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\begin{document}

\title{Genotypic and phenotypic analyses reveal distinct population structures
and ecotypes for sugar beet-associated \emph{Pseudomonas} in Oxford and
Auckland}



\author[1]{Xue-Xian Zhang}%
\author[2]{Stephen Ritchie}%
\author[3]{Hao Chang}%
\author[4]{Dawn Arnold}%
\author[5]{Robert Jackson}%
\author[6]{Paul Rainey}%
\affil[1]{Massey University, Auckland }%
\affil[2]{The University of Auckland Faculty of Medical and Health Sciences}%
\affil[3]{Massey University Auckland}%
\affil[4]{University of the West of England - City Campus}%
\affil[5]{University of Birmingham}%
\affil[6]{Massey University - Albany Campus}%


\vspace{-1em}



  
  \date{June 08, 2024}


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\selectlanguage{english}
\begin{abstract}
Fluorescent pseudomonads represent one of the largest groups of bacteria
inhabiting the surfaces of plants, but their genetic composition in
planta is poorly understood. Here, we examined the population structure
and diversity of fluorescent pseudomonads isolated from sugar beet grown
at two geographic locations (Oxford, UK and Auckland, New Zealand). To
seek evidence for niche adaptation, bacteria were sampled from three
types of leaves (immature, mature and senescent) and then characterized
using a combination of genotypic and phenotypic analysis. We first
performed multilocus sequence analysis (MLSA) of three housekeeping
genes (\emph{gapA}, \emph{gltA}, \emph{acnB}) in a total of 152 isolates
(96 from Oxford, 56 from Auckland). The concatenated sequences were
grouped into 81 sequence types and 22 distinct operational taxonomic
units (OTUs). Significant levels of recombination were detected,
particularly for the Oxford isolates (rate of recombination to mutation
(r/m) = 5.23 for the whole population). Subsequent ancestral analysis
performed in STRUCTURE found evidence of six ancestral populations, and
their distributions significantly differed between Oxford and Auckland
strains. Next, the ability to grow on 95 carbon sources was assessed
using the BiologTM GN2 microtiter plates. A distance matrix was
generated from the raw growth data (A660) and subjected to
multidimensional scaling (MDS) analysis. There was a significant
correlation between the substrate utilization profiles and MLSA
genotypes. Both phenotypic and genotypic analyses indicated presence of
a geographic structure for strains from Oxford and Auckland. Significant
differences were genotypically detected between strains isolated from
immature versus mature/senescent leaves. The fluorescent pseudomonads
thus showed an ecotypic population structure, suggestive of adaptation
to both geographical and local plant environments.%
\end{abstract}%



\sloppy


\textbf{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 \emph{Pseudomonas} , which are ubiquitously found in soil and waters,
but also form intimate associations with plants (Silby et al., 2011).
Among the \textasciitilde{}190 \emph{Pseudomonas} spp. validly described
to date, twenty-one are known to cause plant diseases with over 60
pathovars (Peix et al., 2018). These include \emph{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 \emph{Pseudomonas} spp. (mostly \emph{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, \emph{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 \emph{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 \emph{in vivo} and \emph{in
vitro}studies indicated that SBW25 can protect sugar beet seedlings
against damping-off disease caused by the soilborne fungal
pathogen\emph{Pythium 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 \emph{in
planta} (Rainey, 1999; Silby et al., 2009). Some plant-inducible genes,
particularly those involved in biofilm formation and histidine
utilization (\emph{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
fluorescent\emph{Pseudomonas} is limited.

In a previous study, 30 fluorescent pseudomonads were isolated from the
phyllosphere of field-grown sugar beet in Oxford from where \emph{P.
fluorescens} SBW25 originated (Rainey et al., 1994). These isolates
represented \emph{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
\emph{Pseudomonas}population 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 the\emph{Pseudomonas} 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
\emph{Pseudomonas}diversity has not yet been assessed. Furthermore,
there is no previous research regarding how the sugar beet-associated
\emph{Pseudomonas}populations differ between Oxford and elsewhere.

