Biology subjects

Chang, Y.-J.

Publications and source records attributed to Chang, Y.-J..

17 recordsLinked to original sources

bioRxiv2026

Assessment of variability in the human urinary nucleic acid adductome

Assessment of the urinary nucleic acid adductome provides a valuable, non-invasive approach for evaluating whole-body exposure to genotoxins. To support its application to exposome research, a clear understanding of biological variability independent of exposure is required. In this study, we analyzed a urine biobank from 40 healthy, non-smoking adults. Samples were processed using dual-phase solid-phase extraction, which retains a broad spectrum of nucleic acid adducts, and analyzed using an Orbitrap IQX platform. Data processing was performed using MZmine, FeatureHunter, and MetaboAnalyst. Across approximately 160 adducts per sample, the number of recovered adducts remained consistent between collection times, indicating that qualitative characterization of the urinary nucleic acid adductome is largely unaffected by diurnal variation. Most adducts exhibited stable abundances across sampling times. Diurnal variation was observed only in males, with 10 adducts showing significant differences between collection times, while females showed no temporal differences. Of the total adducts detected, only 32 differed significantly by age and/or sex. Age-related differences were more pronounced, particularly in males, where all significantly altered adducts were present at higher intensities in the younger cohort (20 to 29 y) compared with the older cohort (30 to 60 y). These findings support the use of spot urine samples for adductomic studies, favoring first-void urine. Collectively, these findings provide the first comprehensive assessments of biological variability within the human urinary nucleic acid adductome and support its application as a robust biomonitoring tool for investigating endogenous and environmental exposures across diverse human populations.

pharmacology and toxicology↗

bioRxiv2026

Lifetime-based multiplexed detection of viral RNA using fluorogenic aptamers

Fluorogenic aptamers (FAPs) are emerging molecular probes for viral RNA and DNA sensing. However, their use in multiplexed nucleic acid sensing has been hindered by cross-reactivity and overlapping emission spectra. Here we address these limitations by introducing a fluorescence-lifetime-based multiplexed detection strategy using variants of the DNA fluorogenic aptamer Lettuce that exhibits distinct fluorescence lifetimes when complexed with the fluorogen TO1-biotin. To effectively evolve Lettuce for diverse lifetimes, we developed a large-scale screening platform, termed FAP-FLIM-NGS (fluorogenic aptamer-based fluorescence lifetime imaging microscopy on next-generation sequencing chips), which measures the fluorescence lifetimes of [~]104 Lettuce/TO1-biotin complexes directly on an Illumina MiSeq flow cell. Using this approach, three variants with markedly different lifetimes were identified: a single mutant (smC14T, 6.0 ns) and two double mutants (dmA5T/C14T, 5.2 ns, and dmA5T/T22A, 4.4 ns). To demonstrate the utility of these Lettuce variants in multiplexed detection, a set of split Lettuce probes targeting viral RNA fragments derived from SARS-CoV-2, MERS-CoV, and influenza A were designed and tested. Phasor plot analysis confirmed that these probes can robustly distinguish individual targets as well as mixtures containing any two or all three targets purely based on distinct fluorescence lifetimes of probes, thereby overcoming the challenges of cross-reactivity and spectral overlap. Beyond this proof of concept, our findings establish a generalizable strategy for engineering FAPs with customized photophysical properties, opening new avenues for next-generation diagnostics and molecular sensing technologies.

bioengineering↗

bioRxiv2025

Distinct roles of neuronal phenotypes during neurofeedback adaptation

Learning adaptation allows the brain to refine motor patterns in response to changing environments rapidly. While population-level neural dynamics and single-neuron activity in motor learning have been widely studied, the contributions of individual neuron types remain poorly understood. Here, we employed a brain-machine interface (BMI) task with perturbations of varying difficulty to investigate single-neuron dynamics underlying neurofeedback adaptation in two rhesus macaques. Cortical neurons were classified based on waveform shape into narrow waveform (NW) and broad waveform (BW) categories, representing putative inhibitory interneurons and excitatory pyramidal neurons, respectively. Compared to BW neurons, NW neurons were more active and more strongly involved in the learning process. Moreover, task difficulty modulated neural responsiveness and coordination within both neuron groups, highlighting differential neuron engagement during motor learning. Our findings provide novel insights into single-neuron mechanisms underlying neurofeedback adaptation and emphasize the distinct functional roles of neuronal phenotypes in rapid learning processes. Author SummaryUnderstanding how the brain adapts to changes is crucial for improving treatments for neurological disorders and enhancing brain-machine interface (BMI) technology, which allows control of external devices using neural signals. In our study, we investigated how different types of brain cells contribute to the adaptation process when individuals encounter unexpected challenges during neurofeedback tasks. We discovered that one type of neuron was notably more active and played a more engaged role in rapidly adjusting neural activity compared to another type. These neurons demonstrated stronger coordination in their activity and showed greater responsiveness as the task difficulty increased. Our findings highlight the distinct roles of specific neurons in quickly adapting to neurofeedback tasks, offering insights that could enhance therapies for movement disorders and improve the precision and reliability of brain-controlled prosthetics.

