PLoS Pathogens
Public Library of Science
image
Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?
DOI 10.1371/journal.ppat.1008421, Volume: 16, Issue: 5,
Abstract

Recently, a novel coronavirus, SARS-CoV-2, caused a still ongoing pandemic. Epidemiological study suggested this virus was associated with a wet market in Wuhan, China. However, the exact source of this virus is still unknown. In this study, we attempted to assemble the complete genome of a coronavirus identified from two groups of sick Malayan pangolins, which were likely to be smuggled for black market trade. The molecular and evolutionary analyses showed that this pangolin coronavirus we assembled was genetically associated with the SARS-CoV-2 but was not likely its precursor. This study suggested that pangolins are natural hosts of coronaviruses. Determining the spectrum of coronaviruses in pangolins can help understand the natural history of coronaviruses in wildlife and at the animal-human interface, and facilitate the prevention and control of coronavirus-associated emerging diseases.

Liu, Jiang, Wan, Hua, Li, Zhou, Wang, Hou, Chen, Zou, Chen, and Perlman: Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?

Introduction

In December 2019, there was an outbreak of pneumonia with an unknown cause in Wuhan, Hubei province, China, with an epidemiological link to the Huanan Seafood Wholesale Market, a local live animal and seafood market. Clinical presentations of this disease greatly resembled viral pneumonia. Through deep sequencing on the lower respiratory tract samples of patients, a novel coronavirus named the 2019 novel coronavirus was identified [1], the name of which was then determined as SARS-CoV-2. This virus has spread to all provinces across China and more than 200 additional countries. As of April 11, 2020, the epidemic has resulted in 83,400 laboratory confirmed cases, 3,349 of which were fatal in China, while there were 1,643,047 laboratory confirmed cases and 101,507 deaths in other countries. The global toll of new cases and deaths is still increasing sharply.

To effectively control the disease and prevent new spillovers, it is critical to identify the animal origin of this newly emerging coronavirus. In the Wuhan wet market, high viral loads were reported in environmental samples. However, a variety of animals, including wildlife, were sold in this market, and the daily number and species of animals were very dynamic. Therefore, it remains unclear which animals initiated the first infections.

Coronaviruses usually cause respiratory and gastrointestinal tract infections and are genetically classified into four major genera: Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus . The former two genera primarily infect mammals, whereas the latter two predominantly infect birds [2]. In addition to SARS-CoV-2, other members of the Betacoronavirus genus caused the 2003 SARS (severe acute respiratory syndrome) outbreaks and the 2012 MERS (Middle East respiratory syndrome) outbreaks in humans [3, 4]. SARS-CoV and MERS-CoV are of bat origin, but both coronaviruses had an intermediate host: palm civets for SARS-CoV [5] and dromedary camels for MERS-CoV [6].

Approximately 30,000 base pairs in the coronavirus genome code for up to 11 proteins, including the surface glycoprotein Spike (S) protein binds to receptors on the host cell, which initiates virus infection. Different coronaviruses can use distinct host receptors due to structural variations in the receptor binding domains of their virus S protein. SARS-CoV uses angiotensin-converting enzyme 2 (ACE2) as one of the main receptors [7] with CD209L as an alternative receptor [8], whereas MERS-CoV uses dipeptidyl peptidase 4 (DPP4, also known as CD26) as the primary receptor. A recent study demonstrated that SARS-CoV-2 uses the SARS-CoV receptor ACE2 for entry and the serine protease TMPRSS2 for S protein priming [9].

Soon after the release of the SARS-CoV-2 genome, a scientist released a full genome of a coronavirus, Bat-CoV-RaTG13, from the bat species Rhinolophus affinis , which was colonized in Yunan province, nearly 2,000 km away from Wuhan. Bat-CoV-RaTG13 was 96% identical at the whole genome level to the SARS-CoV-2, suggesting the SARS-CoV-2 could be of bat origin [1]. However, because direct human-bat contact is rare, it seems to be more likely that the spillover of SARS-CoV-2 to humans from an intermediate host rather than directly from bats, as was the cases with both SARS-CoV and MERS-CoV.

The goal of this study was to determine the genetic relationship between a coronavirus from two groups of sick pangolins and SARS-CoV-2, and to assess whether pangolins could be potential intermediate hosts of SARS-CoV-2.

