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Record W4286856569 · doi:10.3791/2094-v

An Assay for Measuring the Activity of <em>Escherichia coli</em> Inducible Lysine Decarboxyase

2010· article· en· W4286856569 on OpenAlexaff
Usheer Kanjee, Walid A. Houry

Bibliographic record

VenueJournal of Visualized Experiments · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPolyamine Metabolism and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Escherichia coli is an enteric bacterium that is capable of growing over a wide range of pH values (pH 5 - 9)1 and, incredibly, is able to survive extreme acid stresses including passage through the mammalian stomach where the pH can fall to as low as pH 1 - 22. To enable such a broad range of acidic pH survival, E. coli possesses four different inducible amino acid decarboxylases that decarboxylate their substrate amino acids in a proton-dependent manner thus raising the internal pH. The decarboxylases include the glutamic acid decarboxylases GadA and GadB3, the arginine decarboxylase AdiA4, the lysine decarboxylase LdcI5, 6 and the ornithine decarboxylase SpeF7. All of these enzymes utilize pyridoxal-5'-phospate as a co-factor8 and function together with inner-membrane substrate-product antiporters that remove decarboxylation products to the external medium in exchange for fresh substrate2. In the case of LdcI, the lysine-cadaverine antiporter is called CadB. Recently, we determined the X-ray crystal structure of LdcI to 2.0 Å, and we discovered a novel small-molecule bound to LdcI the stringent response regulator guanosine 5'-diphosphate,3'-diphosphate (ppGpp) 14. The stringent response occurs when exponentially growing cells experience nutrient deprivation or one of a number of other stresses9. As a result, cells produce ppGpp which leads to a signaling cascade culminating in the shift from exponential growth to stationary phase growth10. We have demonstrated that ppGpp is a specific inhibitor of LdcI 14. Here we describe the lysine decarboxylase assay, modified from the assay developed by Phan et al.11, that we have used to determine the activity of LdcI and the effect of pppGpp/ppGpp on that activity. The LdcI decarboxylation reaction removes the α-carboxy group of L-lysine and produces carbon dioxide and the polyamine cadaverine (1,5-diaminopentane)5. L-lysine and cadaverine can be reacted with 2,4,6-trinitrobenzensulfonic acid (TNBS) at high pH to generate N,N'-bistrinitrophenylcadaverine (TNP-cadaverine) and N,N′-bistrinitrophenyllysine (TNP-lysine), respectively11. The TNP-cadaverine can be separated from the TNP-lysine as the former is soluble in organic solvents such as toluene while the latter is not (See Figure 1). The linear range of the assay was determined empirically using purified cadaverine.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.374
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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