Genome-Guided Analysis of Seven Weed Species Reveals Conserved Sequence and Structural Features of Key Gene Targets for Herbicide Development.

Herbicides are commonly deployed as the front-line treatment to control infestations of weeds in native ecosystems and among crop plants in agriculture. However, the prevalence of herbicide resistance in many species is a major global challenge. The specificity and effectiveness of herbicides acting on diverse weed species are tightly linked to targeted proteins. The conservation and variance at these sites among different weed species remain largely unexplored. Using novel genome data in a genome-guided approach, 12 common herbicide-target genes and their coded proteins were identified from seven species of Weeds of National Significance in Australia: Alternanthera philoxeroides (alligator weed), Lycium ferocissimum (African boxthorn), Senecio madagascariensis (fireweed), Lantana camara (lantana), Parthenium hysterophorus (parthenium), Cryptostegia grandiflora (rubber vine), and Eichhornia crassipes (water hyacinth). Gene and protein sequences targeted by the acetolactate synthase (ALS) inhibitors and glyphosate were recovered. Compared to structurally resolved homologous proteins as reference, high sequence conservation was observed at the herbicide-target sites in the ALS (target for ALS inhibitors), and in 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase (target for glyphosate). Although the sequences are largely conserved in the seven phylogenetically diverse species, mutations observed in the ALS proteins of fireweed and parthenium suggest resistance of these weeds to ALS-inhibiting and other herbicides. These protein sites remain as attractive targets for the development of novel inhibitors and herbicides. This notion is reinforced by the results from the phylogenetic analysis of the 12 proteins, which reveal a largely consistent vertical inheritance in their evolutionary histories. These results demonstrate the utility of high-throughput genome sequencing to rapidly identify and characterize gene targets by computational methods, bypassing the experimental characterization of individual genes. Data generated from this study provide a useful reference for future investigations in herbicide discovery and development.