Transcriptome features of trained immunity in <i>Drosophila</i>
Bibliographic record
Abstract
Summary Immune memory is an ability of organisms to potentiate immune responses at secondary infection. Current studies have revealed that innate immunity, as well as adaptive immunity, exhibits the memory character called “trained immunity”. Although it is suggested that epigenetic reprogramming plays important roles in trained immunity, its underlying mechanism is not fully understood, especially on the individual level. Here we established experimental systems for detecting trained immunity in Drosophila melanogaster . Namely, training infection with low-pathogenic bacteria enhanced the survival rate of the flies at subsequent challenge infection with high-pathogenic bacteria. We found that among low-pathogenic bacteria, Micrococcus luteus (Ml) and Salmonella typhimurium (St) mediated apparent training effects in fly, but seemed to act through different ways. Ml left training effects even after its removal from flies, while living St persisted inside flies for a long time. Our RNA-Seq analysis revealed that Ml-training enhanced the expression of immune-related genes during the challenge infection, but did not do so without challenge infection. In contrast, St-training maintained high expression of the immune-related genes with or without challenge. These results suggest that training effects with Ml and St were due to memory and persistence of immune responses, respectively. Furthermore, we searched the factor involved in Ml-training and identified a candidate, Ada2b, which is a component of the histone modification complex. We found that the Ada2b RNAi and mutant flies showed dampened enhancement of survival rates after Ml-training. These results suggest that Ada2b is involved in the Drosophila trained immunity.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".