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.