A general urban spreading pattern of COVID-19 and its underlying mechanism

Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

Natural land cover positively correlates with COVID-19 health outcomes.

BACKGROUND: The coronavirus disease 2019 (COVID-19) poses special challenges for societies, as the disease causes millions of deaths. Although the direct prevention measures affect the prevalence and mortality the most, the other indirect factors, including natural environments and economics, could not be neglected. Evaluating the effect of natural land cover on COVID-19 health outcomes is an urgent and crucial public health topic. METHODS: Here, we examine the relationships between natural land cover and the prevalence and mortality of COVID-19 in the United States. To probe the effects of long-term living with natural land cover, we extract county-level land cover data from 2001 to 2019. Based on statistically spatial tests, we employ the Spatial Simultaneous Autoregressive (SAC) Model to estimate natural land cover's impact and monetary values on COVID-19 health outcomes. To examine the short-term effects of natural environments, we build a seasonal panel data set about the greenery index and COVID-19 health outcomes. The panel SAC model is used to detect the relationship between the greenery index and seasonal COVID-19 health outcomes. RESULTS: A 1% increase in open water or deciduous forest is associated with a 0.004-death and 0.163-conformed-case, or 0.006-death and 0.099-confirmed-case decrease in every 1,000 people. Converting them into monetary value, for the mortality, a 1% increase in open water, deciduous forest, or evergreen forest in a county is equivalent to a 212-, 313-, or 219-USD increase in household income in the long term. Moreover, for the prevalence, a 1% change in open water, deciduous forest, or mixed forest is worth a 382-, 230-, or 650-USD increase in household income. Furthermore, a rational development intensity is also critical to reduce the risk of the COVID-19 pandemic. More greenery in the short term is also linked to lower prevalence and mortality. CONCLUSIONS: Our study underscores the importance of incorporating natural land cover as a means of mitigating the risks and negative consequences of future pandemics like COVID-19 and promoting overall public health.

Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic.

Background: Efficiently combating with the coronavirus disease 2019 (COVID-19) has been challenging for medics, police and other service providers. To reduce human interaction, multi-robot systems are promising for performing various missions such as disinfection, monitoring, temperature measurement and delivering goods to people quarantined in prescribed homes and hotels. This paper studies the task assignment problem for multiple dispersed homogeneous robots to visit a set of prescribed hotels to perform tasks such as body temperature assessment or oropharyngeal swabs for people quarantined in the hotels while trying to minimize the robots' total operation time. Each robot can move to multiple hotels sequentially within its limited maximum operation time to provide the service. Methods: The task assignment problem generalizes the multiple traveling salesman problem, which is an NP-hard problem. The main contributions of the paper are twofold: (I) a lower bound on the robots' total operation time to serve all the people has been found based on graph theory, which can be used to approximately evaluate the optimality of an assignment solution; (II) several efficient marginal-cost-based task assignment algorithms are designed to assign the hotel-serving tasks to the robots. Results: In the Monte Carlo simulations where different numbers of robots need to serve the people quarantined in 30 and 90 hotels, the designed task assignment algorithms can quickly (around 30 ms) calculate near-optimal assignment solutions (within 1.15 times of the optimal value). Conclusions: Numerical simulations show that the algorithms can lead to solutions that are close to the optimal compared with the competitive genetic algorithm and greedy algorithm.

Synthetic nanobodies as tools to distinguish IgG Fc glycoforms.

Protein glycosylation is a crucial mediator of biological functions and is tightly regulated in health and disease. However, interrogating complex protein glycoforms is challenging, as current lectin tools are limited by cross-reactivity while mass spectrometry typically requires biochemical purification and isolation of the target protein. Here, we describe a method to identify and characterize a class of nanobodies that can distinguish glycoforms without reactivity to off-target glycoproteins or glycans. We apply this technology to immunoglobulin G (IgG) Fc glycoforms and define nanobodies that specifically recognize either IgG lacking its core-fucose or IgG bearing terminal sialic acid residues. By adapting these tools to standard biochemical methods, we can clinically stratify dengue virus and SARS-CoV-2 infected individuals based on their IgG glycan profile, selectively disrupt IgG-Fcγ receptor binding both in vitro and in vivo, and interrogate the B cell receptor (BCR) glycan structure on living cells. Ultimately, we provide a strategy for the development of reagents to identify and manipulate IgG Fc glycoforms.

