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3. Além disso, estudar o material genético do vírus nos ajuda a compreender sua infectividade e transmissão em humanos.
4. E por último, desmitificar teorias em que o vírus possa ter sido manipulado propositalmente em laboratórios!

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Mas qual a importância de saber de onde o vírus surgiu?

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Mas qual a importância de saber de onde o vírus surgiu?

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Muitos podem questionar o porquê de saber de onde surgiu, uma vez que este já se espalhou. Resolvi listar alguns motivos importantes:
1. Saber como um vírus que circulava em animais se tornou capaz de infectar humanos de forma tão eficaz, ajudará a prevenir futuros eventos zoonóticos.
2. Identificar vírus parecidos com o SARS-CoV 2 circulante em animais irá ajudar nos estudos de infecções virais como um todo.

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Um grupo de pesquisadores fez o sequenciamento do genoma total do SARS-CoV-2 dos 1ºs casos de COVID-19 no Brasil. Há forte indícios que houve múltiplas importações independentes da Itália até o início da transmissão comunitária.

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ORIGINAL ARTICLE

Importation and early local transmission of COVID-19 in Brazil, 2020

We conducted the genome sequencing and analysis of the first confirmed COVID-19 infections in Brazil. Rapid sequencing coupled with phylogenetic analyses in the context of travel history corroborate multiple independent importations from Italy and local spread during the initial stage of COVID-19 transmission in Brazil.

Key words: Public health surveillance; COVID-19; SARS-CoV-2

INTRODUCTION

The severe acute respiratory syndrome 2 (SARS-CoV-2) was first identified in Wuhan, Central China, in early December 2019, and reported to the World Health Organization (WHO) country office in China on December 31, 2019. SARS-CoV-2 infection causes coronavirus-associated acute respiratory disease in humans, a disease named corona virus disease 19 (COVID-19). COVID-19 is the third documented spill over of a coronavirus from an animal reservoir to humans in the last two decades to have caused a serious public health threat.

On January 30, 2020, COVID-19 was declared a Public Health Emergency of International Concern. On March 16, 2020, WHO reported 153,517 confirmed cases across the globe, and 5,735 confirmed deaths in 143 countries, territories or areas. Within Latin America, Brazil is the country with the largest number of confirmed cases. The country has reported 234 cases across 15 federal States; Sao Paulo, Rio de Janeiro and Bahia States have confirmed local transmission.

During the early stages of an epidemic disease, molecular surveillance can inform on the tracking and control of the virus spread across the global and at local scales. Moreover, viral genomes can help to design effective molecular diagnostics, improve vaccine design and complement the contact tracking. However, the resolution of the transmission networks reconstructed from genetic data will depend on the rate at which genetic changes accumulate across viral genomes. Within outbreaks, short timescales mean that not all the observed changes will become fixed at the population level. To investigate the early transmission dynamics of imported and local cases in Brazil, we set up a genomic observatory in Sao Paulo where we sequenced and analysed two complete SARS-CoV-2 genomes in less than 48 h after the cases confirmation. Here we investigate the transmission patterns from phylogenetic analysis of the earliest six SARS-CoV-2 cases in Brazil.

MATERIALS AND METHODS

Samples from suspected SARS-CoV-2 cases underwent confirmatory diagnostic real-time RT-PCR testing at the Instituto Adolfo Lutz (IAL), the regional reference laboratory for SARS-CoV-2 detection in Sao Paulo State, Southeast Brazil. Samples obtained from the Reference Centre for Arbovirus of Sao Paulo, Adolfo Lutz Institute (IAL) have been processed in agreement with routine surveillance activities from the Brazilian Ministry of Health.

