In prior studies, our lab found that costs of resistance to pathogens in the absence of disease was ~5-10% for the resistance (R) genes Rps5 and Rpm1, respectively. However, Arabidopsis thaliana has 149 R-genes so it is unlikely that many R genes incur such a high cost. The now published research of former PhD student Alice MacQueen focuses on Rps2 that exists as an ancient balanced polymorphism with two long-lived clades of alleles. Alice conducted field trials that show that Arabidopsis thaliana plants with resistant Rps2 are no less fit than those with a susceptible Rps2 allele in the absence of disease. Both resistant and susceptible Rps2 alleles contribute to controlling defense and stress gene expression thus presenting a pleiotropic effect to explain the maintenance of both alleles.
“These results demonstrate how profoundly the magnitude of fitness costs associated with disease resistance may be shaped by genomic architecture and pleiotropy… These findings shed much-needed light on how the full repertoire of R genes is maintained in the A. thaliana genome. More broadly, these results show that the nature of fitness costs and trade-offs of disease resistance vary among loci even within the same host. Such information is crucial for crop breeding, where the challenge lies in producing high-yield crops while minimizing the cost of disease control.”
We illustrated this post with Sir John Tenniel’s drawing of the Red Queen and Alice from Lewis Carroll’s Through the Looking-Glass. The Red Queen tells Alice: “Now, here, you see, it takes all the running you can do, to keep in the same place”. This is commonly used as an analogy for co-evolution, as hosts and parasites have to rapidly adapt to each other in order to not loose the race. A concept introduced by Leigh Van Valen’s 1973 article. The rate of this co-evolutionary arms race is expected to be constrained by fitness costs.
Alice MacQueen performed fitness experiments as part of her doctoral dissertation and is now a post doctoral researcher with the Juenger lab in Austin Texas.
Xiaoqin Sun worked with the Bergelson lab from 2007-2009 and is now at the Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing.
The 1001 Genomes Consortium set out to provide detailed whole-genome sequences of at least 1001 genotypes of the model plant Arabidopsis thaliana. In a worldwide collaboration, including (past) lab members Angela Hancock, Matthew Horton, Wayan Muliyati, Gianluca Sperone and Joy Bergelson, the consortium released 1,135 genome sequences of A. thaliana. The joint effort results in a publicly available, invaluable resource to study phenotypic variation and adaptation in plants.
The release of the genomes in Cell 2016, 166, provides a fascinating insight into A. thaliana’s global population structure, migration patterns, and evolutionary history. When combined with the RegMap panel, we now have 2,029 natural A. thaliana genotypes with high quality polymorphism data that will greatly expand our ability to study how wild plants adapt to biotic and abiotic environments.
Origins of the 1001 Genomes Accessions (A) Collection locations of the 1001 Genomes accessions by diversity set (colors correspond to Venn diagram in B). (B) Relationships between 1001 Genomes accessions and other A. thaliana diversity sets (Nordborg et al., 2005; Cao et al., 2011; Horton et al., 2012; Long et al., 2013; Schmitz et al., 2013).
In scientific manuscripts, we tell stories of our research, generally in straight-line fashion with clear motivations and results. This type of research is rare (in my experience), with stories, motivations, and applications only realized post hoc. This is the nature of science, and our recent ISMEJ publication is no different.
With “16Stimator: statistical estimation of ribosomal gene copy numbers from draft genome assemblies“, we introduce an exciting method to generate 16S rRNA gene (16S) copy number estimates for bacterial genomes based on comparison of sequencing read depths of ribosomal and single copy gene regions. Application of this method resulted in 16S copy number estimates for hundreds of bacterial species without closed genome representatives. This extended database of known 16S copy numbers combined with phylogenetic based normalization methods [ – PICRUSt] for 16S amplicon sequencing studies will lead to more accurate organismal abundance measurements. Note: Our article is not open access but the code is freely available.
These are valid and important motivations and applications, but really, we just wanted to know the 16S copy numbers for a handful of isolates so we could properly measure their abundances by amplicon sequencing in controlled community studies.