A systematic review and meta-analysis of long term physical and mental sequelae of COVID-19 pandemic: call for research priority and action.

The long-term physical and mental sequelae of COVID-19 are a growing public health concern, yet there is considerable uncertainty about their prevalence, persistence and predictors. We conducted a comprehensive, up-to-date meta-analysis of survivors' health consequences and sequelae for COVID-19. PubMed, Embase and the Cochrane Library were searched through Sep 30th, 2021. Observational studies that reported the prevalence of sequelae of COVID-19 were included. Two reviewers independently undertook the data extraction and quality assessment. Of the 36,625 records identified, a total of 151 studies were included involving 1,285,407 participants from thirty-two countries. At least one sequelae symptom occurred in 50.1% (95% CI 45.4-54.8) of COVID-19 survivors for up to 12 months after infection. The most common investigation findings included abnormalities on lung CT (56.9%, 95% CI 46.2-67.3) and abnormal pulmonary function tests (45.6%, 95% CI 36.3-55.0), followed by generalized symptoms, such as fatigue (28.7%, 95% CI 21.0-37.0), psychiatric symptoms (19.7%, 95% CI 16.1-23.6) mainly depression (18.3%, 95% CI 13.3-23.8) and PTSD (17.9%, 95% CI 11.6-25.3), and neurological symptoms (18.7%, 95% CI 16.2-21.4), such as cognitive deficits (19.7%, 95% CI 8.8-33.4) and memory impairment (17.5%, 95% CI 8.1-29.6). Subgroup analysis showed that participants with a higher risk of long-term sequelae were older, mostly male, living in a high-income country, with more severe status at acute infection. Individuals with severe infection suffered more from PTSD, sleep disturbance, cognitive deficits, concentration impairment, and gustatory dysfunction. Survivors with mild infection had high burden of anxiety and memory impairment after recovery. Our findings suggest that after recovery from acute COVID-19, half of survivors still have a high burden of either physical or mental sequelae up to at least 12 months. It is important to provide urgent and appropriate prevention and intervention management to preclude persistent or emerging long-term sequelae and to promote the physical and psychiatric wellbeing of COVID-19 survivors.

Respiratory mucosal vaccination of peptide-poloxamine-DNA nanoparticles provides complete protection against lethal SARS-CoV-2 challenge.

The ongoing SARS-CoV-2 pandemic represents a brutal reminder of the continual threat of mucosal infectious diseases. Mucosal immunity may provide robust protection at the predominant sites of SARS-CoV-2 infection. However, it remains unclear whether respiratory mucosal administration of DNA vaccines could confer protective immune responses against SARS-CoV-2 challenge due to insurmountable barriers posed by the airway. Here, we applied self-assembled peptide-poloxamine nanoparticles with mucus-penetrating properties for pulmonary inoculation of a COVID-19 DNA vaccine (pSpike/PP-sNp). The pSpike/PP-sNp not only displays superior gene transfection and favorable biocompatibility in the mouse airway, but also promotes a tripartite immunity consisting of systemic, cellular, and mucosal immune responses that are characterized by mucosal IgA secretion, high levels of neutralizing antibodies, and resident memory phenotype T-cell responses in the lungs of mice. Most importantly, immunization with pSpike/PP-sNp completely eliminates SARS-CoV-2 infection in both upper and lower respiratory tracts and enables 100% survival rate of mice following lethal SARS-CoV-2 challenge. Our findings indicate PP-sNp is a promising platform in mediating DNA vaccines to elicit all-around mucosal immunity against SARS-CoV-2.

Guanylate-binding protein 1 restricts avian coronavirus infectious bronchitis virus-infected HD11 cells.

