Transcriptional profiling of Actinobacillus pleuropneumoniae under iron-restricted conditions
Bibliographic record
Abstract
BACKGROUND: To better understand effects of iron restriction on Actinobacillus pleuropneumoniae and to identify new potential vaccine targets, we conducted transcript profiling studies using a DNA microarray containing all 2025 ORFs of the genome of A. pleuropneumoniae serotype 5b strain L20. This is the first study involving the use of microarray technology to monitor the transcriptome of A. pleuropneumoniae grown under iron restriction. RESULTS: Upon comparing growth of this pathogen in iron-sufficient versus iron-depleted medium, 210 genes were identified as being differentially expressed. Some genes (92) were identified as being up-regulated; many have confirmed or putative roles in iron acquisition, such as the genes coding for two TonB energy-transducing proteins and the hemoglobin receptor HgbA. Transcript profiling also led to identification of some new iron acquisition systems of A. pleuropneumoniae. Genes coding for a possible Yfe system (yfeABCD), implicated in the acquisition of chelated iron, were detected, as well as genes coding for a putative enterobactin-type siderophore receptor system. ORFs for homologs of the HmbR system of Neisseria meningitidis involved in iron acquisition from hemoglobin were significantly up-regulated. Down-regulated genes included many that encode proteins containing Fe-S clusters or that use heme as a cofactor. Supplementation of the culture medium with exogenous iron re-established the expression level of these genes. CONCLUSION: We have used transcriptional profiling to generate a list of genes showing differential expression during iron restriction. This strategy enabled us to gain a better understanding of the metabolic changes occurring in response to this stress. Many new potential iron acquisition systems were identified, and further studies will have to be conducted to establish their role during iron restriction.
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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 | 0.001 |
| 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) | 0.001 | 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".