Introduction
The health and wellbeing of plants is to a large extent determined by
the microorganisms
with which they co-exist (Compant et al., 2019). While some bacteria can
cause disease, others have the capacity to confer disease resistance and
promote plant growth. A typical example concerns bacteria from the genus
of Pseudomonas , which are ubiquitously found in soil and waters,
but also form intimate associations with plants (Silby et al., 2011).
Among the ~190 Pseudomonas spp. validly described
to date, twenty-one are known to cause plant diseases with over 60
pathovars (Peix et al., 2018). These include P. syringae , a well
described pathogen for many important crops, such as kiwifruit, tomato
and beans (Arnold & Preston, 2019; Straub et al., 2018). However,
certain strains of other Pseudomonas spp. (mostly P.
fluorescens ) possess plant growth promotion and disease suppression
activities (Hsu & Micallef, 2017; Z. Liu et al., 2018; Stritzler et
al., 2018). They are capable of producing plant hormones and secondary
metabolites (e.g., organic acids) that can help release nutritional
substrates from the soil, particular phosphate (Hol et al., 2013; Oteino
et al., 2015). Pseudomonads can potentially exclude pathogens that are
in direct competition for available niches in the plant environments,
owing to their ability to rapidly colonise plant surfaces. Additionally,
pseudomonads are known to produce various antimicrobial compounds, such
as cyclic lipopeptides, hydrogen cyanide and 2,4-diacetylphloroglucinol,
which provide protection against plant infectious diseases (Flury et
al., 2017; Frapolli et al., 2007).
Sugar beet is commercially grown in Europe for sugar production. Early
studies performed in the late 1980s with field-grown sugar beets at the
University of Oxford farm (Wytham, Oxford) showed that fluorescent
pseudomonads are the largest group of bacteria inhabiting the
phyllosphere of sugar beet, and their species composition changes during
the growing season (Rainey et al., 1994). As a representative of sugar
beet-associated pseudomonads, P. fluorescens SBW25 was used as a
model for further genetic and biological analysis of plant-bacterial
interactions (Bailey et al., 1995; Rainey, 1999; Silby et al., 2009).
First of all, it is interesting to note that P. fluorescens SBW25
is able to aggressively colonise other crops such as wheat, maize and
peas, suggesting that the interactions are not species-specific
(Humphris et al., 2005; Jaderlund et al., 2008). This bacterium has,
thus, likely evolved functional traits for successful plant colonization
in general (Rainey, 1999). Both in vivo and in vitrostudies indicated that SBW25 can protect sugar beet seedlings against
damping-off disease caused by the soilborne fungal pathogenPythium ultimum (Ellis et al., 2000). A non-proteinogenic amino
acid (L-furannomycin) was identified as one of the antimicrobial
compounds produced by SBW25 (Trippe et al., 2013). Furthermore, promoter
trapping techniques were developed for the SBW25/sugar beet model, and
their subsequent application led to identification of 139 loci, which is
expressed at elevated levels during bacterial colonization in
planta (Rainey, 1999; Silby et al., 2009). Some plant-inducible genes,
particularly those involved in biofilm formation and histidine
utilization (hut ), have been investigated in great detail (Gal et
al., 2003; Y. Liu et al., 2015). However, our understanding of the
genetic diversity and population structure of fluorescentPseudomonas is limited.
In a previous study, 30 fluorescent pseudomonads were isolated from the
phyllosphere of field-grown sugar beet in Oxford from where P.
fluorescens SBW25 originated (Rainey et al., 1994). These isolates
represented Pseudomonas present during a single growing season.
They were subjected to restriction fragment length polymorphism (RFLP)
analysis and phenotypic characterization using methods including fatty
acid methyl ester (FAME), biochemical properties and carbon source
assimilation. The phenotypic data consistently showed that these
isolates were grouped according to their time of sampling and leaf type
(immature, mature and senescent). While the RFLP data were complicated
by the presence of megaplasmids, the derived genotypic groups were
closely correlated with clusters generated on the basis of the
phenotypic data (Tett et al., 2007). The data thus implicated adaptation
of pseudomonads to the local plant conditions.
This initial finding prompted further analysis of the Pseudomonaspopulation structure whereby a total of 108 isolates were collected in a
single sampling occasion in the same field in Oxford (Haubold & Rainey,
1996). These isolates were phenotypically characterised using 10
allozyme and 23 biotype markers. The allozyme data indicated that thePseudomonas population was in overall linkage disequilibrium and
showed an ecotypic structure. There was a significant correlation
between isolate distribution and habitat, i.e. leaf type and plot.
Moreover, the data also suggested a probability of frequent large-scale
recombination among certain isolates. However, these fluorescent
pseudomonads were not genotypically characterized, and consequently, the
extent of recombination and its potential impacts on Pseudomonasdiversity has not yet been assessed. Furthermore, there is no previous
research regarding how the sugar beet-associated Pseudomonaspopulations differ between Oxford and elsewhere.
Multilocus sequence analysis (MLSA) has become a universal technique for
studying the population genetics of bacteria, includingPseudomonas (Bennasar et al., 2010; Castaneda-Montes et al.,
2018b; Ogura et al., 2019). It involves a comparative sequence analysis
of three or more housekeeping genes, which together provide higher
resolution of the phylogenetic relationships, when compared with
analysis of 16S rRNA genes. Nucleotide sequences can be obtained from
DNAs amplified by PCR or directly extracted from genome sequences if
available. While whole genome sequencing (WGS) can provide information
about the entire gene content, and thus, an idea of the pan-genome
(McCann et al., 2017), inferences on parameters governing molecular
evolution and geographic structure can readily be obtained from a
detailed analysis of a small set of conserved genes (Ogura et al., 2019;
Straub et al., 2018). In analyses of Pseudomonas populations,
MLSA has most frequently been used for analysis of the plant pathogenic
bacterium P. syringae (Akira & Hemmi, 2003; Straub et al.,
2018), and the opportunistic human and animal pathogen P.
aeruginosa (Castaneda-Montes et al., 2018b; Kidd et al., 2012). MLSA
schemes have also been developed for P. putida and P.
fluorescens (Andreani et al., 2014; Garrido-Sanz et al., 2016; Ogura et
al., 2019). However, MLSA has rarely been applied to plant-associated
fluorescent Pseudomonas (Alvarez-Perez et al., 2013), which
comprise several phylogenetically distinct species with a common feature
of pyoverdine production. Pyoverdines are siderophores secreted by
fluorescent pseudomonads for iron acquisition. They are normally used as
a marker for strain identification because of the distinguishable
fluorescent yellow-green colour.
Here, we describe the population structure and diversity of fluorescentPseudomonas inhabiting the phyllosphere of sugar beet (Beta
vulgaris var. Amethyst). The same plant cultivar was grown in two
geographic locations (Oxford, UK and Auckland, New Zealand), and
bacterial samples were taken from three leaf types (immature, mature and
senescent). We first performed MLSA analysis, and obtained complete
sequences of three genes (gapA , gltA , acnB ) for a
total of 152 isolates. The MLSA data indicated that thePseudomonas population was primarily associated with geographic
location and leaf type from where they were isolated. We found evidence
of significant recombination and identified six ancestral genotypes.
Next, we performed BiologTM assays to determine the
ability of Pseudomonas to grow on 95 unique carbon sources,
including histidine and its derivate urocanate. The data allowed
assessment of the potential correlations between the observed genotypes
and phenotypes, and a discussion of the underlying mechanisms of
bacterial diversification using the dissimilation of histidine and
urocanate as an example.