Metabolic enzyme UAP1 mediates IRF3 pyrophosphorylation to facilitate innate immune response.

Post-translational modifications (PTMs) of proteins are crucial to guarantee the proper biological functions in immune responses. Although protein phosphorylation has been extensively studied, our current knowledge of protein pyrophosphorylation, which occurs based on phosphorylation, is very limited. Protein pyrophosphorylation is originally considered to be a non-enzymatic process, and its function in immune signaling is unknown. Here, we identify a metabolic enzyme, UDP-N-acetylglucosamine pyrophosphorylase 1 (UAP1), as a pyrophosphorylase for protein serine pyrophosphorylation, by catalyzing the pyrophosphorylation of interferon regulatory factor 3 (IRF3) at serine (Ser) 386 to promote robust type I interferon (IFN) responses. Uap1 deficiency significantly impairs the activation of both DNA- and RNA-viruse-induced type I IFN pathways, and the Uap1-deficient mice are highly susceptible to lethal viral infection. Our findings demonstrate the function of protein pyrophosphorylation in the regulation of antiviral responses and provide insights into the crosstalk between metabolism and innate immunity.

Tetherin Suppresses Type I Interferon Signaling by Targeting MAVS for NDP52-Mediated Selective Autophagic Degradation in Human Cells.

Tetherin (BST2/CD317) is an interferon-inducible antiviral factor known for its ability to block the release of enveloped viruses from infected cells. Yet its role in type I interferon (IFN) signaling remains poorly defined. Here, we demonstrate that Tetherin is a negative regulator of RIG-I like receptor (RLR)-mediated type I IFN signaling by targeting MAVS. The induction of Tetherin by type I IFN accelerates MAVS degradation via ubiquitin-dependent selective autophagy in human cells. Moreover, Tetherin recruits E3 ubiquitin ligase MARCH8 to catalyze K27-linked ubiquitin chains on MAVS at lysine 7, which serves as a recognition signal for NDP52-dependent autophagic degradation. Taken together, our findings reveal a negative feedback loop of RLR signaling generated by Tetherin-MARCH8-MAVS-NDP52 axis and provide insights into a better understanding of the crosstalk between selective autophagy and optimal deactivation of type I IFN signaling.

Data derived extrapolation factors (DDEFs) for rat to human interspecies extrapolation for the HPPD inhibitor mesotrione.

In the risk assessment of agrochemicals, there has been a historical paucity of using data to refine the default adjustment factors, even though large datasets are available to support this. The current state of the science for addressing uncertainty regarding animal to human extrapolation (AFA) is to develop a "data-derived" adjustment factor (DDEF) to quantify such differences, if data are available. Toxicokinetic (TK) and toxicodynamic (TD) differences between species can be utilized for the DDEF, with human datasets being ideal yet rare. We identified a case for a currently registered herbicide, mesotrione, in which human TK and TD are available. This case study outlines an approach for the development of DDEFs using comparative human and animal data and based on an adverse outcome pathway (AOP) for inhibition of 4-hydroxyphenol pyruvate dioxygenase (HHPD). The calculated DDEF for rat to human extrapolation (AFA) for kinetics (AFAK = 2.5) was multiplied by the AFA for dynamics (AFAD = 0.3) resulting in a composite DDEF of ∼1 (AFA = 0.75). This reflects the AOP and available scientific evidence that humans are less sensitive than rats to the effects of HPPD inhibitors. Further analyses were conducted utilizing in vitro datasets from hepatocytes and liver cytosols and extrapolated to whole animal using in vitro to in vivo extrapolation (IVIVE) to support toxicodynamic extrapolation. The in vitro datasets resulted in the same AFAD as derived for in vivo data (AFAD = 0.3). These analyses demonstrate that a majority of the species differences are related to toxicodynamics. Future work with additional in vitro/in vivo datasets for other HPPD inhibitors and cell types will further support this result. This work demonstrates utilization of all available toxicokinetic and toxicodynamic data to replace default uncertainty factors for agrochemical human health risk assessment.

Virus-specific editing identification approach reveals the landscape of A-to-I editing and its impacts on SARS-CoV-2 characteristics and evolution.

