Glucose-Sensing Transcription Factor MondoA/ChREBP as Targets for Type 2 Diabetes: Opportunities and Challenges
Bibliographic record
Abstract
The worldwide increase in type 2 diabetes (T2D) is becoming a major health concern, thus searching for novel preventive and therapeutic strategies has become urgent. In last decade, the paralogous transcription factors MondoA and carbohydrate response element-binding protein (ChREBP) have been revealed to be central mediators of glucose sensing in multiple metabolic organs. Under normal nutrient conditions, MondoA/ChREBP plays vital roles in maintaining glucose homeostasis. However, under chronic nutrient overload, the dysregulation of MondoA/ChREBP contributes to metabolic disorders, such as insulin resistance (IR) and T2D. In this review, we aim to provide an overview of recent advances in the understanding of MondoA/ChREBP and its roles in T2D development. Specifically, we will briefly summarize the functional similarities and differences between MondoA and ChREBP. Then, we will update the roles of MondoA/ChREBP in four T2D-associated metabolic organs (i.e., the skeletal muscle, liver, adipose tissue, and pancreas) in physiological and pathological conditions. Finally, we will discuss the opportunities and challenges of MondoA/ChREBP as drug targets for anti-diabetes. By doing so, we highlight the potential use of therapies targeting MondoA/ChREBP to counteract T2D and its complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".