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Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model-based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene's essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model's broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/.

BACKGROUND:Bicoid (Bcd) is a Drosophila morphogenetic protein responsible for patterning the anterior structures in embryos. Recent experimental studies have revealed important insights into the behavior of this morphogen gradient, making it necessary to develop a model that can recapitulate the biological features of the system, including its dynamic and scaling properties. METHODOLOGY/PRINCIPAL FINDINGS:We present a biologically realistic 2-D model of the dynamics of the Bcd gradient in Drosophila embryos. This model is based on equilibrium binding of Bcd molecules to non-specific, low affinity DNA sites throughout the Drosophila genome. It considers both the diffusion media within which the Bcd gradient is formed and the dynamic and other relevant properties of bcd mRNA from which Bcd protein is produced. Our model recapitulates key features of the Bcd protein gradient observed experimentally, including its scaling properties and the stability of its nuclear concentrations during development. Our simulation model also allows us to evaluate the effects of other biological activities on Bcd gradient formation, including the dynamic redistribution of bcd mRNA in early embryos. Our simulation results suggest that, in our model, Bcd protein diffusion is important for the formation of an exponential gradient in embryos. CONCLUSIONS/SIGNIFICANCE:The 2-D model described in this report is a simple and versatile simulation procedure, providing a quantitative evaluation of the Bcd gradient system. Our results suggest an important role of Bcd binding to non-specific, low-affinity DNA sites in proper formation of the Bcd gradient in our model. They demonstrate that highly complex biological systems can be effectively modeled with relatively few parameters.

Identification of voltage-gated sodium channel NaV1.7 inhibitors for chronic pain therapeutic development is an area of vigorous pursuit. In an effort to identify more potent leads compared to our previously reported GpTx-1 peptide series, electrophysiology screening of fractionated tarantula venom discovered the NaV1.7 inhibitory peptide JzTx-V from the Chinese earth tiger tarantula Chilobrachys jingzhao. The parent peptide displayed nominal selectivity over the skeletal muscle NaV1.4 channel. Attribute-based positional scan analoging identified a key Ile28Glu mutation that improved NaV1.4 selectivity over 100-fold, and further optimization yielded the potent and selective peptide leads AM-8145 and AM-0422. NMR analyses revealed that the Ile28Glu substitution changed peptide conformation, pointing to a structural rationale for the selectivity gains. AM-8145 and AM-0422 as well as GpTx-1 and HwTx-IV competed for ProTx-II binding in HEK293 cells expressing human NaV1.7, suggesting that these NaV1.7 inhibitory peptides interact with a similar binding site. AM-8145 potently blocked native tetrodotoxin-sensitive (TTX-S) channels in mouse dorsal root ganglia (DRG) neurons, exhibited 30- to 120-fold selectivity over other human TTX-S channels and exhibited over 1,000-fold selectivity over other human tetrodotoxin-resistant (TTX-R) channels. Leveraging NaV1.7-NaV1.5 chimeras containing various voltage-sensor and pore regions, AM-8145 mapped to the second voltage-sensor domain of NaV1.7. AM-0422, but not the inactive peptide analog AM-8374, dose-dependently blocked capsaicin-induced DRG neuron action potential firing using a multi-electrode array readout and mechanically-induced C-fiber spiking in a saphenous skin-nerve preparation. Collectively, AM-8145 and AM-0422 represent potent, new engineered NaV1.7 inhibitory peptides derived from the JzTx-V scaffold with improved NaV selectivity and biological activity in blocking action potential firing in both DRG neurons and C-fibers. [ABSTRACT FROM AUTHOR]

Large-scale systematic analysis of gene essentiality is an important step closer toward unraveling the complex relationship between genotypes and phenotypes. Such analysis cannot be accomplished without unbiased and accurate annotations of essential genes. In current genomic databases, most of the essential gene annotations are derived from whole-genome transposon mutagenesis (TM), the most frequently used experimental approach for determining essential genes in microorganisms under defined conditions. However, there are substantial systematic biases associated with TM experiments. In this study, we developed a novel Poisson model–based statistical framework to simulate the TM insertion process and subsequently correct the experimental biases. We first quantitatively assessed the effects of major factors that potentially influence the accuracy of TM and subsequently incorporated relevant factors into the framework. Through iteratively optimizing parameters, we inferred the actual insertion events occurred and described each gene’s essentiality on probability measure. Evaluated by the definite mapping of essential gene profile in Escherichia coli, our model significantly improved the accuracy of original TM datasets, resulting in more accurate annotations of essential genes. Our method also showed encouraging results in improving subsaturation level TM datasets. To test our model’s broad applicability to other bacteria, we applied it to Pseudomonas aeruginosa PAO1 and Francisella tularensis novicida TM datasets. We validated our predictions by literature as well as allelic exchange experiments in PAO1. Our model was correct on six of the seven tested genes. Remarkably, among all three cases that our predictions contradicted the TM assignments, experimental validations supported our predictions. In summary, our method will be a promising tool in improving genomic annotations of essential genes and enabling large-scale explorations of gene essentiality. Our contribution is timely considering the rapidly increasing essential gene sets. A Webserver has been set up to provide convenient access to this tool. All results and source codes are available for download upon publication at http://research.cchmc.org/essentialgene/. [ABSTRACT FROM AUTHOR]