Upon SARS-CoV-2 infection, viral intermediates specifically activate the IFN response through MDA5-mediated sensing and accordingly induce ADAR1 p150 expression, which might lead to viral A-to-I RNA editing. Here, we developed an RNA virus-specific editing identification pipeline, surveyed 7622 RNA-seq data from diverse types of samples infected with SARS-CoV-2, and constructed an atlas of A-to-I RNA editing sites in SARS-CoV-2. We found that A-to-I editing was dynamically regulated, varied between tissue and cell types, and was correlated with the intensity of innate immune response. On average, 91 editing events were deposited at viral dsRNA intermediates per sample. Moreover, editing hotspots were observed, including recoding sites in the spike gene that affect viral infectivity and antigenicity. Finally, we provided evidence that RNA editing accelerated SARS-CoV-2 evolution in humans during the epidemic. Our study highlights the ability of SARS-CoV-2 to hijack components of the host antiviral machinery to edit its genome and fuel its evolution, and also provides a framework and resource for studying viral RNA editing.

Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE-based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

Zika virus elicits inflammation to evade antiviral response by cleaving cGAS via NS1-caspase-1 axis.

Viral infection triggers host innate immune responses, which primarily include the activation of type I interferon (IFN) signaling and inflammasomes. Here, we report that Zika virus (ZIKV) infection triggers NLRP3 inflammasome activation, which is further enhanced by viral non-structural protein NS1 to benefit its replication. NS1 recruits the host deubiquitinase USP8 to cleave K11-linked poly-ubiquitin chains from caspase-1 at Lys134, thus inhibiting the proteasomal degradation of caspase-1. The enhanced stabilization of caspase-1 by NS1 promotes the cleavage of cGAS, which recognizes mitochondrial DNA release and initiates type I IFN signaling during ZIKV infection. NLRP3 deficiency increases type I IFN production and strengthens host resistance to ZIKVin vitro and in vivo Taken together, our work unravels a novel antagonistic mechanism employed by ZIKV to suppress host immune response by manipulating the interplay between inflammasome and type I IFN signaling, which might guide the rational design of therapeutics in the future.

NLRP11 disrupts MAVS signalosome to inhibit type I interferon signaling and virus-induced apoptosis.

MAVS signalosome plays an important role in RIG-I-like receptor (RLR)-induced antiviral signaling. Upon the recognition of viral RNAs, RLRs activate MAVS, which further recruits TRAF6 and other signaling proteins to initiate type I interferon (IFN) activation. MAVS signalosome also regulates virus-induced apoptosis to limit viral replication. However, the mechanisms that control the activity of MAVS signalosome are still poorly defined. Here, we report NLRP11, a Nod-like receptor, is induced by type I IFN and translocates to mitochondria to interact with MAVS upon viral infection. Using MAVS as a platform, NLRP11 degrades TRAF6 to attenuate the production of type I IFNs as well as virus-induced apoptosis. Our findings reveal the regulatory role of NLRP11 in antiviral immunity by disrupting MAVS signalosome.

Selective Autophagy Regulates Innate Immunity Through Cargo Receptor Network.

Autophagy, an evolutionarily conserved cargo degradation process, is responsible to remove superfluous and unwanted cytoplasmic materials and maintain cellular homeostasis. Autophagy can be highly selective and target specific cargoes by utilizing multiple cargo receptors, which bind both ubiquitinated cargoes and autophagosomes. Mounting evidence has revealed the deep involvement of selective autophagy in innate immunity upon pathogen invasion, including eliminating microbial pathogens, initiating the anti-microbe responses, and inhibiting excessive immune responses. Given the importance of selective autophagy in innate immunity, how cargo receptors deliver pathogens and intracellular host constitutes to autophagosomes during infection remains to be elucidated. In this review, we summarize current evidence for the regulation of innate immunity by selective autophagy and try to elucidate the mechanisms employed by cargo receptor network in mediating diverse innate immune responses.

Autophagy and Immune Tolerance.

The immune system plays a critical role in defense against invading pathogens, and its function must be strictly controlled to maintain intracellular homeostasis. Once suffering microbial invasion or receiving danger signals, the immune system initiates the responses timely. After the threat removal, the immune system should be shut down to avoid the harm caused by excessive immune activation. Additionally, the immune system needs to be internally adjusted so that it does not respond to self-antigens to avoid autoimmune diseases. The states of nonresponse in immunity are termed as immune tolerance. Numerous studies indicated that macroautophagy (hereafter named as autophagy) is involved in T cells and B cells related immune tolerance. Recently, more and more researches demonstrated that autophagy is not only capable of nonselective degradation of cellular macromolecular components but also responsible for sorting and transporting autophagic substrates through a group of cargo receptors for selective degradation, which is called as selective autophagy. Recent studies indicated that selective autophagy can effectively regulate the immune tolerance and avoid over-activation of immune response by targeting multiple receptors and effectors of immune cells. In this chapter, we will focus on how autophagy participates explicitly in the adaptive and innate immune tolerance.

