Commensal Pseudomonas strains facilitate protective response against pathogens in the host plant

Barcoding of Pseudomonas isolates and experimental design

To test possible host–commensal–pathogen dynamics in a local population, we spray inoculated six A. thaliana accessions with synthetic bacterial communities composed of pathogenic and commensal Pseudomonas candidates. Because we wanted to study interactions that are likely to occur in nature, we used A. thaliana genotypes that originated from the same plant populations near Tübingen, Germany²⁸, from which the Pseudomonas strains had been isolated (Fig. 1a). Classification of Pseudomonas lineages as pathogenic or commensal was based on observed effects in axenic infections¹¹. Only one lineage, previously named OTU5, which dominated local plant populations, was associated with pathogenicity, both based on negative impact on rosette weight and visible disease symptoms¹¹. We henceforth call this lineage ATUE5 (isolates sampled from ‘Around TUEbingen, group 5’) and all other Pseudomonas lineages from the Karasov collection non-ATUE5. We interchangeably use the terms ‘pathogens’ or ‘ATUE5’, and ‘commensals’ or ‘non-ATUE5’.

Seven pathogenic Pseudomonas and seven commensal isolates were selected, prioritizing those with the highest prevalence in the field collection¹¹ (Fig. 1b), estimated from the number of similar isolates with nucleotide sequence divergence of less than 0.0001 in their core genome. The 14 Pseudomonas isolates were classified as belonging to four OTUs based on 16S rDNA clustering at 99% sequence identity. Because of the high relatedness of several of the isolates, we could not rely upon a single endogenous genetic marker to distinguish them in a community context, and we therefore genome barcoded all of the isolates. We employed the mini-Tn7 system²⁹ to insert a single copy of a 22 bp-long unique sequence, flanked by universal priming sites, into the chromosome of each isolate (Extended Data Fig. 1a). We validated the sequence of all barcodes in the corresponding isolates using Sanger sequencing (Supplementary Table 1) and confirmed correct integration by barcode-specific polymerase chain reaction (PCR, Supplementary Fig. 1a). Barcode amplification yielded the expected products on DNA extracted from infected A. thaliana individuals (Supplementary Fig. 1b). While barcoding slightly impaired the in vitro growth of isolates P3 and P4, most barcoded strains grew similarly well as the non-barcoded parental strains when tested in a lysogeny Broth (LB) medium (Supplementary Fig. 2).

Next, we constructed three synthetic communities using the barcoded isolates: an exclusively pathogenic community, comprising the seven ATUE5 isolates (PathoCom); an exclusively commensal community, comprising the seven non-ATUE5 isolates (CommenCom); and a joint community comprising all 14 isolates, both pathogens and commensals (MixedCom). Isolates were mixed in equal proportions (based on OD₆₀₀ readout), and their absolute starting concentration was identical in each synthetic community. Thus, the inoculum of the MixedCom with 14 isolates had twice the total number of bacterial cells per volume as either the PathoCom or CommenCom inoculum.

The community experiments were conducted with plants grown on soil in the presence of other microbes acquired from the environment. We chose to perform non-axenic experiments rather than with axenically grown plants because infection outcomes on soil seemed more consistent with phenotypes observed in the field. Specifically, the focal bacterial strains had been isolated from wild plants that were not obviously diseased¹¹. In the lab, axenic infections with these strains often had rapid and dramatic effects, killing plants as early as three days post infection (DPI) (Supplementary Fig. 3). In contrast, inoculated soil-grown plants had only mild disease symptoms and decreased size even 12 DPI (Supplementary Fig. 3). Also, to more closely mimic natural infections, which probably occur through the air, we chose to inoculate plants by spraying with bacterial suspensions rather than direct leaf infiltration, the more common method for testing the effects of leaf pathogenic bacteria in A. thaliana.

Twenty-one days after sowing, we spray inoculated rosettes of plants raised in growth chambers with the three synthetic communities or with a buffer (Control). At 12 DPI, we sampled the fresh rosettes, weighed them and extracted DNA from them. We measured absolute abundance of each isolate by coupling barcode-specific PCR and sequencing-based amplicon counting with quantitative PCR (qPCR). Including an amplicon from an A. thaliana gene in the qPCR assay allowed us to estimate absolute isolate abundances as the ratio of bacterial to plant cells (Extended Data Fig. 1b).

