Background: The mitogen-activated extracellular signal-regulated kinase 1/2 (MEK1/2) inhibitor trametinib has shown promising therapeutic effects on melanoma, but its efficacy on colorectal cancer (CRC) is limited. Synthetic lethality arises with a combination of two or more separate gene mutations that causes cell death, whereas individual mutations keep cells alive. This study aimed to identify the genes responsible for resistance to trametinib in CRC cells, using a synthetic lethal short hairpin RNA (shRNA) screening approach. Methods: We infected HT29 cells with a pooled lentiviral shRNA library and applied next-generation sequencing to identify shRNAs with reduced abundance after 8-day treatment of 20 nmol/L trametinib. HCT116 and HT29 cells were used in validation studies. Stable ring finger protein 183 (RNF183)-overexpressing cell lines were generated by pcDNA4-myc/his-RNF183 transfection. Stable RNF183-knockdown cell lines were generated by infection of lentivi-ruses that express RNF183 shRNA, and small interference RNA (siRNA) was used to knock down RNF183 transiently. Quantitative real-time PCR was used to determine the mRNA expression. Western blotting, immunohistochemical analysis, and enzyme-linked immunosorbent assay (ELISA) were used to evaluate the protein abundance. MTT assay, colony formation assay, and subcutaneous xenograft tumor growth model were used to evaluate cell proliferation. Results: In the primary screening, we found that the abundance of RNF183 shRNA was markedly reduced after treatment with trametinib. Trametinib induced the expression of RNF183, which conferred resistance to drug-induced cell growth repression and apoptotic and non-apoptotic cell deaths. Moreover, interleukin-8 (IL-8) was a downstream gene of RNF183 and was required for the function of RNF183 in facilitating cell growth. Additionally, elevated RNF183 expression partly reduced the inhibitory effect of trametinib on IL-8 expression. Finally, xenograft tumor model showed the synergism of RNF183 knockdown and trametinib in repressing the growth of CRC cells in vivo. Conclusion: The RNF183-IL-8 axis is responsible for the resistance of CRC cells to the MEK1/2 inhibitor trametinib and may serve as a candidate target for combined therapy for CRC.

Protein nitration and nitrosylation are essential post-translational modifications (PTMs) involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases. Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65 for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively, demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%-42% improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http://deepnitro.renlab.org.
Amino Acid Sequence ; Amino Acids ; metabolism ; Deep Learning ; Humans ; Internet ; Neural Networks (Computer) ; Nitrosation ; Proteins ; chemistry ; metabolism ; Reproducibility of Results ; Software