Journal of Microbiology

Review

[MINIREVIEW]Bacterial bug-out bags: outer membrane vesicles and their proteins and functions: Kesavan Dineshkumar , Vasudevan Aparna , Liang Wu , Jie Wan , Mohamod Hamed Abdelaziz , Zhaoliang Su , Shengjun Wang , Huaxi Xu; J. Microbiol. 2020;58(7):531-542. Published online June 10, 2020; DOI: https://doi.org/10.1007/s12275-020-0026-3

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11 Citations

Abstract: Among the major bacterial secretions, outer membrane vesicles (OMVs) are significant and highly functional. The proteins and other biomolecules identified within OMVs provide new insights into the possible functions of OMVs in bacteria. OMVs are rich in proteins, nucleic acids, toxins and virulence factors that play a critical role in bacteria-host interactions. In this review, we discuss some proteins with multifunctional features from bacterial OMVs and their role involving the mechanisms of bacterial survival and defence. Proteins with moonlighting activities in OMVs are discussed based on their functions in bacteria. OMVs harbour many other proteins that are important, such as proteins involved in virulence, defence, and competition. Overall, OMVs are a power-packed aid for bacteria, harbouring many defensive and moonlighting proteins and acting as a survival kit in
case
of an emergency or as a defence weapon. In summary, OMVs can be defined as bug-out bags for bacterial defence and, therefore, survival.

Research Support, Non-U.S. Gov't

Genotyping, Morphology and Molecular Characteristics of a Lytic Phage of Neisseria Strain Obtained from Infected Human Dental Plaque: Ahmed N Aljarbou , Mohamad Aljofan; J. Microbiol. 2014;52(7):609-618. Published online May 30, 2014; DOI: https://doi.org/10.1007/s12275-014-3380-1

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6 Citations

Abstract: The lytic bacteriaphage (phage) A2 was isolated from human dental plaques along with its bacterial host. The virus was found to have an icosahedron-shaped head (60±3 nm), a sheathed and rigid long tail (~175 nm) and was categorized into the family Siphoviridae of the order Caudovirales, which are dsDNA viral family, characterised by their ability to infect bacteria and are nonenveloped with a noncontractile tail. The isolated phage contained a linear dsDNA genome having 31,703 base pairs of unique sequence, which were sorted into three contigs and 12 single sequences. A latent period of 25 minutes and burst size of 24±2 particles was determined for the virus. Bioinformatics approaches were used to identify ORFs in the genome. A phylogenetic analysis confirmed the species inter-relationship and its placement in the family.

Journal Article

Computational Detection of Prokaryotic Core Promoters in Genomic Sequences: Ki-Bong Kim , Jeong Seop Sim; J. Microbiol. 2005;43(5):411-416.; DOI: https://doi.org/2282 [pii]

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Abstract: The high-throughput sequencing of microbial genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable research attention in recent years. This paper addresses the development of a predictive model, known as the dependence decomposition weight matrix model (DDWMM), which was designed to detect the core promoter region, including the -10 region and the transcription start sites (TSSs), in prokaryotic genomic DNA sequences. This is an issue of some importance with regard to genome annotation efforts. Our predictive model captures the most significant dependencies between positions (allowing for non-adjacent as well as adjacent dependencies) via the maximal dependence decomposition (MDD) procedure, which iteratively decomposes data sets into subsets, based on the significant dependence between positions in the promoter region to be modeled. Such dependencies may be intimately related to biological and structural concerns, since promoter elements are present in a variety of combinations, which are separated by various distances. In this respect, the DDWMM may prove to be appropriate with regard to the detection of core promoter regions and TSSs in long microbial genomic contigs. In order to demonstrate the effectiveness of our predictive model, we applied 10-fold cross-validation experiments on the 607 experimentally-verified promoter sequences, which evidenced good performance in terms of sensitivity.

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