ArticleScientific reports2020
Genome-scale exploration of transcriptional regulation in the nisin Z producer Lactococcus lactis subsp. lactis IO-1.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 10 citations in OpenAlex.
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- Understanding the Effect of Different Glucose Concentrations in the Oligotrophic BacteriumMicroorganisms · 2023Article
- Metabiotics Signature through Genome Sequencing and In Vitro Inhibitory Assessment of a NovelInternational journal of molecular sciences · 2023Article
- BusR senses bipartite DNA binding motifs by a unique molecular ruler architecture.Nucleic acids research · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
Transcription is of the most crucial steps of gene expression in bacteria, whose regulation guarantees the bacteria's ability to adapt to varying environmental conditions. Discovering the molecular basis and genomic principles of the transcriptional regulation is thus one of the most important tasks in cellular and molecular biology. Here, a comprehensive phylogenetic footprinting framework was implemented to predict maximal regulons of Lactococcus lactis subsp. lactis IO-1, a lactic acid bacterium known for its high potentials in nisin Z production as well as efficient xylose consumption which have made it a promising biotechnological strain. A total set of 321 regulons covering more than 90% of all the bacterium's operons have been elucidated and validated according to available data. Multiple novel biologically-relevant members were introduced amongst which arsC, mtlA and mtl operon for BusR, MtlR and XylR regulons can be named, respectively. Moreover, the effect of riboflavin on nisin biosynthesis was assessed in vitro and a negative correlation was observed. It is believed that understandings from such networks not only can be useful for studying transcriptional regulatory potentials of the target organism but also can be implemented in biotechnology to rationally design favorable production conditions.
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