Results

In March and July of 2019, we detected Betacoronaviruses in three individuals from two sets of smuggled Malayan pangolins (Manis javanica ) (n = 27) that were intercepted by Guangdong customs [10]. All three animals suffered from serious respiratory disease and failed to be rescued by the Guangdong Wildlife Rescue Center [10] (S1 Table). Through metagenomic sequencing and de novo assembling, we recovered 38 contigs ranging from 380 to 3,377 nucleotides, and the nucleotide sequence identity among the contigs from these three samples were 99.54%. Thus, we pooled sequences from three samples and assembled the draft genome of this pangolin origin coronavirus. After that, gap filling with amplicon sequencing was conducted to obtain a nearly full genome sequence. This pangolin-CoV-2020 genome (Genbank No.: MT121216) was found to be comprised of 29,521 nucleotides.

Strikingly, genomic analyses suggested the pangolin-CoV-2020 has a high identity with both SARS-CoV-2 and Bat-CoV-RaTG13, the proposed origin of SARS-CoV-2 (Fig 1A, S2 Table). The nucleotide sequence identity between pangolin-CoV-2020 and SARS-CoV-2 was 90.32%, whereas the protein sequence identity for individual proteins can be up to 100% (Table 1; Table 2). The nucleotide sequence identity between pangolin-CoV-2020 and Bat-CoV-RaTG13 was 90.24%, while that for the corresponding regions between SARS-CoV-2 and Bat-CoV-RaTG13 was 96.18% (Table 1, S1 Table).

Genomic comparison of pangolin-CoV-2020, SARS-CoV-2, and other coronaviruses.
Fig 1
A) Similarity plot based on the full-length genome sequence of SARS-CoV-2. Full-length genome sequences of Bat-CoV-RaTG13, Bat-CoV-ZXC21, SARS-CoV, Bat-CoV-ZC45, MERS-CoV, and pangolin-CoV-2020 were used as subject sequences. The green line indicates MERS-CoV, the dark blue line indicates SARS-CoV, the grey line indicates Bat-CoV-ZXC21, the yellow line indicates Bat-CoV-ZC45, the orange line indicates pangolin-CoV-2020, while the light blue line indicates Bat-CoV-RaTG13; B) Phylogenetic analyses of whole genome sequences depicting the evolutionary relationship among SARS-CoV-2, pangolin-CoV-2020, and other coronaviruses from different hosts. The phylogenies were estimated using the MrBayes approach employing the GTR+I+G nucleotide substitution model.Genomic comparison of pangolin-CoV-2020, SARS-CoV-2, and other coronaviruses.
Table 1
Nucleotide sequence identity among the whole genome, each gene or region of pangolin-CoV-2020 and other representative coronavirus against SARS-CoV-2.
Nucleotide sequence identity (%)
Whole genomeSRBDEMNORF1abRdRpORF3aORF6ORF7aORF7bORF8ORF10
Pangolin-CoV-202090.3284.5286.6499.1193.2496.1890.3691.3193.2195.7093.3991.4791.8299.15
Bat-CoV-RaTG1396.1893.1586.1999.5695.9396.9096.5297.8096.2498.3995.5999.2296.9999.15
Bat-CoV-ZXC2188.0476.7467.3286.6793.3991.1789.1286.9988.8595.1689.6295.3588.53100.00
Bat-CoV-ZC4588.0677.1468.6486.6793.3991.0989.1586.7087.7695.1689.3194.5788.5399.15
SARS-CoV79.7574.0573.3094.6784.9288.6280.0288.5875.6776.8882.6586.1852.8793.16
Table 2
Protein sequence identity among the whole genome, each gene or region of pangolin-CoV-2020 and other representative coronaviruses against SARS-CoV-2.
Amino acid sequence identity (%)
Whole genomeSRBDEMNORF1abRdRpORF3aORF6ORF7aORF7bORF8ORF10
Pangolin-CoV-202096.0090.1896.80100.0098.1897.8396.7399.3597.0596.6797.4995.2494.1297.33
Bat-CoV-RaTG1398.4397.6989.56100.0099.0999.0498.5599.5797.79100.0097.4997.6594.9197.33
Bat-CoV-ZXC2193.4579.6666.35100.0098.6494.1095.5695.6991.6693.2287.7092.7794.04100.00
Bat-CoV-ZC4593.5980.3666.35100.0098.6494.1095.7196.0390.4793.2286.7792.7794.0497.33
SARS-CoV83.3974.5470.1795.9289.0190.4985.5796.4868.0262.6884.8684.1840.0082.31

