Allograft Inflammatory Factor 1 Functions as a Pro-Inflammatory Cytokine in the Oyster, Crassostrea ariakensis
Bibliographic record
Abstract
The oyster Crassostrea ariakensis is an economically important bivalve species in China, unfortunately it has suffered severe mortalities in recent years caused by rickettsia-like organism (RLO) infection. Prevention and control of this disease is a priority for the development of oyster aquaculture. Allograft inflammatory factor-1 (AIF-1) was identified as a modulator of the immune response during macrophage activation and a key gene in host immune defense reaction and inflammatory response. Therefore we investigated the functions of C. ariakensis AIF-1 (Ca-AIF1) and its antibody (anti-CaAIF1) in oyster RLO/LPS-induced disease and inflammation. Ca-AIF1 encodes a 149 amino acid protein containing two typical Ca2+ binding EF-hand motifs and shares a 48-95% amino acid sequence identity with other animal AIF-1s. Tissue-specific expression analysis indicates that Ca-AIF1 is highly expressed in hemocytes. Significant and continuous up-regulation of Ca-AIF1 is detected when hemocytes are stimulated with RLO/LPS (RLO or LPS). Treatment with recombinant Ca-AIF1 protein significantly up-regulates the expression levels of LITAF, MyD88 and TGFβ. When anti-CaAIF1 antibody is added to RLO/LPS-challenged hemocyte monolayers, a significant reduction of RLO/LPS-induced LITAF is observed at 1.5-12 h after treatment, suggesting that interference with Ca-AIF1 can suppress the inflammatory response. Furthermore, flow cytometric analysis indicated that anti-CaAIF1 administration reduces RLO/LPS-induced apoptosis and necrosis rates of hemocytes. Collectively these findings suggest that Ca-AIF1 functions as a pro-inflammatory cytokine in the oyster immune response and is a potential target for controlling RLO infection and LPS-induced inflammation.
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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) | 0.000 | 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".