Widespread promiscuous alkaline phosphatases underscore ancient microbial phosphite utilization
Bibliographic record
Abstract
Phosphate is often a limiting resource, directly affecting the availability of key biomolecules such as nucleotides. To cope with phosphate scarcity, bacteria have evolved enzymes that utilize alternative phosphorus compounds, including phosphite (Pt). Although a few enzymes oxidize Pt to produce phosphate, the enzymes responsible for Pt oxidation in many environmental bacteria remain unidentified, and the role of microbial Pt oxidation in the global phosphorus cycle is not yet fully understood. In this study, we performed bioinformatic analyses of three Pt-oxidizing enzymes: the native Pt oxidase, phosphite dehydrogenase (PtxD), and two promiscuous Pt oxidases, alkaline phosphatase (PhoA) and carbon-phosphorus lyase. Among these, PhoA was found to be widely distributed across bacteria since the early stages of their evolution. In contrast, PtxD emerged later in a limited number of bacterial lineages that had lost PhoA. Our biochemical characterizations revealed that most extant and reconstructed ancestral PhoAs tested exhibited Pt oxidation activity. Moreover, disruption of active-site residues diminished Pt oxidase activity in PhoA, while only partially affecting its native function. This promiscuous function of PhoA reveals an overlooked mechanism in bacterial phosphate metabolism and underscores the role of Pt in the cycling of bioavailable phosphorus in ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".