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Record W2946362644 · doi:10.1128/jb.00707-18

Metabolomics Studies To Decipher Stress Responses in Mycobacterium smegmatis Point to a Putative Pathway of Methylated Amine Biosynthesis

2019· article· en· W2946362644 on OpenAlexfundno aff
Arshad Rizvi, Saleem Yousf, Kannan Balakrishnan, Harish Kumar Dubey, Shekhar C. Mande, Jeetender Chugh, Sharmistha Banerjee

Bibliographic record

VenueJournal of Bacteriology · 2019
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsnot available
FundersIndian Institute of Science Education and Research MohaliIndian Institute of Science Education and Research PuneUniversity of HyderabadQueen's UniversityIndian Council of Medical ResearchUniversity Grants CommissionDepartment of Science and Technology, Ministry of Science and Technology, IndiaDepartment of Biotechnology, Ministry of Science and Technology, IndiaQueen's University Belfast
KeywordsBiologyBiochemistryMycobacterium smegmatisMetabolic pathwayEctoineOsmolyteBiosynthesisMetabolomicsPhenylpropanoidMycobacteriumMycobacterium tuberculosisMicrobiologyMetabolismEnzymeOsmoprotectantBacteriaProlineAmino acidGenetics

Abstract

fetched live from OpenAlex

Alterations in metabolite levels provide fast and direct means to regulate enzymatic reactions and, therefore, metabolic pathways. This study documents, for the first time, the metabolic changes that occur in Mycobacterium smegmatis as a response to three stresses, namely, acidic stress, oxidative stress, and nutrient starvation. These stresses are also faced by intracellular mycobacteria during infection and therefore may be extended to frame therapeutic interventions for pathogenic mycobacteria. In addition to the purine-pyrimidine, amino acid, nicotinate-nicotinamide, and energy metabolism pathways that were found to be affected in response to different stresses, a novel putative methylamine biosynthesis pathway was identified to be present in Mycobacterium smegmatis .

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.033
GPT teacher head0.341
Teacher spread0.308 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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