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.

Suppression of ACE2 SUMOylation protects against SARS-CoV-2 infection through TOLLIP-mediated selective autophagy.

In addition to investigating the virology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), discovering the host-virus dependencies are essential to identify and design effective antiviral therapy strategy. Here, we report that the SARS-CoV-2 entry receptor, ACE2, conjugates with small ubiquitin-like modifier 3 (SUMO3) and provide evidence indicating that prevention of ACE2 SUMOylation can block SARS-CoV-2 infection. E3 SUMO ligase PIAS4 prompts the SUMOylation and stabilization of ACE2, whereas deSUMOylation enzyme SENP3 reverses this process. Conjugation of SUMO3 with ACE2 at lysine (K) 187 hampers the K48-linked ubiquitination of ACE2, thus suppressing its subsequent cargo receptor TOLLIP-dependent autophagic degradation. TOLLIP deficiency results in the stabilization of ACE2 and elevated SARS-CoV-2 infection. In conclusion, our findings suggest selective autophagic degradation of ACE2 orchestrated by SUMOylation and ubiquitination as a potential way to combat SARS-CoV-2 infection.

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.

TRIM14 Promotes Noncanonical NF-κB Activation by Modulating p100/p52 Stability via Selective Autophagy.

The noncanonical NF-κB signaling pathway plays a critical role in a variety of biological functions including chronic inflammation and tumorigenesis. Activation of noncanonical NF-κB signaling largely relies on the abundance as well as the processing of the NF-κB family member p100/p52. Here, TRIM14 is identified as a novel positive regulator of the noncanonical NF-κB signaling pathway. TRIM14 promotes noncanonical NF-κB activation by targeting p100/p52 in vitro and in vivo. Furthermore, a mechanistic study shows that TRIM14 recruits deubiquitinase USP14 to cleave the K63-linked ubiquitin chains of p100/p52 at multiple sites, thereby preventing p100/p52 from cargo receptor p62-mediated autophagic degradation. TRIM14 deficiency in mice significantly impairs noncanonical NF-κB-mediated inflammatory responses as well as acute colitis and colitis-associated colon cancer development. Taken together, these findings establish the TRIM14-USP14 axis as a crucial checkpoint that controls noncanonical NF-κB signaling and highlight the crosstalk between autophagy and innate immunity.

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.

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.

Development of analysis of volatile polyfluorinated alkyl substances in indoor air using thermal desorption-gas chromatography-mass spectrometry.

The study attempts to utilize thermal desorption (TD) coupled with gas chromatography-mass spectrometry (GC-MS) for determination of indoor airborne volatile polyfluorinated alkyl substances (PFASs), including four fluorinated alcohols (FTOHs), two fluorooctane sulfonamides (FOSAs), and two fluorooctane sulfonamidoethanols (FOSEs). Standard stainless steel tubes of Tenax/Carbograph 1 TD were employed for low-volume sampling and exhibited minimal breakthrough of target analytes in sample collection. The method recoveries were in the range of 88-119% for FTOHs, 86-138% for FOSAs, exhibiting significant improvement compared with other existing air sampling methods. However, the widely reported high method recoveries of FOSEs were also observed (139-210%), which was probably due to the structural differences between FOSEs and internal standards. Method detection limit, repeatability, linearity, and accuracy were reported as well. The approach has been successfully applied to routine quantification of targeted PFASs in indoor environment of Singapore. The significantly shorter sampling time enabled the observation of variations of concentrations of targeted PFASs within different periods of a day, with higher concentration levels at night while ventilation systems were shut off. This indicated the existence of indoor sources and the importance of building ventilation and air conditioning system.