The nucleotide sequence identities among the S protein genes were 93.15% between the Bat-CoV-RaTG13 and SARS-CoV-2, 84.52% between pangolin-CoV-2020 and SARS-CoV-2, as well as 73.43% between pangolin-CoV-2020 and SARS-CoV, respectively (Table 1). Further analyses suggested the S gene was relatively more genetically diverse in the S1 region than the S2 region (Fig 2A, S3 Table). Compared with their nucleotide sequences, the S proteins of pangolin-CoV-2020 and SARS-CoV-2 were more conserved, with a sequence identity of 90.18% (Table 2).

Genetic analyses of the spike (S) surface glycoprotein of pangolin-CoV-2020, SARS-CoV-2, and other coronaviruses.
Fig 2
A) Similarity plot based on the spike surface glycoprotein amino acid and nucleotide sequence of SARS-CoV-2. Bat-CoV-RaTG13, Bat-CoV-ZXC21, Bat-CoV-ZC45, SARS-CoV, and pangolin-CoV-2020 were used as subject sequences. The green lines indicate SARS-CoV, the grey lines indicate Bat-CoV-ZXC21, the yellow lines indicate Bat-CoV-ZC45, the orange lines indicate pangolin-CoV-2020, while the light blue lines indicate Bat-CoV-RaTG13; B) Phylogenetic analysis of S gene sequences depicting the evolutionary relationship among SARS-CoV-2, pangolin-CoV-2020, and other coronaviruses from different hosts. The phylogenies were estimated using MrBayes approach employing the GTR+I+G nucleotide substitution model.Genetic analyses of the spike (S) surface glycoprotein of pangolin-CoV-2020, SARS-CoV-2, and other coronaviruses.

The receptor binding domains (RBD) of the S protein were highly conserved between pangolin-CoV-2020 and SARS-CoV-2, the nucleotide and amino acid sequences identity of RBD of S gene between them was highest in comparison with those between pangolin-CoV-2020 and other SARS-like conronaviruses of 86.64% and 96.80% (Table 1, Table 2). Pangolin-CoV-2020 and SARS-CoV-2 also shared a very conserved receptor binding motif (RBM) (98.6%), which was more conserved than in Bat-CoV-RaTG13 (76.4%) (Fig 3). These results support that pangolin-CoV-2020 and SARS-CoV-2 share the same angiotensin-converting enzyme 2 (ACE2) receptor. Further analyses suggested that there was one variation (Gln498) between the RBM of pangolin-CoV-2020 and that of SARS-CoV-2 but conserved in all other key residues being associated with receptor binding (Gly482, Val483, Glu484, Gly485, Phe486, Gln493, Leu455, Asn501), suggesting a potential binding affinity between pangolin-CoV-2020 and human ACE2 receptor (Fig 3).

Amino acid sequence alignment of the spike (S) surface glycoprotein of the pangolin-CoV-2020 with SARS-CoV-2 and Bat-CoV-RaTG13.
Fig 3
Previously identified critical ACE2-binding residues are in the blue box. An arginine in the core structure that interacts with glycan is displayed within the red box.Amino acid sequence alignment of the spike (S) surface glycoprotein of the pangolin-CoV-2020 with SARS-CoV-2 and Bat-CoV-RaTG13.

On the other hand, unlike RBD, the nucleotide and amino acid sequence identity of NTD (N-terminal domain) were only 66.2% and 63.1% identical between pangolin-CoV-2020 and SARS-CoV-2. However, a loci Arg408 from the RBD core of SARS-CoV-2 could form a hydrogen bond with human ACE2 was conserved in pangolin-CoV-2020 (Fig 3). Both pangolin-CoV-2020 and Bat-CoV-RaTG13 lack an S1/S2 cleavage site (~680–690 aa) whereas SARS-CoV-2 possesses (Fig 3).

Genomic analyses suggested sequence similarities were not homogeneous across the S genes of pangolin-CoV-2020, SARS-CoV-2, Bat-CoV-ZXC21 and Bat-CoV-ZC45. For example, the first S region (i.e., nucleotides 1–1200) of pangolin-CoV-2020 has a higher nucleotide identity to two bat viruses (Bat-CoV-ZXC21 and Bat-CoV-ZC45) than SARS-CoV-2 and Bat-CoV-RaTG13, whereas the remaining S gene of pangolin-CoV-2020 is opposite (Fig 2A). These results suggest that a recombination event could have occurred during the evolution of these coronaviruses.

