Biology subjects

Chou, W.-C.

Publications and source records attributed to Chou, W.-C..

10 recordsLinked to original sources

bioRxiv2026

Microtubules coordinate with actin assembly and disassembly to elongate cortical actin cables

The coordination of actin filament elongation, bundling and turnover within bundled actin structures is poorly understood. Nurse cells extend bundles of actin filaments (cables) from the cortex during Drosophila oogenesis. We previously showed that cable assembly requires a developmentally-regulated microtubule (MT) network. Here we demonstrate that MTs coordinate with actin filament assembly and disassembly to promote the persistent elongation of actin cables. Disrupting the MT network caused actin cables to detach from the cortex and become free in the cytoplasm. The subsequent fate of those free cables, shrinking over time or maintaining a stable length, depended on the MT manipulation. Furthermore, maintaining cortical continuity and persistent cable elongation requires a regulated imbalance between filament assembly and disassembly. Favoring disassembly caused cable detachment and shrinkage while reducing disassembly allowed free cables to continue elongating in the cytoplasm. Our results also suggest that both filament assembly and disassembly occur at the cortical filament barbed-ends protected from excessive disassembly by Capping Protein.

cell biology↗

bioRxiv2026

cGAS bends unpaired DNA to form an unconventional structure that hyperactivates the innate immune response

cGAS is a pattern-recognition receptor for dsDNA and forms 2cGAS:2DNA dimers followed by oligomerization into phase-separated condensates when fully-complementary DNA is studied. However, many DNAs are not fully complementary. We report that DNA with unpaired regions such as those found during transcription, recombination or replication (designated as bubble-DNA, Bu-DNA) causes cGAS hyper-activation. Hyperactivation is observed by Bu-DNA embedded in linear DNA, circular DNA, plasmid DNA and mitochondria DNA. Bu-DNA binds significantly more tightly to the cGAS catalytic domain than paired-DNA but suppresses condensation. Cryo-EM and single-molecule FRET reveal that cGAS forms 2cGAS:1DNA complexes by bending Bu-DNA into a V-shape using the unpaired region as a hinge, limiting its oligomeric state. This uncovers a novel mode of cGAS activation attributed to pattern diversity within pattern ligands.

immunology↗

bioRxiv2025

Tissue-to-Bytes: A Catalytic Digital Twin Platform for Consortium-Scale Integration of Single-Cell Omics Data in BRAIN Initiative Cell Atlas Network

Monolithic Electronic Health Record (EHR) architectures remain fundamentally misaligned with high-resolution, specialty-specific clinical workflows, contributing to significant physician burnout and challangies in secondary use of data. This study introduces the Enterprise Health Twin (EHT) framework, a paradigm grounded in the Digital Twin concept, with the goal to transform static clinical ledgers into dynamic decision-support systems. The EHT establishes continuous bidirectional feedback (feed-forward ingestion and feed-backward decision support) between the physical entities of clinical workflows and the digital counterparts of interfaces reflecting the evolving states of the workflow, for each individual patient in the EHT. We conceptualized, prototyped, and operationalized the EHT framework for epilepsy care for seven years, in production, at the point of care. Deployed at the Epilepsy Center of the University of Texas Health Science Center at Houston, a [~]400% increase productivity has been achieved in specialized surgical evaluation throughput without additional staffing. The EHT achieved de novo capture of ontology-anchored structured data at the point of care, mitigating cognitive load while growing the volume of high-resolution, annotated longitudinal datasets for clinical research, and driving an increase of care velocity in a learning healthcare system.

neuroscience↗

bioRxiv2025

T cell AIM2 Exacerbates Lung Inflammation in OVA-LPS and HDM-induced Asthma Models

