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Record W3195987580 · doi:10.1186/s12934-021-01653-9

Efficient conversion of phytosterols into 4-androstene-3,17-dione and its C1,2-dehydrogenized and 9α-hydroxylated derivatives by engineered Mycobacteria

2021· article· en· W3195987580 on OpenAlexfundno aff
Xin Li, Tian Chen, Fei Peng, Shikui Song, Jingpeng Yu, Douanla Njimeli Sidoine, Xiyao Cheng, Yongqi Huang, Yijun He, Zhengding Su

Bibliographic record

VenueMicrobial Cell Factories · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSteroid Chemistry and Biochemistry
Canadian institutionsnot available
FundersHubei Technological Innovation Special FundDepartment of Science and Technology, Hubei Provincial People's GovernmentUniversity of Toronto
KeywordsMutantStrain (injury)KetosteroidMycobacteriumGeneGene knockoutPhytosterolBiologyYield (engineering)ChemistryEnzymeMicrobiologyBiochemistryGeneticsBacteria

Abstract

fetched live from OpenAlex

Abstract 4-Androstene-3,17-dione (4-AD), 1,4-androstadiene-3,17-dione (ADD) and 9α-hydroxyl-4-androstene-3,17-dione (9OH-AD), which are important starting compounds for the synthesis of steroidal medicines, can be biosynthetically transformed from phytosterols by Mycobacterium strains. Genomic and metabolic analyses have revealed that currently available 4-AD-producing strains maintain the ability to convert 4-AD to ADD and 9OH-AD via 3-ketosteroid-1,2-dehydrogenase (KstD) and 3-ketosteroid-9α-hydroxylase (Ksh), not only lowering the production yield of 4-AD but also hampering its purification refinement. Additionally, these 4-AD industrial strains are excellent model strains to construct ADD- and 9OH-AD-producing strains. We recently found that Mycobacterium neoaurum HGMS2, a 4-AD-producing strain, harbored fewer kstd and ksh genes through whole-genomic and enzymatic analyses, compared with other strains (Wang et al. in Microbial Cell Fact 19:187, 2020). In this study, we attempted to construct an efficient 4-AD-producing strain by knocking out the kstd and ksh genes from the M. neoaurum HGMS2 strain. Next, we used kstd - and ksh -default HGMS2 mutants as templates to construct ADD- and 9OH-AD-producing strains by knocking in active kstd and ksh genes, respectively. We found that after knocking out its endogenous kstd and ksh genes, one of these knockout mutants, HGMS2 Δkstd211 + ΔkshB122 , showed a 20% increase in the rate of phytosterol to 4-AD conversion, compared relative to the wild-type strain and an increase in 4-AD yield to 38.3 g/L in pilot-scale fermentation. Furthermore, we obtained the ADD- and 9OH-AD-producing strains, HGMS2 kstd2 + Δkstd211+ΔkshB122 and HGMS2 kshA51 + Δkstd211+ΔkshA226 , by knocking in heterogenous active kstd and ksh genes to selected HGMS2 mutants, respectively. During pilot-scale fermentation, the conversion rates of the ADD- and 9OH-AD-producing mutants transforming phytosterol were 42.5 and 40.3%, respectively, and their yields reached 34.2 and 37.3 g/L, respectively. Overall, our study provides efficient strains for the production of 4-AD, ADD and 9OH-AD for the pharmaceutical industry and provides insights into the metabolic engineering of the HGMS2 strain to produce other important steroidal compounds.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.004
GPT teacher head0.187
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2021
Admission routes1
Has abstractyes

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