The SARS-CoV-2 subgenome landscape and its novel regulatory features.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently a global pandemic. CoVs are known to generate negative subgenomes (subgenomic RNAs [sgRNAs]) through transcription-regulating sequence (TRS)-dependent template switching, but the global dynamic landscapes of coronaviral subgenomes and regulatory rules remain unclear. Here, using next-generation sequencing (NGS) short-read and Nanopore long-read poly(A) RNA sequencing in two cell types at multiple time points after infection with SARS-CoV-2, we identified hundreds of template switches and constructed the dynamic landscapes of SARS-CoV-2 subgenomes. Interestingly, template switching could occur in a bidirectional manner, with diverse SARS-CoV-2 subgenomes generated from successive template-switching events. The majority of template switches result from RNA-RNA interactions, including seed and compensatory modes, with terminal pairing status as a key determinant. Two TRS-independent template switch modes are also responsible for subgenome biogenesis. Our findings reveal the subgenome landscape of SARS-CoV-2 and its regulatory features, providing a molecular basis for understanding subgenome biogenesis and developing novel anti-viral strategies.

Cholesterol Homeostatic Regulator SCAP-SREBP2 Integrates NLRP3 Inflammasome Activation and Cholesterol Biosynthetic Signaling in Macrophages.

Cholesterol metabolism has been linked to immune functions, but the mechanisms by which cholesterol biosynthetic signaling orchestrates inflammasome activation remain unclear. Here, we have shown that NLRP3 inflammasome activation is integrated with the maturation of cholesterol master transcription factor SREBP2. Importantly, SCAP-SREBP2 complex endoplasmic reticulum-to-Golgi translocation was required for optimal activation of the NLRP3 inflammasome both in vitro and in vivo. Enforced cholesterol biosynthetic signaling by sterol depletion or statins promoted NLPR3 inflammasome activation. However, this regulation did not predominantly depend on changes in cholesterol homeostasis controlled by the transcriptional activity of SREBP2, but relied on the escort activity of SCAP. Mechanistically, NLRP3 associated with SCAP-SREBP2 to form a ternary complex which translocated to the Golgi apparatus adjacent to a mitochondrial cluster for optimal inflammasome assembly. Our study reveals that, in addition to controlling cholesterol biosynthesis, SCAP-SREBP2 also serves as a signaling hub integrating cholesterol metabolism with inflammation in macrophages.

Development and validation of MRI-based scoring models for predicting placental invasiveness in high-risk women for placenta accreta spectrum.

OBJECTIVES: To develop and validate MRI-based scoring models for predicting placenta accreta spectrum (PAS) invasiveness. MATERIALS AND METHODS: This retrospective study comprised a derivation cohort and a validation cohort. The derivation cohort came from a systematic review of published studies evaluating the diagnostic performance of MRI signs for PAS and/or placenta percreta in high-risk women. The significant signs were identified and used to develop prediction models for PAS and placenta percreta. Between 2016 and 2021, consecutive high-risk pregnant women for PAS who underwent placental MRI constituted the validation cohort. Two radiologists independently evaluated the MRI signs. The reference standard was intraoperative and pathologic findings. The predictive ability of MRI-based models was evaluated using the area under the curve (AUC). RESULTS: The derivation cohort included 26 studies involving 2568 women and the validation cohort consisted of 294 women with PAS diagnosed in 258 women (88%). Quantitative meta-analysis revealed that T2-dark bands, placental/uterine bulge, loss of T2 hypointense interface, bladder wall interruption, placental heterogeneity, and abnormal intraplacental vascularity were associated with both PAS and placenta percreta, and myometrial thinning and focal exophytic mass were exclusively associated with PAS. The PAS model was validated with an AUC of 0.90 (95% CI: 0.86, 0.93) for predicting PAS and 0.85 (95% CI: 0.79, 0.90) for adverse peripartum outcome; the placenta percreta model showed an AUC of 0.92 (95% CI: 0.86, 0.98) for predicting placenta percreta. CONCLUSION: MRI-based scoring models established based on quantitative meta-analysis can accurately predict PAS, placenta percreta, and adverse peripartum outcome. CLINICAL RELEVANCE STATEMENT: These proposed MRI-based scoring models could help accurately predict PAS invasiveness and provide evidence-based risk stratification in the management of high-risk pregnant women for PAS. KEY POINTS: • Accurately identifying placenta accreta spectrum (PAS) and assessing its invasiveness depending solely on individual MRI signs remained challenging. • MRI-based scoring models, established through quantitative meta-analysis of multiple MRI signs, offered the potential to predict PAS invasiveness in high-risk pregnant women. • These MRI-based models allowed for evidence-based risk stratification in the management of pregnancies suspected of having PAS.