We used the open COVID-19 sequencing available and the bioinformatics protocols developed by the ARTIC network. Sequencing protocols, multiplex PCR primers, and bioinformatic pipelines are described in detail at https://artic.network/ncov-2019. In brief, cDNA synthesis was conducted in duplicate for each sample and the concentration of PCR products was measured using a Qubit dsDNA High Sensitivity kit on a Qubit 3.0 fluorometer (Thermo Fisher Scientific, Waltham, USA). Library preparation was conducted without a barcoding step and libraries were sequenced on an R9.4.1 flow cell using MinKNOW version 19.10.1 (Oxford Nanopore Technologies, Oxford, UK) for over 12 h. The open-source software RAMPART version 10.5 was used to assign and map reads in real-time. Raw files were base-called with Guppy, demultiplexed and trimmed with Porechop (https://github.com/rrwick/Porechop) and mapped against reference sequence Wuhan-Hu-1 (GenBank Accession Number MN908947). Variants were called using nanopolish 0.11.3. Low coverage regions were masked with N characters. Coverage for the SPBR1 and SPBR2 was 96.9 and 99.6%, with 552730 and 3461754 mapped reads, respectively ( Table S1). Raw read data for SPBR1 is available for inspection from https://cadde.s3.climb.ac.uk/covid-19/BR1.sorted.bam.

We investigated the transmission dynamics of the early COVID-19 cases in Brazil (SPBR1 to SPBR6) by analysing genetic changes among the early genomes from Brazil belonging to imported and local cases, and by estimating a maximum likelihood phylogenetic tree together with a set of global reference sequences. We added the SBPBR1 and SPBR2 consensus sequences from Sao Paulo to a curated dataset of complete genomes available from GISAID that included four additional sequences from Brazil (available on GISAID of 15 th March 2020). A multiple sequence alignment comprising 347 complete genomes from several countries was generated using MAFFT and manually edited. A maximum likelihood (ML) phylogenetic tree was estimated using PhyML version 3.0 using a Hasegawa-Kishino-Yano nucleotide substitution model with a gamma-distributed rate variation across sites.

RESULTS

Four of the six patients self-reported travelling from European countries to Sao Paulo city (SPBR1 to SPBR4) ( Table S1). Two patients (SPBR5 and SPBR6) reported direct contact with SPBR1 and no travel outside Brazil. Patient SPBR1 (60-65 year old male) self-reported arriving from Italy on the February 21, 2020he started symptoms on the February 24 and tested positive two days later. Patients SPBR5 and SPBR6 were in direct contact with patient SPBR1 on February 22, and tested positive on February 29, 2020. Figure 1 shows the clade containing Brazilian sequences along with location of infection (squares) and reporting (circles). Our analyses show that the SPBR1 genome is identical to the SPBR5 and SPBR6 contacts (illustrated by zero branch lengths in Figure 1; detailed tree with annotated tips and travel history information for the clade containing Brazilian sequences can be found in the Figure S1).

We found that the SPBR1, SPBR5 and SPBR6 are identical to several other genomes circulating in Italy and elsewhere collected between February 20 and March 2, 2020 ( Figure 1). The lack of changes among SARS-CoV-2 genomes collected during this period is not surprising given the evolutionary rate of the virus that results in an average of 1 to 2 mutations per month. These data highlight the critical importance of contextualizing phylogenetic information with travel history when investigating early transmission dynamics of SARS-CoV-2. As no epidemiological information was available for SPBR5 and SPBR6, one could not exclude an alternative scenario based on sequencing data alone that would suggest additional independent introductions from Italy or elsewhere.

Patients SPBR2, SPBR3 and SPBR4 all reported travelling to Italy, where the of incidence of COVID-19 has been the highest outside Wuhan in China. Consistent with the travel history, sequences from these patients are found interspersed in the tree in agreement with the multiple independent introductions of SARS-CoV-2 to Sao Paulo from Italy. This finding highlights the key role of human mobility in the early stages of the pandemic and is in line with a recent analysis on the risk of importation of COVID-19 based on the history of air traveling data and the incidence data. Given that the air traveling to Brazil from Italy has reduced, it is possible that the proportion of SARS-CoV-2 imported cases from other countries, particularly the USA, may increase.

DISCUSSION

Our study provides a snapshot of the early establishment of the COVID-19 pandemic in Brazil, characterized by multiple independent introductions from Italy, followed by local transmission of the virus in Sao Paulo. Phylogenetic analyses are broadly consistent with the patients' self-reported traveling histories. We show that the two genomes associated with local transmission are linked to a patient infected in Italy and are identical to other Italian genomes collected in the same time window. Given the within-outbreak rate of evolutionary change estimated for SARS-CoV-2, we caution against inferring directionality of transmission based on genetic data alone. Such inferences can further be overshadowed by incomplete sampling due to delays, reflecting the lack of equitable access to diagnosis and genomic sequencing.

