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