NETosis promotes chronic inflammation and fibrosis in systemic lupus erythematosus and COVID-19.

Pulmonary fibrosis, a serious complication of systemic lupus erythematosus (SLE) and coronavirus disease 2019 (COVID-19), leads to irreversible lung damage. However, the underlying mechanism of this condition remains unclear. In this study, we revealed the landscape of transcriptional changes in lung biopsies from individuals with SLE, COVID-19-induced pulmonary fibrosis, and idiopathic pulmonary fibrosis (IPF) using histopathology and RNA sequencing, respectively. Despite the diverse etiologies of these diseases, lung expression of matrix metalloproteinase genes in these diseases showed similar patterns. Particularly, the differentially expressed genes were significantly enriched in the pathway of neutrophil extracellular trap formation, showing similar enrichment signature between SLE and COVID-19. The abundance of Neutrophil extracellular traps (NETs) was much higher in the lungs of individuals with SLE and COVID-19 compared to those with IPF. In-depth transcriptome analyses revealed that NETs formation pathway promotes epithelial-mesenchymal transition (EMT). Furthermore, stimulation with NETs significantly up-regulated α-SMA, Twist, Snail protein expression, while decreasing the expression of E-cadherin protein in vitro. This indicates that NETosis promotes EMT in lung epithelial cells. Given drugs that are efficacious in degrading damaged NETs or inhibiting NETs production, we identified a few drug targets that were aberrantly expressed in both SLE and COVID-19. Among these targets, the JAK2 inhibitor Tofacitinib could effectively disrupted the process of NETs and reversed NET-induced EMT in lung epithelial cells. These findings support that the NETs/EMT axis, activated by SLE and COVID-19, contributes to the progression of pulmonary fibrosis. Our study also highlights that JAK2 as a potential target for the treatment of fibrosis in these diseases.

CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma.

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which is expected to be noninvasively identified using radiomics approaches. Purpose To construct a CT radiomics model to predict the MTM subtype and to investigate the underlying immune infiltration patterns. Materials and Methods This study included five retrospective data sets and one prospective data set from three academic medical centers between January 2015 and December 2021. The preoperative liver contrast-enhanced CT studies of 365 adult patients with resected HCC were evaluated. The Third Xiangya Hospital of Central South University provided the training set and internal test set, while Yueyang Central Hospital and Hunan Cancer Hospital provided the external test sets. Radiomic features were extracted and used to develop a radiomics model with machine learning in the training set, and the performance was verified in the two test sets. The outcomes cohort, including 58 adult patients with advanced HCC undergoing transarterial chemoembolization and antiangiogenic therapy, was used to evaluate the predictive value of the radiomics model for progression-free survival (PFS). Bulk RNA sequencing of tumors from 41 patients in The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing from seven prospectively enrolled participants were used to investigate the radiomics-related immune infiltration patterns. Area under the receiver operating characteristics curve of the radiomics model was calculated, and Cox proportional regression was performed to identify predictors of PFS. Results Among 365 patients (mean age, 55 years ± 10 [SD]; 319 men) used for radiomics modeling, 122 (33%) were confirmed to have the MTM subtype. The radiomics model included 11 radiomic features and showed good performance for predicting the MTM subtype, with AUCs of 0.84, 0.80, and 0.74 in the training set, internal test set, and external test set, respectively. A low radiomics model score relative to the median value in the outcomes cohort was independently associated with PFS (hazard ratio, 0.4; 95% CI: 0.2, 0.8; P = .01). The radiomics model was associated with dysregulated humoral immunity involving B-cell infiltration and immunoglobulin synthesis. Conclusion Accurate prediction of the macrotrabecular-massive subtype in patients with hepatocellular carcinoma was achieved using a CT radiomics model, which was also associated with defective humoral immunity. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue.

P200 family protein IFI204 negatively regulates type I interferon responses by targeting IRF7 in nucleus.

