SARS-CoV-2 envelope protein-derived extracellular vesicles act as potential media for viral spillover.

Extracellular vesicles (EVs) are shown to be a novel viral transmission model capable of increasing a virus's tropism. According to our earlier research, cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or transfected with envelope protein plasmids generate a novel type of EVs that are micrometer-sized and able to encase virus particles. Here, we showed the capacity of these EVs to invade various animals both in vitro and in vivo independent of the angiotensin-converting enzyme 2 receptor. First, via macropinocytosis, intact EVs produced from Vero E6 (monkey) cells were able to enter cells from a variety of animals, including cats, dogs, bats, hamsters, and minks, and vice versa. Second, when given to zebrafish with cutaneous wounds, the EVs showed favorable stability in aqueous environments and entered the fish. Moreover, infection of wild-type (WT) mice with heterogeneous EVs carrying SARS-CoV-2 particles led to a strong cytokine response and a notable amount of lung damage. Conversely, free viral particles did not infect WT mice. These results highlight the variety of processes behind viral transmission and cross-species evolution by indicating that EVs may be possible vehicles for SARS-CoV-2 spillover and raising risk concerns over EVs' potential for viral gene transfer.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis.

Background: Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D. Methods: The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database. Results: A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes. Conclusion: "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.

Nanopore-based glycan sequencing: state of the art and future prospects.

Sequencing of biomacromolecules is a crucial cornerstone in life sciences. Glycans, one of the fundamental biomolecules, derive their physiological and pathological functions from their structures. Glycan sequencing faces challenges due to its structural complexity and current detection technology limitations. As a highly sensitive sensor, nanopores can directly convert nucleic acid sequence information into electrical signals, spearheading the revolution of third-generation nucleic acid sequencing technologies. However, their potential for deciphering complex glycans remains untapped. Initial attempts demonstrated the significant sensitivity of nanopores in glycan sensing, which provided the theoretical basis and insights for the realization of nanopore-based glycan sequencing. Here, we present three potential technical routes to employ nanopore technology in glycan sequencing for the first time. The three novel technical routes include: strand sequencing, capturing glycan chains as they translocate through nanopores; sequential hydrolysis sequencing, capturing released monosaccharides one by one; splicing sequencing, mapping signals from hydrolyzed glycan fragments to an oligosaccharide database/library. Designing suitable nanopores, enzymes, and motors, and extracting characteristic signals pose major challenges, potentially aided by artificial intelligence. It would be highly desirable to design an all-in-one high-throughput glycan sequencer instrument by integrating a sample processing unit, nanopore array, and signal acquisition system into a microfluidic device. The nanopore sequencer invention calls for intensive multidisciplinary cooperation including electrochemistry, glycochemistry, engineering, materials, enzymology, etc. Advancing glycan sequencing will promote the development of basic research and facilitate the discovery of glycan-based drugs and disease markers, fostering progress in glycoscience and even life sciences.

Direct Identification of Complex Glycans via a Highly Sensitive Engineered Nanopore.

The crucial roles that glycans play in biological systems are determined by their structures. However, the analysis of glycan structures still has numerous bottlenecks due to their inherent complexities. The nanopore technology has emerged as a powerful sensor for DNA sequencing and peptide detection. This has a significant impact on the development of a related research area. Currently, nanopores are beginning to be applied for the detection of simple glycans, but the analysis of complex glycans by this technology is still challenging. Here, we designed an engineered α-hemolysin nanopore M113R/T115A to achieve the sensing of complex glycans at micromolar concentrations and under label-free conditions. By extracting characteristic features to depict a three-dimensional (3D) scatter plot, glycans with different numbers of functional groups, various chain lengths ranging from disaccharide to decasaccharide, and distinct glycosidic linkages could be distinguished. Molecular dynamics (MD) simulations show different behaviors of glycans with ß1,3- or ß1,4-glycosidic bonds in nanopores. More importantly, the designed nanopore system permitted the discrimination of each glycan isomer with different lengths in a mixture with a separation ratio of over 0.9. This work represents a proof-of-concept demonstration that complex glycans can be analyzed using nanopore sequencing technology.