Because we used a non-sterile system, interactions of barcoded isolates with the plant or with each other could potentially be affected by the presence of other bacteria that colonized the plants from the environment. To gauge how important other environmental bacteria, especially other Pseudomonas strains, were, we quantitatively measured the total bacterial community profile based on the fourth hypervariable (V4) region of 16S rDNA, employing a recent method that measures not only community composition but also absolute bacterial load³⁰. Although 16S rDNA-based profiling is not suitable to differentiate our Pseudomonas isolates from all other environmental Pseudomonas strains or from each other, comparing the uninfected with infected plants (among all three synthetic communities) showed that (1) environmental Pseudomonas load was small in uninfected plants and (2) total Pseudomonas load alone was higher than the cumulative load of all non-Pseudomonas bacteria in infected plants. We therefore conclude that cumulative bacterial load was mainly driven by the inoculated Pseudomonas strains in infected plants (Extended Data Fig. 2), suggesting that environmental microbes do not interfere in a specific manner with our system.

Host effects on composition of synthetic communities

The six A. thaliana genotypes used were originally sampled from a maximum of 40 km apart²⁸ in the same geographic region (Fig. 1a) and they also were all from the same host genetic group³¹. In accordance, we expected host genotype to have limited effects on the composition of our synthetic communities of co-occurring Pseudomonas isolates. While not large, there was nevertheless a significant effect of host genotype, explaining 5% to 12% of compositional variation in the different communities, as determined by permutational multivariate analysis of variance (PERMANOVA) with Bray–Curtis distances (Table 1). For comparison, the difference between experiments explained 4–26% of compositional variation. Analysis of similarities within each experiment indicated similar trends as PERMANOVA, with genotype having a significant effect on isolate composition in each synthetic community (Supplementary Table 2a).

We then examined bacterial composition clustering according to host genotype by applying multi-level comparison using pairwise ‘adonis’ based on Bray–Curtis distances. Some pairs of genotypes differed in their effects on all three communities (Supplementary Table 2b), an observation that was supported by non-metric multi-dimensional scaling (NMDS) ordination of bacterial composition in each treatment (Extended Data Fig. 3a). The cumulative load of all isolates was associated with the loading on the NMDS1 axis (Pearson’s r >0.99 and P value <2.2 × 10⁻¹⁶ for all three communities), suggesting that a part of the compositional differences between host genotypes was due to absolute rather than relative abundance. In agreement, we observed differences in total bacterial load among the host genotypes, and the nature of the differences was treatment dependent (Extended Data Fig. 3b).

Host-dependent pathogenicity, growth promotion or protection

Plant weight in our experiments was a function of treatment and host genotype, and interaction between the two, implying that the six A. thaliana accessions were differentially affected by similar treatments, as inferred from model comparison using leave-one-out cross validation and a two-way ANOVA test (Supplementary Table 3a,b). PathoCom infection reduced plant growth during the 12 days of the experiment (Fig. 2, Extended Data Fig. 4a and Supplementary Fig. 4), with weight decrease being the least in Lu3-30 and TueWal-2, indicating a certain level of resistance to PathoCom members in these accessions. The mean reduction to Control for the individual host genotypes was 29.1 mg (59.3, 1.4) for Lu3-30; 30.0 mg (46.4, 13.4) for TueWal-2; 77.2 mg (96.4, 54.2) for Kus3-1; 93.1 mg (123.5, 67.7) for Schl-7; 92.5 mg (116.4, 66.0) for Ey15-2; and 53.9 mg (82.6, 27.0) for HE-1 (95% confidence intervals in brackets, Extended Data Fig. 4b).

To validate that the effect of the PathoCom on plant weight was due to bacterial activity and not merely a host response to the inoculum (for example, pathogen-associated molecular pattern [‘PAMP’] triggered immunity), we infected plants with heat-killed PathoCom. We found a minor weight decrease in three out of the six accessions, but the overall contribution to weight reduction was small (Supplementary Fig. 5; heat-killed PathoCom accounts for 14% of the variation explained by the living PathoCom in the model shown).

