Coronavirus SARS COV II

CORONAVIRUS SARS_COV_II Danilo Nori Contents Genomic epidemiology of novel coronavirus (hCoV-19) .................................................................................... 3 Origin and continuing evolution of SARS-CoV-2” ...................................................................................... 4 Additional methodological issue ................................................................................................................... 8 On the origin and continuing evolution of SARS-CoV-2 ............................................................................. 9 Mutations in 103 SARS-CoV-2 genomes .............................................................................................. 9 Identification of a Novel Coronavirus in Patients with Severe Acute Respiratory Syndrome. .................... 9 Isolation And Characterization Of A Novel Coronavirus ....................................................................... 10 Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform. ............................................ 12 Cells and general culture conditions. ...................................................................................................... 13 Cultured viruses. ..................................................................................................................................... 14 Bacterial and yeast strains. ...................................................................................................................... 14 Identification of leader-body junctions of viral mRNAs. ....................................................................... 14 5' rapid amplification of cDNA ends (5’-RACE). .................................................................................. 14 Remdesivir experiment ........................................................................................................................... 15 Two major types of SARS-CoV-2 are defined by two SNPs that show complete linkage. ............. 15 The evolutionary history of L and S types of SARS-CoV-2 .............................................................. 16 Amino acid replacements ............................................................................................................................ 16 Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. ............................... 17 Data collection ....................................................................................................................................... 17 Cytokine and chemokine measurement .............................................................................................. 17 Detection of coronavirus in plasma ..................................................................................................... 18 Role of the funding source .................................................................................................................... 18 Results .................................................................................................................................................... 19 Table 3Treatments and outcomes of patients infected with 2019-nCoV.......................................... 26 Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. ............................................. 29 Fig. 1 Structure of 2019-nCoV S in the prefusion conformation. ....................................................... 29 Fig. 2 Structural comparison between 2019-nCoV S and SARS-CoV S. ............................................ 31 Fig. 3 2019-nCoV S binds human ACE2 with high affinity. ............................................................... 32 Fig. 4 Antigenicity of the 2019-nCoV RBD......................................................................................... 33 Crystal structure of the 2019-nCoV spike receptor-binding domain bound with the ACE2 receptor. ................... 34 The overall structure of 2019-nCoV RBD bound with ACE2. ................................................................... 35 Structural comparisons of 2019-nCoV and SARS-CoV RBDs and their binding modes to the ACE2 receptor. ...................................................................................................................................................... 36 X-ray Structure of Main Protease of the Novel Coronavirus SARS-CoV-2 Enables Design of α-Ketoamide Inhibitors ...................................................................................................................................................... 38 Chemical structures of α-ketoamide inhibitors 11r, 13a, and 13b ..................................................... 40 Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein..................................................... 41 RESULTS ................................................................................................................................................... 43 ACE2 is an entry receptor for SARS-CoV-2 ...................................................................................... 43 SARS-CoV-2 recognizes human ACE2 with comparable affinity than SARS-CoV. ...................... 45 The architecture of the SARS-CoV-2 spike glycoprotein trimer ................................................................ 45 Figure 3.CryoEM structures of the SARS-CoV-2 S glycoprotein. ............................................... 46 References. ................................................................................................................................................... 47 Genomic epidemiology of novel coronavirus (hCoV-19) This phylogeny shows evolutionary relationships of HCoV-19 viruses from the ongoing novel coronavirus COVID-19 pandemic. All samples are still closely related with few mutations relative to a common ancestor, suggesting a shared common ancestor sometime in Nov-Dec 2019. This indicates an initial human infection in Nov-Dec 2019 followed by sustained human-tohuman transmission leading to sampled infections. Site numbering and genome structure use Wuhan-Hu-1/2019 as a reference. The phylogeny is rooted relative to early samples from Wuhan. Temporal resolution assumes a nucleotide substitution rate of 5 × 10^-4 subs per site per year. Full details on bioinformatic processing can be found here. We gratefully acknowledge the authors, originating and submitting laboratories of the genetic sequence and metadata made available through GISAID on which this research is based. A full listing of all originating and submitting laboratories is available below. An attribution table is available by clicking on "Download Data" at the bottom of the page and then clicking on "Strain Metadata" in the resulting dialog box[1]. Origin and continuing evolution of SARS-CoV-2” This criticism concerns the claim that there are two definable “major types” of SARS-CoV2 in this outbreak and that they have differentiable transmission rates. Tang et al. term these two types L and S type: “two major types (L and S types): the S type is ancestral, and the L type evolved from S type. Intriguingly, the S and L types can be clearly defined by just two tightly linked SNPs at positions 8,782 (orf1ab: T8517C, synonymous) and 28,144 (ORF8: C251T, S84L).” One nonsynonymous mutation, which has not been assessed for functional significance, is not sufficient to define a distinct “type” nor “major type”. As of 2nd March 2020, 111 nonsynonymous mutations have been identified in the outbreak, these have been cataloged here in the CoV-GLUE resource 191 and can be visualized in Figure 1. At current, there is no evidence that any of these 111 mutations have any significance in a functional context of within-host infections or transmission rates. Additionally, when you choose to define “types” purely based on two mutations, it is not intriguing that these “types” then differ by those two mutations[2]. Figure 1. A visualization of the 111 nonsynonymous mutations (red) observed to date in the COVID-19 outbreak by plotting a grid of mutations where each column is a sample and each row is one of the observed mutations in the phylogeny. The columns are ordered by the position of each sample in the phylogeny. Synonymous mutations are shown in yellow. The C251T (nonsynonymous) and T8517C (synonymous) mutations are visible on the right side of the plot. However, they further claim that these two types have different transmission rates: “Thus far, we found that, although the L type is derived from the S type, L (~70%) is more prevalent than S (~30%) among the sequenced SARS-CoV-2 genomes we examined. This pattern suggests that L has a higher transmission rate than the S type.” The abstract of the paper goes even further, stating outright that: “the S type, which is evolutionarily older and less aggressive…” It is, however, important to appreciate that finding a majority of samples with a particular mutation is not evidence that viruses with that mutation transmit more readily. To make this claim would, at very minimum, require a comparison to be made to expectations under a null distribution assuming equal transmission rates. As this has not been performed by the authors, we believe there is insufficient evidence to make this suggestion, and therefore it is incorrect (and irresponsible) to state that there is any difference in transmission rates. Differences in the observed numbers of samples with and without this mutation are far more likely to be due to stochastic epidemiological effects. Basic evolutionary theory predicts that selectively neutral mutations change in frequency over time through the process of genetic drift. In a viral outbreak, each transmission event from one infected person to another is a random probabilistic event, with some infected individuals transmitting more or less often than others. People may transmit at higher rates than others for a variety of reasons, e.g. because they cough onto their palms and use overcrowded public transport, or just because their friends and coworkers got lucky (or unlucky!). These small-scale epidemiological phenomena add up over time to create substantial variation in the frequencies of mutations observed during an outbreak. Additionally, when a virus spreads to a new area/country that was previously uninfected, a founder effect can occur. As a small number of virus copies rapidly spread into an epidemic, any mutations in the initial viral infections will rapidly become very common, even if they were initially rare in the country that seeded the transmission. This is particularly likely to be the case in an outbreak caused by a novel virus such as COVID-19 as there are a