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.