In contrast to PathoCom, infections with CommenCom led to a slight increase in fresh weight, suggesting plant growth promotion activity or alternatively, protection from resident environmental pathogens (Extended Data Fig. 4a). This effect was independent of the host genotype (Extended Data Fig. 4b).

Importantly, the negative effects of the PathoCom members were greatly reduced in the MixedCom experiment. Plants infected with MixedCom grew to a similar extent as the control, with the exception of the genotype Ey15-2, which continued to suffer a substantial weight reduction when infected by the mixed community, with a mean reduction relative to Control of 48.5 mg (74.8, 22.6; Fig. 2, Extended Data Fig. 4b and Supplementary Fig. 4). Nonetheless, the growth reduction of Ey15-2 was less than that caused by PathoCom. Hence, co-colonization of pathogenic Pseudomonas with commensals led to enhanced growth, with the exact extent depending on host genotype.

Because the pathogenic Pseudomonas strains used here are much more lethal when inoculated on sterile plants¹¹, we wanted to test whether environmentally derived microbes affected the observed interactions in a specific manner. We therefore performed a similar experiment in an axenic system on MS agar. The major trends that we had observed on soil were recapitulated, including the protection against ATUE5 by CommenCom members and the reduced protection of Ey15-2 by CommenCom members against pathogens (Supplementary Fig. 6). This does not exclude that members of the environmental microbiota enhance or dampen some of the observed effects, but if they do, they do so in a general manner and they are not essential for the observed effects.

In aggregate, these results support the role of ATUE5 strains as pathogenic and provide additional evidence for protection against ATUE5 by commensal Pseudomonas strains that coexist with ATUE5 in nature. Next, we therefore wanted to learn whether and how changes in bacterial abundance or shifts in Pseudomonas community composition led to differential impacts on growth of the infected plants.

Load-dependent impact of pathogens and commensals

We hypothesized that the total cumulative load of all inoculated strains, regardless of their taxonomy, should be an important explanatory variable for weight differences among treatments. We based this expectation on the association previously found between prevalence in the field and pathogenicity for similar Pseudomonas isolates¹¹. Contrary to our hypothesis, we found that while the differences in plant weight between treatments were considerable, the bacterial loads of MixedCom and PathoCom were similar with high probability, as deduced from quantification of barcodes (Fig. 3a). This result implies that plant weight is also a function of bacterial composition and not load per se. In agreement with this inference, the load–weight relationships were found to be treatment dependent, indicating that weight can be better predicted by load within a treatment than by load among treatments (Delta Elpd = −52.9, standard error = 9.4, when comparing the model [weight ~ treatment × log₁₀(isolate load) + treatment + log₁₀(isolate load) + genotype + experiment + error] to the same model without the interaction factor [treatment × log₁₀(isolate load)] using leave-one-out cross validation; Methods).

We noticed that the regression slope of PathoCom was more negative than the regression slope of CommenCom, suggesting that ATUE5 isolates had a stronger negative impact on weight per bacterial cell than non-ATUE5 isolates (Fig. 3b and Extended Data Fig. 5a; CommenCom mean effect difference to PathoCom: 12.0 mg [4.4,19.5] at 95% credible interval of the parameter log₁₀(isolate load) × treatment, based on Extended Data Fig. 5a). From the reciprocal angle, that of the host, it can be seen that plants were less tolerant to ATUE5 isolates than to non-ATUE5 isolates. MixedCom presented a regression slope between the two exclusive synthetic communities, implying that the impact on plant growth resulted from both groups, ATUE5 and non-ATUE5, with the MixedCom mean effect difference to PathoCom being 4.8 mg (based on Extended Data Fig. 5a, [−1.6, 11.8] 95% Bayesian credible interval of the parameter log₁₀(isolate load) × treatment). Lastly, we observed differential regression slopes between the host genotypes, particularly among PathoCom- and CommenCom-infected hosts, revealing different levels of tolerance to the same groups of Pseudomonas isolates (Extended Data Fig. 5b,c).