large number of susceptible hosts for the virus. These founder effects have been observed in previous studies of viral outbreaks (e.g., Foley et al. 2004; Rai et al. 2010; Tsetsarkin et al. 2011). Combined, these factors mean that the frequency of a particular mutation in and of itself is not suggestive of any functional significance. Evidence from the widespread media uptake (35 articles at last count), and many comments on social media in response to this article, suggests that the unsupported claims made by Tang et al. have already spread undue fear. It’s also important to appreciate that the smaller the population of viruses is, the more these small scale variations are likely to affect the frequency of mutations (in the same way that the more coins you flip, the closer to the 0.5 heads average you expect to be). Given that this mutation appears to have occurred very early on in the outbreak when fewer individuals were infected, it’s frequency will very likely have been particularly influenced by genetic drift. Tang et al. compare the frequencies of nonsynonymous and synonymous mutations in the data, claiming that there is significant evidence of selection suppressing the frequency of nonsynonymous mutations in the outbreak. This analysis is flawed on three grounds: (1) The numbers in this figure do not make sense. According to the presented data, seven (synonymous) mutations have a derived frequency of >50%, and two of these mutations have derived frequencies greater than 95% in the population. A cursory glance at the tree (Figure 2; taken from Nextstrain 213) shows that this cannot be true. “Derived” in this context should mean since the last common ancestor of the outbreak. For two mutations to have derived frequencies greater than 95%, there would need to be a small number of samples which branch as a sister lineage to the rest of the outbreak tree. However, this is not the case. 833×655 Figure 2. A screenshot of the SARS-CoV-2 time tree phylogeny from NextStrain 213. Colors indicate the geographic location of the sample. The date of sampling is shown below the tree. The only way Tang et al. can get the results they present is by defining the ancestral state as being at some point way back in the bat coronavirus tree before the outbreak began. They then estimate the ancestral state for each mutation independently, ignoring the very informative tree of the current outbreak. This method only makes sense when using a much more closely related outgroup species, to infer the ancestral states of mutations in a freely recombinant species with unlinked mutations with independent ancestry. Whereas the most recent common ancestor of SARS-CoV-2 and the nearest bat sarbecovirus is shared many decades. Additionally, such methods should incorporate the inherent uncertainty in inferring the ancestral state (e.g., est-sfs; Keightley and Jackson 2018), which Tang’s implementation does not. Implementing this method of inferring ancestral states in a viral context, where we assume there is no recombination, means that “high frequency derived mutations” are just new mutations in the outbreak that have mutated back to the inferred ancestral state (in bats). This is a completely meaningless definition of “derived”. These high frequencies derived mutations should instead be classed as low frequency derived mutations. Tang et al. claim 16.3% of (7 out of 43) synonymous mutations have a derived frequency >0.5. However, given the levels of synonymous divergence, and remembering that mutations probabilities are biased, which increases the likelihood of back-mutations, this 16.3% figure is broadly in line with the expected proportion of synonymous mutations that would back-mutate to the nucleotide found in bat infecting strains. Because nonsynonymous sites are much less diverged (<4%) than synonymous sites (19%) to the most closely related bat sequence, new nonsynonymous mutations are much more likely to be away from the inferred ancestral state in bats than new synonymous mutations are. Therefore, using this flawed definition of “derived”, a much smaller proportion of nonsynonymous mutations are expected to be high frequency “derived” mutations without any action of natural selection at all. (2) The way this data has been presented in Tang et al.’s Figure 2 will falsely suggest that purifying selection is acting even if their methodology was sensible, and there was no such selection. The height of the bars in their figure compares the raw numbers of mutations at each frequency without scaling the heights of the bars for the number of each class of mutation. Because there is a greater number of nonsynonymous polymorphisms than synonymous polymorphisms in the population, and as most mutations are expected to be at low frequency (regardless of the action of natural selection), this presentation will always make it look like there are proportionately more low-frequency nonsynonymous mutations. (3) When interpreting their results, Tang et al. do not consider that sequencing error could be a driver of a relative excess of singleton nonsynonymous mutations. This possibility is important because sequencing errors will be at low frequency as they are rare and cannot be transmitted, but real mutations can be at any frequency because they can be transmitted. Additionally, purifying selection can only act on real mutations and not sequencing errors. Therefore sequencing error may have a higher nonsynonymous to synonymous ratio, and these mutations will be at low frequency, which will mimic the action of purifying selection suppressing the frequency of nonsynonymous mutations. Taken together, Tang’s analysis tells us absolutely nothing about purifying selection within the viral outbreak. We have performed an additional analysis below to test for signatures of purifying selection in the SARS-CoV2 outbreak. Additional methodological issue The authors used the software PAML 8 (Yang et al. 2007) to estimate selection parameters. PAML does not allow for synonymous rate variation, but they explicitly state in the paper they believe there are mutational hotspots. Recent work has shown that false-positive rates of positive selection inference are unacceptably high when such synonymous rate variation occurs (Wisotsky et al. 2020). Therefore, if there truly is synonymous rate variation, to reliably identify signatures of positive selection within the phylogeny of SARS-CoV2, methods in which model mutation rate variation must be used (e.g., provided by many of the models from the Hyphy 14 package). Our Additional análisis To test for potential purifying selection simply and robustly, the number of observed synonymous and nonsynonymous mutations was compared to the null expectation by comparing the relative number of synonymous and nonsynonymous sites. The relative number of sites was estimated using the Goldman and Yang (1994) codon model. This model estimates mutation probabilities between all 61 possible coding codons using the observed frequencies of each of the 61 codons weighted by the transition to transversion ratio estimated from the data (2.9). It estimates there are 2.43 times more nonsynonymous than synonymous sites in the SARS-CoV2 genome. This null expectation under no selection was compared to that observed from the outbreak data using a chi-squared test on the below table. This yielded a non-significant P-value of 0.113. This result is not unexpected, as the current rapid growth rate of the viral population is likely to allow viruses with unfit mutations, as well as viruses with neutral mutations to be transmitted. However, we urge caution in over analyzing these results, as statistical power is limited until more sequencing data accumulates. On the origin and continuing evolution of SARS-CoV-2 Mutations in 103 SARS-CoV-2 genomes We downloaded 103 publicly available SARS-CoV-2 genomes, aligned the sequences, and identified the genetic variants. For ease of visualization, we marked each virus strain based on the location and date the virus was isolated with the format of "Location_Date” throughout this study (see Table S1 for details; Each ID did not contain information of the patient's race or ethnicity). Although SARS-CoV-2 is an RNA virus, for simplicity, we presented our results based on DNA sequencing results throughout this study (i.e., the nucleotide T (thymine) means U (uracil) in SARS-CoV-2). For each variant, the ancestral state was inferred based on the genome and CDS alignments of SARS-CoV-2 (NC_045512), RaTG13, and GD Pangolin-CoV (Materials and Methods). In total, we identified mutations in 149 sites across the 103 sequenced strains. Ancestral states for 43 synonymous, 83 non-synonymous, and two stop-gain mutations were unambiguously inferred. The frequency spectra of synonymous and nonsynonymous mutations. Most derived mutations were singletons (67.4% (29/43) of synonymous mutations and 84.3% (70/83) of nonsynonymous mutations), indicating either a recent origin [30] or population growth [31]. In general, the derived alleles of synonymous mutations were significantly skewed towards higher frequencies than those of nonsynonymous ones (P < 0.01, Wilcoxon rank-sum test; Fig. 2), suggesting the nonsynonymous mutations tended to be selected against. However, 16.3% (7 out of 43) synonymous mutations, and one nonsynonymous (ORF8 (L84S, 28,144) The mutation had a derived frequency of ≥ 70% across the SARS-CoV2 strains. The nonsynonymous mutations that had derived alleles in at least two SARS-CoV-2 strains affected six proteins: orf1ab (A117T, I1607V, L3606F, I6075T), S (H49Y, V367F), ORF3a (G251V), ORF7a (P34S), ORF8 (V62L, S84L), and N (S194L, S202N, P344S). Identification of a Novel Coronavirus in Patients with Severe Acute Respiratory Syndrome. A large number of tests for known respiratory pathogens were performed with specimens from all three patients in Frankfurt. The test results were negative, except as follows. Paramyxovirus-like particles were seen in throat swabs and sputum samples from the index patient by electron microscopy. The particles were scarce. However, several PCR tests specific for virus species of the family Paramyxoviridae were negative (including tests for human metapneumovirus), as were PCR assays based on primers designed to react broadly with all members of that family. Isolation And Characterization Of A Novel Coronavirus After six days of incubation (on March 21), a cytopathic effect was seen on Vero-cell cultures inoculated with sputum obtained from the index patient on day 7. Twenty-four hours after a single passage, nucleic acids were purified from the supernatant. Random amplification was performed with 15 different PCRs under low-stringency conditions. We had previously shown that this method can detect unknown pathogens growing in cell culture (unpublished data). To detect RNA viruses, an initial reversetranscription step was included. Ilustración 1Genetic Characterization of the Novel Coronavirus. About 20 distinct DNA fragments were obtained and sequenced. The resulting sequences were subjected to BLAST database searches. Most of the fragments matched human chromosome sequences, indicating that the genetic material of the cultured cells had been amplified (Vero cells are derived from monkeys). Three of the fragments did not match any nucleotide sequence in the database. However, when a translated BLAST search was performed (comparison of the amino acid translation in all six possible reading frames with the database), these fragments showed homology to coronavirus amino acid sequences, indicating that a coronavirus had been isolated. Two of the fragments were 300 nucleotides in length and identical in sequence, and the third fragment was 90 nucleotides in length (sequences BNI-1 and BNI-2, respectively, as reported on the Web site of the WHO network on March 25). Detailed sequence analysis revealed that both fragments were located in the open reading frame 1b of coronaviruses and did not overlap with a 400-nucleotide coronavirus fragment identified by colleagues at the Centers for Disease Control and Prevention (CDC) (sequence CDC, reported on the Web site of the WHO network on March 24). Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform. The emergence of a novel CoV in China at the end of 2019 prompted us to test the applicability of our synthetic genomics platform to reconstruct the virus based on the genome sequences released on January 10-11, 2020. We divided the genome into 12 overlapping DNA fragments. In parallel, we aimed to generate a SARS-CoV-2 expressing GFP that could be valuable to facilitate the screening of antiviral compounds and to establish diagnostic assays (e.g. virus neutralization assay). Fourteen synthetic DNA fragments were ordered as sequence-confirmed plasmids and all but fragments 5 and 7 were delivered. Since we received at the same time SARS-CoV-2 viral RNA from an isolate of a Munich patient (BetaCoV/Germany/BavPat1/2020), we amplified the regions of fragments 5 and 7 by RT-PCR (Supplementary Table 1). TAR cloning was immediately initiated, and for all six SARS-CoV2/SARS-CoV-2-GFP constructs we obtained correctly assembled molecular. Since sequence verification was not possible within this short time frame, we randomly selected two clones for each construct, isolated the YAC DNA, and performed in vitro transcription. The resulting RNAs were electroporated together with an mRNA encoding the SARS-CoV-2 N protein into BHK-21 and, in parallel, into BHKSARS-N cells expressing the SARS-CoV N protein19 Electroporated cells were seeded over VeroE6 cells and two days later we observed green fluorescent signals in cells that received the GFP-encoding SARS-CoV-2 RNAs. Indeed, we could rescue infectious viruses for almost all rSARS-CoV-2 and rSARS-CoV-2-GFP.[3] As shown in Figure 3b for rSARS-CoV-2 clones 1.1, 2.2, and 3.1, plaques were readily detectable, demonstrating that infectious virus has been recovered irrespectively of the 5’-termini. Sequencing of the YACs and corresponding rescued viruses revealed that almost all DNA clones and viruses contained the correct sequence, except for some individual clones carrying mutations within fragments 5 and 7, likely introduced by RT-PCR. Nevertheless, we obtained at least one correct YAC clone for all constructs except for construct 6. To correct this, we re-assembled construct 6 by replacing the RT-PCR-generated fragments 5 and 7 with 4 and 3 shorter synthetic dsDNA fragments, respectively. The resulting molecular clone was used to rescue the synSARS-CoV-2-GFP virus without any mutations exclusively from chemically synthesized DNA. Next, we assessed the 5’-end of the recombinant viruses and the Munich virus isolate and confirmed the published 5’-end sequence of SARS-CoV-2 (5’-AUUAAAGG; Genbank MN996528.3). Full-length sequencing of the viral genomes and 5’-RACE analysis of each recombinant virus confirmed the identity of each virus and showed that each virus 5’-end variant retained the cloned 5terminus. This demonstrates that the 5’-ends of SARS-CoV and bat SARS-related CoVs ZXC21 and ZC45 are compatible with the replication machinery of SARS-CoV-2. Sequencing results also revealed the identity of leader-body junctions of SARS-CoV-2 subgenomic mRNAs, which are identical to those of SARS-CoV. We also analyzed rSARS-CoV-2 clone 3.1 for protein expression and demonstrated the presence of SARS-CoV-2 nucleocapsid protein in dsRNA-positive cells. Replication kinetics of rSARSCoV-2 clone 3.1 containing the authentic 5’-terminus was indistinguishable from replication of the SARS-CoV-2 isolate, while clones 1.1 and 2.2 showed slightly reduced replication. All rSARS-CoV-GFP clones and synSARS-CoV-GFP displayed similar growth kinetics but were significantly reduced compared to the SARS-CoV-2 isolate, suggesting that the insertion of GFP and/or the partial deletion of ORF7a affects replication. Despite the reduced replication, green fluorescence was readily detectable and we demonstrated the utility of synSARS-CoV-GFP for antiviral drug screening by testing remdesivir, a promising compound for COVID19 treatment. Similarly, the simple readout of green fluorescence greatly facilitates the demonstration of virus neutralisations with human serum. Reconstruction, rescue, and characterization of rSARS-CoV-2, rSARS-CoV-2-GFP, and synSARS-CoV2-GFP. a Schematic representation of the SARS-CoV-2 genome organization and DNA fragments used to clone rSARS-CoV-2, rSARS-CoV-2-GFP, and synSARS-CoV-2-GFP. Inserts show synthetic subfragments comprising fragments 5 (A-D) and 7 (Aa, Ab, B), and the fragments used to insert the GFP gene (fragments 13-15). kb, kilobase. Cells and general culture conditions. Vero, VeroB4 and VeroB6 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM); BHK21, BHK-MHV-N (BHK-21 cells expressing MHV-A59 N protein), BHK-SARS-N (BHK-21 cells expressing SARS N protein)19, Huh-723, L92923, and murine 17Cl-123 cells were grown in minimal essential medium (MEM). Both types of media were supplemented with 10% fetal bovine serum, 1X nonessential amino acids, 100 units/ml penicillin, and 100 µg/ml streptomycin. BHK-SARS-N cells were grown using MEM supplemented with 5% fetal bovine serum, 1X non-essential amino acids, 100 units/ml penicillin, and 100 µg/ml streptomycin, 500 µg/ml G418 and 10 µg/ml puromycin. Twenty-four hours before electroporation, BHK-MHV-N and BHK-SARS-N were treated with 1 µg/ml Doxycyclin. All cells were maintained at 37o C and in a 5% CO2 atmosphere. Cultured viruses. MHV-GFP and HCoV-229E were cultured in murine 17Cl-1 and Huh-7 cells, respectively. MERS-CoVEMC24 was cultured in VeroB4 cells. HCoV-HKU1 strain Caen-1 (GenBank: NC_006577) was cultured on human airway epithelial cultures. ZIKA virus strain PRVABC-59 (GenBank: KX377337) was kindly provided by Marco Alves (Institute of Virology and Immunology) and cultured on Vero cells. SARSCoV-2 (SARS-CoV-2/München-1.1/2020/929) was cultured on VeroE6 cells. Bacterial and yeast strains. Escherichia coli DH5α (Thermo Scientific™) and TransforMax™ Epi300™ (Epicentre) were used to propagate the pVC604 and pCC1BAC-His3 TAR vectors, respectively. The bacteria were grown in lysogeny media (LB) supplemented with appropriate antibiotics at 37 °C overnight. E. coli Epi300™ cells harboring different SARS-CoV-2 synthetic fragments on pUC57/pUC57mini were grown at 30 °C to lower instability/toxicity risks. Saccharomyces cerevisiae VL6-48N (MATα trp1-Δ1 ura3-Δ1 ade2-101 his3-Δ200 lys2 met14 cir°) was used for all yeast transformation experiments26. Yeast cells were first grown in YPDA broth (Takara Bio), and transformed cells were plated on minimal synthetic defined (SD) agar without histidine (SD-His) (Takara Bio). S. cerevisiae VL6- 48N derived clones carrying different YACs were never streaked out together on the same agar dishes since mating switching and resulting recombination might occur at a very low frequency. Identification of leader-body junctions of viral mRNAs. To identify reads that mapped discontinuously to the SARS-CoV-2 genome and determine the location of potential transcription regulatory sites (TRS), we pooled reads that mapped to the viral genome as well as unmapped reads and searched for the sequence TTCTCTAA ACGAAC (nucleotides 62 to 75 of MT108784; leader TRS underlined). We then filtered for reads that had at least 18 nucleotides 3’ of the aforementioned sequence and evaluated whether these reads were compatible with any of the SARSCoV-2 mRNA sequences. Reads matching these criteria were used as input for the generation of a consensus sequence for each TRS site and analyzed using a combination of SAMtools (version 1.10), R, and the Integrative Genomics Viewer (IGV). Mapped read depth was also calculated for the discontinuously mapped reads as explained in the previous section. 5' rapid amplification of cDNA ends (5’-RACE). Recombinant SARS-CoV-2(-GFP) poly(A)-purified RNA used for NGS was also used to determine the genome 5’-ends by 5’-RACE. M-MLV Reverse transcription (Promega) was performed according to the manufacturer's instructions using the gene-specific primer pWhSF-ORF1a-R18-655 (Supplementary Table 1) and RNase Inhibitor RNasin plus (Promega) 10U per 25 µl reaction volume. Following the reverse transcription, 1 µl RNase H (5U/µl, New England Biolabs) per 25 µl reaction was added, and the mixture was incubated at 37 °C for 20 min. The cDNA was immediately purified with the High Pure PCR Product Purification Kit (Roche) according to the manufacturer's instructions. A poly (A) tail was added to the cDNA with Terminal Transferase (New England Biolabs) according to the manufacturer's instructions. Subsequently, a PCR reaction with the tailed cDNA was performed with the primer pair pWhSF-ORF1a-R18-655/TagRACE_ dT16 (Supplementary Table 1) using the HotStarTaq Master Mix (Qiagen) according to the manufacturer's instructions with a touchdown cycling protocol: 95 °C for 15 min; 15 cycles of 94 °C for 30 sec, 65 °C touch down to 50 °C for 1 min, 72 °C for 1 min; 25 cycles of 94 °C for 30 sec, 50 °C for 1 min, 72 °C for 1 min. Subsequently, 1µl of this reaction was used for a nested pre-amplification with the primer pair pWhSF-5utr-R17-273/TagRACE (Supplementary Table 1) in a final volume of 50 µl following the same cycling protocol as described above. The PCR fragment was purified using the NucleoSpin™ Gel and PCR Clean-up Kit (Macherey-Nagel) according to the manufacturer's instructions, and