Toolkit for cellular studies of mammalian mitochondrial inorganic polyphosphate
Bibliographic record
Abstract
Introduction: Inorganic polyphosphate (polyP) is an ancient polymer which is extremely well-conserved throughout evolution, and found in every studied organism. PolyP is composed of orthophosphates linked together by high-energy bonds, similar to those found in ATP. The metabolism and the functions of polyP in prokaryotes and simple eukaryotes are well understood. However, little is known about its physiological roles in mammalian cells, mostly due to its unknown metabolism and lack of systematic methods and effective models for the study of polyP in these organisms. Methods: Here, we present a comprehensive set of genetically modified cellular models to study mammalian polyP. Specifically, we focus our studies on mitochondrial polyP, as previous studies have shown the potent regulatory role of mammalian polyP in the organelle, including bioenergetics, via mechanisms that are not yet fully understood. Results: Using SH-SY5Y cells, our results show that the enzymatic depletion of mitochondrial polyP affects the expression of genes involved in the maintenance of mitochondrial physiology, as well as the structure of the organelle. Furthermore, this depletion has deleterious effects on mitochondrial respiration, an effect that is dependent on the length of polyP. Our results also show that the depletion of mammalian polyP in other subcellular locations induces significant changes in gene expression and bioenergetics; as well as that SH-SY5Y cells are not viable when the amount and/or the length of polyP are increased in mitochondria. Discussion: Our findings expand on the crucial role of polyP in mammalian mitochondrial physiology and place our cell lines as a valid model to increase our knowledge of both mammalian polyP and mitochondrial physiology.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.025 |
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".