Equilibrium Relationship between SVOCs in PVC Products and the Air in Contact with the Product.

Phthalates and phthalate alternatives are semivolatile organic compounds (SVOCs) present in many PVC products as plasticizers to enhance product performance. Knowledge of the mass-transfer parameters, including the equilibrium concentration in the air in contact with the product surface ( y0), will greatly improve the ability to estimate the emission rate of SVOCs from these products and to assess human exposure. The objective of this study was to measure y0 for different PVC products and to evaluate its relationship with the material-phase concentrations ( C0). Also, C0 and y0 data from other sources were included, resulting in a substantially larger data set ( Ntotal = 34, T = 25 °C) than found in previous studies. The results show that the material/gas equilibrium relationship does not follow Raoult's law and that therefore the assumption of an ideal solution is invalid. Instead, Henry's law applies, and the Henry's law constant for all target SVOCs consists of the respective pure liquid vapor pressure and an activity coefficient γ, which accounts for the nonideal nature of the solution. For individual SVOCs, a simple partitioning relationship exists, but Henry's law is more generally applicable and will be of greater value in rapid exposure assessment procedures.

Particle/Gas Partitioning of Phthalates to Organic and Inorganic Airborne Particles in the Indoor Environment.

The particle/gas partition coefficient Kp is an important parameter affecting the fate and transport of indoor semivolatile organic compounds (SVOCs) and resulting human exposure. Unfortunately, experimental measurements of Kp exist almost exclusively for atmospheric polycyclic aromatic hydrocarbons, with very few studies focusing on SVOCs that occur in indoor environments. A specially designed tube chamber operating in the laminar flow regime was developed to measure Kp of the plasticizer di-2-ethylhexyl phthalate (DEHP) for one inorganic (ammonium sulfate) and two organic (oleic acid and squalane) particles. The values of Kp for the organic particles (0.23 ± 0.13 m3/µg for oleic acid and 0.11 ± 0.10 m3/µg for squalane) are an order of magnitude higher than those for the inorganic particles (0.011 ± 0.004 m3/µg), suggesting that the process by which the particles accumulate SVOCs is different. A mechanistic model based on the experimental design reveals that the presence of the particles increases the gas-phase concentration gradient in the boundary layer, resulting in enhanced mass transfer from the emission source into the air. This novel approach provides new insight into experimental designs for rapid Kp measurement and a sound basis for investigating particle-mediated mass transfer of SVOCs.

Adsorption of Phthalates on Impervious Indoor Surfaces.

Sorption of semivolatile organic compounds (SVOCs) onto interior surfaces, often referred to as the "sink effect", and their subsequent re-emission significantly affect the fate and transport of indoor SVOCs and the resulting human exposure. Unfortunately, experimental challenges and the large number of SVOC/surface combinations have impeded progress in understanding sorption of SVOCs on indoor surfaces. An experimental approach based on a diffusion model was thus developed to determine the surface/air partition coefficient K of di-2-ethylhexyl phthalate (DEHP) on typical impervious surfaces including aluminum, steel, glass, and acrylic. The results indicate that surface roughness plays an important role in the adsorption process. Although larger data sets are needed, the ability to predict K could be greatly improved by establishing the nature of the relationship between surface roughness and K for clean indoor surfaces. Furthermore, different surfaces exhibit nearly identical K values after being exposed to kitchen grime with values that are close to those reported for the octanol/air partition coefficient. This strongly supports the idea that interactions between gas-phase DEHP and soiled surfaces have been reduced to interactions with an organic film. Collectively, the results provide an improved understanding of equilibrium partitioning of SVOCs on impervious surfaces.

Simple Method To Measure the Vapor Pressure of Phthalates and Their Alternatives.

