Biology subjects

Chang, I.

Publications and source records attributed to Chang, I..

11 recordsLinked to original sources

bioRxiv2026

Disruption of the ARID1A-containing SWI/SNF complex reprograms tumor-associated macrophages and enhances immunotherapy response

Tumor-associated macrophages (TAMs) contribute to tumor immune evasion and therapeutic resistance. However, the epigenetic and transcriptional regulators that control TAM function remain largely unidentified. Here we investigated the role of the SWI/SNF chromatin remodeling complex in TAMs and whether disruption of SWI/SNF function in TAMs could improve immunotherapy. Pharmacologic inhibition of SWI/SNF ATPase activity improved the efficacy of checkpoint blockade immunotherapy, slowing tumor growth and reprogramming transcription broadly in tumor cells, tumor-infiltrating lymphocytes, and TAMs. To define the role of SWI/SNF in TAMs specifically, we genetically deleted the SWI/SNF subunit Arid1a in myeloid cells and found this was sufficient to suppress tumor progression and enhance checkpoint blockade response. Epigenomic and single cell analyses indicated that SWI/SNF inhibition and ARID1A deletion in TAMs reduced accessibility at enhancers of genes associated with poor prognosis, such as Spp1, and increased accessibility at promoters of interferon-stimulated genes (ISGs). CD86 was elevated on ARID1A-deficient TAMs and the enhanced immunotherapy response required CD86 costimulation and CD8+ T cells. These findings establish ARID1A-dependent chromatin remodeling as a determinant of TAM gene expression programs and show that disruption of myeloid SWI/SNF function improves checkpoint blockade immunotherapy via TAM reprogramming.

cancer biology↗

bioRxiv2024

Dynamic Changes in Chloride Homeostasis Coordinate Midbrain Inhibitory Network Activity during Reward Learning

The ability to associate environmental stimuli with positive outcomes is a fundamental form of learning. While extensive research has focused on the response profiles of midbrain dopamine neurons during associative learning, less is known about learning-mediated changes in the afferents that shape their responses. We demonstrate that during critical phases of learning, anion homeostasis in midbrain GABA neurons - a primary source of input to dopamine neurons - is disrupted due to downregulation of the chloride transporter KCC2. This alteration in GABA neurons preferentially impacted lateral mesoaccumbal dopamine pathways and was not observed after learning was established. At the network level, learning-mediated KCC2 downregulation was associated with enhanced synchronization between individual GABA neurons and increased dopamine responses to reward-related stimuli. Conversely, enhancing KCC2 function during learning reduced GABA synchronization, diminished relevant dopamine signaling, and prevented cue-reward associations. Thus, circuit-specific adaptations in midbrain GABA neurons are crucial for forming new reward-related behaviors.

neuroscience↗

bioRxiv2024

Wavenumber-dependent transmission of subthreshold waves on electrical synapses network model of Caenorhabditis elegans

Recent experimental studies showed that electrically coupled neural networks like in mammalian inferior olive nucleus generate synchronized rhythmic activity by the subthreshold sinusoidal-like oscillations of the membrane voltage. Understanding the basic mechanism and its implication of such phenomena in the nervous system bears fundamental importance and requires preemptively the connectome information of a given nervous system. Inspired by these necessities of developing a theoretical and computational model to this end and, however, in the absence of connectome information for the inferior olive nucleus, here we investigated interference phenomena of the subthreshold oscillations in the reference system Caenorhabditis elegans for which the structural anatomical connectome was completely known recently. We evaluated how strongly the sinusoidal wave was transmitted between arbitrary two cells in the model network. The region of cell-pairs that are good at transmitting waves changed according to the wavenumber of the wave, for which we named a wavenumber-dependent transmission map. Also, we unraveled that 1) the transmission of all cell-pairs disappeared beyond a threshold wavenumber, 2) long-distance and regular patterned transmission existed in the body-wall muscles part of the model network, and 3) major hub cell-pairs of the transmission were identified for many wavenumber conditions. A theoretical and computational model presented in this study provided fundamental insight for understanding how the multi-path constructive/destructive interference of the subthreshold oscillations propagating on electrically coupled neural networks could generate wavenumber-dependent synchronized rhythmic activity.

biophysics↗

bioRxiv2023

ElegansBot: Development of equation of motion deciphering locomotion including omega turns of Caenorhabditis elegans

Locomotion is a fundamental behavior of Caenorhabditis elegans (C. elegans). Previous works on kinetic simulations of animals helped researchers understand the physical mechanisms of locomotion and the muscle-controlling principles of neuronal circuits as an actuator part. It has yet to be understood how C. elegans utilizes the frictional forces caused by the tension of its muscles to perform sequenced locomotive behaviors. Here, we present a two-dimensional rigid body chain model for the locomotion of C. elegans by developing Newtonian equations of motion for each body segment of C. elegans. Having accounted for friction-coefficients of the surrounding environment, elastic constants of C. elegans, and its kymogram from experiments, our kinetic model (ElegansBot) reproduced various locomotion of C. elegans such as, but not limited to, forward-backward-(omega turn)-forward locomotion constituting escaping behavior and delta-turn navigation. Additionally, ElegansBot precisely quantified the forces acting on each body segment of C. elegans to allow investigation of the force distribution. This model will facilitate our understanding of the detailed mechanism of various locomotive behaviors at any given friction-coefficients of the surrounding environment. Furthermore, as the model ensures the performance of realistic behavior, it can be used to research actuator-controller interaction between muscles and neuronal circuits.

