Biology subjects

Miyaki, S.

Publications and source records attributed to Miyaki, S..

3 recordsLinked to original sources

bioRxiv2026

Extracellular Sulfatase Regulation of BMP-7 and FGF-2 Signaling in Human Articular Chondrocytes

We performed a new set of experiments to confirm findings reported in our previous publication regarding the role of Sulf-1 and Sulf-2 in regulating BMP-7 and FGF-2 signaling in human articular chondrocytes. Using primary chondrocytes from five independent human donors, we examined the effects of Sulf knockdown on Smad1/5 and Erk1/2 phosphorylation. Sulf-1 and Sulf-2 knockdown consistently reduced BMP-7-induced Smad1/5 phosphorylation and enhanced FGF-2-induced Erk1/2 phosphorylation. Although the magnitude of Erk1/2 activation was somewhat lower than originally reported, the direction and statistical significance of the effects were preserved. These results confirm the original conclusions and support the role of Sulfs as dual regulators of BMP-7 and FGF-2 signaling pathways in human chondrocytes.

cell biology↗

bioRxiv2024

Cell segmentation without annotation by unsupervised domain adaptation based on cooperative self-learning

Semantic cell segmentation from microscopic images is essential for the quantitative evaluation of cell morphology. Although supervised deep-learning-based models offer accurate segmentation, their performance degrades for unknown cell types. To address this problem, unsupervised domain adaptation methods based on adversarial training, self-training, or a combination of these approaches have been developed in recent years. These methods train the model using inference labels from the unknown domain as pseudo labels with reliability to resolve the discrepancy between the features of the unknown and known domains. However, conventional methods require a predefined threshold to calculate pseudo-labels reliability, leading to costly hyperparameter tuning. Here, we developed an unsupervised domain adaptation for semantic cell segmentation with cooperative self-learning (CULPICO: Cooperative Unsupervised Learning for PIxel-wise COloring) that does not require predefined threshold of the pseudo-labels reliability. The proposed method consists of two independent segmentation models and a mutual exchange mechanism of inference data. The models infer a label probability at each pixel and generate a pseudo-label as unsupervised learning. The pseudo-labels created by each model are mutually used as ground truth in the other model. Loss function is corrected by considering pixel-level discrepancies between the label probabilities inferred by the two models. The proposed method, despite being an unsupervised learning method, can segment efficiently the unknown cell types without labels with an accuracy comparable to supervised learning models. Our method, which could solve the performance degradation problem without constructing new datasets, is expected to accelerate life science by reducing the cost of extracting quantitative biological knowledge.

systems biology↗

bioRxiv2019

Exhaustive identification of conserved upstream open reading frames with potential translational regulatory functions from animal genomes

Upstream open reading frames (uORFs) are present in the 5-untranslated regions of many eukaryotic mRNAs, and some peptides encoded by these regions play important regulatory roles in controlling main ORF (mORF) translation. We previously developed a novel pipeline, ESUCA, to comprehensively identify plant uORFs encoding functional peptides, based on genome-wide identification of uORFs with conserved peptide sequences (CPuORFs). Here, we applied ESUCA to diverse animal genomes, because animal CPuORFs have been identified only by comparing uORF sequences between a limited number of species, and how many previously identified CPuORFs encode regulatory peptides is unclear. By using ESUCA, 1,517 (1,373 novel and 144 known) CPuORFs were extracted from four evolutionarily divergent animal genomes. We examined the effects of 17 human CPuORFs on mORF translation using transient expression assays. Through these analyses, we identified seven novel regulatory CPuORFs that repressed mORF translation in a sequence-dependent manner, including one conserved only among Eutheria. We discovered a much higher number of animal CPuORFs than previously identified. Since most human CPuORFs identified in this study are conserved across a wide range of Eutheria or a wider taxonomic range, many CPuORFs encoding regulatory peptides are expected to be found in the identified CPuORFs.

bioinformatics↗