Combined genomic and imaging techniques show intense arsenic enrichment caused by detoxification in a microbial mat of the Dead Sea shore
Bibliographic record
Abstract
Microbial mats and microbialites are essential tools for reconstructing early life and its environments. To better understand microbial trace element cycling, a microbial mat was collected from the sinkhole systems of the western shores of the Dead Sea, a dynamic environment exhibiting diverse extreme environments. Intense arsenic enrichment (up to 6.5 million times higher than current water concentrations, and 400 times the bulk concentration in the mat) was measured. Arsenic was dominantly found as As(V) in organic molecules, as shown by XANES spectra and high-resolution elemental mapping. Arsenic cycling genes obtained from metagenomic analysis were associated with arsenic detoxification, supporting an active mechanism of As(V) uptake, As(III) efflux and organo-arsenic accumulation in microbial mat extracellular polymeric substances. Thus, we propose that such localized enrichment of As can be attributed to a transient increase in As(V) concentrations in the circulating subsurface water of the Dead Sea shore and its subsequent incorporation in organoarsenic molecules through microbial detoxification processes. Our dataset supports the possibility of metalloid enrichments recorded in very localized facies due to rapid geogenic fluctuations in chemistry of the water flowing over a biofilm. In this context, this example calls for caution when interpreting metal(loid) enrichment in organic matter-rich layers and microbialites of Paleoproterozoic origins. Arsenic signatures in Precambrian organic matter and carbonate rocks may host biosignatures, including evidence of extracellular polymeric susbtances, As-binding and detoxification processes, without supporting arsenotrophy. They do, however, provide clues to better assess paleoenvironmental conditions at the time of microbial mat formation and sedimentation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".