Intestinal microbiome and metabolome signatures in patients with chronic granulomatous disease
Bibliographic record
Abstract
BACKGROUND: Chronic granulomatous disease (CGD) is caused by defects in any 1 of the 6 subunits forming the nicotinamide adenine dinucleotide phosphate oxidase complex 2 (NOX2), leading to severely reduced or absent phagocyte-derived reactive oxygen species production. Almost 50% of patients with CGD have inflammatory bowel disease (CGD-IBD). While conventional IBD therapies can treat CGD-IBD, their benefits must be weighed against the risk of infection. Understanding the impact of NOX2 defects on the intestinal microbiota may lead to the identification of novel CGD-IBD treatments. OBJECTIVE: We sought to identify microbiome and metabolome signatures that can distinguish individuals with CGD and CGD-IBD. METHODS: We conducted a cross-sectional observational study of 79 patients with CGD, 8 pathogenic variant carriers, and 19 healthy controls followed at the National Institutes of Health Clinical Center. We profiled the intestinal microbiome (amplicon sequencing) and stool metabolome, and validated our findings in a second cohort of 36 patients with CGD recruited through the Primary Immune Deficiency Treatment Consortium. RESULTS: We identified distinct intestinal microbiome and metabolome profiles in patients with CGD compared to healthy individuals. We observed enrichment for Erysipelatoclostridium spp, Sellimonas spp, and Lachnoclostridium spp in CGD stool samples. Despite differences in bacterial alpha and beta diversity between the 2 cohorts, several taxa correlated significantly between both cohorts. We further demonstrated that patients with CGD-IBD have a distinct microbiome and metabolome profile compared to patients without CGD-IBD. CONCLUSION: Intestinal microbiome and metabolome signatures distinguished patients with CGD and CGD-IBD, and identified potential biomarkers and therapeutic targets.
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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 | 0.000 | 0.001 |
| 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".