Relationship Between Hepatic Gene Expression, Intestinal Microbiota, and Inferred Functional Metagenomic Analysis in NAFLD
Bibliographic record
Abstract
INTRODUCTION: We previously reported a lower fecal abundance of Ruminococcus spp., Faecalibacterium prausnitzii , and Coprococcus spp. in nonalcoholic fatty liver disease (NAFLD). In this article, we assess the associations between hepatic gene expression, the specific taxa, and bacterial pathways. METHODS: The relationships between hepatic genes that were differentially expressed in patients with NAFLD vs healthy controls (HC) and the abundance of these specific taxa were studied. Inferred functional metagenomic analysis using Piphillin was also performed to investigate associations with bacterial pathways. RESULTS: Fifteen patients with NAFLD and 6 HC participated. Of 728 hepatic genes examined, 176 correlated with the abundance of Ruminococcus spp., 138 with F. prausnitzii , and 92 with Coprococcus spp. For Ruminococcus spp., genes were enriched in gene ontology (GO) terms related to apoptotic process, response to external and cytokine stimuli, and regulation of signaling. Several genes related to the Kyoto Encyclopedia of Genes and Genomes pathway insulin resistance were correlated with F. prausnitzii . The hepatic genes associated with F. prausnitzii were enriched in GO terms related to cellular response to different stimuli, apoptotic process, and regulation of metabolic pathways. For Coprococcus spp., only the GO term response to external stimulus was enriched. There was a distinct pattern of associations between hepatic genes and bacterial taxa in NAFLD vs HC. For bacterial pathways, 65 and 18 hepatic genes correlated with bacterial metabolic functions in NAFLD and HC, respectively. DISCUSSION: Hepatic gene expression related to insulin resistance, inflammation, external stimuli, and apoptosis correlated with bacterial taxa. Patients with NAFLD showed a higher presence of bacterial pathways associated with lipid metabolism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".