Journal of Microbiology

Journal Article

The putative C2H2 transcription factor RocA is a novel regulator of development and secondary metabolism in Aspergillus nidulans: Dong Chan Won , Yong Jin Kim , Da Hye Kim , Hee-Moon Park , Pil Jae Maeng; J. Microbiol. 2020;58(7):574-587. Published online April 22, 2020; DOI: https://doi.org/10.1007/s12275-020-0083-7

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Multiple transcriptional regulators play important roles in the coordination of developmental processes, including asexual and sexual development, and secondary metabolism in the filamentous fungus Aspergillus nidulans. In the present study, we characterized a novel putative C2H2-type transcription factor (TF), RocA, in relation to development and secondary metabolism. Deletion of rocA increased conidiation and caused defective sexual development. In contrast, the overexpression of rocA exerted opposite effects on both phenotypes. Additionally, nullifying rocA resulted in enhanced brlA expression and reduced nsdC expression, whereas its overexpression exerted the opposite effects. These results suggest that RocA functions as a negative regulator of asexual development by repressing the expression of brlA encoding a key asexual development activator, but as a positive regulator of sexual development by enhancing the expression of nsdC encoding a pivotal sexual development activator. Deletion of rocA increased the production of sterigmatocystin (ST), as well as the expression of its biosynthetic genes, aflR and stcU. Additionally, the expression of the biosynthetic genes for penicillin (PN), ipnA and acvA, and for terrequinone (TQ), tdiB and tdiE, was increased by rocA deletion. Thus, it appears that RocA functions as a negative transcriptional modulator of the secondary metabolic genes involved in ST, PN, and TQ biosynthesis. Taken together, we propose that RocA is a novel transcriptional regulator that may act either positively or negatively at multiple target genes necessary for asexual and sexual development and secondary metabolism.

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srdA mutations suppress the rseA/cpsA deletion mutant conidiation defect in Aspergillus nidulans
Masahiro Ogawa, Ryouichi Fukuda, Ryo Iwama, Yasuji Koyama, Hiroyuki Horiuchi
Scientific Reports.2023;[Epub] CrossRef
Identification of a Novel Pleiotropic Transcriptional Regulator Involved in Sporulation and Secondary Metabolism Production in Chaetomium globosum
Shanshan Zhao, Kai Zhang, Congyu Lin, Ming Cheng, Jinzhu Song, Xin Ru, Zhengran Wang, Wan Wang, Qian Yang
International Journal of Molecular Sciences.2022; 23(23): 14849. CrossRef

Editorial

User guides for biologists to learn computational methods: Dokyun Na; J. Microbiol. 2020;58(3):173-175. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9723-1

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Abstract

System-wide studies of a given molecular type are referred to as “omics.” These include genomics, proteomics, and metabolomics, among others. Recent biotechnological advances allow for high-throughput measurement of cellular components, and thus it becomes possible to take a snapshot of all molecules inside cells, a form of omics study. Advances in computational modeling methods also make it possible to predict cellular mechanisms from the snapshots. These technologies have opened an era of computation-based biology. Component snapshots allow the discovery of gene-phenotype relationships in diseases, microorganisms in the human body, etc. Computational models allow us to predict new outcomes, which are useful in strain design in metabolic engineering and drug discovery from protein-ligand interactions. However, as the quantity of data increases or the model becomes complicated, the process becomes less accessible to biologists. In this special issue, six protocol articles are presented as user guides in the field of computational biology.

