Application of a gut-immune co-culture system for the study of N-glycan-dependent host-pathogen interactions of <i>Campylobacter jejuni</i>
Bibliographic record
Abstract
Abstract An in vitro gut-immune co-culture model with apical and basal accessibility, designed to more closely resemble a human intestinal microenvironment, was employed to study the role of the Nlinked protein glycosylation (Pgl) pathway in Campylobacter jejuni pathogenicity. The gutimmune co-culture (GIC) was developed to model important aspects of the human small intestine by the inclusion of mucin producing goblet cells, human enterocytes, and dendritic cells, bringing together a mucus-containing epithelial monolayer with elements of the innate immune system. The utility of the system was demonstrated by characterizing host-pathogen interactions facilitated by N-linked glycosylation, such as host epithelial barrier functions, bacterial invasion and immunogenicity. Changes in human intestinal barrier functions in the presence of 11168 C. jejuni (wildtype) strains were quantified using GICs. The glycosylationdeficient strain 11168 ΔpglE was 100-fold less capable of adhering to and invading this intestinal model in cell infectivity assays. Quantification of inflammatory signaling revealed that 11168ΔpglE differentially modulated inflammatory responses in different intestinal microenvironments, suppressive in some but activating in others. Virulence-associated outer membrane vesicles produced by wildtype and 11168ΔpglE C. jejuni were shown to have differential composition and function, with both leading to immune system activation when provided to the gut-immune co-culture model. This analysis of aspects of C. jejuni infectivity in the presence and absence of its N-linked glycome, is enabled by application of the gut-immune model and we anticipate that this system will be applicable to further studies of C. jejuni and other enteropathogens of interest.
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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".