the purified PCR fragment was sent to Microsynth AG (Switzerland) for Sanger sequencing with the primer pWhSF-5utr-R17-273 (Supplementary Table 1). Sequencing raw data were assessed using the SeqManTM II sequence analysis software (DNASTAR Inc., Madison, USA). Remdesivir experiment Remdesivir (MedChemExpress) was dissolved in DMSO and stored at -80 °C in 20 mM stock aliquots. One day before the experiment, VeroE6 cells were seeded in 24-well plates at a density of 8 x 104 cells per well. Cells were infected with synSARS-CoV-2-GFP (passage 1) at MOI = 0.01, or mock-infected as control. Inocula were removed at 1 h.p.i, and replaced with medium containing Remdesivir at a concentration of 0.2 µM, 2 µM or the equivalent amount of DMSO. At 48 h.p.i., cells were washed once with PBS and incubated in fresh PBS. Images were acquired using an EVOS fluorescence microscope equipped with a 10x air objective. Brightness and contrast were adjusted identically for each condition and their corresponding control using FIJI. Two major types of SARS-CoV-2 are defined by two SNPs that show complete linkage. To detect the possible recombination among SARS-CoV2 viruses, we used Haploview [32] to analyze and visualize the patterns of linkage disequilibrium (LD) between variants with minor alleles in at least two SARS-CoV-2 strains (Fig. 3A). Since most mutations were at very low frequencies, it is not surprising that many pairs had a very low r2 or LOD value (Fig. 3B-C). Consistent with another recent report [31], we did not find evidence of recombination between the SARS-CoV2 strains. However, we found that SNPs at location 8,782 (orf1ab: T8517C, synonymous) and 28,144 (ORF8: C251T, S84L) showed significant linkage, with an r2 value of 0.954 (Fig. 3B, red) and a LOD value of 50.13 (Fig. 3C, red). Among the 103 SARS-CoV-2 virus strains, 101 of they exhibited complete linkage between the two SNPs: 72 strains exhibited a “CT” haplotype (defined as “L” type because T28,144 is in the codon of Leucine) and 29 strains exhibited a “TC” haplotype (defined as “S” type because C28,144 is in the codon of Serine) at these two sites. Thus, we categorized the SARS-CoV-2 viruses into two major types, with L being the major type (~70%) and S being the minor type (~30%). The evolutionary history of L and S types of SARS-CoV-2 Although we defined the L and S types based on two tightly linked SNPs, strikingly, the the separation between the L (blue) and S (red) types was maintained when we reconstructed the haplotype networks using all the SNPs in the SARS-CoV-2 genomes (Fig. 4A; the number of mutations between two neighboring haplotypes were inferred parsimoniously). This analysis further supports the idea that the two linked SNPs at sites 8,782 and 28,144 adequately define the L and S types of SARS-CoV-2[4]. Amino acid replacements [5] Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Initial investigations included a complete blood count, coagulation profile, and serum biochemical test (including renal and liver function, creatine kinase, lactate dehydrogenase, and electrolytes). Respiratory specimens, including nasal and pharyngeal swabs, bronchoalveolar lavage fluid, sputum, or bronchial aspirates were tested for common viruses, including influenza, avian influenza, respiratory syncytial virus, adenovirus, parainfluenza virus, SARS-CoV and MERS-CoV using real-time RT-PCR assays approved by the China Food and Drug Administration. Routine bacterial and fungal examinations were also performed. Given the emergence of the 2019-nCoV pneumonia cases during the influenza season, antibiotics (orally and intravenously) and oseltamivir (orally 75 mg twice daily) were empirically administered. Corticosteroid therapy (methylprednisolone 40–120 mg per day) was given as a combined regimen if severe community-acquired pneumonia was diagnosed by physicians at the designated hospital. Oxygen support (eg, nasal cannula and invasive mechanical ventilation) was administered to patients according to the severity of hypoxaemia. Repeated tests for 2019-nCoV were done in patients confirmed to have 2019nCoV infection to show viral clearance before hospital discharge or discontinuation of isolation. Data collection We reviewed clinical charts, nursing records, laboratory findings, and chest x-rays for all patients with laboratory-confirmed 2019-nCoV infection who were reported by the local health authority. The admission data of these patients were from Dec 16, 2019, to Jan 2, 2020. Epidemiological, clinical, laboratory, and radiological characteristics and treatment and outcomes data were obtained with standardized data collection forms (modified case record form for severe acute respiratory infection clinical characterization shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium) from electronic medical records. Two researchers also independently reviewed the data collection forms to double-check the data collected. To ascertain the epidemiological and symptom data, which were not available from electronic medical records, the researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Cytokine and chemokine measurement To characterize the effect of coronavirus on the production of cytokines or chemokines in the acute phase of the illness, plasma cytokines and chemokines (IL1B, IL1RA, IL2, IL4, IL5, IL6, IL7, IL8 (also known as CXCL8), IL9, IL10, IL12p70, IL13, IL15, IL17A, Eotaxin (also known as CCL11), basic FGF2, GCSF (CSF3), GMCSF (CSF2), IFNγ, IP10 (CXCL10), MCP1 (CCL2), MIP1A (CCL3), MIP1B (CCL4), PDGFB, RANTES (CCL5), TNFα, and VEGFA were measured using Human Cytokine Standard 27-Plex Assays panel and the Bio-Plex 200 system (Bio-Rad, Hercules, CA, USA) for all patients according to the manufacturer's instructions. The plasma samples from four healthy adults were used as controls for crosscomparison. The median time from being transferred to a designated hospital to the blood sample collection was 4 days (IQR 2–5). Detection of coronavirus in plasma Each 80 μL plasma sample from the patients and contacts was added into 240 μL of Trizol LS (10296028; Thermo Fisher Scientific, Carlsbad, CA, USA) in the Biosafety Level 3 laboratory. Total RNA was extracted by Direct-zol RNA Miniprep kit (R2050; Zymo Research, Irvine, CA, USA) according to the manufacturer's instructions and 50 μL elution was obtained for each sample. 5 μL RNA was used for realtime RT-PCR, which targeted the NP gene using AgPath-ID One-Step RT-PCR Reagent (AM1005; Thermo Fisher Scientific). The final reaction mix concentration of the primers was 500 nM and the probe was 200 nM. Real-time RT-PCR was performed using the following conditions: 50°C for 15 min and 95°C for 3 min, 50 cycles of amplification at 95°C for 10 s and 60°C for 45 s. Since we did not perform tests for detecting the infectious virus in the blood, we avoided the term viraemia and used RNAaemia instead. RNAaemia was defined as a positive result for real-time RT-PCR in the plasma sample. Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. Results By Jan 2, 2020, 41 admitted hospital patients were identified as laboratory-confirmed 2019-nCoV infection in Wuhan. 20 [49%]) of the 2019-nCoV-infected patients were aged 25–49 years, and 14 (34%) were aged 50–64 years (figure 1A). The median age of the patients was 49·0 years (IQR 41·0–58·0; table 1). In our cohort of the first 41 patients as of Jan 2, no children or adolescents were infected. Of the 41 patients, 13 (32%) were admitted to the ICU because they required high-flow nasal cannula or higherlevel oxygen support measures to correct the hypoxaemia. Most of the infected patients were men (30 [73%]); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). The blood counts of patients on admission showed leucopenia (white blood cell count less than 4 × 109/L; ten [25%] of 40 patients) and lymphopenia (lymphocyte count <1·0 × 109/L; 26 [63%] patients; table 2). Prothrombin time and D-dimer level on admission were higher in ICU patients (median prothrombin time 12·2 s [IQR 11·2–13·4]; median D-dimer level 2·4 mg/L [0·6–14·4]) than non-ICU patients (median prothrombin time 10·7 s [9·8–12·1], p=0·012; median D-dimer level 0·5 mg/L [0·3–0·8], p=0·0042). Levels of aspartate aminotransferase were increased in 15 (37%) of 41 patients, including eight (62%) of 13 ICU patients and seven (25%) of 28 non-ICU patients. Hypersensitive troponin I (hs-cTnI) was increased substantially in five patients, in whom the diagnosis of virus-related cardiac injury was made. Table 2Laboratory findings of patients infected with 2019-nCoV on admission to hospital All patients (n=41) ICU care (n=13) No ICU care (n=28) p value 6·2 (4·1– 10·5) 11·3 (5·8– 12·1) 5·7 (3·1– 7·6) 0·011 <4 10/40 (25%) 1/13 (8%) 9/27 (33%) 0·041 4–10 18/40 (45%) 5/13 (38%) 13/27 (48%) .. >10 12/40 (30%) 7/13 (54%) 5/27 (19%) .. Neutrophil count, × 109/L 5·0 (3·3–8·9) 10·6 (5·0– 11·8) 4·4 (2·0– 6·1) 0·00069 Lymphocyte count, × 109/L 0·8 (0·6–1·1) 0·4 (0·2– 0·8) 1·0 (0·7– 1·1) 0·0041 <1·0 26/41 (63%) 11/13 (85%) 15/28 (54%) 0·045 ≥1·0 15/41 (37%) 2/13 (15%) 13/28 (46%) .. White blood cell count, × 109/L All patients (n=41) ICU care (n=13) No ICU care (n=28) p value 126·0 (118·0– 140·0) 122·0 (111·0– 128·0) 130·5 (120·0– 140·0) 0·20 164·5 (131·5– 263·0) 196·0 (165·0– 263·0) 149·0 (131·0– 263·0) 0·45 <100 2/40 (5%) 1/13 (8%) 1/27 (4%) 0·45 ≥100 38/40 (95%) 12/13 (92%) 26/27 (96%) .. Prothrombin time, s 11·1 (10·1– 12·4) 12·2 (11·2– 13·4) 10·7 (9·8– 12·1) 0·012 Activated partial thromboplastin time, s 27·0 (24·2– 34·1) 26·2 (22·5– 33·9) 27·7 (24·8– 34·1) 0·57 D-dimer, mg/L 0·5 (0·3–1·3) 2·4 (0·6– 14·4) 0·5 (0·3– 0·8) 0·0042 Albumin, g/L 31·4 (28·9– 36·0) 27·9 (26·3– 30·9) 34·7 (30·2– 36·5) 0·00066 Alanine aminotransferase, U/L 32·0 (21·0– 50·0) 49·0 (29·0– 115·0) 27·0 (19·5– 40·0) 0·038 Hemoglobin, g/L Platelet count, × 10 /L 9 All patients (n=41) ICU care (n=13) No ICU care (n=28) p value 34·0 (26·0– 48·0) 44·0 (30·0– 70·0) 34·0 (24·0– 40·5) 0·10 ≤40 26/41 (63%) 5/13 (38%) 21/28 (75%) 0·025 >40 15/41 (37%) 8/13 (62%) 7/28 (25%) .. Total bilirubin, mmol/L 11·7 (9·5– 13·9) 14·0 (11·9– 32·9) 10·8 (9·4– 12·3) 0·011 Potassium, mmol/L 4·2 (3·8–4·8) 4·6 (4·0– 5·0) 4·1 (3·8– 4·6) 0·27 Sodium, mmol/L 139·0 (137·0– 140·0) 138·0 (137·0– 139·0) 139·0 (137·5– 140·5) 0·26 Creatinine, μmol/L 74·2 (57·5– 85·7) 79·0 (53·1– 92·7) 73·3 (57·5– 84·7) 0·84 ≤133 37/41 (90%) 11/13 (85%) 26/28 (93%) 0·42 >133 4/41 (10%) 2/13 (15%) 2/28 (7%) .. 132·5 (62·0– 219·0) 132·0 (82·0– 493·0) 133·0 (61·0– 189·0) 0·31 Aspartate aminotransferase, U/L Creatine kinase, U/L All patients (n=41) ICU care (n=13) No ICU care (n=28) p value ≤185 27/40 (68%) 7/13 (54%) 20/27 (74%) 0·21 >185 13/40 (33%) 6/13 (46%) 7/27 (26%) .. 