Although these results suggest general ATUE5 and non-ATUE5 effects, they may still be due to a few dominant strains that outcompeted the others. For example, high competition in the early phases of plant colonization may lead to later exclusion of a subset of strains. In such a scenario, these latter strains would not become established in the plant and would therefore not be particularly relevant. In contrast, we found that in all three synthetic communities, each strain had robustly colonized the plants at the end of the experiment (Extended Data Fig. 6a), confirming that the observed weight differences in host plants are compatible with effects of entire communities. As expected, some strains were more abundant than others, although there was no individual dominant strain in any of the communities (Extended Data Fig. 6b).

We have described two general differences between pathogenic and commensal Pseudomonas: (1) on average, pathogens have a greater impact per a given load on plant growth than commensals do and (2) pathogens can reach higher titre in A. thaliana leaves than commensals can. Together, this points to dual effects of pathogens on plant health. To explain how commensal non-ATUE5 isolates were able to mitigate the harmful impact of pathogenic ATUE5 in MixedCom, we next addressed the bacterial compositionality in MixedCom-infected hosts.

Protection by commensal members and pathogen suppression

Given that (1) MixedCom-infected plants grew better than PathoCom-infected plants (Fig. 2, Extended Data Fig. 4a and Supplementary Fig. 4), (2) there was no considerable difference in total load between PathoCom- and MixedCom-infected plants (Fig. 3a) and (3) pathogens were found to cause more damage per cell (Fig. 3b and Extended Data Fig. 5a), we expected commensal members to dominate MixedCom.

Consistent with our expectations, the composition of MixedCom was more similar to CommenCom than PathoCom (Fig. 4a). We then analysed the change in bacterial abundance due to the mixture of pathogens and commensals at the isolate level. We compared the absolute abundance of each isolate among the treatments: pathogenic isolates were compared between PathoCom and MixedCom, and commensals between CommenCom and MixedCom. In general, the abundance of pathogens was substantially lower in MixedCom, while the abundance of commensals was either similar or slightly higher in MixedCom (Fig. 4b). Thus, the mixture of pathogens and commensals led to pathogen suppression, while commensal load was largely unchanged in MixedCom compared with CommenCom. Therefore, non-ATUE5 isolates appear to be more competitive in the MixedCom context than ATUE5 isolates. The abundance change of each isolate in the presence of additional community members was similar among the host genotypes, implying that commensal–pathogen interactions were mostly conserved (Extended Data Fig. 7 and Supplementary Table 4). We therefore tested for direct, host-independent interactions among isolates with an in vitro growth-inhibition assay (Methods).

Each of the 14 isolates was examined for growth inhibition against all other isolates, covering all possible combinations of binary interactions. In total, three strains out of the 14 had inhibitory activity; all were non-ATUE5 (Fig. 4c). Specifically, C4 and C5 showed the same pattern: both inhibited all pathogenic isolates but P1, and both inhibited the same two commensals, C6 and weakly C3. C3 inhibited three ATUE5 isolates: P5, P6 and P7. In summary, the in vitro assay provides evidence that among the tested Pseudomonas isolates, direct inhibition was a trait unique to commensals, and susceptible bacteria were primarily pathogens. This supports the notion that ATUE5 and non-ATUE5 isolates employ divergent competition strategies, or that if they use the same mechanism, they differ in the effectiveness of such a mechanism.

