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).
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.
References
[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).
