Evidence map›Paper›PMID 36198866›Full record

ArticleApplied microbiology and biotechnology2022

Construction of automated high-throughput screening method for finding efficient 3-ketosteroid 1,2-dehydrogenating strains.

Menglei Xia, XinYao Yan, Zehui Zan, Fan Yang, Mengjiao Liu, Danni Xue, Yanbing Shen, Min Wang

Abstract read
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In one paragraph

Article in Applied microbiology and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Menglei XiaState Key Laboratory of Food Nutrition and Safety, Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, College of Biotechnology, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457, China.
XinYao YanState Key Laboratory of Food Nutrition and Safety, Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, College of Biotechnology, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457, China.
Zehui ZanState Key Laboratory of Food Nutrition and Safety, Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, College of Biotechnology, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457, China.
Fan YangState Key Laboratory of Food Nutrition and Safety, Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, College of Biotechnology, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457, China.
Mengjiao LiuState Key Laboratory of Food Nutrition and Safety, Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, College of Biotechnology, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457, China.
Danni XueState Key Laboratory of Food Nutrition and Safety, Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, College of Biotechnology, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457, China.
Yanbing ShenKey Laboratory of Industrial Fermentation Microbiology, Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, College of Biotechnology, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457, China. shenyb@tust.edu.cn.
Min WangState Key Laboratory of Food Nutrition and Safety, Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, College of Biotechnology, Ministry of Education, Tianjin University of Science and Technology, Tianjin, 300457, China. minw@tust.edu.cn.ORCID http://orcid.org/0000-0001-5752-4065
Tianjin University of Science and Technology · CN

Funding

Agricultural Variety Improvement Project of Shandong Province TSBICIP-KJGG-001-08Natural Science Foundation of Tianjin Municipal Science and Technology Commission 21978221Natural Science Foundation of Tianjin Municipal Science and Technology Commission 22178362Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province TD13-5013the National Key R&D Program of China, Synthetic Biology Research no. 2019YFA0905300
6 · The paper itself

Abstract

Dehydrogenation reaction at C1(2) positions is typical and representative of industrial production of steroid drugs. Anti-inflammatory activity can be doubled when the nucleus of the anti-inflammatory steroid hormone drug introduces double bonds at the C1(2) positions. Arthrobacter simplex is currently the most widely studied and used strain for C1(2) dehydrogenation. Therefore, breeding Arthrobacter simplex with high-efficiency dehydrogenation ability is of great significance. In order to obtain high-efficiency strains, the research proposed a new screening strategy based on image process technique: firstly, a color reaction between 2,4-dinitrophenylhydrazine (DNPH) and 9α-hydroxyandrost-4-ene-3,17-dione (9α-OH-AD) was established to characterize the dehydrogenation ability of the strain; secondly, the color data of strains mutated by atmospheric and room temperature plasma (ARTP) in the "color reaction" were automated and analyzed for dehydrogenation ability prediction using optimized support vector machine model. Result showed that the prediction accuracy reached as high as 96% in verification experiments. After a series of mutagenesis, including breaking the bottleneck of a single mutation in ARTP, the dominant strain ARLU-146 was finally obtained from 5168 strains. Its initial conversion rate was 0.8059 g/L/h, with a conversion of 94.41% at 24 h, compared to the original strain ASP which increased the transformation rate by more than 10%. By further process optimization, a high conversion (94.34% within 20 h) with high substrate (85 g/L cortisone acetate) was achieved. According to literature research, it is the highest conversion at this substrate concentration. KEY POINTS: • A high-throughput screening method was developed by using image processing and machine learning technique. • "Mutation bottleneck" of single ARTP mutagenesis was surpassed by complex mutagenesis. • A high substrate (85 g/L CA) and high transformation rate craft (94.34% within 20 h) were built.

Indexed as

ActinobacteriaArthrobacterCortisoneHigh-Throughput Screening AssaysKetosteroidsMutagenesisCortisoneKetosteroidsArthrobacter simplexARTP compound mutagenesisAutomated high-throughput image processingC1(2) dehydrogenationMachine learning

Identifiers

PMID36198866
OpenAlexW4302278272

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.