The human microbiota is a beneficial reservoir for SARS-CoV-2 mutations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations are rapidly emerging. In particular, beneficial mutations in the spike (S) protein, which can either make a person more infectious or enable immunological escape, are providing a significant obstacle to the prevention and treatment of pandemics. However, how the virus acquires a high number of beneficial mutations in a short time remains a mystery. We demonstrate here that variations of concern may be mutated due in part to the influence of the human microbiome. We searched the National Center for Biotechnology Information database for homologous fragments (HFs) after finding a mutation and the six neighboring amino acids in a viral mutation fragment. Among the approximate 8,000 HFs obtained, 61 mutations in S and other outer membrane proteins were found in bacteria, accounting for 62% of all mutation sources, which is 12-fold higher than the natural variable proportion. A significant proportion of these bacterial species-roughly 70%-come from the human microbiota, are mainly found in the lung or gut, and share a composition pattern with COVID-19 patients. Importantly, SARS-CoV-2 RNA-dependent RNA polymerase replicates corresponding bacterial mRNAs harboring mutations, producing chimeric RNAs. SARS-CoV-2 may collectively pick up mutations from the human microbiota that change the original virus's binding sites or antigenic determinants. Our study clarifies the evolving mutational mechanisms of SARS-CoV-2. IMPORTANCE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations are rapidly emerging, in particular advantageous mutations in the spike (S) protein, which either increase transmissibility or lead to immune escape and are posing a major challenge to pandemic prevention and treatment. However, how the virus acquires a high number of advantageous mutations in a short time remains a mystery. Here, we provide evidence that the human microbiota is a reservoir of advantageous mutations and aids mutational evolution and host adaptation of SARS-CoV-2. Our findings demonstrate a conceptual breakthrough on the mutational evolution mechanisms of SARS-CoV-2 for human adaptation. SARS-CoV-2 may grab advantageous mutations from the widely existing microorganisms in the host, which is undoubtedly an "efficient" manner. Our study might open a new perspective to understand the evolution of virus mutation, which has enormous implications for comprehending the trajectory of the COVID-19 pandemic.

Mapping the Acetylamino and Carboxyl Groups on Glycans by Engineered α-Hemolysin Nanopores.

Glycan is a crucial class of biological macromolecules with important biological functions. Functional groups determine the chemical properties of glycans, which further affect their biological activities. However, the structural complexity of glycans has set a technical hurdle for their direct identification. Nanopores have emerged as highly sensitive biosensors that are capable of detecting and characterizing various analytes. Here, we identified the functional groups on glycans with a designed α-hemolysin nanopore containing arginine mutations (M113R), which is specifically sensitive to glycans with acetamido and carboxyl groups. Molecular dynamics simulations indicated that the acetamido and carboxyl groups of the glycans produce unique electrical signatures by forming polar and electrostatic interactions with the M113R nanopores. Using these electrical features as the fingerprints, we mapped the length of the glycans containing acetamido and carboxyl groups at the monosaccharide, disaccharide, and trisaccharide levels. This proof-of-concept study provides a promising foundation for developing single-molecule glycan fingerprinting libraries and demonstrates the capability of biological nanopores in glycan sequencing.

A small extent of seawater intrusion significantly enhanced Cd uptake by rice in coastal paddy fields.

