Energetic adaptations: Metabolic control of endocytic membrane traffic
Bibliographic record
Abstract
Endocytic membrane traffic controls the access of myriad cell surface proteins to the extracellular milieu, and thus gates nutrient uptake, ion homeostasis, signaling, adhesion and migration. Coordination of the regulation of endocytic membrane traffic with a cell's metabolic needs represents an important facet of maintenance of homeostasis under variable conditions of nutrient availability and metabolic demand. Many studies have revealed intimate regulation of endocytic membrane traffic by metabolic cues, from the specific control of certain receptors or transporters, to broader adaptation or remodeling of the endocytic membrane network. We examine how metabolic sensors such as AMP-activated protein kinase, mechanistic target of rapamycin complex 1 and hypoxia inducible factor 1 determine sufficiency of various metabolites, and in turn modulate cellular functions that includes control of endocytic membrane traffic. We also examine how certain metabolites can directly control endocytic traffic proteins, such as the regulation of specific protein glycosylation by limiting levels of uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) produced by the hexosamine biosynthetic pathway. From these ideas emerge a growing appreciation that endocytic membrane traffic is orchestrated by many intrinsic signals derived from cell metabolism, allowing alignment of the functions of cell surface proteins with cellular metabolic requirements. Endocytic membrane traffic determines how cells interact with their environment, thus defining many aspects of nutrient uptake and energy consumption. We examine how intrinsic signals that reflect metabolic status of a cell regulate endocytic traffic of specific proteins, and, in some cases, exert broad control of endocytic membrane traffic phenomena. Hence, endocytic traffic is versatile and adaptable and can be modulated to meet the changing metabolic requirements of a cell.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".