Interferon-inducible p200 family protein IFI204 was reported to be involved in DNA sensing, and subsequently induces the production of type I interferons and proinflammatory mediators. However, its function in the regulation of antiviral innate immune signaling pathway remains unclear. Here we reported a novel role of IFI204 that specifically inhibits the IRF7-mediated type I interferons response during viral infection. IFI204 and other p200 family proteins are highly expressed in mouse hepatitis coronavirus-infected bone marrow-derived dendritic cells. The abundant IFI204 could significantly interact with IRF7 in nucleus by its HIN domain and prevent the binding of IRF7 with its corresponding promoter. Moreover, other p200 family proteins that possess HIN domain could also inhibit the IRF7-mediated type I interferons. These results reveal that, besides the positive regulation function in type I interferon response at the early stage of DNA virus infection, the interferon-inducible p200 family proteins such as IFI204 could also negatively regulate the IRF7-mediated type I interferon response after RNA virus infection to avoid unnecessary host damage from hyper-inflammatory responses.

Clinical and immunological characteristics in COVID-19 convalescent patients.

The humoral and cellular immunity of convalescent COVID-19 patients is involved in pathogenesis and vaccine immunity. In this study, through CoV-psV neutralization assay and IFN-γ ELISpot testing in 30 cases of COVID-19 patients after 9 months post-SARS-CoV-2 infection, it found that the ratio of memory/naive CD4+ T lymphocytes cells and levels of anti-SARS-CoV-2-IgM and RBD-IgM were slightly but significantly higher in COVID-19 severe convalescent patients than that in non-severe patients. The specific cellular and humoral immunity against SARS-CoV-2 were detectable, regardless of the severity of the disease in the acute phase. This information may help understanding the immune status after SARS-CoV-2 infection.

Emissions and Secondary Formation of Air Pollutants from Modern Heavy-Duty Trucks in Real-World Traffic-Chemical Characteristics Using On-Line Mass Spectrometry.

Complying with stricter emissions standards, a new generation of heavy-duty trucks (HDTs) has gradually increased its market share and now accounts for a large percentage of on-road mileage. The potential to improve air quality depends on an actual reduction in both emissions and subsequent formation of secondary pollutants. In this study, the emissions in real-world traffic from Euro VI-compliant HDTs were compared to those from older classes, represented by Euro V, using high-resolution time-of-flight chemical ionization mass spectrometry. Gas-phase primary emissions of several hundred species were observed for 70 HDTs. Furthermore, the particle phase and secondary pollutant formation (gas and particle phase) were evaluated for a number of HDTs. The reduction in primary emission factors (EFs) was evident (∼90%) and in line with a reduction of 28-97% for the typical regulated pollutants. Secondary production of most gas- and particle-phase compounds, for example, nitric acid, organic acids, and carbonyls, after photochemical aging in an oxidation flow reactor exceeded the primary emissions (EFAged/EFFresh ratio ≥2). Byproducts from urea-selective catalytic reduction systems had both primary and secondary sources. A non-negative matrix factorization analysis highlighted the issue of vehicle maintenance as a remaining concern. However, the adoption of Euro VI has a significant positive effect on emissions in real-world traffic and should be considered in, for example, urban air quality assessments.

Reviving chloroquine for anti-SARS-CoV-2 treatment with cucurbit[7]uril-based supramolecular formulation.

The wide-spreading SARS-CoV-2 virus has put the world into boiling water for more than a year, however pharmacological therapies to act effectively against coronavirus disease 2019 (COVID-19) remain elusive. Chloroquine (CQ), an antimalarial drug, was found to exhibit promising antiviral activity in vitro and in vivo at a high dosage, thus CQ was approved by the FDA for the emergency use authorization (EUA) in the fight against COVID-19 in the US, but later was revoked the EUA status due to the severe clinical toxicity. Herein, we show that supramolecular formulation of CQ by a macrocyclic host, curcurbit[7]uril (CB[7]), reduced its non-specific toxicity and improved its antiviral activity against coronavirus, working in synergy with CB[7]. CB[7] was found to form 1:1 host-guest complexes with CQ, with a binding constant of ∼104 L/mol. The CQ-CB[7] formulation decreased the cytotoxicity of CQ against Vero E6 and L-02 cell lines. In particular, the cytotoxicity of CQ (60 µmol/L) against both Vero E6 cell line and L-02 cell lines was completely inhibited in the presence of 300 µmol/L and 600 µmol/L CB[7], respectively. Furthermore, the CB[7] alone showed astonishing antiviral activity in SARS-CoV-2 infected Vero E6 cells and mouse hepatitis virus strain A59 (MHV-A59) infected N2A cells, and synergistically improved the antiviral activity of CQ-CB[7], suggesting that CB[7]-based CQ formulation has a great potential as a safe and effective antiviral agent against SARS-CoV-2 and other coronavirus.

