Biology subjects

Wu, J.-T.

Publications and source records attributed to Wu, J.-T..

4 recordsLinked to original sources

bioRxiv2022

Microsecond pulse electrical stimulation modulates cell migration

Wound healing is a complicated process for maintaining skin integrity after injury, for which electrical stimulations (ES) are ascribed to promote wound healing by facilitating cell migration. Time-shortening of the stimulation treatment from current hours to minutes for efficient wound healing but free of cell damage in return, is however rather a challenge. Here, a novel mechanism of ultrashort pulse electric field (PEF), microsecond PEF at higher voltage, is proposed and realized to promote wound healing under a much short time (seconds) for the total treatment. We revealed that microsecond PEF regulated actin cytoskeleton reorganization and focal adhesion turnover, promoting fibroblasts migration in 2D cell cultures under the pulse stimulation. This accelerated fibroblast migration was accompanied by the mutual promotion with extracellular matrix (ECM) alignment in 3D microenvironments, which cooperatively benefit the eventual wound healing, and these findings were further confirmed by the enhanced skin wound healing in a classic mouse model. Additionally, we coined an actin- and collagen-dependent mechanism of microsecond PEF-mediated wound healing. The quantitative mechanism proposed here for our novel microsecond pulse electric filed (sPEF) methodology orients the new practical electric treatment in a wide range of biomedical applications, such as wound healing, regenerative medicine, and tissue engineering.

bioengineering↗

bioRxiv2020

accuEnhancer: Accurate enhancer prediction by integration of multiple cell type data with deep learning

Enhancers are one class of the regulatory elements that have been shown to act as key components to assist promoters in modulating the gene expression in living cells. At present, the number of enhancers as well as their activities in different cell types are still largely unclear. Previous studies have shown that enhancer activities are associated with various functional data, such as histone modifications, sequence motifs, and chromatin accessibilities. In this study, we utilized DNase data to build a deep learning model for predicting the H3K27ac peaks as the active enhancers in a target cell type. We propose joint training of multiple cell types to boost the model performance in predicting the enhancer activities of an unstudied cell type. The results demonstrated that by incorporating more datasets across different cell types, the complex regulatory patterns could be captured by deep learning models and the prediction accuracy can be largely improved. The analyses conducted in this study demonstrated that the cell type-specific enhancer activity can be predicted by joint learning of multiple cell type data using only DNase data and the primitive sequences as the input features. This reveals the importance of cross-cell type learning, and the constructed model can be applied to investigate potential active enhancers of a novel cell type which does not have the H3K27ac modification data yet. AvailabilityThe accuEnhancer package can be freely accessed at: https://github.com/callsobing/accuEnhancer

bioinformatics↗

bioRxiv2020

Long-term warming weakens stabilizing effects of biodiversity in aquatic ecosystems

Despite the consensus that warming will affect biodiversity, alter physicochemical environments, and disrupt biological interactions, the relative importance of these key processes and how they interact to determine overall ecosystem function is poorly understood. Here, we analyze long-term (16[~]39 years) time series data from ten aquatic ecosystems and use convergent cross mapping (CCM) to quantify the hidden causal network linking species diversity, ecosystem function, and physicochemical factors. We observe that aquatic ecosystems subject to stronger warming exhibit decreased stability (larger fluctuations in phytoplankton biomass). We further show that this effect can be attributed to a weakening of stabilizing causal pathways between biodiversity, nutrient cycling, and phytoplankton biomass. Thus, rather than thinking in terms of separate factors, a more holistic view, that causally links biodiversity and the other ecosystem components, is required to understand and predict climate impacts on the temporal stability of aquatic ecosystems.

ecology↗

bioRxiv2018

GenEpi: Gene-based Epistasis Discovery Using Machine Learning

Genome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimers disease (AD). In this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power. Availability: GenEpi is an open-source python package and available free of charge only for non-commercial users. The package can be downloaded from https://github.com/Chester75321/GenEpi, and has also been published on The Python Package Index.

bioinformatics↗