Phthalates and alternative plasticizers are semivolatile organic compounds (SVOCs), an important class of indoor pollutants that may have significant adverse effects on human health. Unfortunately, models that predict emissions of and the resulting exposure to SVOCs have substantial uncertainties. One reason is that the characteristics governing emissions, transport, and exposure are usually strongly dependent on vapor pressure. Furthermore, available data for phthalates exhibit significant variability, and vapor pressures for the various alternatives are usually unavailable. For these reasons, a new approach based on modeling of the evaporation process was developed to determine vapor pressures of phthalates and alternate plasticizers. A laminar flow forced convection model was used in the design of a partial saturator (PS) tube. The mass transfer mechanisms in the PS tube are accurately modeled and enable the determination of vapor pressure even when the carrier gas is not completely saturated, avoiding the complicated procedure to establish vapor saturation. The measured vapor pressures ranged from about 10(-2) to 10(-7) Pa. Compared to the traditional gas saturation method, the model-based approach is advantageous in terms of both predictability and simplicity. The knowledge provides new insight into experimental design and a sound basis for further method development.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation.

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

USP18 Antagonizes Pyroptosis by Facilitating Selective Autophagic Degradation of Gasdermin D.

As a key executioner of pyroptosis, Gasdermin D (GSDMD) plays a crucial role in host defense and emerges as an essential therapeutic target in the treatment of inflammatory diseases. So far, the understanding of the mechanisms that regulate the protein level of GSDMD to prevent detrimental effects and maintain homeostasis is currently limited. Here, we unveil that ubiquitin-specific peptidase 18 (USP18) works as a negative regulator of pyroptosis by targeting GSDMD for degradation and preventing excessive innate immune responses. Mechanically, USP18 recruits E3 ubiquitin ligase mind bomb homolog 2 (MIB2) to catalyze ubiquitination on GSDMD at lysine (K) 168, which acts as a recognition signal for the selective autophagic degradation of GSDMD. We further confirm the alleviating effect of USP18 on LPS-triggered inflammation in vivo. Collectively, our study demonstrates the role of USP18 in regulating GSDMD-mediated pyroptosis and reveals a previously unknown mechanism by which GSDMD protein level is rigorously controlled by selective autophagy.

Moso bamboo (Phyllostachys edulis) expansion enhances soil pH and alters soil nutrients and microbial communities.

Amid global environmental concerns, the issue of bamboo expansion has garnered significant attention due to its extensive and profound impacts on the ecosystems. Bamboo expansion occurs in native and introduced habitats worldwide, particularly in Asia. However, the effects of bamboo expansion on soil pH, nutrient levels, and microbial communities are complex and vary across different environments. To address this knowledge gap, we conducted a meta-analysis with 2037 paired observations from 81 studies. The results showed that soil pH increased by 6.99 % (0-20 cm) and 4.49 % (20-40 cm) after bamboo expansion. Notably, soil pH increased more in the coniferous forest with bamboo expansion than in the broadleaf forest. Soil pH progressively increased over time since the establishment of bamboo stands. The extent of soil pH elevation was significantly positively correlated with the proportion of bamboo within the forest stand and mean annual solar radiation. In contrast, it was significantly negatively correlated with the mean annual temperature. The elevation of pH is closely related to expansion stage and expanded forest type rather than primarily shaped by climatic factors across a large scale. We also found that bamboo expansion into coniferous forests brought about a notable 14.14 % reduction in total nitrogen (TN). Varied expansion stages resulted in TN reductions of 6.88 % and 7.99 % for mixed forests and bamboo stands, respectively, compared to native forests. Pure bamboo stands exhibited a remarkable 30.39 % increase in ammonium nitrogen and a significant 21.12 % decrease in nitrate nitrogen compared to their native counterparts. Furthermore, bamboo expansion contributed to heightened soil fungal diversity. Taken together, our findings highlight that bamboo expansion leads to an increase in soil pH and alters soil N components and fungal microbial communities, providing valuable insights for future ecological conservation and resource management.

Refining acute human exposure assessment to pesticides in surface water: An integrated data-driven modeling approach.