Emerging coronaviruses: Genome structure, replication, and pathogenesis.

The recent emergence of a novel coronavirus (2019-nCoV), which is causing an outbreak of unusual viral pneumonia in patients in Wuhan, a central city in China, is another warning of the risk of CoVs posed to public health. In this minireview, we provide a brief introduction of the general features of CoVs and describe diseases caused by different CoVs in humans and animals. This review will help understand the biology and potential risk of CoVs that exist in richness in wildlife such as bats.

Secondary Organic Aerosol Formation from Urban Roadside Air in Hong Kong.

Motor vehicle emissions are an important but poorly constrained source of secondary organic aerosol (SOA). Here, we investigated in situ SOA formation from urban roadside air in Hong Kong during winter time using an oxidation flow reactor (OFR), with equivalent atmospheric oxidation ranging from several hours to several days. The campaign-average mass enhancement of OA, nitrate, sulfate, and ammonium upon OFR aging was 7.0, 7.2, 0.8, and 2.6 µg m-3, respectively. To investigate the sources of SOA formation potential, we performed multilinear regression analysis between measured peak SOA concentrations from OFR and the concentrations of toluene that represent motor vehicle emissions and cooking OA from positive matrix factorization (PMF) analysis of ambient OA. Traffic-related SOA precursors contributed 92.3%, 92.4%, and 83.1% to the total SOA formation potential during morning rush hours, noon and early afternoon, and evening meal time, respectively. The SOA production factor (PF) was approximately 5.2 times of primary OA (POA) emission factor (EF) and the secondary particulate matter (PM) PF was approximately 2.6 times of primary particles EF. This study highlights the potential benefit of reducing secondary PM production from motor vehicle emissions in mitigating PM pollutions.

Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma.

OBJECTIVE: To evaluate the diagnostic performance of machine-learning based quantitative texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). METHODS: This single-institutional retrospective study included 58 patients with pathologically proven small renal mass (17 in AMLwvf and 41 in RCC groups). Texture features were extracted from the largest possible tumorous regions of interest (ROIs) by manual segmentation in preoperative three-phase CT images. Interobserver reliability and the Mann-Whitney U test were applied to select features preliminarily. Then support vector machine with recursive feature elimination (SVM-RFE) and synthetic minority oversampling technique (SMOTE) were adopted to establish discriminative classifiers, and the performance of classifiers was assessed. RESULTS: Of the 42 extracted features, 16 candidate features showed significant intergroup differences (P < 0.05) and had good interobserver agreement. An optimal feature subset including 11 features was further selected by the SVM-RFE method. The SVM-RFE+SMOTE classifier achieved the best performance in discriminating between small AMLwvf and RCC, with the highest accuracy, sensitivity, specificity and AUC of 93.9 %, 87.8 %, 100 % and 0.955, respectively. CONCLUSION: Machine learning analysis of CT texture features can facilitate the accurate differentiation of small AMLwvf from RCC. KEY POINTS: • Although conventional CT is useful for diagnosis of SRMs, it has limitations. • Machine-learning based CT texture analysis facilitate differentiation of small AMLwvf from RCC. • The highest accuracy of SVM-RFE+SMOTE classifier reached 93.9 %. • Texture analysis combined with machine-learning methods might spare unnecessary surgery for AMLwvf.

Size-resolved effective density of submicron particles during summertime in the rural atmosphere of Beijing, China.

Particle density is an important physical property of atmospheric particles. The information on high time-resolution size-resolved particle density is essential for understanding the atmospheric physical and chemical aging processes of aerosols particles. In the present study, a centrifugal particle mass analyzer (CPMA) combined with a differential mobility analyzer (DMA) was deployed to determine the size-resolved effective density of 50 to 350nm particles at a rural site of Beijing during summer 2016. The measured particle effective densities decreased with increasing particle sizes and ranged from 1.43 to 1.55g/cm3, on average. The effective particle density distributions were dominated by a mode peaked at around 1.5g/cm3 for 50 to 350nm particles. Extra modes with peaks at 1.0, 0.8, and 0.6g/cm3 for 150, 240, and 350nm particles, which might be freshly emitted soot particles, were observed during intensive primary emissions episodes. The particle effective densities showed a diurnal variation pattern, with higher values during daytime. A case study showed that the effective density of Aitken mode particles during the new particle formation (NPF) event decreased considerably, indicating the significant contribution of organics to new particle growth.

