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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Journal Article

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation: Bilal Shaker , Myung-Sang Yu , Jingyu Lee , Yongmin Lee , Chanjin Jung , Dokyun Na; J. Microbiol. 2020;58(3):235-244. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9563-z

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Abstract

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate- L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

Citations

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In Silico and In Vitro Studies of Antibacterial Activity of Cow Urine Distillate (CUD)
LokRaj Pant, Shankar Thapa, Bibek Dahal, Ravindra Khadka, Mahalakshmi Suresha Biradar, Rajeev K. Singla
Evidence-Based Complementary and Alternative Medicine.2024; 2024: 1. CrossRef
Investigating the role of PmrB mutation on Colistin antibiotics drug resistance in Klebsiella Pneumoniae
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International Journal of Biological Macromolecules.2024; 281: 136414. CrossRef
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