Genomic Evolution of Growth and Reproduction Related Genes in Shrimp
Bibliographic record
Abstract
As globally important economic aquaculture species, the growth and reproductive capacities of shrimp directly determine the efficiency and sustainability of the industry. In recent years, the rapid advancement of genomics and multi-omics technologies has provided critical support for elucidating the functions and evolutionary trajectories of genes related to shrimp growth and reproduction. This study systematically reviews the progress in shrimp genomics, focusing on the functional classification, expression characteristics, phylogenetic relationships, and selection pressure analyses of growth- and reproduction-related genes. Through case studies of three representative species—Litopenaeus vannamei (Pacific white shrimp), Penaeus monodon (black tiger shrimp), and Macrobrachium nipponense (oriental river prawn)—the evolutionary features of key genes in terms of function and regulation are analyzed. Furthermore, the roles of gene family expansion, gene duplication and pseudogenes, and transcriptional regulatory elements in genomic functional evolution are summarized. By integrating transcriptomic, proteomic, and epigenetic data, the study reveals adaptive evolutionary mechanisms of shrimp under environmental stressors such as salinity changes, pollution, and pathogens. The findings provide a theoretical foundation for molecular breeding and biological mechanism studies in shrimp.
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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".