Comprehensive survey of transposon mPing insertion sites and transcriptome analysis for identifying candidate genes controlling high protein content of rice
Bibliographic record
Abstract
Rice is the most important crop species in the world, being staple food of more than 80% of people in Asia. About 80% of rice grain is composed of carbohydrates (starch), with its protein content as low as 7–8%. Therefore, increasing the protein content of rice offers way to create a stable protein source that contributes to improving malnutrition and health problems worldwide. We detected two rice lines harboring a significantly higher protein content (namely, HP5-7 and HP7-5) in the EG4 population. The EG4 strain of rice is a unique material in that the transposon mPing has high transpositional activity and high copy numbers under natural conditions. Other research indicated that mPing is abundant in the gene-rich euchromatic regions, suggesting that mPing amplification should create new allelic variants, novel regulatory networks, and phenotypic changes in the EG4 population. Here, we aimed to identify the candidate genes and/or mPing insertion sites causing high protein content by comprehensively identifying the mPing insertion sites and carrying out an RNA-seq-based transcriptome analysis. By utilizing the next-generation sequencing (NGS)-based methods, ca. 570 mPing insertion sites were identified per line in the EG4 population. Our results also indicated that mPing apparently has a preference for inserting itself in the region near a gene, with 38 genes in total found to contain the mPing insertion in the HP lines, of which 21 and 17 genes were specific to HP5-7 and HP7-5, respectively. Transcriptome analysis revealed that most of the genes related to protein synthesis (encoding glutelin, prolamin, and globulin) were up-regulated in HP lines relative to the control line. Interestingly, the differentially expressed gene (DEG) analysis revealed that the expression levels of many genes related to photosynthesis decreased in both HP lines; this suggests the amount of starch may have decreased, indirectly contributing to the increased protein content. The high-protein lines studied here are expected to contribute to the development of high protein-content rice by introducing valuable phenotypic traits such as high and stable yield, disease resistance, and abundant nutrients.
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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".