Cost-effective production of <i>Escherichia coli</i> “GABase” for spectrophotometric determination of γ-aminobutyrate (GABA) levels or glutamate decarboxylase activity
Bibliographic record
Abstract
Abstract γ-aminobutyrate (GABA) is a non-proteinogenic amino acid produced by glutamate decarboxylase (GAD) that functions as a vital neurotransmitter in animals, and as an important metabolite and signaling molecule in plants and microbes. “GABase” consists of a mixture of recombinant GABA transaminase (GABA-T) and succinic semialdehyde dehydrogenase (SSDH) that is widely used for spectrophotometric quantification of glutamate decarboxylase (GAD) activity or GABA levels in tissue extracts. Both can be conveniently monitored at 340 nm owing to the sequential conversion of GABA into succinate by GABA-T and SSDH, and concomitant reduction of NADP+ into NADPH by SSDH. Currently, these assays rely on commercially available GABase from Pseudomonas fluorescens. However, the excessive cost of commercial GABase prompted us to develop an inexpensive and rapid “DIY” method for producing GABase by cloning, expressing and purifying His6-tagged GABA-T and SSDH from Escherichia coli. We validated our in-house GABase preparation by comparing GAD activities and GABA levels of the model plant Arabidopsis thaliana with those obtained using commercial GABase. Both pET30a plasmids for expressing E. coli His6-GABA-T and His6-SSDH have been deposited into AddGene (www.addgene.com). Our protocols for producing and using recombinant E. coli GABase should be of interest to any researcher who studies eukaryotic or prokaryotic GABA and/or GAD activity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.005 |
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".