So here is the actual development route of 16Stimator:
A caveat of 16S amplicon sequencing studies is that, due to variation in bacterial 16S copy number, sequencing read and organismal abundances are not equivalent. For our controlled community experiments using leaf endophytic bacteria originally isolated from Arabidopsis thaliana, we needed to determine each isolate’s 16S copy number. We chose whole genome sequencing and assembly for this task. That was a horrible choice.
Current assembly algorithms do a poor job resolving repetitive genomic regions. Longer reads or larger insert sizes can overcome this limitation, but alas, we had short read, Illumina sequencing libraries with insert sizes smaller than ribosomal rRNA gene regions. After assembly, the 16S rRNA gene was found in one to few contigs. When we mapped reads back to the assembly, the coverage of the 16S contig was much greater than the average genomic coverage, so we sought to use read-depths to resolve 16S copy numbers. By statistical coverage comparisons of 16S to single copy, conserved genes, we were able to accurately estimate copy numbers.
16Stimator pipeline overview.
Though the focus of the paper is on the sequencing read-depth approach, we did confirm 16S copy numbers experimentally, using an efficient qPCR approach. We compared amplification of 16S to single copy, conserved genes to determine copy number. The IDT-DNA gBlocks provided a convenient alternative to plasmid construction for creating standards with a 1:1 ratio of 16S to single copy gene.
16S copy number estimates from de novo assemblies. For each endophytic isolate, paired-end sequencing reads (R1, R2) were generated on the Illumina HiSeq 2000 from short (~250 bp) and long (~2500) insert libraries (Short_Ins and Long_Ins, respectively). For closed-genome controls, similarly generated sequencing reads were downloaded from SRA: Escherichia coli TY-2482 (GCA_000217695.2, SRR292678, SRR292862), Bacteroides fragilis HMW 615 (GCA_000297735.1, SRR488169, SRR488170), Pseudomonas aeruginosa PAO1 (GCA_000006765.1, SRR032420, SRR032832) and Staphylococcus aureus KPL1828 (GCA_000507725.1, SRR835799, SRR958927). The 16Stimator pipeline was used to estimate 16S copy number as the ratio of median coverage for 16S and single-copy genes. Confidence intervals (95%) were either calculated as in Price and Bonett (2002) (PB), or via permutations (Perm). For endophytic isolates, 16S copy numbers were independently verified by absolute quantification via qPCR with the mean and standard deviation of technical replicates shown. For closed-genome controls, each horizontal line marks the rrnDB (Stoddard et al., 2014) consensus 16S copy number for each species. Note: the short-insert library for MEDvA23 and the long-insert library for MEB061 did not meet quality thresholds. 16S copy number was not experimentally determined by qPCR for E. coli TY-2482, B. fragilis HMW 615, P. aeruginosa PAO1 and S. aureus KPL1828.
Only after resolving 16S copy numbers for our isolates of interest did we realize that this method could be applied to thousands of other draft genomes. We scaled 16Stimator to process tens of thousands of sequencing libraries deposited in SRA, resulting in 16S copy number estimations for hundreds of species without closed genome representatives. A large and diverse database of 16S copy numbers combined with methods to correct for copy number bias in 16S amplicon sequencing studies will ultimately result in more accurate abundance and diversity estimates. If sequencing reads are publicly deposited along with draft genome sequences, then the database can continue to grow.
Though we did not initially intend to create a method to estimate 16S copy numbers from draft genomes, science threw us a curveball and 16Stimator was our response. All the scripts and data are publicly available at https://bitbucket.org/perisin/16stimator. We look forward to feedback on our method to continue to improve and generate 16Stimates!
Kembel, S. W., Wu, M., Eisen, J. A., & Green, J. L. (2012). Incorporating 16S Gene Copy Number Information Improves Estimates of Microbial Diversity and Abundance. PLoS Comput Biol, 8(10), e1002743. https://doi.org/10.1371/journal.pcbi.1002743Cite
Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., Beiko, R. G., & Huttenhower, C. (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology, 31(9), 814–821. https://doi.org/10.1038/nbt.2676Cite
Perisin, Matthew, Madlen Vetter, Jack A. Gilbert, and Joy Bergelson. 2015. “16Stimator: Statistical Estimation of Ribosomal Gene Copy Numbers from Draft Genome Assemblies.” The ISME Journal. https://doi.org/10.1038/ismej.2015.161.
