Author response: A bacterial immunomodulatory protein with lipocalin-like domains facilitates host–bacteria mutualism in larval zebrafish
Bibliographic record
Abstract
Animals, including humans, harbor vast numbers of bacteria inside our digestive tracts. But rather than wage constant war, we have learned to coexist peacefully, and many of these bacteria are important to keep us healthy. Our immune system controls the number of bacteria, but in some diseases, this balance fails, and immune cells called neutrophils start a defense response. However, such attacks also cause inflammation in our guts, which can damage the tissues and organs. Understanding the delicate balance between immune cells and individual bacteria in humans remains a challenge. Our gut bacteria live in complex communities with many species of microorganisms. Using animals like zebrafish can help to find out if gut bacteria are able to prevent such inflammation. These fish can grow under sterile conditions in the laboratory, allowing the study of added individual bacteria and the molecules they release. They are also transparent, making it easy to capture images of their organs under the microscope. In the wild, they live side-by-side with a type of bacteria called Aeromonas. Here, Rolig et al. added Aeromonas bacteria to sterile zebrafish and observed how they interacted with the fish immune system. This revealed that the bacteria produce a protein to pacify immune cells, named 'Aeromonas immune modulator' (AimA). The sequence of this new protein did not look like any known molecules, but its 3D structure resembled a protein found in animals called lipocalin. AimA dampened inflammation in the gut. When Aeromonas without AimA colonized the zebrafish, their neutrophils went to war. Their guts became inflamed and the bacteria started to die. But when the zebrafish had no neutrophils in their gut, nothing could stop the AimA-deficient bacteria and they grew too much. They produced toxic products, triggering septic shock and killing the fish. Adding AimA back into the fish stopped the inflammation and prevented septic shock, restoring balance. Both partners need AimA to survive. The bacteria need it to shield them from the immune response and the fish need it for protection against toxic products made by the bacteria. Understanding proteins like AimA could help us to control bacteria and gut inflammation. Further work could reveal ways to use similar molecules to treat inflammation or to boost the growth of friendly bacteria.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".