Adipose tissue gene expression profiling reveals distinct molecular pathways that define visceral adiposity in offspring of maternal protein-restricted rats
Bibliographic record
Abstract
There is increasing evidence that poor early growth confers an increased risk of type 2 diabetes, hypertension, and other features of the metabolic syndrome in later life. We hypothesized that this may result from poor nutrition during early life exerting permanent effects on the structure and function of key metabolic organ systems. To study the long-term impact of early-life undernutrition on susceptibility to visceral adiposity, we used a rat model of maternal protein restriction (MPR) in which dams were fed a low-protein diet (containing 8% instead of 20% protein in control diet) throughout pregnancy and lactation. MPR offspring were born smaller than controls (offspring of dams on control diet) and in adulthood developed visceral adiposity. We compared the pattern of gene expression in visceral adipose tissue (VAT) between MPR offspring and controls with Affymetrix rat expression arrays. Of the total number of genes and expressed sequence tags analyzed (15,923 probe sets), 9,790 (61.5%) were expressed in VAT. We identified 650 transcripts as differentially expressed > or =1.5-fold in the VAT of MPR offspring. Gene ontology analysis revealed a global upregulation of genes involved in carbohydrate, lipid, and protein metabolism. A number of genes involved in adipocyte differentiation, angiogenesis, and extracellular matrix remodeling were also upregulated. However, in marked contrast to other rodent models of obesity, the expression of a large number of genes associated with inflammation was reduced in this rat model. Thus visceral adiposity in this early-life programmed rat model is marked by dynamic changes in the transcriptional profile of VAT. Our data provide new insights into the molecular mechanisms that underlie the early-life programming of visceral adiposity.
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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 | 0.001 | 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 | 0.000 |
| 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".