Physiological and molecular insights into the resilience of biological nitrogen fixation to applied nitrogen in Saccharum spontaneum, wild progenitor of sugarcane
Bibliographic record
Abstract
Excessive use of nitrogen (N) fertilizer for sugarcane cultivation is a significant cause of greenhouse gas emission. N use-efficiency (NUE) of sugarcane is relatively low, and considerable effort is now directed to exploit biological nitrogen fixation (BNF) in sugarcane. We hypothesize that genetic base-broadening of sugarcane using high-BNF Saccharum spontaneum, a wild progenitor of sugarcane, will help develop N-efficient varieties. We found remarkable genetic variation for BNF and growth in S. spontaneum accessions, and BNF in some accessions remained highly resilient to inorganic N application. Physiological and molecular analyses of two S. spontaneum accessions with high-BNF capacity and growth, namely G152 and G3, grown under N replete and low N conditions showed considerable similarity for total N, NH4-N, soluble sugar, indoleacetic acid, gibberellic acid, zeatin and abscisic acid content; yet, they were strikingly different at molecular level. Global gene expression analysis of G152 and G3 grown under contrasting N supply showed genotype effect explaining much of the gene expression variation observed. Differential gene expression analysis found an over-representation of carbohydrate and amino acid metabolism and transmembrane transport genes in G152 and an enrichment of lipid metabolism and single-organism processes genes in G3, suggesting that distinctly divergent metabolic strategies are driving N-related processes in these accessions. This was attested by the remarkable variation in carbon, N, amino acid and hormone metabolism-related gene expression in G152 and G3 under high- and low-N supply. We conclude that both accessions may be achieving similar BNF and growth phenotypes through overlapping but distinctly different biochemical and molecular mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".