Journal Article
- Effects of mycosubtilin homolog algicides from a marine bacterium, Bacillus sp. SY-1, against the harmful algal bloom species Cochlodinium polykrikoides
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Seong-Yun Jeong , Hong-Joo Son
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J. Microbiol. 2021;59(4):389-400. Published online March 29, 2021
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DOI: https://doi.org/10.1007/s12275-021-1086-8
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Abstract
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The marine bacterium, Bacillus sp. SY-1, produced algicidal
compounds that are notably active against the bloom-forming
alga Cochlodinium polykrikoides. We isolated three algicidal
compounds and identified these as mycosubtilins with
molecular weights of 1056, 1070, and 1084 (designated MS
1056, 1070, and 1084, respectively), based on amino acid
analyses and 1H, 13C, and two-dimensional nuclear magnetic
resonance spectroscopy, including 1H-15N heteronuclear
multiple bond correlation analysis. MS 1056 contains a β-
amino acid residue with an alkyl side chain of C15, which has
not previously been seen in known mycosubtilin families.
MS 1056, 1070, and 1084 showed algicidal activities against
C. polykrikoides with 6-h LC50 values of 2.3 ± 0.4, 0.8 ± 0.2,
and 0.6 ± 0.1 μg/ml, respectively. These compounds also
showed significant algicidal activities against other harmful
algal bloom species. In contrast, MS 1084 showed no significant
growth inhibitory effects against other organisms, including
bacteria and microalgae, although does inhibit the
growth of some fungi and yeasts. These observations imply
that the algicidal bacterium Bacillus sp. SY-1 and its algicidal
compounds could play an important role in regulating the
onset and development of harmful algal blooms in natural
environments.
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Citations
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Review
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Justine L. Murray , Jodi L. Connell , Apollo Stacy , Keith H. Turner , Marvin Whiteley
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J. Microbiol. 2014;52(3):188-199. Published online March 1, 2014
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DOI: https://doi.org/10.1007/s12275-014-4067-3
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48
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Abstract
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Communities of microbes can live almost anywhere and contain many different species. Interactions between members of these communities often determine the state of the habitat in which they live. When these habitats include sites on the human body, these interactions can affect health and disease. Polymicrobial synergy can occur during infection, in which the combined effect of two or more microbes on disease is worse than seen with any of the individuals alone.
Powerful genomic methods are increasingly used to study microbial communities, including metagenomics to reveal the members and genetic content of a community and metatranscriptomics to describe the activities of community members. Recent efforts focused toward a mechanistic understanding of these interactions have led to a better appreciation of the precise bases of polymicrobial synergy in communities
containing bacteria, eukaryotic microbes, and/or viruses. These studies have benefited from advances in the development of in vivo models of polymicrobial infection and modern techniques to profile the spatial and chemical bases of intermicrobial communication. This review describes the breadth of mechanisms microbes use to interact in ways that impact pathogenesis and techniques to study polymicrobial communities.
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Research Support, Non-U.S. Gov't
- NOTE] rRNASelector: A Computer Program for Selecting Ribosomal RNA Encoding Sequences from Metagenomic and Metatranscriptomic Shotgun Libraries
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Jae-Hak Lee , Hana Yi , Jongsik Chun
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J. Microbiol. 2011;49(4):689-691. Published online September 2, 2011
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DOI: https://doi.org/10.1007/s12275-011-1213-z
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Abstract
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Metagenomic and metatranscriptomic shotgun sequencing techniques are gaining popularity as more cost-effective
next-generation sequencing technologies become commercially available. The initial stage of bioinformatic
analysis generally involves the identification of phylogenetic markers such as ribosomal RNA genes.
The sequencing reads that do not code for rRNA can then be used for protein-based analysis. Hidden Markov
model is a well-known method for pattern recognition. Hidden Markov models that are trained on well-curated
rRNA sequence databases have been successfully used to identify DNA sequence coding for rRNAs in prokaryotes.
Here, we introduce rRNASelector, which is a computer program for selecting rRNA genes from
massive metagenomic and metatranscriptomic sequences using hidden Markov models. The program successfully
identified prokaryotic 5S, 26S, and 23S rRNA genes from Roche 454 FLX Titanium-based metagenomic and
metatranscriptomic libraries. The rRNASelector program is available at http://sw.ezbiocloud.net/rrnaselector.