Phylogenetic analyses suggested that the S genes of pangolin-CoV-2020, SARS-CoV-2 and three bat origin coronaviruses (Bat-CoV-RaTG13, Bat-CoV-ZXC21, and Bat-CoV-ZC45) were genetically more similar to each other than other viruses in the same family (Fig 2B). The S gene of Bat-CoV-RaTG13 was genetically closer to pangolin-CoV-2020 than Bat-CoV-ZXC21 and Bat-CoV-ZC45. Similar tree topologies were observed for the encoding ORFs of RNA-dependent RNA polymerase (RdRp gene) and other genes (S1S3 Figs). At the genomic level, SARS-CoV-2 was also genetically closer to Bat-CoV-RaTG13 than pangolin-CoV-2020 (Fig 1B).

Discussion

In this study, we assembled the genomes of coronaviruses identified in sick pangolins and our results showed that pangolin-CoV-2020 is genetically associated with both SARS-CoV-2 and a group of bat coronaviruses. There is a high sequence identity between pangolin-CoV-2020 and SARS-CoV-2. However, phylogenetic analyses and a special amino acid sequence in the S gene of SARS-CoV-2 did not support the hypothesis of SARS-CoV-2 arising directly from the pangolin-CoV-2020.

It is of interest that the genomic sequences of coronaviruses detected from two batches of smuggled pangolins intercepted by different customs at different dates were all be associated with bat coronaviruses. In addition, the genetic identity of coronavirus contigs assembled in each animal was extremely high (99.54%). The reads from the third pangolin acquired in July 2019 were relatively less abundant than those from the two pangolin samples acquired in March 2019. Although it is unclear whether coronaviruses in these two batches of smuggled pangolins had the same origin, our results indicated that the pangolins can be a natural host for Betacoronaviruses, which could be enzootic in pangolins.

All three exotic pangolins detected with Betacoronaviruses were sick with serious respiratory diseases and failed to be rescued. However, these pangolins were very stressful in the transportation freight when being intercepted by the customs. It is unclear whether this coronavirus is a common virus flora in the respiratory tract of pangolins. Nevertheless, the pathogenesis of this coronavirus in pangolins remains to be elucidated.

Phylogenetic trees suggested that Bat-CoV-RaTG13 was more genetically close to SARS-CoV-2 at both individual gene and genomic sequence level compared with the genomic sequence of pangolin-CoV-2020 assembled in this study. Recombination analysis showed that S gene of pangolin-CoV-2020 might be constructed by fragment from Bat-CoV-ZC45 or Bat-CoV-ZXC21 and fragment from Bat-CoV-RaTG13. Interestingly, the cleavage site between S1 and S2 in SARS-CoV-2 had multiple insertions (i.e. PRRA), compared with those of Bat-CoV-RaTG13 and pangolin-CoV-2020, which may result from an additional recombination event. A new study reported a novel bat-derived coronavirus (RmYN02) identified from a metagenomics analysis of samples from 227 bats collected from the Yunnan province in China between May and October of 2019. Although RmYN02 showed a relatively low nucleotide sequence identity (93.3%) to SARS-CoV-2, it had a similar manner of the insertion of multiple amino acids at the junction site of the S1 and S2 subunits of the S protein as SARS-CoV-2, providing strong evidence that such insertion events can occur in nature [11]. Thus, these data suggest that SARS-CoV-2 originated from multiple naturally occurring recombination events among viruses present in bats and other wildlife species.

The S protein of coronaviruses binds to host receptors via RBDs and plays an essential role in initiating viral infection and determining host tropism [2]. A prior study suggested that SARS-CoV-2 and SARS-CoV bind to the same ACE2 receptor [9]. Our analyses showed that pangolin-CoV-2020 has a much conserved RBD to these viruses compared to MERS-CoV, suggesting that pangolin-CoV is very likely to use ACE2 as its receptor as well. A comparative analysis of the interaction of the S proteins of coronaviruses with ACE2 proteins of humans and pangolins showed that the S proteins of SARS-CoV-2 and pangolin-CoV can potentially recognize ACE2 in both humans and pangolins [12]. A recent study found that a human ACE2-binding ridge in SARS-CoV-2 RBD takes a more compact conformation compared with the SARS-CoV RBD; moreover, several residue changes in SARS-CoV-2 RBD may also enhance its human ACE2-binding affinity [13]. The core residues in RBM which may related to higher human ACE2-binding affinity than SARS-CoV are 100% identical between SARS-CoV-2 and CoV-Pangolin-2020. Therefore, pangolin-CoV-2020 (CoV-pangolin/GD) potentially recognizes human ACE2 better than the SARS-CoV.