CONCLUSION

Given the findings of the present study, we conclude that phylogenetic data from the pandemic needs to be contextualized with appropriate metadata, including basic demographics, symptoms onset date, the sample collection date, the country of reporting and the self-reported travel history. Joint epidemiological and genomic surveillance of COVID-19 cases will be critical to rapidly identify possible clusters of local transmission in Brazil and in other countries, and to better understand and help mitigating the transmission in the community.

ACKNOWLEDGMENTS

We thank GISAID database for supporting rapid and open access real-time SARS-CoV-2 genomic data sharing. We would like to thank the authors who generated and shared data with the research community and all the patients involved, the staff and research groups who assisted with patient care, sample collection, genome sequencing and data sharing. We thank Josh Quick, Lucy Matkin and Julien Thezé for support.

REFERENCES

1. World Health Organization. Critical preparedness, readiness and response actions for COVID-19. [cited 2020 Apr 22]. Available from: https://www.who.int/publications-detail/critical-preparedness-readiness-and-response-actions-for-covid-19 [ Links ]

2. Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol. 2020 In Press. [ Links ]

3. Andersen KG, Rambaut A, Lipkin WI, Holmes EC, Garry RF. The proximal origin of SARS-CoV-2. Nat Med. 2020;26:450-2. [ Links ]

4. Brasil. Ministério da Saúde. Notificação de casos de doença pelo coronavírus 2019 (COVID-19). [cited 2020 March 15]; Available from: https://www.saude.go.gov.br/component/content/article/34-page/9895-coronav%C3%ADrus.html? highlight=WyJjb3JvbmF2XHUwMGVkcnVzIl0=&Itemid=101 [ Links ]

5. Park DJ, Dudas G, Wohl S, Goba A, Whitmer SL, Andersen KG, et al. Ebola virus epidemiology, transmission, and evolution during seven nonths in Sierra Leone. Cell. 2015;161:1516-26. [ Links ]

6. Gire SK, Goba A, Andersen KG, Sealfon RS, Park DJ, Kanneh L, et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science. 2014;345:1369-72. [ Links ]

7. Holmes EC, Dudas G, Rambaut A, Andersen KG. The evolution of Ebola virus: insights from the 2013-2016 epidemic. Nature. 2016;538:193-200. [ Links ]

8. Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 2020;25:2000045. [ Links ]

9. Katoh K, Standley DM. MAFFT: iterative refinement and additional methods. In: Russel DJ, editor. Multiple sequence alignment methods. New York: Humana; 2014. p.131-46. [ Links ]

10. Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307-21. [ Links ]

11. Rambaut A. Phylogenetic analysis of nCoV-2019 genomes. [cited 2020 Apr 22]. Available from: http://virological.org/t/phylogenetic-analysis-of-23-ncov-2019-genomes-2020-01-23/335 [ Links ]

12. World Health Organization. Coronavirus disease (COVID-2019) situation reports. [cited 2020 Apr 22]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports [ Links ]

13. Candido DS, Watts A, Abade L, Kraemer MU, Pybus OG, Croda J, et al. Routes for COVID-19 importation in Brazil. J Travel Med. 2020 In press. [ Links ]

SUPPLEMENTARY MATERIAL

We gratefully acknowledge the authors, originating and submitting laboratories of the sequences from GISAID's EpiFlu ™ Database on which this research is based. The list is detailed on Supplementary Table 2, which is available on GitHub ( https://github.com/CADDE-CENTRE/REPOSITORY/blob/master/First%20genomes%20from%20Americas.docx). All submitters of data may be contacted directly via www.gisaid.org.

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Importation and early local transmission of COVID-19 in Brazil, 2020

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ORIGINAL ARTICLE Importation and early local transmission of COVID-19 in Brazil, 2020 We conducted the genome sequencing and analysis of the first confirmed COVID-19 infections in Brazil. Rapid sequencing coupled with phylogenetic analyses in the context of travel history corroborate multiple independent importations from Italy and local spread during the initial stage of COVID-19 transmission in Brazil.

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O SARS-CoV 2 sofre mutações desde quando começou a se espalhar no fim do ano passado. E uma nova variante, a 'G' (D614G), passou a ser dominante nos EUA e no resto do mundo. Porque isso? Leia mais nesta reportagem do 'The Washington Post'

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July 2
When the first coronavirus cases in Chicago appeared in January, they bore the same genetic signatures as a germ that emerged in China weeks before.