Multilocus sequence analysis (MLSA) has become a universal technique for
studying the population genetics of bacteria,
including\emph{Pseudomonas} (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 \emph{Pseudomonas} populations,
MLSA has most frequently been used for analysis of the plant pathogenic
bacterium \emph{P. syringae} (Akira \& Hemmi, 2003; Straub et al.,
2018), and the opportunistic human and animal pathogen \emph{P.
aeruginosa} (Castaneda-Montes et al., 2018b; Kidd et al., 2012). MLSA
schemes have also been developed for \emph{P. putida} and \emph{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 \emph{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
fluorescent\emph{Pseudomonas} inhabiting the phyllosphere of sugar beet
(\emph{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 (\emph{gapA} , \emph{gltA} , \emph{acnB} ) for
a total of 152 isolates. The MLSA data indicated that
the\emph{Pseudomonas} 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 Biolog\textsuperscript{TM} assays to
determine the ability of \emph{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.

\textbf{Materials and Methods}

\textbf{Isolation and culture of pseudomonads.} A total of 170
leaf-colonizing \emph{Pseudomonas} strains were isolated from nine sugar
beet plants (\emph{Beta vulgaris} var. \emph{Amethyst} ) in three
experimental plots at the University farm, Wytham, Oxford, UK in 1993.
Some of these strains (108 isolates) were subjected to a previous
multi-locus enzyme electrophoresis (MLEE) analysis wherein details of
the sampling methods are provided (Haubold \& Rainey, 1996). Briefly,
each plant was divided into three leaf types (immature, mature and
senescent), and for each leaf type two replicate leaves were placed in a
sterile plastic bag containing 5 ml sterile water. After a 1 min
``massage'', the bacterial suspensions were dilution-plated onto King's
Medium B (KB) supplemented with CFC (10 \selectlanguage{greek}µ\selectlanguage{english}g/ml cetrimide, 10 \selectlanguage{greek}µ\selectlanguage{english}g/ml
fucidin, and 50 \selectlanguage{greek}µ\selectlanguage{english}g/ml cephalosporin) from Oxoid (Hampshire, UK). After
incubation at 28 \textsuperscript{o}C for 48 hrs, two colonies were
randomly picked for each leaf sample and purity of the isolates was
further checked by streaking onto KB agar plates. The obtained isolates
were sub-cultured in Luria broth (LB), and subsequently stored frozen at
-80 \textsuperscript{o}C by mixing 1.0 ml culture with 0.8 ml glycerol
saline solution (70\% glycerol, 0.85\% NaCl).

In 2004, five plants of the same \emph{B. vulgaris} variety were grown
at one site located in West Auckland, New Zealand;
\emph{Pseudomonas}strains were isolated similarly using the KB + CFC
selective plates. Isolates were coded similarly as strains from Oxford,
indicating their origin of isolation: plot (1), plant (1 to 5), leaf
type (1, senescent; 3, mature; 5, immature) and replicate leaf (a or b).
A prefix X was assigned to distinguish the Auckland isolates from those
from Oxford. For example, isolate X131b1 identifies isolate 1 from
Auckland plot 1, from plant 3, from senescent (1) leaf ``b''. Isolates
from Oxford were additionally assigned a more simplified code from U100
to U269.

\textbf{PCR amplification and DNA sequencing} . Total DNA was extracted
from bacterial cells using the CTAB-based method as previously described
(Zhang et al., 2001). Briefly, bacteria were resuspended in 567 \selectlanguage{greek}µ\selectlanguage{english}l TE
buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0) to which 30 \selectlanguage{greek}µ\selectlanguage{english}l of 10\% SDS
and 3 \selectlanguage{greek}µ\selectlanguage{english}l of 20 mg/ml proteinase K were added. After lysis at
37\textsuperscript{o}C for 1 hour, 100 \selectlanguage{greek}µ\selectlanguage{english}l of 5 M NaCl and 80 \selectlanguage{greek}µ\selectlanguage{english}l of
CTAB/NaCl solution (10\% hexadecyltrimethyl ammonium bromide, 0.7 m
NaCl) were added and incubated at 65 \textsuperscript{o}C for 10 min.
DNAs were subsequently extracted with equal volume of phenol/chloroform
and precipitated with 0.6 volume of isopropanol. PCR was performed in a
25 \selectlanguage{greek}µ\selectlanguage{english}l reaction containing 1x PCR buffer, 0.2 mM dNTPs, 1.6 mM MgCl2, 2\%
DMSO, 10 pmol for each primer, 30-100 ng template DNA and 0.2 units Taq
polymerase (Invitrogen). After an initial denaturation at
94\textsuperscript{o}C for 3 min, DNAs were amplified in 30 cycles of
denaturation at 94 \textsuperscript{o}C for 30 s, annealing at
58\textsuperscript{o}C for 30 s elongation at 72 \textsuperscript{o}C
for I min, followed by a 3-min final extension at 72
\textsuperscript{o}C. The PCR products were purified using the Exo-CIP
Rapid PCR Cleanup Kit (New England Biolabs), before they were sent to
Macrogen Inc (South Korea) for DNA sequencing.

