Host immunoglobulin G selectively identifies pathobionts in pediatric inflammatory bowel diseases
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel diseases (IBD) are a group of complex and multifactorial disorders with unknown etiology. Chronic intestinal inflammation develops against resident intestinal bacteria in genetically susceptible hosts. We hypothesized that host intestinal immunoglobulin (Ig) G can be used to identify bacteria involved in IBD pathogenesis. RESULTS: IgG-bound and -unbound microorganisms were collected from 32 pediatric terminal ileum aspirate washes during colonoscopy [non-IBD (n = 10), Crohn disease (n = 15), and ulcerative colitis (n = 7)], and composition was assessed using the Illumina MiSeq platform. In vitro analysis of invasive capacity was evaluated by fluorescence in situ hybridization and gentamicin invasion assay; immune activation was measured by qPCR. Despite considerable inter-individual variations, IgG binding favored specific and unique mucosa-associated species in pediatric IBD patients. Burkholderia cepacia, Flavonifractor plautii, and Rumminococcus sp. demonstrated increased IgG binding, while Pseudomonas ST29 demonstrated reduced IgG binding, in IBD. In vitro validation confirmed that B. cepacia, F. plautii, and Rumminococcus display invasive potential while Pseudomonas protogens did not. CONCLUSION: Using IgG as a marker of pathobionts in larger patient cohorts to identify microbes and elucidate their role in IBD pathogenesis will potentially underpin new strategies to facilitate development of novel, targeted diagnostic, and therapeutic approaches. Interestingly, this method can be used beyond the scope of this manuscript to evaluate altered gut pathobionts in a number of diseases associated with altered microbiota including arthritis, obesity, diabetes mellitus, alcoholic liver disease, cirrhosis, metabolic syndrome, and carcinomas.
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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 | 0.001 | 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 | 0.000 |
| 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".