Plants recognize potential pathogens and induce a complex immune response by detecting pathogen-associated molecular patterns (PAMPs). While immune responses are beneficial for mitigating the detrimental effects of pathogens, PAMP perception comes at the cost of growth reduction in seedlings. The genetic basis of growth versus defense trade-offs is poorly understood. A genome-wide association study identified the genetic loci contributing to natural variation in expenses in innate immune responses. We experimentally validated several a priori and de novo candidate genes, which significantly contribute to de- or increase of biomass after PAMP-triggered seedling growth inhibition.
Vetter, Madlen, Talia L. Karasov, and Joy Bergelson. 2016. “Differentiation between MAMP Triggered Defenses in Arabidopsis Thaliana.” PLOS Genet 12 (6): e1006068. https://doi.org/10.1371/journal.pgen.1006068. Cite
Understanding how organisms adapt to their environment has been a long standing question in evolutionary biology. While demonstrating local adaptation with reciprocal transplants is an old idea (KAWECKI and EBERT 2004), the recent technological advances in genomics present us with an opportunity to better understand the genetics and the process of adaptive evolution.
This is particularly true for the model plant Arabidopsis thaliana. A. thaliana is a small, mostly selfing, winter-annual brassica that was introduced as a model species for its short life cycle and small genome size (THE ARABIDOPSIS GENOME INITIATIVE 2000). Naturally occurring inbred lines (accessions) also have the advantage that once they have been genotyped or sequenced, seeds generated through selfing can be used for multiple experiments with high levels of replication. In addition to being a convenient model, A. thaliana is also a wild plant, found across the world in a great diversity of natural environments and displays great phenotypic variation between and within populations (see for example STINCHCOMBE et al. 2004; KRONHOLM et al. 2012; ZÜST et al. 2012). The recent genomic resources developed for this plant opens an unprecedented opportunity to investigate the genetics underlying adaptive variation
(HORTON et al. 2012; LONG et al. 2013) while the great effort that went into understanding the function of many, if not most, of its genes provides us with a new window into the functions, traits and environmental factors driving in local adaptation.
In this project we investigate local adaptation in natural populations of this small winter annual plant in Sweden. Prior work showed strong population structure (NORDBORG et al. 2005) and isolation by distance (PLATT et al. 2010) throughout the species range, suggesting that populations are stable and have had the opportunity to adapt to local environments. Local adaptation to climate variation was also found to be ubiquitous across Europe (FOURNIER-LEVEL et al. 2011; HANCOCK et al. 2011).
In Sweden we focus on two regions: the High Coast, about 4h drive North of Stockholm, and Skåne, in the South (Figure 1).
Figure 1: Maps of Sweden showing the regions where experiments are located in Sweden (red dots) and the location of origin of each of the 200 accessions used in this study.
These two regions display contrasting climates, with the Northern region of the High Coast displaying colder temperatures, longer snow cover, and a broader range of photoperiod. The High Coast is close to the Northern limit of the species range and in this region Arabidopsis populations are only found on South facing slopes where they can capture the low incidence sun’s rays. In Skåne, in the South, A. thaliana is found in agricultural meadows, fields and on beaches along the Baltic sea.
Building on the old idea of reciprocal transplants (KAWECKI and EBERT 2004), and combining it with cutting edge genomics, we set up experiments designed to test for local adaptation, identify important phenotypes and selective pressures, and detect the molecular bases of local adaptation among natural populations of A. thaliana.
We use a set of 200 accessions all re-sequenced (LONG et al. 2013) in a dual experimental strategy. The first part our experimental design consists of experimental natural selection experiments. In both the High Coast (North) and Skåne (South), we selected 5 locations where the environment seemed suitable for an Arabidopsis population to establish. In each location we set up five-1 m2 plots in which we dispersed a mixture of seeds from the 200 re-sequenced accessions (LONG et al. 2013). Populations were allowed to establish without further intervention and we collected samples three times a year for the last 2 years. After low depth sequencing of the samples, we will be able to track changes in the frequency of individual genotypes, but also changes in allele frequency across the genome.
The second experimental strategy builds more directly on the idea of reciprocal transplants and consists of 4 large common garden type experiments (two in each region). Experiments were installed to coincide with local germination flushes among local natural populations and consisted of three complete randomized blocks, each block included 8 replicates per accessions. These experiments were used to gather data on flowering time, herbivore damage, rosette size, shape and growth, pathogen infections and microbial community composition (see Microbial community paragraph). We also directly measured over-winter survival and estimated seed production, two major fitness components for any annual plant. These experiments will allow us to directly test for local adaptation, investigate the contribution of various significant traits, and to identify the underlying molecular bases of adaptive variation using Genome-Wide Association mapping (ATWELL et al. 2010). While estimating fitness component in the common garden experiment is likely biased because it doesn’t include all components of fitness, the results will help us understand and validate results from the selection experiments.