But as Egon Ozer, an infectious-disease specialist at the Northwestern University Feinberg School of Medicine, examined the genetic structure of virus samples from local patients, he noticed something different.

A change in the virus was appearing again and again. This mutation, associated with outbreaks in Europe and New York, eventually took over the city. By May, it was found in 95 percent of all the genomes Ozer sequenced.

At a glance, the mutation seemed trivial. About 1,300 amino acids serve as building blocks for a protein on the surface of the virus. In the mutant virus, the genetic instructions for just one of those amino acids - number 614 - switched in the new variant from a "D" (shorthand for aspartic acid) to a "G" (short for glycine).

But the location was significant, because the switch occurred in the part of the genome that codes for the all-important "spike protein" - the protruding structure that gives the coronavirus its crownlike profile and allows it to enter human cells the way a burglar picks a lock.
[Stay safe and informed with our free Coronavirus Updates newsletter]
And its ubiquity is undeniable. Of the approximately 50,000 genomes of the new virus that researchers worldwide have uploaded to a shared database, about 70 percent carry the mutation, officially designated D614G but known more familiarly to scientists as "G."

"G" hasn't just dominated the outbreak in Chicago - it has taken over the world. Now scientists are racing to figure out what it means.

At least five laboratory experiments suggest that the mutation makes the virus more infectious, although only one of those studies has been peer-reviewed. That study, led by scientists at Los Alamos National Laboratory and published Thursday in the journal Cell, also asserts that patients with the G variant actually have more virus in their bodies, making them more likely to spread it to others.

The mutation doesn't appear to make people sicker, but a growing number of scientists worry that it has made the virus more contagious.

"The epidemiological study and our data together really explain why the [G variant's] spread in Europe and the U.S. was really fast," said Hyeryun Choe, a virologist at Scripps Research and lead author of an unpublished study on the G variant's enhanced infectiousness in laboratory cell cultures. "This is not just accidental."

But there may be other explanations for the G variant's dominance: biases in where genetic data are being collected, quirks of timing that gave the mutated virus an early foothold in susceptible populations.

"The bottom line is, we haven't seen anything definitive yet," said Jeremy Luban, a virologist at the University of Massachusetts Medical School.
[Coronavirus pandemic exposes layers of government failure]
The scramble to unravel this mutation mystery embodies the challenges of science during the coronavirus pandemic. With millions of people infected and thousands dying every day around the world, researchers must strike a high-stakes balance between getting information out quickly and making sure that it's right.

A better lock pick

SARS-CoV-2, the novel coronavirus that causes the disease covid-19, can be thought of as an extremely destructive burglar. Unable to live or reproduce on its own, it breaks into human cells and co-opts their biological machinery to make thousands of copies of itself. That leaves a trail of damaged tissue and triggers an immune system response that for some people can be disastrous.
[How the virus kills]
This replication process is messy. Even though it has a "proofreading" mechanism for copying its genome, the coronavirus frequently makes mistakes, or mutations. The vast majority of mutations have no effect on the behavior of the virus.

But since the virus's genome was first sequenced in January, scientists have been on the lookout for changes that are meaningful. And few genetic mutations could be more significant than ones that affect the spike protein - the virus's most powerful tool.

This protein attaches to a receptor on respiratory cells called ACE2, which opens the cell and lets the virus slip inside. The more effective the spike protein, the more easily the virus can break into the bodies of its hosts. Even when the original variant of the virus emerged in Wuhan, China, it was obvious that the spike protein on SARS-CoV-2 was already quite effective.

But it could have been even better, said Choe, who has studied spike proteins and the way they bind to the ACE2 receptor since the severe acute respiratory syndrome outbreak in 2003.

The spike protein for SARS-CoV-2 has two parts that don't always hold together well. In the version of the virus that arose in China, Choe said, the outer part - which the virus needs to attach to a human receptor - frequently broke off. Equipped with this faulty lock pick, the virus had a harder time invading host cells.

"I think this mutation happened to compensate," Choe said.

