Plasmids as Tools for Containment

Gene Editing of Candida glycerinogenes by Designed Toxin-Antitoxin Cassette

Candida glycerinogenes is an industrial yeast with excellent multistress resistance. However, due to the diploid genome and the lack of meiosis and screening markers, its molecular genetic operation is limited. Here, a gene editing system using the toxin-antitoxin pair relBE from the type II toxin-antitoxin system in Escherichia coli as a screening marker was constructed. The RelBE complex can specifically and effectively regulate cell growth and arrest through a conditionally controlled toxin RelE switch, thereby achieving the selection of positive recombinants. The constructed editing system achieved precise gene deletion, replacement, insertion, and gene episomal expression in C. glycerinogenes. Compared with the traditional amino acid deficiency complementation editing system, this editing system produced higher biomass and the gene deletion efficiency was increased by 3.5 times. Using this system, the production of 2-phenylethanol by C. glycerinogenes was increased by 11.5-13.5% through metabolic engineering and tolerance engineering strategies. These results suggest that the stable gene editing system based on toxin-antitoxin pairs can be used for gene editing of C. glycerinogenes to modify metabolic pathways and promote industrial applications. Therefore, the constructed gene editing system is expected to provide a promising strategy for polyploid industrial microorganisms lacking gene manipulation methods.

Plasmer: an Accurate and Sensitive Bacterial Plasmid Prediction Tool Based on Machine Learning of Shared k-mers and Genomic Features

Identification of plasmids in bacterial genomes is critical for many factors, including horizontal gene transfer, antibiotic resistance genes, host-microbe interactions, cloning vectors, and industrial production. There are several in silico methods to predict plasmid sequences in assembled genomes. However, existing methods have evident shortcomings, such as unbalance in sensitivity and specificity, dependency on species-specific models, and performance reduction in sequences shorter than 10 kb, which has limited their scope of applicability. In this work, we proposed Plasmer, a novel plasmid predictor based on machine-learning of shared k-mers and genomic features. Unlike existing k-mer or genomic-feature based methods, Plasmer employs the random forest algorithm to make predictions using the percent of shared k-mers with plasmid and chromosome databases combined with other genomic features, including alignment E value and replicon distribution scores (RDS). Plasmer can predict on multiple species and has achieved an average the area under the curve (AUC) of 0.996 with accuracy of 98.4%. Compared to existing methods, tests of both sliding sequences and simulated and de novo assemblies have consistently shown that Plasmer has outperforming accuracy and stable performance across long and short contigs above 500 bp, demonstrating its applicability for fragmented assemblies. Plasmer also has excellent and balanced performance on both sensitivity and specificity (both >0.95 above 500 bp) with the highest F1-score, which has eliminated the bias on sensitivity or specificity that was common in existing methods. Plasmer also provides taxonomy classification to help identify the origin of plasmids. IMPORTANCE In this study, we proposed a novel plasmid prediction tool named Plasmer. Technically, unlike existing k-mer or genomic features-based methods, Plasmer is the first tool to combine the advantages of the percent of shared k-mers and the alignment score of genomic features. This has given Plasmer (i) evident improvement in performance compared to other methods, with the best F1-score and accuracy on sliding sequences, simulated contigs, and de novo assemblies; (ii) applicability for contigs above 500 bp with highest accuracy, enabling plasmid prediction in fragmented short-read assemblies; (iii) excellent and balanced performance between sensitivity and specificity (both >0.95 above 500 bp) with the highest F1-score, which eliminated the bias on sensitivity or specificity that commonly existed in other methods; and (iv) no dependency of species-specific training models. We believe that Plasmer provides a more reliable alternative for plasmid prediction in bacterial genome assemblies.

Engineered Probiotics: Applications and Biological Containment

Bioengineered probiotics represent the next generation of whole cell-mediated biotherapeutics. Advances in synthetic biology, genome engineering, and DNA sequencing and synthesis have enabled scientists to design and develop probiotics with increased stress tolerance and the ability to target specific pathogens and their associated toxins, as well as to mediate targeted delivery of vaccines, drugs, and immunomodulators directly to host cells. Herein, we review the most significant advances in the development of this field. We discuss the critical issue of biological containment and consider the role of synthetic biology in the design and construction of the probiotics of the future.

Heterologous Expression of Toxins from Bacterial Toxin-Antitoxin Systems in Eukaryotic Cells: Strategies and Applications

Toxin-antitoxin (TA) systems are found in nearly all prokaryotic genomes and usually consist of a pair of co-transcribed genes, one of which encodes a stable toxin and the other, its cognate labile antitoxin. Certain environmental and physiological cues trigger the degradation of the antitoxin, causing activation of the toxin, leading either to the death or stasis of the host cell. TA systems have a variety of functions in the bacterial cell, including acting as mediators of programmed cell death, the induction of a dormant state known as persistence and the stable maintenance of plasmids and other mobile genetic elements. Some bacterial TA systems are functional when expressed in eukaryotic cells and this has led to several innovative applications, which are the subject of this review. Here, we look at how bacterial TA systems have been utilized for the genetic manipulation of yeasts and other eukaryotes, for the containment of genetically modified organisms, and for the engineering of high expression eukaryotic cell lines. We also examine how TA systems have been adopted as an important tool in developmental biology research for the ablation of specific cells and the potential for utility of TA systems in antiviral and anticancer gene therapies.

Genetically engineered microorganisms for the detection of explosives' residues

The manufacture and use of explosives throughout the past century has resulted in the extensive pollution of soils and groundwater, and the widespread interment of landmines imposes a major humanitarian risk and prevents civil development of large areas. As most current landmine detection technologies require actual presence at the surveyed areas, thus posing a significant risk to personnel, diverse research efforts are aimed at the development of remote detection solutions. One possible means proposed to fulfill this objective is the use of microbial bioreporters: genetically engineered microorganisms “tailored” to generate an optical signal in the presence of explosives’ vapors. The use of such sensor bacteria will allow to pinpoint the locations of explosive devices in a minefield. While no study has yet resulted in a commercially operational system, significant progress has been made in the design and construction of explosives-sensing bacterial strains. In this article we review the attempts to construct microbial bioreporters for the detection of explosives, and analyze the steps that need to be undertaken for this strategy to be applicable for landmine detection.