Analysis of wild plant pathogen populations reveals a signal of adaptation in genes evolving for survival in agriculture in the beet rust pathogen ( Uromyces beticola )

  1. Mark McMullan
  2. Lawrence Percival-Alwyn
  3. Kevin Sawford
  4. Gemy Kaithakottil
  5. Michelle Grey
  6. Hélène Yvanne
  7. Ross Low
  8. Sally Warring
  9. Darren Heavens
  10. Ned Peel
  11. Jakob Kroboth
  12. Mark Stevens
  13. David Swarbreck
  14. Matt Clark
  15. Neil Hall

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Abstract

Improvements in crop resistance to pathogens can reduce yield losses and address global malnourishment today. Gene-for-gene -type interactions can identify new sources of resistance but genetic resistance is often short lived. Ultimately an understanding of how pathogens rapidly adapt will allow us to both increase resistance gene durability and more effectively target chemical treatments. Until recently all agricultural pathogens were living on wild hosts. To understand crop pathogen evolution, we compared genetic diversity in agricultural and wild populations. Wild reservoirs may be the source of emergent pathogen lineages, but here we outline a strategy for comparison of wild and agricultural pathogen populations to highlight genes adapting to agriculture. To address this, we have selected and developed the beet rust system ( Beta vulgaris , Uromyces beticola , respectively) as our wild-agricultural model. Our hypothesis is that pathogen adaptation to agricultural crops will be evident as divergence in comparisons of wild and agricultural plant pathogen populations. We sampled isolates in both the wild and agriculture, sequenced and assembled and annotated a large fungal genome and analysed genetic diversity in 42 re-sequenced rust isolates. We found population differentiation between isolates in the wild compared to a predominantly agricultural group. Fungal effector genes are co-evolving with host resistance and are important for successful colonisation. We predicted (and found) that these exhibit a greater signal of diversification and adaptation and more importantly displayed increased wild agricultural divergence. Finding a signal of adaptation in these genes highlights this as an important strategy to identify genes which are key to pathogen success, that analysis of agricultural isolates alone cannot.

Author Summary

As quickly as we develop new strategies for crop defence, pathogens evolve to circumvent them. Novel crop pathogen strains emerge periodically and sweep through the agricultural system. However, because of the (often) clonal nature of these crop pathogens it is difficult to identify the trait that is key to their success. In other words, if there is a trait that is key for success in agriculture, all agricultural isolates will have it (or die without it). What we need is a case and control system where we identify genes important to pathogen success in agricultural by comparing them to pathogens that live in the wild. Here we exemplify this strategy by focussing on genes already known to specifically adapt for the successful colonisation of the host, the fungal effector genes. We find that these genes appear to be evolving quickly and that they are more different between the wild and agriculture than other non-effector genes. These differences between wild and agricultural pathogens suggest we are observing adaptation to agriculture. We do this work in the sugar beet rust system because of its tractability to sample but this understanding about how to identify genetic variation that is key to pathogen success in agriculture is applicable to crop systems where pathogen reservoirs exist as well as other pathogen reservoir systems (e.g. zoonoses).

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  1. Review Commons

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    Reply to the reviewers

    Reviewer #1

    Further work required on divergence to address the observed level of gene flow between wild and crop populations and the lack of admixed/hybrid genomes.

    • Authors should plot Fst and Dxy along the main scaffolds. It would permit to see whether there is huge peaks of divergence and differentiation between the two populations.

    We will measure Dxy across the genome and plot against Fst in the larger contigs. Check for similarities and differences in these two measures and their relationship with effector locations. Relate results to effectors as well as broader demography as highlighted by the network.

    Virulence can be mediated at the expression level by effector silencing and the reviewer suggests looking for premature stop codons, deletion of an exons, or mutations in the promoter region.

    We will run SNPeff to annotate and classify the severity of variants. We will relate that to potential roles in silencing in effectors relative to non-effector genes.

    Simulations would provide stronger support for conclusions.