Studying both versions of the gene using a proxy virus in a petri dish of human cells, Choe and her colleagues found that viruses with the G variant had more spike proteins, and the outer parts of those proteins were less likely to break off. This made the virus approximately 10 times more infectious in the lab experiment.

The mutation does not seem to lead to worse outcomes in patients. Nor did it alter the virus's response to antibodies from patients who had the D variant, Choe said, suggesting that vaccines being developed based on the original version of the virus will be effective against the new strain.

Choe has uploaded a manuscript describing this study to the website bioRxiv, where scientists can post "preprint" research that has not yet been peer reviewed. She has also submitted the paper to an academic journal, which has not yet published it.

The distinctive infectiousness of the G strain is so strong that scientists have been drawn to the mutation even when they weren't looking for it.

Neville Sanjana, a geneticist at the New York Genome Center and New York University, was trying to figure out which genes enable SARS-CoV-2 to infiltrate human cells. But in experiments based on a gene sequence taken from an early case of the virus in Wuhan, he struggled to get that form of the virus to infect cells. Then the team switched to a model virus based on the G variant.

"We were shocked," Sanjana said. "Voilà! It was just this huge increase in viral transduction." They repeated the experiment in many types of cells, and every time the variant was many times more infectious.

Their findings, published as a preprint on bioRxiv, generally matched what Choe and other laboratory scientists were seeing.

But the New York team offers a different explanation as to why the variant is so infectious. Whereas Choe's study proposes that the mutation made the spike protein more stable, Sanjana said experiments in the past two weeks, not yet made public, suggest that the improvement is actually in the infection process. He hypothesized that the G variant is more efficient at beginning the process of invading the human cell and taking over its reproductive machinery.

Luban, who has also been experimenting with the D614G mutation, has been drawn to a third possibility: His experiments suggest that the mutation allows the spike protein to change shape as it attaches to the ACE2 receptor, improving its ability to fuse to the host cell.

Different approaches to making their model virus might explain these discrepancies, Luban said. "But it's quite clear that something is going on."

Unanswered questions

Although these experiments are compelling, they're not conclusive, said Kristian Andersen, a Scripps virologist not involved in any of the studies. The scientists need to figure out why they've identified different mechanisms for the same effect. All the studies still have to pass peer review, and they have to be reproduced using the real version of the virus.

Even then, Andersen said, it will be too soon to say that the G variant transmits faster among people.

Cell culture experiments have been wrong before, noted Anderson Brito, a computational biologist at Yale University. Early experiments with hydroxychloroquine, a malaria drug, hinted that it was effective at fighting the coronavirus in a petri dish. The drug was touted by President Trump, and the Food and Drug Administration authorized it for emergency use in hospitalized covid-19 patients. But that authorization was withdrawn this month after evidence showed that the drug was "unlikely to be effective" against the virus and posed potential safety risks.

So far, the biggest study of transmission has come from Bette Korber, a computational biologist at Los Alamos National Laboratory who helped build one of the world's biggest viral genome databases for tracking HIV. In their paper published this week, Korber's team argues that the G variant "may have a fitness advantage" that explains its overwhelming dominance.

Using two statistical approaches, the researchers analyzed some 30,000 sequences from across the world; they found that most places where the original virus was dominant before March were eventually taken over by the mutated version. This switch often took place in a matter of weeks. This was especially apparent in the United States: Ninety-six percent of early sequences here belonged to the D variant, but by the end of March, more than two thirds of sequences carried the G amino acid instead.

Korber's collaborators in the U.K. also found evidence that people with the G variant had more viral particles in their bodies. Although this higher viral load didn't seem to make people sicker, it might explain the G variant's rapid spread, the scientists wrote. People with more virus to shed are more likely to infect others.

Meanwhile, the paper describes two laboratory experiments confirming what Sanjana, Choe and others have found: the G variant was 3 to 6 times more infectious.

A preliminary version of the Los Alamos paper, which did not include these laboratory findings, drew intense scrutiny when it was released as a pre-print in the spring. Though the additional data and the credibility of peer review make the case for increased transmissibility even stronger, many scientists -- including Korber -- say more research is needed.

"There are so many biases in the data set here that you can't control for and you might not know exist," Andersen said. In a time when as many as 90 percent of U.S. infections are still undetected and countries with limited public health infrastructure are struggling to keep up with surging cases, a shortage of data means "we can't answer all the questions we want to answer."

