1.Biosynthesis of steroidal intermediates using Mycobacteria: a review.
Shikui SONG ; Jianxin HE ; Yongqi HUANG ; Zhengding SU
Chinese Journal of Biotechnology 2023;39(3):1056-1069
Steroids are a class of medicines with important physiological and pharmacological effects. In pharmaceutical industry, steroidal intermediates are mainly prepared through Mycobacteria transformation, and then modified chemically or enzymatically into advanced steroidal compounds. Compared with the "diosgenin-dienolone" route, Mycobacteria transformation has the advantages of abundant raw materials, cost-effective, short reaction route, high yield and environmental friendliness. Based on genomics and metabolomics, the key enzymes in the phytosterol degradation pathway of Mycobacteria and their catalytic mechanisms are further revealed, which makes it possible for Mycobacteria to be used as chassis cells. This review summarizes the progress in the discovery of steroid-converting enzymes from different species, the modification of Mycobacteria genes and the overexpression of heterologous genes, and the optimization and modification of Mycobacteria as chassis cells.
Mycobacterium/metabolism*
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Steroids/metabolism*
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Phytosterols/metabolism*
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Genomics
2.Establishment of Antibiotics Use Rationality Evaluation Model in Patients Underwent Type Ⅰ Incision Surgery by Means of Machine Learning Method
Liqiang ZHU ; Yonggan WANG ; Weihua LI ; Qingjun SU ; Guihua BAI ; Deguang SHI ; Lihua CUI
China Pharmacy 2019;30(9):1260-1265
OBJECTIVE: To establish antibiotics use rationality evaluation model in type Ⅰ incision surgery patients, and to provide reference for prescription review of clinical pharmacists. METHODS: Totally 432 inpatients underwent type Ⅰ surgical incision in a hospital from Jan. 1st- Dec. 31st, 2017 were selected as the research objects. The information of diagnosis and treatment including age, nosocomial infection, the number of kinds of antibiotics used were extracted. Based on the results of clinical pharmacists’ comments on the antibiotics use rationality in patients’ prevention and treatment, non-conditional Logistic regression and support vector machine (SVM) in machine learning method were used to convert clinical pharmacists’ comments into objective index that can be recognized by the machine learning model, using categories of antibiotics (preventive or therapeutic use) as dependent variables and the patient’s diagnosis and treatment information as independent variables. Classification and identification model was established for antibiotics use rationality in type Ⅰ incision surgery patients. Using sensitivity, specificity and Youden index as indexes, established mode was validated on the other 61 samples of type Ⅰ incision surgery patients. The rationality of antibiotics prescriptions in type Ⅰ incision surgery patients before (by manual review, Jan.-Dec. 2017) and after (Jan.-Oct. 2018) using the model were collected, and the effects of the model were evaluated. RESULTS: The sensitivity, specificity and Youden index of non-conditional Logistic regression model were 65.63%, 75.00% and 40.63%, respectively. Main parameters of the model established by SVM included gamma 0.01, cost 10, sensitivity 92.19%, specificity 87.50%, Youden index 79.69%. The model established by SVM was better than non-conditional Logistic regression. SVM was used to validate established mode, and sensitivity, specificity and Youden index were 100%, 88.57% and 88.57%, respectively. Compared with before using the model, the evaluation ratio increased from 69.44% to 100%, the rate of prophylactic use of antibiotics decreased from 23.84% to 16.43%, the rate of rational drug type selection increased from 37.86% to 54.39%, and treatment course shortened from 5.01 days to 3.26 days after using the model. CONCLUSIONS: Established antibiotics use rationality evaluation model in typeⅠincision surgery patients by SVM in machine learning method fully covers all the patients, promotes rational use of antibiotics in typeⅠincision surgery patients, and provides a new idea for pharmacist prescription comment.
3.Identification of a new C-23 metabolite in sterol degradation of Mycobacterium neoaurum HGMS2 and analysis of its metabolic pathways.
Jianxin HE ; Xinlin DONG ; Yongqi HUANG ; Shikui SONG ; Zhengding SU
Chinese Journal of Biotechnology 2023;39(11):4550-4562
Mycobacterium neoaurum has the ability to produce steroidal intermediates known as 22-hydroxy-23, 24-bisnorchol-4-en-3-one (BA) upon the knockout of the genes for either the hydroxyacyl-CoA dehydrogenase (Hsd4A) or acyl-CoA thiolase (FadA5). In a previous study, we discovered a novel metabolite in the fermentation products when the fadA5 gene was deleted. This research aims to elucidate the metabolic pathway of this metabolite through structural identification, homologous sequence analysis of the fadA5 gene, phylogenetic tree analysis of M. neoaurum HGMS2, and gene knockout. Our findings revealed that the metabolite is a C23 metabolic intermediate, named 24-norchol-4-ene-3, 22-dione (designated as 3-OPD). It is formed when a thioesterase (TE) catalyzes the formation of a β-ketonic acid by removing CoA from the side chain of 3, 22-dioxo-25, 26-bisnorchol-4-ene-24-oyl CoA (22-O-BNC-CoA), followed by spontaneously undergoing decarboxylation. These results have the potential to contribute to the development of novel steroid intermediates.
Mycobacterium/metabolism*
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Phylogeny
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Steroids/metabolism*
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Metabolic Networks and Pathways
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Sterols/metabolism*