Draft genome sequence of Staphylococcus agnetis 4244, a strain with gene clusters encoding distinct post-translationally modified antimicrobial peptides
Bibliographic record
Abstract
OBJECTIVES: Here we report the draft genome sequence of Staphylococcus agnetis 4244, a strain involved in bovine mastitis, and its ability to inhibit different species of antibiotic-resistant Gram-positive bacteria owing to bacteriocin production. METHODS: An Illumina MiSeq platform was used for genome sequencing. De novo genome assembly was done using the A5-miseq pipeline. Genome annotation was performed by the RAST server, and mining of bacteriocinogenic gene clusters was done using the BAGEL4 and antiSMASH v.5.0 platforms. Investigation of the spectrum of activity of S. agnetis 4244 was performed on BHI agar by deferred antagonism assay. RESULTS: The total scaffold size was determined to be 2 511 708 bp featuring a G+C content of 35.6%. The genome contains 2431 protein-coding sequences and 80 RNA sequences. Genome analyses revealed three prophage sequences inserted in the genome as well as several genes involved in drug resistance and two bacteriocin gene clusters (encoding a thiopeptide and a sactipeptide) encoded on the bacterial chromosome. Staphylococcus agnetis 4244 was able to inhibit all 44 strains of antibiotic-resistant Gram-positive bacteria tested in this study, including vancomycin-resistant enterococci (VRE), methicillin-resistant Staphylococcus aureus (MRSA) and other antibiotic-resistant staphylococcal strains. CONCLUSION: This study emphasises the potential biotechnological application of this strain for production of bacteriocins that could be used in the food industry as biopreservatives and/or in medicine as alternative therapeutic options against VRE, MRSA, vancomycin-intermediate S. aureus and other antibiotic-resistant Gram-positive bacteria, including biofilm-forming isolates. It also provides some genetic features of the draft genome of S. agnetis 4244.
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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 | 0.001 |
| 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) | 0.000 | 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".