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Guo, F.-B.

Publications and source records attributed to Guo, F.-B..

2 recordsLinked to original sources

Co-adaption of tRNA Gene Copy Number and Amino Acid Usage Influences Translation Rates in Three Life Domains

The cellular translation process should obey the principle of maximizing efficiency and minimizing resource and energy costs. Here, we validated this principle by focusing on the basic translation components of tRNAs and amino acids. To most efficiently utilize these components, we reasoned that the quantities of the 20 tRNAs and their corresponding amino acids would be consistent in an organism. The two values should match at both the organismal and protein scales. For the former, they co-vary to meet the need to translate more proteins in fast-growing or larger cells. For the latter, they are consistent to different extents for various proteins in an organism to comply with different needs of translation speed. In this work, 310 out of 410 genomes in three domains had significant co-adaptions between the tRNA gene copy number and amino acid composition, and thus validating the principle at the organism scale. Furthermore, fast-growing bacteria co-adapt better than slow-growing ones. Highly expressed proteins and those connected to acute responses have better co-adaption, illustrating the principle at the individual protein scale. Experimentally, manipulating the tRNA gene copy number to optimize co-adaption between enhanced green fluorescent protein (EGFP) and tRNA gene set of Escherichia coli indeed lifted the translation rate (speed). Our results also contribute to revealing a translation rate-associated factor with universal and global effects. From a practical perspective, our findings suggest a strategy to increase the expression of target proteins and have implications for designing chassis cells in the field of synthetic biology field.

genomics

Accurate prediction of human essential genes using only nucleotide composition and association information

Three groups recently identified essential genes in human cancer cell lines using wet experiments, and these genes are of high values. Herein, we improved the widely used Z curve method by creating a {lambda}-interval Z curve, which considered interval association information. With this method and recursive feature elimination technology, a computational model was developed to predict human gene essentiality. The 5-fold cross-validation test based on our benchmark dataset obtained an area under the receiver operating characteristic curve (AUC) of 0.8814. For the rigorous jackknife test, the AUC score was 0.8854. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. However, previous classifiers in three eukaryotes can gave satisfactory prediction only combining sequence with other features. It is also demonstrated that although the information contributed by interval association is less than adjacent nucleotides, this information can still play an independent role. Integrating the interval information into adjacent ones can significantly improve our classifiers prediction capacity. We re-predicted the benchmark negative dataset by Pheg server (https://cefg.uestc.edu.cn/Pheg), and 118 genes were additionally predicted as essential. Among them, 21 were found to be homologues in mouse essential genes, indicating that at least a part of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the {lambda}-interval Z curve method could be effectively extended to classification issues of other DNA elements.

bioinformatics