In addition to RBD, NTD is also important in recognizing acetylated sialic acids on glycosylated cell-surface receptors [14]. It is reported that SARS-CoV-2 can bind to human ACE2 via the viral CTD (the same as RBD), but not NTD, and that the glycan attached to Asn90 from human ACE2 forms a hydrogen bond with Arg408 from the RBD core [15]. This glycan interacting Arginine is conserved between SARS-CoV-2 and pangolin-CoV-2. Therefore, there is structural similarity in glycan binding between SARS-CoV-2 and pangolin-CoV-2020. On the other hand, ACE2 receptor is present in pangolins with a high sequence conservation with those in the gene homolog in humans. However, the zoonosis of pangolin-CoV-2020 remains unclear.

The coronaviruses are shown to have a wide range of hosts, and some of them can infect humans [16]. Thus, it is critical to determine the natural reservoir and the host tropisms of these coronaviruses, especially their potential of causing zoonosis. In the last two decades, apart from SARS-CoV-2, SARS and MERS have caused serious outbreaks in humans, leading to thousands of deaths [3, 4, 17, 18]. Although these three zoonotic coronaviruses were shown to be of bat origin, they seemed to use different intermediate hosts. For example, farmed palm civets were suggested to be an intermediate host for SARS-CoV, although the details of the link from bats to farmed palm civets remain unclear [1921]. Most recently, dromedary camels in Saudi Arabia were shown to harbor three different coronaviruses, including the dominant MERS-CoV lineage that was responsible for the outbreaks in the Middle East and South Korea during 2015 [22]. Although this present study does not support that pangolins would be intermediate hosts for the emergence of SARS-CoV-2, our results do not exclude the possibility that other CoVs could be circulating in pangolins. Thus, surveillance of coronaviruses in pangolins could improve our understanding of the spectrum of coronaviruses in pangolins. In addition to conservation of wildlife, minimizing the exposures of humans to wildlife will be important to reduce the spillover risks coronaviruses from wild animals to humans.

In summary, we suggest that pangolins could be natural hosts of Betacoronaviruses with an unknown potential to infect humans. However, our study does not support that SARS-CoV-2 evolved directly from the pangolin-CoV.

Materials and methods

Ethics statement

The study design was approved by the ethics committee for animal experiments at the Guangdong Institute of Applied Biological Resources (reference number: GIABR20170720; 20 July 2017) and followed basic principles outlined by this committee.

Data selection

During our routine wildlife rescue efforts, one of the goals was to identify pathogens causing wildlife diseases. In 2019, we were involved in two events of pangolin rescues: one involved with 21 smuggling pangolins in March and the second with 6 smuggling pangolins in July. Although extensive rescue efforts were made, the majority of thse pangolins were dead. Most of the dead pangolins had a swollen lung, which contained a frothy liquid, and symptoms of pulmonary fibrosis. In the minority of these dead ones, we observed hepatomegaly and splenomegaly. From 11 pangolins failed to be rescued, we collected samples from their lung, lymph and spleen tissues and subjected for metagenomic analyses. Coronaviruses were detected in three individuals by mapping clean reads without ribosomes and host sequences to an in-house virus reference dataset separated from the GenBank non-redundant nucleotide database using the Burrows-Wheeler Aligner (BWA) ver 0.7.17 [10, 23]. Two of these animals were from the first batch of smuggled Malayan pangolins intercepted by Meizhou, Yangjiang, and Jiangmen customs in March, 2019, and the third one was from the second batch in a freight being transported from Qingyuan to Heyuan in July, 2019. The RNA samples from these three individuals were subjected to deep sequencing.

