Metabolic Reprogramming in Response to Freund’s Adjuvants: Insights from Serum Metabolomics
Bibliographic record
Abstract
Freund’s adjuvants have been used in vaccine and autoimmune settings, and their effects can be overlapping or unique to each. While both incomplete Freund’s adjuvants (IFA) and complete Freund’s adjuvants (CFA) influence antibody and T cell responses, the robust T helper 1 cytokines induced by the mycobacterial components make CFA the powerful immunostimulating adjuvant. In these studies, the adjuvant effects are investigated in a select population of cells, and the changes, if any, with the metabolic alterations in the systemic compartment are unclear. We investigated whether the effects of IFA and CFA can be influenced by the metabolic shifts in mice immunized with saline, IFA, or CFA using Mycobacterium tuberculosis var. bovis Bacillus Calmette–Guérin (BCG) as a positive control. After seven days of immunization, we analyzed the serum metabolite profiles using liquid chromatography coupled with high-resolution mass spectrometry and multivariate statistical analysis to identify metabolic features between the groups. The data revealed that, in the scores space, the CFA and BCG groups were more closely aligned compared to the saline group, while the IFA group displayed an intermediate profile. Furthermore, comparisons between the CFA and BCG groups showed more significant perturbations in lipid and amino acid metabolism, particularly involving glycerophospholipids, cysteine, and aromatic amino acids. In contrast, comparisons between the BCG and IFA groups indicated a more pronounced disruption in central energy metabolism pathways, such as the citric acid cycle and pyruvate metabolism. Together, the data suggest that the serum metabolite profiles in response to IFA and CFA might play a role in modulating the immune responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".