Metagenomic insights into soil microbial diversity and antibiotic resistance genes in pristine karst tiankeng ecosystems
Bibliographic record
Abstract
ABSTRACT Surveys of microorganisms and antibiotic resistance genes (ARGs) in edaphic systems have centered on those in human-impacted environments, with relatively little information from primitive environments. The karst tiankeng (also known as sinkholes) is the largest negative terrain on the earth’s surface, and the trapped terrain keeps the interior relatively pristine. In this study, three of the most representative tiankeng types (severely, moderately, and non-degraded tiankengs) were selected, and microbial composition, function, and their association with ARGs were determined using metagenetic techniques. The dominant phyla in karst tiankengs were Proteobacteria , Actinobacteria , and Acidobacteria ; the dominant archaea were Crenarchaeota ; and the dominant fungi were Ascomycota . The non-degrade tiankeng maintains a complex and stable microbial network. The major functional profiles of the microorganisms are involved in amino acid metabolism and carbohydrate metabolism. A total of 145 ARGs were annotated, and the dominant ARGs in karst tiankeng were CeoB , AcrB , and MexF. Paraburkholderia , Rhodococcus , Bradyrhizobium, and Agromyces were the main hosts of ARGs in karst tiankengs. Compared with ARGs, microorganisms were more influenced by soil factors. These results provide a novel insight into microbes and ARGs in unexplored karst tiankeng ecosystems. IMPORTANCE Currently, knowledge regarding the origin of antibiotic resistance genes (ARGs) in pristine soil environments remains limited, with some potentially linked to ancestral genetic diversity. In this study, metagenomics was employed to investigate the distribution of ARGs across nine relatively pristine karst tiankengs. We identified the predominant microbial communities and prevalent types of ARGs within these tiankengs. Soil factors primarily influenced the microbial community structure but had little effect on ARGs. This study offers insights for in-depth research on the microbial composition and risk assessment of antibiotic resistance genes within pristine karst tiankeng 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".