Pardis Sabeti, a computational biologist at Harvard University and the Broad Institute, noted that the vast majority of sequenced genomes come from Europe, where the G variant first emerged, and the United States, where infections thought to have been introduced by travelers from Europe spread undetected for weeks before the country shut down. This could at least partly explain why it appears so dominant.

The mutation's success might also be a "founder effect," she said. Arriving in a place like Northern Italy - where the vast majority of sequenced infections are caused by the G variant - it found easy purchase in an older and largely unprepared population, which then unwittingly spread it far and wide.

Scientists may be able to rule out these alternative explanations with more rigorous statistical analyses or a controlled experiment in an animal population. And as studies on the D614G mutation accumulate, researchers are starting to be convinced of its significance.

"I think that slowly we're beginning to come to a consensus," said Judd Hultquist, a virologist at Northwestern University.

Solving the mystery of the D614G mutation won't make much of a difference in the short term, Andersen said. "We were unable to deal with D," he said. "If G transmits even better, we're going to be unable to deal with that one."

But it's still essential to understand how the genome influences the behavior of the virus, scientists say. Identifying emerging mutations allows researchers to track their spread. Knowing what genes affect how the virus transmits enables public health officials to tailor their efforts to contain it. Once therapeutics and vaccines are distributed on a large scale, having a baseline understanding of the genome will help pinpoint when drug resistance starts to evolve.

"Understanding how transmissions are happening won't be a magic bullet, but it will help us respond better," Sabeti said. "This is a race against time."
Correction: An initial version of this article gave an incorrect affiliation for Jeremy Luban. He is a virologist at the University of Massachusetts Medical School.

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This coronavirus mutation has taken over the world. Scientists are trying to understand why.

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July 2 When the first coronavirus cases in Chicago appeared in January, they bore the same genetic signatures as a germ that emerged in China weeks before. But as Egon Ozer, an infectious-disease specialist at the Northwestern University Feinberg School of Medicine, examined the genetic structure of virus samples from local patients, he noticed something different.

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Vírus de RNA está sempre sujeito a mutações a cada ciclo de replicação. Com isso podem surgir novas linhagens do mesmo vírus. Conheça as linhagens que existem do SARS-CoV-2 (S e a L) no paper abaixo.

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Abstract

The SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China, and has since impacted a large portion of China and raised major global concern. Herein, we investigated the extent of molecular divergence between SARS-CoV-2 and other related coronaviruses. Although we found only 4% variability in genomic nucleotides between SARS-CoV-2 and a bat SARS-related coronavirus (SARSr-CoV; RaTG13), the difference at neutral sites was 17%, suggesting the divergence between the two viruses is much larger than previously estimated. Our results suggest that the development of new variations in functional sites in the receptor-binding domain (RBD) of the spike seen in SARS-CoV-2 and viruses from pangolin SARSr-CoVs are likely caused by natural selection besides recombination. Population genetic analyses of 103 SARS-CoV-2 genomes indicated that these viruses had two major lineages (designated L and S), that are well defined by two different SNPs that show nearly complete linkage across the viral strains sequenced to date. We found that L lineage was more prevalent than the S lineage within the limited patient samples we examined. The implication of these evolutionary changes on disease etiology remains unclear. These findings strongly underscores the urgent need for further comprehensive studies that combine viral genomic data, with epidemiological studies of coronavirus disease 2019 (COVID-19).

INTRODUCTION

SARS-CoV-2 was detected in late December 2019 in Wuhan, the capital of Central China's Hubei Province. Since then, it has rapidly spread across China and in other countries, raising major global concerns. This novel coronavirus, SARS-CoV-2, was named for the similarity of its structure to severe acute respiratory syndrome related coronaviruses. As of February 28, 2020, 78,959 cases of SARS-CoV-2 infection have been confirmed in China, with 2,791 deaths. Worryingly, there have also been more than 3,664 confirmed cases outside of China in 46 countries and areas ( https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/), raising significant issues for successful containment. Further, the genomic sequences of SARS-CoV-2 viruses isolated from a number of patients share sequence identity higher than 99.9%, suggesting a very recent host shift into humans [ 1-3].