Genomic assembly and sequence analyses

Clean reads from each of the three coronavirus positive animals were de novo assembled using MEGAHIT v1.2.9 [24]. After examining the high similarity of 99.54% among the samples from three animals, to maximize the coverage of the virus genome, we pooled clean reads and de novo assembled them. The assembled contigs were used as references for extracting unmapped reads using Salmon v0.14.1 [25], and multiple rounds were implemented to maximize the mapping.

A total of 38 contigs were identified to be highly similar to the SARS-CoV-2 genome using BLASTn and tBLASTx. GapFiller v1.10 and SSPACE v3.0 were used to fill gaps and draft pangolin-CoV-2020 genome was constructed with ABACAS v1.3.1 (http://abacas.sourceforge.net/) [2628].

Gaps in the draft genomes were filled using the 2x PCR Mix (Gentech, China) by reverse transcription PCR (RT-PCR). Primers were designed based on the draft genome sequence of the pangolin-CoV-2020 we assembled (S4 Table). After gap filling, the whole genome sequence of pangolin-CoV-2020 was submitted to GenBank databases (accession no. MT121216).

Multiple sequence alignments were conducted using MUSCLE [29]. Changing patterns of sequence identity were analyzed using SimPlot v3.5.1 to determine the sequence identity among SARS-CoV-2 (MN908947.3), pangolin-CoV-2020, Bat-CoV-RaTG13 (MN996532.1), Bat-CoV-ZXC21 (MG772934.1), Bat-CoV-ZC45 (MG772933.1), SARS-CoV (AY395003.1), and MERS-CoV (NC_019843.3) at both the genomic sequence level and the individual gene level [30]. The sequence identity between the whole genome and different genes or regions was calculated utilizing p-distance in MEGA v10.1.7 [31].

Phylogenetic analyses and recombination

We downloaded 44 full-length genome sequences of coronaviruses isolated from different hosts from the public database (S5 Table), with the data kindly shared by the submitters. Phylogenetic analyses were performed based on their whole genome sequences, RdRp gene, S gene, small envelope protein (E gene), as well as all other gene sequences. We constructed multiple sequence alignments of their complete genomes and individual genes using MAFFT v7.407 [32]. Phylogenetic analyses were estimated using MrBayes [33] with 50,000,000 generations and the 25% of the generations as burnin. The best models were determined by jModelTest v2.1.7 [34]. Then, the trees were visualized and exported as vector diagrams with FigTree v1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/). Potential recombination events and the location of possible breakpoints in coronavirus genomes were detected using SimPlot v3.5.1 [30].

Acknowledgements

We thank De-Chun Lin and Tao Jin from Magigene Biotech and Hanghui Kong from South China Botanical Garden for their support in the bioinformatics analysis.

References

1 

P Zhou, XL Yang, XG Wang, B Hu, L Zhang, W Zhang, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020; , pp.1–4.

2 

A Wu, Y Peng, B Huang, X Ding, X Wang, P Niu, et al. Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China. Cell Host & Microbe. 2020, doi: 10.1016/j.chom.2020.02.001

3 

C Drosten, S Günther, W Preiser, S Van Der Werf, HR Brodt, S Becker, et al. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. New England journal of medicine. 2003; 348(20):, pp.1967–1976. , doi: 10.1056/NEJMoa030747

4 

AM Zaki, S Van Boheemen, TM Bestebroer, AD Osterhaus, RA Fouchier. . Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. New England Journal of Medicine. 2012; 367(19):, pp.1814–1820. , doi: 10.1056/NEJMoa1211721

5 

Y Guan, BJ Zheng, YQ He, XL Liu, ZX Zhuang, CL Cheung, et al. Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China. Science. 2003; 302(5643):, pp.276–8. , doi: 10.1126/science.1087139

6 

EI Azhar, SA El-Kafrawy, SA Farraj, AM Hassan, MS Al-Saeed, AM Hashem, et al. Evidence for camel-to-human transmission of MERS coronavirus. New England Journal of Medicine. 2014; 370(26):, pp.2499–2505. , doi: 10.1056/NEJMoa1401505

7 

XY Ge, JL Li, XL Yang, AA Chmura, G Zhu, JH Epstein, et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature. 2013; 503(7477):, pp.535–8. , doi: 10.1038/nature12711

8 

SA Jeffers, SM Tusell, L Gillim-Ross, EM Hemmila, JE Achenbach, GJ Babcock, et al. CD209L (L-SIGN) is a receptor for severe acute respiratory syndrome coronavirus. Proceedings of the National Academy of Sciences. 2004; 101(44):, pp.15748–53.

