Trafficked Malayan pangolins contain viral pathogens of humans.

Pangolins are the most trafficked wild animal in the world according to the World Wildlife Fund. The discovery of SARS-CoV-2-related coronaviruses in Malayan pangolins has piqued interest in the viromes of these wild, scaly-skinned mammals. We sequenced the viromes of 161 pangolins that were smuggled into China and assembled 28 vertebrate-associated viruses, 21 of which have not been previously reported in vertebrates. We named 16 members of Hunnivirus, Pestivirus and Copiparvovirus pangolin-associated viruses. We report that the L-protein has been lost from all hunniviruses identified in pangolins. Sequences of four human-associated viruses were detected in pangolin viromes, including respiratory syncytial virus, Orthopneumovirus, Rotavirus A and Mammalian orthoreovirus. The genomic sequences of five mammal-associated and three tick-associated viruses were also present. Notably, a coronavirus related to HKU4-CoV, which was originally found in bats, was identified. The presence of these viruses in smuggled pangolins identifies these mammals as a potential source of emergent pathogenic viruses.

Co-infecting pathogens can contribute to inflammatory responses and severe symptoms in COVID-19.

Background: The current COVID-19 pandemic is posing a major challenge to public health on a global scale. While it is generally believed that severe COVID-19 results from over-expression of inflammatory mediators (i.e., a "cytokine storm"), it is still unclear whether and how co-infecting pathogens contribute to disease pathogenesis. To address this, we followed the entire course of the disease in cases with severe or critical COVID-19 to determine the presence and abundance of all potential pathogens present-the total "infectome"-and how they interact with the host immune system in the context of severe COVID-19. Methods: We examined one severe and three critical cases of COVID-19, as well as a set of healthy controls, with longitudinal samples (throat swab, whole blood, and serum) collected from each case. Total RNA sequencing (meta-transcriptomics) was performed to simultaneously investigate pathogen diversity and abundance, as well as host immune responses, in each sample. A Bio-Plex method was used to measure serum cytokine and chemokine levels. Results: Eight pathogens, SARS-CoV-2, Aspergillus fumigatus (A. fumigatus), Mycoplasma orale (M. orale), Myroides odoratus (M. odoratus), Acinetobacter baumannii (A. baumannii), Candida tropicalis, herpes simplex virus (HSV) and human cytomegalovirus (CMV), identified in patients with COVID-19 appeared at different stages of the disease. The dynamics of inflammatory mediators in serum and the respiratory tract were more strongly associated with the dynamics of the infectome compared with SARS-CoV-2 alone. Correlation analysis revealed that pulmonary injury was directly associated with cytokine levels, which in turn were associated with the proliferation of SARS-CoV-2 and co-infecting pathogens. Conclusions: For each patient, the cytokine storm that resulted in acute lung injury and death involved a dynamic and highly complex infectome, of which SARS-CoV-2 was a component. These results indicate the need for a precision medicine approach to investigate both the infection and host response as a standard means of infectious disease characterization.

Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion

As influenza viruses mutate rapidly, a prediction model for potential outbreaks of influenza-like illnesses helps detect the spread of the illnesses in real time. In order to create a better prediction model, in this study, in addition to using the traditional hydrological and atmospheric data, features, such as popular search keywords on Google Trends, public holiday information, population density, air quality indices, and the numbers of COVID-19 confirmed cases, were also used to train the model in this research. Furthermore, Random Forest and XGBoost were combined and used in the proposed prediction model to increase the prediction accuracy. The training data used in this research were the historical data taken from 2016 to 2021. In our experiments, different combinations of features were tested. The results show that features, such as popular search keywords on Google Trends, the numbers of COVID-19 confirmed cases, and air quality indices can improve the outcome of the prediction model. The evaluation results showed that the error rate between the predicted results and the actual number of influenza-like cases form Week 15 to Week 18 fell to less than 5%. The outbreak of COVID-19 in Taiwan began in Week 19 and resulted in a sharp rise in the number of clinic or hospital visits by patients of influenza-like illnesses. After that, from Week 21 to Week 26, the error rate between the predicted and actual numbers of influenza-like cases in the later period dropped down to 13%. It can be confirmed from the actual experimental results in this research that the use of the ensemble learning prediction model proposed in this research can accurately predict the trend of influenza-like cases.

Total infectome characterization of respiratory infections in pre-COVID-19 Wuhan, China.

