Category Archives: project

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).

Rpp8 three-copy paralog intergenic gene conversion polymorphism

Here we examined the population genetics of the three-copy R-gene family of Rpp8. Across Rpp8, nucleotide diversity ranged from double to 27x the genomic background rate and amino acid substitutions were 5 to 16x higher due to intergenic gene conversion (IGC) between the 3 paralogs.  Simulation models suggest IGC coupled with balancing selection to maintain copy number polymorphism drives the high level of diversity we observe in Rpp8.  If we consider paralogs undergoing IGC as analogous to a single gene, then IGC between paralogs could effectively create a heterozygous locus in a predominantly homozygous individual thus establishing reservoirs of variation for the generation of new R-gene recognition specificities via some sort of recombination event.

Bootstrap consensus trees for the maximum parsimony phylogenies of the leucine-rich repeat region (LRR) and non-LRR regions of all three Rpp8 paralogs. Clades comprised of alleles from one paralog are boxed. Green, blue, and orange boxes represent P1, P2, and P3, respectively.

a) Phylogeny of non-LRR region (239 parsimony-informative sites out of 1701 sites).
b) Phylogeny of the framed LRR region for the same accessions as in (a). There were 236 parsimony-informative sites out of 1019 in this phylogeny

Co-infections with pathogenic and beneficial bacteria

Our preliminary experimental data reveals that coinfection has exceptionally strong impacts on pathogen performance in Arabidopsis. To test for these effects, we conducted infections consisting of a luciferase-labeled focal strain of P. viridiflava that was separately co-inoculated with each of 60 randomly chosen strains from the P. syringae complex. At 36 hours post-inoculation, luciferase activity (i.e., photon counts) in each infected plant was measured to quantify abundance of the focal strain. This was replicated in triplicate for two different focal strains. Four aspects of these results are favorable for the proposed work:

  • First, the effects of coinfection on pathogen performance are large. The mean abundance of both focal strains differed by two orders of magnitude between the most and least favorable coinfection combinations.
  • Second, these effects are highly consistent and dwarf experimental noise. The identity of the coinfecting strain explained over 70% of the variance in focal strain abundance in our experiments (linear mixed model, abundance ~ coinfecting strain + batch effect covariates), and this effect was statistically significant (P < 1e-16).
  • Third, our data indicate the potential for both costs and benefits to coinfection, depending on the identity of the coinfecting strains. In 40% of the pairwise coinfection combinations, the focal strain grew to a higher abundance than when singly inoculated; conversely, in 60% of cases, its abundance decreased relative to single infections.
  • Fourth, the two focal strains differed in how their abundance was affected by the coinfecting strains (aforementioned linear mixed model; focal strain x coinfecting strain interaction, P < 0.001). This underscores the importance of accounting for genotype x genotype interactions, as we propose to do, when predicting infection outcomes.
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
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.

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.

Local adaptation and the accessory genome in an endemic plant-pathogen

infected crop cultivars from the ongoing adaptation experiment



Genetic variation is fodder for evolution, and microbial plant-pathogens have it in spades. The Pseudomonas syringae genome is characterized by many rare “accessory” genes that co-occur with “core” genes found in all individuals. In fact, accessory genes outnumber core genes 2:1, even though accessory genes are not essential for survival. Moreover, there is tremendous variation in the gene content of P. syringae; isolates from different crop species, for example, differ in gene content by ~32% (Karasov et al. 2017). Whether these strain-specific genes have adaptive potential remains unknown; they may simply be a consequence of high rates of mutation and lateral gene transfer, even if purifying selection to remove deleterious variants is strong. Another, not mutually exclusive possibility is that accessory genes are maintained by positive selection as pathogens adapt to alternative hosts. Indeed, local adaptation has been hypothesized to explain the presence of rare alleles in P. syringae, which causes major agricultural loss in multiple crop species each year. To address these hypotheses, I have paired a set of P. syringae isolates with their original hosts of isolation. I first test for local adaptation by comparing the in planta fitness of each isolate in its own, and in each other’s, native host. Next, I ask to what degree strain-specific genes influence adaptive patterns by using Tn-seq to track the in planta gene frequencies of each pathogen over the course of infection in each host. From this combination of experiments, we will learn to what extent host ecology influences genome evolution and virulence in P. syringae; this is important not only to inform our understanding of the selective process, but also to fields concerned with the emergence and spread of infectious disease.

P. syringae transposon mutants!

Genetic basis of a natural plant pathosystem

During the last two decades, scientists achieved a better understanding of the molecular basis of host-parasite co-evolution. However, many studies focused on the interaction of the genetic plant model species Arabidopsis thaliana and the highly pathogenic but non-specific tomato pathogen Pseudomonas syringae pv. tomato DC3000.

The Bergelson lab studies the interaction of A. thaliana and one of its highly abundant bacterial resident, P. viridiflava . We previously identified broad-scale natural variation in resistance phenotypes towards two distinct clades of P. viridiflava . While some genotypes of A. thaliana show little signs of disease or low bacteria titer, others suffer from severe hydrolysis of leaf tissue.

In a collaboration with Fabrice Roux, Joy Bergelson and Madlen Vetter, we currently identify and confirm the genetic loci underlying strain-specific and general defense mechanisms of A. thaliana against its natural pathogen P. viridiflava.

Host control of bacteria community composition in Arabidopsis thaliana

The outcome of host-microbe interactions is influenced by host genetics and interactions among bacterial community members. Previous studies described the bacterial community associated with Arabidopsis thaliana in the field. Using controlled greenhouse experiments we now aim to characterize how endophytic species composition influences plant-pathogen interactions. We furthermore seek to identify host genetic loci underlying the putative control of bacterial community composition.