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Metabolic Engineering.2024; 83: 172. CrossRef
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Seungmok Han, Ji-Won Byun, Minho Lee
Journal of Microbiology.2024; 62(1): 33. CrossRef
Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope
Smita Singh, Pranjal Kumar Singh, Kapil Sachan, Mukesh Kumar, Poonam Bhardwaj
Current Computer-Aided Drug Design.2024; 20(6): 723. CrossRef
A review of Ribosome profiling and tools used in Ribo-seq data analysis
Mingso Sherma Limbu, Tianze Xiong, Sufang Wang
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Nasim Azari Torbat, Iman Akbarzadeh, Niloufar Rezaei, Zahra Salehi Moghaddam, Saba Bazzazan, Ebrahim Mostafavi
Coordination Chemistry Reviews.2023; 492: 215233. CrossRef
Regulator of RNase E activity modulates the pathogenicity of Salmonella Typhimurium
Jaejin Lee, Eunkyoung Shin, Ji-Hyun Yeom, Jaeyoung Park, Sunwoo Kim, Minho Lee, Kangseok Lee
Microbial Pathogenesis.2022; 165: 105460. CrossRef
Transcript-specific selective translation by specialized ribosomes bearing genome-encoded heterogeneous rRNAs in V. vulnificus CMCP6
Younkyung Choi, Minju Joo, Wooseok Song, Minho Lee, Hana Hyeon, Hyun-Lee Kim, Ji-Hyun Yeom, Kangseok Lee, Eunkyoung Shin
Journal of Microbiology.2022; 60(12): 1162. CrossRef
Omics-based microbiome analysis in microbial ecology: from sequences to information
Jang-Cheon Cho
Journal of Microbiology.2021; 59(3): 229. CrossRef
Trans-acting regulators of ribonuclease activity
Jaejin Lee, Minho Lee, Kangseok Lee
Journal of Microbiology.2021; 59(4): 341. CrossRef
Regulator of ribonuclease activity modulates the pathogenicity of Vibrio vulnificus
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Journal of Microbiology.2021; 59(12): 1133. CrossRef

Journal Article

Setup of a scientific computing environment for computational biology: Simulation of a genome-scale metabolic model of Escherichia coli as an example: Junhyeok Jeon , Hyun Uk Kim; J. Microbiol. 2020;58(3):227-234. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9516-6

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Abstract

Computational analysis of biological data is becoming increasingly important, especially in this era of big data. Computational analysis of biological data allows efficiently deriving biological insights for given data, and sometimes even counterintuitive ones that may challenge the existing knowledge. Among experimental researchers without any prior exposure to computer programming, computational analysis of biological data has often been considered to be a task reserved for computational biologists. However, thanks to the increasing availability of user-friendly computational resources, experimental researchers can now easily access computational resources, including a scientific computing environment and packages necessary for data analysis. In this regard, we here describe the process of accessing Jupyter Notebook, the most popular Python coding environment, to conduct computational biology. Python is currently a mainstream programming language for biology and biotechnology. In particular, Anaconda and Google Colaboratory are introduced as two representative options to easily launch Jupyter Notebook. Finally, a Python package COBRApy is demonstrated as an example to simulate 1) specific growth rate of Escherichia coli as well as compounds consumed or generated under a minimal medium with glucose as a sole carbon source, and 2) theoretical production yield of succinic acid, an industrially important chemical, using E. coli. This protocol should serve as a guide for further extended computational analyses of biological data for experimental researchers without computational background.

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The Application of Web‐Based Scientific Computing System in Innovation and Entrepreneurship
Tingli Cheng, Lele Qin
Discrete Dynamics in Nature and Society.2022;[Epub] CrossRef
Numerical Analysis and Scientific Calculation Considering the Management Mechanism of College Students’ Innovation and Entrepreneurship Education
Sheng Wang, Xiantao Jiang
Mathematical Problems in Engineering.2022; 2022: 1. CrossRef
Omics-based microbiome analysis in microbial ecology: from sequences to information
Jang-Cheon Cho
Journal of Microbiology.2021; 59(3): 229. CrossRef
Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data
Anurag Passi, Juan D. Tibocha-Bonilla, Manish Kumar, Diego Tec-Campos, Karsten Zengler, Cristal Zuniga
Metabolites.2021; 12(1): 14. CrossRef
User guides for biologists to learn computational methods
Dokyun Na
Journal of Microbiology.2020; 58(3): 173. CrossRef

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