neuroscience↗

bioRxiv2025

Population-level constraints on single neuron tuning changes during behavioral adaptation

In neurofeedback training, select neurons - of the billions that comprise the brain - are able to adapt their activity to produce a desired behavior, even though the feedback signal is low-dimensional. However, the extent that individual neurons are able to alter their activity within the context of the neurofeedback population is unclear. Using a brain-computer interface in monkeys (n=2; Macaca mulatta), we tested how the tuning of individual neurons changed in response to various feedback perturbations. Overall, we found that neurons that co-varied their activity the most with the population and those that contributed most to the behavior prior to the perturbation were most resistant to change- regardless of perturbation type. The degree to which individual neurons counteracted the applied perturbation explained daily differences in how well neurons collectively produced adapted behavior. Our work provides important insight into how population-level constraints impact the ability of individual neurons to adapt and support behavioral flexibility.

bioengineering↗

bioRxiv2024

Neural population variance explains adaptation differences during learning

Variability, a ubiquitous feature of neural activity, plays an integral role in behavior. However, establishing a causal relationship between neural signals and behavior is difficult. By defining a mathematical mapping between neural spiking activity and behavior, we investigate the role of spiking variability in adaptation during a brain-computer interface (BCI) behavior in male rhesus macaques (Macaca mulatta, n=2). Recent BCI evidence demonstrates that creating novel neural patterns is harder than repurposing existing patterns to respond to changes in external input. However, what limits the ability to repurpose, or adapt, patterns under different magnitudes of change is less well-characterized. Here, we present evidence that variance in neural spiking activity reveals differences in learnability between easy and hard adaptation conditions and across sessions. Furthermore, our study illuminates the limitations in neural changes underlying behavior within a neurofeedback paradigm. Significance StatementVariability in neural activity is a major driver of behavioral variability, though it is unclear how variability is balanced with stable neural activity as new behaviors become more practiced. By using a brain-computer interface methodology, we define a mathematical mapping between neural spiking activity and a behavioral control signal. Through thoughtful manipulation of this mapping, we incite the subjects (rhesus macaques) to learn and adapt neural activity to regain behavioral proficiency. We find that metrics of neural population variability are differentially modulated depending on difficulty of the imposed manipulation. Our exciting results provide important implications for brain-computer interface applications as well as our understanding of learning and adaptation more broadly. Our work represents an important step forwards towards understanding population neural dynamics in this critical component of behavior.

bioengineering↗

bioRxiv2023

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes. Author SummaryMulti-modal measurements have become an emerging trend in recent years due to the capability of studying brain dynamics at disparate scales. However, an integrative framework to systematically capture the multi-scale nonlinear dynamics in brain networks is lacking. A major challenge for creating a cohesive model is a mismatch in the timescale and subsequent sampling rate of the dynamics for disparate modalities. In this work, we introduce a deep learning-based approach to characterize brain communications between regions and scales. By modeling the continuous dynamics of hidden states using the neural network-based ordinary differential equations, the requirement of downsampling the faster sampling signals is discarded, thus preventing from losing dynamics information. Another advantageous feature of the proposed method is flexibility. An adaptable framework to bridge the gap between scales is necessary. Depending on the neural recording modalities utilized in the experiment, any suitable pair of well-established models can be plugged into the proposed multi-scale modeling framework. Thus, this method can provide insight into the brain computations of multi-scale brain activity.

neuroscience↗

bioRxiv2023

A Novel Genome Optimization Tool for Chromosome-Level Assembly across Diverse Sequencing Techniques

