Biology subjects

Huang, Y.-J.

Publications and source records attributed to Huang, Y.-J..

12 recordsLinked to original sources

bioRxiv2026

Ribophorin-1 Governs Spike Abundance of Highly Pathogenic Coronaviruses by ER-associated degradation

The spike protein of highly pathogenic coronaviruses is indispensable for viral entry, pathogenesis, and immune evasion. However, specific host factors governing spike protein degradation and abundance on progeny virions remain largely uncharacterized. Here, we identify Ribophorin-1 (RPN1), a non-catalytic subunit of the oligosaccharyltransferase (OST) complex, as an ER-localized host restriction factor that selectively depletes spike to inhibit coronavirus infection. Conditional knockout of Rpn1 in mouse lung markedly exacerbates SARS-CoV-2 pathogenesis, increasing viral load, immune cell infiltration, and syncytium formation. In cells, RPN1 knockdown enhances susceptibility to diverse variants, whereas its overexpression attenuates infection. Nano-flow cytometry, cryo-EM and LSCM show that RPN1 reduces spike abundance on progeny virions, resulting in reduction in subsequent syncytium formation across SARS-CoV, SARS-CoV-2, and MERS-CoV. Mechanistically, RPN1 promotes VCP/p97-dependent retrotranslocation and AMFR-mediated ubiquitination of spike protein. Heterologous expression of RPN1s functional domain (residues 180-307) confers significant protection against SARS-CoV-2 both in vitro and in vivo. Collectively, our findings define an RPN1-initiated ERAD pathway that selectively targets coronavirus spike for proteasomal destruction, and provide proof-of-concept evidence that targeting this RPN1-dependent pathway represents a promising broad-spectrum antiviral strategy.

microbiology↗

bioRxiv2025

Viral protease-Initiated Pyroptosis Activator mRNA therapy as a Universal Antiviral Strategy

Although therapeutic drugs targeting gasdermin (GSDM)-mediated pyroptosis have made remarkable progress in treating various diseases, their potential for antiviral therapy remains largely unexplored. Inspired by the autoinhibitory mechanism of GSDM proteins and the clinical success of mRNA vaccines, herein we propose a concept of viral protease-initiated pyroptosis (VIP) mRNA therapy as a universal antiviral strategy. Using Hepatitis A virus (HAV) as a model, we rationally engineered an HAV-specific VIP activator (VIPA) by replacing the native gasdermin D (GSDMD) cleavage motif with that of HAV 3C protease. Expression of this VIPA selectively triggered pyroptosis in HAV-infected cells, thereby terminating viral replication. Remarkably, in vivo delivery of VIPA mRNA via lipid nanoparticles (LNPs) significantly suppressed viral replication and shedding, alleviated liver injury, and elicited a pyroptosis-driven antiviral immune response in uninfected cells. Similarly, VIPA against Zika virus (ZIKV) also exhibited potent antiviral efficacy in both cell culture and mouse models. Furthermore, a generative artificial intelligence (AI) platform was deployed to design de novo cleavage motifs for the SARS-CoV-2 main protease NSP5, together with experimental screening, yielding optimized VIPAs with enhanced antiviral potency. Our work establishes the concept of VIPA mRNA therapy as a versatile platform for the treatment of diverse viral infections via "kill-and-alert" mechanism.

microbiology↗

bioRxiv2025

Identifying Phenotype-Indicative Molecules from the Structure of Biochemical Reaction Networks

Multistability in chemical reaction networks (CRNs) is a key mechanism underlying the diversity in cellular phenotypes. Identifying the steady state a cell occupies requires measuring molecular concentrations. This in turn raises a fundamental question: which chemical species should be selected to reliably distinguish among multiple steady states? We introduce a network-structural approach for identifying indicator species--a subset of species whose concentrations alone suffice to identify which steady state the system occupies. By decomposing a CRN into subnetworks based on structural criteria and applying topological degree theory, we show that the concentrations within certain subnetworks uniquely determine those of all remaining species. These subnetworks thus provide indicator species that distinguish all possible steady states. Crucially, our method relies only on stoichiometric and regulatory information, without requiring kinetic parameters. Implemented via a computational algorithm, the framework is validated through numerical simulations and applied to biochemical networks. This work provides a principled strategy for steady-state identification in high-dimensional biochemical networks from partial observations, with potential applications in single-cell data analysis and biomarker discovery.

