Commensal-pathogen interactions in the intestinal tract
Bibliographic record
Abstract
The intestinal microbiota are pivotal in determining the developmental, metabolic and immunological status of the mammalian host. However, the intestinal tract may also accommodate pathogenic organisms, including helminth parasites which are highly prevalent in most tropical countries. Both microbes and helminths must evade or manipulate the host immune system to reside in the intestinal environment, yet whether they influence each other's persistence in the host remains unknown. We now show that abundance of Lactobacillus bacteria correlates positively with infection with the mouse intestinal nematode parasite, Heligmosomoides polygyrus, as well as with heightened regulatory T cell (Treg) and Th17 responses. Moreover, H. polygyrus raises Lactobacillus species abundance in the duodenum of C57BL/6 mice, which are highly susceptible to H. polygyrus infection, but not in BALB/c mice, which are relatively resistant. Sequencing of samples at the bacterial gyrB locus identified the principal Lactobacillus species as L. taiwanensis, a previously characterized rodent commensal. Experimental administration of L. taiwanensis to BALB/c mice elevates regulatory T cell frequencies and results in greater helminth establishment, demonstrating a causal relationship in which commensal bacteria promote infection with an intestinal parasite and implicating a bacterially-induced expansion of Tregs as a mechanism of greater helminth susceptibility. The discovery of this tripartite interaction between host, bacteria and parasite has important implications for both antibiotic and anthelmintic use in endemic human populations.
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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".