Coronaviruses are naturally hosted and evolutionarily shaped by bats [ 4, 5]. Indeed, it has been postulated that most of the coronaviruses in humans are derived from the bat reservoir [ 6, 7]. Several teams have recently confirmed the genetic similarity between SARS-CoV-2 and a bat betacoronavirus of the sub-genus Sarbecovirus [ 8-13]. The whole-genome sequence of the novel virus has 96.2% similarity to that of a bat SARS-related coronavirus (SARSr-CoV; RaTG13) collected in Yunnan province, China [ 2, 14], but has low similarity to that of SARS-CoV (about 79%) or MERS-CoV (about 50%) [ 1, 15]. It has also been confirmed that the SARS-CoV-2 uses the same receptor, the angiotensin converting enzyme II (ACE2), as the SARS-CoV [ 2]. Although the specific route of transmission from natural reservoirs to humans remains unclear [ 5, 13], several studies have shown that pangolins may have provided a partial spike gene to SARS-CoV-2; the critical functional sites in the spike protein of SARS-CoV-2 are nearly identical to those identified in a virus isolated from a pangolin [ 16-18].

Despite these recent discoveries, several fundamental issues related to the evolutionary patterns and driving forces behind this outbreak of SARS-CoV-2 remain to be fully characterized [ 19-21]. Herein, we investigated the extent of molecular divergence between SARS-CoV-2 and other related coronaviruses and carried out population genetic analyses of 103 sequenced genomes of SARS-CoV-2. This work provides new insights into evolution of SARS-CoV-2 and its pattern of spread through the human population.

RESULTS

Molecular phylogeny and divergence between SARS-CoV-2 and related coronaviruses

For each annotated ORF in the reference genome of SARS-CoV-2 (NC_045512), we extracted the orthologous sequences in human SARS-CoV, four bat SARS-related coronaviruses (SARSr-CoV: RaTG13, ZXC21, ZC45, and BM48-31), one pangolin SARSr-CoV from Guangdong (GD), and six pangolin SARSr-CoV genomes from Guangxi (GX) [ 18] (Table S1). We aligned the coding sequences (CDSs) based on the protein alignments (see Materials and Methods). Most ORFs annotated from SARS-CoV-2 were found to be conserved in other viruses, except for ORF8 and ORF10 (Table 1). The protein sequence of SARS-CoV-2 ORF8 shared very low similarity with those sequences in SARS-CoV and BM48-31, and ORF10 had a premature stop codon in both SARS-CoV and BM48-31 (Fig. S1). A one-base deletion caused a frame-shift mutation in ORF10 of ZXC21 (Fig. S1).

To investigate the phylogenetic relationship between these viruses at the genomic scale, we concatenated coding regions (CDSs) of the nine conserved ORFs ( orf1ab, E, M, N, S, ORF3a, ORF6, ORF7a, and ORF7b) and reconstructed the phylogenetic tree using the synonymous sites (Fig. 1A). We also used CODEML in the PAML [ 22] to infer the ancestral sequence of each node and calculated the dN (nonsynonymous substitutions per nonsynonymous site), dS (synonymous substitutions per synonymous site), and dN/dS (ω) values for each branch (Fig. 1A). In parallel, we also calculated the pairwise dN, dS, and ω values between SARS-CoV-2 and another virus (Table 1).

The genome-wide phylogenetic tree indicated that SARS-CoV-2 was closest to RaTG13, followed by GD Pangolin SARSr-CoV, then by GX Pangolin SARSr-CoVs, then by ZC45 and ZXC21, then by human SARS-CoV, and finally by BM48-31(Fig. 1A). Notably, we found that the nucleotide divergence at synonymous sites between SARS-CoV-2 and other viruses was much higher than previously anticipated. For example, although the overall genomic nucleotides differ ∼4% between SARS-CoV-2 and RaTG13, the genomic average dS was 0.17, which means the divergence at the neutral sites is 17% between these two viruses (Table 1). Note that nonsynonymous sites are usually under stronger negative selection than synonymous sites, and calculating sequence differences without separating these two classes of sites may underestimate the extent of molecular divergence by several folds.

