341 Effects of different iron sources and chelators on growth in wild type and iron-uptake defective mutants of non-typhoidal Salmonella enterica serovars.
Bibliographic record
Abstract
Non-typhoidal Salmonella enterica serovars are well equipped to acquire iron for their survival to cause disease in their hosts. The present study investigated effects of mutations in specific iron-uptake systems of S. enterica serovars Typhimurium and Enteritidis on their growth in the presence of: 1) different sources of iron, including ferric chloride, ferric citrate, ferric EDTA, ferrous-L-ascorbate and ferrous sulfate, 2) different iron chelators including ethylenediaminetetraacetic acid (EDTA) and citric acid. A total of 10 Salmonella strains, including S. enterica Enteritidis 3346, S. enterica Typhimurium 3128 and their iron-uptake defective singles (DtonB or DcirA), double (DfepADiroN) and triple(DfepADfhuADiroN) mutants were used in this study. The iron concentrations in the different studied compounds ranged from 0.1 to 50 µM. Results indicated that both wild-type and mutant Salmonella serovars had better growth in the presence of a higher dose of iron regardless the iron sources (P < 0.05). Iron-uptake defective mutants showed significant reduction of the growth rate of both Typhimurium and Enteritidis(P < 0.05). Complementation result of mutants having tonB and fepA iroN showed recovery of their phenotype (P < 0.05). Moreover, EDTA at 1000 µM in the medium inhibited the growth of investigated Salmonella strains (P < 0.05). However, there was no significant difference between the growth of Salmonella treated with citric acid and control (P > 0.05). These results confirm that reducing iron availability to Salmonella though iron chelating or outcompeting natural compoundscompromise the growth and survival of both Typhimurium and Enteritidis. Further studies are underway to investigate the role of specific iron-uptake systems in the use of specific iron-sources and on the importance of some other key genes relevant to iron-metabolism related to the virulence of Salmonella.
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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) | 0.001 | 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".