing leaf from young plants (sampled 64 days after planting) treated with inocula.
Group II contained samples taken only from the
senescent leaves of 231-day-old plants from each of
the three treatments. Group III, the largest cluster,
included a mixture of profiles from the phyllospheres
of treated and untreated plants sampled throughout
the study. Group IV clustered the profiles of all the
mature leaf communities taken on day 231 irrespective of the original inocula.
To assess the relationship between the sole carbon
source utilization profiles of microbial communities
isolated from the different habitats in greater detail,
the profiles of the communities
isolated from both
leaf and root samples on day 231 clustered as shown
in Fig. 2. The dendrogram revealed five clusters: A,
B, C, D and E. Clusters A and C differentiated
samples extracted from senescent leaves and roots,
irrespective
of inocula. Cluster B contained only
samples collected from the immature leaves of inocula-treated plants. Cluster D contained a mixture of
samples, which demonstrates
the overlap between
Ecology 16 (19955) 9-18
13
the metabolic profiles of communities from roots and
senescing and immature leaves. This cluster also
contained the untreated immature leaves and one
treated immature leaf sample, R2-5’. Closer analysis
of the profiles from immature leaves revealed that
the R2-5’ community utilized 8 substrates not utilized by other communities
from treated immature
leaves but utilized by all communities from untreated
immature leaves. Cluster E, as with Group IV (Fig.
l), contained all the samples taken from mature
leaves together with a single senescing leaf sample,
Wl-l’. Sample Wl-1’ had a relatively low pseudomonad population compared to others of the same
tissue type, W2-1’ and W3-l’ (Fig. 3).
The proportion of the 95 different carbon sources
that were utilized by a mixture of organisms was a
measure of the complexity of that group, in terms of
its metabolic versatility. An estimate of metabolic
diversity was made by calculating the proportion of
reaction-positive
wells for each sample taken on day
231 examined by the BIOLOG method (Table 1).
The metabolic diversity of the samples in Group E
(mature leaves) was significantly
(P = 0.05) lower
than all other groups. Group D showed significantly
(P = 0.05) greater metabolic diversity than Group B
-Total
bacteria OPseudomonads
Fig. 3. Total bacterial and pseudomonad counts of leaf and root microbial communities from glasshouse-grown
sugar beet plants 231 days
after planting. Counts are expressed as cfu per gram dry weight of tissue and grouped according to the clusters identified in Fig. 2; for key
see Fig. 2.
14
R.J. Ellis et al./FEMS
Table 1
Estimations
of metabolic diversity
communities from glasshouse-grown
after planting.
Microbiology
of leaf and root microbial
sugar beet plants 231 days
Cluster
group a
Mean % positive
wells b
Standard
deviation ’
A
B
C
D
E
62.4
55.2
63.5
71.0
16.6
9.5
5.7
4.7
1.3
12.5
a Cluster group as indicated in Fig. 2.
b Mean percentage of positive wells on BIOLOG plate per sample.
’ Standard Deviation of the mean.
The least significant difference (LSD) for the data set at 95%
probability was 10.1.
(immature leaves from inoculated plants). Furthermore, the percentage carbon sources used on each
BIOLOG plate correlated with the corresponding
total bacteria counts and pseudomonad counts (Fig.
3). Total bacteria gave a Pearson correlation coefficient (r) of 0.7057 and the value for the number of
pseudomonads
was r = 0.8067. The significance of
both these values was high (P < 0.0005) indicating
that the proportion of substrates utilized was related
to the number of total bacteria and pseudomonads
that could be cultivated.
3.2. Bacterial
numbers determined
by colony counts
Total bacterial and pseudomonad counts from the
samples taken 231 days after planting are shown in
Fig. 3. Data were placed in the same order along the
X-axis as recorded in Fig. 2. Analysis of variance of
the log transformed data revealed that the different
tissue types bore significantly different total bacteria
and pseudomonad
numbers (P < 0.0001). Only the
variance of count data from Group C was significantly less than the variance of the complete data set
(P = 0.0006), indicating that the groupings were not
based solely on the similarity of count data. For
example, similar count data were recorded for samples U3-RT
and Rl-RT
but classification
by
metabolic profiling placed them in different groups
(C and D respectively, Fig. 2).
Analysis of variance revealed that the seed treatment (GMM treated, wild-type treated or untreated)
Ecology 16 (1995) 9-18
had no significant effect on either the numbers of
total bacteria or pseudomonads
recovered from any
tissue type sampled from the mature plants at day
231. However, metabolic profiling showed distinction between the immature leaves from treated and
untreated plants. The numerical abundance of the
inocula in this habitat was confirmed by the identification of SBW25EeZY6KX
on PSA-CFC indicator
agars containing X-gal, which is converted to a blue
pigment by /3-galactosidase
(1acZ gene product).
GMM represented 30-50% of total bacteria isolated
from the 3 replicates of immature leaves sampled
231 days after planting (data not shown). Metabolic
profiles from immature leaves of GMM-treated plants
showed an average of 92% similarity to the metabolic
profiles of pure cultures of SBW25EeZY6KX,
indicating the metabolic dominance of the inocula.
4. Discussion
Characterization
of microbial communities on the
basis of nutrient utilization profiles using BIOLOG
was found to be a rapid and effective means of
differentiating communities isolated from plant associated habitats. Comparison of the metabolic profiles
obtained allowed differentiation of communities that
could not have been otherwise separated by the
classical plate count techniques used in this study.
The BIOLOG approach was rapid and permitted a
simple and direct evaluation of whether the establishment of microbial communities
was disrupted
following the introduction of a large inoculum.
The metabolic profiles of the extracted communities were grouped according to habitat (Figs. 1 and
2). Fig. 1 illustrates that the community metabolic
profiles were influenced by the extent of the differentiation of the leaves and plant age at sampling.