biophysics↗

bioRxiv2024

Uncovering Bifurcation Behaviors of Biochemical Reaction Systems from Network Topology

The regulation of biological functions is achieved through the modulation of biochemical reaction network dynamics. The diversity of cell states and the transitions between them have been interpreted as bifurcations in these dynamics. However, due to the complexity of networks and limited knowledge of reaction kinetics, bifurcation behaviors in biological systems remain largely underexplored. To address this, we developed a mathematical method, Structural Bifurcation Analysis (SBA), which decomposes the system into substructures and determines important aspects of bifurcation behaviors--such as substructures responsible for bifurcation conditions, bifurcation-inducing parameters, and bifurcating variables--solely from network topology. We establish a direct relationship between SBA and classical bifurcation analysis, enabling the study of systems even in the presence of conserved quantities. Additionally, we provide a step-by-step bifurcation analysis for general use. We applied our method to the macrophage M1/M2 polarization system. Our analysis reveals that the network structure strongly constrains possible patterns of polarization. We also clarify the dependency of the M1/M2 balance on gene expression levels and predict the emergence of intermediate polarization patterns under gene deletions, including SOCS3, which are experimentally testable.

biophysics↗

bioRxiv2022

Probabilistic Edge Inference of Gene Networks with Bayesian Markov Random Field Modelling

Gaussian graphical models (GGMs), also known as Gaussian Markov random field (MRF) models, are commonly used for gene regulatory network construction. Most current approaches to estimating network structure via GGMs can be categorized into a binary decision that determines if an edge exists through penalized optimization and a probabilistic approach that incorporates graph uncertainty. Analyses in the first category usually adopt the perspective of variable (edge) selection without consideration of probabilistic interpretation. Methods in the second group, particularly the Bayesian approach, often quantify the uncertainty in the network structure with a stochastic measure on the precision matrix. Nevertheless, these methods overlook the existence probability of an edge and its strength related to the dependence between nodes. This study simultaneously investigates the existence and intensity of edges for network structure learning. We propose a method that combines the Bayesian MRF model and conditional autoregressive model for the relationship between gene nodes. This analysis can evaluate the relative strength of the edges and further prioritize the edges of interest. Simulations and a glioblastoma cancer study were carried out to assess the proposed models performance and compare it with existing methods. The proposed approach shows stable performance and may identify novel structures with biological insights.

bioinformatics↗

bioRxiv2022

Energetic decoupling of phycobilisomes from photosystem II involved in nonphotochemical quenching in red algae

To mitigate photodamage under fluctuating light conditions, photosynthetic organisms respond by regulating light energy absorbed by light-harvesting complexes and used for photochemistry. Nonphotochemical quenching acts as a frontline response to prevent excitation energy from reaching the photochemical reaction center of photosystem II. The mechanisms underlying nonphotochemical quenching in red algae, which display unique combination of light-harvesting transmembrane antenna proteins and membrane-attached phycobilisomes, appear to be different from those in cyanobacteria, green algae, and plants. Several single-process models have been proposed for red algal nonphotochemical quenching, yet the possibility of more than one process being involved in nonphotochemical quenching awaits further investigation. To assess multiple nonphotochemical quenching processes in the extremophilic red alga Cyanidioschyzon merolae, fluorescence analyses with light preferentially absorbed by phycobilisomes or photosystems were utilized. Energetic decoupling of phycobilisomes from photosystem II and intrinsic photosystem II quenching were identified as two dominant processes involved in nonphotochemical quenching and distinguished by their kinetics. Whereas the degrees of energetic decoupling remained similar after its induction, the degrees of intrinsic photosystem II quenching varied depending on the illumination period and intensity. The respective effects of protein crosslinkers, osmolytes, ionophores, and photosynthetic inhibitors on the kinetics of nonphotochemical quenching suggested that the energetic decoupling involved conformational changes associated with the connection between the PBS and PSII. Furthermore, the surface charge on the thylakoid membrane played a significant role in the modulation of red algal nonphotochemical quenching. One-sentence summaryEnergetic decoupling of phycobilisomes from photosystem II and intrinsic photosystem II quenching were involved in nonphotochemical quenching of the extremophilic red alga Cyanidioschyzon merolae.

plant biology↗

bioRxiv2021

Rice NIN-LIKE PROTEIN 3 plays a significant role in nitrogen use efficiency and grain yield under nitrate-sufficient conditions