The in vitro results recapitulated the general trend of pathogen inhibition found among treatments in planta. Nevertheless, we observed major discrepancies between the two assays. First, P1 was not inhibited by any isolate in the host-free assay (Fig. 4c), though it was the most inhibited member in planta among the communities (Fig. 4b). Second, no commensal isolate was inhibited in planta among communities (Fig. 4b), while two commensals, C3 and C6, were inhibited in vitro (Fig. 4c). Both observations are compatible with an effect of the host on microbe–microbe interactions. To explore such effects, we analysed all pairwise microbe–microbe abundance correlations within MixedCom-infected hosts. When we used absolute abundances, all pairwise correlations were positive, also in CommenCom and PathoCom (Extended Data Fig. 8a), consistent with there being a positive correlation between absolute abundance of individual isolates and total abundance of the entire community (Supplementary Fig. 7), that is, no isolate was less abundant in highly colonized plants than in sparsely colonized plants. This indicates that there does not seem to be active killing of competitors in planta in the CommenCom, which is probably not surprising. With relative abundances, however, a clear pattern emerged with a cluster of commensals that were positively correlated, possibly reflecting mutual growth promotion, and several commensal strains being negatively correlated with both P6 and C7, possibly reflecting unidirectional growth inhibition (Fig. 4d). We did not observe the same correlations within CommenCom among commensals and within PathoCom among pathogens as we did for either subgroup in MixedCom, reflecting higher-order interactions. Thus, interactions among pathogens were constrained by the presence of commensals and vice versa (Extended Data Fig. 8b).

The in planta patterns measured in complex communities did not fully recapitulate what we had observed in vitro with pairwise interactions. We therefore investigated individual commensal isolates for their ability to suppress pathogens in planta and also tested the entourage effect. We focused on the three commensals C3, C4 and C5, which had directly inhibited pathogens in vitro, and as a control C7, which had not shown any inhibition activity in vitro. We infected plants with mixtures of PathoCom and each of the four individual commensals and also with PathoCom mixed with all four commensals. Because pathogen inhibition seemed to be independent of the host genotype, we arbitrarily chose HE-1. Regardless of the commensal isolate, only P1 was suppressed with high probability in all commensal-including treatments (Fig. 4e), with P2, P3 and P4 being substantially inhibited only by the mixture of all four commensals. Together with the lack of meaningful differences between individual commensals, this indicates that pathogen inhibition is either a function of commensal dose or a result of interaction among commensals.

An important finding was that four commensal strains had much more similar inhibitory activity in planta than in vitro and that the combined action was greater than the individual effects. Together, this suggested that the host contributes to the observed interactions between commensal and pathogenic Pseudomonas isolates. To begin to investigate this possibility, we next studied potential host immune responses with RNA sequencing.

Defensive response elicited by non-ATUE5

For the RNA-sequencing experiment, we treated plants of the genotype Lu3-30 with the three synthetic communities and also used a bacteria-free control treatment. We sampled the treated plants 3 DPI and 4 DPI, thus increasing the ability to pinpoint differentially expressed genes (DEGs) between treatments that are not highly time specific. Exploratory analysis indicated that the two time points behaved similarly, and they were combined for further in-depth analysis.

We first looked at DEGs in a comparison between infected plants and control (Supplementary Table 5); with PathoCom, there were only 14 DEGs; with CommenCom, there were 1,112 DEGs; and with MixedCom, there were 1,949 DEGs, suggesting that the CommenCom isolates, which are also present in the MixedCom, elicited a stronger host response than the PathoCom members. Furthermore, the high number of DEGs in MixedCom, higher than both PathoCom and CommenCom together, suggested a synergistic response derived from inclusion of both PathoCom and CommenCom members. Alternatively, this could also be a consequence of the higher initial inoculum in the 14-member MixedCom than either the 7-member PathoCom or 7-member CommenCom, or a combination of the two effects (Fig. 5a,b and Extended Data Fig. 9). The genes induced by the MixedCom fell into two classes: Group 5 (Fig. 5a,b) was also induced, albeit more weakly, by the CommenCom but not by the PathoCom. This group was overrepresented for non-redundant gene ontology (GO) categories linked to defence (Fig. 5c) and most likely explains the protective effects of commensals in the MixedCom. Specifically, among the top ten enriched GO categories in the shared MixedCom and CommenCom set, eight relate to immune response or response to another organism (‘defence response’, ‘multi-organism process’, ‘immune response’, ‘response to stimulus’, ‘response to biotic stimulus’, ‘response to other organism’, ‘immune system process’, ‘response to stress’; Fig. 5c).

