Genomic potential for photoferrotrophy in a seasonally anoxic Boreal Shield lake
Bibliographic record
Abstract
Abstract Photoferrotrophy, the light-induced oxidation of ferrous iron, is thought to have contributed to primary production within Earth’s early anoxic oceans yet is presumed to be of little modern environmental relevance. Here we use genome-resolved metagenomics and enrichment cultivation to explore the potential for photoferrotrophy in the anoxic water columns of globally abundant Boreal Shield lakes. We recovered four high-completeness and low-contamination draft genome bins assigned to the class Chlorobia (formerly phylum Chlorobi ) from environmental metagenome data and enriched two novel sulfide-oxidizing species, also from the Chlorobia . The sequenced genomes of both enriched species, including the novel “ Candidatus Chlorobium canadense”, encoded the cyc2 candidate gene marker for iron oxidation, suggesting the potential for photoferrotrophic growth. Surprisingly, one of the environmental genome bins encoded cyc2 and lacked sulfur oxidation gene pathways altogether. Despite the presence of cyc2 in the corresponding draft genome, we were unable to induce photoferrotrophy in “ Ca. Chlorobium canadense”, suggesting that yet-unexplored mechanisms regulate expression of sulfide and ferrous iron oxidation gene systems, or that previously unrecognized functions for this outer membrane cytochrome exist. Doubling the known diversity of Chlorobia -associated cyc2 genes, metagenome data showed that putative photoferrotrophic populations occurred in one lake but that only sulfide-oxidizing populations were present in a neighboring lake, implying that strong ecological or geochemical controls govern the favourability of photoferrotrophy in aquatic environments. These results indicate that anoxygenic photoautotrophs in Boreal Shield lakes could have unexplored metabolic diversity that is controlled by ecological and biogeochemical drivers pertinent to understanding Earth’s early microbial communities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".