Paddy fields located around estuaries suffer from seawater intrusion, and how and to what extent salinity levels influence Cd accumulation in rice grains is still unclear. Pot experiments were carried out by cultivating rice under alternating flooding and drainage conditions with different salinity levels (0.2‰, 0.6‰ and 1.8‰). The Cd availability was greatly enhanced at 1.8‰ salinity due to the competition for binding sites by cations and the formation of Cd complexation with anions, which also contributed to Cd uptake by rice roots. The soil Cd fractions were investigated and found that the Cd availability significantly decreased during flooding stage, while it rapidly increased after soil drainage. During drainage stage, Cd availability was greatly enhanced at 1.8‰ salinity mainly attributed to the formation of CdCln2-n. The kinetic model was established to quantitatively evaluate Cd transformation, and it found that the release of Cd from organic matter and Fe-Mn oxides was greatly enhanced at 1.8‰ salinity. The results of pot experiments showed that there was a significant increase in Cd content in rice roots and grains in the treatment of 1.8‰ salinity, because the increasing salinity induced an increase in Cd availability and upregulation of key genes regulating Cd uptake in rice roots. Our findings elucidated the key mechanisms by which high salinity enhanced Cd accumulation in rice grains, and more attention should be given to the food safety of rice cultivated around estuaries.

Extracellular vesicles mediate antibody-resistant transmission of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic. Antibody resistance dampens neutralizing antibody therapy and threatens current global Coronavirus (COVID-19) vaccine campaigns. In addition to the emergence of resistant SARS-CoV-2 variants, little is known about how SARS-CoV-2 evades antibodies. Here, we report a novel mechanism of extracellular vesicle (EV)-mediated cell-to-cell transmission of SARS-CoV-2, which facilitates SARS-CoV-2 to escape from neutralizing antibodies. These EVs, initially observed in SARS-CoV-2 envelope protein-expressing cells, are secreted by various SARS-CoV-2-infected cells, including Vero E6, Calu-3, and HPAEpiC cells, undergoing infection-induced pyroptosis. Various SARS-CoV-2-infected cells produce similar EVs characterized by extra-large sizes (1.6-9.5 µm in diameter, average diameter > 4.2 µm) much larger than previously reported virus-generated vesicles. Transmission electron microscopy analysis and plaque assay reveal that these SARS-CoV-2-induced EVs contain large amounts of live virus particles. In particular, the vesicle-cloaked SARS-CoV-2 virus is resistant to neutralizing antibodies and able to reinfect naïve cells independent of the reported receptors and cofactors. Consistently, the constructed 3D images show that intact EVs could be taken up by recipient cells directly, supporting vesicle-mediated cell-to-cell transmission of SARS-CoV-2. Our findings reveal a novel mechanism of receptor-independent SARS-CoV-2 infection via cell-to-cell transmission, provide new insights into antibody resistance of SARS-CoV-2 and suggest potential targets for future antiviral therapeutics.

Image quality and whole-lesion histogram and texture analysis of diffusion-weighted imaging of breast MRI based on advanced ZOOMit and simultaneous multislice readout-segmented echo-planar imaging.

Objectives: To investigate the image quality and diagnostic capability a of whole-lesion histogram and texture analysis of advanced ZOOMit (A-ZOOMit) and simultaneous multislice readout-segmented echo-planar imaging (SMS-RS-EPI) to differentiate benign from malignant breast lesions. Study design: From February 2020 to October 2020, diffusion-weighted imaging (DWI) using SMS-RS-EPI and A-ZOOMit were performed on 167 patients. Three breast radiologists independently ranked the image datasets. The inter-/intracorrelation coefficients (ICCs) of mean image quality scores and lesion conspicuity scores were calculated between these three readers. Histogram and texture features were extracted from the apparent diffusion coefficient (ADC) maps, respectively, based on a WL analysis. Student's t-tests, one-way ANOVAs, Mann-Whitney U tests, and receiver operating characteristic curves were used for statistical analysis. Results: The overall image quality scores and lesion conspicuity scores for A-ZOOMit and SMS-RS-EPI showed statistically significant differences (4.92 ± 0.27 vs. 3.92 ± 0.42 and 4.93 ± 0.29 vs. 3.87 ± 0.47, p < 0.0001). The ICCs for the image quality and lesion conspicuity scores had good agreements among the three readers (all ICCs >0.75). To differentiate benign and malignant breast lesions, the entropy of ADCA-Zoomit had the highest area (0.78) under the ROC curve. Conclusions: A-ZOOMit achieved higher image quality and lesion conspicuity than SMS-RS-EPI. Entropy based on A-ZOOMit is recommended for differentiating benign from malignant breast lesions.

Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis.

BACKGROUND: Kidney disease progression rates vary among patients. Rapid and accurate prediction of kidney disease outcomes is crucial for disease management. In recent years, various prediction models using Machine Learning (ML) algorithms have been established in nephrology. However, their accuracy have been inconsistent. Therefore, we conducted a systematic review and meta-analysis to investigate the diagnostic accuracy of ML algorithms for kidney disease progression. METHODS: We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, the Chinese Biomedicine Literature Database, Chinese National Knowledge Infrastructure, Wanfang Database, and the VIP Database for diagnostic studies on ML algorithms' accuracy in predicting kidney disease prognosis, from the establishment of these databases until October 2020. Two investigators independently evaluate study quality by QUADAS-2 tool and extracted data from single ML algorithm for data synthesis using the bivariate model and the hierarchical summary receiver operating characteristic (HSROC) model. RESULTS: Fifteen studies were left after screening, only 6 studies were eligible for data synthesis. The sample size of these 6 studies was 12,534, and the kidney disease types could be divided into chronic kidney disease (CKD) and Immunoglobulin A Nephropathy, with 5 articles using end-stage renal diseases occurrence as the primary outcome. The main results indicated that the area under curve (AUC) of the HSROC was 0.87 (0.84-0.90) and ML algorithm exhibited a strong specificity, 95% confidence interval and heterogeneity (I2) of (0.87, 0.84-0.90, [I2 99.0%]) and a weak sensitivity of (0.68, 0.58-0.77, [I2 99.7%]) in predicting kidney disease deterioration. And the the results of subgroup analysis indicated that ML algorithm's AUC for predicting CKD prognosis was 0.82 (0.79-0.85), with the pool sensitivity of (0.64, 0.49-0.77, [I2 99.20%]) and pool specificity of (0.84, 0.74-0.91, [I2 99.84%]). The ML algorithm's AUC for predicting IgA nephropathy prognosis was 0.78 (0.74-0.81), with the pool sensitivity of (0.74, 0.71-0.77, [I2 7.10%]) and pool specificity of (0.93, 0.91-0.95, [I2 83.92%]). CONCLUSION: Taking advantage of big data, ML algorithm-based prediction models have high accuracy in predicting kidney disease progression, we recommend ML algorithms as an auxiliary tool for clinicians to determine proper treatment and disease management strategies.

Loss of MLKL ameliorates liver fibrosis by inhibiting hepatocyte necroptosis and hepatic stellate cell activation.

Background: Liver fibrosis affects millions of people worldwide without an effective treatment. Although multiple cell types in the liver contribute to the fibrogenic process, hepatocyte death is considered to be the trigger. Multiple forms of cell death, including necrosis, apoptosis, and necroptosis, have been reported to co-exist in liver diseases. Mixed lineage kinase domain-like protein (MLKL) is the terminal effector in necroptosis pathway. Although necroptosis has been reported to play an important role in a number of liver diseases, the function of MLKL in liver fibrosis has yet to be unraveled. Methods and Results: Here we report that MLKL level is positively correlated with a number of fibrotic markers in liver samples from both patients with liver fibrosis and animal models. Mlkl deletion in mice significantly reduces clinical symptoms of CCl4- and bile duct ligation (BDL) -induced liver injury and fibrosis. Further studies indicate that Mlkl-/- blocks liver fibrosis by reducing hepatocyte necroptosis and hepatic stellate cell (HSC) activation. AAV8-mediated specific knockdown of Mlkl in hepatocytes remarkably alleviates CCl4-induced liver fibrosis in both preventative and therapeutic ways. Conclusion: Our results show that MLKL-mediated signaling plays an important role in liver damage and fibrosis, and targeting MLKL might be an effective way to treat liver fibrosis.

