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Effects of mycosubtilin homolog algicides from a marine bacterium, Bacillus sp. SY-1, against the harmful algal bloom species Cochlodinium polykrikoides
Seong-Yun Jeong , Hong-Joo Son
J. Microbiol. 2021;59(4):389-400.   Published online March 29, 2021
DOI: https://doi.org/10.1007/s12275-021-1086-8
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  • 12 Web of Science
  • 11 Crossref
AbstractAbstract
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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Review
REVIEW] Mechanisms of Synergy in Polymicrobial Infections
Justine L. Murray , Jodi L. Connell , Apollo Stacy , Keith H. Turner , Marvin Whiteley
J. Microbiol. 2014;52(3):188-199.   Published online March 1, 2014
DOI: https://doi.org/10.1007/s12275-014-4067-3
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  • 124 Crossref
AbstractAbstract
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
Jae-Hak Lee , Hana Yi , Jongsik Chun
J. Microbiol. 2011;49(4):689-691.   Published online September 2, 2011
DOI: https://doi.org/10.1007/s12275-011-1213-z
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AbstractAbstract
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.

Journal of Microbiology : Journal of Microbiology
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