Ca <sup>2+</sup> /Calmodulin‐Dependent Protein Kinase Kinase β Negatively Regulates Progesterone Mediated PGRMC1 Signaling and the Warburg Effect
Bibliographic record
Abstract
Objectives Ca 2+ /calmodulin‐dependent protein kinase kinase β (CaMKKβ) signaling cascades directly regulate a variety of physiological processes and have been implicated in several human diseases, including cancer and neurodegenerative disorders. We discovered p rogesterone‐ r eceptor m embrane c omponent 1 (PGRMC1) is differentially phosphorylated in CaMKKβ knockout (KO) HEK293 cells. PGRMC1 is a member of the membrane‐associated progesterone receptor (MAPR) family with a cytochrome b5‐like heme‐binding region. The gene is known to be involved in diverse functions, including regulation of cytochrome P450, steroidogenesis, vesicle trafficking, progesterone signaling and mitotic spindle and cell cycle regulation. PGRMC1 is associated with multiple progesterone‐dependent effects in diverse cell types. However, direct progesterone binding to PGRMC1 is yet to be demonstrated. The lack of credible ligand binding to PGRMC1 may be due to the fact that bacterially expressed PGRMC1 preparations may not possess the necessary post‐translational modifications (PTMs) required for progesterone binding. Therefore, we hypothesized that CaMKKβ is an upstream kinase that mediates phosphorylation of PGRMC1 and, thereby, regulates progesterone signaling. Methodology We used TiO 2 column enrichment followed by mass spectrometry to identify differentially expressed phosphopeptides derived from CaMKKβ KO versus wild type HEK293 cells. We interrogated cellular metabolism by measuring oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) as an indicator of mitochondrial respiration and glycolysis, respectively. Further, we used isolectric focusing (IEF) followed by SDS‐PAGE and immunoblotting to identify different charged fractions of PGRMC1. Results Loss of CaMKKβ significantly decreased PGRMC1 protein expression in multiple CaMKKβ KO cell lines. TiO 2 column enriched phosphopeptide analysis revealed the presence of phosphorylated S57, T178, Y180 and S181 peptides in CaMKKβ KO cells whereas only S181 phosphorylation was found in wild type cells. In addition, IEF analysis revealed multiple charged fractions of PGRMC1. The ~pI/pH‐3 fraction was significantly higher in CaMKKβ KO cells which may correspond to increased phosphorylation. Loss of CaMKKβ significantly increased the rate of glycolysis but reduced mitochondrial OCR. Treatment with 10μM progesterone significantly increased the rate of glycolysis within 30 mins in wild type cells; however it failed to show a similar effect in CaMKKβ KO cells. In contrast, progesterone treatment significantly lowered OCR in both wild type and CaMKKβ KO cells within 30 mins. Conclusion CaMKKβ negatively regulates PRGMC1 phosphorylation which may, in turn, control the relative turnover of the protein. CaMKKβ negatively regulates the progesterone‐mediated Warburg effect which may explain the role of CaMKKβ in tumorigenesis and neurodegenerative disease. Our study identifies a link between CaMKKβ and progesterone signaling which may be used for therapeutic targeting. Support or Funding Information Supported by CIHR grant # MOP‐130282 (PF) This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".