Strong negative correlation between codon usage bias and protein structural disorder impedes protein expression after codon optimization.

As a common phenomenon existing in almost all genomes, codon usage bias has been studied for a long time. Codon optimization is a frequently used strategy to accelerate protein synthesis rate. Besides regulating protein translation speed, codon usage bias has also be reported to affect co-translation folding and transcription. P. pastoris is a well-developed expression system, whose efficiency is tightly correlated with commercial value. However, few studies focus on the role of codon usage bias in affecting protein expression in P. pastoris. Besides, many genes in P. pastoris genome show significant negative correlation between codon usage bias and protein structural disorder tendency. It's not known whether this feature is important for their expression. In order to answer these questions, we picked 4P. pastoris gene candidates with strong negative correlation between codon usage bias and protein structural disorder. We then performed full-length codon optimization which completely eliminated the correlation. Protein and RNA assays were then used to compare protein and mRNA levels before and after codon optimization. As a result, codon optimization failed to elevate their protein expression levels, and even resulted in a decrease. As represented by the trypsin sensitivity assays, codon optimization also altered the protein structure of 0616 and 0788. Besides protein, codon optimization also affected mRNA levels. Shown by in vitro and in vivo RNA degradation assays, the mRNA stability of 0616, 0788 and 0135 were also altered by codon optimization. For each gene, the detailed effect may be related with its specific sequence and protein structure. Our results suggest that codon usage bias is an important factor to regulate gene expression level, as well as mRNA and protein stabilities in P. pastoris. "Extreme" codon optimization in genes with strong negative correlation between codon usage bias and protein structural disorder tendency may not be favored. Compromised strategies should be tried if expression is not successful. Besides, codon optimization may affect protein structural conformation more severely in structural disordered proteins.

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers.

Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.