Genome of <i>Serratia</i> <i>nematodiphila</i> MB307 offers unique insights into its diverse traits
Bibliographic record
Abstract
A pigment-producing species of Serratia was isolated from the rhizosphere of a heavy metal resistant Cannabis sativa plant growing in effluent-affected soil of Hattar Industrial Estate, Haripur, Pakistan. Here, we report the genome sequence of this bacterium, which has been identified as Serratia nematodiphila on the basis of whole genome comparison using the OrthoANI classification scheme. The bacterium exhibited diverse traits, including plant growth promotion, antimicrobial, bioremediation, and pollutant tolerance capabilities including metal tolerance, azo dye degradation, ibuprofen degradation, etc. Plant growth-promoting exoenzyme production as well as phosphate solubilisation properties were observed. Genes for phosphate solubilisation, siderophore production, and chitin destruction were identified in addition to other industrially important enzymes like nitrilase and lipase. Secondary metabolite producing apparatus for high value chemicals in the whole genome was also analysed. The number of antibiotic resistance genes was then profiled in silico, through a match with Antibiotic Resistant Gene and CAR database. This is the first report of a S. nematodiphila genome from a polluted environment. This could significantly contribute to the understanding of pollution tolerance, antibiotic resistance, association with nematodes, production of bio-pesticide, and their role in plant growth promotion.
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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 | 0.000 | 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 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".