286·0 (242·0– 408·0) 400·0 (323·0– 578·0) 281·0 (233·0– 357·0) 0·0044 ≤245 11/40 (28%) 1/13 (8%) 10/27 (37%) 0·036 >245 29/40 (73%) 12/13 (92%) 17/27 (63%) .. 3·4 (1·1–9·1) 3·3 (3·0– 163·0) 3·5 (0·7– 5·4) 0·075 5/41 (12%) 4/13 (31%) 1/28 (4%) 0·017 0·1 (0·1–0·1) 0·1 (0·1– 0·4) 0·1 (0·1– 0·1) 0·031 <0·1 27/39 (69%) 6/12 (50%) 21/27 (78%) 0·029 ≥0·1 to <0·25 7/39 (18%) 3/12 (25%) 4/27 (15%) .. Lactate dehydrogenase, U/L Hypersensitive troponin I, pg/mL >28 (99th percentile) Procalcitonin, ng/mL All patients (n=41) ICU care (n=13) No ICU care (n=28) p value ≥0·25 to <0·5 2/39 (5%) 0/12 2/27 (7%) .. ≥0·5 3/39 (8%) 0/27 .. 3/12 (25%) * Bilateral involvement of chest radiographs 40/41 (98%) 13/13 (100%) 27/28 (96%) 0·68 Cycle threshold of the respiratory tract 32·2 (31·0– 34·5) 31·1 (30·0– 33·5) 32·2 (31·1– 34·7) 0·39 Data are median (IQR) or n/N (%), where N is the total number of patients with available data. p values comparing ICU care and no ICU care are from χ2, Fisher's exact test, or Mann-Whitney U test. 2019nCoV=2019 novel coronavirus. ICU=intensive care unit. * Complicated typical secondary infection during the first hospitalization. Most patients had normal serum levels of procalcitonin on admission (procalcitonin <0·1 ng/mL; 27 [69%] patients; table 2). Four ICU patients developed secondary infections. Three of the four patients with secondary infection had procalcitonin greater than 0·5 ng/mL (0·69 ng/mL, 1·46 ng/mL, and 6·48 ng/mL). On admission, abnormalities in chest CT images were detected among all patients. Of the 41 patients, 40 (98%) had bilateral involvement (table 2). The typical findings of chest CT images of ICU patients on admission were bilateral multiple lobular and subsegmental areas of consolidation (figure 3A). The representative chest CT findings of non-ICU patients showed bilateral ground-glass opacity and subsegmental areas of consolidation (figure 3B). Later chest CT images showed bilateral ground-glass opacity, whereas the consolidation had been resolved (figure 3C). Initial plasma IL1B, IL1RA, IL7, IL8, IL9, IL10, basic FGF, GCSF, GMCSF, IFNγ, IP10, MCP1, MIP1A, MIP1B, PDGF, TNFα, and VEGF concentrations were higher in both ICU patients and non-ICU patients than in healthy adults (appendix pp 6–7). Plasma levels of IL5, IL12p70, IL15, Eotaxin, and RANTES were similar between healthy adults and patients infected with 2019-nCoV. Further comparison between ICU and non-ICU patients showed that plasma concentrations of IL2, IL7, IL10, GCSF, IP10, MCP1, MIP1A, and TNFα were higher in ICU patients than non-ICU patients. All patients had pneumonia. Common complications included ARDS (12 [29%] of 41 patients), followed by RNAaemia (six [15%] patients), acute cardiac injury (five [12%] patients), and secondary infection (four [10%] patients; table 3). Invasive mechanical ventilation was required in four (10%) patients, with two of them (5%) had refractory hypoxaemia and received extracorporeal membrane oxygenation as salvage therapy. All patients were administered with empirical antibiotic treatment, and 38 (93%) patients received antiviral therapy (oseltamivir). Additionally, nine (22%) patients were given systematic corticosteroids. A comparison of clinical features between patients who received and did not receive systematic corticosteroids is in the appendix (pp 1–5). Table 3Treatments and outcomes of patients infected with 2019-nCoV All patients (n=41) ICU care (n=13) No ICU care (n=28) p value 7·0 (4·0– 8·0) 7·0 (4·0– 8·0) 7·0 (4·0– 8·5) 0·87 Acute respiratory distress syndrome 12 (29%) 11 (85%) 1 (4%) <0·0001 RNAaemia 6 (15%) 2 (15%) 4 (14%) 0·93 Cycle threshold of RNAaemia 35·1 (34·7– 35·1) 35·1 (35·1– 35·1) 34·8 (34·1– 35·4) 0·35 Acute cardiac injury 5 (12%) 4 (31%) 1 (4%) 0·017 Duration from illness onset to the first admission Complications All patients (n=41) ICU care (n=13) No ICU care (n=28) p value Acute kidney injury 3 (7%) 3 (23%) 0 0·027 Secondary infection 4 (10%) 4 (31%) 0 0·0014 Shock 3 (7%) 3 (23%) 0 0·027 Antiviral therapy 38 (93%) 12 (92%) 26 (93%) 0·46 Antibiotic therapy 41 (100%) 13 (100%) 28 (100%) NA Use of corticosteroid 9 (22%) 6 (46%) 3 (11%) 0·013 Continuous renal replacement therapy 3 (7%) 3 (23%) 0 0·027 Oxygen support .. .. .. <0·0001 27 (66%) 1 (8%) 26 (93%) .. * Treatment Nasal cannula All patients (n=41) ICU care (n=13) No ICU care (n=28) p value Non-invasive ventilation or high-flow nasal cannula 10 (24%) 8 (62%) 2 (7%) .. Invasive mechanical ventilation 2 (5%) 2 (15%) 0 .. Invasive mechanical ventilation and ECMO 2 (5%) 2 (15%) 0 .. .. .. .. 0·014 Hospitalisation 7 (17%) 1 (8%) 6 (21%) .. Discharge 28 (68%) 7 (54%) 21 (75%) .. Death 6 (15%) 5 (38%) 1 (4%) .. Prognosis Data are median (IQR) or n (%). p values are comparing ICU care and no ICU care. 2019-nCoV=2019 novel coronavirus. ICU=intensive care unit. NA=not applicable. ECMO=extracorporeal membrane oxygenation.[6] Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Based on the first reported genome sequence of 2019-nCoV (4), we expressed ectodomain residues 1 to 1208 of 2019-nCoV S, adding two stabilizing proline mutations in the C-terminal S2 fusion machinery using a previous stabilization strategy that proved effective for other betacoronavirus S proteins (11, 14). Figure 1A shows the domain organization of the expression construct, and figure S1 shows the purification process. We obtained ~0.5 mg/liter of the recombinant prefusion-stabilized S ectodomain from FreeStyle 293 cells and purified the protein to homogeneity by affinity chromatography and sizeexclusion chromatography (fig. S1). Cryo-electron microscopy (cryo-EM) grids were prepared using this purified, fully glycosylated S protein, and preliminary screening revealed a high particle density with little aggregation near the edges of the holes. Fig. 1 Structure of 2019-nCoV S in the prefusion conformation. (A) Schematic of 2019-nCoV S primary structure colored by domain. Domains that were excluded from the ectodomain expression construct or could not be visualized in the final map are colored white. SS, signal sequence; S2′, S2′ protease cleavage site; FP, fusion peptide; HR1, heptad repeat 1; CH, central helix; CD, connector domain; HR2, heptad repeat 2; TM, transmembrane domain; CT, cytoplasmic tail. Arrows denote protease cleavage sites. (B) Side and top views of the prefusion structure of the 2019nCoV S protein with a single RBD in the up conformation. The two RBD down protomers are shown as cryo-EM density in either white or gray and the RBD up protomer is shown in ribbons colored corresponding to the schematic in (A). After collecting and processing 3207 micrograph movies, we obtained a 3.5-Å-resolution threedimensional (3D) reconstruction of an asymmetrical trimer in which a single RBD was observed in the up conformation. (Fig. 1B, fig. S2, and table S1). Because of the small size of the RBD (~21 kDa), the asymmetry of this conformation was not readily apparent until ab initio 3D reconstruction and classification were performed (Fig. 1B and fig. S3). By using the 3D variability feature in cryoSPARC v2 (15), we observed breathing of the S1 subunits as the RBD underwent a hinge-like movement, which likely contributed to the relatively poor local resolution of S1 compared with the more stable S2 subunit (movies S1 and S2). This seemingly stochastic RBD movement has been captured during structural characterization of the closely related beta coronaviruses SARS-CoV and MERS-CoV, as well as the more distantly related alphacoronavirus porcine epidemic diarrhea virus (PEDV) (10, 11, 13, 16). The observation of this phenomenon in 2019nCoV S suggests that it shares the same mechanism of triggering that is thought to be conserved among the Coronaviridae, wherein receptor binding to exposed RBDs leads to an unstable three-RBD up conformation that results in shedding of S1 and refolding of S2 (11, 12). Because the S2 subunit appeared to be a symmetric trimer, we performed a 3D refinement imposing C3 symmetry, resulting in a 3.2-Å-resolution map with excellent density for the S2 subunit. Using both maps, we built most of the 2019-nCoV S ectodomain, including glycans at 44 of the 66 N-linked glycosylation sites per trimer (fig. S4). Our final model spans S residues 27 to 1146, with several flexible loops omitted. Like all previously reported coronavirus S ectodomain structures, the density for 2019-nCoV S begins to fade after the connector domain, reflecting the flexibility of the heptad repeat 2 domain in the prefusion conformation (fig. S4A) (13, 16–18). The overall structure of 2019-nCoV S resembles that of SARS-CoV S, with a root mean square deviation (RMSD) of 3.8 Å over 959 Cα atoms (Fig. 2A). One of the larger differences between these two structures (although still relatively minor) is the position of the RBDs in their respective down conformations. Whereas the SARS-CoV RBD in the down conformation packs tightly against the Nterminal domain (NTD) of the neighboring protomer, the 2019-nCoV RBD in the down conformation is angled closer to the central cavity of the trimer (Fig. 2B). Despite this observed conformational difference, when the individual structural domains of 2019-nCoV S are aligned to their counterparts from SARS-CoV S, they reflect the high degree of structural homology between the two proteins, with the NTDs, RBDs, subdomains 1 and 2 (SD1 and SD2), and S2 subunits yielding individual RMSD values of 2.6 Å, 3.0 Å, 2.7 Å, and 2.0 Å, respectively (Fig. 2C). Fig. 2 Structural comparison between 2019-nCoV S and SARS-CoV S. (A) Single protomer of 2019-nCoV S with the RBD in the down conformation (left) is shown in ribbons colored according to Fig. 1. A protomer of 2019-nCoV S in the RBD up conformation is shown (center) next to a protomer of SARS-CoV S in the RBD up conformation (right), displayed as ribbons and colored white (PDB ID: 6CRZ). (B) RBDs of 2019-nCoV and SARS-CoV aligned based on the position of the adjacent NTD from the neighboring protomer. The 2019-nCoV RBD is colored green and the SARS-CoV RBD is colored white. The 2019-nCoV NTD is colored blue. (C) Structural domains from 2019-nCoV S have been aligned to their counterparts from SARS-CoV S as follows: NTD (top left), RBD (top right), SD1, and SD2 (bottom left), and S2 (bottom right). 