The Infectious Bronchitis Virus (IBV), a coronavirus, is a key avian pathogen that causes acute and highly infectious viral respiratory diseases. IBV is an enveloped, positive-sense RNA virus, and the host factors that restrict infection and replication of the virus remain poorly understood. Guanylate-binding protein 1 (GBP1), an interferon-gamma (IFN-γ)-inducible guanosine triphosphatase (GTPase), is a major player in host immunity and provides defense against viral replication. However, the role of chicken GBP1 (chGBP1) in the IBV-life cycle is not well understood. Therefore, this study aimed to reveal the potential role of IFN-γ-induced chGBP1 in mediating host anti-IBV infection responses. We identified the host restriction factor, chGBP1, in IBV-infected chicken macrophages HD11 cell lines. We showed that chGBP1 was upregulated by treatment with both IFN-γ and IBV in HD11 cells. chGBP1 inhibited IBV replication in a dose-dependent manner and enhanced IFN-γ anti-IBV activity. Importantly, the GTPase domain of chGBP1 played a pivotal role in its anti-IBV activity. Furthermore, chGBP1 interacts with IBV Nucleocapsids protein to degrade IBV-N protein through the autophagy pathway. Taken together, our results demonstrate a critical role of chGBP1 in anti-IBV in macrophages HD11 cells.

A general urban spreading pattern of COVID-19 and its underlying mechanism

Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis.

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task.

Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data.

Digital contact tracing has been recently advocated by China and many countries as part of digital prevention measures on COVID-19. Controversies have been raised about their effectiveness in practice as it remains open how they can be fully utilized to control COVID-19. In this article, we show that an abundance of information can be extracted from digital contact tracing for COVID-19 prevention and control. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location-related data contributed by 10,527,737 smartphone users in Wuhan, China. The temporal contact graph reveals five time-varying indicators can accurately capture actual contact trends at population level, demonstrating that travel restrictions (e.g., city lockdown) in Wuhan played an important role in containing COVID-19. We reveal a strong correlation between the contacts level and the epidemic size, and estimate several significant epidemiological parameters (e.g., serial interval). We also show that user participation rate exerts higher influence on situation evaluation than user upload rate does, indicating a sub-sampled dataset would be as good at prediction. At individual level, however, the temporal contact graph plays a limited role, since the behavior distinction between the infected and uninfected individuals are not substantial. The revealed results can tell the effectiveness of digital contact tracing against COVID-19, providing guidelines for governments to implement interventions using information technology.

Design of UVC Surface Disinfection Robot with Coverage Path Planning Using Map-Based Approach At-The-Edge

In response to the issue of virus contamination in the cold-chain warehouse or hospital environment under the influence of the COVID-19, we propose the design work of a disinfection robot based on the UVC radiation mechanism using the low-computational path optimization at-the-edge. To build a surface disinfection robot with less computing power to generate a collision-free path with shorter total distance in studies, a 2D map is used as a graph-based approach to automatically generate a closed-loop disinfection path to cover all the accessible surfaces. The discrete disinfection points from the map are extracted with effective disinfection distances and sorted by a nearest-neighbor (NN) search over historical trajectory data and improved A * algorithm to obtain an efficient coverage path to all accessible boundaries of the entire area. The purpose of improved A * algorithm with NN is not to find the optimal path solution but to optimize one with reasonable computing power. The proposed algorithm enhances the path-finding efficiency by a dynamically weighted heuristic function and reduces the path turning angles, which improves the path smoothness significantly requiring less computing power. The Gazebo simulation is conducted, and the prototype disinfection robot has been built and tested in a real lab environment. Compared with the classic A * algorithm, the improved A * algorithm with NN has improved the path-finding efficiency and reduced the path length while covering the same area. Both the simulation and experimental results show that this approach can provide the design to balance the tradeoffs among the path-finding efficiency, smoothness, disinfection coverage, and computation resources.

