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Asai, K.

Publications and source records attributed to Asai, K..

13 recordsLinked to original sources

Mountain Centroid: RNA Ensemble Representation with Mountain Profiles

Background: RNA molecules form thermodynamic ensembles, but interpretation often requires a single representative structure. Existing base-pair centroid estimators assess agreement at the level of individual base pairs and do not directly target nesting depth along the sequence. Methods: We introduce Mountain Centroid, which minimizes expected squared mountain-profile distance, and derive dynamic programming algorithms with and without RNA pairing constraints. We also combine the Mountain Centroid objective with the base-pair centroid gain. Results: Across 21,254 RNAStrAlign sequences, Mountain Centroid had lower median normalized mean squared mountain distance (NMSMD) than minimum-free-energy (MFE) and base-pair centroid ({gamma} = 1) structures, whereas its median base-pair F1 was lower. Imposing RNA pairing constraints improved base-pair F1 for 59.35% of sequences and reduced it for 3.58%. At an illustrative weight, the combined objective had median base-pair F1 similar to MFE while retaining lower median NMSMD than MFE and all tested {gamma}-centroid settings. Conclusions: Mountain Centroid represents an RNA structural ensemble with a single secondary structure that reflects how nesting depth varies across nucleotide positions. Combining mountain-profile and individual-base-pair criteria allows their relative contributions to be varied.

bioinformatics↗

PKProbDesign: RNA inverse folding including pseudoknots by optimizing thermodynamic folding probability

Motivation: RNA inverse folding, the design of RNA sequences that fold into specified target secondary structures, is a central problem in RNA design, with applications in functional RNA engineering, synthetic biology, and nucleic-acid therapeutics. This task becomes especially challenging for pseudoknotted target structures because pseudoknots break the nested structure assumed by standard thermodynamic folding models. Existing pseudoknot inverse-folding methods often rely on structure-predictor-based objectives. These methods do not directly optimize the probability that a sequence folds into the specified pseudoknotted target structure. Such optimization requires an evaluator that can assign a folding probability to the specified target within a pseudoknot-aware ensemble. Results: We present PKProbDesign, a sampling-based inverse-folding framework that directly optimizes a thermodynamic folding-probability objective for pseudoknotted targets. For each target, sampled sequences are scored by combining the folding probability of a pseudoknot-free scaffold with the conditional folding probability of the remaining extension component. On 254 unique density-2 PseudoBase++ targets, PKProbDesign achieved the highest pseudo-joint folding probability on 245 targets, compared with 6 for DesiRNA, 3 for MODENA, and none for antaRNA. Conclusions: PKProbDesign demonstrates that pseudoknot inverse folding can be formulated with target folding probability as its objective rather than structure-prediction agreement alone. By combining scaffold decomposition with conditional folding-probability evaluation based on CParty, the method provides a practical folding-probability-based approach to designing sequences for density-2 pseudoknotted targets. Availability: The source code of PKProbDesign is available at https://github.com/TakumiOtagaki/PKProbDesign.

bioinformatics↗

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↗

Designing mRNA coding sequence via multimodal reverse translation language modeling with Pro2RNA

mRNA coding sequence design is a critical component in the development of mRNA vaccines, nucleic acid therapeutics, and heterologous gene expression systems. While large language models have recently been successfully applied to protein design and RNA modeling, designing optimal mRNA coding sequences for a given protein, particularly in a species-specific manner, remains a major challenge. Here, we present Pro2RNA, a multimodal reverse-translation language model that generates mRNA coding sequences from their corresponding protein sequences while explicitly conditioning on host organism taxonomy information. Pro2RNA integrates multiple pretrained language models across different modalities, including ESM2 for protein representation, SciBERT for taxonomy understanding, and a generative RNA language model for mRNA codon-level sequence generation. By training on mRNA-protein pairs from eukaryote and bacteria datasets respectively, Pro2RNA learns species-dependent genetic codes and codon usage patterns, enabling the generation of host-adapted and natural-like mRNA coding sequences. Across multiple benchmark evaluations, Pro2RNA matches or surpasses existing optimization methods, demonstrating its potential as a powerful and flexible framework for species-aware mRNA coding sequence design.

bioinformatics↗

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↗

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↗

FOXM1 Expression in Invasive Ductal Breast Carcinoma of No Special Type: Insights from RNA-seq and Immunohistochemical Analysis

BackgroundInvasive ductal carcinoma of no special type (IDC-NST) is the most common subtype of breast cancer, characterized by significant clinical heterogeneity. Forkhead box M1 (FOXM1) is a key transcription factor involved in cell cycle regulation and tumor progression, but its expression profile and clinical significance in IDC-NST remain incompletely understood. MethodsWe analyzed FOXM1 expression in a cohort of 100 IDC-NST patients using RNA sequencing and immunohistochemistry (IHC). FOXM1 mRNA levels were quantified, and protein expression was scored based on the percentage of positive tumor cells. Differentially expressed genes (DEGs) between high and low FOXM1 expression groups were identified, followed by pathway enrichment and protein-protein interaction (PPI) network analyses. The prognostic value of FOXM1 was evaluated by recurrence-free survival (RFS) analysis. ResultsFOXM1 protein expression correlated significantly with mRNA abundance in 22 representative cases (p < 0.05). Receiver operating characteristic (ROC) curve analysis identified a cut-off value of 4.954 CPM for FOXM1 mRNA to predict recurrence (AUC = 0.642). High FOXM1 expression was associated with larger tumor size, higher histological grade, and negative hormone receptor status. Patients with high FOXM1 expression exhibited significantly poorer 5-year RFS (73.6% vs. 92.3%, p < 0.001). Multivariate analysis confirmed FOXM1 as an independent prognostic factor (HR 15.26; p = 0.026). Transcriptomic profiling revealed 190 upregulated and 197 downregulated genes in the FOXM1-high group, enriched in cell cycle and mitotic pathways. PPI network analysis positioned FOXM1 as a central hub coordinating genes involved in chromosomal stability and mitosis. ConclusionsFOXM1 overexpression is a strong independent predictor of poor prognosis in IDC-NST and plays a critical role in tumor proliferation and genome integrity. These findings support FOXM1 as a potential prognostic biomarker and therapeutic target, warranting further investigation into FOXM1-targeted therapies for breast cancer management.

