Nucleoprotein (265-273)

Inflammation mediated by the crosstalk between leukocytes and resident tissue cells is crucial for the maintenance of homeostasis. Because chemokine ligands and receptors, which recruit a variety of leukocytes, are widely distributed among tissues, it is important to understand the mechanisms regulating inflammatory disease. Chemokines such as CC-chemokine ligand 2 (CCL2) amplify and maintain inflammation through chemokine-cytokine networks after the recruitment of circulating leukocytes. Chemokine-dependent nonresolving inflammation occurs in the peripheral and central nervous systems, and underlies several intractable diseases, including cancer and neuropathic pain. The chronic upregulation of chemokines is often mediated by epigenetic mechanisms consisting of DNA methylation, histone modification, and nucleosome positioning. In particular, histone acetylation and methylation have been shown to play important roles in the upregulation of chemokine expression. In addition to CCL2, several other chemokines strongly contribute to neuropathic pain through epigenetic induction. Consequently, targeting epigenetic changes may have therapeutic potential for nonresolving inflammatory diseases such as neuropathic pain. Further research into the epigenetics of inflammatory diseases should promote the development of novel and effective treatment strategies for intractable inflammatory diseases.

Histone lysine crotonylation (Kcr) is a post-translational modification of histone proteins that is involved in the regulation of gene transcription, acute and chronic kidney injury, spermatogenesis, depression, cancer, and so forth. The identification of Kcr sites in proteins is important for characterizing and regulating primary biological mechanisms. The use of computational approaches such as machine learning and deep learning algorithms have emerged in recent years as the traditional wet-lab experiments are time-consuming and costly. We propose as part of this study a deep learning model based on a recurrent neural network (RNN) termed as Sohoko-Kcr for the prediction of Kcr sites. Through the embedded encoding of the peptide sequences, we investigate the efficiency of RNN-based models such as long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and bidirectional gated recurrent unit (BiGRU) networks using cross-validation and independent tests. We also established the comparison between Sohoko-Kcr and other published tools to verify the efficiency of our model based on 3-fold, 5-fold, and 10-fold cross-validations using independent set tests. The results then show that the BiGRU model has consistently displayed outstanding performance and computational efficiency. Based on the proposed model, a webserver called Sohoko-Kcr was deployed for free use and is accessible at https://sohoko-research-9uu23.ondigitalocean.app.

The post-transcriptional operon provides a means of synexpression of mRNAs encoding interrelated proteins. The coordination of gene expression may be achieved by a trans-acting RNA-binding protein attaching to similar cis-elements in different, yet functionally clustered, mRNAs. The RNP granule can be regarded as a supramolecular assembly of RNA and protein, probably representing several overlapping post-transcriptional operons. The present protocol describes how RNP granules may be isolated by the transgenic expression of a 3X FLAG version of an RNA-binding protein under tetracycline control via the tetracycline receptor/operator complex. In this way, inclusion of an appropriate tetracycline concentration ensures expression of the tagged version at the endogenous level, and the 3X FLAG tag is a convenient "handle" for the subsequent immunoprecipitation by immobilized anti-FLAG antibody.