Asthma is a complex airway inflammatory disease characterized by immune dysregulation, with diverse cellular contributors to inflammation that result in airway hyperresponsiveness (AHR) and excessive immune responses. The role of the inflammasome in asthma is conflicted. AIM2 is an innate immune receptor that activates the inflammasome through DNA binding. The clinical relevance of AIM2 in asthma is supported by analysis of blood or endobronchial biopsies from patients with severe asthma, showing increased AIM2 but not NLRP3 expression. We investigated the role of AIM2 in allergic asthma using ovalbumin (OVA)-LPS and house dust mite (HDM)- induced models. AIM2 expression was elevated in lung homogenates and bronchoalveolar lavage fluid (BALF) cells from allergen-induced groups compared to controls. In the OVA-LPS model, deletion of Aim2 led to reduced airway smooth muscle actin expression and DNA damage, contributing to decreased AHR and lung inflammation in whole body Aim2-/- mice. Surprisingly, cell-specific deletion in CD4+ T cells but not regulatory T cells or myeloid cells significantly affected AHR, indicating AIM2 in CD4+ T cells is a main driver of pathogenesis. Additionally, in HDM-induced asthma, Aim2-/- mice also exhibited reduced AHR. Cell-specific deletion reveals that AIM2 in CD4+ T cells promotes AHR and cytokine production, while AIM2 in myeloid cells modulates IgG1 levels and IL-13-producing CD4+ T cells, and the percentage of alveolar macrophages in BALF. These findings reveal that AIM2 differentially regulates HDM-induced allergic asthma through distinct exacerbating roles in T cells and myeloid cells, highlighting its potential as a therapeutic target. SignificanceAllergic asthma is a complex, heterogeneous disease characterized by airway inflammation and hyper-responsiveness. AIM2, an inflammasome activated by DNA, has emerged as a critical regulator of inflammatory diseases. In humans, AIM2 expression is elevated in severe asthma. In vivo studies highlight AIM2 as a key modulator of allergic asthma in both OVA-LPS and HDM- induced models. Surprisingly, its functional relevance in both models is mediated by its intrinsic role in T cells, while a modest role in myeloid cells is restricted to the HDM model. AIM2 promotes airway hyper-responsiveness and lung inflammation through enhancing type II cytokines, immune subpopulations and DNA damage response. These findings suggest AIM2 as a biomarker and therapeutic target for managing asthma, particularly in severe asthma subtypes.

immunology↗

bioRxiv2024

Developmentally regulated actin-microtubule crosstalk in Drosophila oogenesis

Actin-microtubules (MT) interactions are essential, but how those mechanisms are orchestrated in complex developing systems is poorly understood. Here we show that actin-MT crosstalk regulates actin cable assembly and the assembly and organization of MTs in Drosophila nurse cells during oogenesis. We found that a stable MT meshwork develops concurrently with actin cable initiation and requires acetylation for its maintenance. These {gamma}-tubulin nucleated MTs are cortically tethered via Patronin and Shortstop, extend into the cytoplasm, and coalign with the elongating actin cables. The MT network promotes the cortical enrichment of the actin assembly factors Diaphanous and Enabled and loss of MTs dramatically reduced actin cable density and growth rate. We further demonstrated that actin filament assembly promotes cortical tethering of MTs and that loss of the actin filament bundler Villin/Quail results in fewer, shorter, and more highly coaligned MTs. Together, our data reveal multiple modes of coordinated actin-MT crosstalk producing actin cable-MT network synergy that is instrumental for oogenesis. SummaryThis study demonstrates actin-microtubule crosstalk in Drosophila oogenesis, revealing coregulation between actin filament assembly/bundling and microtubule nucleation/organization, highlighting the coordinated regulation of the cytoskeleton required during development.

cell biology↗

bioRxiv2024

A mutant ASXL1-EHMT complex contributes to heterochromatin dysfunction in clonal hematopoiesis and chronic monomyelocytic leukemia