Stoddard, Steven F., Byron J. Smith, Robert Hein, Benjamin R. K. Roller, and Thomas M. Schmidt. 2015. “RrnDB: Improved Tools for Interpreting RRNA Gene Abundance in Bacteria and Archaea and a New Foundation for Future Development.” Nucleic Acids Research 43 (D1): D593–D598. https://doi.org/10.1093/nar/gku1201.
Price, Robert M., and Douglas G. Bonett. 2002. “Distribution-Free Confidence Intervals for Difference and Ratio of Medians.” Journal of Statistical Computation and Simulation 72 (2): 119–124. https://doi.org/10.1080/00949650212140.
(a) Accumulation of m6A-IP reads along transcripts. Each transcript is divided into three parts: 5′ UTRs, CDs and 3′ UTRs. (b) The m6A peak distribution within different gene contexts. Left panel: total genes with m6A peaks; right panel: genes conserved in human and Arabidopsis.
Graduate student Alice MacQueen investigated the transcriptome-wide patterns of mRNA editing in a collaboration with the group of Chuan He at the Department of Chemistry and Institute for Biophysical Dynamics at the University of Chicago. m6A mRNA editing is essential for plant development, but the role this editing mark plays in the cell is still unknown. The research team found that m6A editing in plants is distinct from editing in yeast and mammals, enriched not only around the stop codon and within 3′-untranslated regions, but also around the start codon .
Deposition of this editing mark around the start codon was associated with chloroplast-specific genes and increased mRNA abundance, which suggests a regulatory role for m6A editing in plants distinct from other eukaryotes described to date.
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Luo, G.-Z., MacQueen, A., Zheng, G., Duan, H., Dore, L. C., Lu, Z., Liu, J., Chen, K., Jia, G., Bergelson, J., & He, C. (2014). Unique features of the m6A methylome in Arabidopsis thaliana. Nature Communications, 5. https://doi.org/10.1038/ncomms6630Cite
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.
Araki, Hitoshi, Hideki Innan, Martin Kreitman, and Joy Bergelson. 2007. “Molecular Evolution of Pathogenicity-Island Genes in Pseudomonas Viridiflava.” Genetics 177 (2): 1031–41. https://doi.org/10.1534/genetics.107.077925. Cite
Traw, M Brian, Joel M Kniskern, and Joy Bergelson. 2007. “SAR Increases Fitness of Arabidopsis Thaliana in the Presence of Natural Bacterial Pathogens.” Evolution; International Journal of Organic Evolution 61 (10): 2444–9. https://doi.org/10.1111/j.1558-5646.2007.00211.x. Cite
Araki, Hitoshi, Dacheng Tian, Erica M Goss, Katrin Jakob, Solveig S Halldorsdottir, Martin Kreitman, and Joy Bergelson. 2006. “Presence/Absence Polymorphism for Alternative Pathogenicity Islands in Pseudomonas Viridiflava, a Pathogen of Arabidopsis.” Proceedings of the National Academy of Sciences of the United States of America 103 (15): 5887–92. https://doi.org/10.1073/pnas.0601431103. Cite
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.
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
Durable resistance in agriculture is difficult to achieve, and in fact most resistance factors that are introduced into crops are effective for fewer than five years. In contrast, resistance polymorphisms in nature often persist for thousands, if not millions, of years. Why are these dynamics so different?
In this work, Talia Karasov with recent members of the Bergelson group and in collaboration with Richard Hudson and Roger Innes investigated how polymorphisms in resistance (R) genes are maintained over long time scales.
Through dissecting a resistance polymorphism in nature the authors show that the complexity inherent in ecological communities is key to its longevity. This suggests that the simplicity of agricultural communities may not be conducive to long-term resistance. Our study highlights the value of understanding natural species interactions for resistance management.
Karasov, T. L., Kniskern, J. M., Gao, L., DeYoung, B. J., Ding, J., Dubiella, U., … & Bergelson, J. (2014). The long-term maintenance of a resistance polymorphism through diffuse interactions. Nature, 512(7515), 436-440.
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.