9 

M Hoffmann, H Kleine-Weber, S Schroeder, N Krüger, T Herrler, S Erichsen, et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 2020, doi: 10.1016/j.cell.2020.02.052

10 

P Liu, W Chen, JP Chen. . Viral Metagenomics Revealed Sendai Virus and Coronavirus Infection of Malayan Pangolins (Manis javanica). Viruses. 2019; 11(11), , pp.979.

11 

H Zhou, X Chen, T Hu, J Li, H Song, Y Liu, et al. A novel bat coronavirus reveals natural insertions at the S1/S2 cleavage site of the Spike protein and a possible recombinant origin of HCoV-19. bioRxiv. 2020, doi: 10.1101/2020.03.02.974139

12 

K Xiao, J Zhai, Y Feng, N Zhou, X Zhang, JJ Zou, et al. Isolation and Characterization of 2019-nCoV-like Coronavirus from Malayan Pangolins. bioRxiv. 2020, doi: 10.1101/2020.02.17.951335

13 

J Shang, G Ye, K Shi, Y Wan, C Luo, H Aihara, et al. Structural basis of receptor recognition by SARS-CoV-2. Nature. 2020, doi: 10.1038/s41586-020-2179-y

14 

RN Kirchdoerfer, CA Cottrell, N Wang, J Pallesen, HM Yassine, HL Turner, et al. Pre-fusion structure of a human coronavirus spike protein. Nature. 2016; 531(7592):, pp.118–121. , doi: 10.1038/nature17200

15 

Q Wang, Y Zhang, L Wu, S Niu, C Song, Z Zhang, et al. Structural and functional basis of SARS-CoV-2 entry by using 2 human ACE2. Cell. 2020, doi: 10.1016/j.cell.2020.03.045

16 

MacLachlan NJ, Dubovi EJ. Fenner’s Veterinary Virology Fifth Ed., Chapter 29, Flaviviridae, West Nile Virus. 2017.

17 

World Health Organization. Consensus document on the epidemiology of severe acute respiratory syndrome (SARS) (No. WHO/CDS/CSR/GAR/2003.11). World Health Organization2003.

18 

CB Cunha, SM Opal. . Middle East respiratory syndrome (MERS) A new zoonotic viral pneumonia. Virulence. 2014; 5(6):, pp.650–654. , doi: 10.4161/viru.32077

19 

W Li, Z Shi, M Yu, W Ren, C Smith, JH Epstein, et al. Bats are natural reservoirs of SARS-like coronaviruses. Science. 2005; 310(5748):, pp.676–679. , doi: 10.1126/science.1118391

20 

LF Wang, Z Shi, S Zhang, H Field, P Daszak, BT Eaton. . Review of bats and SARS. Emerging infectious diseases. 2006; 12(12):, pp.1834, doi: 10.3201/eid1212.060401

21 

LF Wang, BT Eaton. Bats, civets and the emergence of SARS In Wildlife and emerging zoonotic diseases: the biology, circumstances and consequences of cross-species transmission (pp. 325–344). Springer, Berlin, Heidelberg2007.

22 

JS Sabir, TTY Lam, MM Ahmed, L Li, Y Shen, SE Abo-Aba, et al. Co-circulation of three camel coronavirus species and recombination of MERS-CoVs in Saudi Arabia. Science. 2016; 351(6268):, pp.81–84. , doi: 10.1126/science.aac8608

23 

H Li, R Durbin. . Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):, pp.1754–1760. , doi: 10.1093/bioinformatics/btp324

24 

D Li, R Luo, CM Liu, CM Leung, HF Ting, K Sadakane, et al. MEGAHIT v1. 0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016; 102:, pp.3–11. , doi: 10.1016/j.ymeth.2016.02.020

25 

R Patro, G Duggal, MI Love, RA Irizarry, C Kingsford. . Salmon provides fast and bias-aware quantification of transcript expression. Nature methods. 2017; 14(4):, pp.417, doi: 10.1038/nmeth.4197

26 

F Nadalin, F Vezzi, A Policriti. . GapFiller: a de novo assembly approach to fill the gap within paired reads. BMC bioinformatics. 2012; 13(S14):, pp.S8.

