Author response: A conserved bacterial protein induces pancreatic beta cell expansion during zebrafish development
Bibliographic record
Abstract
For a long time, genes and carefully orchestrated chemical signals from the mother’s body have been known to shape the earliest phases of an embryo’s development. More recently, researchers have started to appreciate that environmental cues – such as signals from nearby bacteria – also shape animal development. The body is home to a teeming community of bacteria and other microbes, called the microbiota. Because the gut houses more microbes than any other part of the body, the role that the microbiota plays in gut development has been investigated. Less is known about how the microbiota affects the development of the other organs involved in digestion, such as the pancreas. In developing fish and mammals, the pancreas grows its population of insulin-producing beta cells during the same period of development in which the microbial population of the gut becomes established. Now, Hill et al. show that certain gut bacteria are necessary for the pancreas to populate itself with a robust number of beta cells during development. Normally, the number of beta cells in zebrafish larvae increases steadily in the first few days after hatching. However, developing zebrafish that were reared in a microbe-free environment maintained the same number of beta cells as they had before hatching. Exposing the microbe-free fish to certain bacteria restored their beta cell populations to normal levels. Further investigation revealed that these bacteria release a protein called BefA that causes the beta cells to multiply. Some bacteria in humans produce proteins that are similar to BefA. Hill et al. performed experiments that showed that these proteins also stimulate beta cell development in microbe-free fish. Future studies are now needed to investigate the mechanism by which the proteins affect beta cell development, and to find out whether they have the same effect in humans and other animals. This will help us to understand whether a lack of gut microbes could contribute to the development of diseases, such as diabetes, that are characterized by insufficient numbers of beta cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.015 |
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".