Chicken or Porcine Aminopeptidase N Mediates Cellular Entry of Pseudoviruses Carrying Spike Glycoprotein from the Avian Deltacoronaviruses HKU11, HKU13, and HKU17.

Members of deltacoronavirus (DCoV) have mostly been identified in diverse avian species as natural reservoirs, though the porcine DCoV (PDCoV) is a major swine enteropathogenic virus with global spread. The important role of aminopeptidase N (APN) orthologues from various mammalian and avian species in PDCoV cellular entry and interspecies transmission has been revealed recently. In this study, comparative analysis indicated that three avian DCoVs, bulbul DCoV HKU11, munia DCoV HKU13, and sparrow DCoV HKU17 (Chinese strain), and PDCoV in the subgenera Buldecovirus are grouped together at whole-genome levels; however, the spike (S) glycoprotein and its S1 subunit of HKU17 are more closely related to night heron DCoV HKU19 in Herdecovirus. Nevertheless, the S1 protein of HKU11, HKU13, or HKU17 bound to or interacted with chicken APN (chAPN) or porcine APN (pAPN) by flow cytometry analysis of cell surface expression of APN and by coimmunoprecipitation in APN-overexpressing cells. Expression of chAPN or pAPN allowed entry of pseudotyped lentiviruses with the S proteins from HKU11, HKU13 and HKU17 into nonsusceptible cells and natural avian and porcine cells, which could be inhibited by the antibody against APN or anti-PDCoV-S1. APN knockdown by siRNA or knockout by CRISPR/Cas9 in chicken or swine cell lines significantly or almost completely blocked infection of these pseudoviruses. Hence, we demonstrate that HKU11, HKU13, and HKU17 with divergent S genes likely engage chAPN or pAPN to enter the cells, suggesting a potential interspecies transmission from wild birds to poultry and from birds to mammals by certain avian DCoVs. IMPORTANCE The receptor usage of avian deltacoronaviruses (DCoVs) has not been investigated thus far, though porcine deltacoronavirus (PDCoV) has been shown to utilize aminopeptidase N (APN) as a cell receptor. We report here that chicken or porcine APN also mediates cellular entry by three avian DCoV (HKU11, HKU13, and HKU17) spike pseudoviruses, and the S1 subunit of three avian DCoVs binds to APN in vitro and in the surface of avian and porcine cells. The results fill the gaps in knowledge about the avian DCoV receptor and elucidate important insights for the monitoring and prevention of potential interspecies transmission of certain avian DCoVs. In view of the diversity of DCoVs, whether this coronavirus genus will cause novel virus to emerge in other mammals from birds, are worthy of further surveillance and investigation.

Bile acids promote the caveolae-associated entry of swine acute diarrhea syndrome coronavirus in porcine intestinal enteroids.

Intestinal microbial metabolites have been increasingly recognized as important regulators of enteric viral infection. However, very little information is available about which specific microbiota-derived metabolites are crucial for swine enteric coronavirus (SECoV) infection in vivo. Using swine acute diarrhea syndrome (SADS)-CoV as a model, we were able to identify a greatly altered bile acid (BA) profile in the small intestine of infected piglets by untargeted metabolomic analysis. Using a newly established ex vivo model-the stem cell-derived porcine intestinal enteroid (PIE) culture-we demonstrated that certain BAs, cholic acid (CA) in particular, enhance SADS-CoV replication by acting on PIEs at the early phase of infection. We ruled out the possibility that CA exerts an augmenting effect on viral replication through classic farnesoid X receptor or Takeda G protein-coupled receptor 5 signaling, innate immune suppression or viral attachment. BA induced multiple cellular responses including rapid changes in caveolae-mediated endocytosis, endosomal acidification and dynamics of the endosomal/lysosomal system that are critical for SADS-CoV replication. Thus, our findings shed light on how SECoVs exploit microbiome-derived metabolite BAs to swiftly establish viral infection and accelerate replication within the intestinal microenvironment.

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

Background and Aims The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. Methods Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. Results Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4+ T, and CD8+ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4+ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot. Conclusions XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.

First evidence that an emerging mammalian alphacoronavirus is able to infect an avian species.

A novel swine enteric alphacoronavirus, swine acute diarrhoea syndrome coronavirus (SADS-CoV), related to Rhinolophus bat CoV HKU2 in the subgenus Rhinacovirus emerged in southern China in 2017, causing diarrhoea in newborn piglets, and critical questions remain about the pathogenicity, cross-species transmission and potential animal reservoirs. Our laboratory's previous research has shown that SADS-CoV can replicate in various cell types from different species, including chickens. Here, we systematically explore the susceptibility of chickens to a cell-adapted SADS-CoV strain both in vitro and in vivo. First, evidence of SADS-CoV replication in primary chicken cells, including cytopathic effects, immunofluorescence staining, growth curves and structural protein expression, was proven. Furthermore, we observed that SADS-CoV replicated in chicken embryos without causing gross lesions and that experimental infection of chicks resulted in mild respiratory symptoms. More importantly, SADS-CoV shedding and viral distribution in the lungs, spleens, small intestines and large intestines of infected chickens were confirmed by quantitative reverse transcription polymerase chain reaction and immunohistochemistry. The genomic sequence of the original SADS-CoV from the pig source sample in 2017 was determined to have nine nucleotide differences compared to the cell-adapted strain used; among these were three nonsynonymous mutations in the spike gene. These results collectively demonstrate that chickens are susceptible to SADS-CoV infection, suggesting that they are a potential animal reservoir. To our knowledge, this study provides the first experimental evidence of cross-species infection in which a mammalian alphacoronavirus is able to infect an avian species.

