Biology subjects

Chiang, A. W. T.

Publications and source records attributed to Chiang, A. W. T..

9 recordsLinked to original sources

bioRxiv2026

Targeting phosphodiesterase 10A disrupts MAPK signaling pathways in the tumor microenvironment to unleash antitumor immunity

Phosphodiesterase 10A (PDE10A), a cyclic nucleotide-degrading enzyme, is overexpressed in various human cancers. While PDE10A inhibition using small-molecule inhibitors or gene silencing suppresses tumor growth in xenograft models, its precise mechanism of action and immunological impact remain unclear. Here, we report that ADT-030, a novel PDE10A inhibitor, exhibits potent cytotoxicity against a broad range of murine tumor cell lines. ADT-030 is orally bioavailable and effectively suppresses tumor growth across multiple syngeneic mouse models. Notably, its efficacy is diminished in immunodeficient mice or upon CD8+ T cell depletion, highlighting a critical dependence on host immunity. The immunostimulatory properties of ADT-030 are further supported by its ability to induce immunogenic tumor cell death and promote dendritic cell (DC) maturation, its reliance on Batf3-expressing DCs to elicit antitumor CD8+ T cell response, and its synergy with anti-PD-1 therapy. Comprehensive immune profiling in the 4T1 breast cancer model, both in orthotopic and metastatic settings, revealed that ADT-030 selectively reduces myeloid-derived suppressor cells (MDSCs) while normalizing the immune landscape within the tumor. Mechanistically, ADT-030 disrupts multiple components of the mitogen-activated protein kinase (MAPK) signaling network in both tumor cells and MDSCs, leading to induction of apoptosis in these populations. These findings highlight the multi-faceted impact of PDE10A inhibition as a therapeutic strategy that not only disrupts tumor-intrinsic oncogenic signaling to inhibit tumor progression but also reshapes the tumor immune microenvironment to unleash antitumor immunity.

immunology↗

bioRxiv2025

Glycoengineering of the hepatitis C virus E2 glycoprotein leads to improved biochemical properties and enhanced immunogenicity

An effective vaccine against hepatitis C virus (HCV) must elicit the production of broadly neutralizing antibodies (bnAbs) reproducibly against the E1E2 glycoprotein complex. Little is known about how glycan content affects this process. Ideally, glycans would maximize epitope exposure without compromising antigen stability or exposing new epitopes. However, typical recombinant vaccines contain considerable heterogeneity in glycan content, which can affect the antibody response and neutralization potency. Here we employed glycoengineered Chinese hamster ovary (geCHO) cell lines that impart nearly homogeneous glycosylation as a means to test how specific glycan features influence antigenicity and immunogenicity for the secreted HCV E2 ectodomain (sE2). Specific geCHO antigens exhibited a modest but reproducible increase in affinity for some mAbs relative to CHO- and HEK293-produced sE2. Surprisingly, one geCHO sE2 antigen failed to bind the CD81 receptor, indicating the potential for significant glycan effects on biochemical properties. We immunized mice with the four antigens and found the total antibody response to be the same for all groups. However, sera from one geCHO group exhibited a 7-fold improvement in neutralization against the homologous HCV pseudovirus and had the most mice whose sera exhibited neutralization activity against genotypes 1b, 2a, 2b, and 3. Further analysis identified beneficial and deleterious glycan features, and the glycan that correlated the most with decreased potency was relatively small. However, size was not the sole determinant of glycan-driven effects on the antibody response. In summary, glycan content impacts biochemical properties of antigens to varying degrees and such effects can influence immune response quality and uniformity.

immunology↗

bioRxiv2024

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/587044v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@11687e4org.highwire.dtl.DTLVardef@33b146org.highwire.dtl.DTLVardef@1bb6bcborg.highwire.dtl.DTLVardef@1a1e47e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2021

The Human Milk Oligosaccharide 3-Sialyllactose Promotes Inflammation Resolution and Reduces Atherosclerosis Development in Mice.

