Abstract 414: Transcriptome Analysis by RNA Sequencing of Left Ventricular Tissue From Mice Fed a High-Fat Diet
Bibliographic record
Abstract
A high-fat diet induces obesity and is associated with heart disease characterized by cardiac hypertrophy, diastolic dysfunction, fibrosis, and mitochondrial dysfunction. However, the molecular mechanisms involved in the pathogenesis of cardiac disease are not known. Therefore, the objective of this study was to investigate the molecular changes in the heart associated with a high-fat diet at early time points in the progression of cardiac disease. To do this, male C57BL/6 mice were fed either a chow diet or a high-fat (58 kcal%), low-sucrose (HFLS) diet starting at 5 weeks of age for 14 days and 100 days. At each time point, hearts were extracted and morphometric measurements were recorded. HFLS diet had no effect on weight gain at 14 days but did significantly increase body weight by two-fold at 100 days. At 14 days, there was no significant difference in cardiac size as measured by heart weight-to-tibia length ratio (HW/TL) between mice on HFLS diet and chow diet. However, there was a 24% increase in HW/TL following 100 days on the HFLS diet. Total RNA was extracted from left ventricular tissue from each group and gene expression was determined by RNA-Seq transcriptome analysis. DAVID functional pathway analysis of the top 50 genes with differential expression between diets at 14 days and 100 days identified significant enrichment for mitochondrial proteins and lipid metabolism at both time points on the HFLS diet. Moreover, there was enrichment for hypoxia response genes following 100 days on the HFLS diet. Analysis of genes with differential expression between 14 days and 100 days on HFLS diet identified enrichment for actin binding proteins/Z disc, glutathione transferase activity, and oxidation/reduction activity. These findings demonstrate significant differences in gene regulation in the hearts of mice fed a high fat diet at early time points. It is possible that these early molecular changes play a role in the progression of cardiac disease.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".