CpG-ODN induced antimicrobial immunity in neonatal chicks involves a substantial shift in serum metabolic profiles
Bibliographic record
Abstract
Synthetic CpG-ODNs can promote antimicrobial immunity in neonatal chicks by enriching immune compartments and activating immune cells. Activated immune cells undergo profound metabolic changes to meet cellular biosynthesis and energy demands and facilitate the signaling processes. We hypothesize that CpG-ODNs induced immune activation can change the host's metabolic demands in neonatal chicks. Here, we used NMR-based metabolomics to explore the potential of immuno-metabolic interactions in the orchestration of CpG-ODN-induced antimicrobial immunity. We administered CpG-ODNs to day-old broiler chicks via intrapulmonary (IPL) and intramuscular (IM) routes. A negative control group was administered IPL distilled water (DW). In each group (n = 60), chicks (n = 40) were challenged with a lethal dose of Escherichia coli, two days post-CpG-ODN administration. CpG-ODN administered chicks had significantly higher survival (P < 0.05), significantly lower cumulative clinical scores (P < 0.05), and lower bacterial loads (P < 0.05) compared to the DW control group. In parallel experiments, we compared NMR-based serum metabolomic profiles in neonatal chicks (n = 20/group, 24 h post-treatment) treated with IM versus IPL CpG-ODNs or distilled water (DW) control. Serum metabolomics revealed that IM administration of CpG-ODN resulted in a highly significant and consistent decrease in amino acids, purines, betaine, choline, acetate, and a slight decrease in glucose. IPL CpG-ODN treatment resulted in a similar decrease in purines and choline but less extensive decrease in amino acids, a stronger decrease in acetate, and a considerable increase in 2-hydroxybutyrate, 3-hydroxybutyrate, formic acid and a mild increase in TCA cycle intermediates (all P < 0.05 after FDR adjustment). These perturbations in pathways associated with energy production, amino acid metabolism and nucleotide synthesis, most probably reflect increased uptake of nutrients to the cells, to support cell proliferation triggered by the innate immune response. Our study revealed for the first time that CpG-ODNs change the metabolomic landscape to establish antimicrobial immunity in neonatal chicks. The metabolites highlighted in the present study can help future targeted studies to better understand immunometabolic interactions and pinpoint the key molecules or pathways contributing to 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) | 0.001 | 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".