Impact of Three Different Mutations in Ehrlichia chaffeensis in Altering the Global Gene Expression Patterns
Bibliographic record
Abstract
The rickettsial pathogen Ehrlichia chaffeensis causes a tick-borne disease, human monocytic ehrlichiosis. Mutations within certain genomic locations of the pathogen aid in understanding the pathogenesis and in developing attenuated vaccines. Our previous studies demonstrated that mutations in different genomic sites in E. chaffeensis caused variable impacts on their growth and attenuation in vertebrate and tick hosts. Here, we assessed the effect of three mutations on transcriptional changes using RNA deep-sequencing technology. RNA sequencing aided in detecting 66-80% of the transcripts of wildtype and mutant E. chaffeensis. Mutation in an antiporter gene (ECH_0379) causing attenuated growth in vertebrate hosts resulted in the down regulation of many transcribed genes. Similarly, a mutation downstream to the ECH_0490 coding sequence resulted in minimal impact on the pathogen's in vivo growth, but caused major changes in its transcriptome. This mutation caused enhanced expression of several host stress response genes. Even though the ECH_0660 gene mutation caused the pathogen's rapid clearance in vertebrate hosts and aids in generating a protective response, there was minimal impact on the transcriptome. The transcriptomic data offer novel insights about the impact of mutations on global gene expression and how they may contribute to the pathogen's resistance and/or clearance from the host.
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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 | 0.000 |
| 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) | 0.000 | 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 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".