Microbial degradation and assimilation of veratric acid in oxic and anoxic groundwaters
Bibliographic record
Abstract
Microbial communities are key players in groundwater ecosystems. In this dark environment, heterotrophic microbes rely on biomass produced by the activity of lithoautotrophs or on the degradation of organic matter seeping from the surface. Most studies on bacterial diversity in groundwater habitats are based on 16S gene sequencing and full genome reconstructions showing potential metabolic pathways used in these habitats. However, molecular-based studies do not allow for the assessment of population dynamics over time or the assimilation of specific compounds and their biochemical transformation by microbial communities. Therefore, in this study, we combined DNA-, phospholipid fatty acid-, and metabolomic-stable isotope probing to target and identify heterotrophic bacteria in the groundwater setting of the Hainich Critical Zone Exploratory (CZE), focusing on 2 aquifers with different physico-chemical conditions (oxic and anoxic). We incubated groundwater from 4 different wells using either 13C-labeled veratric acid (a lignin-derived compound) (single labeling) or a combination of 13CO2 and D-labeled veratric acid (dual labeling). Our results show that heterotrophic activities dominate all groundwater sites. We identified bacteria with the potential to break down veratric acid (Sphingobium or Microbacterium). We observed differences in heterotrophic activities between the oxic and anoxic aquifers, indicating local adaptations of bacterial populations. The dual labeling experiments suggested that the serine pathway is an important carbon assimilation pathway and that organic matter was an important source of hydrogen in the newly produced lipids. These experiments also yielded different labeled taxa compared to the single labeling experiments, showing that there exists a complex interaction network in the groundwater habitats.
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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 | 0.000 |
| 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".