Back

Metabolomic profiling of glyphosate resistance evolution in green alga Chlamydomonas reinhardtii

Hansson, E. M.; Walker, H. J.; Childs, D. Z.; Beckerman, A. P.

2025-07-15 evolutionary biology
10.1101/2025.07.10.664120 bioRxiv
Show abstract

The widespread use of glyphosate has led to a rapid increase in glyphosate resistant weeds at a considerable cost to agriculture. Furthermore, agricultural run-off means non-target, natural ecosystems are also affected. Resistance may arise both through mutations to target enzyme EPSPS in the shikimate pathway generating insensitivity to glyphosate and through non-target site mutations regulating the dose of glyphosate reaching the shikimate pathway. Either mechanism may trade-off against normal cell functioning. Here, using experimental evolution of replicated Chlamydomonas reinhardtii populations facing glyphosate, we reveal the metabolomic fingerprint of both glyphosate action and emerging resistance by applying untargeted metabolomic screens throughout the course of resistance evolution. Furthermore, this allows us to evaluate potential underlying molecular mechanisms and fitness costs. We find evidence of build-up of shikimate pathway metabolites, a characteristic signal of glyphosate action, that subsides but does not disappear as resistance evolves, suggesting a fitness trade-off. Concurrent with this is evidence of cell wide disruption of the amino acid pool, that stabilises as resistance evolves. We also found evidence of effects of glyphosate on membrane lipids and increased levels of reactive oxygen species persisting after resistance had evolved. This suggests a considerable effect of the recently described secondary effect of glyphosate: oxidative damage. These data highlight several metabolic processes disrupted by and persisting with the evolution of resistance to glyphosate and may provide a template for enhancing predictions of population and ecosystem effects.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.