Biology subjects

Terai, G.

Publications and source records attributed to Terai, G..

5 recordsLinked to original sources

bioRxiv2026

Pathway-Centric Integration of CRISPR Fitness with Molecular Features Draws Cancer State Maps

Cancer cells display heterogeneous pathway activity that shapes therapeutic vulnerability, but mapping it remains challenging. Transcriptomic scores do not directly measure functional activity, and CRISPR knockout data alone lack molecular interpretability. We introduce StateMap, a pathway-centric framework integrating gene expression and genome-wide CRISPR knockout fitness data from the Cancer Dependency Map. For a given pathway, StateMap selects features by co-dependency and mutual information, then projects cell lines into a low-dimensional space reflecting pathway activity and molecular state. Applied to the Hippo pathway, it resolved five functional states refining the YAP-on/YAP-off dichotomy. Notably, the Hippo-strong state showed selective dependence on integrin V{beta}5; ITGAV depletion triggered Hippo-dependent cell aggregation and G1 arrest via enhanced cell-cell adhesion. Machine learning transfer to TCGA identified a matching subtype with poor prognosis, nominated NNMT as a biomarker, and predicted sensitivity to the V inhibitor Cilengitide. StateMap enables pathway-specific state mapping and discovery of state-selective therapeutic vulnerabilities.

cancer biology↗

bioRxiv2025

LinearCapR: Linear-time computation of per-nucleotide structural-context probabilities of RNA without base-pair span limits

MotivationRNA molecules adopt dynamic ensembles of secondary structures, where the local structural context of each nucleotide-such as whether it resides in a stem or a specific type of loop-strongly shapes molecular interactions and regulatory function. Structural-context probabilities therefore provide a more functionally informative view of RNA folding than the minimum free energy structures or base-pairing probabilities. However, existing tools either require O (N3) time or employ span-restricted approximations that omit long-range base-pairs, limiting their applicability to large and biologically important RNAs. ResultsWe introduce LinearCapR, enabling linear-time, span-unrestricted computation of structural-context marginalized probabilities, using beam-pruned Stochastic Context Free Grammar-based computation. LinearCapR retains global ensemble features lost by span-limited methods and yields superior predictive power on bpRNA-1m(90) dataset, especially for multiloops and exterior regions, as well as long-distance stems. LinearCapR supports analysis of long RNAs, demonstrated on the full genome of SARS-CoV-2. ConclusionsLinearCapR provides the first base-pair-span-unrestricted, linear-time framework for RNA structural-context analysis, retaining key thermodynamic ensemble features essential for functional interpretation. It enables large-scale studies of viral genomes, long non-coding RNAs, and downstream analyses such as RNA-binding protein site prediction. AvailabilityThe source code of LinearCapR is available at https://github.com/hoget157/LinearCapR.

bioinformatics↗

bioRxiv2025

Gradient-based Optimization for mRNA Sequence Design

MotivationOptimization of mRNA sequences presents fundamental challenges to balance multiple physicochemical/biological properties--including accessibility, stability, and translation efficiency--while preserving amino acid sequences. The discrete nature of RNA sequence design hinders direct application of gradient-based methods, despite their potential for leveraging modern deep learning predictors of the properties in biological sequence design. ResultsWe present the Input Data Differentiable Designer (ID3) framework, a unified computational approach for mRNA sequence optimization that enables gradient-based optimization of discrete RNA sequences through innovative mathematical techniques. ID3 framework encompasses 12 constrained variants across four base configurations and three constraint mechanisms: Codon Profile Constraint, Amino Matching Softmax, and Lagrangian multipliers. The ID3 framework treats trained models as fixed differentiable functions while optimizing input data through continuous probability distributions. We also provide convergence analyses from the perspective of trained model input optimization. Availability and implementationhttps://github.com/Li-Hongmin/ID3.git Contactlihongmin@edu.k.u-tokyo.ac.jp, terai@edu.k.u-tokyo.ac.jp, asai@k.u-tokyo.ac.jp

bioinformatics↗

bioRxiv2022

Remarkable improvement in detection of readthrough downstream-of-gene transcripts by semi-extractable RNA-sequencing

The mammalian cell nucleus contains dozens of membrane-less nuclear bodies that play significant roles in various aspects of gene expression. Several nuclear bodies are nucleated by specific architectural noncoding RNAs (arcRNAs) acting as structural scaffolds. We have reported that a minor population of cellular RNAs exhibits an unusual semi-extractable feature upon using the conventional procedure of RNA preparation and that needle shearing or heating of cell lysates remarkably improves extraction of dozens of RNAs. Because semi-extractable RNAs, including known arcRNAs, commonly localize in nuclear bodies, this feature may be a hallmark of arcRNAs. Using the semi-extractability of RNA, we performed genome-wide screening of semi-extractable long non-coding RNAs to identify new candidate arcRNAs for arcRNA under hyperosmotic and heat stress conditions. After screening stress-inducible and semi-extractable RNAs, hundreds of readthrough downstream-of-gene (DoG) transcripts over several hundreds of kilobases, many of which were not detected among RNAs prepared by the conventional extraction procedure, were found to be stress-inducible and semi-extractable. We further characterized some of the abundant DoGs and found that stress-inducible transient extension of the 3-UTR made DoGs semi-extractable. Furthermore, they were localized in distinct nuclear foci that were sensitive to 1,6-hexanediol. These data suggest that semi-extractable DoGs exhibit arcRNA-like features and our semi-extractable RNA-seq is a powerful tool to extensively monitor DoGs that are induced under specific physiological conditions.

molecular biology↗

bioRxiv2019

LncRNA-dependent nuclear stress bodies promote intron retention through SR protein phosphorylation

A number of long noncoding RNAs (lncRNAs) are induced in response to specific stresses to construct membrane-less nuclear bodies; however, their function remains poorly understood. Here, we report the role of nuclear stress bodies (nSBs) formed on highly repetitive satellite III (HSATIII) lncRNAs derived from primate-specific satellite III repeats upon thermal stress exposure. A transcriptomic analysis revealed that depletion of HSATIII lncRNAs, resulting in elimination of nSBs, promoted splicing of 533 retained introns during thermal stress recovery. A HSATIII-Comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS) analysis identified multiple splicing factors in nSBs, including serine and arginine-rich pre-mRNA splicing factors (SRSFs), the phosphorylation states of which affect splicing patterns. SRSFs are rapidly dephosphorylated upon thermal stress exposure. During stress recovery, CDC like kinase 1 (CLK1) was recruited to nSBs and accelerated the re-phosphorylation of SRSF9, thereby promoting target intron retention. Our findings suggest that HSATIII-dependent nSBs serve as a conditional platform for phosphorylation of SRSFs by CLK1 to promote the rapid adaptation of gene expression through intron retention following thermal stress exposure.

molecular biology↗