Central Metabolism and Growth Rate Impacts on Hydrogen and Carbon Isotope Fractionation During Amino Acid Synthesis in E. coli
Bibliographic record
Abstract
Compound specific stable isotope analysis (CSIA) of amino acids from bacterial biomass is a newly emerging powerful tool for exploring central carbon metabolism pathways and fluxes. By comparing isotopic values and fractionations relative to water and growth substrate, the impact of variable flow path for metabolites through different central metabolic pathways, perturbations of these paths, and their resultant consequences on intracellular pools and resultant biomass may be elucidated. Here, we explore the effects that central carbon metabolism and growth rate can have on stable hydrogen (δ 2 H) and carbon (δ 13 C) compound specific isotopic values of amino acids, and whether diagnostic isotopic fingerprints are revealed by these paired analyses. We measured δ 2 H and δ 13 C in amino acids in the wild type Escherichia coli (MG1655) across a range of growth rates in chemostat cultures to address the unknown isotopic consequences as metabolic fluxes are shuffled between catabolic and anabolic metabolisms. Additionally, two E. coli knockout mutants, one with deficiency in glycolysis – pgi (LC1888) and another inhibiting the oxidative pentose phosphate pathway (OPPP) – zwf (LC1889), were grown on glucose and used as a comparison against the wild type E. coli (MG1655) to address the isotopic changes of amino acids produced in these perturbed metabolic pathways. Amino acid δ 2 H values, which collectively vary in composition by more than 400‰, are altered along with δ 13 C values demonstrating fundamental shifts in central metabolic pathways and/or fluxes. Within our linear discriminant analysis with a simple model organism to examine potential amino acid fingerprinting, our knockout strains and variable growth rate samples plot across a wider array of organism classification than merely within the boundaries of other bacterial data.
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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) | 0.000 | 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".