Biology subjects

Nishi, Y.

Publications and source records attributed to Nishi, Y..

4 recordsLinked to original sources

bioRxiv2026

Profiling and Targeting of Regulatory RNAs to Upregulate Gene Expression

Transcription of long noncoding RNAs (lncRNAs), including enhancer RNAs (eRNAs) and promoter-associated RNAs (paRNAs), collectively termed regulatory RNAs (regRNAs), is a hallmark of active gene expression, yet it remains unknown whether regRNAs can be targeted to selectively enhance transcription in cis. We developed regRNA Capture-seq, a high-throughput method to profile regRNAs, and applied it to primary human hepatocytes, annotating thousands of regRNAs at [~]2,000 enhancers and promoters. Using this approach, we interrogated a genetically validated enhancer of the ornithine transcarbamylase (OTC) gene, mutations of which cause OTC deficiency (OTCD), the most common urea cycle disorder. Antisense oligonucleotides (ASOs) targeting enhancer-derived regRNAs led to dose-dependent upregulation of OTC in hepatocytes. Mechanistically, ASOs altered regRNA structure, elevated regRNA levels, displaced transcriptional repressors, and increased H3K27 acetylation at the targeted enhancer. This work establishes a potential therapeutic strategy for addressing haploinsufficiency and highlights regRNAs as actionable targets for ASO-mediated upregulation of gene expression.

genomics↗

bioRxiv2025

Excellent agreement between automated deep learning-based and manual DWI infarct volume measurement in hyperacute stroke

BackgroundDiffusion-weighted imaging (DWI) lesion volume and infarct growth are important imaging markers in acute ischemic stroke, but manual volume measurement is time-consuming and resource-intensive. Deep learning (DL)-based automated segmentation may facilitate rapid assessment; however, its performance on hyperacute DWI has not been sufficiently assessed. The aim was to evaluate the agreement between DL-based automated and manual infarct volume measurements and to compare their ability to predict clinical outcomes in patients treated with mechanical thrombectomy (MT). MethodConsecutive MT-treated patients (September 2014-December 2019) who underwent DWI at admission and at approximately 24 hours were retrospectively analyzed. Manual infarct volume was measured by stroke neurologists. Automated measurements were obtained using DL-based software. Agreement was assessed using Pearsons correlation, Bland-Altman analysis, and intraclass correlation coefficients (ICC 2,1). Inter- and intra-rater reliabilities were evaluated in a randomly selected subgroup of 150 patients. Predictive ability for a good outcome at 3 months (modified Rankin Scale score 0-2 or stable/improved from premorbid status) was compared using C-statistics and DeLongs test. ResultsA total of 371 patients (677 DWI scans) were included. Manual and automated measurements showed very strong correlation (r = 0.96) with minimal bias (-1.77 mL). The ICC for manual-automated agreement was 0.959 (95% CI, 0.952-0.965), comparable to inter- and intra-rater ICCs. Agreement remained high across onset-to-imaging times and lesion sizes. Predictive abilities for a good outcome were similar for manual and automated admission DWI volume (C-statistics 0.867 vs. 0.861) and infarct growth (0.859 vs. 0.853). Manual follow-up DWI volume showed slightly better predictive ability than automated measurement (0.880 vs. 0.866). ConclusionDL-based automated infarct volume measurement shows excellent agreement with experienced clinicians, with predictive performance comparable to manual assessment. Automated DWI-based quantification is reliable and feasible for use in hyperacute stroke management.

neuroscience↗

bioRxiv2025

Online supervised learning of temporal patterns in biological neural networks under feedback control

In vitro biological neural networks (BNNs) provide a well-defined model system to constructively investigate how living cells interact with their environment to shape high-dimensional dynamics that could be used to generate a coherent temporal output, such as those required for motor control. Here, we developed a real-time closed-loop BNN system capable of generating periodic and chaotic temporal signals by integrating cultured cortical neurons with microfluidic devices and high-density microelectrode arrays. We show that training a simple linear decoder with fixed feedback weights enables the system to learn and autonomously generate diverse temporal patterns. When feedback was switched on, irregular activity in BNNs is transformed into low-dimensional, structured dynamics, producing coherent trajectories characterized by stable transitions between neural states. BNNs trained on different target frequencies--ranging from 4 to 30 s--could be trained to sustain oscillations at distinct frequencies, demonstrating their adaptability. Importantly, a top-down control of self-organized network formation with microfluidic devices is the key to suppress excessive synchronization and increase dynamical complexity in BNNs, facilitating the training and robust output generation. This work offers a biologically inspired platform for understanding the physical basis of cortical computation and for advancing energy-efficient neuromorphic computation. Significance StatementReservoir computing is a machine learning paradigm that exploits the transient dynamics of high-dimensional nonlinear systems. Although it was originally inspired by the mammalian brain and widely explored in physical systems, its implementations in biological neural networks (BNNs) have been limited due to their excessive connectivity and global synchrony in vitro. Here, we use microfluidic devices to construct modular, nonrandomly connected BNNs and integrate them with microelectrode arrays in a closed-loop reservoir computing environment. We show that the system can be trained to autonomously output various temporal signals, with the modular connectivity that is essential for learning. In vitro BNNs provide unique alternatives for physical reservoirs with dynamic adaptability.

neuroscience↗

bioRxiv2021

Post-transcriptionally regulated genes are essential for pluripotent stem cell survival

The effects of transcription factors on the maintenance and differentiation of pluripotent stem cells (PSCs) have been well studied. However, the importance of post-transcriptional regulatory mechanisms, which cause the quantitative dissociation of mRNA and protein expression, has not been explored in detail. Here, by combining transcriptome and proteome profiling, we identified 228 post-transcriptionally regulated genes with strict upregulation of the protein level in PSCs. Among them, we found that 84 genes were vital for the survival of PSCs and HDFs, including 20 genes that were specifically necessary for the survival of PSCs. These 20 proteins were upregulated only in PSCs and not in differentiated cells derived from the three germ layers. Subcellular fractionation of the mRNA showed that the expression of most of the 20 proteins was regulated at the mRNA localization stage from the nucleus to the cytoplasm, but their translation efficiency was constant. Together, these results revealed that post-transcriptionally regulated genes have a crucial role in PSC survival.

molecular biology↗