Biology subjects

Chang, W. Y.

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

3 recordsLinked to original sources

bioRxiv2026

FXR2 Promotes Autophagic Clearance of Sindbis Virus Capsid and Protects Against Viral Encephalitis

Autophagy protects neurons from viral infection by removing toxic viral components, but the host factors that recognize viral proteins and direct their clearance remain incompletely defined. Here, we identify fragile X mental retardation syndrome-related protein 2 (FXR2) as a mediator of protective autophagy during Sindbis virus (SINV) infection. Quantitative proteomic analysis of host proteins associated with SINV capsid identified FXR2, an RNA-binding protein linked to stress granules and protein homeostasis. Co-immunoprecipitation and immunofluorescence confirmed that FXR2 associates and colocalizes with viral capsid in infected cells. Loss of FXR2 increased capsid accumulation, reduced LC3 lipidation and autophagosome formation, and impaired p62 degradation following infection. Reconstitution of FXR2-deficient cells restored autophagy and promoted capsid clearance. Although FXR2 deficiency did not significantly increase viral replication, it enhanced virus-induced cell death, suggesting that FXR2 protects infected cells by limiting viral protein toxicity rather than by directly restricting virus production. Consistent with this model, both Fxr2-null and heterozygous mice exhibited increased mortality following neurotropic SINV challenge, and Fxr2-null mice developed greater neuronal degeneration, inflammation, and cell death despite viral titers comparable to those in wild-type animals. Together, these findings identify FXR2 as a previously unrecognized component of the antiviral autophagy pathway that promotes viral capsid clearance and protects neural tissue from infection-associated damage.

microbiology↗

bioRxiv2025

Decrypting cellular engagement and recruitment from spatially resolved transcriptomics

The recruitment of the various types of cells into the tissue microenvironment and how these cells engage with other cells in the tissue sites play critical biological roles. However, it is difficult to study these processes on a genome-wide scale using traditional low-throughput experiments. To address this gap, we developed a fully interpretable multi-instance deep learner, coined "spacer". Spacer digests the transcriptomics and spatial modalities of spatially resolved transcriptomics (SRT) data in alignment with the biological principles of the tissue spatial architecture. We deployed spacer to a panel of 17 high definition and 20 low definition SRT datasets, for studying how stromal and immune cells were recruited into tumors and heart during myocarditis. Spacer discovered novel biological insights not affordable by prior spatial data analysis tools, which were validated by orthogonal immuno-peptidomics, spatial T cell receptor (TCR) sequencing, and single cell sequencing experiments. We discovered genes that encode more immunogenic peptides and that are involved in developmental pathways are more potent in recruiting T cells to local tumor sites, while the recruitment of B cells and macrophages is stipulated by a different molecular program. On the other hand, expression of mucins in the tumor cells was found to repel T cell localization. For the engaging cell side, we also uncovered T cell-intrinsic features that determine their localization, validated by spatial-TCR-seq data. Spacer also unexpectedly revealed that CD4+ T cells, though fewer in numbers, are more responsive than CD8+ T cells in the heart during myocarditis. Collectively, this study establishes a new spatially resolved paradigm for studying cellular localization mechanisms in situ and shifts the paradigm from descriptive mapping to mechanistic discovery.

bioinformatics↗

bioRxiv2023

Mapping Cell-to-cell Interactions from Spatially Resolved Transcriptomics Data

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacias power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates, and most importantly the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of spacia for all three commercialized single cell resolution ST technologies: MERSCOPE/Vizgen, CosMx/Nanostring, and Xenium/10X. Spacia unveiled how endothelial cells, fibroblasts and B cells in the tumor microenvironment contribute to Epithelial-Mesenchymal Transition and lineage plasticity in prostate cancer cells. We deployed spacia in a set of pan-cancer datasets and showed that B cells also participate in PDL1/PD1 signaling in tumors. We demonstrated that a CD8+ T cell/PDL1 effectiveness signature derived from spacia analyses is associated with patient survival and response to immune checkpoint inhibitor treatments in 3,354 patients. We revealed differential spatial interaction patterns between {gamma}{delta} T cells and liver hepatocytes in healthy and cancerous contexts. Overall, spacia represents a notable step in advancing quantitative theories of cellular communications.

bioinformatics↗