Two unique biological response-modifier glucans beneficially regulating gut microbiota and faecal metabolome in a non-alcoholic steatohepatitis animal model, with potential for applications in human health and disease
Bibliographic record
Abstract
Abstract Objective The gut microbiome and its metabolites, influenced by age and stress, reflect the metabolism and immune system’s health. We assessed the gut microbiota and faecal metabolome in a Stelic Animal Model of non-alcoholic steatohepatitis (NASH). Design This model was subjected to the following treatments: reverse osmosis water, AFO-202, N-163, AFO-202+N-163, and telmisartan. Faecal samples were collected at 6 weeks and 9 weeks of age. The gut microbiome was analysed using 16S ribosomal RNA sequence acquired by next-generation sequencing and the faecal metabolome using gas chromatography-mass spectrometry. Results The gut microbial diversity increased greatly in the AFO-202+N-163 group. Post-intervention, the abundance of Firmicutes decreased, while that of Bacteroides increased and was the highest in the AFO-202+N-163 group. The decrease in the Enterobacteria and other Firmicutes abundance and in the Turicibacter and Bilophila abundance was the highest in the AFO-202 and N-163 groups, respectively. The Lactobacillus abundance increased the most in the AFO-202+N-163 group. The faecal metabolites spermidine and tryptophan, beneficial against inflammation and NASH, respectively, were greatly increased in the N-163 group. Succinic acid, beneficial in neurodevelopmental and neurodegenerative diseases, increased in the AFO-202 group. Decrease in fructose was the highest in the AFO-202 group. Leucine and phenylalanine decreased, whereas ornithine, which is beneficial against chronic immune-metabolic-inflammatory pathologies, increased in the AFO-202+N-163 group. Conclusion AFO-202 treatment in mice is beneficial against neurodevelopmental and neurodegenerative diseases and has prophylactic potential against metabolic conditions. N-163 treatment has anti-inflammatory effects against organ fibrosis and neuroinflammatory conditions. In combination, they present anticancer activity. Key messages The influence of gut microbiome on fecal metabolome and their association to several diseases is already known. This study proves the efficacy of 1,3-1,6 beta glucans with pre-biotic potentials, beneficially influencing both gut microbiome and metabolome. These results recommends for an in-depth exploration of relationship among pre-biotics, gut microbiome and gut-multi-organ axes on the fundamentals of disease onset. Hidden prophylactic and therapeutic solutions to non-contagious diseases with Aureobasidium pullulans produced 1,3-1,6 beta glucans may be unveiled.
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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 | 0.001 |
| 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".