Iron Sequestration in Microbiota Biofilms As A Novel Strategy for Treating Inflammatory Bowel Disease
Bibliographic record
Abstract
Significant alterations of intestinal microbiota and anemia are hallmarks of inflammatory bowel disease (IBD). It is widely accepted that iron is a key nutrient for pathogenic bacteria, but little is known about its impact on microbiota associated with IBD. We used a model device to grow human mucosa-associated microbiota in its physiological anaerobic biofilm phenotype. Compared to microbiota from healthy donors, microbiota from IBD patients generate biofilms ex vivo that were larger in size and cell numbers, contained higher intracellular iron concentrations, and exhibited heightened virulence in a model of human intestinal epithelia in vitro and in the nematode Caenorhabditis elegans. We also describe an unexpected iron-scavenging property for an experimental hydrogen sulfide-releasing derivative of mesalamine. The findings demonstrate that this new drug reduces the virulence of IBD microbiota biofilms through a direct reduction of microbial iron intake and without affecting bacteria survival or species composition within the microbiota. Metabolomic analyses indicate that this drug reduces the intake of purine nucleosides (guanosine), increases the secretion of metabolite markers of purine catabolism (urate and hypoxanthine), and reduces the secretion of uracil (a pyrimidine nucleobase) in complex multispecies human biofilms. These findings demonstrate a new pathogenic mechanism for dysbiotic microbiota in IBD and characterize a novel mode of action for a class of mesalamine derivatives. Together, these observations pave the way towards a new therapeutic strategy for treatment of patients with IBD.
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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) | 0.000 | 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".