However, difficulties were encountered in the characterisation of the leaves from some of the younger
plants. The distinction between expanding immature
leaves and early mature leaves was not always obvious, as highlighted in the data set obtained for the
untreated sample collected on day 64. Attempts to
minimise the variation in community size and composition between replicated samples were made by
the analysis of pooled samples. In some instance this
variation remained too great to accurately cluster
R.J. Ellis et al./FEMS
Microbioology Ecology 16 (1995) 9-18
samples into habitat specific groups. Such variety in
composition was observed in Group III which represented a mixture of treatments and habitats from
maturing plants.
Fig. 2 details the clustering of metabolic profiles
from a single sampling occasion where variation
within the sampled habitats was still observed (Fig.
2; Group D). Explanations for this variation include
the inability to effectively
distinguish
habitats or
more probably, the further demonstration
of natural
variation in numbers and species composition
as
determined by traditional identification methods [2].
The ability to detect disruption of microbial community structure following
seed inoculation
with
SBW25 (recombinant
or wild-type) was dependent
on the tissue type sampled. In the root, senescing and
mature leaves the inoculum had no detectable effect
as both treated and untreated samples cluster within
the same groups (Fig. 2; Groups C, A, and E,
respectively). However, the immature leaves of inoculated plants had significantly
different profiles to
those from uninoculated
plants. Both wild-type and
recombinant treated immature leaf samples grouped
in cluster B (Fig. 2) whereas all three untreated
immature leaf samples were clustered in Group D.
The observation
that R2-5’ (day 231) utilized 8
substrates in common with the untreated immature
leaf samples implied that this community contained
an additional component or components not found in
the other treated communities. Group B also showed
significantly reduced metabolic diversity (P = 0.05)
compared to Group D. This was attributed to SBW25
dominating the metabolic activity of the community
on the emerging leaves. This metabolic dominance
was confirmed by the degree of similarity between
pure cultures of inoculum SBW25 and the community profiles determined for the immature leaves of
treated plants. The insertion of the marker genes
provided a unique opportunity to assess the relative
numbers of inocula (GMM) in each habitat. In immature tissue the inoculum represented more than 30%
of the community and although GMM persisted in
other tissue habitats it represented less than 10% of
these communities.
As the leaves mature, regardless
of the presence of the original inoculum of SBW25,
new components become established either as a result of immigration or effective competition against
the initial colonising inocula.
15
The method described was adapted from the approach described by Garland and Mills for comparing aquatic and soil environments
[13]. However, in
our study the data collection procedures were simplified, whilst retaining the same degree of sensitivity,
by determining
the end point rather than rate of
substrate utilization. This allowed the ability of a
community to utilize a substrate, rather than the rate
at which it was utilized, to be the differentiating
factor. Investigations
of the nature of the assay
demonstrated
that little growth (replication) of the
bacteria occurred in the wells of the BIOLOG plates.
This was an important consideration for the validation of the assay. Metabolic profiling therefore reflects the potential for activity within the community
sample rather than the rapid response of an individual population which might mask the activity of
slower growing organisms. The limited growth observed implied that this approach might overcome
the problems of selectivity and inhibition normally
associated with characterizing
microbial communities by agar plate methods. The BIOLOG method
provides a means of characterizing
microbial communities with the minimum of selective pressure to
include the less active as well as the more dominant
components of the community.
By analysing colony count data it has been possible to determine some of the underlying
factors
influencing differences in the metabolic profiles. The
comparisons revealed that community metabolic profiles were a function of total bacterial and pseudomonad numbers as well as community composition. For example, significantly lower counts of total
bacteria and pseudomonads
were recorded in the
mature leaves sampled on day 231 and these samples
not only clustered away from the other samples but
also utilized the smallest proportion of the substrates
(i.e. had lowest metabolic diversity). This lower
diversity reflects the more limited nutrient availability of the fully expanded mature sugar beet leaf
compared to the degrading senescent leaves and the
rapidly expanding immature leaves. These samples
also provided the smallest inocula to each of the
assay wells, approximately 1 X lo3 cfu. Studies with
pure cultures demonstrate that fewer than 10 cfu are
required for a positive reaction (results not shown). It
has been suggested that in habitats in which lower
numbers of bacteria are present there is less competi-
16
R.J. Ellis et al. /FEMS
Microbiology
tion and thus metabolic versatility is not a prerequisite for survival in this habitat [151.
The BIOLOG system was originally developed to
characterize isolates for bacterial identification [16].
Studies of pure cultures revealed that the fluorescent
pseudomonads were capable of utilizing over 55% of
the substrates provided. Of these, only 20% were
simple sugars. Other species common in the phytosphere, such as the Enterobacteriacae,
were capable
of utilizing less than 45% of the substrates, 50% of
which were sugars. Despite the greater metabolic
diversity of the pseudomonads,
the preference for
substrates other than sugars [15] means that the
metabolic activity of other phytosphere colonizers
will not be masked. The use of BIOLOG MicroPlates allowed the activity of many different components to be assessed and therefore provides a
useful tool for characterizing microbial communities.
An important advantage of the BIOLOG over
other methods that attempt to characterize microbial
communities is the ability to comment on the function of that community.
This may be especially
relevant when attempting to assess community perturbation following the introduction
of organisms.
Other attempts to assess inocula impact have focused
on evaluating the processes of individuals in isolation from the parent community [22,23]. Metabolic
profiling by BIOLOG permits the rapid and simple
assessment of whole community processes and the
detection of interactive processes between two or
more components of a community.
Acknowledgements
R.J.E. and I.P.T. were funded by the Department
of the Environment,
UK contract PECD 6/S/ 143.
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