Cancer proteome and metabolite changes linked to SHMT2
Bibliographic record
Abstract
Serine hydroxymethyltransferase 2 (SHMT2) converts serine plus tetrahydrofolate (THF) into glycine plus methylene-THF and is upregulated at the protein level in lung and other cancers. In order to better understand the role of SHMT2 in cancer a model system of HeLa cells engineered for inducible over-expression or knock-down of SHMT2 was characterized for cell proliferation and changes in metabolites and proteome as a function of SHMT2. Ectopic over-expression of SHMT2 increased cell proliferation in vitro and tumor growth in vivo. Knockdown of SHMT2 expression in vitro caused a state of glycine auxotrophy and accumulation of phosphoribosylaminoimidazolecarboxamide (AICAR), an intermediate of folate/1-carbon-pathway-dependent de novo purine nucleotide synthesis. Decreased glycine in the HeLa cell-based xenograft tumors with knocked down SHMT2 was potentiated by administration of the anti-hyperglycinemia agent benzoate. However, tumor growth was not affected by SHMT2 knockdown with or without benzoate treatment. Benzoate inhibited cell proliferation in vitro, but this was independent of SHMT2 modulation. The abundance of proteins of mitochondrial respiration complexes 1 and 3 was inversely correlated with SHMT2 levels. Proximity biotinylation in vivo (BioID) identified 48 mostly mitochondrial proteins associated with SHMT2 including the mitochondrial enzymes Acyl-CoA thioesterase (ACOT2) and glutamate dehydrogenase (GLUD1) along with more than 20 proteins from mitochondrial respiration complexes 1 and 3. These data provide insights into possible mechanisms through which elevated SHMT2 in cancers may be linked to changes in metabolism and mitochondrial function.
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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".