Bacterial Essential Genes Research Articles

Information dimension analysis of bacterial essential and nonessential genes based on chaos game representation

Essential genes are indispensable for the survival of an organism. Investigating features associated with gene essentiality is fundamental to the prediction and identification of the essential genes. Selecting features associated with gene essentiality is fundamental to predict essential genes with computational techniques. We use fractal theory to make comparative analysis of essential and nonessential genes in bacteria. The information dimensions of essential genes and nonessential genes available in the DEG database for 27 bacteria are calculated based on their gene chaos game representations (CGRs). It is found that weak positive linear correlation exists between information dimension and gene length. Moreover, for genes of similar length, the average information dimension of essential genes is larger than that of nonessential genes. This indicates that essential genes show less regularity and higher complexity than nonessential genes. Our results show that for bacterium with a similar number of essential genes and nonessential genes, the CGR information dimension is helpful for the classification of essential genes and nonessential genes. Therefore, the gene CGR information dimension is very probably a useful gene feature for a genetic algorithm predicting essential genes.

Predicting bacterial essential genes using only sequence composition information.

Essential genes are those genes that are needed by organisms at any time and under any conditions. It is very important for us to identify essential genes from bacterial genomes because of their vital role in synthetic biology and biomedical practices. In this paper, we developed a support vector machine (SVM)-based method to predict essential genes of bacterial genomes using only compositional features. These features are all derived from the primary sequences, i.e., nucleotide sequences and protein sequences. After training on the multiple samplings of the labeled (essential or not essential) features using a library for SVM, we obtained an average area under the ROC curve (AUC) of about 0.82 in a 5-fold cross-validation for Escherichia coli and about 0.74 for Mycoplasma pulmonis. We further evaluated the performance of the method proposed using the dataset consisting of 16 bacterial genomes, and an average AUC of 0.76 was achieved. Based on this training dataset, a model for essential gene prediction was established. Another two independent genomes, Shewanella oneidensis RW1 and Salmonella enterica serovar Typhimurium SL1344 were used to evalutate the model. Results showed that the AUC sores were 0.77 and 0.81, respectively. For the convenience of the vast majority of experimental scientists, a web server has been constructed, which is freely available at http://cefg.uestc.edu.cn:9999/egp.

Geptop: A Gene Essentiality Prediction Tool for Sequenced Bacterial Genomes Based on Orthology and Phylogeny

Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the Porphyromonas gingivalis, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at http://cefg.uestc.edu.cn/geptop/ free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.

Peptide Conjugated Phosphorodiamidate Morpholino Oligomers Increase Survival of Mice Challenged with AmesBacillus anthracis

Targeting bacterial essential genes using antisense phosphorodiamidate morpholino oligomers (PMOs) represents an important strategy in the development of novel antibacterial therapeutics. PMOs are neutral DNA analogues that inhibit gene expression in a sequence-specific manner. In this study, several cationic, membrane-penetrating peptides were conjugated to PMOs (PPMOs) that target 2 bacterial essential genes: acyl carrier protein (acpP) and gyrase A (gyrA). These were tested for their ability to inhibit growth of Bacillus anthracis, a gram-positive spore-forming bacterium and causative agent of anthrax. PPMOs targeted upstream of both target gene start codons and conjugated with the bacterium-permeating peptide (RFF)(3)R were found to be most effective in inhibiting bacterial growth in vitro. Both of the gene-targeted PPMOs protected macrophages from B. anthracis induced cell death. Subsequent, in vivo testing of the PPMOs resulted in increased survival of mice challenged with the virulent Ames strain of B. anthracis. Together, these studies suggest that PPMOs targeting essential genes have the potential of being used as antisense antibiotics to treat B. anthracis infections.

A statistical feature of Hurst exponents of essential genes in bacterial genomes

At present, methods for determining essential genes depend on biochemical experiments. There is therefore a demand for the development of analysis methods and software for identifying essential genes, based on the common features of these genes. In this study, we employed the Hurst exponent as a characteristic parameter and analyzed its distribution among nine bacterial species. We found that most of the significance levels of the Hurst exponents of essential genes were higher than those of the corresponding full-gene-set. Conversely, most of the significance levels of the Hurst exponents of nonessential genes remained unchanged or only increased slightly. Therefore, we propose that this feature represents a restraint for pre- or post-design checking of bacterial essential genes in computer-aided design.

Enzymes Are Enriched in Bacterial Essential Genes

Essential genes, those indispensable for the survival of an organism, play a key role in the emerging field, synthetic biology. Characterization of functions encoded by essential genes not only has important practical implications, such as in identifying antibiotic drug targets, but can also enhance our understanding of basic biology, such as functions needed to support cellular life. Enzymes are critical for almost all cellular activities. However, essential genes have not been systematically examined from the aspect of enzymes and the chemical reactions that they catalyze. Here, by comprehensively analyzing essential genes in 14 bacterial genomes in which large-scale gene essentiality screens have been performed, we found that enzymes are enriched in essential genes. Essential enzymes have overrepresented ligases (especially those forming carbon-oxygen bonds and carbon-nitrogen bonds), nucleotidyltransferases and phosphotransferases, while have underrepresented oxidoreductases. Furthermore, essential enzymes tend to associate with more gene ontology domains. These results, from the aspect of chemical reactions, provide further insights into the understanding of functions needed to support natural cellular life, as well as synthetic cells, and provide additional parameters that can be integrated into gene essentiality prediction algorithms.

MGenomeSubtractor: a web-based tool for parallel in silico subtractive hybridization analysis of multiple bacterial genomes

mGenomeSubtractor performs an mpiBLAST-based comparison of reference bacterial genomes against multiple user-selected genomes for investigation of strain variable accessory regions. With parallel computing architecture, mGenomeSubtractor is able to run rapid BLAST searches of the segmented reference genome against multiple subject genomes at the DNA or amino acid level within a minute. In addition to comparison of protein coding sequences, the highly flexible sliding window-based genome fragmentation approach offered can be used to identify short unique sequences within or between genes. mGenomeSubtractor provides powerful schematic outputs for exploration of identified core and accessory regions, including searches against databases of mobile genetic elements, virulence factors or bacterial essential genes, examination of G+C content and binucleotide distribution bias, and integrated primer design tools. mGenomeSubtractor also allows for the ready definition of species-specific gene pools based on available genomes. Pan-genomic arrays can be easily developed using the efficient oligonucleotide design tool. This simple high-throughput in silico ‘subtractive hybridization’ analytical tool will support the rapidly escalating number of comparative bacterial genomics studies aimed at defining genomic biomarkers of evolutionary lineage, phenotype, pathotype, environmental adaptation and/or disease-association of diverse bacterial species. mGenomeSubtractor is freely available to all users without any login requirement at: http://bioinfo-mml.sjtu.edu.cn/mGS/.