Nitrogen (N) is an essential macronutrient for crop growth and yield, but excessive application of N fertilizer has caused serious environmental pollution and greatly increased the cost of agricultural production. One of the effective and economical solutions to this dilemma is to improve the N use efficiency (NUE) of crops. Although some components involved in regulating NUE have been identified, the underlying molecular mechanism remains largely elusive in rice. Here we report that the OsNLP3 (NIN-like protein 3) is an important regulator in NUE and grain yield under nitrate-sufficient conditions. Both NUE and grain yield were significantly improved by enhanced OsNLP3 expression in the field, but reduced in osnlp3 mutants. The expression of OsNLP3 responds to both nitrate and ammonium, while OsNLP3 nuclear retention is only induced by nitrate, not by ammonium. OsNLP3 regulates the expression of a set of genes involved in N transport and assimilation by directly binding to the nitrate-responsive cis-element in the promoters of these genes. Our study demonstrates that OsNLP3 is significant for the regulation of NUE and grain yield, particularly in nitrate-rich conditions, thus providing a candidate for improving NUE and grain yield in rice.

plant biology↗

bioRxiv2021

The impact of distributional assumptions in gene-set and pathway analysis: how far can it go wrong?

Gene-set analysis (GSA) has been one of the standard procedures for exploring potential biological functions when a group of differentially expressed genes have been derived. The development of its methodology has been an active research topic in recent decades. Many GSA methods, when newly proposed, rely on simulation studies to evaluate their performance with a common implicit assumption that the multivariate expression values are normally distributed. The validity of this assumption has been disputed in several studies but no systematic analysis has been carried out to assess the influence of this distributional assumption. Our goal in this study is not to propose a new GSA method but to first examine if the multi-dimensional gene expression data in gene sets follow a multivariate normal distribution (MVN). Six statistical methods in three categories of MVN tests were considered and applied to a total of twenty-two datasets of expression data from studies involving tumor and normal tissues, with ten signaling pathways chosen as the gene sets. Second, we evaluated the influence of non-normality on the performance of current GSA tools, including parametric and non-parametric methods. Specifically, the scenario of mixture distributions representing the case of different tumor subtypes was considered. Our first finding suggests that the MVN assumption should be carefully dealt with. It does not hold true in many applications tested here. The second investigation of the GSA tools demonstrates that the non-normality does affect the performance of these GSA methods, especially when subtypes exist. We conclude that the use of the inherent multivariate normality assumption should be assessed with care in evaluating new GSA tools, since this MVN assumption cannot be guaranteed and this assumption affects strongly the performance of GSA methods. If a newly proposed GSA method is to be evaluated, we recommend the incorporation of multivariate non-normal distributions or sampling from large databases if available.

bioinformatics↗

bioRxiv2020

Epigenetic therapy remodels the immune synaptic cytoskeleton to potentiate cancer susceptibility to γδ T cells

{gamma}{delta} T cells are a distinct subgroup of T cells that bridge the innate and adaptive immune systems and can attack cancer or virus-infected cells in an MHC-unrestricted manner. Despite its antitumor ability in both autologous and allogeneic settings, earlier trials of adoptive {gamma}{delta} T cell transfer in solid tumors had limited success due to limitations in cell expansion and the lack of a strategy to modulate tumor lytic interactions between {gamma}{delta} T and cancer cells. Here, we show through quantitative surface proteomics and gene enrichment analyses that DNA methyltransferase inhibitors (DNMTis) upregulate multiple surface molecules related to {gamma}{delta} T cell activation in cancer cells. DNMTi treatment of human lung cancer potentiates tumor lysis by ex vivo-expanded {gamma}{delta} T cells using a clinical-grade expansion protocol developed by our team to enrich for the V{delta}1 subset while preserving their antitumor effector functions. Mechanistically, DNMTis enhance immune synapse formation and stabilize the synaptic cleft to facilitate {gamma}{delta} T-mediated tumor lysis. Through integrated analysis of RNA-seq, DNA methylation, and ATAC-seq, we demonstrate that depletion of DNMTs induces coordinated pattern alterations of immune synaptic-cytoskeletal networks at the cancer side of the immune synapse. In addition, single-cell mass cytometry reveals enrichment of polyfunctional {gamma}{delta} T subsets by DNMTis. Combined DNMTi and adoptive {gamma}{delta} T transfer in a mouse lung cancer model offers a significant survival benefit. Consistently, the DNMTi-associated cytoskeleton signature identifies a subset of lung cancer patients with improved survival. Our results demonstrate that epigenetic mechanisms are crucial for cytoskeletal remodeling in cancer to potentiate immune attack and support a combinatorial strategy of DNMTis and {gamma}{delta} T cell-based immunotherapy in lung cancer management. One Sentence SummaryDNA methyltransferase inhibitors potentiate the killing of lung cancer by {gamma}{delta} T cells through remodeling cytoskeletal-immune synaptic networks.

