All Publications

  • Pathways for macrophage uptake of cell-free circular RNAs. Molecular cell Amaya, L., Abe, B., Liu, J., Zhao, F., Zhang, W. L., Chen, R., Li, R., Wang, S., Kamber, R. A., Tsai, M. C., Bassik, M. C., Majeti, R., Chang, H. Y. 2024


    Circular RNAs (circRNAs) are stable RNAs present in cell-free RNA, which may comprise cellular debris and pathogen genomes. Here, we investigate the phenomenon and mechanism of cellular uptake and intracellular fate of exogenous circRNAs. Human myeloid cells and B cells selectively internalize extracellular circRNAs. Macrophage uptake of circRNA is rapid, energy dependent, and saturable. CircRNA uptake can lead to translation of encoded sequences and antigen presentation. The route of internalization influences immune activation after circRNA uptake, with distinct gene expression programs depending on the route of RNA delivery. Genome-scale CRISPR screens and chemical inhibitor studies nominate macrophage scavenger receptor MSR1, Toll-like receptors, and mTOR signaling as key regulators of receptor-mediated phagocytosis of circRNAs, a dominant pathway to internalize circRNAs in parallel to macropinocytosis. These results suggest that cell-free circRNA serves as an "eat me" signal and danger-associated molecular pattern, indicating orderly pathways of recognition and disposal.

    View details for DOI 10.1016/j.molcel.2024.04.022

    View details for PubMedID 38761795

  • Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis. bioRxiv : the preprint server for biology Shiau, C., Cao, J., Gregory, M. T., Gong, D., Yin, X., Cho, J., Wang, P. L., Su, J., Wang, S., Reeves, J. W., Kim, T. K., Kim, Y., Guo, J. A., Lester, N. A., Schurman, N., Barth, J. L., Weissleder, R., Jacks, T., Qadan, M., Hong, T. S., Wo, J. Y., Roberts, H., Beechem, J. M., Fernandez-Del Castillo, C., Mino-Kenudson, M., Ting, D. T., Hemberg, M., Hwang, W. L. 2023


    In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

    View details for DOI 10.1101/2023.06.28.546848

    View details for PubMedID 37425692

  • Protein-protein interaction networks as miners of biological discovery. Proteomics Wang, S., Wu, R., Lu, J., Jiang, Y., Huang, T., Cai, Y. D. 2022; 22 (15-16): e2100190


    Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid (Y2H), mass spectrometry (MS), co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.

    View details for DOI 10.1002/pmic.202100190

    View details for PubMedID 35567424

  • Competitive Endogenous RNA Landscape in Epstein-Barr Virus Associated Nasopharyngeal Carcinoma. Frontiers in cell and developmental biology Lin, X., Wang, S., Lin, K., Zong, J., Zheng, Q., Su, Y., Huang, T. 2021; 9: 782473


    Non-coding RNAs have been shown to play important regulatory roles, notably in cancer development. In this study, we investigated the role of microRNAs and circular RNAs in Nasopharyngeal Carcinoma (NPC) by constructing a circRNA-miRNA-mRNA co-expression network and performing differential expression analysis on mRNAs, miRNAs, and circRNAs. Specifically, the Epstein-Barr virus (EBV) infection has been found to be an important risk factor for NPC, and potential pathological differences may exist for EBV+ and EBV- subtypes of NPC. By comparing the expression profile of non-cancerous immortalized nasopharyngeal epithelial cell line and NPC cell lines, we identified differentially expressed coding and non-coding RNAs across three groups of comparison: cancer vs. non-cancer, EBV+ vs. EBV- NPC, and metastatic vs. non-metastatic NPC. We constructed a ceRNA network composed of mRNAs, miRNAs, and circRNAs, leveraging co-expression and miRNA target prediction tools. Within the network, we identified the regulatory ceRNAs of CDKN1B, ZNF302, ZNF268, and RPGR. These differentially expressed axis, along with other miRNA-circRNA pairs we identified through our analysis, helps elucidate the genetic and epigenetic changes central to NPC progression, and the differences between EBV+ and EBV- NPC.

    View details for DOI 10.3389/fcell.2021.782473

    View details for PubMedID 34805186

    View details for PubMedCentralID PMC8600047

  • Integrative genomic characterization of therapeutic targets for pancreatic cancer. Guo, J. A., Zhao, D., Ginebaugh, S. P., Wang, S., Jambhale, A. D., Yu, P. Z., Wu, W. W., Chen, P., Zhao, M., Lowder, K. E., Kapner, K. S., Hoffman, H. I., Cheng, S. W., Kim, D. Y., Boiarsky, R., Aguet, F., Paolella, B., Krill-Burger, J. M., McFarland, J. M., Oni, T., Jacks, T., Regev, A., Getz, G., Hwang, W. L., Singh, H., Aguirre, A. J. AMER ASSOC CANCER RESEARCH. 2021
  • Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis. Current gene therapy Zhang, H., Wang, S., Huang, T. 2021; 21 (4): 299-303


    This study aims to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP.Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the task of differentiating CHP and other interstitial lung diseases, especially idiopathic pulmonary fibrosis (IPF), was challenging.In this study, we analyzed the publically available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers.The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis.There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control.The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential coexpression network showed great promise in revealing the underlying mechanisms of CHP.

    View details for DOI 10.2174/1566523220666201208093325

    View details for PubMedID 33292121

  • Decipher the connections between proteins and phenotypes. Biochimica et biophysica acta. Proteins and proteomics Ren, X., Wang, S., Huang, T. 2020; 1868 (11): 140503


    As the outward-most representation of life, phenotype is the fundamental basis with which humans understand life and disease. But with the advent of molecular and sequencing technique and research, a growing portion of science research focuses primarily on the molecular level of life. Our understanding in molecular variations and mechanisms can only be fully utilized when they are translated into the phenotypic level. In this study, we constructed similarity network for phenotype ontology, and then applied network analysis methods to discover phenotype/disease clusters. Then, we used machine learning models to predict protein-phenotype associations. Each protein was characterized by the functional profiles of its interaction neighbors on the protein-protein interaction network. Our methods can not only predict protein-phenotype associations, but also reveal the underlying mechanisms from protein to phenotype.

    View details for DOI 10.1016/j.bbapap.2020.140503

    View details for PubMedID 32707349