Comparison of two arylsulfatases for targeted mass spectrometric analysis of microbiota-derived metabolites
Bibliographic record
Abstract
Sulfation of metabolites is the second highest phase II modification in humans, which plays a critical role in the xenobiotics clearance process and gut microbiota-host co-metabolism. Besides the main function to remove xenobiotics from the body, sulfated metabolites have also been linked to inflammation, bacterial pathogenesis and metabolic disorders. A better understanding of how these metabolites impact the human body has turned into an important research area. Analytical methods for selective identification of this metabolite class are scarce. We have recently developed an assay utilizing the arylsulfatase from Helix pomatia due to a high substrate promiscuity combined with state-of-the-art metabolomics bioinformatic analysis for the selective identification of O-sulfated metabolites in human samples. This enzyme requires a multistep purification process as highest purity is needed for the developed mass spectrometric assay. In this study, we have utilized a new and recombinant overexpressed arylsulfatase (ASPC) for the selective identification of organic sulfate esters in human urine samples. We have compared the substrate conversion in urine samples and substrate specificity of this enzyme with purified arylsulfatase from Helix pomatia. Our analysis of urine samples revealed that both enzymes can be utilized for the selective analysis and discovery of sulfated metabolites with high promiscuity as demonstrated by equal hydrolysis of 108 substrates including sulfated conjugates of 27 metabolites of microbial origin. Importantly, we also identified 21 substrates in human urine samples that are exclusively hydrolyzed by ASPC and application of this enzyme increases the discovery of unknown sulfated metabolites with a higher scaffold diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".