Preliminary results show evidence for local adaptation. In the Southern Sweden common garden experiments, Southern accessions grew bigger and produced more seeds than Northern accessions (Figure 2).
Figure 2: Relationship between the latitude of origin of accessions and lifetime fecundity, in the four common garden experiments (North: top panels, South: bottom panels). Significant, negative relationships were found in the two Southern experiments Rathckegården and Ullstorp. “cor” indicates the Spearman rank correlation coefficient and p-value, the associated p-value.
Interestingly, genome-wide association mapping clearly identifies a disease resistance gene explaining a significant fraction of seed production in one of the Southern experiments (Figure 3).
Figure 3: Manhattan plot for lifetime fecundity in Ullstorp, Southern Sweden. The y-axis gives the associations score between an estimate of lifetime fecundity and approximately 2 millions SNPs with allele frequencies over 5%. The x-axis gives the location of the SNPs along the 5 chromosome of Arabidopsis thaliana. The peak annotated as one on Chromosome 1 is located in the vicinity of RLM1.
In the other Southern experiment, herbivore attacks in the fall are associated with SNP polymorphisms located near the known glucosinolate genes AOP2 and AOP3. The amount of herbivore damage also significantly decreases the probability of overwinter survival. Overall, preliminary results seem to indicate a large contribution of biotic interaction to fitness components. This prompted us to further investigate the leaf microbial community variation among accessions in our four common garden experiments.
Microbial community variation.
In a prior study from our lab, M. Horton and N. Bodenhausen performed a common garden experiment in Michigan in which they grew a set of 196 worldwide accessions in natural conditions (BODENHAUSEN et al. 2013, HORTON et al. In Press). They characterized the bacterial and fungal communities in leaves and roots for each accession by sequencing the taxonomically informative genes 16S rRNA in bacteria and ITS in fungi. One of the major results of these studies is the effect of the plant’s genotype on the composition and diversity of the plant microbiota. Using methods developed in these studies, we aim at better understanding adaptive variation in Sweden by characterizing the leaf microbial communities of the plants in the common garden installed in Sweden. The specific questions here are:
1) Are there differences in the bacterial community among the four study sites? Observing different communities of microbes among our study sites would suggest that plants experience a different biotic environment depending on the location and climate.
2) Can we see differences in the leaf bacterial community among accessions of Arabidopsis thaliana, and if so then what are the genetics driving those differences? Differences in leaf microbial community among accessions grown in the same location would suggest that plants shape the microbial community they host. Because all accessions used in this study have been genome sequenced, we will have the opportunity to study the genetics shaping the leaf microbial community with GWA mapping.
3) Are natural populations of Arabidopsis thaliana locally adapted to the pathogens they encounter in their natural habitat?
Fitness estimates based on seed production will be generated via high throughput analysis of mature plants images. We will investigate if polymorphisms at genes shaping the bacterial community in the different experiments have effects on plant fitness and determine their importance relative to genes underlying other adaptive traits.
Main contributors:
Benjamin Brachi
Daniele Filiault (Gregor Mendel Institute, Vienna)
Svante Holms (Mid-Sweden University)
Principal investigators:
Joy Bergelson
Magnus Nordborg
Caroline Dean
Other contributors:
Envel Kerdaffrec (Gregor Mendel Institute, Austria)
Fernando Rabanal (Gregor Mendel Institute, Austria)
Polina Novikova (Gregor Mendel Institute, Austria)
Takashi Tsuchimatsu (Gregor Mendel Institute, Austria)
Susan Duncan (John Innes Centre, UK)
Timothy Morton (University of Chicago, USA)
Roderick Wooley (University of Chicago)
Matthew Box (John Innes Centre, UK)
Alison Anastasio (University of Chicago, USA)
Arthur Korte (Gregor Mendel Institute, Austria)
Pamela Korte (Gregor Mendel Institute, Austria)
Viktoria Nizhynska (Gregor Mendel Institute, Austria)
Stéphanie Arnoux (Gregor Mendel Institute, Austria)
References:
Atwell S., Huang Y. S., Vilhjálmsson B. J., Willems G., Horton M., Li Y., Meng D., Platt A., Tarone A. M., Hu T. T., Jiang R., Muliyati N. W., Zhang X., Amer M. A., Baxter I., Brachi B., Chory J., Dean C., Debieu M., de Meaux J., Ecker J. R., Faure N., Kniskern J. M., Jones J. D. G., Michael T., Nemri A., Roux F., Salt D. E., Tang C., Todesco M., Traw M. B., Weigel D., Marjoram P., Borevitz J. O., Bergelson J., Nordborg M., 2010 Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465: 627–631.
