Development of a high-throughput assay for rapid screening of butanologenic strains
Bibliographic record
Abstract
Abstract We report a Thermotoga hypogea ( Th ) alcohol dehydrogenase (ADH)-dependent spectrophotometric assay for quantifying the amount of butanol in growth media, an advance that will facilitate rapid high-throughput screening of hypo- and hyper-butanol-producing strains of solventogenic Clostridium species. While a colorimetric nitroblue tetrazolium chloride-based assay for quantitating butanol in acetone-butanol-ethanol (ABE) fermentation broth has been described previously, we determined that Saccharomyces cerevisiae ( Sc ) ADH used in this earlier study exhibits approximately 13-fold lower catalytic efficiency towards butanol than ethanol. Any Sc ADH-dependent assay for primary quantitation of butanol in an ethanol-butanol mixture is therefore subject to “ethanol interference”. To circumvent this limitation and better facilitate identification of hyper-butanol-producing Clostridia , we searched the literature for native ADHs that preferentially utilize butanol over ethanol and identified Th ADH as a candidate. Indeed, recombinant Th ADH exhibited a 6-fold higher catalytic efficiency with butanol than ethanol, as measured using the reduction of NADP + to NADPH that accompanies alcohol oxidation. Moreover, the assay sensitivity was not affected by the presence of acetone, acetic acid or butyric acid (typical ABE fermentation products). We broadened the utility of our assay by adapting it to a high-throughput microtiter plate-based format, and piloted it successfully in an ongoing metabolic engineering initiative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".