Seven housekeeping genes were first tested in this work using available
universal primers (Hwang et al., 2005): \emph{gapA} , glyceraldehyde
3-phosphate

dehydrogenase; \emph{gltA} , citrate synthase, \emph{acnB} , aconitate
hydratase; \emph{gyrB,} gyrase B; \emph{coxC} , cytochrome c
oxidase;\emph{pgi} , glucose- 6-phosphate isomerase; \emph{rpoD} , RNA
polymerase sigma factor D. Final MLSA was performed with three genes
(\emph{gapA, gltA and acnB} ) whose primer sequences are provided in
Table 1. The three genes are almost equally distributed in the genome of
\emph{P. fluorescens} SBW25: \emph{gapA, gltA and acnB} are annotated
to\emph{pflu4965} (5448514-5449515), \emph{pflu1815}
(1979150-1980439)\emph{pflu3489} (3858403-3861012), respectively.

\textbf{Assays for bacterial growth:} The GN2 microtiter plates (Biolog
Inc., Hayward, CA) were used following the manufacturer's instructions.
Inoculants were prepared by first growing stored bacteria in LB broth,
and sub-cultured once in R2A broth (Reasoner \& Geldreich, 1985). Cells
were spun down and resuspended in the same volume of sterile deionized
water; 100 \selectlanguage{greek}µ\selectlanguage{english}l of this bacterial suspension was then inoculated into 15
ml of the IF-0 inoculating fluid from Biolog, and subsequently starved
at 28 \textsuperscript{o}C for 2 hrs. Next, 150 \selectlanguage{greek}µ\selectlanguage{english}l of the starved cells
was pipetted into each well of the GN2 MicroPlate and incubated at
28\textsuperscript{o}C for 48 hrs. The initial and final cell density
was estimated by measuring absorbance at the wavelength of 660 nM
(\emph{A\textsubscript{600}} ) in a Synergy 2 plate reader equipped with
the Gen5 software (BioTek Instruments). Growth utilization of histidine
and urocanates was assessed with the minimal M9 salt medium as
previously described (Zhang et al., 2012).

\textbf{Data analysis:} Geneious (Biomatters Ltd; Auckland, New Zealand)
was used to manipulate the DNA sequences, including multiple sequence
alignment, trimming and concatenation. For some analyses, unique
sequence types (STs) were grouped into operational taxonomic units
(OTUs) that included all STs that varied by the mean pairwise distance
(0.06) of the total sample. Rarefaction analysis was performed using
MOTHUR v.1.34.4 (Schloss et al., 2009). The numbers of STs and OTUs were
determined by selecting the isolates at random from the population, and
the procedure was repeated 1,000 times. The Simpson's index (1-\emph{D}
) of diversity was calculated to compare variability in isolates from
Oxford and Auckland, and it takes into account both the number of taxa
(i.e. STs or OTUs) and the relative abundance of each taxa (Simpson,
1949). Phylogenetic trees generated in Mothur on the MLSA data were
visualized with FigTree v1.4.4
(http://tree.bio.ed.ac.uk/software/figtree/). Statistical testing of
differences in genetic diversity was performed using the permutational
multivariate analysis of variance (PERMANOVA) implemented in PRIMER V6
(Anderson, 2001). Pairwise distances were used as input and the testes
were run with 9999 permutations. Visual comparison of isolates from
different ecological sources (location, plot, plant and leaf type) were
performed by multidimensional scaling (MDS) using PRIMER v6. Ancestral
subpopulations of \emph{Pseudomonas} were determined using STRUCTURE
software (Pritchard et al., 2000).

Recombination was detected using the likelihood permutation test in
LDhat v2.1 (McVean et al., 2002) as previously described (Kidd et al.,
2012). Evidence of recombination breakpoints were further obtained from
the aligned multilocus sequences using the SBP/GARD server (Kosakovsky
Pond et al., 2006). GARD examined 2717 models in 00:16:25 wallclock
time, at a rate of 2.76 models/second. The alignment contained 340
potential breakpoints, translating into the search space of 6550950
models with up to 3 breakpoints, of which 0.04\% was explored by the
genetic algorithm. Significance of the toplogical incongruence was
inferred by KH test (Kishino \& Hasegawa, 1989).

