Seed protein biotyping in Amaranthus species: a tool for rapid identification of weedy amaranths of concern
Bibliographic record
Abstract
BACKGROUND: The Amaranthus genus contains at least 20 weedy and invasive species, including Amaranthus palmeri (palmer's amaranth) and Amaranthus tuberculatus (tall waterhemp), two species of regulatory concern in North America, impacting production and yield in crops like corn, soybean and cotton. Amaranthus tuberculatus is regulated in Canada with limited establishment, while current climate models predict a range expansion of A. palmeri impacting crop growing areas in Ontario, Quebec and Manitoba. Since many Amaranthus species are similar in their morphology, especially at the seed stage, this demands the development of additional methods that can efficiently aid in the detection and identification of these species. Protein biotyping using Matrix-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS) has been traditionally used to identify microorganism species, races and pathotypes. Major protein fractions extracted from an organism, ionized and run through a biotyper using mass spectrometry, result in protein spectra that represent a fingerprint at the species or lower taxonomic rank, providing an efficient molecular diagnostics method. Here we use a modified protein biotyping protocol to extract major protein fractions from seeds of the family Brassicaceae to test our protocol, and then implemented the standardized approach in seeds from Amaranthus species. We then created a database of Amaranthus protein spectra that can be used to test blind samples for a quick identification of species of concern. RESULTS: We generated a protein spectra database with 16 Amaranthus species and several accessions per species, spanning target species of regulatory concern and species which are phylogenetically related or easily confused at the seed stage due to phenotypic plasticity. Testing of two Amaranthus blind sample seed sets against this database showed accuracies of 100% and 87%, respectively. CONCLUSIONS: Our method is highly efficient in identifying Amaranthus species of regulatory concern. The mismatches between our protein biotyping approach and phenotypic identification of seeds are due to absence of the species in the database or close phylogenetic relationship between the species. While A. palmeri cannot be distinguished from A. watsonii, there is evidence these two species have the same native range and are closely related.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".