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Gapmer antisense oligonucleotides cleave target RNA effectively in vivo, and is considered as promising therapeutics. Especially, gapmers modified with locked nucleic acid (LNA) shows potent knockdown activity; however, they also cause hepatotoxic side effects. For developing safe and effective gapmer drugs, a deeper understanding of the mechanisms of hepatotoxicity is required. Here, we investigated the cause of hepatotoxicity derived from LNA-modified gapmers. Chemical modification of gapmer’s gap region completely suppressed both knockdown activity and hepatotoxicity, indicating that the root cause of hepatotoxicity is related to intracellular gapmer activity. Gene silencing of hepatic ribonuclease H1 (RNaseH1), which catalyses gapmer-mediated RNA knockdown, strongly supressed hepatotoxic effects. Small interfering RNA (siRNA)-mediated knockdown of a target mRNA did not result in any hepatotoxic effects, while the gapmer targeting the same position on mRNA as does the siRNA showed acute toxicity. Microarray analysis revealed that several pre-mRNAs containing a sequence similar to the gapmer target were also knocked down. These results suggest that hepatotoxicity of LNA gapmer is caused by RNAseH1 activity, presumably because of off-target cleavage of RNAs inside nuclei.

For advanced materials characterization, a novel and extremely effective approach of pattern recognition in optical microscopic images of steels is demonstrated. It is based on fast Random Forest statistical algorithm of machine learning for reliable and automated segmentation of typical steel microstructures. Their percentage and location areas excellently agreed between machine learning and manual examination results. The accurate microstructure pattern recognition/segmentation technique in combination with other suitable mathematical methods of image processing and analysis can help to handle the large volumes of image data in a short time for quality control and for the quest of new steels with desirable properties.

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