Lysosomal ion channels involved in cellular entry and uncoating of enveloped viruses: Implications for therapeutic strategies against SARS-CoV-2.

Ion channels are necessary for correct lysosomal function including degradation of cargoes originating from endocytosis. Almost all enveloped viruses, including coronaviruses (CoVs), enter host cells via endocytosis, and do not escape endosomal compartments into the cytoplasm (via fusion with the endolysosomal membrane) unless the virus-encoded envelope proteins are cleaved by lysosomal proteases. With the ongoing outbreak of severe acute respiratory syndrome (SARS)-CoV-2, endolysosomal two-pore channels represent an exciting and emerging target for antiviral therapies. This review focuses on the latest knowledge of the effects of lysosomal ion channels on the cellular entry and uncoating of enveloped viruses, which may aid in development of novel therapies against emerging infectious diseases such as SARS-CoV-2.

The porcine deltacoronavirus accessory protein NS6 is expressed in vivo and incorporated into virions.

Porcine deltacoronavirus (PDCoV) is one of the emerged coronaviruses posing a significant threat to the swine industry. Previous work showed the presence of a viral accessory protein NS6 in PDCoV-infected cells. In this study, we detected the expression of the NS6 protein in small intestinal tissues of PDCoV-infected piglets. In addition, SDS-PAGE and Western blot analysis of sucrose gradient-purified virions showed the presence of a 13-kDa NS6 protein. Further evidences of the presence of NS6 in the PDCoV virions were obtained by immunogold staining of purified virions with anti-NS6 antiserum, and by immunoprecipitation of NS6 from purified virions. Finally, the anti-NS6 antibody was not able to neutralize PDCoV in cultured cells. These data establish for the first time that the accessory protein NS6 is expressed during infection in vivo and incorporated into PDCoV virions.

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks.

BACKGROUND: Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be benefit for the patients in the underdevelopment areas lack of the experienced radiologists. In recent years, with the development of large neural network architectures and medical imaging datasets, deep learning methods have become a methodology of choice for analyzing medical images. The objective of this study was to the construct convolutional neural networks to localize the pneumothorax lesions in chest radiographs. METHODS AND FINDINGS: We developed a convolutional neural network, called CheXLocNet, for the segmentation of pneumothorax lesions. The SIIM-ACR Pneumothorax Segmentation dataset was used to train and validate CheXLocNets. The training dataset contained 2079 radiographs with the annotated lesion areas. We trained six CheXLocNets with various hyperparameters. Another 300 annotated radiographs were used to select parameters of these CheXLocNets as the validation set. We determined the optimal parameters by the AP50 (average precision at the intersection over union (IoU) equal to 0.50), a segmentation evaluation metric used by several well-known competitions. Then CheXLocNets were evaluated by a test set (1082 normal radiographs and 290 disease radiographs), based on the classification metrics: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV); segmentation metrics: IoU and Dice score. For the classification, CheXLocNet with best sensitivity produced an AUC of 0.87, sensitivity of 0.78 (95% CI 0.73-0.83), and specificity of 0.78 (95% CI 0.76-0.81). CheXLocNet with best specificity produced an AUC of 0.79, sensitivity of 0.46 (95% CI 0.40-0.52), and specificity of 0.92 (95% CI 0.90-0.94). For the segmentation, CheXLocNet with best sensitivity produced an IoU of 0.69 and Dice score of 0.72. CheXLocNet with best specificity produced an IoU of 0.77 and Dice score of 0.79. We combined them to form an ensemble CheXLocNet. The ensemble CheXLocNet produced an IoU of 0.81 and Dice score of 0.82. Our CheXLocNet succeeded in automatically detecting pneumothorax lesions, without any human guidance. CONCLUSIONS: In this study, we proposed a deep learning network, called, CheXLocNet, for the automatic segmentation of chest radiographs to detect pneumothorax. Our CheXLocNets generated accurate classification results and high-quality segmentation masks for the pneumothorax at the same time. This technology has the potential to improve healthcare delivery and increase access to chest radiograph expertise for the detection of diseases. Furthermore, the segmentation results can offer comprehensive geometric information of lesions, which can benefit monitoring the sequential development of lesions with high accuracy. Thus, CheXLocNets can be further extended to be a reliable clinical decision support tool. Although we used transfer learning in training CheXLocNet, the parameters of CheXLocNet was still large for the radiograph dataset. Further work is necessary to prune CheXLocNet suitable for the radiograph dataset.

Trypsin promotes porcine deltacoronavirus mediating cell-to-cell fusion in a cell type-dependent manner.

Porcine deltacoronavirus (PDCoV) is a newly emerging threat to the global porcine industry. PDCoV has been successfully isolated using various medium additives including trypsin, and although we know it is important for viral replication, the mechanism has not been fully elucidated. Here, we systematically investigated the role of trypsin in PDCoV replication including cell entry, cell-to-cell membrane fusion and virus release. Using pseudovirus entry assays, we demonstrated that PDCoV entry is not trypsin dependent. Furthermore, unlike porcine epidemic diarrhea virus (PEDV), in which trypsin is important for the release of virus from infected cells, PDCoV release was not affected by trypsin. We also demonstrated that trypsin promotes PDCoV replication by enhancing cell-to-cell membrane fusion. Most importantly, our study illustrates two distinct spreading patterns from infected cells to uninfected cells during PDCoV transmission, and the role of trypsin in PDCoV replication in cells with different virus spreading types. Overall, these results clarify that trypsin promotes PDCoV replication by mediating cell-to-cell fusion transmission but is not crucial for viral entry. This knowledge can potentially contribute to improvement of virus production efficiency in culture, not only for vaccine preparation but also to develop antiviral treatments.