The kynurenine pathway is essential for rhodoquinone biosynthesis in <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
ABSTRACT A key metabolic adaptation for some species that face hypoxia as part of their life-cycle involves an alternative electron transport chain in which rhodoquinone (RQ) is required for fumarate reduction and ATP production. RQ biosynthesis in bacteria and protists requires ubiquinone (Q) as a precursor. In contrast, Q is not a precursor for RQ biosynthesis in animals such as parasitic helminths, and this pathway has remained elusive. We used Caenorhabditis elegans as a model animal to elucidate several key steps in RQ biosynthesis. Through RNA interference and a series of mutants, we found that arylamine metabolites from the kynurenine pathway are essential precursors for RQ biosynthesis de novo . Deletion of kynu-1 , which encodes a kynureninase that converts L-kynurenine (KYN) into anthranilic acid (AA), and 3-hydroxykynurenine (HKYN) into 3-hydroxyanthranilic acid (3HAA), completely abolishes RQ biosynthesis, but does not affect Q levels. Deletion of kmo-1 , which encodes a kynurenine 3-monooxygenase that converts KYN to HKYN, drastically reduces RQ, but not Q levels. Knockdown of the Q biosynthetic genes, coq-5 and coq-6 , affects both Q and RQ levels demonstrating that common enzymes are used in both biosynthetic pathways. Our study reveals that two pathways for RQ biosynthesis have independently evolved. In contrast to bacteria, where amination is the last step in RQ biosynthesis, worms begin with the arylamine precursor, AA or 3HAA. Since RQ is absent in mammalian hosts of helminths, inhibition of RQ biosynthesis may have broad implications for targeting parasitic infections which cause important neglected tropical diseases.
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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 | 0.000 | 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".