cancer biology↗

Tethering NDC80-NUF2 to microparticles is sufficient to enable their biorientation in the spindle

Faithful chromosome segregation requires biorientation, where the pair of kinetochores on the chromosome establish bipolar microtubule attachment. The integrity of the kinetochore, a macromolecular complex built on centromeric DNA, is required for biorientation, but components sufficient for biorientation remain unknown. In this study, we show that tethering the outer kinetochore heterodimer NDC80-NUF2 to the surface of microbeads with no bipolar cue is sufficient for them to establish a biorientation-like state in mouse oocytes. NDC80-NUF2 microbeads efficiently and stably align at the spindle equator, forming bipolar microtubule attachments. Furthermore, they can self-correct alignment errors. They align independently of the outer kinetochore proteins SPC24-SPC25, KNL1, the MIS12 complex, or inner kinetochore proteins. Aurora-mediated NDC80 phosphoregulation promoted microbead alignment under challenging conditions. Interestingly, larger microbeads align more rapidly, suggesting that large platform size enhances NDC80-NUF2-mediated biorientation. This study shows a biohybrid kinetochore design for synthetic biorientation of microscale particles in cells.

cell biology↗

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↗

RNAfamProb Plus NeoFold: Estimations of Posterior Probabilities on RNA Structural Alignment and RNA Secondary Structures with Incorporating Homologous-RNA Sequences

MotivationThe simultaneous optimization of the sequence alignment and secondary structures among RNAs, structural alignment, has been required for the more appropriate comparison of functional ncRNAs than sequence alignment. Pseudo-probabilities given RNA sequences on structural alignment have been desired for more-accurate secondary structures, sequence alignments, consensus secondary structures, and structural alignments. However, any algorithms have not been proposed for these pseudo-probabilities.\n\nResultsWe invented the RNAfamProb algorithm, an algorithm for estimating these pseudo-probabilities. We performed the application of these pseudo-probabilities to two biological problems, the visualization with these pseudo-probabilities and maximum-expected-accuracy secondary-structure (estimation). The RNAfamProb program, an implementation of this algorithm, plus the NeoFold program, a maximum-expected-accuracy secondary-structure program with these pseudo-probabilities, demonstrated prediction accuracy better than three state-of-the-art programs of maximum-expected-accuracy secondary-structure while demanding running time far longer than these three programs as expected due to the intrinsic serious problem-complexity of structural alignment compared with independent secondary structure and sequence alignment. Both the RNAfamProb and NeoFold programs estimate matters more accurately with incorporating homologous-RNA sequences.\n\nAvailabilityThe source code of each of these two programs is available on each of \"https://github.com/heartsh/rnafamprob\" and \"https://github.com/heartsh/neofold\".\n\nContact\"tagashira_masaki_17@stu-cbms.k.u-tokyo.ac.jp\" and \"asai@k.u-tokyo.ac.jp\".\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

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↗

RintC: fast and accuracy-aware decomposition of distributions of RNA secondary structures with extended logsumexp

The analysis of secondary structures is essential to understanding the function of RNAs. Because RNA molecules thermally fluctuate, it is necessary to analyze the probability distribution of secondary structures. Existing methods, however, are not applicable to long RNAs owing to their high computational complexity. Additionally, previous research has suffered from two numerical difficulties: overflow and significant numerical error. In this research, we reduced the computational complexity in calculating the landscape of the probability distribution of secondary structures by introducing a maximum-span constraint. In addition, we resolved numerical computation problems through two techniques: extended logsumexp and accuracy-guaranteed numerical computation. We analyzed the stability of the secondary structures of 16S ribosomal RNAs at various temperatures without overflow. The results obtained are consistent with in vivo assay results reported in previous research. Furthermore, we quantitatively assessed numerical stability using our method. These results demonstrate that the proposed method is applicable to long RNAs. Source code is available on https://github.com/eukaryo/rintc.

bioinformatics↗

Nearest-neighbor parameter for inosine-cytosine pairs through a combined experimental and computational approach

In RNA secondary structure prediction, nearest-neighbor parameters are used to determine the stability of a given structure. We derived the nearest-neighbor parameters for RNAs containing inosine-cytosine pairs. For parameter derivation, we developed a method that combines UV adsorption measurement experiments with free-energy calculations using molecular dynamics simulations. The method provides fast drop-in parameters for modified bases. Derived parameters were compared and found to be consistent with existing parameters for canonical RNAs. A duplex with an internal inosine-cytosine pair is 0.9 kcal/mol more unstable than the same duplex with an internal guanine-cytosine pair, and is as stable as the one with an internal adenine-uracil pair (only 0.1 kcal/mol more stable) on average.

bioinformatics↗