Genetically engineered<i>E. coli</i>Nissle attenuates hyperammonemia and prevents memory impairment in bile‐duct ligated rats
Bibliographic record
Abstract
Hyperammonemia associated with chronic liver disease (CLD) is implicated in the pathogenesis of hepatic encephalopathy (HE). The gut is a major source of ammonia production that contributes to hyperammonemia in CLD and HE and remains the primary therapeutic target for lowering hyperammonemia. As an ammonia-lowering strategy, Escherichia coli Nissle 1917 bacterium was genetically modified to consume and convert ammonia to arginine (S-ARG). S-ARG was further modified to additionally synthesize butyrate (S-ARG + BUT). Both strains were evaluated in bile-duct ligated (BDL) rats; experimental model of CLD and HE. METHODS: CFU/day) or vehicle until sacrifice at 3 or 5 weeks. Plasma (ammonia/pro-inflammatory/liver function), liver fibrosis (hydroxyproline), liver mRNA (pro-inflammatory/fibrogenic/anti-apoptotic) and colon mRNA (pro-inflammatory) biomarkers were measured post-sacrifice. Memory, motor-coordination, muscle-strength and locomotion were assessed at 5 weeks. RESULTS: In BDL-Veh rats, hyperammonemia developed at 3 and further increased at 5 weeks. This rise was prevented by S-ARG and S-ARG + BUT, whereas EcN was ineffective. Memory impairment was prevented only in S-ARG + BUT vs BDL-Veh. Systemic inflammation (IL-10/MCP-1/endotoxin) increased at 3 and 5 weeks in BDL-Veh. S-ARG + BUT attenuated inflammation at both timepoints (except 5-week endotoxin) vs BDL-Veh, whereas S-ARG only attenuated IP-10 and MCP-1 at 3 weeks. Circulating ALT/AST/ALP/GGT/albumin/bilirubin and gene expression of liver function markers (IL-10/IL-6/IL-1β/TGF-β/α-SMA/collagen-1α1/Bcl-2) were not normalized by either strain. Colonic mRNA (TNF-α/IL-1β/occludin) markers were attenuated by synthetic strains at both timepoints vs BDL-Veh. CONCLUSION: S-ARG and S-ARG + BUT attenuated hyperammonemia, with S-ARG + BUT additional memory protection likely due to greater anti-inflammatory effect. These innovative strategies, particularly S-ARG + BUT, have potential to prevent HE.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".