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
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
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).
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:
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.
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).
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
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.
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
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.
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
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.
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.
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.
University of Chicago, Dept. of Ecology & Evolution