Antisense-RNA-Mediated Gene Downregulation in Clostridium pasteurianum
Bibliographic record
Abstract
Clostridium pasteurianum is receiving growing attention for its unique metabolic properties, particularly its ability to convert waste glycerol and glycerol-rich byproducts into butanol, a prospective biofuel. Genetic tool development and whole genome sequencing have recently been investigated to advance the genetic tractability of this potential industrial host. Nevertheless, methodologies for tuning gene expression through plasmid-borne expression and chromosomal gene downregulation are still absent. Here we demonstrate plasmid-borne heterologous gene expression and gene knockdown using antisense RNA in C. pasteurianum. We first employed a common thermophilic β-galactosidase (lacZ) gene reporter system from Thermoanaerobacterium thermosulfurogenes to characterize two promoters involved in the central fermentative metabolism of C. pasteurianum. Due to a higher level of constitutive lacZ expression compared to the ferredoxin gene (fdx) promoter, the thiolase (thl) promoter was selected to drive expression of asRNA. Expression of a lacZ asRNA resulted in 52%–58% downregulation of β-galactosidase activity compared to the control strain throughout the duration of culture growth. Subsequent implementation of our asRNA approach for downregulation of the native hydrogenase I gene (hydA) in C. pasteurianum resulted in altered end product distribution, characterized by an increase in production of reduced metabolites, particularly butyrate (40% increase) and ethanol (25% increase). Knockdown of hydA was also accompanied by increased acetate formation and lower levels of 1,3-propanediol, signifying a dramatic shift in cellular metabolism in response to inhibition of the hydrogenase enzyme. The methodologies described herein for plasmid-based heterologous gene expression and antisense-RNA-mediated gene knockdown should promote rational metabolic engineering of C. pasteurianum for enhanced production of butanol as a prospective biofuel.
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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 | 0.001 |
| 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".