Spatial–Temporal Urban Mobility Pattern Analysis During COVID-19 Pandemic

In response to the repeated outbreaks of the COVID-19, many countries implement the region-specific, multilevel epidemic prevention and control policies. To fully understand the impact of these interventions on urban mobility, it is urgent to analyze spatial-temporal mobility pattern at the neighborhood level and structural changes in urban mobility networks. Here, we construct urban mobility networks among points of interest (POIs), using large-scale anonymous mobility data from de-identified mobile phone users. We comprehensively investigate the changes of urban mobility networks during two waves of the COVID-19 pandemic in Beijing from both graph and subgraph perspectives. Beyond an overall mobility reduction in Beijing, we find that the mobility change is spatially and temporally heterogeneous among different urban regions. We uncover a disproportionately large reduction in long-distance, nighttime, and non-essential travel. This results in a more geographically fragmented, local, and regional network in the pandemic. We demonstrate that these structural changes slow down the spatial spread of the COVID-19 in the mobility network.

Efficient Transfection of In vitro Transcribed mRNA in Cultured Cells Using Peptide-Poloxamine Nanoparticles.

In vitro transcribed messenger RNA (mRNA) vaccines have displayed enormous potential in fighting against the coronavirus disease 2019 (COVID-19) pandemic. Efficient and safe delivery systems must be included in the mRNA vaccines due to the fragile properties of mRNA. A self-assembled peptide-poloxamine nanoparticle (PP-sNp) gene delivery system is specifically designed for the pulmonary delivery of nucleic acids and displays promising capabilities in mediating successful mRNA transfection. Here, an improved method for preparing PP-sNp is described to elaborate on how the PP-sNp encapsulates Metridia luciferase (MetLuc) mRNA and successfully transfects cultured cells. MetLuc-mRNA is obtained by an in vitro transcription process from a linear DNA template. A PP-sNp is produced by mixing synthetic peptide/poloxamine with mRNA solution using a microfluidic mixer, allowing for the self-assembly of PP-sNp. The charge of PP-sNp is subsequently evaluated by measuring the zeta potential. Meanwhile, the polydispersity and hydrodynamic size of PP-sNp nanoparticles are measured using dynamic light scattering. The mRNA/PP-sNp nanoparticles are transfected into cultured cells, and supernatants from the cell culture are assayed for luciferase activity. The representative results demonstrate their capacity for in vitro transfection. This protocol may shed light on developing next-generation mRNA vaccine delivery systems.

5-Hydroxymethylcytosine Signatures in Circulating Cell-Free DNA as Early Warning Biomarkers for COVID-19 Progression and Myocardial Injury.

Background: The symptoms of coronavirus disease 2019 (COVID-19) range from moderate to critical conditions, leading to death in some patients, and the early warning indicators of the COVID-19 progression and the occurrence of its serious complications such as myocardial injury are limited. Methods: We carried out a multi-center, prospective cohort study in three hospitals in Wuhan. Genome-wide 5-hydroxymethylcytosine (5hmC) profiles in plasma cell-free DNA (cfDNA) was used to identify risk factors for COVID-19 pneumonia and develop a machine learning model using samples from 53 healthy volunteers, 66 patients with moderate COVID-19, 99 patients with severe COVID-19, and 38 patients with critical COVID-19. Results: Our warning model demonstrated that an area under the curve (AUC) for 5hmC warning moderate patients developed into severe status was 0.81 (95% CI 0.77-0.85) and for severe patients developed into critical status was 0.92 (95% CI 0.89-0.96). We further built a warning model on patients with and without myocardial injury with the AUC of 0.89 (95% CI 0.84-0.95). Conclusion: This is the first study showing the utility of 5hmC as an accurate early warning marker for disease progression and myocardial injury in patients with COVID-19. Our results show that phosphodiesterase 4D and ten-eleven translocation 2 may be important markers in the progression of COVID-19 disease.

A Novel Nanobody-Horseradish Peroxidase Fusion Based-Competitive ELISA to Rapidly Detect Avian Corona-Virus-Infectious Bronchitis Virus Antibody in Chicken Serum.