2019-nCoV S shares 98% sequence identity with the S protein from the bat coronavirus RaTG13, with the most notable variation arising from an insertion in the S1/S2 protease cleavage site that results in an “RRAR” furin recognition site in 2019-nCoV (19) rather than the single arginine in SARS-CoV (fig. S5) (20–23). Notably, amino acid insertions that create a polybasic furin site in a related position in hemagglutinin proteins are often found in highly virulent avian and human influenza viruses (24). In the structure reported here, the S1/S2 junction is in a disordered, solvent-exposed loop. In addition to this insertion of residues in the S1/S2 junction, 29 variant residues exist between 2019-nCoV S and RaTG13 S, with 17 of these positions mapping to the RBD (figs. S5 and S6). We also analyzed the 61 available 2019-nCoV S sequences in the Global Initiative on Sharing All Influenza Data database (https://www.gisaid.org/) and found that there were only nine amino acid substitutions among all deposited sequences. Most of these substitutions are relatively conservative and are not expected to have a substantial effect on the structure or function of the 2019-nCoV S protein (fig. S6). Recent reports demonstrating that 2019-nCoV S and SARS-CoV S share the same functional host cell receptor, angiotensin-converting enzyme 2 (ACE2) (22, 25–27), prompted us to quantify the kinetics of this interaction by surface plasmon resonance. ACE2 bound to the 2019-nCoV S ectodomain with ~15 nM affinity, which is ~10- to 20-fold higher than ACE2 binding to SARS-CoV S (Fig. 3A and fig. S7) (14). We also formed a complex of ACE2 bound to the 2019-nCoV S ectodomain and observed it by negative-stain EM, which showed that it strongly resembled the complex formed between SARS-CoV S and ACE2 that has been observed at high resolution by cryo-EM (Fig. 3B) (14, 28). The high affinity of 2019-nCoV S for human ACE2 may contribute to the apparent ease with which 2019-nCoV can spread from human to human (1); however, additional studies are needed to investigate this possibility. Fig. 3 2019-nCoV S binds human ACE2 with high affinity. (A) Surface plasmon resonance sensorgram showing the binding kinetics for human ACE2 and immobilized 2019-nCoV S. Data are shown as black lines, and the best fit of the data to a 1:1 binding model is shown in red. (B) Negative-stain EM 2D class averages of 2019-nCoV S bound by ACE2. Averages have been rotated so that ACE2 is positioned above the 2019-nCoV S protein concerning the viral membrane. A diagram depicting the ACE2-bound 2019-nCoV S protein is shown (right) with ACE2 in blue and S protein protomers colored tan, pink, and green. The overall structural homology and shared receptor usage between SARS-CoV S and 2019-nCoV S prompted us to test published SARS-CoV RBD-directed monoclonal antibodies (mAbs) for crossreactivity to the 2019-nCoV RBD (Fig. 4A). A 2019-nCoV RBD-SD1 fragment (S residues 319 to 591) was recombinantly expressed, and appropriate folding of this construct was validated by measuring ACE2 binding using biolayer interferometry (BLI) (Fig. 4B). Cross-reactivity of the SARS-CoV RBD-directed mAbs S230, m396, and 80R was then evaluated by BLI (12, 29–31). Despite the relatively high degree of structural homology between the 2019-nCoV RBD and the SARS-CoV RBD, no binding to the 2019nCoV RBD could be detected for any of the three mAbs at the concentration tested (1 μM) (Fig. 4C), in contrast to the strong binding that we observed to the SARS-CoV RBD (fig. S8). Although the epitopes of these three antibodies represent a relatively small percentage of the surface area of the 2019-nCoV RBD, the lack of observed binding suggests that SARS-directed mAbs will not necessarily be cross-reactive and that future antibody isolation and therapeutic design efforts will benefit from using 2019-nCoV S proteins as probes. Fig. 4 Antigenicity of the 2019-nCoV RBD. (A) SARS-CoV RBD is shown as a white molecular surface (PDB ID: 2AJF), with residues that vary in the 2019-nCoV RBD colored red. The ACE2-binding site is outlined with a black dashed line. (B) Biolayer interferometry sensorgram showing binding to ACE2 by the 2019-nCoV RBD-SD1. Binding data are shown as a black line, and the best fit of the data to a 1:1 binding model is shown in red. (C) Biolayer interferometry to measure cross-reactivity of the SARS-CoV RBD-directed antibodies S230, m396, and 80R. Sensor tips with immobilized antibodies were dipped into wells containing 2019-nCoV RBD-SD1, and the resulting data are shown as a black line[7]. Crystal structure of the 2019-nCoV spike receptor-binding domain bound with the ACE2 receptor. Phylogenetic analysis on the coronavirus genomes has revealed that 2019-nCoV is a new member of the betacoronavirus genus, which includes SARS-CoV, MERS-CoV, bat SARS-related coronaviruses (SARSr-CoV), as well as others identified in humans and diverse animal species1–3,7. Bat coronavirus RaTG13 appears to be the closest relative of the 2019-nCoV sharing over 93.1% homology in the spike (S) gene. SARS-CoV and other SARSr-CoVs, however, are rather distinct with less than 80% homology1. Coronaviruses utilize the homotrimeric spike glycoprotein (S1 subunit and S2 subunit in each spike monomer) on the envelope to bind their cellular receptors. Such binding triggers a cascade events leading to the fusion between a cell and viral membranes for cell entry. Our cryo-EM studies have shown that the binding of the SARS-CoV spike to the cell receptor ACE2 induces the dissociation of the S1 with ACE2, prompting the S2 to transition from a metastable prefusion to a more stable postfusion state that is essential for membrane fusion8,9. Therefore, binding to the ACE2 receptor is a critical initial step for SARS-CoV to entry into the target cells. Recent studies also pointed to the important role of ACE2 in mediating entry of 2019-nCoV1,10. HeLa cells expressing ACE2 is susceptible to 2019-nCoV infection while those without failed to do so1. In vitro SPR experiments also showed that the binding affinity of ACE2 to the spike glycoprotein and the receptor-binding domain (RBD) are equivalent, with the former of 14.7 nM and the latter of 15.2 nM11,12. These results indicate that the RBD is the key functional component within the S1 subunit responsible for binding to ACE2. The cryo-EM structure of the 2019-nCoV spike trimer at 3.5 Å resolution has just been reported12. The coordinates are not yet available for detailed characterization. However, an inspection of the structure features presented in the uploaded manuscript on bioRxiv indicated incomplete resolution of RBD in the model, particularly for the receptor-binding motif (RBM) that interacts directly with ACE2. Computer modeling of the interaction between 2019-CoV RBD and ACE2 has identified some residues potentially involved in the actual interaction but the actual interaction remained elusive13. Furthermore, despite impressive cross-reactive neutralizing activity from serum/plasma of SARS-CoV recovered patients14, no SARS-CoV monoclonal antibodies targeted to RBD so far isolated can bind and neutralize 2019nCoV11,12. These findings highlight some intrinsic sequence and structure differences between the SARS-CoV and 2019-nCoV RBDs. The overall structure of 2019-nCoV RBD bound with ACE2 . (a) The overall topology of 2019-nCoV spike monomer. NTD, N-terminal domain. RBD, receptorbinding domain. RBM, receptor-binding motif. SD1, subdomain 1. SD2, subdomain 2. FP, fusion peptide. HR1, heptad repeat 1. HR2, heptad repeat 2. TM, transmembrane region. IC, intracellular domain. (b) Sequence and secondary structures of 2019-nCoV RBD. The RBM is colored red. (c) The overall structure of 2019-nCoV RBD bound with ACE2. ACE2 is colored green. 2019-nCoV RBD core is colored cyan and RBM is colored red. Disulfide bonds in the 2019-nCoV RBD are shown as the stick and indicated by yellow arrows. The N-terminal helix of ACE2 responsible for binding is labeled. The 2019-nCoV RBD has a twisted four-stranded antiparallel β sheet (β1, β2, β3, and β6) with short connecting helices and loops forming as the core (Fig. 1b and 1c). Between the β3 and β6 strands in the core, there is an extended insertion containing short β4 and β5 strands, α4 and α5 helices and loops (Fig. 1b and 1c). This extended insertion is the receptor-binding motif (RBM) containing most of the contacting residues of 2019-nCoV for ACE2 binding. A total of nine cysteine residues are found in the RBD, six of which forming three pairs of disulfide bonds that are resolved in the final model. Among these three pairs, two are in the core (Cys336-Cys361 and Cys379-Cys432) to help stabilize the β sheet structure (Fig. 1c) while the remaining one (Cys480-Cys488) connects loops in the distal end of the RBM (Fig. 1c). The N-terminal peptidase domain of ACE2 has two lobes, forming the peptide substrate binding site between them. The extended RBM in the 2019-nCoV RBD contacts the bottom side of the ACE2 small lobe, with a concave outer surface in the RBM accommodating the N-terminal helix of the ACE2 Structural comparisons of 2019-nCoV and SARS-CoV RBDs and their binding modes to the ACE2 receptor. (a) Alignment of the 2019-nCoV RBD (core in cyan and RBM in red) and SARS-CoV RBD (core in orange and RBM in blue) structures. (b) Structural alignment of 2019-nCoV RBD/ACE2 and SARS-CoV RBD/ACE2 complexes. 2019-nCoV RBD is colored cyan and red, its interacting ACE2 is colored green. SARS-CoV RBD is colored orange and blue, its interacting ACE2 is colored salmon. The PDB code for SARS-CoV RBD/ACE2 complex. The cradling of the ACE2 N-terminal helix by the RBM outer surface results in a large buried surface of ~1700 Å2 between the 2019-nCoV RBD and ACE2 receptor (Fig. 1c). With a distance cutoff of 4 Å, a total of 18 residues of the RBD contact 20 residues of the ACE2 (Fig. 3a and Table S2). Analysis of interface between SARS-CoV RBD and ACE2 revealed a total of 16 residues of the SARS-CoV RBD contact 20 residues of the ACE2 (Fig. 3a and Table S2). Among the 20 residues interacting with the two different RBDs, 17 are shared and most of which are located at the N-terminal helix (Fig. 2a). One prominent and common feature presented at both interfaces is the networks of hydrophilic interactions. There are 17 hydrogen bonds and 1 salt bridge at the 2019-nCoV RBD/ACE2 interface, and 12 hydrogen bonds and 2 salt bridges at the SARS-CoV RBD/ACE2 interface[8]. X-ray Structure of Main Protease of the Novel Coronavirus SARS-CoV-2 Enables Design of α-Ketoamide Inhibitors In the active site of 2019-nCoV Mpro, Cys145 and His41 form a catalytic dyad. Like in SARS-CoV Mpro and other coronavirus homologs, a buried water molecule is found hydrogen-bonded to His41; this could be considered the third component of a catalytic triad. Previously, we have designed and synthesized peptidomimetic a-keto amides as broadspectrum inhibitors of the main proteases of betacoronaviruses and alphacoronaviruses as well as the 3C proteases of enteroviruses (Zhang et al., 2020). The best of these compounds (11r; see Scheme 1) showed an EC50 of 400 picomolar against MERS-CoV in Huh7 cells as well as low micromolar EC50 values against SARS-CoV