Transcriptional regulation of metabolism in disease: From transcription factors to epigenetics
Bibliographic record
Abstract
Every cell in an individual has largely the same genomic sequence and yet cells in different tissues can present widely different phenotypes. This variation arises because each cell expresses a specific subset of genomic instructions. Control over which instructions, or genes, are expressed is largely controlled by transcriptional regulatory pathways. Each cell must assimilate a huge amount of environmental input, and thus it is of no surprise that transcription is regulated by many intertwining mechanisms. This large regulatory landscape means there are ample possibilities for problems to arise, which in a medical context means the development of disease states. Metabolism within the cell, and more broadly, affects and is affected by transcriptional regulation. Metabolism can therefore contribute to improper transcriptional programming, or pathogenic metabolism can be the result of transcriptional dysregulation. Here, we discuss the established and emerging mechanisms for controling transcription and how they affect metabolism in the context of pathogenesis. Cis- and trans-regulatory elements, microRNA and epigenetic mechanisms such as DNA and histone methylation, all have input into what genes are transcribed. Each has also been implicated in diseases such as metabolic syndrome, various forms of diabetes, and cancer. In this review, we discuss the current understanding of these areas and highlight some natural models that may inspire future therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".