Universal Design of Luciferase Fusion Proteins for Epigenetic Modifications Detection Based on Bioluminescence Resonance Energy Transfer.

Global hypomethylation and promoter hypermethylation of tumor-suppressor genes are the hallmarks of cancer. We previously reported a global DNA methylation level sensing system based on dual-color bioluminescence resonance energy transfer (BRET) using methyl-CpG binding domain (MBD)-fused firefly luciferase (Fluc) and unmethyl-CpG binding domain (CXXC)-fused Oplophorus luciferase (Oluc). Moreover, BRET-based hydroxymethylation and hemi-methylation level sensing systems have been developed using hydroxymethyl-CpG and hemi-methyl-CpG binding domain-fused Fluc. These studies suggest that target epigenetic modifications can be simultaneously quantified using target-modification-binding protein-fused luciferases. In this study, we focused on the SnoopTag (SnT)/SnoopCatcher (SnC) protein ligation system to establish a universal design for fusion protein construction for any combination. SnT spontaneously forms an isopeptide bond with SnC; therefore, any kind of fusion protein would be constructed by the SnT/SnC system. To establish the proof of concept, MBD-SnT, CXXC-SnT, and SnC-Oluc were prepared and ligated MBD-SnT or CXXC-SnT to SnC-Oluc. The ligation products of MBD-SnT-SnC-Oluc and CXXC-SnT-SnC-Oluc showed luciferase activity and specific binding activity to methyl-CpG and unmethyl-CpG, respectively. The BRET signal using MBD-SnT-SnC-Oluc and CXXC-SnT-SnC-Oluc increased the amount of methyl-CpG and unmethyl-CpG in genomic DNA, respectively. There was a significant negative correlation between the BRET signals; therefore, the global DNA methylation level was quantified using the BRET signals (R2 = 0.99, and R.S.D. <3.5%). These results indicate that the SnT/SnC protein ligation system can be utilized to construct target modification-binding protein-fused luciferases in any combination that detects target modifications in genomic DNA based on BRET.

Cartoon-texture image decomposition using blockwise low-rank texture characterization.

Using a novel characterization of texture, we propose an image decomposition technique that can effectively decomposes an image into its cartoon and texture components. The characterization rests on our observation that the texture component enjoys a blockwise low-rank nature with possible overlap and shear, because texture, in general, is globally dissimilar but locally well patterned. More specifically, one can observe that any local block of the texture component consists of only a few individual patterns. Based on this premise, we first introduce a new convex prior, named the block nuclear norm (BNN), leading to a suitable characterization of the texture component. We then formulate a cartoon-texture decomposition model as a convex optimization problem, where the simultaneous estimation of the cartoon and texture components from a given image or degraded observation is executed by minimizing the total variation and BNN. In addition, patterns of texture extending in different directions are extracted separately, which is a special feature of the proposed model and of benefit to texture analysis and other applications. Furthermore, the model can handle various types of degradation occurring in image processing, including blur+missing pixels with several types of noise. By rewriting the problem via variable splitting, the so-called alternating direction method of multipliers becomes applicable, resulting in an efficient algorithmic solution to the problem. Numerical examples illustrate that the proposed model is very selective to patterns of texture, which makes it produce better results than state-of-the-art decomposition models.