Epigenetics of trans-generational defense Induction

Some of the best evidence for environmentally induced epigenetic inheritance comes from studies of pathogen infection in A. thaliana. When infected by the common laboratory strain of the bacterial pathogen Pseudomonas syringae (DC3000), A. thaliana plants undergo extensive DNA methylation changes that regulate defense gene expression. Furthermore, some of these induced methylation changes can be transmitted to offspring, trans-generationally ‘priming’ offspring for more effective defense responses when they encounter similar pathogens.

However, plants in nature are typically subject to simultaneous infection by pathogens that induce different defense responses. The defense systems activated by different pathogens may even antagonize each other via hormonal crosstalk. The effects of such co-infection on DNA methylation patterns and trans-generational defense priming remain entirely unexplored, as does the extent of host genetic variation for these epigenetic responses.

To address these issues, we generated A. thaliana lineages with different histories of bacterial infection across generations. This framework enables several key determinations, including the specific DNA methylation changes that are induced in parents by single- versus co-infection, which of these changes are inherited by offspring, and how inherited methylation changes influence offspring defense responses when offspring are infected. To date, we have characterized the genome-wide DNA methylomes of the founding (parental) plants of these lineages, which were infected by the natural bacterial pathogens Pseudomonas syringae (Michigan strain NP29.1A) and P. viridiflava (Michigan strain RMX3.1B),separately and in combination (i.e., co-infection).


Genomics of Pathogen-pathogen interactions during co-infection

Host-pathogen interactions are often viewed as a conflict between two organisms: the pathogen thrives if it can exploit its host, and the host thrives if it can fend off infection. This restricted view, however, overlooks the fact that organisms in nature are exposed to many pathogens simultaneously, which can lead to co-infection by multiple pathogen strains or species within each host [1].

Interactions between co-infecting pathogens can profoundly alter the ability of individual pathogens to invade and exploit a host, with consequences for disease progression and host fitness. For example, we have previously found that pathogen strains lacking key virulence genes proliferate better during mixed infections with more aggressive strains [3]. In general, interactions among pathogens range from antagonistic to beneficial: microbes not only compete for resources but can also mount synergistic strategies to subvert host defenses and better exploit host resources [1,2]. How pathogens maximize their performance during co-infections, and the genes underlying these strategies, is largely unknown.

We are studying co-infections using a genetic model plant species, Arabidopsis thaliana, and one of its most abundant bacterial pathogens, Pseudomonas viridiflava. Recent ecological genomics work has revealed that co-infection is remarkably common in this system: most individual Arabidopsis plants in natural settings harbor multiple virulent P. viridflava strains within their leaves [4]. Our work uses high throughput assays to answer the following questions in this system:

Figure 1. Experimental system for plant growth and infections. Above, gnotobiotic Arabidopsis in plant growth (MS) media in 24-well microplates. Below, from left: a lightly, moderately, and heavily diseased plant, 36 hours after infection with P. viridiflava strains differing in pathogenicity
  • Do pathogen strains differ in performance depending on which other strains are present during co-infections? Our earliest results suggest this is overwhelmingly the case (Figure 1). We are therefore pursuing the genetic basis of these microbe-microbe interactions.
  • What genes, and combinations of alleles, determine co-infection outcomes? This knowledge will help us understand pathogen ecology and evolution. But it also has practical applications: it could improve our ability to model, predict, and prevent disease outbreaks in natural populations.

One of the main advantages of the Arabidopsis-Pseudomonas study system is its amenity to large-scale experiments (Figure 2). Hundreds of plants can quickly be inoculated using pipetting robot, infection outcomes (pathogen abundance) can be monitored by high-throughput DNA sequencing, and new statistical approaches in two-organism genome-wide association mapping [5] are poised to identify combinations of alleles in the pathogens’ genomes with beneficial or deleterious effects on their performance. Although this project focuses on a single plant and pathogen species, we hope to generate conceptual and statistical insights relevant to ecological, evolutionary, and biomedical models of host-enemy interactions.

Figure 2. Pairwise co-infections strongly shape pathogen performance in gnotobiotic Arabidopsis. The abundance of two luciferase-tagged strains of P. viridiflava (strain p13.G4, “b”; strain p25.A12, “c”) were measured 36 hours after co-inoculation with each of 60 different strains from the P. syringae complex, whose phylogenetic relationship is shown in “a”. Abundances of the two focal strains in each pairwise co-infection are expressed relative to their abundance in single infections. Effects on focal strain abundance are expressed as log10 units of photon counts/second.


[1] Tollenaere, Charlotte, et al. “Evolutionary and epidemiological implications of multiple infection in plants.” Trends in Plant Science (2016).

[2] Abdullah, Araz S., et al. “Host–multi-pathogen warfare: pathogen interactions in co-infected plants.” Frontiers in Plant Science (2017).

[3] Barrett, Luke G., et al. “Cheating, trade‐offs and the evolution of aggressiveness in a natural pathogen population.” Ecology Letters (2011).

[4] Karasov, Talia L., et al. “Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales.” Cell Host & Microbe (2018).

[5] Wang, Miaoyan, et al. “Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes.” Proceedings of the National Academy of Sciences (2018).