The Biolog\textsuperscript{TM} growth data (\emph{A\textsubscript{660}}
) were analyzed by first eliminating the 28 carbon sources which didn't
support any bacterial growth for all tested \emph{Pseudomonas} isolates.
To cluster the growth phenotypes, a distance matrix was generated from
absorbance data of the remaining 67 carbon sources using PRIMER v6. The
matrix was then subjected to PERMANOVA implemented in PRIMER V6 as
described above for genotypic data. Correlation between phenotype and
genotype was performed using Principal Component Analysis (PCA) and
Canonical Variate Analysis (CVA) (Renaud et al., 2015).

\textbf{Results}

\textbf{Multilocus sequencing of pseudomonads isolated from the
phyllosphere of sugar beet}

A total of 230 isolates (164 from Oxford, 66 from Auckland) were
initially subjected to sequencing of seven housekeeping genes
(\emph{gapA} , \emph{gltA} , \emph{acnB} , \emph{gyrB} , \emph{coxC}
,\emph{pgi} and \emph{ropD} ) using the universal primers (Hwang et al.,
2005). However, the rates of PCR or sequencing failure were very high,
suggesting that the leaf-colonising \emph{Pseudomonas} populations are
highly diverse. After modification of the primer sequences (Table 1),
high-quality DNA sequences were obtained for three genes (\emph{gapA}
,\emph{gltA} , \emph{acnB} ) in a total of 152 isolates (96 from Oxford,
56 from Auckland). The concatenated aligned sequences were 897 bp in
length, and encompassed 81 unique STs and 22 distinct OTUs. Rarefaction
analysis indicates near saturation of sampling at the OTU level (Fig.
1A), indicating that the sample was a good representation of the genetic
diversity. As summarised in Table 2, the number of segregation sites
ranged from 18 to 23 for each of the three genes, and 59 identified for
the concatenated sequences. Results obtained from~a coalescent-based
likelihood permutation test (LPT) from LDHat (McVean, 2002) identified a
recombination point within the \emph{gapA} gene (\emph{P} = 0.007), but
not in the \emph{gltA} or \emph{acnB} genes.

\textbf{Genetic diversity of the \emph{Pseudomonas} population}

Representative Neighbour-Joining (NJ) trees are shown in Figure 2 and
Figure 3A. The \emph{Pseudomonas} population was clearly separated into
two major clusters according to origin of isolation, i.e., Oxford or
Auckland (ANOSIM in PRIMER, \emph{r} = 0.477, \emph{P} \textless{} 0.001
(Fig. 3B); Libshuff in Mothur, dCXYScore 0.0254, \emph{P} \textless{}
0.0001). The Oxford cluster contains the reference strain, \emph{P.
fluorescen} s SBW25, which was separately isolated from the phyllosphere
of sugar beet grown in the same field. Interestingly, the Oxford cluster
contains few isolates from Auckland, and vice versa (highlighted by
asterisk in Fig. 2).

A total of 44 and 37 unique STs (81 in total) were detected in the
Oxford and Auckland populations, respectively (Table 3). The two
populations had no shared STs. However, when the closely related STs
were grouped into OTUs at the distance level of 0.06, four shared OTUs
were identified for the 11 and 15 OTUs present in the population of
Oxford and Auckland, respectively (Table 3). The Oxford population
displayed lower levels of nucleotide sequence polymorphism (Fig. 3C),
and lower diversity in terms of the Simpson's index on the basis of both
unique STs and OTUs (Fig. 3D). Single likelihood ancestor counting
(SLAC) analysis indicated no sites under positive or negative selection
for these housekeeping genes.

Recombination, as mentioned above, was detected in a single gene
(\emph{gapA} ) but not in the concatenated sequence by the likelihood
permutation test. However, when the analysis was performed with data
from Oxford and Auckland separately, significant levels of recombination
were noted in the concatenated sequence, particularly for the Oxford
population; these were further supported by single breakpoint
recombination analysis using the 10340 model (Table 3). Further GARD
analysis revealed two breakpoints with significant topological
incongruence, indicating recombination breakpoints between the MLSA
genes (Fig. 4A).

Recombination events can be utilized to separate the isolates into
populations on the basis of ancestry, i.e. the presence of linked
alleles in multiple isolates mandates that they have shared ancestry to
some degree. Obviously, multiple sources of ancestry can be present in
any one isolate. Ancestral analysis performed in STRUCTURE found
evidence of six ancestral populations, and their distributions at the
level of the population and isolate are presented in Figure 4B and 4C,
respectively. Few isolates were ``highly recombinant'' without any
predominant source of their ancestry (8\% in total, mostly from Oxford).

