A newborn derived monoclonal IgM antibody selectively modulates microbial metabolism in the gut
Bibliographic record
Abstract
Metabolism and gut microbiota are essential for newborn health, influencing immune function, energy balance, and growth. Breast milk provides IgA, crucial for shaping gut microbiota in infants. In non-breastfed newborns, we observe the presence of IgM antibodies that can recognize various bacteria, influence bacterial clustering, and alter bacterial metabolism, such as carbon source utilization in vitro within small bacterial communities. Based on these findings, we developed a monoclonal IgM, M291, derived from a newborn-B cell, which mimics naturally occurring antibodies and could serve as a surrogate tool to modulate intestinal bacterial functions and metabolism. Oral administration of M291 alters the metabolome of germ-free mice colonized with a defined bacterial consortium or an infant gut microbiota, by modulating the bacterial transcriptome, while maintaining microbial abundance and diversity. This study establishes proof of concept for the design and application of newborn-derived antibodies to modulate microbial and host metabolism, including lipid metabolism and bile acid secretion, without significantly altering microbiota composition. Here, the authors characterize a newborn-derived monoclonal IgM, showing it influences bacterial clustering, gene expression, and metabolic activity, thereby modulating gut bacterial functions and host metabolism without altering microbiota composition.
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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".