This paper introduces a novel genome assembly optimization tool named LOCLA, which stands for "Local Optimization for Chromosome-Level Assembly". It identifies reads and contigs aligned locally with high quality on gap flanks or scaffold boundaries of draft assemblies for gap filling and scaffold connection. LOCLA applies to both de novo and reference-based assemblies. It can also utilize reads produced by diverse sequencing techniques, e.g., 10x Genomics (10xG) Linked-Reads, and PacBio HiFi reads. We validated LOCLA on three human samples and one non-model organism. For the first two human samples, LLD0021C and CHM13, we generated de novo draft assemblies from 10xG Linked-Reads. On LLD0021C, LOCLA improves the draft assembly by adding 23.3 million bases using only 10xG Linked-Reads. These additional bases cover 28,746 protein-coding regions, particularly in pericentromeric and telomeric regions. On the CHM13 sample, we took 10xG Linked-Reads and PacBio HiFi reads as input. As a result, LOCLA added 46.2 million bases to the draft assembly. The increased content enables us to identify genes linked to complex diseases (e.g., ARHGAP11A) and critical biological pathways. We created two reference-guided draft assemblies on the third human sample, HG002, using contigs assembled from PacBio HiFi reads. LOCLA enhances the two draft assemblies by recovering 27.9 million bases (22.26%) and 35.7 million bases (30.93%) of the sequences discarded by the reference-guided assembly tool. The results indicate the robustness of LOCLAs contig detection algorithm on gap flanks. Furthermore, we show that 95% of the sequences filled in by LOCLA have over 80% accuracy compared with the HG002 reference genome published by the Human Pan-genome Reference Consortium. On the non-model organism, LOCLA enhanced the genome assembly of Bruguiera sexangula (JAHLGP000000000) by decreasing 41.4% of its gaps and raising the Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis score to 98.10%. LOCLA can optimize de novo and reference-guided assemblies using varied sequencing reads. The final assemblies produced by LOCLA have improved in both quantity and quality. The increased gene content may provide a valuable resource in advancing personalized medicine.

genomics↗

bioRxiv2022

Nucleic Acid Adductomics - the Next Generation of Adductomics for Assessing Environmental Health Risk

The exposome describes the totality of internal and external environmental exposures, across the life course. Components of the exposome have been linked to an increased risk of various, major diseases. To identify the precise nature, and size, of risk, in this complex mixture of exposures, powerful tools are needed to link exposure, cellular consequences, and health/disease. The most biologically informative biomarkers of the exposome should, to varying extents, reflect the dose of the exposure on the body or target organ(s), a subsequent effect on the biological system and, ideally, possess a role in disease. Modification of nucleic acids (NA) is a key consequence of environmental exposures, and while cellular DNA adductomics aims to evaluate the totality to DNA modifications in the genome, an approach which encompasses modifications of all nucleic acids, would be far more comprehensive, and therefore informative. To address this, we propose a cellular and urinary NA adductomics approach for the assessment of both DNA and RNA modifications, including modified (2-deoxy)ribonucleosides (2dN/rN), modified nucleobases (nB), plus: DNA-DNA, RNA-RNA, DNA-RNA, DNA-protein, and RNA-protein crosslinks (DDCL, RRCL, DRCL, DPCL, and RPCL, respectively). Supporting the feasibility of this approach, we presented preliminary, proof-of-principle results, which revealed the presence of over 1,000 modified NA moieties, and at least six types of NA modifications, in a representative, pooled urine from healthy subjects, including modified 2-dN, modified rN, modified nB, DRCL, RRCL and RPCL, many of which were novel/unexpected. We suggest that NA adductomics will provide a more comprehensive approach to the study of nucleic acid modifications, which will facilitate a range of advances, including the identification of novel, unexpected modifications e.g., RNA-RNA, and DNA-RNA crosslinks; key modifications associated with mutagenesis; agent-specific mechanisms; and adductome signatures of key environmental agents, leading to the dissection of the exposome, and its role in human health/disease, across the life course.

pharmacology and toxicology↗

bioRxiv2021

GABOLA: A Reliable Gap-Filling Strategy for de novo Chromosome-Level Assembly

We propose a novel method, GABOLA, which utilizes long-range genomic information provided by accurate linked short reads jointly with long reads to improve the integrity and resolution of whole genome assemblies especially in complex genetic regions. We validated GABOLA on human and Japanese eel genomes. On the two human samples, we filled in more bases spanning 23.3Mbp and 46.2Mbp than Supernova assembler, covering over 3,200 functional genes which includes 8,500 exons and 15,000 transcripts. Among them, multiple genes related to various types of cancer were identified. Moreover, we discovered additional 11,031,487 base pairs of repeat sequences and 218 exclusive repeat patterns, some of which are known to be linked to several disorders such as neuron degenerative diseases. As for the eel genome, we successfully raised the genetic benchmarking score to 94.6% while adding 24.7 million base pairs. These results manifest the capability of GABOLA in the optimization of whole genome assembly and the potential in precise disease diagnosis and high-quality non-model organism breeding. Availability: The docker image and source code of GABOLA assembler are available at https://hub.docker.com/r/lsbnb/gabola and https://github.com/lsbnb/gabola respectively.

