Biology subjects

Tasaki, S.

Publications and source records attributed to Tasaki, S..

23 records · Page 2Linked to original sources

bioRxiv2023

How far are we from personalized gene expression prediction using sequence-to-expression deep neural networks?

Introductory Paragraph Deep learning methods have recently become the state-of-the-art in a variety of regulatory genomic tasks1-6 including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions, however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluates their utility as personal DNA interpreters. We used paired Whole Genome Sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learnt sequence motif grammar, and suggest new model training strategies to improve performance.

bioinformatics↗

bioRxiv2022

The molecular genetic landscape of human brain size variation

Human brain size increases dynamically through early development, peaks in adolescence, and varies up to two-fold among adults. Although previous studies have elucidated changes in brain size across evolution, development, traits, and diseases, the molecular underpinnings of interindividual variation in brain size remain unknown. Here, we leverage postmortem brain RNA sequencing and estimates of brain weight (BW) in 2,531 individuals across three independent datasets, to identify 928 genes that show genome-wide significant associations with either higher or lower BW (BW+, BW-, respectively). These BW gene sets showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto developmental, functional and cellular axes of brain organization. Expression differences among evolutionarily conserved BW genes were predictive of interspecies differences in brain size, and functional annotation of BW genes revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses of in vivo neuroimaging phenotypes confirmed that the genetic regulation of BW-transcripts influences cortical surface area and volume, as well as behavioral traits related to brain function and disease. Cumulatively, our study represents a major step towards the goal of delineating the causal mechanisms of human brain size variation in health and disease.

neuroscience↗

bioRxiv2022

Migrating mesoderm cells self-organize into a dynamic meshwork structure during chick gastrulation

Migration of cell populations is a fundamental process in morphogenesis and disease. The mechanisms of collective cell migration of epithelial cell populations have been well studied. It remains unclear, however, how the highly motile mesenchymal cells, which migrate extensively throughout the embryo, are connected with each other and coordinated as a collective. During gastrulation in chick embryos, the mesoderm cells, that are formed by an epithelial-to-mesenchymal transition (EMT), migrate in the 3D space between ectoderm and endoderm of the embryo. Using live imaging and quantitative analysis, such as topological data analysis (TDA), we found that the mesoderm cells undergo a novel form of collective migration, in which they form a meshwork structure while moving away from the primitive streak. This meshwork is supported by N-cadherin-mediated cell-cell adhesion, which undergoes rapid reorganization. Overexpressing a mutant form of N-cadherin decreases the speed of tissue progression and the directionality of the collective cell movement, whereas the speed of individual cells remains unchanged. To investigate how this meshwork arises and how it contributes to the cell movement, we utilized an agent-based theoretical model, showing that cell elongation, cell-cell adhesion, and cell density are the key parameters for the meshwork formation. These data provide novel insights into how a supracellular structure of migrating mesenchymal cells forms and how it facilitates efficient migration during early mesoderm formation.

developmental biology↗

bioRxiv2020

Life cycle of Bacillus subtilis population: biofilm life cycle generation and response to environmental pH

Microbial populations are ubiquitous, many of which form robust, multicellular structures called biofilms that protect cells from environmental damage. Understanding their migration cycles is crucial for controlling target microbial populations. Here we develop a multilevel mathematical model that captures the migration cycle of Bacillus subtilis cell populations and reproduces the periodic expansion of colonies into concentric circles. We construct an input/output model that regulates cell types in response to environmental conditions; based on this, we propose a theoretical model for the generation of cell population migration cycles. We then construct a multilevel system that links the cell-level state regulation model with tissue-level partial differential equations governing cell density, signal, and resource fields. Through this approach, we successfully simulate cyclically expanding colonies as well as their environmental pH dependence. The proposed models provide a robust basis for describing highly self-regulating multicellular systems and predicting broader biofilm-related phenomena.

microbiology↗

bioRxiv2020

Decoding differential gene expression

Identifying the molecular mechanisms that control differential gene expression (DE) is a major goal of basic and disease biology. Combining the strengths of systems biology and deep learning in a model called DEcode, we are able to predict DE more accurately than traditional sequence-based methods, which do not utilize systems biology data. To determine the biological origins of this accuracy, we identify the most predictive regulators and types of regulatory interactions in DEcode, contrasting their roles across many human tissues. Diverse systems biology, ontological and disease-related assessments all point to the predominant influence of post-translational RNA-binding factors on DE. Through the combinatorial gene regulation that is captured in DEcode, it is even possible to predict relatively subtle person-to-person variation in gene expression. We demonstrate the broad applicability of these clinically-relevant predictions by predicting drivers of aging throughout the human lifespan, gene coexpression relationships on a genome-wide scale, and frequent DE in diverse conditions. Researchers can freely access DEcode to utilize genomic big data in identifying influential molecular mechanisms for any human expression data - www.differentialexpression.org.

systems biology↗