The substantial spatial and temporal variability of pesticides has led to large uncertainties when determining their peak aqueous concentrations. There is however a lack of large-scale studies dealing with accurate determination of annual maximum daily concentration (AMDC) across the landscape and over time based on the publicly available monitoring data. We developed a novel data-driven approach that firstly used time series modeling to generate AMDCs for qualified water monitoring sites in the conterminous U.S. With feature variables such as pesticide use and land cover compiled into the dataset, machine learning models using eXtreme Gradient Boosting (XGBoost) and Random Forest Regressor (RF) were then developed to estimate AMDCs in surface waters across the U.S. Both models exhibited significant predictability, while a hybrid model consisting of the average predictions by XGBoost and RF model had the highest prediction accuracy (mean absolute error (MAE): 1.23; R2: 0.61). The analysis of permutation variable importance indicated that pesticide use and drainage area were the two most important drivers. Partial dependence analysis revealed that pesticide use, precipitation, cultivated crop land cover and solubility exhibited concentration-promoting effects, whereas drainage area and molecular weight had concentration-demoting effects. Soil adsorption coefficient (Koc) showed nonmonotonic effects. The hybrid model was used to predict and map AMDCs of four example pesticides, including 2,4-dichlorophenoxyacetic acid (2,4-D), atrazine, glyphosate and imidacloprid during 2016-2019 at national scale. The predictive capability was validated using independent monitoring datasets. The fully evaluated approach significantly reduced the uncertainties in modeling annual peak concentrations and served as a valuable solution for conducting geographically oriented, highly refined exposure assessments for pesticides.

Molecular mechanisms and cellular functions of liquid-liquid phase separation during antiviral immune responses.

Spatiotemporal separation of cellular components is vital to ensure biochemical processes. Membrane-bound organelles such as mitochondria and nuclei play a major role in isolating intracellular components, while membraneless organelles (MLOs) are accumulatively uncovered via liquid-liquid phase separation (LLPS) to mediate cellular spatiotemporal organization. MLOs orchestrate various key cellular processes, including protein localization, supramolecular assembly, gene expression, and signal transduction. During viral infection, LLPS not only participates in viral replication but also contributes to host antiviral immune responses. Therefore, a more comprehensive understanding of the roles of LLPS in virus infection may open up new avenues for treating viral infectious diseases. In this review, we focus on the antiviral defense mechanisms of LLPS in innate immunity and discuss the involvement of LLPS during viral replication and immune evasion escape, as well as the strategy of targeting LLPS to treat viral infectious diseases.

ATG4B antagonizes antiviral immunity by GABARAP-directed autophagic degradation of TBK1.

ABBREVIATIONS: Baf A1: bafilomycin A1; GABARAP: GABA type A receptor-associated protein; GFP: green fluorescent protein; IFN: interferon; IKBKE/IKKi: inhibitor of nuclear factor kappa B kinase subunit epsilon; IRF3: interferon regulatory factor 3; ISG: interferon-stimulated gene; ISRE: IFN-stimulated response element; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MAVS: mitochondrial antiviral signaling protein; MOI: multiplicity of infection; PAMPs: pathogen-associated molecule patterns; RIGI/DDX58: RNA sensor RIG-I; SeV: Sendai virus; siRNA: small interfering RNA; TBK1: TANK binding kinase 1; WT: wild-type; VSV: vesicular stomatitis virus.

Phase-separated nucleocapsid protein of SARS-CoV-2 suppresses cGAS-DNA recognition by disrupting cGAS-G3BP1 complex.

Currently, the incidence and fatality rate of SARS-CoV-2 remain continually high worldwide. COVID-19 patients infected with SARS-CoV-2 exhibited decreased type I interferon (IFN-I) signal, along with limited activation of antiviral immune responses as well as enhanced viral infectivity. Dramatic progresses have been made in revealing the multiple strategies employed by SARS-CoV-2 in impairing canonical RNA sensing pathways. However, it remains to be determined about the SARS-CoV-2 antagonism of cGAS-mediated activation of IFN responses during infection. In the current study, we figure out that SARS-CoV-2 infection leads to the accumulation of released mitochondria DNA (mtDNA), which in turn triggers cGAS to activate IFN-I signaling. As countermeasures, SARS-CoV-2 nucleocapsid (N) protein restricts the DNA recognition capacity of cGAS to impair cGAS-induced IFN-I signaling. Mechanically, N protein disrupts the assembly of cGAS with its co-factor G3BP1 by undergoing DNA-induced liquid-liquid phase separation (LLPS), subsequently impairs the double-strand DNA (dsDNA) detection ability of cGAS. Taken together, our findings unravel a novel antagonistic strategy by which SARS-CoV-2 reduces DNA-triggered IFN-I pathway through interfering with cGAS-DNA phase separation.