Radiogenomic analysis reveals tumor heterogeneity of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a subset of breast cancer with an adverse prognosis and significant tumor heterogeneity. Here, we extract quantitative radiomic features from contrast-enhanced magnetic resonance images to construct a breast cancer radiomic dataset (n = 860) and a TNBC radiogenomic dataset (n = 202). We develop and validate radiomic signatures that can fairly differentiate TNBC from other breast cancer subtypes and distinguish molecular subtypes within TNBC. A radiomic feature that captures peritumoral heterogeneity is determined to be a prognostic factor for recurrence-free survival (p = 0.01) and overall survival (p = 0.004) in TNBC. Combined with the established matching TNBC transcriptomic and metabolomic data, we demonstrate that peritumoral heterogeneity is associated with immune suppression and upregulated fatty acid synthesis in tumor samples. Collectively, this multi-omic dataset serves as a useful public resource to promote precise subtyping of TNBC and helps to understand the biological significance of radiomics.

Kinetics of antimony biogeochemical processes under pre-definite anaerobic and aerobic conditions in a paddy soil.

While the transformation of antimony (Sb) in paddy soil has been previously investigated, the biogeochemical processes of highly chemical active Sb in the soil remain poorly understood. In addition, there is a lack of quantitative understanding of Sb transformation in soil. Therefore, in this study, the kinetics of exogenous Sb in paddy soils were investigated under anaerobic and aerobic incubation conditions. The dissolved Sb(V) and the Sb(V) extracted by diffusive gradient technique decreased under anaerobic conditions and then increased under aerobic conditions. The redox reaction of Sb occurred, and Sb bioavailability significantly decreased after 55 days of incubation. The kinetics of Fe and the scanning transmission electron microscopy analysis revealed that the Fe oxides were reduced and became dispersed under anaerobic conditions, whereas they were oxidized and re-aggregated during the aerobic stage. In addition, the redox processes of sulfur and nitrogen were detected under both anaerobic and aerobic conditions. Based on these observations, a simplified kinetic model was established to distinguish the relative contributions of the transformation processes. The bioavailability of Sb was controlled by immobilization as a result of S reduction and by mobilization as a result of Fe reductive dissolution and S oxidation, rather than the pH. These processes coupled with the redox reaction of Sb jointly resulted in the complex behavior of Sb transformation under anaerobic and aerobic conditions. The model-based method and findings of this study provide a comprehensive understanding of the Sb transformation in a complex soil biogeochemical system under changing redox conditions.

Discovery of SARS-CoV-2-E channel inhibitors as antiviral candidates.

Lack of efficiency has been a major problem shared by all currently developed anti-SARS-CoV-2 therapies. Our previous study shows that SARS-CoV-2 structural envelope (2-E) protein forms a type of cation channel, and heterogeneously expression of 2-E channels causes host cell death. In this study we developed a cell-based high throughput screening (HTS) assay and used it to discover inhibitors against 2-E channels. Among 4376 compounds tested, 34 hits with cell protection activity were found. Followed by an anti-viral analysis, 15 compounds which could inhibit SARS-CoV-2 replication were identified. In electrophysiological experiments, three representatives showing inhibitory effect on 2-E channels were chosen for further characterization. Among them, proanthocyanidins directly bound to 2-E channel with binding affinity (KD) of 22.14 µM in surface plasmon resonance assay. Molecular modeling and docking analysis revealed that proanthocyanidins inserted into the pore of 2-E N-terminal vestibule acting as a channel blocker. Consistently, mutations of Glu 8 and Asn 15, two residues lining the proposed binding pocket, abolished the inhibitory effects of proanthocyanidins. The natural product proanthocyanidins are widely used as cosmetic, suggesting a potential of proanthocyanidins as disinfectant for external use. This study further demonstrates that 2-E channel is an effective antiviral drug target and provides a potential antiviral candidate against SARS-CoV-2.

A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy.