genomics↗

bioRxiv2021

Breast tumor IGF-1R regulates cell adhesion and metastasis: Alignment of mouse single cell and human breast cancer transcriptomics

The acquisition of a metastatic phenotype is the critical event that determines patient survival from breast cancer. Several receptor tyrosine kinases have functions both in promoting and inhibiting metastasis in breast tumors. Although the insulin-like growth factor 1 receptor (IGF-1R) has been considered a target for inhibition in breast cancer, low levels of IGF-1R expression are associated with worse overall patient survival. To determine how reduced IGF-1R impacts tumor phenotype, we used weighted gene correlation network analysis (WGCNA) of METABRIC patient data and identified gene modules specific to cell cycle, adhesion, and immune cell signaling inversely correlated with IGF-1R expression in human breast cancers. Integration of human patient data with data from mouse tumors revealed similar pathways necessary for promoting metastasis in basal-like tumors with reduced signaling or expression of the IGF-1R. Functional analyses revealed the basis for the enhanced metastatic phenotype including alterations in E- and P-cadherins.

cancer biology↗

bioRxiv2020

Bi-directional Modeling Between Cross-Scale Neural Activity

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas the population brain activity has been studied in the micro-to meso-scale in building the connections between the dynamical patterns and the behaviors, such studies were often done at a single length scale and lacked an explanatory theory that identifies the neuronal origin across multiple scales. Here we introduce the NeuroBondGraph Network, a dynamical system incorporating both biological-inspired components and deep learning techniques to capture cross-scale dynamics that can infer and map the neural data from multiple scales. We demonstrated our model is not only 3.5 times more accurate than the popular sphere head model but also able to predict synchronous neural activity and extract correlated low-dimensional latent dynamics. We also showed that we can extend our methods to robustly predict held-out data across several weeks. The extracted effective connectivity agreed with the neuroanatomical hierarchy of motor control. Accordingly, the NeuroBondGraph Network opens the door to revealing comprehensive understanding of the brain computation, where network mechanisms of multi-scale communications are critical.

neuroscience↗

bioRxiv2020

Deep learning enables rapid and robust analysis of fluorescence lifetime imaging in photon-starved conditions

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study the molecular states in the complex cellular environment as the lifetime readings are not biased by the fluorophore concentration or the excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is not only 258 times faster than the most popular time-domain least-square estimation (TD_LSE) method but also provide more accurate analysis in barcode identification, cellular structure visualization, Forster resonance energy transfer characterization, and metabolic state analysis. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where ultrafast analysis is critical.

cell biology↗

bioRxiv2020

Single-cell Sequencing Reveals Brain/Spinal Cord Oligodendrocyte Precursor Heterogeneity and Requirement for mTOR in Cholesterol Biosynthesis and Myelin Maintenance

Brain and spinal cord oligodendroglia have distinct functional characteristics, and cell autonomous loss of individual genes can result in different regional phenotypes. However, sequencing studies to date have not revealed distinctions between brain and spinal cord oligodendroglia. Using single-cell analysis of oligodendroglia during myelination, we demonstrate that brain and spinal cord precursors are transcriptionally distinct, defined predominantly by cholesterol biosynthesis. We further identify mechanistic target of rapamycin (mTOR) as a major regulator promoting cholesterol biosynthesis in oligodendroglia. Oligodendroglial-specific loss of mTOR compromises cholesterol biosynthesis in both the brain and spinal cord. Importantly, mTOR loss has a greater impact on cholesterol biosynthesis in spinal cord oligodendroglia that corresponds with more pronounced developmental deficits. However, loss of mTOR in brain oligodendroglia ultimately results in oligodendrocyte death, spontaneous demyelination, and impaired axonal function, demonstrating that mTOR is required for myelin maintenance in the adult brain.

neuroscience↗

bioRxiv2020

Discovery of isoplumbagin as a novel NQO1 substrate and anti-cancer quinone

Isoplumbagin (5-hydroxy-3-methyl-1,4-naphthoquinone), a naturally occurring quinone from Lawsonia inermis and Plumbago europaea, that has been reported to have anti-inflammatory and anti-microbial activity. Inflammation has long been implicated in cancer progression. In this study, we examined the anti-cancer effect of chemically-synthesized isoplumbagin. Our results revealed that isoplumbagin treatment suppressed cell viability and invasion of highly invasive oral squamous cell carcinoma (OSCC) OC3-IV2 cells, glioblastoma U87 cells, non-small cell lung carcinoma H1299 cells, prostate cancer PC3 cells, and cervical cancer Hela cells by using MTT and Boyden chamber assays. In vivo studies demonstrate the inhibitory effect of 2 mg/kg isoplumbagin on the growth of orthotopic xenograft tumors derived from OSCC cells. Mechanistically, isoplumbagin exerts its cytotoxic effect through acting as a substrate of NAD(P)H quinone dehydrogenase 1 (NQO1) to generate hydroquinone, which reverses mitochondrial fission phenotype, reduces mitochondrial complex IV activity and thus compromises mitochondrial function. Collectively, this work reveals an anti-cancer activity of isoplumbagin mainly through modulating mitochondrial dynamics and function. Chemical compounds: Isoplumbagin (PubChem CID: 375105)