ASXL1 is one of the three most frequently mutated genes in age-related clonal hematopoiesis (CH), alongside DNMT3A and TET2. CH can progress to myeloid malignancies including chronic monomyelocytic leukemia (CMML), and is also strongly associated with inflammatory cardiovascular disease and all-cause mortality in humans. DNMT3A and TET2 regulate DNA methylation and demethylation pathways respectively, and loss-of-function mutations in these genes reduce DNA methylation in heterochromatin, allowing de-repression of silenced elements in heterochromatin. In contrast, the mechanisms that connect mutant ASXL1 and CH are not yet fully understood. CH/CMML-associated ASXL1 mutations encode C-terminally truncated proteins that enhance the deubiquitinase activity of the ASXL-BAP1 "PR-DUB" deubiquitinase complex, which removes mono-ubiquitin from H2AK119Ub. Here we show that ASXL1 mutant proteins interact with the EHMT1-EHMT2 methyltransferase complex, which generates H3K9me1 and me2, the latter a repressive modification in constitutive heterochromatin. Compared to cells from age-matched wildtype mice, we found that expanded myeloid cells from old ([≥]18-month-old) Asxl1tm/+ mice, a heterozygous knock-in mouse model of CH, display genome-wide decreases of H3K9me2, H3K9me3 and H2AK119Ub as well as an associated increase in expression of transposable elements (TEs) and satellite repeats. Increased TE expression was also observed in monocytes from ASXL1-mutant CMML patients compared to monocytes from healthy controls. Our data suggest that mutant ASXL1 proteins compromise the integrity of both constitutive and facultative heterochromatin in an age-dependent manner, by reducing the levels of H3K9me2/3 and H2AK119Ub. This increase in TE expression correlated with increased expression of nearby genes, including many interferon-inducible (inflammation-associated) genes (ISGs). Significance StatementAge-related clonal hematopoiesis (CH) is a premalignant condition associated with inflammatory cardiovascular disease. ASXL1 mutations are very frequent in CH. We show that ASXL1 interacts with EHMT1 and EHMT2, H3K9 methyltransferases that deposit H3K9me1 and me2. Using a mouse model of mutant ASXL1 to recapitulate CH, we found that old ASXL1-mutant mice showed marked expansion of myeloid cells in bone marrow, with decreased H3K9me2/3 and increased expression of transposable elements (TEs) in heterochromatin. In humans, ASXL1-mutant CH progresses to chronic monomyelocytic leukemia (CMML); CMML patient samples showed striking upregulation of many TE families, suggesting that ASXL1 mutations compromise heterochromatin integrity, hence causing derepression of TEs. Targeting heterochromatin-associated proteins and TEs might counter the progression of CH, CMML and other myeloid malignancies.

cancer biology↗

bioRxiv2023

Annotation-Free Deep Learning for Predicting Gene Mutations from Whole Slide Images of Acute Myeloid Leukemia

The rapid development of deep learning in recent years has revolutionized the field of medical image processing, including the applications of using high-resolution whole slide images (WSIs) in acute myeloid leukemia (AML) diagnosis. Although the potential of characterizing gene mutations directly from WSIs has been demonstrated in some cancers, it still faces challenges due to image resolutions and manual annotations. To address this, we propose a deep learning model based on multiple instance learning (MIL) with ensemble learning to predict gene mutations from AML annotation-free WSIs. Our deep learning model offers a promising solution for gene mutation prediction on NPM1 mutations and FLT3 -ITD without the need for patch-level or cell-level manual annotations, reducing the manpower and time costs associated with traditional supervised learning approaches. The dataset of 572 WSIs from AML patients that we used to train our MIL models is currently the largest independent database with both WSI and genetic mutation information. By leveraging upsampling and ensemble learning techniques, our final model achieved an AUC of 0.90 for predicting NPM1 mutations and 0.81 for FLT3 -ITD. This confirms the feasibility of directly obtaining gene mutation data through WSIs without the need for expert annotation and training involvement. Our study also compared the proportional representation of cell types before and after applying the MIL model, finding that blasts are consistently important indicators for gene mutation predictions, with their proportion increasing in mutated WSIs and decreasing in non-mutated WSIs after MIL application. These enhancements, leading to more precise predictions, have brought AML WSI analysis one step closer to being utilized in clinical practice.

bioinformatics↗

bioRxiv2021

Analysis of single-cell RNA-sequencing data to identify quiescent and proliferating neural cell populations in Glioblastoma