\textbf{Phenotypic analysis of the \emph{Pseudomonas} population}

Growth data were obtained for a total of 95 carbon substrates included
in the Biolog GN2 MicroPlate. To ensure that the phenotypic and
genotypic data could be compared, the raw \emph{A}
\textsubscript{660}values were used to create a distance matrix for
further analysis. The results clearly indicate the presence of
geographic structure, which is almost identical to the above-described
genotypic variation by location (ANOSIM, \emph{r} = 0.454, P \textless{}
\emph{0.001} ). Principal component analysis (PCA) indicated that 62\%
of the phenotypic variability can be accounted for by the principal
component, whereas only 29\% of the genotypic variability can be
accounted for by the principal component (Fig. 5). There was significant
correlation between the phenotypic and genotypic principal components
(\emph{R\textsuperscript{2}} = 0.5303).

Further Canonical variate analysis separated the
\emph{Pseudomonas}isolates into discrete populations, which matched well
with the ancestral genotypes identified by STRUCTURE (Fig. 6A). There
was a significant correlation between carbon source utilization with the
canonical axes CAP1 and CAP2 (Fig. 6A). The following eight substrates
had an \emph{r} value \textgreater{} 0.8 (location in GN2 MicoPlate
shown in parenthesis): \selectlanguage{greek}γ-\selectlanguage{english}hydroxybutyric acid (D12), xylitol (C10),
D-galactose (B4), inosine (H2), urocanic acid (H1), L-arabinose (A10),
D-glucosaminic acid (D8) and m-inositol (B7).

The roles of individual carbon substrate in separating
the\emph{Pseudomonas} populations by location are summarized in Figure
6B. Of particular note are histidine (his) and urocanic acid (urocanate,
uro) whose genetic basis in \emph{Pseudomonas} has been well
characterized (Zhang \& Rainey, 2007). Both substrates are
co-catabolised with the involvement of only one additional enzyme
histidase (HutC) catalysing the conversion from histidine and urocanate.
Interestingly, there was a strong genotypic association with urocanate
utilization (ANOSIM, \emph{r} = 0.758, \emph{P} \textless{} 0.001), but
not with histidine utilization (ANOSIM, \emph{r} = 0.066, \emph{P} =
0.145). Further analysis indicated a clear linkage between urocanate
utilization and location (location, PERMANOVA \emph{P} = 0.0001; uro,
PERMANOVA \emph{P} = 0.0002; location x uro, PERMANOVA \emph{P} =
0.0146). Figure 7 clearly shows that the Oxford and Auckland populations
are well separated by their ability to grow on urocanate
(His\textsuperscript{-}, Uro\textsuperscript{+} versus
His\textsuperscript{-} Uro\textsuperscript{-}).

Finally, it should be noted that minor but significant differences were
detected between genotypes for strains isolated from young vs. mature
leaves (ANOSIM, \emph{r} = 0.103, \emph{P} = 0.01), and also for young
vs. old leaves (\emph{r} = 0.075, \emph{P} = 0.01), but not for mature
vs. old leaves (\emph{r} = 0.015, \emph{P} = 0=7.4). Similar results
were found in terms of growth phenotypes on histidine and urocanate:
young vs. mature leave (\emph{r} = 0.185, \emph{P} = 0.01), young vs.
old leaves (\emph{r} = 0.136, \emph{P} = 0.01), and mature vs. old
leaves (\emph{r} = 0.05, \emph{P} = 1.7). No significant differences
were detected for the potential effects of plants and plots.

\textbf{Discussion}

Fluorescent \emph{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
Biolog\textsuperscript{TM} analyses indicated that the sugar
beet-associated \emph{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
Biolog\textsuperscript{TM} 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 \emph{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
in\emph{Pseudomonas} diversification has not been reported before. Only
a handful of MLSA studies are available for plant-associated
fluorescent\emph{Pseudomonas} , 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 \emph{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
(\emph{r/m} \textgreater{} 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 \emph{Pseudomonas} , particularly in the Oxford
subpopulation. An overall \emph{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 \emph{P.
aeruginosa}and \emph{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 \emph{P. putida} and
\emph{P. fluorescens} (Ogura et al., 2019). However, frequent
recombination was detected in a MLSA study with 501 \emph{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 \emph{Pseudomonas}and 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\textsuperscript{+}) 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\textsuperscript{+}, Uro\textsuperscript{-}),
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 \emph{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
urocanate\emph{in 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.