pharmacology and toxicology↗

bioRxiv2020

Mitochondrial ROS1 increases mitochondrial fission and respiration in oral squamous cancer carcinoma

Increased ROS1 oncogene expression has been implicated in the invasiveness of human oral squamous cell carcinoma (OSCC). The cellular distribution of ROS1 has long been assumed at the plasma membrane. However, a previous work reported a differential cellular distribution of mutant ROS1 derived from chromosomal translocation, resulting in increased carcinogenesis. We thus hypothesized that cellular distribution of up-regulated ROS1 in OSCC may correlate with invasiveness. We found that ROS1 can localize to mitochondria in the highly invasive OSCC and identified a mitochondria-targeting signal sequence in ROS1. We also demonstrated that ROS1 targeting to mitochondria is required for mitochondrial fission phenotype in the highly invasive OSCC cells. OSCC cells expressing high levels of ROS1 consumed more oxygen and had increased levels of cellular ATP levels. Our results also revealed that ROS1 regulates mitochondrial biogenesis and cellular metabolic plasticity. Together, these findings demonstrate that ROS1 targeting to mitochondria enhances OSCC invasion through regulating mitochondrial morphogenesis and cellular respiratory. Summary StatementThis study discovers a new role for the ROS1 in mitochondrial fission and metabolic activities.

cancer biology↗

bioRxiv2020

Three-dimensional genome structure and chromatin accessibility reorganization during in vivo induction of human T cell tolerance

Achieving T cell tolerance is a pivotal goal for the field of transplantation and autoimmune diseases. Here, we characterized the gene expression profiles, 3D genome architecture and chromatin accessibility in human steady-state and tolerant T cells, which had been induced in healthy donors by granulocyte-colony-stimulating factor in vivo. We provided the first high-resolution 3D genomic landscape of human tolerant T cells in vivo and identified highly expressed suppressor of cytokine signaling 1 (SOCS1), which is essential for maintaining T cell tolerance and was validated by ex vivo experiments. Mechanistically, SOCS1 is activated by STAT3, which mediates a new interaction between the SOCS1 locus and downstream super-enhancers and is accompanied by the disruption of the CTCF loop between the SOCS1 locus and upstream heterochromatin. This competitive regulation pattern between STAT3 and CTCF is present in the whole genome. Our study defines a regulatory model of transcription factors and provides insight into the induction of immune tolerance.

bioinformatics↗

bioRxiv2019

Semilunar Granule Cells Maintain Distinct Dendritic Morphology and Inhibition through Postnatal Development and Receive Heightened Inhibition in Adolescence

Semilunar granule cells (SGCs) have been proposed as a morpho-functionally distinct class of hippocampal dentate projection neurons contributing to feedback inhibition and memory processing in juvenile rats. However, the structural and physiological features that can reliably classify granule cells (GCs) from SGCs through postnatal development remain unresolved. Focusing on postnatal days 11-13, 28-42, and >120, corresponding with human infancy, adolescence, and adulthood, we examined the somatodendritic morphology and inhibitory regulation in SGCs and GCs to determine the cell-type specific features. Unsupervised cluster analysis confirmed that morphological features reliably distinguish SGCs from GCs irrespective of animal age. SGCs maintain higher spontaneous inhibitory postsynaptic current (sIPSC) frequency than GCs from infancy through adulthood. Although sIPSC frequency in SGCs was particularly enhanced during adolescence, sIPSC amplitude and cumulative charge transfer declined from infancy to adulthood and were not different between GCs and SGCs. Extrasynaptic GABA current amplitude peaked in adolescence in both cell types and was significantly greater in SGCs than in GCs only during adolescence. Although GC input resistance was higher than in SGCs during infancy and adolescence, input resistance decreased with developmental age in GCs while it progressively increased in SGCs. Consequently, GCs input resistance was significantly lower than SGCs in adults. The data delineate the structural features that can reliably distinguish GCs from SGCs through development. The results reveal developmental differences in passive membrane properties and steady state inhibition between GCs and SGCs which could confound their use in classifying the cell types.

neuroscience↗