In mammals, the SET1 family of lysine methyltransferases (KMTs), which includes MLL1-5, SET1A and SET1B, catalyzes the methylation of lysine-4 (Lys-4) on histone H3. Recent reports have demonstrated that a three-subunit complex composed of WD-repeat protein-5 (WDR5), retinoblastoma-binding protein-5 (RbBP5) and absent, small, homeotic disks-2-like (ASH2L) stimulates the methyltransferase activity of MLL1. On the basis of studies showing that this stimulation is in part controlled by an interaction between WDR5 and a small region located in close proximity of the MLL1 catalytic domain [referred to as the WDR5-interacting motif (Win)], it has been suggested that WDR5 might play an analogous role in scaffolding the other SET1 complexes. We herein provide biochemical and structural evidence showing that WDR5 binds the Win motifs of MLL2-4, SET1A and SET1B. Comparative analysis of WDR5-Win complexes reveals that binding of the Win motifs is achieved by the plasticity of WDR5 peptidyl-arginine-binding cleft allowing the C-terminal ends of the Win motifs to be maintained in structurally divergent conformations. Consistently, enzymatic assays reveal that WDR5 plays an important role in the optimal stimulation of MLL2-4, SET1A and SET1B methyltransferase activity by the RbBP5-ASH2L heterodimer. Overall, our findings illustrate the function of WDR5 in scaffolding the SET1 family of KMTs and further emphasize on the important role of WDR5 in regulating global histone H3 Lys-4 methylation.

Background: CCAAT/Enhancer-binding Protein ␤ (C/EBP␤) inhibits differentiation of muscle satellite cells and is rapidly down-regulated in early myogenesis. Results: The E3 ubiquitin ligase Mouse double minute 2 homolog (Mdm2) targets C/EBP␤ for degradation thereby promoting entry into myogenesis. Conclusion: Mdm2 expression is necessary for entry into myogenesis. Significance: Establishes a new role for Mdm2 in cellular differentiation.

Spam detection is an essential and unavoidable problem in today’s society. Most of the existing studies have used string-based detection methods with models and have been conducted on a single language, especially with English datasets. However, in the current global society, research on languages other than English is needed. String-based spam detection methods perform different preprocessing steps depending on language type due to differences in grammatical characteristics. Therefore, our study proposes a text-processing method and a string-imaging method. The CNN 2D visualization technology used in this paper can be applied to datasets of various languages by processing the data as images, so they can be equally applied to languages other than English. In this study, English and Korean spam data were used. As a result of this study, the string-based detection models of RNN, LSTM, and CNN 1D showed average accuracies of 0.9871, 0.9906, and 0.9912, respectively. On the other hand, the CNN 2D image-based detection model was confirmed to have an average accuracy of 0.9957. Through this study, we present a solution that shows that image-based processing is more effective than string-based processing for string data and that multilingual processing is possible based on the CNN 2D model.

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