Discussion
Fluorescent Pseudomonas are a diverse group of bacteria
predominantly inhabiting the phyllosphere of sugar beet. They play an
important role in determining plant health, but our understanding of the
population structure is limited. Here, we present results of a MLSA
analysis of fluorescent pseudomonads associated with sugar beets. The
obtained MLSA data were analysed and compared with the utilization
patterns of 95 unique carbon substrates. Both MLSA and
BiologTM analyses indicated that the sugar
beet-associated Pseudomonas has an ecotypic population structure
with geographic location and leaf type as the most significant
determining factors. Interestingly, the MLSA data revealed an unusually
high recombination rate relative to the mutation rate. This led to
subsequent identification of six “ancestral” genotypes, which
significantly differed in the Oxford and Auckland sub-populations. There
was a clear significant correlation between the MLSA genotypes and
BiologTM phenotypes. Together, our results indicates
that MLSA analysis with only three genes can provide an excellent basis
on which to explore population structure, and a concurrent phenotypic
assay can enhance our understanding of bacterial core genome evolution
revealed by MLSA.
Our data consistently indicated that pseudomonads isolated from Oxford
and Auckland have distinct population structures. The two subpopulations
didn’t share any unique sequence types, and only have four common OTUs
clustered according to the mean pairwise distance of the population.
Given the large geographic distance between Oxford and Auckland, this
finding is not surprising as it is generally consistent with our current
knowledge of microbial biogeography (Nemergut et al., 2013). The
observed difference can be explained by the combined effects of
historical contingencies and contemporary environmental disturbances
(Sun et al., 2014). More specifically, soils are a reservoir of
plant-associated microorganisms; and the species composition of
microbial communities, including Pseudomonas , likely differ
between the Oxford and Auckland sites. Of particular note is that sugar
beets have been cultivated in Oxford soil for years prior to sampling,
but they have never been grown in Auckland, New Zealand. Hence, the
lower level of diversity in Oxford (Fig. 3D) and fewer unique OTUs are
likely a result of long-term selection by the sugar beet plant.
However, such a strong effect of geographic location inPseudomonas diversification has not been reported before. Only a
handful of MLSA studies are available for plant-associated fluorescentPseudomonas , but all previous reports suggested a cosmopolitan
distribution, and the same genotypes were often found in geographically
distant locations (Alvarez-Perez et al., 2013; Andreani et al., 2014;
Frapolli et al., 2007). For example, Frapolli et al. (2012) examined a
worldwide collection of plant-colonizing fluorescent pseudomonads using
MLSA of 10 housekeeping genes and 14 functional loci involved in the
production of secondary antimicrobial metabolites. Their results
revealed no specific linkage between genotype and geographic locations.
Although the work was performed with only 30 isolates from six crops,
the phenomenon was consistent with their prior work using the methods of
both MLSA and PCR-RFLP analysis (Frapolli et al., 2007; Wang et al.,
2001). Therefore, further larger scale MLSA analyses with multiple
cultivars and multiple geographic locations are necessary to verify the
roles of geographic factors and local plant environmental conditions in
shaping Pseudomonas population structure.
Recombination is a major driving force in shaping bacterial genetic
diversity, but its relative importance to mutation varies greatly among
different species (Gonzalez-Torres et al., 2019). A previous survey
showed that the highest and lowest r/m values differed by three orders
of magnitude in bacteria and archaea (Vos & Didelot, 2009). The ability
of recombination to cause changes in the genome exceed that of mutation
(r/m > 1) in more than half of the analyzed
bacterial and archaeal species (56%, 27 out of 48). A significant
finding in this work is the high recombination-to-mutation rate ratios
in sugar beet-associated Pseudomonas , particularly in the Oxford
subpopulation. An overall r/m of 5.23 was detected for the
concatenated sequences of three genes. This is in contrast to previous
MLSA and comparative genomic studies showing that P. aeruginosaand P. syringae populations were mostly clonal, and diversity is
largely determined by the process of mutation rather recombination
(Castaneda-Montes et al., 2018a; Nowell et al., 2016; Sarkar & Guttman,
2004; Straub et al., 2018). Relatively lower recombination levels were
also reported for P. putida and P. fluorescens (Ogura et
al., 2019). However, frequent recombination was detected in a MLSA study
with 501 P. areuginosa isolates collected from environmental,
animal and human samples in South East Queensland, Australia (Kidd et
al., 2012). Interestingly, contrasting recombination patterns were
revealed by MLSA of 38 nectar-inhabiting pseudomonads associated with
Mediterranean and South African plants (Alvarez-Perez et al., 2013).
Among the three main clades identified, two nectar groups have a mostly
clonal population structure, whereas the third one showed predominant
effects of recombination over mutation and exclusively consisted of
isolates from floral nectar of insect-pollinated Mediterranean plants.
Given the lack of consensus marker genes for MSLA in Pseudomonasand the variation in strain sampling in different studies, it is
difficult to understand the underlying causes for the observed higher or
lower recombination to mutation rates.
Mutation and recombination are the major sources of genetic diversity,
yet natural selection acts at the level of the phenotypes. A combination
of phenotypic and genotypic analysis is thus necessary for the proper
description of a bacterial population. Patterns of nutrient utilization
are important descriptors of physiological capability, and the data can
be obtained using the Biolog GN2 Microplate. This technique was
developed for rapid identification of Gram-negative bacteria through the
assessment of their ability to utilize a panel of 95 different carbon
sources. Data presented here revealed a significant correlation between
phenotypes and genotypes defined by Biolog analysis and MLSA,
respectively. Overall, the first principal component of phenotypes can
explain 62% of the observed diversity, but only 29% for first
principal component of genotypes. Furthermore, we identified that
utilization of eight carbon substrates were primarily responsible for
separating the Oxford and Auckland sub-populations. These included
urocanic acid (or urocanate).
Urocanate is the first intermediate of the histidine degradation pathway
(Zhang & Rainey, 2007). Both histidine and urocanate were included in
the GN2 MicroPlate, and their utilization patterns were tested in this
work. If a strain can grow on histidine (His+) it must
have all the catabolic enzymes required for the utilization of
urocanate. However, certain pseudomonads can grow on histidine, but not
on urocanate (His+, Uro-),
suggesting that these strains lack a functional transport system for
urocanate uptake. This led to a hypothesis that variation in histidine
and urocanate utilization is attributable to genetic differences in
transport systems (Zhang et al., 2012). This hypothesis was confirmed in
a previous study by heterogeneous complementation after the
urocanate-specific transporter (HutTu) was identified in the model
strain of P. fluorescens SBW25 (Zhang et al., 2012). Here, we
found a significant association of genotypes with the utilization of
urocanate but not histidine. While almost all Oxford strains (95%) were
capable of growing on urocanate, only one-third could grow on urocanate
in the Auckland sub-population (Fig. 7). Given the absence of historical
sugar beet cultivation in the Auckland soil, our finding sits in accord
with the previously proposed niche-specific accumulation of urocanatein planta (Zhang et al., 2013). Urocanate may act not only as a
nutrient, but also an important signal for successful bacterial
colonization. Based on this, it is logical that urocanate utilization is
widespread in the Oxford sub-population as a result of adaptation to the
local conditions of sugar beet phyllosphere. Fitness improvements
associated with urocanate utilization are likely to occur through
changes in the uptake systems (Dean, 1995; Zhang et al., 2012).
Together, our data provide an example of the genetic basis of phenotypic
variation for plant-associated fluorescent pseudomonads.