Proteome and MicroRNAs in Human Milk: Term‐Dependent Changes in Exosome Composition
Bibliographic record
Abstract
Infants born prematurely are a vulnerable population. Human milk is considered the best source of nutrition for infants as it contains not only nutrients, but also a variety of bioactive components to help infants adjust to life outside the womb. Among these components are exosomes, which are lipid bilayer vesicles that contain messenger RNA (mRNA), microRNA (miR) and proteins. Human milk exosomes contain various immunomodulatory components that may provide protection and promote microbial tolerance in the GI tract of newborns. However, mothers birth infants at very different times. Could preterm birth signal mother’s mammary gland to manufacture magical milk? We propose that term and preterm human milk convey different exosome constituents to the infant gut. We hypothesized that term and preterm human milk have distinct protein and miR content, and the exosomal isolates have unique immunomodulatory effects on intestinal cells. To investigate this, exosomes were isolated from human milk after term and preterm births. The exosomal miRs were characterized using RNA sequencing. Exosomal proteome enabled determination of abundant protein classes, narrowing of protein profile and discovery of essential proteins in preterm versus term milk. To investigate downstream pathways of either exosomes or specific exosomal‐contained proteins, we used the Caco 2/15 intestinal cell line to measure levels of immunomodulatory proteins following treatments with milk derived exosomes or proteins. These results provide novel insights for adaptations that exist in human term and preterm milk on both the proteome and expressional level. Ultimately, this research can guide the development for therapeutics in infant gastro‐intestinal diseases, such as necrotizing enterocolitis, and elucidate unique proteins that are released into human milk for downstream action(s) in the developing infant. Therapeutic benefits of preterm milk constituents are being explored. Support or Funding Information Canadian Institutes of Health Research
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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".