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).


Collecting microbial network members and hub species

As microbial ecology has advanced in recent decades, the importance and incredible diversity of microbial communities has become apparent. However, the processes that determine the composition of microbial communities remain poorly understood. Determining what gives rise to a certain community composition may help us manipulate microbial communities into healthier or more productive forms.

To gather candidates for our synthetic microbial community studies, we are coordinating two A. thaliana microbial collections: one from Sweden and one from the Midwestern United States.  We are attempting to collect as many microbes from our samples as possible, creating a permanent “living library” for future research. We are collecting microbes primarily from internal leaf tissue. However, collections from the Midwest also include microbes from roots and siliques.

We are currently processing over 5000 new bacterial and hundreds of fungal isolates (we already hold >6,000 Midwestern bacterial and 50 fungal isolates).  We seek taxa that match hub OTUs that have not been previously cultured in order test them in controlled growth chamber experiments with sterile plants and ultimately combine them with other OTUs to form synthetic communities in which the network of interactions among microbes has been empirically verified.  Such a community will be used to assess the accuracy of various interaction inference approaches. This evaluation of our ability to identify microbial interactions is fundamental for our continued application of network science to microbial communities.             Future work will expand the application of this experimental community to address questions and hypotheses from network science and ecology. This may include topics such as: the importance of competitive interactions in community stability, and the effect of higher order interactions on community dynamics and composition.


Spatial and temporal dynamics of Arabidopsis thaliana associated bacterial communities

Seven Arabidopsis Midwestern accessions in HPG1 were grown in two locations, Warren Woods and the Michigan Research and Extension Center, for two successive years and sampled monthly during the growing seasons over the span of two years.  The aim was to collect samples for bacterial microbiome analysis using 16S rRNA from all developmental stages of the plants to understand how the microbiome changes in space and time.

Figure 1. PCoA showing separation of bacteria from soil, roots, and rosettes (colors) and location (shapes).

We find that the phyllosphere and rhizosphere communities have distinct compositions compared to each other and to the surrounding soil (Figure 1 above). Figure 2 (below) shows the networks constructed for each developmental stage in the roots at two different sites. The taxa richness, and thus the number of members in the network, increased as plant development progressed. An increase in community diversity at later stages can be seen as the number of different types of bacteria represented increases.

Figure 2. Bacterial networks sampled from A. thaliana roots by developmental stage. WW vegetative not sampled.

Bacterial networks also show more modularity in their structure as plant development progresses. Relative to random networks of the same size, networks from later developmental stages in both tissues were more modular than the networks from earlier developmental stages. There is more analysis that can be done on the modules present in the plant and soil networks to determine what variables in the data (microbe relatedness, site, or year) can best explain the patterns in community structure.

Previous studies on plant microbial networks identified sets of fungal or bacterial taxa as “hubs” because they were exceptionally well connected in inferred interaction networks. It is posited that this small set of microbes has outsized influence on phyllosphere and rhizosphere communities through interactions. However, in this dataset we find that the bacteria identified as hubs based on their connections in the network varied across plant development in both the phyllosphere and rhizosphere. This suggests the influence of a hub microbe may not be predictable across different tissues and developmental stages in plants.


The Microbiome and Evolution

We are investigating the importance of the microbiome and the holobiont in evolution. To test this, we are experimentally evolving the model plant, Arabidopsis thaliana in conjunction with a synthetic microbial community. Plant genetic diversity is supplied with a set of A. thaliana recombinant inbred lines. The synthetic microbial community is composed of bacteria, fungi, and other eukaryotes. These microbes were isolated from the tissues and rhizosphere of A. thaliana growing in the field.


Comparative R-Genes Project

Our current understanding of how polymorphism is maintained relies on models of obligate pairwise species interactions but at least half of all plant pathogens have multiple hosts. This raises the possibility that pathogens drive convergent evolution in co-occurring plants. We propose to test this hypothesis by studying co-occurring Brassicaceae plant species, and how shared plant pathogens can potentially maintain ancient balanced polymorphism of resistance genes in plants. Arabidopsis thaliana has long been the plant model for genetics. We will focus on a set of approximately 180 natural plant populations in the southern France (Midi-Pyrenees) that contain A. thaliana, as well as two closely related weedy Brassicaceae; Cardamine hirsuta and Erophila verna. Our major aim is to unravel the genetic architecture and evolutionary dynamics behind all the R genes shared among these three species. We do this by sequencing all R genes in natural populations of co-occurring C. hirsuta, A. thaliana and E. verna plants. This project has 4 specific aims.
1. Reconstruction of R gene evolution. We will isolate DNA and perform R-gene enrichment sequencing (RENSeq) on 60 natural populations of co-occurring Arabidopsis thaliana, Erophila verna and Cardamine hirsuta, collected in Southern France. After orthologous genes have been detected among the three species, several statistical approaches can be applied to study the evolution of those R genes. We propose to explain evolutionary dynamics observed in these R genes through functional characterization from an ecological perspective (component 2), physiological costs of maintaining resistance in the absence of disease (component 3) and genomic and functional constraints of these R genes (component 4).
2. Functional Ecology of homologs. Shared pathogens are likely driving some of the R gene evolution dynamics we have observed in the past in A. thaliana (Karasov, Kniskern, et al., 2014a; Karasov, Horton, et al., 2014). Pathogen effectors will be isolates in the co-occurring pathobiomes with effector enrichment sequencing (PATHSeq) and metagenomics. We propose a two-tiered approach to test how shared pathogens and their effectors might shape R gene evolution. Transient expression on R- avr protein pairs of a small set of divergently evolving R genes will be performed to study what effectors interact with different homologs, and ancestral protein reconstruction on these R genes will be performed to understand how neofunctionalization of homologs could lead to potential differences in Avr recognition.
3. Physiological burden of resistance. Fitness trade-offs exist for carrying the resistance allele in absence of disease, as demonstrated for RPM1 (Stahl, Dwyer, Mauricio, Kreitman, & Bergelson, 1999) and RPS 5 (Karasov, Kniskern, et al., 2014a) although we also demonstrated an exemption with RPS2 (MacQueen, Sun, & Bergelson, 2016). We will test the costs of carrying functional R alleles (candidates of interest derived from first two components of this grant) by creating isogenic lines in all three species, and test fitness effects of disease resistance in climate cell and common garden experiments in the field.
4. Genomic and functional constraints. Does any given R gene recognize a set of effectors, or only one? Do they function as direct recognition proteins, or are they guarding other plant receptors to initiate a defence response? Are they located in tandem repeats, and where in the genome? Both genomic location and genetic architecture can influence the evolution of a gene. Single copy homologs have less freedom to diversify, and genes in recombination hotspots are more likely to undergo rapid evolution. In this component, we are taking a closer look at the genomic and functional constraints of R gene homologs through bio-informatics approaches and functional characterization of protein/protein interactions to establish roles of different R genes. We will perform a phenotype free joint GWAS on geographically linked plant and P. syringae pairs to gain insights in the genomic scale at which this diffuse evolution across different species occurs.