Metatranscriptomic analysis reveals gut microbiome bacterial genes in pyruvate and amino acid metabolism associated with hyperuricemia and gout in humans
Bibliographic record
Abstract
Several pathologies with metabolic origin, such as hyperuricemia and gout, have been associated with the gut microbiota taxonomic profile. However, there is no evidence of which bacterial genes are being expressed in the gut microbiome, and of their potential effects on hyperuricemia and gout. We sequenced the RNA of 26 fecal samples from 10 healthy normouricemic controls, 10 with asymptomatic hyperuricemia (AH), and six gout patients. The coding sequences were mapped to KEGG orthologues (KO). We compared the expression levels using generalized linear models and validated the expression of four KO in a larger sample by qRT-PCR. A distinct genetic expression pattern was identified among groups. AH individuals and gout patients showed an over-expression of KOs mainly related to pyruvate metabolism (Log2foldchange > 23, p -adj ≤ 3.56 × 10 − 9 ), the pentose pathway (Log2foldchange > 24, p -adj < 1.10 × 10 −12 ) and purine metabolism (Log2foldchange > 22, p -adj < 1.25 × 10 − 7 ). AH subjects had lower expression of KO related to glycine metabolism (Log2foldchange=-18, p -adj < 1.72 × 10 −6 ) than controls. Gout patients had lower expression (Log2foldchange=-22.42, p -adj < 3.31 × 10 − 16 ) of a KO involved in phenylalanine biosynthesis, in comparison to controls and AH subjects. The over-expression seen for the KO related to pyruvate metabolism and the pentose pathway in gout patients´ microbiome was validated. There is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects and gout patients. These differences are mainly located in metabolic pathways involved in acetate precursors and bioavailability of amino acids.
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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) | 0.001 | 0.001 |
| 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".