At the end of 2019 Wuhan witnessed an outbreak of "atypical pneumonia" that later developed into a global pandemic. Metagenomic sequencing rapidly revealed the causative agent of this outbreak to be a novel coronavirus denoted SARS-CoV-2. To provide a snapshot of the pathogens in pneumonia-associated respiratory samples from Wuhan prior to the emergence of SARS-CoV-2, we collected bronchoalveolar lavage fluid samples from 408 patients presenting with pneumonia and acute respiratory infections at the Central Hospital of Wuhan between 2016 and 2017. Unbiased total RNA sequencing was performed to reveal their "total infectome", including viruses, bacteria and fungi. We identified 35 pathogen species, comprising 13 RNA viruses, 3 DNA viruses, 16 bacteria and 3 fungi, often at high abundance and including multiple co-infections (13.5%). SARS-CoV-2 was not present. These data depict a stable core infectome comprising common respiratory pathogens such as rhinoviruses and influenza viruses, an atypical respiratory virus (EV-D68), and a single case of a sporadic zoonotic pathogen-Chlamydia psittaci. Samples from patients experiencing respiratory disease on average had higher pathogen abundance than healthy controls. Phylogenetic analyses of individual pathogens revealed multiple origins and global transmission histories, highlighting the connectedness of the Wuhan population. This study provides a comprehensive overview of the pathogens associated with acute respiratory infections and pneumonia, which were more diverse and complex than obtained using targeted PCR or qPCR approaches. These data also suggest that SARS-CoV-2 or closely related viruses were absent from Wuhan in 2016-2017.

A Convenient and Biosafe Replicon with Accessory Genes of SARS-CoV-2 and Its Potential Application in Antiviral Drug Discovery.

SARS-CoV-2 causes the pandemic of COVID-19 and no effective drugs for this disease are available thus far. Due to the high infectivity and pathogenicity of this virus, all studies on the live virus are strictly confined in the biosafety level 3 (BSL3) laboratory but this would hinder the basic research and antiviral drug development of SARS-CoV-2 because the BSL3 facility is not commonly available and the work in the containment is costly and laborious. In this study, we constructed a reverse genetics system of SARS-CoV-2 by assembling the viral cDNA in a bacterial artificial chromosome (BAC) vector with deletion of the spike (S) gene. Transfection of the cDNA into cells results in the production of an RNA replicon that keeps the capability of genome or subgenome replication but is deficient in virion assembly and infection due to the absence of S protein. Therefore, such a replicon system is not infectious and can be used in ordinary biological laboratories. We confirmed the efficient replication of the replicon by demonstrating the expression of the subgenomic RNAs which have similar profiles to the wild-type virus. By mutational analysis of nsp12 and nsp14, we showed that the RNA polymerase, exonuclease, and cap N7 methyltransferase play essential roles in genome replication and sgRNA production. We also created a SARS-CoV-2 replicon carrying a luciferase reporter gene and this system was validated by the inhibition assays with known anti-SARS-CoV-2 inhibitors. Thus, such a one-plasmid system is biosafe and convenient to use, which will benefit both fundamental research and development of antiviral drugs.

RNA based mNGS approach identifies a novel human coronavirus from two individual pneumonia cases in 2019 Wuhan outbreak.

From December 2019, an outbreak of unusual pneumonia was reported in Wuhan with many cases linked to Huanan Seafood Market that sells seafood as well as live exotic animals. We investigated two patients who developed acute respiratory syndromes after independent contact history with this market. The two patients shared common clinical features including fever, cough, and multiple ground-glass opacities in the bilateral lung field with patchy infiltration. Here, we highlight the use of a low-input metagenomic next-generation sequencing (mNGS) approach on RNA extracted from bronchoalveolar lavage fluid (BALF). It rapidly identified a novel coronavirus (named 2019-nCoV according to World Health Organization announcement) which was the sole pathogens in the sample with very high abundance level (1.5% and 0.62% of total RNA sequenced). The entire viral genome is 29,881 nt in length (GenBank MN988668 and MN988669, Sequence Read Archive database Bioproject accession PRJNA601736) and is classified into ß-coronavirus genus. Phylogenetic analysis indicates that 2019-nCoV is close to coronaviruses (CoVs) circulating in Rhinolophus (Horseshoe bats), such as 98.7% nucleotide identity to partial RdRp gene of bat coronavirus strain BtCoV/4991 (GenBank KP876546, 370 nt sequence of RdRp and lack of other genome sequence) and 87.9% nucleotide identity to bat coronavirus strain bat-SL-CoVZC45 and bat-SL-CoVZXC21. Evolutionary analysis based on ORF1a/1b, S, and N genes also suggests 2019-nCoV is more likely a novel CoV independently introduced from animals to humans.