animal behavior and cognition↗

bioRxiv2023

Effects of six pyrimidine analogs on the growth of Tetrahymena thermophila and their implications in pyrimidine metabolism

Tetrahymena are ciliated protists that have been used to study the effects of toxic chemicals, including anticancer drugs. In this study, we tested the inhibitory effects of six pyrimidine analogs (5-fluorouracil, floxuridine, 5-deoxy-5-fluorouridine, 5-fluorouridine, gemcitabine, cytarabine) on wild-type CU428 and conditional mutant NP1 Tetrahymena thermophila at room temperature and the restrictive temperature (37{degrees}C) where NP1 does not form the oral apparatus. We found that cytarabine was the only tested analog that did not inhibit growth, and phagocytosis was not required for pyrimidine analog entry. IC50 values did not significantly differ between strains for the same analog at either temperature. To investigate the mechanism of inhibition, we used two pyrimidine bases (uracil and thymine) and three nucleosides (uridine, thymidine, 5-methyluridine) to help determine whether the inhibitory effects from analogs were reversible. We found that the inhibitory effects from 5-fluorouracil could be reversed by uracil and thymine, from floxuridine could be reversed by thymidine, and from 5-deoxy-5-fluorouridine could be reversed by uracil. None of the tested nucleobases or nucleosides could reverse the inhibitory effects of gemcitabine or 5-fluorouridine. Our results suggest that the five pyrimidine analogs act on different sites to inhibit T. thermophila growth and that nucleobases and nucleosides are metabolized differently.

cell biology↗

bioRxiv2021

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-{kappa}B immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression. SummaryFeyaerts et al. demonstrate that an integrated analysis of plasma and single-cell proteomics differentiates COVID-19 severity and reveals severity-specific biological signatures associated with the dysregulation of the JAK/STAT, MAPK/mTOR, and NF-{kappa}B immune signaling networks and the mobilization of the renin-angiotensin and hemostasis systems.

immunology↗

bioRxiv2020

Targeted long-read sequencing resolves complex structural variants and identifies missing disease-causing variants

BACKGROUNDDespite widespread availability of clinical genetic testing, many individuals with suspected genetic conditions do not have a precise diagnosis. This limits their opportunity to take advantage of state-of-the-art treatments. In such instances, testing sometimes reveals difficult-to-evaluate complex structural differences, candidate variants that do not fully explain the phenotype, single pathogenic variants in recessive disorders, or no variants in specific genes of interest. Thus, there is a need for better tools to identify a precise genetic diagnosis in individuals when conventional testing approaches have been exhausted. METHODSTargeted long-read sequencing (T-LRS) was performed on 33 individuals using Read Until on the Oxford Nanopore platform. This method allowed us to computationally target up to 100 Mbp of sequence per experiment, resulting in an average of 20x coverage of target regions, a 500% increase over background. We analyzed patient DNA for pathogenic substitutions, structural variants, and methylation differences using a single data source. RESULTSThe effectiveness of T-LRS was validated by detecting all genomic aberrations, including single-nucleotide variants, copy number changes, repeat expansions, and methylation differences, previously identified by prior clinical testing. In 6/7 individuals who had complex structural rearrangements, T-LRS enabled more precise resolution of the mutation, which led, in one case, to a change in clinical management. In nine individuals with suspected Mendelian conditions who lacked a precise genetic diagnosis, T-LRS identified pathogenic or likely pathogenic variants in five and variants of uncertain significance in two others. CONCLUSIONST-LRS can accurately predict pathogenic copy number variants and triplet repeat expansions, resolve complex rearrangements, and identify single-nucleotide variants not detected by other technologies, including short-read sequencing. T-LRS represents an efficient and cost-effective strategy to evaluate high-priority candidate genes and regions or to further evaluate complex clinical testing results. The application of T-LRS will likely increase the diagnostic rate of rare disorders.