BackgroundDiffuse Glioblastoma (GBM) has high mortality and remains one of the most challenging type of cancer to treat. Identifying and characterizing the cells populations driving tumor growth and therapy resistance has been particularly difficult owing to marked inter and intra tumoral heterogeneity observed in these tumors. These tumorigenic populations contain long lived cells associated with latency, immune evasion and metastasis. MethodsHere, we analyzed the single-cell RNA-sequencing data of high grade glioblastomas from four different studies using integrated analysis of gene expression patterns, cell cycle stages and copy number variation to identify gene expression signatures associated with quiescent and cycling neuronal tumorigenic cells. ResultsThe results show that while cycling and quiescent cells are present in GBM of all age groups, they exist in a much larger proportion in pediatric glioblastomas. These cells show similarities in their expression patterns of a number of pluripotency and proliferation related genes. Upon unbiased clustering, these cells explicitly clustered on their cell cycle stage. Quiescent cells in both the groups specifically overexpressed a number of genes for ribosomal protein, while the cycling cells were enriched in the expression of high-mobility group and heterogeneous nuclear ribonucleoprotein group genes. A number of well-known markers of quiescence and proliferation in neurogenesis showed preferential expression in the quiescent and cycling populations identified in our analysis. Through our analysis, we identify ribosomal proteins as key constituents of quiescence in glioblastoma stem cells. ConclusionsThis study identifies gene signatures common to adult and pediatric glioblastoma quiescent and cycling stem cell niches. Further research elucidating their role in controlling quiescence and proliferation in tumorigenic cells in high grade glioblastoma will open avenues in more effective treatment strategies for glioblastoma patients.

cancer biology↗

bioRxiv2021

Analysis of next- and third-generation RNA-Seq data reveals the structures of alternative transcription units in bacterial genomes

Alternative transcription units (ATUs) are dynamically encoded under different conditions or environmental stimuli in bacterial genomes, and genome-scale identification of ATUs is essential for studying the emergence of human diseases caused by bacterial organisms. However, it is unrealistic to identify all ATUs using experimental techniques, due to the complexity and dynamic nature of ATUs. Here we present the first-of-its-kind computational framework, named SeqATU, for genome-scale ATU prediction based on next-generation RNA-Seq data. The framework utilizes a convex quadratic programming model to seek an optimum expression combination of all of the to-be-identified ATUs. The predicted ATUs in E. coli reached a precision of 0.77/0.74 and a recall of 0.75/0.76 in the two RNA-Sequencing datasets compared with the benchmarked ATUs from third-generation RNA-Seq data. We believe that the ATUs identified by SeqATU can provide fundamental knowledge to guide the reconstruction of transcriptional regulatory networks in bacterial genomes.

bioinformatics↗

bioRxiv2019

rSeqTU -- a machine-learning based R package for prediction of bacterial transcription units

A transcription unit (TU) is composed of one or multiple adjacent genes on the same strand that are co-transcribed in mostly prokaryotes. Accurate identification of TUs is a crucial first step to delineate the transcriptional regulatory networks and elucidate the dynamic regulatory mechanisms encoded in various prokaryotic genomes. Many genomic features, e.g., gene intergenic distance, and transcriptomic features including continuous and stable RNA-seq reads count signals, have been collected from a large amount of experimental data and integrated into classification techniques to computationally predict genome-wide TUs. Although some tools and web servers are able to predict TUs based on bacterial RNA-seq data and genome sequences, there is a need to have an improved machine-learning prediction approach and a better comprehensive pipeline handling QC, TU prediction, and TU visualization. To enable users to efficiently perform TU identification on their local computers or high-performance clusters and provide a more accurate prediction, we develop an R package, named rSeqTU. rSeqTU uses a random forest algorithm to select essential features describing TUs and then uses support vector machine (SVM) to build TU prediction models. rSeqTU (available at https://s18692001.github.io/rSeqTU/) has six computational functionalities including read quality control, read mapping, training set generation, random-forest-based feature selection, TU prediction, and TU visualization.

bioinformatics↗