\textbf{Acknowledgments}

We thank Annabel Gunn and Yunhao Liu for technical assistance. XXZ
appreciates financial support by MBIE Catalyst Fund, New Zealand
(project no. 92846082). RWJ was supported by a British Society for Plant
Pathology fellowship award.

\textbf{Data Accessibility}

DNA sequences are available with GenBank Accession Numbers MT073416 -
MT073567

for \emph{gapA} , MT073568 - MT073719 for \emph{gltA} and MT073720 -
MT073871 for \emph{acnB} .

\textbf{Figure captions}

\textbf{Figure 1.} Rarefaction curves showing a near saturation of
bacterial sampling.

Each curve represents the mean of 1000 replicates. Data of 95\%
confidence intervals are displayed with discontinuous lines.

\textbf{Figure 2.} Phylogenetic relationships of sugar
beet-associated\emph{Pseudomonas} .

Isolates from Oxford and Auckland are differentiated by blue and red
colors, respectively. Six reference \emph{Pseudomonas} species were
included in the analysis. Asterisks indicate the few Oxford isolates in
the Auckland cluster, and vice versa.

\textbf{Figure 3.} Comparative analysis of \emph{Pseudomonas} isolated
from Oxford and Auckland

\begin{enumerate}
\tightlist
\item
  Clonal frame output displayed as an unrooted network, indicating the
  origination of isolation. Distance was calculated using the NJ method
  with Kimura 2 correction. Sizes of the circles are proportional to the
  number of isolates.
\item
  Multi-dimensional scaling plot showing separation of\emph{Pseudomonas}
  from Oxford (blue) and Auckland (green).
\item
  Nucleotide and amino acid polymorphism of the concatenated sequences
  by location.
\item
  Simpson's index of diversity (1-D) calculated on the basis of unique
  STs and OTUs clustered at the level of 0.06. Error bars are 95\% CIs.
\end{enumerate}

\textbf{Figure 4.} Evidence of recombination and structure of the Oxford
and Auckland subpopulations.

\begin{enumerate}
\tightlist
\item
  Recombination breakpoints were detected using the method of GARD.
  Results of the Kishino-Hasegawa (KH) test are shown below the two
  detected breakpoints.
\item
  Distribution of the six ``ancestral'' genotypes revealed by STRUCTURE
  analysis.
\item
  Individual isolates sharing ancestry for the Oxford and Auckland
  subpopulations.
\end{enumerate}

\textbf{Figure 5.} Principal component analysis showing correlations
between genotypes and phenotypes.

\textbf{Figure 6.} Genotypic relatedness of carbon source utilization
(A) and the role of individual substrate in separating pseudomonads from
Oxford and Auckland (B).

\begin{enumerate}
\tightlist
\item
  Canonical analysis of principal coordinates (CAP) based on a Euclidean
  distance similarity matrix generated from the Biolog and MLSA data.
  The six ``ancestral'' populations revealed by STRUCTURE analysis are
  indicated as A to E, while X denotes the other ancestral types. Only
  carbon substrates with \emph{r} value larger than 0.8 are shown. A to
  E denotes the six an
\item
  Ratio of the mean value of bacterial growth
  (\emph{A\textsubscript{660}} ) on each carbon source was calculated
  for isolates from Oxford and Auckland. The carbon substrates are
  listed in\emph{y} -axis in order of their location in the Biolog GN2
  MicroPlate from A2 to H12 labelled from number 1 to 95. Red circles
  denote the eight carbon substrates of strong correlation with
  genotypes (Fig. 6B).
\end{enumerate}

\textbf{Figure 7.} An unrooted phylogenetic tree showing the association
of urocanate utilization with genotypes.

The capability of bacterial growth on histidine (His) and urocanate
(Uro) are marked in four different colours. Number of isolates showing
the same phenotype is provided in a table below the tree, and percentage
of each phenotype in the Auckland, Oxford and total population is shown
in parenthesis.

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Zhang, X. X., Ritchie, S. R., \& Rainey, P. B. (2013). Urocanate as a
potential signaling molecule for bacterial recognition of eukaryotic
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doi:10.1007/s00018-013-1527-6

\textbf{Hosted file}

\verb`Tables 1-3.docx`  available at \url{https://authorea.com/users/298952/articles/428333-genotypic-and-phenotypic-analyses-reveal-distinct-population-structures-and-ecotypes-for-sugar-beet-associated-pseudomonas-in-oxford-and-auckland}\selectlanguage{english}
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