and a whole range of enteroviruses in various cell lines. To improve the half-life and the solubility of the compounds in human plasma, and to reduce the binding to plasma proteins, we have modified the compound by hiding the P3 - P2 amide bond within a pyridone ring and by replacing the cinnamoyl group. For a compound related to 11r but modified this way (compound 13a), the half-life in human plasma was increased by 50%, solubility was improved, and plasma protein binding was reduced from 99% to 94%. There was no sign of toxicity in mice. In addition, 13a showed good metabolic stability using mouse and human microsomes, with intrinsic clearance rates Clint_mouse= 32.00 μL/min/mg protein and Clint_human= 20.97 μL/min/mg protein. This means that after 30 min, around 80% for mouse and 60% for humans, respectively, of residual compound remained metabolically stable. Pharmacokinetic studies in CD-1 mice using the subcutaneous route at 20 mg/kg showed that 13a stayed in plasma for up to only 4 hrs, but was excreted via urine up to 24 hrs. The Cmax was determined at 334.50 ng/mL and the mean residence time was about 1.59 hrs. Although 13a seemed to be cleared very rapidly from plasma, it was found at 24 hrs at 135 ng/g tissue in the lung and at 52.7 ng/mL in broncheo-alveolar lavage fluid (BALF) suggesting that it was mainly distributed to tissue. In light of the current CoV outbreak, it is advisable to develop compounds with lung tropism such as 13a. However, compared to 11r, the structural modification led to some loss of inhibitory activity against the main protease of 2019-nCoV (IC50 = 2.39 ± 0.63 uM) as well as the 3C proteases of enteroviruses. To enhance the antiviral activity against betacoronaviruses of clade b (2019-nCoV and SARS-CoV), we sacrificed the goal of broad-spectrum activity including the enteroviruses for the time being and replaced the P2 cyclohexyl moiety of 13a by cyclopropyl in 13b, because the S2 pocket of the betacoronavirus main proteases shows pronounced plasticity enabling it to adapt to the shape of smaller inhibitor moieties entering this site (Zhang et al., 2020). Here we present X-ray crystal structures in two different crystal forms, at 1.95 and 2.20 Å resolution, of the complex between α-ketoamide 13b optimized this way and the Mpro of 2019-nCoV (Fig. 2). One structure is in space group C2, where both protomers of the Mpro dimer are bound by crystal symmetry to have identical conformations, the other is in space group P212121, where the two protomers are independent of each other and free to adopt different conformations. Indeed, we find that in the latter crystal structure, the key residue Glu166 adopts an inactive conformation (as evidenced by its prolonged distance from His172 and the lack of H-bonding interaction between Glu166 and the P1 moiety of the inhibitor (see below)), even though compound 13b is bound in the same mode as in molecule A. This phenomenon has also been observed, in a more pronounced form, with the SARS-CoV Mpro (Yang et al., 2003) and is consistent with the half-site activity described for this enzyme (Chen et al., 2006). In all copies of the inhibited 2019-nCoV Mpro, the inhibitor binds to the shallow substrate-binding site at the surface of each protomer, between domains I and II (Fig. 2). Chemical structures of α-ketoamide inhibitors 11r, 13a, and 13b Fig. 2: Compound 13b in the substrate-binding cleft located between domains I and II of the Mpro, in the monoclinic crystal form (space group C2). 2Fo-Fc electron density is shown for the inhibitor (contouring level: 1σ). Carbon atoms of the inhibitor are magenta, oxygens red, nitrogens blue, and sulfur yellow. Note the interaction between the N-terminal residue of chain B, S1*, and E166 of chain A. Through the nucleophilic attack of the catalytic Cys145 onto the a-keto group of the inhibitor, a thiohemiketal is formed in a reversible reaction. This is reflected in the electron density (Fig. 2); the stereochemistry of this chiral moiety is S in all three copies of compound 13b in these structures. The oxyanion (or hydroxyl) group of this thiohemiketal is stabilized by a hydrogen bond from His41, whereas the amide oxygen of 13b accepts a hydrogen bond from the main-chain amides of Gly143, Cys145, and partly Ser144, which form the canonical “oxyanion hole” of the cysteine protease[9]. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. MERS-CoV was suggested to originate from bats but the reservoir host fueling spillover to humans is unequivocally dromedary camels (Haagmans et al., 2014; Memish et al., 2013). Both SARS-CoV and SARS-CoV-2 are closely related to each other and originated in bats which most likely serve as a reservoir host for these two viruses (Ge et al., 2013; Hu et al., 2017; Li et al., 2005b; Yang et al., 2015a; Zhou et al., 2020). Whereas palm civets and raccoon dogs have been recognized as an intermediate host for zoonotic transmission of SARS-CoV between bats and humans (Guan et al., 2003; Kan et al., 2005; Wang et al., 2005), the intermediate host of SARS-CoV-2 remains unknown. The recurrent spillovers of coronaviruses in humans along with detection of numerous coronaviruses in bats, including many SARS-related coronaviruses (SARSr-CoVs), suggest that future zoonotic transmission events may continue to occur (Anthony et al., 2017; Ge et al., 2013; Hu et al., 2017; Li et al., 2005b; Menachery et al., 2015; Menachery et al., 2016; Yang et al., 2015a; Zhou et al., 2020). In addition to the highly pathogenic zoonotic pathogens SARS-CoV, MERS-CoV, and SARS-CoV-2, all belonging to the β-coronavirus genus, four low pathogenicity coronaviruses are endemic in humans: HCoV-OC43, HCoV-HKU1, HCoV-NL63, and HCoV-229E.To date, no therapeutics or vaccines are approved against any human infecting coronaviruses. Coronavirus entry into host cells is mediated by the transmembrane spike (S) glycoprotein that forms homotrimers protruding from the viral surface (Tortorici and Veesler, 2019). S comprises two functional subunits responsible for binding to the host cell receptor (S1 subunit) and fusion of the viral and cellular membranes (S2 subunit). For many CoVs, S is cleaved at the boundary between the S1 and S2 subunits which remain non-covalently bound in the prefusion conformation (Belouzard et al., 2009; Bosch et al., 2003; Burkard et al., 2014; Kirchdoerfer et al., 2016; Millet and Whittaker, 2014, 2015; Park et al., 2016; Walls et al., 2016a). The distal S1 subunit comprises the receptor-binding domain(s) and contributes to the stabilization of the prefusion state of the membrane-anchored S2 subunit that contains the fusion machinery (Gui et al., 2017; Kirchdoerfer et al., 2016; Pallesen et al., 2017; Song et al., 2018; Walls et al., 2016a; Walls et al., 2017b; Yuan et al., 2017). For all CoVs, S is further cleaved by host proteases at the so-called S2’ site located immediately upstream of the fusion peptide (Madu et al., 2009; Millet and Whittaker, 2015). This cleavage has been proposed to activate the protein for membrane fusion via extensive irreversible conformational changes (Belouzard et al., 2009; HealdSargent and Gallagher, 2012; Millet and Whittaker, 2014, 2015; Park et al., 2016; Walls et al., 2017b). As a result, coronavirus entry into susceptible cells is a complex process that requires the concerted action of receptor-binding and proteolytic processing of the S protein to promote virus-cell fusion. Different coronaviruses use distinct domains within the S1 subunit to recognize a variety of attachment and entry receptors, depending on the viral species. Endemic human coronaviruses OC43 and HKU1 attach via their S domain A (SA) to 5-N-acetyl-9-O-acetyl-sialosides found on glycoproteins and glycolipids at the host cell surface to enable entry into susceptible cells (Hulswit et al., 2019; Tortorici et al., 2019; Vlasak et al., 1988). MERS-CoV S, however, uses domain A to engage non-acetylated sialosides as attachment receptors (Li et al., 2017; Park et al., 2019) and promote subsequent binding of domain B (SB) to the entry receptor, dipeptidyl-peptidase 4 (Lu et al., 2013; Raj et al., 2013). SARS- and several SARS-related coronaviruses (SARSr-CoV) interact directly with angiotensin-converting enzyme 2 (ACE2) via SB to enter target cells (Ge et al., 2013; Kirchdoerfer et al., 2018; Li et al., 2005a; Li et al., 2003; Song et al., 2018; Yang et al., 2015a). As the coronavirus S glycoprotein is surface-exposed and mediates entry into host cells, it is the main target of neutralizing antibodies (Abs) upon infection and the focus of therapeutic and vaccine design. S trimers are extensively decorated with N-linked glycans that are important for proper folding (Rossen et al., 1998) and to modulate accessibility to host proteases and neutralizing antibodies (Walls et al., 2016b; Walls et al., 2019; Xiong et al., 2017; Yang et al., 2015b). We previously characterized potent human neutralizing Abs from rare memory B cells of individuals infected with SARS-CoV (Traggiai et al., 2004) or MERS-CoV (Corti et al., 2015) in complex with SARS-CoV S and MERS-CoV S to provide molecular-level information of the mechanism of competitive inhibition of SB attachment to the host receptor (Walls et al., 2019). The S230 anti-SARS-CoV antibody also acted by functionally mimicking receptor-attachment and promoting spike fusogenic conformational rearrangements through a ratcheting mechanism that elucidated the unique nature of the coronavirus membrane fusion activation (Walls et al., 2019)[10]. We report here that ACE2 could mediate SARS-CoV-2 S-mediated entry into cells, establishing it as a functional receptor for this newly emerged coronavirus. The SARS-CoV-2 SB engages human ACE2 (hACE2) with comparable affinity than SARS-CoV SB from viral isolates associated with the 2002-2003 epidemic (i.e. binding with high affinity to hACE2). Tight binding to hACE2 could explain the efficient transmission of SARS-CoV-2 in humans, as was the case for SARS-CoV. We identified the presence of an unexpected furin cleavage site at the S1/S2 boundary of SARS-CoV-2 S, which is cleaved during biosynthesis, a novel feature setting this virus apart from SARS-CoV and SARSr-CoVs. Abrogation of this cleavage motif moderately affected SARS-CoV-2 S-mediated entry into VeroE6 or BHK cells but may contribute to expanding the tropism of this virus, as reported for several highly pathogenic avian influenza viruses and pathogenic Newcastle disease virus (Klenk and Garten, 1994; Steinhauer, 1999). We determined a cryo-electron microscopy structure of the SARS-CoV-2 S ectodomain trimer and reveal that it adopts multiple SB conformations that are reminiscent of previous reports on both SARS-CoV S and MERS-CoV