Effects of Cadmium Stress on Bacterial and Fungal Communities in the Whitefly Bemisia tabaci
Bibliographic record
Abstract
Heavy metal contamination is among the most prominent environmental problems in China, posing serious threats to both ecosystem and human health. Among the diverse heavy metal contaminants, cadmium is the most serious. The whitefly Bemisia tabaci is a cosmopolitan pest capable of causing severe damage to a broad range of agricultural crops, especially vegetables. At present, little is known about the effects of cadmium stress on B. tabaci, including on its bacterial and fungal communities. In the current study, we investigated the effects of cadmium on bacterial and fungal communities in whiteflies. Meta-barcode sequencing of the 16S rRNA gene revealed that the whitefly bacterial community contained 264 operational taxonomic units (OTUs) belonging to 201 known genera and 245 known species. The top five most frequent bacterial genera were Rickettsia, Rhodococcus, Candidatus Portiera, Candidatus Hamiltonella, and Achromobacter. Meta-barcode sequencing of the fungal ITS locus revealed that the whitefly fungal community contained 357 OTUs belonging to 187 known genera and 248 known species. The top five most frequent fungal genera were Wallemia, unclassified_f_Dipodascaceae, Apiotrichum, Penicillium, and unclassified_o_Saccharomycetales. Cadmium exposure reduced the fungal OTU richness but increased the bacterial Shannon and Simpson diversity indices in whiteflies. In addition, upon exposure to cadmium, the microbial community composition in whiteflies changed significantly, with increased prevalence of the bacterial genera Rhodococcus and Exiguobacterium and fungal genus Wallemia. Our results indicate that the whitefly microbiota likely contributed to their adaptation and resistance to cadmium and suggested that whiteflies may contain microbes that could help remediate cadmium contamination in natural environments and agricultural fields.
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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) | 0.001 | 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".