Search bioRxiv⌕ Search

Biology subjects

Han, S.-H.

Publications and source records attributed to Han, S.-H..

2 recordsLinked to original sources

A nuclear pore complex component HOS1 mediates UV-B light-induced UVR8 nuclear localization for activating HY5 cascade in Arabidopsis

Upon ultraviolet-B (UV-B) exposure, the photoreceptor UVR8 translocates to the nucleus and interacts with COP1 to activate downstream transcription factors, notably including HY5. However, the mechanism of UVR8 nuclear import has remained unclear1,2. Here, we identified HOS1, a component of the nuclear pore complex (NPC), as a key mediator of UVR8 nuclear translocation under UV-B conditions. HOS1 directly interacts with UVR8 specifically in response to UV-B, facilitating its efficient import into the nucleus. Consistent with this role, HOS1-deficient mutants exhibit reduced HY5 protein accumulation, compromised UV-B tolerance, and impaired expression of HY5- regulated genes, including those involved in anthocyanin synthesis. These findings reveal a novel role of HOS1 in bridging UV-B perception in the cytoplasm and gene transcriptional activation in the nucleus by enabling UVR8 nuclear import. They highlight a distinct broader role of NPC components in coordinating plant transcriptional response to environmental stimuli.

plant biology↗

Empirical optimization of dual-sgRNA design for in vivo CRISPR/Cas9-mediated exon deletion in mice

CRISPR/Cas9 has transformed gene editing, enabling precise genetic modifications across species. However, existing sgRNA design prediction models based on in vitro data are difficult to generalize to in vivo contexts. In particular, approaches based on single-sgRNA design require additional filtering of in-frame mutations, which is inefficient in terms of both time and cost. In this study, we developed the first mammalian in vivo-trained prediction model to evaluate the efficiency of a dual-sgRNA-based exon deletion strategy. Using 230 editing outcomes of postnatal viable individuals, eight prediction models were constructed and evaluated based on generalized linear models and random forests. The final selected model, a Combined GLM, integrated the DeepSpCas9 score with k-mer sequence features, achieving an AUC of 0.759 (95% Confidence Interval: 0.697-0.821). Motif analysis revealed that CC sequences were associated with high efficiency and TT sequences were associated with low editing efficiency. This study demonstrates that integrating sequence-based features with existing design scores can improve sgRNA efficiency prediction in vivo. The proposed framework can be applied to the development of next-generation sgRNA design tools, with direct implications for gene therapy, effective animal model generation, and precision genome engineering. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/675005v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@12e6170org.highwire.dtl.DTLVardef@1ff2599org.highwire.dtl.DTLVardef@1fd61dborg.highwire.dtl.DTLVardef@23a426_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