A TetR-family protein activates transcription from a new promoter motif associated with essential genes for autotrophic growth in acetogens
Bibliographic record
Abstract
Abstract Acetogens can fix carbon (CO or CO 2 ) into acetyl-CoA via the Wood-Ljungdahl pathway (WLP) that also makes them attractive cell factories for the production of fuels and chemicals from waste feedstocks. Although most biochemical details of the WLP are well understood and systems-level characterisation of acetogen metabolism has recently improved, key transcriptional features such as promoter motifs and transcriptional regulators are still unknown in acetogens. Here, we use differential RNA-sequencing to identify a previously undescribed promoter motif associated with essential genes for autotrophic growth of the model-acetogen Clostridium autoethanogenum . RNA polymerase was shown to bind to the new promoter motif using a DNA-binding protein assay and proteomics enabled the discovery of four candidates to potentially function directly in control of transcription of the WLP and other key genes of C 1 fixation metabolism. Next, in vivo experiments showed that a TetR-family transcriptional regulator (CAETHG_0459) and the housekeeping sigma factor (σ A ) activate expression of a reporter protein (GFP) in-frame with the new promoter motif from a fusion vector in E. coli . Lastly, a protein-protein interaction assay with the RNA polymerase (RNAP) shows that CAETHG_0459 directly binds to the RNAP. Together, the data presented here advance the fundamental understanding of transcriptional regulation of C 1 fixation in acetogens and provide a strategy for improving the performance of gas-fermenting bacteria by genetic engineering.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".