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 13C nuclear magnetic resonance. We prepared all six singly 13C-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 1l-5-myo-inosose preferentially, but also two minor products, 1d-chiro-inosose and 1l-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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".