Comprehensive Metabolic and Taxonomic Reconstruction of an Ancient Microbial Mat From the McMurdo Ice Shelf (Antarctica) by Integrating Genetic, Metaproteomic and Lipid Biomarker Analyses
Bibliographic record
Abstract
Paleobiological reconstructions based on molecular fossils may be limited by degradation processes causing differential preservation of biomolecules, the distinct taxonomic specificity of each biomolecule type, and analytical biases. Here, we combined the analysis of DNA, proteins and lipid biomarkers using 16S and 18S rRNA gene metabarcoding, metaproteomics and lipid analysis to reconstruct the taxonomic composition and metabolisms of a desiccated microbial mat from the McMurdo Ice Shelf (MIS) (Antarctica) dated ~1,000 years BP. The different lability, taxonomic resolution and analytical bias of each biomolecule type led to a distinct microbial community profile. DNA analysis showed selective preservation of DNA remnants from the most resistant taxa (e.g., spore-formers). In contrast, the proteins profile revealed microorganisms missed by DNA sequencing, such as Cyanobacteria, and showed a microbial composition similar to fresh microbial mats in the MIS. Lipid hydrocarbons also confirmed Cyanobacteria and suggested the presence of mosses or vascular plant remnants from a period in Antarctica when the climate was warmer (e.g., Mid-Miocene or Eocene). The combined analysis of the three biomolecule types also revealed diverse metabolisms that operated in the microbial mat before desiccation: oxygenic and anoxygenic photosynthesis, nitrogen fixation, nitrification, denitrification, sulfur reduction and oxidation, and methanogenesis. Therefore, the joint analysis of DNA, proteins and lipids resulted in a powerful approach that improved taxonomic and metabolic reconstructions overcoming information gaps derived from using individual biomolecules types.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".