Gene duplication and cellular divergence in crops
Bibliographic record
Abstract
Abstract Different plant species within the grasses were parallel targets of domestication, giving rise to crops with distinct evolutionary histories and traits. Key traits that distinguish these species are mediated by specialized cell types within organs. Here, we compare the transcriptomes of all cells within roots in three grasses—Zea mays (maize), Sorghum bicolor (sorghum), and outgroup Setaria viridis (Setaria). We first show that single-cell and single-nucleus RNA-seq provide complementary readouts of cell identity, warranting a combined analysis. Comparative cellular analysis shows that the transcriptomes of some cell types diverged more rapidly than others, in part by recruiting gene modules from other cell types. Furthermore, examining the whole genome duplication in maize, we detect extensive dosage compensation in surviving co-expressed homeologs, reinforcing genomic balance1. Homeolog pairs that underwent subfunctionalization2, partitioning their expression among cell types, represented a minor pattern but showed the highest rate of acquiring a novel (non-ancestral) domain. These results fit a conjecture in which mechanisms that maintain stoichiometric balance at the molecular level aid in homeolog retention for extended periods to allow new functions to arise. An unexpected synergy between spatial sub- and neo-functionalization then contributes to changes in transcriptional cell identity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".