Background: To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signatures, MRI findings, and clinicopathological variables to predict DFS. Methods: Patients (n = 150) from two hospitals who received NAC from August 2011 to May 2017 were diagnosed with TNBC by pathological biopsy, and follow-up through May 2020 was retrospectively analysed. Patients from one hospital (n = 109) were used as the training group, and patients from the other hospital (n = 41) were used as the validation group. ROIs were drawn on 1.5 T MRI T1W enhancement images of the whole volume of the tumour obtained with a 3D slicer. Radiomics signatures predicting DFS were identified, optimal cut-off value for Rad-score was determined, and the associations between DFS and radiomics signatures, MRI findings, and clinicopathological variables were analysed. A nomogram was developed and validated for individualized DFS estimation. Results: The median follow-up time was 53.5 months, and 45 of 150 (30.0%) patients experienced recurrence and metastasis. The optimum cut-off value of the Rad-score was 0.2528, which stratified patients into high- and low-risk groups for DFS in the training group (p<0.001) and was validated in the external validation group. Multivariate analysis identified three independent indicators: multifocal/centric disease status, pCR status, and Rad-score. A nomogram based on these factors showed discriminatory ability, the C-index of the model was 0.834 (95% CI, 0.761-0.907) and 0.868 (95% CI, 0.787-949) in the training and the validation groups, respectively, which is better than clinicoradiological nomogram(training group: C-index = 0.726, 95% CI = 0.709-0.743; validation group: C-index = 0.774,95% CI = 0.743-0.805). Conclusion: The Rad-score derived from preoperative MRI features is an independent biomarker for DFS prediction in patients with TNBC to NAC, and the combined radiomics nomogram improved individualized DFS estimation.

Increasing the heterologous production of spinosad in Streptomyces albus J1074 by regulating biosynthesis of its polyketide skeleton.

Spinosyns are natural broad-spectrum biological insecticides with a double glycosylated polyketide structure that are produced by aerobic fermentation of the actinomycete, Saccharopolyspora spinosa. However, their large-scale overproduction is hindered by poorly understood bottlenecks in optimizing the original strain, and poor adaptability of the heterologous strain to the production of spinosyn. In this study, we genetically engineered heterologous spinosyn-producer Streptomyces albus J1074 and optimized the fermentation to improve the production of spinosad (spinosyn A and spinosyn D) based on our previous work. We systematically investigated the result of overexpressing polyketide synthase genes (spnA, B, C, D, E) using a constitutive promoter on the spinosad titer in S. albus J1074. The supply of polyketide synthase precursors was then increased to further improve spinosad production. Finally, increasing or replacing the carbon source of the culture medium resulted in a final spinosad titer of ∼70 mg/L, which is the highest titer of spinosad achieved in heterologous Streptomyces species. This research provides useful strategies for efficient heterologous production of natural products.

SARS-CoV-2 envelope protein causes acute respiratory distress syndrome (ARDS)-like pathological damages and constitutes an antiviral target.

Cytokine storm and multi-organ failure are the main causes of SARS-CoV-2-related death. However, the origin of excessive damages caused by SARS-CoV-2 remains largely unknown. Here we show that the SARS-CoV-2 envelope (2-E) protein alone is able to cause acute respiratory distress syndrome (ARDS)-like damages in vitro and in vivo. 2-E proteins were found to form a type of pH-sensitive cation channels in bilayer lipid membranes. As observed in SARS-CoV-2-infected cells, heterologous expression of 2-E channels induced rapid cell death in various susceptible cell types and robust secretion of cytokines and chemokines in macrophages. Intravenous administration of purified 2-E protein into mice caused ARDS-like pathological damages in lung and spleen. A dominant negative mutation lowering 2-E channel activity attenuated cell death and SARS-CoV-2 production. Newly identified channel inhibitors exhibited potent anti-SARS-CoV-2 activity and excellent cell protective activity in vitro and these activities were positively correlated with inhibition of 2-E channel. Importantly, prophylactic and therapeutic administration of the channel inhibitor effectively reduced both the viral load and secretion of inflammation cytokines in lungs of SARS-CoV-2-infected transgenic mice expressing human angiotensin-converting enzyme 2 (hACE-2). Our study supports that 2-E is a promising drug target against SARS-CoV-2.