genomics↗

bioRxiv2020

Parallel Movement-suppressing Striatal Output Pathways

The classic basal ganglia circuit model asserts a complete segregation of the two striatal output pathways. Empirical data argue that, in addition to indirect-pathway striatal projection neurons (iSPNs), direct-pathway striatal projection neurons (dSPNs) innervate the external globus pallidus (GPe). However, the functions of the latter were not known. In this study, we interrogated the organization principles of striatopallidal projections and their roles in full-body movement in mice (both males and females). In contrast to the canonical motor-promoting response of dSPNs in the dorsomedial striatum (DMSdSPNs), optogenetic stimulation of dSPNs in the dorsolateral striatum (DLSdSPNs) suppressed locomotion. Circuit analyses revealed that dSPNs selectively target Npas1+ neurons in the GPe. In a chronic 6-hydroxydopamine lesion model of Parkinsons disease, the dSPN-Npas1+ projection was dramatically strengthened. As DLSdSPN-Npas1+ projection suppresses movement, the enhancement of this projection represents a circuit mechanism for the hypokinetic symptoms of Parkinsons disease that has not been previously considered. In sum, our results suggest that dSPN input to the GPe is a critical circuit component that is involved in the regulation of movement in both healthy and parkinsonian states. Significance statementIn the classic basal ganglia model, the striatum is described as a divergent structure--it controls motor and adaptive functions through two segregated, opposing output streams. However, the experimental results that show the projection from direct-pathway neurons to the external pallidum have been largely ignored. Here, we showed that this striatopallidal sub-pathway targets a select subset of neurons in the external pallidum and is motor-suppressing. We found that this sub-pathway undergoes changes in a Parkinsons disease model. In particular, our results suggest that the increase in strength of this sub-pathway contributes to the slowness or reduced movements observed in Parkinsons disease.

neuroscience↗

bioRxiv2020

Inference and analysis of cell-cell communication using CellChat

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We constructed a database of interactions among ligands, receptors and their cofactors that accurately represents known heteromeric molecular complexes. Based on mass action models, we then developed CellChat, a tool that is able to quantitively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applications of CellChat to several mouse skin scRNA-seq datasets for embryonic development and adult wound healing shows its ability to extract complex signaling patterns, both previously known as well as novel. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer (http://www.cellchat.org/) will help discover novel intercellular communications and build a cell-cell communication atlas in diverse tissues.

bioinformatics↗

bioRxiv2019

Structural and molecular insight into the pH-induced low-permeability of the voltage-gated potassium channel Kv1.2 through dewetting of the water cavity

Understanding the gating mechanism of ion channel proteins is key to understanding the regulation of cell signaling through these channels. Channel opening and closing are regulated by diverse environmental factors that include temperature, electrical voltage across the channel, and proton concentration. Low permeability in voltage-gated potassium ion channels (Kv) is intimately correlated with the prolonged action potential duration observed in many acidosis diseases. The Kv channels consist of voltage-sensing domains (S1-S4 helices) and central pore domains (S5-S6 helices) that include a selectivity filter and water-filled cavity. The voltage-sensing domain is responsible for the voltage-gating of Kv channels. While the low permeability of Kv channels to potassium ion is highly correlated with the cellular proton concentration, it is unclear how an intracellular acidic condition drives their closure, which may indicate an additional pH-dependent gating mechanism of the Kv family. Here, we show that two residues E327 and H418 in the proximity of the water cavity of Kv1.2 play crucial roles as a pH switch. In addition, we present a structural and molecular concept of the pH-dependent gating of Kv1.2 in atomic detail, showing that the protonation of E327 and H418 disrupts the electrostatic balance around the S6 helices, which leads to a straightening transition in the shape of their axes and causes dewetting of the water-filled cavity and closure of the channel. Our work offers a conceptual advancement to the regulation of the pH-dependent gating of various voltage-gated ion channels and their related biological functions.\n\nAuthor SummaryThe acid sensing ion channels are a biological machinery for maintaining the cell functional under the acidic or basic cellular environment. Understanding the pH-dependent gating mechanism of such channels provides the structural insight to design the molecular strategy in regulating the acidosis. Here, we studied the voltage-gated potassium ion channel Kv1.2 which senses not only the electrical voltage across the channels but also the cellular acidity. We uncovered that two key residues E327 and H418 in the pore domain of Kv1.2 channel play a role as pH-switch in that their protonation control the gating of the pore in Kv1.2 channel. It offered a molecular insight how the acidity reduces the ion permeability in voltage-gated potassium channels.

biophysics↗

bioRxiv2017

DAFi: A Directed Recursive Filtering and Clustering Approach to Data-Driven Identification of Cell Populations from Polychromatic Flow Cytometry Data

Computational methods for identification of cell populations from high-dimensional flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. We found that combining recursive filtering and clustering with constraints converted from the user manual gating strategy can effectively identify overlapping and rare cell populations from smeared data that would have been difficult to resolve by either a single run of data clustering or manual segregation. We named this new method DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell-based biomarkers, but also makes the results interpretable to experimental scientists as in supervised classification through mapping and merging the high-dimensional data clusters into the user-defined 2D gating hierarchy. By recursive data filtering before clustering, DAFi can uncover small local clusters which are otherwise difficult to identify due to the statistical interference of the irrelevant major clusters. Quantitative assessment of cell type specific characteristics demonstrates that the population proportions calculated by DAFi, while being highly consistent with those by expert centralized manual gating, have smaller technical variance than those from individual manual gating analysis. Visual examination of the dot plots showed that the boundaries of the DAFi-identified cell populations followed the natural shapes of the data distributions. To further exemplify the utility of DAFi, we show that DAFi can incorporate the FLOCK clustering method to identify novel cell-based biomarkers. Implementation of DAFi supports options including clustering, bisecting, slope-based gating, and reversed filtering to meet various auto-gating needs from different scientific use cases.

bioinformatics↗