A Lesson from Wheat Evolution: Wild and Landraces Genetic Diversity the Key to Improving the Nutritional Value of Our Spaghetti Dish
Bibliographic record
Abstract
While wheat has been much maligned recently for it is gluten content, and new suspicions casted about as to its nutritional value, scientists have been eager to trace the evolutionary history of wheat to better understand the pasta wheat currently available. Since the dawn of agriculture, humans have been selecting plants to maximize both the crop yield and food benefits. But which has left the larger genomic footprint, and are there evolutionary tradeoffs with domestication? In a new study published in the early online edition of Molecular Biology and Evolution , scientists Beleggia et al. (2016) , examined the rich evolutionary history of pasta wheat by measuring the changes in metabolic content from three different populations and exploiting innovative population genomics methods. Each represents the two main periods of wheat domestication: the transition from wild to emmer (an easier to harvest grain first used during the Bronze Age about 12,000 years ago) to durum wheats (to be distinguished from bread wheat also known as pasta wheat, about 10,000 years ago), which greatly affected grain sizes and shapes, spreading to the Mediterranean, to forever change the human diets and palates with new foods like pasta and couscous. The authors have now shown that the initial domestication of emmer wheat involved a reduction in unsaturated fatty acids and the secondary domestication of durum wheat from emmer wheat involved changes in amino acid content. “Our results indicating a reduction of the UFA/SFA ratio may suggest that not necessarily all of the metabolic variations that occurred during domestication have proceeded towards an amelioration of the nutritional quality, probably because yield-related traits were given priority during the domestication process,” said Roberto Papa, corresponding author of the study. With the new data, the authors speculate that the selection for nutritional quality on the one side and the selection for adaptation to benefit yield on the other side was characterized by a greater provision of nitrogen. Finally, the authors offer some suggestions, based on a wide use of genetic diversity from landraces and wild germplasm, to help select useful traits that can be identified and further incorporated into modern pasta wheat production.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.019 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".