Macrophages contribute to the induction and resolution of inflammation and play a central role in the chronic low-grade inflammation in cardiovascular diseases caused by atherosclerosis. Human milk oligosaccharides (HMOs) are complex unconjugated glycans unique to human milk that benefit infant health and act as innate immune modulators. Here, we identify the HMO 3sialyllactose (3SL) as a natural inhibitor of TLR4-induced low-grade inflammation in macrophages and endothelium. Transcriptome analysis in macrophages revealed that 3SL attenuates a selected set of inflammatory gene expression and promotes activity of LXR and SREBP. These acute anti-inflammatory effects of 3SL were associated with reduced histone H3K27 acetylation at a subset of LPS-inducible enhancers distinguished by preferential enrichment for CTCF, IRF2, BCL6, and other transcription factor recognition motifs. In a murine atherosclerosis model, both subcutaneous and oral administration of 3SL significantly reduced atherosclerosis development and the associated inflammation. This study provides evidence that 3SL attenuates inflammation by a transcriptional mechanism to reduce atherosclerosis development in the context of cardiovascular disease.

immunology↗

bioRxiv2020

A consensus-based and readable extension of Linear Code for Reaction Rules (LiCoRR)

Systems glycobiology aims to provide models and analysis tools that account for the biosynthesis, regulation, and interactions with glycoconjugates. To facilitate these methods, there is a need for a clear glycan representation accessible to both computers and humans. Linear Code, a linearized and readily parsable glycan structure representation, is such a language. For this reason, Linear Code was adapted to represent reaction rules, but the syntax has drifted from its original description to accommodate new and originally unforeseen challenges. Here, we delineate the consensuses and inconsistencies that have arisen through this adaptation. We recommend options for a consensus-based extension of Linear Code that can be used for reaction rule specification going forward. Through this extension and specification of Linear Code to reaction rules, we aim to minimize inconsistent symbology thereby making glycan database queries easier. With a clear guide for generating reaction rule descriptions, glycan synthesis models will be more interoperable and reproducible thereby moving glycoinformatics closer to compliance with FAIR standards. Reaction rule-extended Linear Code is an unambiguous representation for describing glycosylation reactions in both literature and code.

systems biology↗

bioRxiv2020

What does your cell really do? Model-based assessment of mammalian cells metabolic functionalities using omics data

Large-scale omics experiments have become standard in biological studies, leading to a deluge of data. However, researchers still face the challenge of connecting changes in the omics data to changes in cell functions, due to the complex interdependencies between genes, proteins and metabolites. Here we present a novel framework that begins to overcome this problem by allowing users to infer how metabolic functions change, based on omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. We then used genome-scale metabolic networks to define gene modules responsible for each specific metabolic task. We further developed a framework to overlay omics data on these modules to predict pathway usage for each metabolic task. The proposed approach allows one to directly predict how changes in omics experiments change cell or tissue function. We further demonstrated how this new approach can be used to leverage the metabolic functions of biological entities from the single cell to their organization in tissues and organs using multiple transcriptomic datasets (human and mouse). Finally, we created a web-based CellFie module that has been integrated into the list of tools available in GenePattern (www.genepattern.org) to enable adoption of the approach.

systems biology↗

bioRxiv2019

Correcting for sparsity and non-independence in glycomic data through a system biology framework

Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the roles of glycans, but it remains challenging to properly analyze large glycomics datasets, since the data are sparse (each sample often has only a few measured glycans) and detected glycans are non-independent (sharing many intermediate biosynthetic steps). We address these challenges with GlyCompare, a glycomic data analysis approach that leverages shared biosynthetic pathway intermediates to correct for sparsity and non-independence in glycomics. Specifically, quantities of measured glycans are propagated to intermediate glycan substructures, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we studied diverse N-glycan profiles from glycoengineered erythropoietin. We obtained biologically meaningful clustering of mutant cell glycoprofiles and identified knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyzed human milk oligosaccharide profiles and identified novel impacts that the mothers secretor-status on fucosylation and sialylation. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data.

systems biology↗

bioRxiv2018

Combating viral contaminants in CHO cells by engineering STAT1 mediated innate immunity

Viral contamination in biopharmaceutical manufacturing can lead to shortages in the supply of critical therapeutics. To facilitate the protection of bioprocesses, we explored the basis for the susceptibility of CHO cells, the most commonly used cell line in biomanufacturing, to RNA virus infection. Upon infection with certain ssRNA and dsRNA viruses, CHO cells fail to generate a significant interferon (IFN) response. Nonetheless, the downstream machinery for generating IFN responses and its antiviral activity is intact in these cells: treatment of cells with exogenously-added type I IFN or poly I:C prior to infection limited the cytopathic effect from Vesicular stomatitis virus (VSV), Encephalomyocarditis virus (EMCV), and Reovirus-3 virus (Reo-3) in a STAT1-dependent manner. To harness the intrinsic antiviral mechanism, we used RNA-Seq to identify two upstream repressors of STAT1: Gfi1 and Trim24. By knocking out these genes, the engineered CHO cells exhibited increased resistance to the prototype RNA viruses tested. Thus, omics-guided engineering of mammalian cell culture can be deployed to increase safety in biotherapeutic protein production among many other biomedical applications.

bioengineering↗

bioRxiv2018

Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions

Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.

systems biology↗