Biology subjects

Chang, W. K.

Publications and source records attributed to Chang, W. K..

5 recordsLinked to original sources

bioRxiv2025

Identification and classification of abundant RNA-binding proteins in the mouse lens and interactions of Carhsp1, Igf2bp1/ZBP1, and Ybx1 with crystallin and β-actin mRNAs

RNA-binding proteins (RBPs) are critical regulators of mRNAs controlling all processes such as RNA transcription, transport, localization, translation, mRNA:ncRNA interactions, and decay. Cellular differentiation is driven by tissue-specific and/or tissue-preferred expression of proteins needed for the optimal function of mature cells, tissues and organs. Lens fiber cell differentiation is marked by high levels of expression of crystallin genes encoding critical proteins for lens transparency and light refraction. Herein we performed proteomic and transcriptomic analyses of RBPs in differentiating mouse lenses to identify the most abundant RBPs and establish dynamic changes of their expression in differentiating lens. Expression analyses include highly abundant RBPs, including Carhsp1, Igf2bp1/ZBP1, Ybx1, Pabpc1, Ddx39, and Rbm38. Binding sites of Carhsp1, Ybx1, and Igf2bp1/ZBP1 were predicted in various crystallin and {beta}-actin mRNAs. Immunoprecipitations using antibodies against Carhsp1, Igf2bp1/ZBP1, and Ybx1 confirmed their interactions with A-, B-, and {gamma}A-crystallin mRNAs. A combination of single molecule RNA FISH (smFISH) and immunofluorescence was used to probe in vivo interactions of these RBPs with A-, B-crystallin, and {beta}-actin mRNAs in cytoplasm and nucleoplasm of cultured mouse lens epithelial cells. Together, these results open new avenues to perform comprehensive genetic, cell, and molecular biology studies of individual RBPs in the lens.

developmental biology↗

bioRxiv2024

Motor Hotspot Localization Based on Electroencephalography Using Convolutional Neural Network in Patients with Stroke

BackgroundAlthough transcranial magnetic stimulation (TMS) is the optimal tool for identifying individual motor hotspots for transcranial electrical stimulation (tES), it requires a cumbersome procedure in which patients must visit the hospital each time and rely on expert judgment to determine the motor hotspot. Therefore, in previous study, we proposed electroencephalography (EEG)-based machine learning approach to automatically identify individual motor hotspots. In this study, we proposed an advanced EEG-based motor hotspot identification algorithm using a deep learning model and assessed its clinical feasibility and benefits by applying it to stroke patient EEGs. MethodsEEG data were measured from thirty subjects as they performed a simple hand movement task. We utilized the five types of input data depending on the processing levels to assess the signal processing capability of our proposed deep learning model. The motor hotspot locations were estimated using a two-dimensional convolutional neural network (CNN) model. The error distance between the 3D coordinate information of the individual motor hotspots identified by the TMS (ground truth) and EEGs was calculated using the Euclidean distance. Additionally, we confirmed the clinical benefits of our proposed deep-learning algorithm by applying the EEG of stroke patients. ResultsA mean error distance between the motor hotspot locations identified by TMS and our approach was 2.34 {+/-} 0.19 mm when using raw data from only 9 channels around the motor area. When it was tested on stroke patients, the mean error distance was 1.77 {+/-} 0.15 mm using only 5 channels around the motor area. ConclusionWe have demonstrated that an EEG-based deep learning approach can effectively identify the individual motor hotspots. Moreover, we validated the clinical benefits of our algorithm by successfully implementing it in stroke patients. Our algorithm can be used as an alternative to TMS for identifying motor hotspots and maximizing rehabilitation effectiveness.

bioengineering↗

bioRxiv2022

Investigation of neuromodulatory effect of anodal cerebellar transcranial direct current stimulation on the primary motor cortex using functional near-infrared spectroscopy

BackgroundCerebellar brain inhibition (CBI), a neural connection between the cerebellum and primary motor cortex (M1), has been researched as a target pathway for neuromodulation to improve clinical outcomes in various neurological diseases. However, conflicting results of anodal cerebellar transcranial direct current stimulation (acb-tDCS) on M1 excitability indicate that additional investigation is required to examine its precise effect. Objective/HypothesisThis study aimed to gather evidence of the neuromodulatory effect of acb-tDCS on the M1 using functional near-infrared spectroscopy (fNIRS). MethodsSixteen healthy participants were included in this cross-over study. Participants received real and sham acb-tDCS in a random order, with a minimum one-week washout period between them. The anode and cathode were placed on the right cerebellum and the right buccinator muscle, respectively. Stimulation lasted 20 min at an intensity of 2 mA, and fNIRS data were recorded for 42 min (including a 4 min baseline before stimulation and an 18 min post-stimulation duration) using eight channels attached bilaterally on the M1. Resultsacb-tDCS induced a significant decrease in oxyhemoglobin (HbO) concentration (inhibitory effect) in the left (contralateral) M1, whereas it induced a significant increase in HbO concentration (excitatory effect) in the right (ipsilateral) M1 compared to sham tDCS during (p < 0.05) and after stimulation (p < 0.01) in a group level analysis. At the individual level, variations in the response to acb-tDCS were observed. ConclusionOur findings demonstrate the neuromodulatory effects of acb-tDCS on the bilateral M1 in terms of neuronal hemodynamics.

neuroscience↗

bioRxiv2021

Resolving the structure of phage-bacteria interactions in the context of natural diversity

Microbial communities are shaped by viral predators1. Yet, resolving which viruses (phages) and bacteria are interacting is a major challenge in the context of natural levels of microbial diversity2. Thus, fundamental features of how phage-bacteria interactions are structured and evolve in "the wild" remain poorly resolved3, 4. Here we use large-scale isolation of environmental marine Vibrio bacteria and their phages to obtain quantitative estimates of strain-level phage predator loads, and use all-by-all host range assays to discover how phage and host genomic diversity shape interactions. We show that killing in environmental interaction networks is sparse - with phage predator loads low for most bacterial strains and phages host-strain-specific in their killing. Paradoxically, we also find that although overlap in killing is generally rare between phages, recombination is common. Together, these results indicate that the number of hosts that phages infect is often larger than the number that they kill and suggest that recombination during cryptic co-infections is an important mode of phage evolution in microbial communities. In the development of phages for bioengineering and therapeutics it will be important to consider that nucleic acids of introduced phages may spread into local phage populations through recombination, and that the likelihood of transfer is not predictable based on killing host range.

microbiology↗

bioRxiv2019

Inferring the quasipotential landscape of microbial ecosystems with topological data analysis

Microbiome dynamics influence the health and functioning of human physiology and the environment and are driven in part by interactions between large numbers of microbial taxa, making large-scale prediction and modeling a challenge. Here, using topological data analysis, we identify states and dynamical features relevant to macroscopic processes.We show that gut disease processes and marine geochemical events are associated with transitions between community states, defined as topological features of the data density. We find a reproducible two-state succession during recovery from cholera in the gut microbiomes of multiple patients, evidence of dynamic stability in the gut microbiome of a healthy human after experiencing diarrhea during travel, and periodic state transitions in a marine Prochlorococcus community driven by water column cycling. Our approach bridges small-scale fluctuations in microbiome composition and large-scale changes in phenotype without details of underlying mechanisms, and provides a novel assessment of microbiome stability and its relation to human and environmental health.

systems biology↗