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Record W2598764697 · doi:10.1038/srep44826

Modulation of Central Carbon Metabolism by Acetylation of Isocitrate Lyase in Mycobacterium tuberculosis

2017· article· en· W2598764697 on OpenAlexafffund
Jing Bi, Yihong Wang, Heguo Yu, Xiaoyan Qian, Honghai Wang, Jun Liu, Xuelian Zhang

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchFudan UniversityNational Natural Science Foundation of China
KeywordsIsocitrate lyaseAcetylationLysineBiochemistryGlyoxylate cycleEnzymeMalate synthaseMetabolismBiologyChemistryAmino acidGene

Abstract

fetched live from OpenAlex

Several enzymes involved in central carbon metabolism such as isocitrate lyase and phosphoenolpyruvate carboxykinase are key determinants of pathogenesis of Mycobacterium tuberculosis (M. tb). In this study, we found that lysine acetylation plays an important role in the modulation of central carbon metabolism in M. tb. Mutant of M. tb defective in sirtuin deacetylase exhibited improved growth in fatty acid-containing media. Global analysis of lysine acetylome of M. tb identified three acetylated lysine residues (K322, K331, and K392) of isocitrate lyase (ICL1). Using a genetically encoding system, we demonstrated that acetylation of K392 increased the enzyme activity of ICL1, whereas acetylation of K322 decreased its activity. Antibodies that specifically recognized acetyllysine at 392 and 322 of ICL1 were used to monitor the levels of ICL1 acetylation in M. tb cultures. The physiological significance of ICL1 acetylation was demonstrated by the observation that M. tb altered the levels of acetylated K392 in response to changes of carbon sources, and that acetylation of K392 affected the abundance of ICL1 protein. Our study has uncovered another regulatory mechanism of ICL1.

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)0.000
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 score0.033
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.262
Teacher spread0.252 · 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

Citations43
Published2017
Admission routes2
Has abstractyes

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