    Absolutely. Simulations are important to validate our conclusions and improve our hypotheses as to the levels of selection, rates of gene flow and/or boom and bust. Our simulation would look at:

    • The strength of balancing selection required to preserve diversity in pathogen virulence genes.
    • The strength and direction of selection required to partition that diversity to produce increased Fst we observe at virulence genes.
    • The impact of differential rates of recombination in the wild and crop populations
    • The impact of clonality in the crop population at multiple levels:
    • On divergence between populations and levels of inbreeding (Fis)
    • On boom-and-bust dynamics and the frequency of successive invasions

    To parameterise this model, we would need to estimate the rate of recombination using linkage decay across a near/pseudo chromosomal assembly. Our assembly isn’t contiguous enough to estimate recombination rates for our populations. Given the parameter space we must investigate, combined with unknowns, we feel that the investment required to design and test such a model is significant, including requiring a new (long read, phased) genome assembly. This is our aim but without that data now, we feel that a simulation would not be strong enough to get through the rigor of peer review. We are happy to add a discussion of the importance these next steps to validate our conclusions, in the Discussion section.

    Figure 2 network suggests strong divergence between populations, and this needs further exploration because divergence and the locus level is very low.

    • The reviewer requests a PCA
    • (Reviewer #2 missed the Machine Learning in the supplementary which also speaks to this work).

    Reorder Figure 2 to reduce confusion. Reduce the content in Figure 2 to include only the map, the network, and the admixture plot. Add a new Figure 3 which would include a larger PCA and the supplementary data which uses Machine Learning to attempt to partition individuals into clusters/populations.

    Authors should also look for how many effector genes are non-expressed in cultivated population face to wild population.

    The reviewer’s suggestion of analysis of premature stop codons etc will be done using SNPeff.

    Run Selscan (ZA Szpiech and RD Hernandez (2014) or similar to look at indicators of selection.

    It is feasible to run selection scan software, although this would be heavily caveated because these methods often do not account for clonal expansion in a single population.

    Address Minor Comments

    Reviewer #2

    • Effector candidates were not evaluated/characterized in any form.
    • Authors should compare pathogen features to other related species and try to contrast what stands out, especially the effectors' diversity

    The reviewer referred to a statement in which we suggest that it is difficult to functionally annotate effectors (“According to the authors, it is difficult to functionally annotate these genes in general”). This statement was not intended to suggest that we did not annotate them, only that, because effectors are quickly evolving, fewer of them tend to receive an annotation, as compared to non-effector genes. In fact, we use shared annotations to refer to the presence of shared effector annotations in other rust species.

    Details of cross species functional annotation were included in Supporting Information 01. They included annotation using AHRD, UniProt (Swss-Prot and TrEMBL) blast and InterProScan. AHRD uses a database of unbiased ground truth set of high-quality protein annotations with minimal redundancy to assign GO annotations. UniProt is the world’s leading high-quality protein sequence and functional information. It contains more than 190 million sequences with which to assign functional annotations to proteins. InterProScan was used to assign proteins into families as well as predict domains. All these methods utilise cross species information to assign gene/domain function and ontology (GO), the output from these is included for each gene along with that gene’s population genetic signature (Supporting Information 08).

    In the results section we did highlight effector functional annotations and conservation among the Pucciniales (e.g. Rust Transferred Protein, Alpha-amylase, CSEP-06 and PriA, among others). We will clarify that statement to reflect our efforts in that area and throughout.

    Where exactly the machine learning was used?

    It’s in the supplement. Accounting for this comment as well as Reviewer 1 & 2 about the complexity of Figure 2 we plan to bring those results into the main document. This would allow us to unpack genetic diversity and differentiation as a separate figure from the map and network.

    Address Minor Comments

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    Referee #2

    Evidence, reproducibility and clarity

    Summary: In the present study, using beet (Beta vulgaris) - rust (Uromyces beticola) as a model system, the authors set out to assess how crop pathogens evolve to evade resistance using wild reservoirs. They tested whether 1) the genes necessary to success in wild and crop environments are more genetically differentiated between pathogen populations and 2) the rate of clonal to sexual reproduction is higher in crop pathogen populations. Using freshly obtained 42 pathogen isolates from both crop and wild beets across the east of England, the authors assessed the genetic variation among virulent genes(effectors) between wild and crop pathogens. They found evidence for higher signals of diversity and differentiation in effectors and significant differences in reproductive rates between the wild and crop pathogen populations. They highlight that these findings can be used to identify candidate genes for pathogen survival in crops and develop methods to circumvent crop pathogen resistance. Additionally, they developed a new DNA peel extraction protocol for pathogens and produced a new annotation of Uromyces beticola genome annotation.

