Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Feb 3;16(2):e1008593.
doi: 10.1371/journal.pgen.1008593. eCollection 2020 Feb.

Parallel and Nonparallel Genomic Responses Contribute to Herbicide Resistance in Ipomoea Purpurea, a Common Agricultural Weed

Affiliations
Free PMC article

Parallel and Nonparallel Genomic Responses Contribute to Herbicide Resistance in Ipomoea Purpurea, a Common Agricultural Weed

Megan Van Etten et al. PLoS Genet. .
Free PMC article

Abstract

The repeated evolution of herbicide resistance has been cited as an example of genetic parallelism, wherein separate species or genetic lineages utilize the same genetic solution in response to selection. However, most studies that investigate the genetic basis of herbicide resistance examine the potential for changes in the protein targeted by the herbicide rather than considering genome-wide changes. We used a population genomics screen and targeted exome re-sequencing to uncover the potential genetic basis of glyphosate resistance in the common morning glory, Ipomoea purpurea, and to determine if genetic parallelism underlies the repeated evolution of resistance across replicate resistant populations. We found no evidence for changes in 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), glyphosate's target protein, that were associated with resistance, and instead identified five genomic regions that showed evidence of selection. Within these regions, genes involved in herbicide detoxification-cytochrome P450s, ABC transporters, and glycosyltransferases-are enriched and exhibit signs of selective sweeps. One region under selection shows parallel changes across all assayed resistant populations whereas other regions exhibit signs of divergence. Thus, while it appears that the physiological mechanism of resistance in this species is likely the same among resistant populations, we find patterns of both similar and divergent selection across separate resistant populations at particular loci.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Population locations and relationships among I. purpurea samples.
(A) Populations were sampled from locations in the southeast and ranged from 10% to 100% survival following glyphosate application (proportion of individuals that survived glyphosate treatment shown for each population, red = survived, blue = died). Individuals from resistant populations (>50% survival after treatment; red colored symbols, solid lines) do not group together in a PCoA analysis when using all of the RAD-seq SNP loci (B) but there is some grouping when only considering the outlier loci (C). Ellipses are normal confidence ellipses.
Fig 2
Fig 2. Regions of the I. purpurea genome enriched with outlier loci.
(A) Aligning the target-capture denovo contigs to the I. nil genome showed 5 regions enriched for outliers (regions in grey; symbol colors denote chromosomes; symbol shape denotes significance). The majority of the outliers (71%) fall within the five regions. Significant outliers, noted with triangles, exhibited the most extreme 1% Bayes Factors and the 5% most extreme Spearman correlation coefficients (left y-axis). The average population structure (GST; right y-axis) was calculated per enriched region and is indicated by a thin horizontal line for each outlier enriched region (arrow indicates average GST value over all SNPs). The position of each chromosome’s centromere is indicated by a thick black vertical line on the x-axis. (B) The five outlier-containing regions had multiple copies of gene families potentially involved in non-target site resistance. Numbers in the table indicate the number of genes that fall into each category, whereas Avg genes/mb is the average number of genes per 1MB. (C) Resampling the I. nil genome 1000 times to generate an empirical distribution of gene copy number of each type of gene indicates that the outlier enriched regions contain more of the potential herbicide detoxification genes of interest than expected due to chance. The dashed vertical line indicates the overall number of each type of gene found within the outlier-enriched regions, which was greater than expected from the empirical distribution for the cytochrome P450 (P < 0.01), glycosyltransferase (P = 0.01), and ABC transporter genes (P = 0.05).
Fig 3
Fig 3. Resistant individuals exhibit evidence of selective sweeps in some outlier-enriched regions of genome.
(A) Nucleotide diversity (shown here as log10 πSR) is decreased in resistant individuals within the chr10 region compared to susceptible individuals, and (B) values of Tajima’s D and (C) Fay and Wu’s H across outlier enriched regions both suggest marks of positive selection in the chromosome 10 outlier enriched region, with some indication for positive selection in the outlier enriched region of chromosome 13. Dashed lines show the 95% most extreme genome-wide values for each metric.
Fig 4
Fig 4. Signs of selection across conserved haplotypes of detoxification genes.
Haplotypes are shown for each individual for the (A) seven duplicated glycosyltransferase genes on chromosome 10 (exons above in grey), and (B) an ABC transporter gene on chromosome 13. Blue and yellow indicate homozygotes, red indicates heterozygotes, white indicates missing data; asterisks indicate a non-synonymous change at that location. Black bar above gene models indicates 1kb.
Fig 5
Fig 5. Genetic similarity of haplotypes among resistant populations.
(A) The proportion of each population that exhibited the resistant haplotype are shown for each population. Pairwise genetic distance between each individual was calculated using all SNPs from each I. purpurea contig from the outlier-enriched regions (length of region used shown for each chromosome), and multidimensional scaling was used to reduce the resultant genetic distance matrix to two dimensions. Populations were then hierarchically clustered into two groups, with the group containing less than half of the individuals from the susceptible populations considered the ‘resistant’ group. (B) The average pairwise genetic differentiation for resistant (red) and susceptible (blue) populations. Pairwise FST values were calculated separately for resistant and susceptible populations using contigs from each outlier enriched region of each chromosome.
Fig 6
Fig 6. Test of convergence for the enriched region of chromosome 10.
(A) Likelihood ratio of the following models relative to a neutral model with no selection: standing variant model (blue), migration (green) or independent mutation (red). (B) Likelihood surface for minimum frequency of the standing variant and the strength of selection holding the age of the standing variant constant; the point indicates the highest likelihood; color denotes likelihood (white (high) to yellow to red (low)). (C) Likelihood for the minimum age of standing variant maximizing over the other parameters. Convergence tests across enriched regions on chromosomes 1, 6, 13, and 15 are presented in S8 Fig.

Similar articles

See all similar articles

References

    1. Powles SB, Yu Q. Evolution in action: plants resistant to herbicides. Annu Rev Plant Biol. 2010;61: 317–347. 10.1146/annurev-arplant-042809-112119 - DOI - PubMed
    1. Délye C, Michel S, Bérard A, Chauvel B, Brunel D, Guillemin J-P, et al. Geographical variation in resistance to acetyl-coenzyme A carboxylase-inhibiting herbicides across the range of the arable weed Alopecurus myosuroides (black-grass). New Phytol. Blackwell Publishing Ltd; 2010;186: 1005–1017. - PubMed
    1. Thomsen EK, Strode C, Hemmings K, Hughes AJ, Chanda E, Musapa M, et al. Underpinning sustainable vector control through informed insecticide resistance management. PLoS One. 2014;9: e99822 10.1371/journal.pone.0099822 - DOI - PMC - PubMed
    1. Kuester A, Chang S-M, Baucom RS. The geographic mosaic of herbicide resistance evolution in the common morning glory, Ipomoea purpurea: Evidence for resistance hotspots and low genetic differentiation across the landscape. Evol Appl. 2015;8: 821–833. 10.1111/eva.12290 - DOI - PMC - PubMed
    1. Christin P-A, Weinreich DM, Besnard G. Causes and evolutionary significance of genetic convergence. Trends Genet. 2010;26: 400–405. 10.1016/j.tig.2010.06.005 - DOI - PubMed

Publication types

MeSH terms

Feedback