Maternal allergic sensitization affects the T cell modulatory capacity and molecular cargo of milk derived extracellular vesicles
Bibliographic record
Abstract
Abstract Background Human milk extracellular vesicles (EVs) affect various cell types in the gastrointestinal tract, including T cells, and play a role in the development of the newborn’s immune system by delivering specific molecular cargo to target cells. Although maternal allergic sensitization alters the composition of milk, it is unknown whether this impacts the function of milk EVs. Therefore, we analyzed the T cell modulatory capacity and compared the protein and miRNA cargoes of EVs from milk of allergic and non-allergic mothers. Methods EVs were isolated from human milk from allergic and non-allergic donors by differential centrifugation, density gradient floatation and size exclusion chromatography. Functional modulation of primary human CD4+ T cells by EVs was assessed in vitro . Proteomic analysis and small RNA sequencing was performed on milk EVs to evaluate protein and miRNA abundance and to identify cellular targets of this EV cargo in relevant T cell signaling pathways. Results T cell proliferation, activation and cytokine production were suppressed in the presence of milk EVs. Remarkably, milk EVs from allergic mothers inhibited T cell activation to a lesser extent than EVs from non-allergic mothers. Integrative multi-omics analysis identified EV cargo of which the cellular targets could be linked to differential modulation of T cell activation-associated processes . Conclusions Milk EVs from non-allergic mothers are stronger inhibitors of T cell activation compared to milk EVs from allergic mothers. This altered functionality might be linked to changes in miRNA and protein cargo that modulate T cell signaling pathways in an integrative manner.
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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".