Classifier Model Based on Machine Learning Algorithms: Application to Differential Diagnosis of Suspicious Thyroid Nodules via Sonography.

OBJECTIVE: The purpose of this article is to construct classifier models using machine learning algorithms and to evaluate their diagnostic performances for differentiating malignant from benign thyroid nodules. MATERIALS AND METHODS: This study included 970 histopathologically proven thyroid nodules in 970 patients. Two radiologists retrospectively reviewed ultrasound images, and nodules were graded according to a five-tier sonographic scoring system. Statistically significant variables based on an experienced radiologist's observations were obtained with attribute optimization using fivefold cross-validation and applied as the input nodes to build models for predicting malignancy of nodules. The performances of the machine learning algorithms and radiologists were compared using ROC curve analysis. RESULTS: Diagnosis by the experienced radiologist achieved the highest predictive accuracy of 88.66% with a specificity of 85.33%, whereas the radial basis function (RBF)-neural network (NN) achieved the highest sensitivity of 92.31%. The AUC value for diagnosis by the experienced radiologist (AUC = 0.9135) was greater than those for diagnosis by the less experienced radiologist, the naïve Bayes classifier, the support vector machine, and the RBF-NN (AUC = 0.8492, 0.8811, 0.9033, and 0.9103, respectively; p < 0.05). CONCLUSION: The machine learning algorithms underperformed with respect to the experienced radiologist's readings used to construct them, and the RBF-NN outperformed the other machine learning algorithm models.

Influence of abdominal fat distribution and inflammatory status on post-operative prognosis in non-small cell lung cancer patients: a retrospective cohort study.

PURPOSE: To evaluate the influence of visceral fat area (VFA), subcutaneous fat area (SFA), the systemic immune-inflammation index (SII) and total inflammation-based systemic index (AISI) on the postoperative prognosis of non-small cell lung cancers (NSCLC) patients. METHODS: 266 NSCLC patients received surgery from two academic medical centers were included. To assess the effect of abdominal fat measured by computed tomography (CT) imaging and inflammatory indicators on patients' overall survival (OS) and progression-free survival (PFS), Kaplan-Meier survival analysis and Cox proportional hazards models were used. RESULTS: Kaplan-Meier analysis showed the OS and PFS of patients in high-VFA group was better than low-VFA group (p < 0.05). AISI and SII were shown to be risk factors for OS and PFS (p < 0.05) after additional adjustment for BMI (Cox regression model II). After further adjustment for VFA (Cox regression model III), low-SFA group had longer OS (p < 0.05). Among the four subgroups based on VFA (high/low) and SFA (high/low) (p < 0.05), the high-VFA & low-SFA group had the longest median OS (108 months; 95% CI 74-117 months) and PFS (85 months; 95% CI 65-117 months), as well as the lowest SII and AISI (p < 0.05). Low-SFA was a protective factor for OS with different VFA stratification (p < 0.05). CONCLUSION: VFA, SFA, SII and AISI may be employed as significant prognostic markers of postoperative survival in NSCLC patients. Moreover, excessive SFA levels may encourage systemic inflammation decreasing the protective impact of VFA, which may help to provide targeted nutritional support and interventions for postoperative NSCLC patients with poor prognosis.

Longitudinal changes in the volume of residual lung lobes after lobectomy for lung cancer: a retrospective cohort study.

It is unclear how the residual lobe volume changes over time after lobectomy. This study aims to clarify the temporal patterns of volume changes in each remaining lung lobe post-lobectomy. A retrospective review was conducted on patients who underwent lobectomy for lung cancer at Yueyang Central Hospital from January to December 2021. Lung CT images were reconstructed in three dimensions to calculate the volumes of each lung lobe preoperatively and at 1, 6, and 12 months postoperatively. A total of 182 patients were included. Postoperatively, the median total lung volume change rates relative to preoperative values were -20.1%, -9.3%, and -5.9% at 1, 6, and 12 months, respectively. Except for the right middle lobe in patients who underwent right upper lobectomy, the volumes of individual lung lobes exceeded preoperative values. The volume growth of the lung on the side of the resection was significantly more than that of the lung on the opposite side. For left lobectomy patients, the right lower lobe's volume change rate exceeded that of the right upper and middle lobes. Among right lobectomy patients, the left lower lobe and the relatively inferior lobe of right lung had higher volume change rates than the superior one. Right middle lobe change rate was more in patients with right lower lobectomy than right upper lobectomy. Six months postoperatively, FEV1% and right middle lobectomy were positively correlated with the overall volume change rate. One year postoperatively, only age was negatively correlated with the overall volume change rate. 75 patients had pulmonary function tests. Postoperative FEV1 change linearly correlated with 1-year lung volume change rate, but not with theoretical total lung volume change rate or segmental method calculated FEV1 change. Time-dependent compensatory volume changes occur in remaining lung lobe post-lobectomy, with stronger compensation observed in the relatively inferior lobe compared to the superior one(s). Preoperative lung function and age may affect compensation level.