Avian coronavirus-infectious bronchitis virus (AvCoV-IBV) is the causative agent of infectious bronchitis (IB) that has brought great threat and economic losses to the global poultry industry. Rapid and accurate diagnostic methods are very necessary for effective disease monitoring. At the present study, we screened a novel nanobody against IBV-N protein for development of a rapid, simple, sensitive, and specific competitive ELISA for IBV antibody detection in order to enable the assessment of inoculation effect and early warning of disease infection. Using the phage display technology and bio-panning, we obtained 7 specific nanobodies fused with horseradish peroxidase (HRP) which were expressed in culture supernatant of HEK293T cells. Out of which, the nanobody of IBV-N-Nb66-vHRP has highly binding with IBV-N protein and was easily blocked by the IBV positive serums, which was finally employed as an immunoprobe for development of the competitive ELISA (cELISA). In the newly developed cELISA, we reduce the use of enzyme-conjugated secondary antibody, and the time of whole operation process is approximately 1 h. Moreover, the IBV positive serums diluted at 1:1000 can still be detected by the developed cELISA, and it has no cross reactivity with others chicken disease serums including Newcastle disease virus, Fowl adenovirus, Avian Influenza Virus, Infectious bursal disease virus and Hepatitis E virus. The cut-off value of the established cELISA was 36%, and the coefficient of variation of intra- and inter-assay were 0.55-1.65% and 2.58-6.03%, respectively. Compared with the commercial ELISA (IDEXX kit), the agreement rate of two methods was defined as 98% and the kappa value was 0.96, indicating the developed cELISA has high consistency with the commercial ELISA. Taken together, the novel cELISA for IBV antibody detection is a simple, rapid, sensitive, and specific immunoassay, which has the potential to rapidly test IBV antibody contributing to the surveillance and control of the disease.

Network pharmacology and bioinformatics analysis identified essential genes of Jingulian in the treatment of rheumatoid arthritis and COVID-19.

Background: Patients with rheumatoid arthritis (RA) may be more susceptible to infection by coronavirus disease-19 (COVID-19) due to immune system dysfunction. However, there are still insufficient treatment strategies for patients with RA and COVID-19. Since Jingulian is a traditional Chinese medicine (TCM) with anti-viral and immune regulatory functions, our study aims to explore the detailed mechanisms of Jingulian in treating patients with RA and COVID-19. Methods: All the components of Jingulian were retrieved from pharmacology databases. Then, a series of network pharmacology-based analyses and molecular docking were used to understand the molecular functions, core targets, related pathways, and potential therapeutic targets of Jingulian in patients with RA/COVID-19. Results: A total of 93 genes were identified according to the disease-compound-target network. We investigated that the main targets, signaling pathways, and biological functions of Jingulian in RA and COVID-19. Our results indicated that Jingulian may treat patients with RA/COVID-19 through immune processes and viral processes. Moreover, the results of molecular docking revealed that tormentic acid was one of the top compounds of Jingulian, which had high affinity with Janus kinase 1 (JAK1), signal transducer and activator of transcription 3 (STAT3), and epidermal growth factor receptor (EGFR) in patients with RA/COVID-19. Furthermore, 5 core targets of Jingulian were also identified, including JAK1, Janus kinase 2 (JAK2), STAT3, lymphocyte specific protein tyrosine kinase (LCK), and EGFR. Conclusions: Tormentic acid in Jingulian may regulate JAK1, STAT3, and EGFR, and might play a critical role in RA/COVID-19.

Comparative miRNA transcriptomics of macaques and mice reveals MYOC is an inhibitor for Cryptococcus neoformans invasion into the brain.

Cryptococcal meningoencephalitis (CM) is emerging as an infection in HIV/AIDS patients shifted from primarily ART-naive to ART-experienced individuals, as well as patients with COVID-19 and immunocompetent hosts. This fungal infection is mainly caused by the opportunistic human pathogen Cryptococcus neoformans. Brain or central nervous system (CNS) dissemination is the deadliest process for this disease; however, mechanisms underlying this process have yet to be elucidated. Moreover, illustrations of clinically relevant responses in cryptococcosis are currently limited due to the low availability of clinical samples. In this study, to explore the clinically relevant responses during C. neoformans infection, macaque and mouse infection models were employed and miRNA-mRNA transcriptomes were performed and combined, which revealed cytoskeleton, a major feature of HIV/AIDS patients, was a centric pathway regulated in both infection models. Notably, assays of clinical immune cells confirmed an enhanced macrophage "Trojan Horse" in patients with HIV/AIDS, which could be shut down by cytoskeleton inhibitors. Furthermore, myocilin, encoded by MYOC, was found to be a novel enhancer for the macrophage "Trojan Horse," and an enhanced fungal burden was achieved in the brains of MYOC-transgenic mice. Taken together, the findings from this study reveal fundamental roles of the cytoskeleton and MYOC in fungal CNS dissemination, which not only helps to understand the high prevalence of CM in HIV/AIDS but also facilitates the development of novel therapeutics for meningoencephalitis caused by C. neoformans and other pathogenic microorganisms.