Group 4 was only induced in MixedCom, either indicating synergism between commensals and pathogens or reflecting a consequence of the higher initial inoculum. This group included a small number of redundant GO categories indicative of defence, such as ‘salicylic acid mediated signalling pathway’, ‘multi-organism process’, ‘response to other organism’ and ‘response to biotic stimulus’ (Supplementary Table 6). Moreover, the MixedCom response cannot simply be explained by synergistic effects or commensals suppressing pathogen effects because there was a prominent class, Group 2, which included genes that were induced in the CommenCom but to a much lesser extent in the PathoCom or MixedCom. From their annotation, it was unclear how they can be linked to infection (Fig. 5c). About 500 genes (Group 1) that were downregulated by all bacterial communities are unlikely to contain candidates for commensal protection (Fig. 5a).

Cumulatively, these results imply that the CommenCom members elicited a defensive response in the host regardless of PathoCom members, while the mixture of both led to additional responses. To better understand if selective suppression of ATUE5 in MixedCom infections may have resulted from the recognition of both non-ATUE5 and ATUE5 (reflected by a unique MixedCom set of DEGs) or solely non-ATUE5 (a set of DEGs shared by MixedCom and CommenCom), we examined the expression of key genes related to the salicylic acid pathway and downstream immune responses. Activation of the salicylic acid pathway was previously related to increased fitness of A. thaliana in the presence of wild bacterial pathogens, a phenomenon which was attributed to an increased systemic acquired resistance³².

We observed a general trend of higher expression in MixedCom- and CommenCom-infected hosts for several such genes (Fig. 5d). Examples are PR1 and PR5, marker genes for systemic acquired resistance and resistance execution. Therefore, according to the marker genes we tested, non-ATUE5 elicited a defensive response in the host, regardless of ATUE5 presence.

We conclude that the expression profiles of non-ATUE5-infected Lu3-30 plants point to an increased defensive status, supporting our hypothesis regarding host-mediated ATUE5 suppression. We note that ATUE5 suppression was not associated with full plant protection and thus control-like weight levels in all plant genotypes. One accession, Ey15-2, was only partially protected in the MixedCom (Fig. 2), despite levels of pathogen inhibition being not very different from other host genotypes (Extended Data Fig. 7).

Lack of protection explained by a single pathogenic isolate

The fact that Ey15-2 was only partially protected by MixedCom (Fig. 2) underlines the importance of the host genotype in plant–microbe–microbe interactions, apparently reflecting the dynamics between microbes and plants in wild populations. We therefore wanted to reveal the cause for this differential interaction.

Our first aim was to rank compositional variables in MixedCom according to their impact on plant weight, regardless of host genotype. Next, we asked whether any of the top-ranked variables could explain the lack of protection in Ey15-2. With Random Forest analysis, we estimated the weight-predictive power of all individual isolates in MixedCom and three cumulative variables: total bacterial abundance, total ATUE5 abundance and total non-ATUE5 abundance. We found that the best weight-predictive variable was the abundance of pathogenic isolate P6, followed by total bacterial load and total ATUE5 load, which were probably confounded by the abundance of P6 (Fig. 6a). In agreement, P6 was the dominant ATUE5 in MixedCom (Fig. 6b and Extended Data Fig. 10a). We thus hypothesized that the residual pathogenicity in MixedCom-infected Ey15-2 was caused by P6. Although P6 grew best in Ey15-2, the difference to most other genotypes was unlikely to be important (Extended Data Fig. 10b). However, P6 was particularly dominant in Ey15-2 (Fig. 6b).

Given that pathogen load in Ey15-2 was driven to a substantial extent by P6, we assumed that this isolate had a stronger impact on the weight of Ey15-2 than on other accessions. We experimentally validated that removal of P6 restored protection when Ey15-2 was infected with the MixedCom (Fig. 6c). To confirm that restored protection was due to the interaction of commensals with the five other pathogenic isolates (P1–P5), rather than simply removal of P6, we also treated Ey15-2 with PathoCom only, but not P6. The removal of P6 did not diminish the negative weight impact of PathoCom (P1–P5, Supplementary Fig. 8), implying that it was indeed the interaction between commensals with five out of six pathogenic isolates that mitigated the harmful effect of pathogens in Ey15-2 plants.

Source: Ecology - nature.com