Bodenhausen N., Horton M. W., Bergelson J., 2013 Bacterial Communities Associated with the Leaves and the Roots of Arabidopsis thaliana (AM Ibekwe, Ed.). PLoS ONE 8: e56329.
Fournier-Level A., Korte A., Cooper M. D., Nordborg M., Schmitt J., Wilczek A. M., 2011 A map of local adaptation in Arabidopsis thaliana. Science 334: 86–89.
Hancock A. M., Brachi B., Faure N., Horton M. W., Jarymowycz L. B., Sperone F. G., Toomajian C., Roux F., Bergelson J., 2011 Adaptation to climate across the Arabidopsis thaliana genome. Science 334: 83–86.
Horton M. W., Hancock A. M., Huang Y. S., Toomajian C., Atwell S., Auton A., Muliyati N. W., Platt A., Sperone F. G., Vilhjálmsson B. J., Nordborg M., Borevitz J. O., Bergelson J., 2012 Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nat Genet 44: 212–216.
Kawecki T. J., Ebert D., 2004 Conceptual issues in local adaptation. Ecology Letters 7: 1225–1241.
Kronholm I., Picó F. X., Alonso-Blanco C., Goudet J., Meaux J. de, 2012 Genetic basis of adaptation in Arabidopsis thaliana: local adaptation at the seed dormancy qtl DOG1. Evolution 66: 2287–2302.
Long Q., Rabanal F. A., Meng D., Huber C. D., Farlow A., Platzer A., Zhang Q., lmsson B. J. V. A., Korte A., Nizhynska V., Voronin V., Korte P., Sedman L., aacute T. M. A. K., Lysak M. A., Seren U. M., Hellmann I., Nordborg M., 2013 Massive genomic variation and strong selection in Arabidopsis thaliana lines from Sweden. Nat Genet: 1–8.
Nordborg M., Hu T. T., Ishino Y., Jhaveri J., Toomajian C., Zheng H., Bakker E., Calabrese P., Gladstone J., Goyal R., Jakobsson M., Kim S., Morozov Y., Padhukasahasram B., Plagnol V., Rosenberg N. A., Shah C., Wall J. D., Wang J., Zhao K., Kalbfleisch T., Schulz V., Kreitman M., Bergelson J., 2005 The Pattern of Polymorphism in Arabidopsis thaliana. PLoS Biol 3: e196.
Platt A., Horton M., Huang Y. S., Li Y., Anastasio A. E., Mulyati N. W., Ågren J., Bossdorf O., Byers D., Donohue K., Dunning M., Holub E. B., Hudson A., Le Corre V., Loudet O., Roux F., Warthmann N., Weigel D., Rivero L., Scholl R., Nordborg M., Bergelson J., Borevitz J. O., 2010 The Scale of Population Structure in Arabidopsis thaliana (J Novembre, Ed.). PLoS Genet. 6: e10000843.
Stinchcombe J. R., Weinig C., Ungerer M., Olsen K. M., Mays C., Halldorsdottir S. S., Purugganan M. D., Schmitt J., 2004 A latitudinal cline in flowering time in Arabidopsis thaliana modulated by the flowering time gene FRIGIDA. Proc. Natl. Acad. Sci. USA 101: 4712–4717.
The Arabidopsis genome initiative, 2000 Analysis of the genome sequence of the flowering plantArabidopsis thaliana. Nature 408: 796–815.
Züst T., Heichinger C., Grossniklaus U., Harrington R., Kliebenstein D. J., Turnbull L. A., 2012 Natural enemies drive geographic variation in plant defenses. Science 338: 116–119.
In agriculture, plant resistance to pathogens is typically short-lived, lasting on the order of a few years. In contrast, resistance in natural plant populations seems to persist for millions of years. Why is resistance ephemeral in agriculture, but seemingly indefinite in natural populations? We address this question by studying the coevolution of natural populations of A. thaliana with natural populations of their pathogens using molecular, genomic and ecological techniques.
Our results led us to a hypothesis about what maintains resistance polymorphisms in natural populations: A. thaliana, unlike plants in agriculture, is rarely challenged with one dominant pathogen. Instead, A. thaliana populations are exposed to thousands of microbes, all at low to intermediate abundances, each with different mechanisms of persistence and/or pathogenicity. A. thaliana seems to evolve resistance in response to this diverse microbial community, and not to one pathogen factor. In short, the heterogeneity of the microbial community selects for heterogeneity in resistance traits.
