Author response: Copy number variation and population-specific immune genes in the model vertebrate zebrafish
Bibliographic record
Abstract
Humans and other animals have immune systems that protect them from bacteria, viruses and other potentially harmful microbes. Members of a family of genes known as the NLR family play various roles in helping to recognize and destroy these microbes. Different species have varying numbers of NLR genes, for example, humans have 22 NLRs, but fish can have hundreds. 400 have been found in the small tropical zebrafish, also known as zebra danios. Zebrafish are commonly used as model animals in research studies because they reproduce quickly and are easy to keep in fish tanks. Much of what we know about fish biology comes from studying strains of those laboratory zebrafish, including the 400 NLRs found in a specific laboratory strain. Many NLRs in zebrafish are extremely similar, suggesting that they have only evolved fairly recently through gene duplication. It remains unclear why laboratory zebrafish have so many almost identical NLRs, or if wild zebrafish also have lots of these genes. To find out more, Schäfer et al. sequenced the DNA of NLRs from almost 100 zebrafish from multiple wild and laboratory populations. The approach identified over 1,500 different NLR genes, most of which, were previously unknown. Computational modelling suggested that each wild population of zebrafish may harbour up to around 2,000 NLR genes, but laboratory strains had much fewer NLRs. The numbers of NLR genes in individual zebrafish varied greatly – only 4% of the genes were present in 80% or more of the fish. Many genes were only found in specific populations or single individuals. Together, these findings suggest that the NLR family has expanded in zebrafish as part of an ongoing evolutionary process that benefits the immune system of the fish. Similar trends have also been observed in the NLR genes of plants, indicating there may be an evolutionary strategy across all living things to continuously diversify large families of genes. Additionally, this work highlights the lack of diversity in the genes of laboratory animals compared with those of their wild relatives, which may impact how results from laboratory studies are used to inform conservation efforts or are interpreted in the context of human health.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".