Proteomic and metabolomic analysis reveals new insights into quaternary amine metabolism in <i>Citrobacter amalonaticus</i> CJ25
Bibliographic record
Abstract
ABSTRACT Gut microbiota and human physiology are closely linked, and our microbiota can influence disease through immunological and metabolic activity. Quaternary amines, such as choline and carnitine, are abundant in the human gut and are found in red meat, beef, eggs, seafood, wheat, and beets. Canonically, choline and carnitine are broken down into the pro-atherogenic metabolite, trimethylamine (TMA), by various microbes in the human gut. A glycyl radical enzyme, CutC, is involved in the breakdown of choline to TMA. Carnitine is broken down to TMA via a gamma-butyrobetaine intermediate. TMA from the human gut increases levels of TMA N -oxide in blood and promotes atherosclerosis. Citrobacter amalonaticus CJ25, a gut strain isolated and characterized in our lab, has been shown to grow on choline or carnitine as the sole carbon-energy source without generating TMA. Because the genome lacks canonical enzymes involved in the degradation of choline and no TMA was produced in both choline and carnitine growth conditions, we analyzed the choline and carnitine metabolism using a combined metabolomic and proteomic approach. CJ25 metabolizes choline and carnitine into glycine betaine (GB) via pathways involving novel enzyme homologs, as indicated by proteomic analysis. The proteomics showed putative dehydrogenases that could be oxidizing choline and carnitine to GB. These non-atherogenic pathways involving novel enzyme homologs that we identify in CJ25 may also exist in other gut microbiota, which could amplify the effects of these pathways significantly, possibly reducing the risk of atherosclerotic cardiovascular disease in individuals harboring these microbiota. IMPORTANCE The human gut microbiome has been shown to contribute to atherosclerotic cardiovascular disease with adverse health effects throughout the world. Gut microbes canonically metabolize quaternary amines into proatherogenic TMA. In this study, a gut bacterium, CJ25, metabolizes choline and carnitine to a non-atherogenic product, glycine betaine, potentially using novel dehydrogenase homologs for their oxidation. Notably, the ability of CJ25 to metabolize choline and carnitine in a non-atherogenic manner establishes its potential as a beneficial human gut bacterium. Additionally, enzymes identified in CJ25 for choline and carnitine breakdown may be present in other gut microbes, which could amplify the effects of these pathways and reduce the risk of atherosclerotic cardiovascular disease more universally.
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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 | 0.000 |
| 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) | 0.000 | 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".