Preparation and Application of <sup>13</sup>C-Labeled <i>myo</i>-Inositol to Identify New Catabolic Products in Inositol Metabolism in <i>Lactobacillus casei</i>
Bibliographic record
Abstract
myo -Inositol (mI) is widely distributed in all domains of life and is important for several cellular functions, including bacterial survival. The enzymes responsible for the bacterial catabolism of mI, encoded in the iol operon, can vary from one organism to another, and these pathways have yet to be fully characterized. We previously identified a new scyllo -inositol dehydrogenase (sIDH) in the iol operon of Lactobacillus casei that can oxidize mI in addition to the natural substrate, scyllo -inositol, but the product of mI oxidation was not determined. Here we report the identification of these metabolites by monitoring the reaction with 13 C nuclear magnetic resonance. We prepared all six singly 13 C-labeled mI isotopomers through a biocatalytic approach and used these labeled inositols as substrates for sIDH. The use of all six singly labeled mI isotopomers allowed for metabolite characterization without isolation steps. sIDH oxidation of mI produces 1 l -5- myo -inosose preferentially, but also two minor products, 1 d - chiro -inosose and 1 l - chiro -inosose. Together with previous crystal structure data for sIDH, we were able to rationalize the observed oxidation preference. Our relatively simple procedure for the preparation of isotopically labeled mI standards can have broad applications for the study of mI biotransformations.
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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) | 0.001 | 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) | 0.001 | 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".