The influence of early life exposures on the infant gut virome
Bibliographic record
Abstract
The factors influencing the establishment of the gut bacterial community in early life are fairly well studied. However, the factors shaping the infant gut virome remain elusive. Interestingly, early life gut virome imbalances have recently been linked with increased risk of developing diseases like type 1 diabetes and asthma. We utilized the deeply phenotyped COPSAC2010 cohort to investigate how environmental factors influence the gut virome at one year age. We demonstrate that the presence of older siblings as well as residential location (urban or rural) had the strongest impact on gut virome composition at 1 year of age. A total of 16,118 species-level clustered viral representative contigs (here termed viral Operational Taxonomic Units – vOTUs) were identified and of these 2105 vOTUs varied in abundance with environmental exposures. Of these vOTUs 94.1% were phages mainly predicted to infect Bacteroidaceae, Prevotellaceae, and Ruminococcaceae. Strong co-abundance of phages and their bacterial hosts was confirmed underlining the predicted phage-host connections. Furthermore, we found some gut viruses affected by environmental factors encode enzymes involved in the utilization and degradation of major dietary components, potentially affecting infant health by influencing the bacterial host metabolic capacity. These findings provide a valuable insights for understanding the early life factors that predispose to autoimmune and metabolic disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".