The genetics and genomics of herbicide resistant Kochia scoparia L.
Bibliographic record
Abstract
Weed genomics resources lag behind other plant biology disciplines despite larger annual crop losses occurring due to weeds than to plant pathogens or invertebrate pests.To date only a handful of weed genomes are assembled, and what is available is generally incomplete, poorly annotated, or only useful to a small group of researchers.Recent advancements in sequencing and an increased interest in the genetic foundations of weedy traits have contributed to driving de novo genome assemblies for key weed species.The introduced weed species Kochia scoparia (kochia) is the most important weed species in Colorado and severely impacts yield in various crop systems including sugar beet, wheat, and corn.Additionally, kochia rapidly invades disturbed land including roadsides, drainage areas, rangelands, and pastures.Kochia spans a massive geographic distribution, from as far south as Mexico, as far north as Saskatoon, Canada, as far east as the Mississippi river, and as far west as Oregon.Locally, kochia populations are well adapted to various abiotic stresses including drought, cold, high salinity, and high wind.Recently, and most importantly, kochia has evolved resistance to several modes of herbicide action.Currently kochia populations exist that are resistant to acetolactate synthase (ALS) inhibitors, photosystem II (PSII) inhibitors, several synthetic auxin compounds, and the 5enolpyruvylshikimate-3-phosphate synthase (EPSPS) inhibitor, glyphosate.Individuals have even been identified that are resistant to all four modes of action (MOA) simultaneously.Each herbicide mode of action (MOA) resistance case is caused by different mutations or even different mutation types (target site SNPs, copy number variation, translocation changes, etc.).iii Selection pressure from herbicides is intense as not having the proper allele is lethal; therefore, resistance alleles are selected and go to fixation quickly.Kochia populations may be especially prone to herbicide resistance for a variety of physiological reasons, as kochia plants can produce thousands of seeds, are wind pollinated, are primarily outcrossing, and have tumbleweed seed dispersal in the windier environments like eastern Colorado and Kansas.Additionally, there may be genetic and genomic explanations for rapid herbicide resistance evolution such as rapid mutation rates or dynamic responses to environmental stress.Glyphosate resistance, in particular, has driven a significant amount of herbicide resistance research in this species.In this case, resistance is caused by copy number variation of the target gene, EPSPS.Over production of the EPSPS enzyme makes normally lethal doses of glyphosate inadequate for control.Many of the details underlying gene amplification are missing, such as what are its origins and what genes are included in the duplication event.Understanding mechanisms of gene duplication is fundamental to understanding the evolution of resistance, predicting future gene duplication events, and understanding the significance of fitness and inheritance studies.I especially want to thank my committee: Dr. Todd Gaines, who gave me the opportunity to do the work I love and for always being there as a friend, mentor and colleague, Dr. Dan Sloan who shaped my understanding of plant genomes, Dr. Chris Saski for forcing me to always improve my science and adopt new techniques, and Dr. Stephen Pearce for asking the tough questions.Thank you to the weed lab for being excellent scientists and friends.I would have killed every plant
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".