cancer biology↗

bioRxiv2020

Rice NIN-LIKE PROTEIN 4 is a master regulator of nitrogen use efficiency

Nitrogen (N) is one of the key essential macronutrients that affects rice growth and yield. Inorganic N fertilizers are excessively used to boost yield and generate serious collateral environmental pollution. Therefore, improving crop N use efficiency (NUE) is highly desirable and has been a major endeavor in crop improvement. However, only a few regulators have been identified that can be used to improve NUE in rice to date. Here we show that the NIN-like protein OsNLP4 significantly improves the rice NUE and yield. Field trials consistently showed that loss-of-OsNLP4 dramatically reduced yield and NUE compared with wild type under different N regimes. In contrast, the OsNLP4 overexpression lines remarkably increased yield by 30% and NUE by 47% under moderate N level compared with wild type. Transcriptomic analyses revealed that OsNLP4 orchestrates the expression of a majority of known N uptake, assimilation and signaling genes by directly binding to the nitrate-responsive cis-element in their promoters to regulate their expression. Moreover, overexpression of OsNLP4 can recover the phenotype of Arabidopsis nlp7 mutant and enhance its biomass. Our results demonstrate that OsNLP4 is a master regulator of NUE in rice and sheds light on crop NUE improvement.

plant biology↗

bioRxiv2019

Fucosyltransferase 4 shapes oncogenic glycoproteome to drive metastasis of lung adenocarcinoma

Aberrant fucosylation plays a critical role in lung cancer progression. Identification of the key fucosyltransferase as a therapeutic target may refine lung cancer management. Here, we identified a terminal 1,3-fucosyltransferase, FUT4, as the key prognostic predictor for lung adenocarcinoma through transcriptomic screens in lung cancer cohorts. Overexpression of FUT4 promotes lung cancer invasion, migration and cell adhesion in vitro and provokes distant metastases in mouse xenograft models. RNA-seq and glycoproteomics analyses revealed that FUT4 mediates aberrant fucosylation of intracellular transport and signal transduction proteins, which facilitates concurrent transcriptional activation of multiple cellular processes, including membrane trafficking, cell cycle, and major oncogenic signaling pathways. Notably, knockdown of FUT4 markedly curtailed lung colonization and distant metastases of lung cancer cells in mouse xenograft models. In addition, the metastatic phenotype provoked by FUT4-mediated fucosylproteomic networks can be diminished by targeted pathway inhibitors. Collectively, FUT4 represents a promising therapeutic target in lung cancer metastasis. Our data highlight the potentials for integration of glycomics into precision medicine-based therapeutics.

cancer biology↗

bioRxiv2019

Gut-Liver physiomimetics reveal paradoxical modulation of IBD-related inflammation by short-chain fatty acids

Association between the microbiome, IBD and liver diseases are known, yet cause and effect remain elusive. By connecting human microphysiological systems of the gut, liver and circulating Treg/Th17 cells, we modeled progression of ulcerative colitis (UC) ex vivo. We show that microbiome-derived short-chain fatty acids (SCFA) may either improve or worsen disease severity, depending on the activation state of CD4 T cells. Employing multiomics, we found SCFA increased production of ketone bodies, glycolysis and lipogenesis, while markedly reducing innate immune activation of the UC gut. However, during acute T cell-mediated inflammation, SCFA exacerbated CD4+ T cell effector function, partially through metabolic reprograming, leading to gut barrier disruption and hepatic injury. These paradoxical findings underscore the emerging utility of human physiomimetic technology in combination with systems immunology to study causality and the fundamental entanglement of immunity, metabolism and tissue homeostasis.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC=\"FIGDIR/small/706812v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (31K):\norg.highwire.dtl.DTLVardef@10006b0org.highwire.dtl.DTLVardef@b7344eorg.highwire.dtl.DTLVardef@1cb0c06org.highwire.dtl.DTLVardef@1826e38_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