27 

M Boetzer, CV Henkel, HJ Jansen, D Butler, W Pirovano. . Scaffolding pre-assembled contigs using SSPACE. Bioinformatics. 2011; 27(4):, pp.578–579. , doi: 10.1093/bioinformatics/btq683

28 

S Assefa, TM Keane, TD Otto, C Newbold, M Berriman. . ABACAS: algorithm-based automatic contiguation of assembled sequences. Bioinformatics. 2009; 25(15):, pp.1968–1969. , doi: 10.1093/bioinformatics/btp347

29 

RC Edgar. . MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic acids research. 2004; 32(5):, pp.1792–1797. , doi: 10.1093/nar/gkh340

30 

KS Lole, RC Bollinger, RS Paranjape, D Gadkari, SS Kulkarni, NG Novak, et al. Full-length human immunodeficiency virus type 1 genomes from subtype C-infected seroconverters in India, with evidence of intersubtype recombination. Journal of virology. 1999; 73(1):, pp.152–160.

31 

S Kumar, G Stecher, M Li, C Knyaz, K Tamura. . MEGA X: molecular evolutionary genetics analysis across computing platforms. Molecular biology and evolution. 2018; 35(6):, pp.1547–1549. , doi: 10.1093/molbev/msy096

32 

K Katoh, DM Standley. . MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular biology and evolution. 2013; 30(4):, pp.772–780. , doi: 10.1093/molbev/mst010

33 

JP Huelsenbeck, F Ronquist. . MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics. 2001; 17(8); , pp.754–755. , doi: 10.1093/bioinformatics/17.8.754

34 

D Darriba, GL Taboada, R Doallo, D Posada. . jModelTest 2: more models, new heuristics and parallel computing. Nature methods. 2012; 9(8):, pp.772–772.

https://www.researchpad.co/tools/openurl?pubtype=article&doi=10.1371/journal.ppat.1008421&title=Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?&author=Ping Liu,Jing-Zhe Jiang,Xiu-Feng Wan,Yan Hua,Linmiao Li,Jiabin Zhou,Xiaohu Wang,Fanghui Hou,Jing Chen,Jiejian Zou,Jinping Chen,Stanley Perlman,&keyword=&subject=Research Article,Biology and life sciences,Organisms,Viruses,RNA viruses,Coronaviruses,Biology and Life Sciences,Microbiology,Medical Microbiology,Microbial Pathogens,Viral Pathogens,Coronaviruses,Medicine and Health Sciences,Pathology and Laboratory Medicine,Pathogens,Microbial Pathogens,Viral Pathogens,Coronaviruses,Biology and Life Sciences,Organisms,Viruses,Viral Pathogens,Coronaviruses,Research and analysis methods,Database and informatics methods,Bioinformatics,Sequence analysis,DNA sequence analysis,Biology and Life Sciences,Molecular Biology,Molecular Biology Techniques,Sequencing Techniques,Nucleotide Sequencing,Research and Analysis Methods,Molecular Biology Techniques,Sequencing Techniques,Nucleotide Sequencing,Biology and life sciences,Organisms,Viruses,RNA viruses,Coronaviruses,SARS coronavirus,Biology and life sciences,Microbiology,Medical microbiology,Microbial pathogens,Viral pathogens,Coronaviruses,SARS coronavirus,Medicine and health sciences,Pathology and laboratory medicine,Pathogens,Microbial pathogens,Viral pathogens,Coronaviruses,SARS coronavirus,Biology and life sciences,Organisms,Viruses,Viral pathogens,Coronaviruses,SARS coronavirus,Biology and Life Sciences,Evolutionary Biology,Evolutionary Systematics,Phylogenetics,Phylogenetic Analysis,Biology and Life Sciences,Taxonomy,Evolutionary Systematics,Phylogenetics,Phylogenetic Analysis,Computer and Information Sciences,Data Management,Taxonomy,Evolutionary Systematics,Phylogenetics,Phylogenetic Analysis,Biology and Life Sciences,Organisms,Eukaryota,Animals,Animal Types,Wildlife,Biology and Life Sciences,Zoology,Animal Types,Wildlife,Research and analysis methods,Database and informatics methods,Bioinformatics,Sequence analysis,RNA sequence analysis,Research and Analysis Methods,Database and Informatics Methods,Biological Databases,Sequence Databases,Research and Analysis Methods,Database and Informatics Methods,Bioinformatics,Sequence Analysis,Sequence Databases,