The Response of a 16S Ribosomal RNA Gene Fragment Amplified Community to Lead, Zinc, and Copper Pollution in a Shanghai Field Trial
Bibliographic record
Abstract
Industrial and agricultural activities have caused extensive metal contamination of land throughout China and across the globe. The pervasive nature of metal pollution can be harmful to human health and can potentially cause substantial negative impact to the biosphere. To investigate the impact of anthropogenic metal pollution found in high concentrations in industrial, agricultural and urban environments, 16S ribosomal RNA gene amplicon sequencing was used to track change in the amplified microbial community after metal contamination in a large-scale field experiment in Shanghai. 1,566 operational taxonomic units (OTUs) identified from 448,108 sequences gathered from 20 plots treated as controls or with lead, zinc, copper or all three metals. Constrained Analysis of Principal Coordinates ordination did not separate control and lead treatment but could separate control/lead, zinc, copper and three metal treatment. DESeq2 was applied to identify 93 significantly differentially abundant OTUs varing in 211 pairwise instances between the treatments. Differentially abundant OTUs representing genera or species belonging to the phyla Chloroflexi, Cyanobacteria, Firmicutes, Latescibacteria and Planctomycetes were almost universally reduced in abundance due to zinc, copper or three metal treatment; with three metal treatment abolishing the detection of some OTUs, such as Leptolyngbya, Desmonostoc muscorum and Microcoleus steenstrupii. The greatest increases due to metal treatment were observed in Bacteroidetes, Actinobacteria, Chlamydiae, Nitrospirae and Proteobacteria (α, β, ƍ and γ); the most (relative) abundant being uncharacterised species within the genera Methylobacillus, Solirubrobacter and Ohtaekwangia. Three metal treatment alone resulted in identification of 22 OTUs (genera or species) which were not detected in control soil, notably including Yonghaparkia alkaliphila, Pedobacter steynii, Pseudolabrys taiwanensis, Methylophilus methylotrophus, Nitrosospira, and Lysobacter mobilis. The capacity to track alterations of an amplified microbial community at high taxonomic resolution using modern bioinformatic approaches, as well as identifying where that resolution is lost for technical or biological reasons, provides an insight into the complexity of the microbial world resisting anthropogenic pollution. While functional assessment of uncharacterised organisms within environmental samples is technically challenging, an important step is observing those organisms able to tolerate extreme stress and to recognise the extent to which important amplifiable community members still require characterisation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| 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 teacher head, 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".