Metagenome Assembled Genomes (MAG) Facilitate a Better Understanding of Microbially-mediated Heavy Metal Resistance in Soils from a Former Nuclear Materials Production Facility
Bibliographic record
Abstract
Abstract Shotgun metagenomes is a repository of all the genes present in an environmental sample. With recent advancements in bioinformatic techniques, it is now possible to in-silico retrieve sequences that belong to specific taxa, followed by assembly and annotation and the obtained sequences are called as metagenome-assembled genome (MAG), which facilitates better understanding of metabolic and other traits without having to culture the microorganism. We applied the MAG technique using the nf-core/mag pipeline on shotgun metagenome sequences obtained from a soil ecosystem that has long-term co-contamination with radionuclides (mainly uranium), heavy metals (mercury, nickel etc.) and organic compounds. Annotation of MAGs was performed using SPAdes and MEGAHIT and genomes were binned and taxonomically classified using the GTDBTk and CAT toolkits within nf-core/mag. Additional annotations were done using Prokka and Prodigal and the dRep program was used to choose specific MAGs for further analysis. Initial analysis resulted in a total of 254 MAGs which met the high-quality standard with the completeness > 95% and contamination < 5%, accounting for 26.67% of all the MAGs (Fig SI-1). After bin refinement and de-replication, 27 MAGs (18 from Winter season and 9 from Summer season) were reconstructed. These 27 MAGs span across 6 bacterial phyla and the most predominant ones were Proteobacteria, Bacteroidetes, and Cyanobacteria regardless of the season. Overall, the Arthrobacter MAG was found to be one that was robust for further analysis. Over 1749 genes putatively involved in crucial metabolism of elements viz. nitrogen, phosphorous, sulfur and 598 genes encoding enzymes for metals resistance from cadmium, zinc, chromium, arsenic and copper. In summary, this project enhances our understanding of genes conferring resistance to heavy metals in uranium contaminated soils.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".