S. Finally, we show that SARS-CoV S mouse polyclonal sera potently inhibited entry into target cells of SARS-CoV-2 S pseudotyped viruses. Collectively, these results pave the way for designing vaccines eliciting broad protection against SARS-CoV-2, SARS-CoV, and SARSr-CoV. RESULTS ACE2 is an entry receptor for SARS-CoV-2 The SARS-CoV-2 S glycoprotein shares ~80% amino acid sequence identity with the SARS-CoV S Urbani and with bat SARSr-CoV ZXC21 S and ZC45 S glycoprotein. The latter two SARSr-CoV sequences were identified from Rinolophus sinicus (Chinese horseshoe bats), the species from which SARSr-CoV WIV-1 and WIV-16 were isolated (Ge et al., 2013; Yang et al., 2015a). Furthermore, Zhou et al recently reported that SARS-CoV-2 is most closely related to the bat SARSr-CoV RaTG13 with which it forms a distinct lineage from other SARSr-CoVs, and that their S glycoproteins share 98% amino acid sequence identity (Zhou et al., 2020). SARS-CoV recognizes its entry receptor human ACE2 (hACE2) at the surface of type II pneumocytes, using SB which shares ~75% overall amino acid sequence identity with SARS-CoV-2 SB and 50% identity within their receptor-binding motifs (RBMs) (Li et al., 2005a; Li et al., 2003; Li et al., 2005c; Wan et al., 2020). Previous studies also showed that the host proteases cathepsin L and TMPRSS2 prime SARS-CoV S for membrane fusion through cleavage at the S1/S2 and at the S2’ sites (Belouzard et al., 2009; Bosch et al., 2008; Glowacka et al., 2011; Matsuyama et al., 2010; Millet and Whittaker, 2015; Shulla et al., 2011). We set out to investigate the functional determinants of S-mediated entry into target cells using a murine leukemia virus (MLV) pseudotyping system (Millet and Whittaker, 2016). To assess the ability of SARS-CoV-2 S to promote entry into target cells, we first compared the transduction of SARS-CoV-2 SMLV and SARS-CoV S-MLV into VeroE6 cells, that are known to express ACE2 and support SARSCoV replication (Drosten et al., 2003; Ksiazek et al., 2003). Both pseudoviruses entered cells equally well (Fig. 1 A), suggesting that SARS-CoV-2 S-MLV could potentially use African green monkey ACE2 as an entry receptor. To confirm these results, we evaluated entry into BHK cells and observed that transient transfection with hACE2 rendered them susceptible to transduction with SARS-CoV-2 SMLV (Fig. 1 B). These results demonstrate hACE2 is a functional receptor for SARS-CoV-2, in agreement with recently reported findings (Hoffmann et al., 2020; Letko and Munster, 2020; Zhou et al., 2020). • Download figure • Open in new tab Figure 1.hACE2 is a functional receptor for SARS-CoV-2 S. A. Entry of MLV pseudotyped with SARS-CoV-2 S, SARS-CoV-2 Sfur/mut and SARS-CoV S in VeroE6 cells. B. Entry of MLV pseudotyped with SARS-CoV-2 S or SARS-CoV-2 Sfur/mut in BHK cells transiently transfected with hACE2. The experiments were carried out in triplicate with two independent pseudovirus preparations and a representative experiment is shown. C. Sequence alignment of SARSCoV-2 S with multiple related SARS-CoV and SARSr-CoV S glycoproteins reveals the introduction of an S1/S2 furin cleavage site in this novel coronavirus. Identical and similar positions are respectively shown with white or red font. The four amino acid residue insertion at SARS-CoV-2 S positions 690-693 is indicated with periods. The entire sequence alignment is presented in Fig. S1. D. Western blot analysis of SARS-CoV-2 S-MLV, SARS-CoV-2 Sfur/mut-MLV and SARS-CoV S-MLV pseudovirions using an anti-SARS-CoV S2 antibody. Sequence analysis of SARS-CoV-2 S reveals the presence of a four amino acid residue insertion at the boundary between the S1 and S2 subunits compared to SARS-CoV S and SARSr-CoV S (Fig. 1 C). This results in the introduction of a furin cleavage site, a feature conserved among the 103 SARS-CoV-2 isolates sequenced to date but not in the closely related RaTG13 S (Zhou et al., 2020). Using Western blot analysis, we observed that SARS-CoV-2 S was virtually entirely processed at the S1/S2 site during biosynthesis in HEK293T cells, presumably by furin in the Golgi compartment (Fig. 1 D). This observation contrasts with SARS-CoV S which was incorporated into pseudovirions largely uncleaved (Fig. 1 D). To study the influence on pseudovirus entry of the SARS-CoV-2 S1/S2 furin cleavage site, we designed an S mutant lacking the four amino acid residue insertion and the furin cleavage site by mutating Q686TNSPRRAR↓SV696 (wildtype SARS-CoV-2 S) to Q686TILR↓SV692 (SARS-CoV-2 Sfur/mut). SARS-CoV-2 Sfur/mut preserves only the conserved Arg residue at position 994 of wildtype SARS-CoV-2 S thereby mimicking the S1/S2 cleavage site of the related SARSrCoV S CZX21 (Fig. 1 D). SARS-CoV-2 Sfur/mut is therefore expected to undergo processing at the S1/S2 site upon encounter of a target cell, similar to SARS-CoV S and SARSr-CoV S (i.e. via TMPRSS2 and/or cathepsin L). As expected, SARS-CoV-2 Sfur/mut-MLV harbored uncleaved S upon budding (Fig. 1 D). The observed transduction efficiency of VeroE6 cells was higher for SARS-CoV-2 Sfur/mut-MLV than for SARS-CoV-2 S-MLV Fig. 1 A) whereas the opposite trend was observed for transduction of hACE2expressing BHK cells (Fig. 1 B). These results suggest that S1/S2 cleavage during S biosynthesis was not necessary for S-mediated entry in the conditions of our experiments (Fig. 1 C-D). We speculate that the detection of a polybasic cleavage site in the fusion glycoprotein of SARS-CoV-2 could putatively expand its tropism and/or enhance its transmissibility, compared to SARS-CoV and SARSr-CoV isolates, due to the near-ubiquitous distribution of furin-like proteases and their reported effects on other viruses (Klenk and Garten, 1994; Millet and Whittaker, 2015; Steinhauer, 1999). SARS-CoV-2 recognizes human ACE2 with comparable affinity than SARS-CoV. The binding affinity of SARS-CoV for hACE2 correlates with the overall rate of viral replication in distinct species, transmissibility and disease severity (Guan et al., 2003; Li et al., 2004; Li et al., 2005c; Wan et al., 2020). Indeed, specific SB mutations enabled efficient binding to hACE2 of SARSCoV isolates from the three phases of the 2002-2003 epidemic, which were associated with marked disease severity (Consortium, 2004; Kan et al., 2005; Li et al., 2005c; Sui et al., 2004). In contrast, SARS-CoV isolates detected during the brief 2003-2004 re-emergence interacted more weakly with hACE2, but tightly with civet ACE2, and had low pathogenicity and transmissibility (Consortium, 2004; Kan et al., 2005; Li et al., 2005c). The architecture of the SARS-CoV-2 spike glycoprotein trimer To enable single-particle cryoEM study of the SARS-CoV-2 S glycoprotein, we designed a prefusion stabilized ectodomain trimer construct with an abrogated furin S1/S2 cleavage site (Tortorici et al., 2019; Walls et al., 2017a; Walls et al., 2016a; Walls et al., 2019), two consecutive proline stabilizing mutations (Kirchdoerfer et al., 2018; Pallesen et al., 2017) and a C-terminal fold on trimerization domain (Miroshnikov et al., 1998). 3D classification of the cryoEM data revealed the presence of multiple conformational states of SARS-CoV-2 S corresponding to distinct organization of the SB domains within the S1 apex. Approximately half of the particle images selected correspond to trimers harboring a single SB domain opened whereas the remaining half was accounted for by closed trimers with the 3 SB domains closed. The observed conformational variability of SB domains is reminiscent of observations made with SARS-CoV S and MERS-CoV S trimers although we did not detect trimers with two SB domains open and the distribution of particles across the S conformational landscape varies among studies (Gui et al., 2017; Kirchdoerfer et al., 2018; Pallesen et al., 2017; Song et al., 2018; Walls et al., 2019; Yuan et al., 2017). We determined reconstruction of the closed SARS-CoV-2 S ectodomain trimer at 3.3 Å resolution (applying 3-fold symmetry) and an asymmetric reconstruction of the trimer with a single SB domain opened at 3.7 Å resolution (Fig 3 A-H, Fig S2, Table 2). The S2 fusion machinery is the best-resolved part of the map whereas the SA and SB domains are less well resolved, presumably due to conformational heterogeneity. The final atomic model comprises residues 36-1157, with internal breaks corresponding to flexible regions, and lacks the C-terminal most segment (including the heptad repeat 2) which is not visible in the map, as is the case for all S structures determined to date. Figure 3.CryoEM structures of the SARS-CoV-2 S glycoprotein. A-B. Two orthogonal views of the closed SARS-CoV-2 S trimer cryoEM map. C. Atomic model of the closed SARS-CoV-2 S trimer in the same orientation as in panel A. D-E. Two orthogonal views of the partially open SARS-CoV-2 S trimer cryoEM map (one SB domain is open). F. Atomic model of the closed SARS-CoV-2 S trimer in the same orientation as in panel D. The glycans were omitted for clarity. References. [1] “Genomic epidemiology of novel coronavirus (hCoV-19),” 2020. [Online]. Available: https://nextstrain.org/ncov?dmax=2020-01-02&dmin=2019-12-26&l=radial. [2] D. L. R. Oscar A. MacLean*, Richard Orton, Joshua B. Singer and M.-U. of G. C. for V. R. (CVR)., “Response to ‘On the origin and continuing evolution of SARS-CoV-2,’” 2020. [Online]. Available: http://virological.org/t/response-to-on-the-origin-and-continuing-evolution-of-sars-cov2/418. [3] N. E. Tran Thi Nhu Thao, Fabien Labroussaa, “Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform,” 2020. [4] J. L. Xiaolu Tang, Changcheng Wu, Xiang Li, Yuhe Song, Xinmin Yao, Xinkai Wu, Yuange Duan, Hong Zhang, Yirong Wang, Zhaohui Qian, Jie Cui, “On the origin and continuing evolution of SARS-CoV-2,” 2020. [5] U. of Glasgow, “Amino acid replacements,” 2020. [6] M. * Prof Chaolin Huang et al., “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” 2020. [7] J. S. M. Daniel Wrapp1,*, Nianshuang Wang1,*, Kizzmekia S. Corbett2, Jory A. Goldsmith1, Ching-Lin Hsieh1, Olubukola Abiona2, Barney S. Graham2, “Cryo-EM structure of the 2019nCoV spike in the prefusion conformation,” 2020. [8] X. W. Jun Lan, Jiwan Ge, Jinfang Yu, Sisi Shan, Huan Zhou, Shilong Fan, Qi Zhang, Xuanling Shi, Qisheng Wang, Linqi Zhang, “Crystal structure of the 2019-nCoV spike receptor-binding domain bound with the ACE2 receptor,” 2020. [9] V. O. P. H. Linlin Zhang, Daizong Lin, Xinyuanyuan Sun, Katharina Rox, “X-ray Structure of Main Protease of the Novel Coronavirus SARS-CoV-2 Enables Design of α-Ketoamide Inhibitors,” 2020. [10] D. V. Alexandra C. Walls, Young-Jun Park, M. Alexandra Tortorici, Abigail Wall, Andrew T. McGuire, “Structure, function and antigenicity of the SARS-CoV-2 spike glycoprotein,” 2020.