    Major comments:

    • The study design and the methodologies are appropriately explained and the statistical analyses are strong enough to draw the conclusions presented in the manuscript. The results are adequately explained and the inferences drawn from them are satisfactory.
    • The putative effectors (virulent genes) were identified based on the assumption that gene products secreted outside of the fungal cell and into the host are host interaction genes, potentially facilitating infection. However, these candidates were not evaluated/characterized in any form. According to the authors, it is difficult to functionally annotate these genes in general. However, I believe at least the predicted functionality can be checked with published adequately annotated genomes of related species. This comparison is lacking in the analysis. Not having confirmed the functionality of at least some of the effectors undermines the finding that the study reflects the actual genetic differentiation in infectious genes.
    • Similarly, comparisons of current findings to a related species/system are missing. Authors should compare pathogen features to other related species and try to contrast what stands out, especially the effectors' diversity.
    • Although the authors claim that machine learning was utilized in the manuscript, where exactly the machine learning was used is not clear. The models used in the analyses are already implemented in the software packages and methods described in the manuscript. I did not see any machine learning method being applied to improve the analysis either. If it is actually used, it would be beneficial to highlight for what and where it was used and how it improved specific analyses.

    Minor comments:

    • Lines 184 - 186: Can the lack of admixture and gene low among these wild isolates also explain this observation? what about the levels of FIS in these isolates? Clonality in these populations may have a significant impact on the genetic diversity in these populations.
    • lines 216-220: Is this also reflected in the excess of heterozygosity non-effectors in these crop populations? The mutations should equally accumulate in both gene categories.
    • lines 219-220: it is not clear which CDS are being referred to here; Are you talking about the correlation between the CDSs of wild and crops or effectors and non-effectors?
    • Figure 1: I suggest separating F & G from the rest
    • Figure 3: D. Unless this is a noe to one window comparison of pi, this plot does not necessarily show a correlation. Please explain how the windows were treated in this comparison.
    • Figure 4: A. I would expect a relatively high correlation between the FST and pi in effectors. Does this include both wild and crop effectors?
    • I spotted a number of typos throughout the manuscript. So I suggest the authors pay attention to punctuation and typos.

    Significance

    This study presents a critical comparative analysis of crop pathogens in their wild populations. It highlights the significance of assessing the crop pathogen genetic diversity against their wild background/relatives to identify how crop pathogens evolve to evade crop resistance. And in turn, it will help us to improve our crop varieties to be better resistant to pathogens in this era of ever-increasing demand for crop production.

    Further, the present study also provides a new methodology with an annotated genome of beet pathogen Uromyces beticola to identify candidate crop resistance genes in other related pathogens. The scientific community will also benefit from the protocol they developed to extract pathogens from host peels.

    Therefore, I believe this work will reach a wide audience in genetics, genomics, agriculture, crop development, and landscape genomics.

  3. Review Commons

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    Referee #1

    Evidence, reproducibility and clarity

    Summary:

    This study presents a pathosystem beet/Uromyces beticola in order to understand how reservoirs can play a role in ermergence of new virulences. To do this, the authors sample cultivated beets and wild beets in England and resequence 42 genomes of U beticola found on these two beet species (24 from sugar beet hosts and 18 from coastal places on wild beet hosts). The authors use population genomics tools to explore population structure and compare diversities of the two populations found. Indeed they found a population of U beticola exclusively living on wild beets, and a population infecting both sugar and wild beets. They compare genes encoding for effectors (important in interactions with hosts) with genes encoding for other proteins. They found that genes encoding for effectors are more diverse in wild compartiment than genes encoding for other types of proteins. In general, the wild compartiment is more diverse than the cultivated one. The authors draw conclusions about the role of reservoir of wild population for emergence of new virulence in the cultivated population. At last, as the authors found excess of heterozygosity in the cultivated population, they conclude about clonal reproduction in this population.