Structural and functional insights into the 2'-O-methyltransferase of SARS-CoV-2.

A unique feature of coronaviruses is their utilization of self-encoded nonstructural protein 16 (nsp16), 2'-O-methyltransferase (2'-O-MTase), to cap their RNAs through ribose 2'-O-methylation modification. This process is crucial for maintaining viral genome stability, facilitating efficient translation, and enabling immune escape. Despite considerable advances in the ultrastructure of SARS-CoV-2 nsp16/nsp10, insights into its molecular mechanism have so far been limited. In this study, we systematically characterized the 2'-O-MTase activity of nsp16 in SARS-CoV-2, focusing on its dependence on nsp10 stimulation. We observed cross-reactivity between nsp16 and nsp10 in various coronaviruses due to a conserved interaction interface. However, a single residue substitution (K58T) in SARS-CoV-2 nsp10 restricted the functional activation of MERS-CoV nsp16. Furthermore, the cofactor nsp10 effectively enhanced the binding of nsp16 to the substrate RNA and the methyl donor S-adenosyl-l-methionine (SAM). Mechanistically, His-80, Lys-93, and Gly-94 of nsp10 interacted with Asp-102, Ser-105, and Asp-106 of nsp16, respectively, thereby effectively stabilizing the SAM binding pocket. Lys-43 of nsp10 interacted with Lys-38 and Gly-39 of nsp16 to dynamically regulate the RNA binding pocket and facilitate precise binding of RNA to the nsp16/nsp10 complex. By assessing the conformational epitopes of nsp16/nsp10 complex, we further determined the critical residues involved in 2'-O-MTase activity. Additionally, we utilized an in vitro biochemical platform to screen potential inhibitors targeting 2'-O-MTase activity. Overall, our results significantly enhance the understanding of viral 2'-O methylation process and mechanism, providing valuable targets for antiviral drug development.

An optimized high-throughput SARS-CoV-2 dual reporter trans-complementation system for antiviral screening in vitro and in vivo.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still epidemic around the world. The manipulation of SARS-CoV-2 is restricted to biosafety level 3 laboratories (BSL-3). In this study, we developed a SARS-CoV-2 ΔN-GFP-HiBiT replicon delivery particles (RDPs) encoding a dual reporter gene, GFP-HiBiT, capable of producing both GFP signal and luciferase activities. Through optimal selection of the reporter gene, GFP-HiBiT demonstrated superior stability and convenience for antiviral evaluation. Additionally, we established a RDP infection mouse model by delivering the N gene into K18-hACE2 KI mouse through lentivirus. This mouse model supports RDP replication and can be utilized for in vivo antiviral evaluations. In summary, the RDP system serves as a valuable tool for efficient antiviral screening and studying the gene function of SARS-CoV-2. Importantly, this system can be manipulated in BSL-2 laboratories, decreasing the threshold of experimental requirements.

A broad neutralizing nanobody against SARS-CoV-2 engineered from an approved drug.

SARS-CoV-2 infection is initiated by Spike glycoprotein binding to the human angiotensin-converting enzyme 2 (ACE2) receptor via its receptor binding domain. Blocking this interaction has been proven to be an effective approach to inhibit virus infection. Here we report the discovery of a neutralizing nanobody named VHH60, which was directly produced from an engineering nanobody library based on a commercialized nanobody within a very short period. VHH60 competes with human ACE2 to bind the receptor binding domain of the Spike protein at S351, S470-471and S493-494 as determined by structural analysis, with an affinity of 2.56 nM. It inhibits infections of both ancestral SARS-CoV-2 strain and pseudotyped viruses harboring SARS-CoV-2 wildtype, key mutations or variants at the nanomolar level. Furthermore, VHH60 suppressed SARS-CoV-2 infection and propagation 50-fold better and protected mice from death for twice as long as the control group after SARS-CoV-2 nasal infections in vivo. Therefore, VHH60 is not only a powerful nanobody with a promising profile for disease control but also provides evidence for a highly effective and rapid approach to generating therapeutic nanobodies.