Marine Microbial Community Adaptation and Resiliency to Anthropogenic Stresses Through Horizontal Gene Transfer

2017 ◽  
pp. 109-131
Author(s):  
Suja Rajan ◽  
Patricia A. Sobecky
BMC Genomics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Jiangtao Guo ◽  
Qi Wang ◽  
Xiaoqi Wang ◽  
Fumeng Wang ◽  
Jinxian Yao ◽  
...  

2004 ◽  
Vol 49 (11-12) ◽  
pp. 327-336 ◽  
Author(s):  
S. Wuertz ◽  
S. Okabe ◽  
M. Hausner

Several important advances have been made in the study of biofilm microbial populations relating to their spatial structure (or architecture), their community structure, and their dependence on physicochemical parameters. With the knowledge that hydrodynamic forces influence biofilm architecture came the realization that metabolic processes may be enhanced if certain spatial structures can be forced. An example is the extent of plasmid-mediated horizontal gene transfer in biofilms. Recent in situ work in defined model systems has shown that the biofilm architecture plays a role for genetic transfer by bacterial conjugation in determining how far the donor cells can penetrate the biofilm. Open channels and pores allow for more efficient donor transport and hence more frequent cell collisions leading to rapid spread of the genes by horizontal gene transfer. Such insight into the physical environment of biofilms can be utilized for bioenhancement of catabolic processes by introduction of mobile genetic elements into an existing microbial community. If the donor organisms themselves persist, bioaugmentation can lead to successful establishment of newly introduced species and may be a more successful strategy than biostimulation (the addition of nutrients or specific carbon sources to stimulate the authochthonous population) as shown for an enrichment culture of nitrifying bacteria added to rotating disk biofilm reactors using fluorescent in situ hybridization (FISH) and microelectrode measurements of NH4+, NO2-, NO3-, and O2. However, few studies have been carried out on full-scale systems. Bioaugmentation and bioenhancement are most successful if a constant selective pressure can be maintained favoring the promulgation of the added enrichment culture. Overall, knowledge gain about microbial community interactions in biofilms continues to be driven by the availability of methods for the rapid analysis of microbial communities and their activities. Molecular tools can be grouped into those suitable for ex situ and in situ community analysis. Non-spatial community analysis, in the sense of assessing changes in microbial populations as a function of time or environmental conditions, relies on general fingerprinting methods, like DGGE and T-RFLP, performed on nucleic acids extracted from biofilm. These approaches have been most useful when combined with gene amplification, cloning and sequencing to assemble a phylogenetic inventory of microbial species. It is expected that the use of oligonucleotide microarrays will greatly facilitate the analysis of microbial communities and their activities in biofilms. Structure-activity relationships can be explored using incorporation of 13C-labeled substrates into microbial DNA and RNA to identify metabolically active community members. Finally, based on the DNA sequences in a biofilm, FISH probes can be designed to verify the abundance and spatial location of microbial community members. This in turn allows for in situ structure/function analysis when FISH is combined with microsensors, microautoradiography, and confocal laser scanning microscopy with advanced image analysis.


2020 ◽  
Vol 6 (11) ◽  
Author(s):  
Zhencheng Fang ◽  
Hongwei Zhou

Plasmids are the key element in horizontal gene transfer in the microbial community. Recently, a large number of experimental and computational methods have been developed to obtain the plasmidomes of microbial communities. Distinguishing transmissible plasmid sequences, which are derived from conjugative or at least mobilizable plasmids, from non-transmissible plasmid sequences in the plasmidome is essential for understanding the diversity of plasmids and how they regulate the microbial community. Unfortunately, due to the highly fragmented characteristics of DNA sequences in the plasmidome, effective identification methods are lacking. In this work, we used information entropy from information theory to assess the randomness of synonymous codon usage over 4424 plasmid genomes. The results showed that for all amino acids, the choice of a synonymous codon in conjugative and mobilizable plasmids is more random than that in non-transmissible plasmids, indicating that transmissible plasmids have different sequence signatures from non-transmissible plasmids. Inspired by this phenomenon, we further developed a novel algorithm named PlasTrans. PlasTrans takes the triplet code sequences and base sequences of plasmid DNA fragments as input and uses the convolutional neural network of the deep learning technique to further extract the more complex signatures of the plasmid sequences and identify the conjugative and mobilizable DNA fragments. Tests showed that PlasTrans could achieve an AUC of as high as 84–91%, even though the fragments only contained hundreds of base pairs. To the best of our knowledge, this is the first quantitative analysis of the difference in sequence signatures between transmissible and non-transmissible plasmids, and we developed the first tool to perform transferability annotation for DNA fragments in the plasmidome. We expect that PlasTrans will be a useful tool for researchers who analyse the properties of novel plasmids in the microbial community and horizontal gene transfer, especially the spread of resistance genes and virulence factors associated with plasmids. PlasTrans is freely available via https://github.com/zhenchengfang/PlasTrans


PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4015 ◽  
Author(s):  
Weizhi Song ◽  
Kerrin Steensen ◽  
Torsten Thomas

The development and application of metagenomic approaches have provided an opportunity to study and define horizontal gene transfer (HGT) on the level of microbial communities. However, no current metagenomic data simulation tools offers the option to introduce defined HGT within a microbial community. Here, we present HgtSIM, a pipeline to simulate HGT event among microbial community members with user-defined mutation levels. It was developed for testing and benchmarking pipelines for recovering HGTs from complex microbial datasets. HgtSIM is implemented in Python3 and is freely available at: https://github.com/songweizhi/HgtSIM.


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