The high selective pressures involved in the “arms race” between plants and their pathogens drives rapid evolution of genes involved in immunity on the host side and virulence on the pathogen side (Alcázar et al., 2011). However, plants are not typically infected by individual pathogens: they interact with a community of inter- and intraspecifically diverse microbes that also experience competitive pressures from one another. How these interactions among microbes affect their ability to cause disease and how the host plant influences the microbial community it harbors remain open questions for investigation.
Researchers have observed that P. syringae is a common natural pathogen of A. thaliana and that resistance to P. syringae infection varies among different A. thaliana accessions (Jakob et al., 2002). Recent work has shown that P. syringae strains isolated from A. thaliana leaf tissue are not only genetically diverse but also differ in their degree of virulence: many isolates harbor a polymorphism in the type three secretion system (T3SS), losing the ability to cause disease (Barrett et al., 2011; Kniskern et al., 2011). Such strains show increased growth in plant tissue when co-inoculated with other P. syringae isolates harboring an intact T3SS. This result suggests a model where non-pathogenic strains engage in “cheating” through taking advantage of the nutrients released from host cells infected by pathogenic strains (Barrett et al., 2011).
Works cited
Alcázar, R., Reymond, M., Schmitz, G., and de Meaux, J. (2011). Genetic and evolutionary perspectives on the interplay between plant immunity and development. Curr. Opin. Plant Biol. 14, 378–384.
Barrett, L.G., Bell, T., Dwyer, G., and Bergelson, J. (2011). Cheating, trade-offs and the evolution of aggressiveness in a natural pathogen population. Ecol. Lett. 14, 1149–1157.
Jakob, K., Goss, E.M., Araki, H., Van, T., Kreitman, M., and Bergelson, J. (2002). Pseudomonas viridiflava and P. syringae–natural pathogens of Arabidopsis thaliana. Mol. Plant Microbe Interact. 15, 1195–1203.
Kniskern, J.M., Barrett, L.G., and Bergelson, J. (2011). Maladaptation in wild populations of the generalist plant pathogen Pseudomonas syringae. Evolution 65, 818–830.
Plant pathogen interactions have been thought to undergo arms race dynamics, where there is consistant, dynamic turnover of resistance alleles – R genes – in the host, and avirulence alleles in the pathogen. If this were the case, resistance alleles segregating in natural populations should be relatively young.
However, over a decade ago we learned that, instead, R genes in plants are frequently maintained as ancient, balanced polymorphisms. Through field trials with transgenic A. thaliana lines, we have demonstrated that large fitness costs are associated with R genes experiencing balancing selection for presence/absence polymorphisms.
Not all R genes experiencing balancing selection are presence/absence polymorphisms. We are interested in how ecological and evolutionary forces combine to shape patterns of variation at RPS2, an R gene under balancing selection for disease resistance and susceptibility which is present in all natural populations of A. thaliana sequenced to date.
We have conducted a field trial with transgenic A. thaliana lines differing in only the native allele of RPS2 that they contain. We found that there was no cost of carrying a resistant allele of RPS2 in the field; instead, having any allele of RPS2 was beneficial in the field relative to a mutant that lacked RPS2.
High fitness benefit of RPS2 presence in the absence of pathogen!
Currently we are trying to understand the fitness benefit of RPS2 presence. We are exploring whether RPS2 has some alternative function, aside from the previously known resistance to avrRpt2, which gives plants with RPS2 a fitness benefit relative to plants that don’t. To support our field results, we are also determining if variation in RPS2 is associated with any fitness changes in the Recombinant Inbred Lines phenotyped by Ben Brachi.
To explore the benefit of RPS2 presence, we first asked if there was a difference in metabolome between plants with RPS2 and plants without. We used mass spectrometry to look at the metabolomes of our transgenic lines with and without RPS2, but we found no convincing differences between these lines. We are now using RNAseq to further characterize differences between these different transgenic lines.
We are also exploring the prevalence of homologs of avrRpt2, the avirulence gene that interacts with RPS2, in the environment. Diffuse interactions with common avirulence genes in other species may help to explain the maintenance of RPS2 in all accessions. Though we did not detect avrRpt2 using PCR for any of our field samples, these PCRs may not have picked up other homologs of avrRpt2. A weak homolog of avrRpt2 seems to be present in dot blots of most P. syringae strains from the Midwestern US; if RPS2 can recognize this homolog, it may explain the consistent presence of RPS2 and the fitness benefit of RPS2 presence.
Though we expected that R genes without presence-absence polymorphisms should not carry high fitness costs of resistance, we were surprised that the presence of RPS2 carried a substantial fitness benefit. We think understanding this benefit may help us understand the difference between R genes are under balancing selection for presence/absence polymorphisms and those that always present.
