Metabolome and metaboproteome remodeling in nuclear reprogramming
Bibliographic record
Abstract
Nuclear reprogramming resets differentiated tissue to generate induced pluripotent stem (iPS) cells. While genomic attributes underlying reacquisition of the embryonic-like state have been delineated, less is known regarding the metabolic dynamics underscoring induction of pluripotency. Metabolomic profiling of fibroblasts vs. iPS cells demonstrated nuclear reprogramming-associated induction of glycolysis, realized through augmented utilization of glucose and accumulation of lactate. Real-time assessment unmasked downregulated mitochondrial reserve capacity and ATP turnover correlating with pluripotent induction. Reduction in oxygen consumption and acceleration of extracellular acidification rates represent high-throughput markers of the transition from oxidative to glycolytic metabolism, characterizing stemness acquisition. The bioenergetic transition was supported by proteome remodeling, whereby 441 proteins were altered between fibroblasts and derived iPS cells. Systems analysis revealed overrepresented canonical pathways and interactome-associated biological processes predicting differential metabolic behavior in response to reprogramming stimuli, including upregulation of glycolysis, purine, arginine, proline, ribonucleoside and ribonucleotide metabolism, and biopolymer and macromolecular catabolism, with concomitant downregulation of oxidative phosphorylation, phosphate metabolism regulation, and precursor biosynthesis processes, prioritizing the impact of energy metabolism within the hierarchy of nuclear reprogramming. Thus, metabolome and metaboproteome remodeling is integral for induction of pluripotency, expanding on the genetic and epigenetic requirements for cell fate manipulation.
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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 | 0.000 |
| 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".