Infectious Bronchitis Virus Nsp14 Degrades JAK1 to Inhibit the JAK-STAT Signaling Pathway in HD11 Cells.

Coronaviruses (CoVs) are RNA viruses that can infect a wide range of animals, including humans, and cause severe respiratory and gastrointestinal disease. The Gammacoronavirus avian infectious bronchitis virus (IBV) causes acute and contagious diseases in chickens, leading to severe economic losses. Nonstructural protein 14 (Nsp14) is a nonstructural protein encoded by the CoV genome. This protein has a regulatory role in viral virulence and replication. However, the function and mechanism of IBV Nsp14 in regulating the host's innate immune response remain unclear. Here we report that IBV Nsp14 was a JAK-STAT signaling pathway antagonist in chicken macrophage (HD11) cells. In these cells, Nsp14 protein overexpression blocked IBV suppression induced by exogenous chIFN-γ treatment. Meanwhile, Nsp14 remarkably reduced interferon-gamma-activated sequence (GAS) promoter activation and chIFN-γ-induced interferon-stimulated gene expression. Nsp14 impaired the nuclear translocation of chSTAT1. Furthermore, Nsp14 interacted with Janus kinase 1 (JAK1) to degrade JAK1 via the autophagy pathway, thereby preventing the activation of the JAK-STAT signaling pathway and facilitating viral replication. These results indicated a novel mechanism by which IBV inhibits the host antiviral response and provide new insights into the selection of antiviral targets against CoV.

Rapid and Accurate Detection of SARS Coronavirus 2 by Nanopore Amplicon Sequencing.

Objective: We aimed to evaluate the performance of nanopore amplicon sequencing detection for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical samples. Method: We carried out a single-center, prospective cohort study in a Wuhan hospital and collected a total of 86 clinical samples, including 54 pharyngeal swabs, 31 sputum samples, and 1 fecal sample, from 86 patients with coronavirus disease 2019 (COVID-19) from Feb 20 to May 15, 2020. We performed parallel detection with nanopore-based genome amplification and sequencing (NAS) on the Oxford Nanopore Technologies (ONT) minION platform and routine reverse transcription quantitative polymerase chain reaction (RT-qPCR). In addition, 27 negative control samples were detected using the two methods. The sensitivity and specificity of NAS were evaluated and compared with those of RT-qPCR. Results: The viral read number and reference genome coverage were both significantly different between the two groups of samples, and the latter was a better indicator for SARS-CoV-2 detection. Based on the reference genome coverage, NAS revealed both high sensitivity (96.5%) and specificity (100%) compared with RT-qPCR (80.2 and 96.3%, respectively), although the samples had been stored for half a year before the detection. The total time cost was less than 15 h, which was acceptable compared with that of RT-qPCR (∼2.5 h). In addition, the reference genome coverage of the viral reads was in line with the cycle threshold value of RT-qPCR, indicating that this number could also be used as an indicator of the viral load in a sample. The viral load in sputum might be related to the severity of the infection, particularly in patients within 4 weeks after onset of clinical manifestations, which could be used to evaluate the infection. Conclusion: Our results showed the high sensitivity and specificity of the NAS method for SARS-CoV-2 detection compared with RT-qPCR. The sequencing results were also used as an indicator of the viral load to display the viral dynamics during infection. This study proved the wide application prospect of nanopore sequencing detection for SARS-CoV-2 and may more knowledge about the clinical characteristics of COVID-19.