Faculty Opinions recommendation of Mapping the Burkholderia cenocepacia niche response via high-throughput sequencing.
Bibliographic record
Abstract
Determining how an organism responds to its environment by altering gene expression is key to understanding its ecology. Here, we used RNA-seq to comprehensively and quantitatively assess the transcriptional response of the bacterial opportunistic cystic fibrosis (CF) pathogen and endemic soil dweller, Burkholderia cenocepacia, in conditions mimicking these 2 environments. By sequencing 762 million bases of cDNA from 2 closely related B. cenocepacia strains (one isolated from a CF patient and one from soil), we identified a number of potential virulence factors expressed under CF-like conditions, whereas genes whose protein products are involved in nitrogen scavenging and 2-component sensing were among those induced under soil-like conditions. Interestingly, 13 new putative noncoding RNAs were discovered using this technique, 12 of which are preferentially induced in the soil environment, suggesting that ncRNAs play an important role in survival in the soil. In addition, we detected a surprisingly large number of regulatory differences between the 2 strains, which may represent specific adaptations to the niches from which each strain was isolated, despite their high degree of DNA sequence similarity. Compared with the CF strain, the soil strain shows a stronger global gene expression response to its environment, which is consistent with the need for a more dynamic reaction to the heterogeneous conditions of soil. PMID: 19234113 Funding information This work was supported by: NHLBI NIH HHS, United States Grant ID: R21HL087833 NHLBI NIH HHS, United States Grant ID: R21 HL087833
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.624 | 0.397 |
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".