Identification and Functional Analysis of Male Sterility Genes in Hybrid Rice: Current Status and Future Prospects
Bibliographic record
Abstract
Hybrid rice breeding has significantly enhanced rice productivity worldwide, primarily through the utilization of male sterility (MS) systems. This paper summarizes the current status and future prospects of identifying and functionally analyzing MS genes in hybrid rice. Various types of MS, including cytoplasmic male sterility (CMS) of WA, HL, BT, DT various subtypes, and genic male sterility (GMS), have been characterized, with specific genes and loci identified for their roles in sterility and fertility restoration. For instance, the novel Fujian Abortive CMS system, controlled by the mitochondrial gene FA182 and restored by the nuclear gene OsRf19, has simplified the breeding process by providing stable MS and single-gene fertility restoration. Additionally, the broadly and/or potentially utilized genes PMS3 , TMS5 , and HMS1 , of photoperiod-sensitive genic male sterility (PGMS), temperature-sensitive genic male sterility (TGMS) and humidity-sensitive genic male sterility (HGMS) have been mapped and functional studied , offering insights into their genetic control and potential for hybrid breeding. The identification of new fertility restorer genes, such as Rf18(t) and their chromosomal locations, further broadens our understanding of the genetic mechanisms underlying MS and fertility restoration. The use of novel strategies, such as combining CMS and GMS genes, has led to the creation of third-generation hybrid rice technology, which offers stable sterility and improved hybrid seed production. This review highlights the advancements in genetic mapping, molecular characterization, and the practical applications of MS genes in hybrid rice breeding, paving the way for future research and breeding strategies.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".