Genetic Regulation of Photosynthetic Efficiency in Sugarcane: Molecular Basis of C4 Pathway and Carbon Metabolism
Bibliographic record
Abstract
Photosynthetic efficiency is a crucial determinant of sugarcane yield and biomass accumulation, particularly in tropical environments where sugarcane, a C 4 plant, thrives due to its high carbon assimilation capacity. This study investigates the genetic regulation of photosynthetic efficiency in sugarcane, focusing on the molecular mechanisms underlying the C 4 pathway and carbon metabolism. We provide a comprehensive overview of C 4 photosynthesis, emphasizing Kranz anatomy and CO2-concentrating mechanisms, and examine the genetic regulation of key enzymes such as PEPC, NADP-ME, and PPDK. The study explores transcriptional and post-transcriptional control, including the role of epigenetic factors and non-coding RNAs, alongside genetic components involved in Rubisco regulation, sugar transport, and carbon partitioning. Furthermore, we analyze the integration of light reactions with carbon metabolism and identify regulatory genes for chlorophyll biosynthesis and photoprotection. Advances in biotechnological tools such as CRISPR/Cas9 and omics-based gene discovery are discussed in this study of improving photosynthetic traits. We also present a case study on elite sugarcane varieties, highlighting the association between gene expression and photosynthetic performance under field conditions. Our findings underscore the need for integrative genetic models and systems biology approaches to optimize photosynthetic efficiency and promote sustainable sugarcane production through targeted genetic innovation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".