Nutritional Energy Stimulates NAD+ Production to Promote Tankyrase-Mediated PARsylation in Insulinoma Cells
Bibliographic record
Abstract
The poly-ADP-ribosylation (PARsylation) activity of tankyrase (TNKS) regulates diverse physiological processes including energy metabolism and wnt/β-catenin signaling. This TNKS activity uses NAD+ as a co-substrate to post-translationally modify various acceptor proteins including TNKS itself. PARsylation by TNKS often tags the acceptors for ubiquitination and proteasomal degradation. Whether this TNKS activity is regulated by physiological changes in NAD+ levels or, more broadly, in cellular energy charge has not been investigated. Because the NAD+ biosynthetic enzyme nicotinamide phosphoribosyltransferase (NAMPT) in vitro is robustly potentiated by ATP, we hypothesized that nutritional energy might stimulate cellular NAMPT to produce NAD+ and thereby augment TNKS catalysis. Using insulin-secreting cells as a model, we showed that glucose indeed stimulates the autoPARsylation of TNKS and consequently its turnover by the ubiquitin-proteasomal system. This glucose effect on TNKS is mediated primarily by NAD+ since it is mirrored by the NAD+ precursor nicotinamide mononucleotide (NMN), and is blunted by the NAMPT inhibitor FK866. The TNKS-destabilizing effect of glucose is shared by other metabolic fuels including pyruvate and amino acids. NAD+ flux analysis showed that glucose and nutrients, by increasing ATP, stimulate NAMPT-mediated NAD+ production to expand NAD+ stores. Collectively our data uncover a metabolic pathway whereby nutritional energy augments NAD+ production to drive the PARsylating activity of TNKS, leading to autoPARsylation-dependent degradation of the TNKS protein. The modulation of TNKS catalytic activity and protein abundance by cellular energy charge could potentially impose a nutritional control on the many processes that TNKS regulates through PARsylation. More broadly, the stimulation of NAD+ production by ATP suggests that nutritional energy may enhance the functions of other NAD+-driven enzymes including sirtuins.
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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 | 0.000 | 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".