    Major comments:

    • Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

    Although the paper is well written and the population genomics studies were well done, the analyses are still preliminary and hence conclusions are not accurate. Indeed, in Fig2 authors show a tree representing the clear divergence between strains found in sugar beets and the ones found in wild beets (excepted for five isolates found in wild beets but belonging to the cultivated clade). Despite the fact that this apparent divergence is supported by other analyses, the authors do not conclude about the lack of gene flow between theses two populations. Indeed, the gene flow occurred there would have been admixed genomes and no clearcut delineations between the two populations. In other word, they authors have not found hybrids in their sampling. In general, divergence is not really studied in this paper. The comparison between genes encoding for effectors and the ones encoding for other genes is very interesting. However, the authors just forget that sometimes virulence is acquired by effector silencing. Indeed an effector that is no more expressed can not be recognised by host, and then resistance can be overcome. The authors should look for effectors that are no more expressed (with stop codon for example, deletion of an exon, or mutated in the promoter region) in crop population. They could find other good candidates for adaptation. In general, conclusions are badly supported. The authors should use simulations for their model validation. This study strongly deserves it. I will detail this in the following.

    • Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.
      • First validate your assumations with models simulated. If the authors assume that population infecting cultivated beets come from population infecting wild beets, they should be able to confirm this hypothesis by simulations. For instance, authors could use ABC method in order to check the posterior probability of such a model.
      • Authors should use divergence statistics in order to check whether there is divergence or not on their data. For example, use Dxy in order to check the degree of divergence between wild and cultivated population. As for evidence in Figure 2, there is a strong divergence between the two populations. It could be interesting to check whether there is gene flow or not between these two populations.
      • Authors should also look for how many effector genes are non expressed in cultivated population face to wild population.
      • Authors should plot Fst and Dxy along the main scaffolds. It would permit to see whether there is huge peaks of divergence and differentiation between the two populations.
    • Are the data and the methods presented in such a way that they can be reproduced?

    Yes, the data can be reproduced as well as the analyses.

    • Are the experiments adequately replicated and statistical analysis adequate?

    Both experiments and statistics are adequate.

    Minor comments:

    • Specific experimental issues that are easily addressable.

    Yes they are

    • Are prior studies referenced appropriately?

    Yes they are

    • Are the text and figures clear and accurate?

    Figure 2 is somewhat hard to understand. There is too much data here.

    • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

    I would prefer a PCA plot, just showing strains plotted along the first axis and showing that there is no hybrid.

    Referee Cross-commenting

    Hello everyone I just start the comment session by providing a few points that I think to be important to be treated in a future version of the paper. 1- Characterize the relationships between wild and agricultural populations. As shown on figure 2, the tree presented clearly indicates divergence between wild and agricultural strains. A PCA would be interesting to be plotted, as it may indicate that there is no hybrid between populations. In a general manner, statistics like Dxy or Da as well as Fst should be plotted along the genome. 2- The scenario should be validated using simulations and tested against a null hypothesis. 3- Virulence can be acquired through effector losing function. Thus, variations like occurrence of codon stop, delection in ORF, or mutations altering the promoter region should be checked.

    Significance

    Provide contextual information to readers (editors and researchers) about the novelty of the study, its value for the field and the communities that might be interested.

    The following aspects are important:

    • General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? This study is interesting in showing the crucial role of wild compartiment as a reservoir of virulence. However, as the data are well produced, their analysis suffer from several flaws. As divergence is not analysed, and only differentiation is shown. In addition, the model proposed is not validated by simulation, not even tested by ABC. In order to be more conclusive, selection should be tested. Fst variance is not a good predictor of selection. I would recommend to use Selscan (ZA Szpiech and RD Hernandez (2014) ) in order to test for selection on genomic data. It would give real clues for selection acting on cultivated population.
    • Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field? This paper is of a broad audience as it treats of large problematic of evolution of plant pathogen.
    • Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

    I am a population genomicist working on evolution of pathogens.

  4. Version published to 10.1101/2021.08.12.456076v1 on bioRxiv