We found that the dS value varied considerably across genes in SARS-CoV-2 and the other viruses analyzed. In particular, the spike gene ( S) consistently exhibited larger dS values than other genes (Table 1). This pattern became clear when we calculated the dS value for each branch in Fig. 1A for the spike gene versus the concatenated sequences of the remaining genes (Fig. S2). In each branch, the dS of spike was 2.29 ± 1.45 (mean ± SD) times as large as that of the other genes. This extremely elevated dS value of spike could be caused either by a high mutation rate or by natural selection that favors synonymous substitutions. Synonymous substitutions may serve as another layer of genetic regulation, guiding the efficiency of mRNA translation by changing codon usage [ 23]. If positive selection is the driving force for the higher synonymous substation rate seen in spike, we expect the frequency of optimal codons (FOP) of spike to be different from that of other genes. However, our codon usage bias analysis (Table S2) suggests the FOP of spike was only slightly higher than that of the genomic average (0.717 versus 0.698, see Materials and Methods). Thus, we believe that the elevated synonymous substitution rate measured in spike is more likely caused by higher mutational rates; however, the underlying molecular mechanism remains unclear.

Both SARS-CoV and SARS-CoV-2 bind to ACE2 through the RBD of the spike protein in order to initiate membrane fusion and enter human cells [ 1, 2, 24-28]. Five out of the six critical amino acid (AA) residues in RBD were different between SARS-CoV-2 and SARS-CoV (Fig. 1B), and a 3D structural analysis indicated that the spike of SARS-CoV-2 had a higher binding affinity to ACE2 than SARS-CoV [ 25]. Intriguingly, these same six critical AAs are identical between GD Pangolin-CoV and SARS-CoV-2 [ 16]. In contrast, although the genomes of SARS-CoV-2 and RaTG13 are more similar overall, only one out of the six functional sites are identical between the two viruses (Fig. 1B). It has been proposed that the SARS-CoV-2 RBD region of the spike protein might have resulted from recent recombination events in pangolins [ 16-18]. Although several ancient recombination events have been described in spike [ 29, 30], it also seems likely that the identical functional sites in SARS-CoV-2 and GD Pangolin-CoV may actually result from coincidental convergent evolution [ 18].

If the functional AA residues in the SARS-CoV-2 RBD region were acquired from GD Pangolin-CoV in a very recent recombination event, we would expect the nucleotide sequences of this region to be nearly identical between the two viruses. However, for the CDS sequences that span five critical AA sites in the SARS-CoV-2 spike (ranging from codon 484 to 507, covering five adjacent functional sites: F486, Q493, S494, N501, and Y505; Fig. S3), we estimated dS = 0.411, dN = 0.019, and ω = 0.046 between SARS-CoV-2 and GD Pangolin-CoV. By assuming the synonymous substitution rate ( u) of 1.67-4.67 × 10 −3/site/year, as estimated in SARS-CoV [ 31], the recombination/introgression, if it occurred at all, would be estimated to happen approximately 19.2-53.7 years ago. Here, the formula |$t\ = \ dS/( {u \times \ 2\ \times \ 2.29} )$| was used to calculate divergence time; note that the increased mutational rate of spike was considered for this calculation. Thus, it seems very unlikely that SARS-CoV-2 originated from the GD Pangolin-CoV due to a very recent recombination event. Rather, it seems more likely that a high mutation rate in spike, coupled with strong natural selection, has shaped the identical functional AA residues between these two viruses, as proposed previously [ 18]. Although these sites are maintained in SARS-CoV-2 and GD Pangolin-CoV, mutations may have changed the residues in the RaTG13 lineage after it diverged from SARS-CoV-2 (the blue arrow in Fig. 1 A). In summary, the shared identity of critical AA sites between SARS-CoV-2 and GD Pangolin-CoV may be due to random mutations coupled with natural selection, rather than recombination.

Selective constraints and positive selection during the evolution of SARS-CoV-2 and related coronaviruses

The genome-wide ω value between SARS-CoV-2 and other viruses ranged from 0.044 to 0.124 (Table 1), indicative of strong negative selection on the nonsynonymous sites. In other words, 87.6% to 95.6% of the nonsynonymous mutations were removed by negative selection during viral evolution. To determine the extent of positive selection, we concatenated the CDS sequences of 9 conserved ORFs in all the viruses in Fig. 1A and fitted the M7 (beta: neutral and negative selection) and M8 (beta + ω > 1: neutral, negative selection, and positive selection) model using CODEML (Materials and Methods). The M8 model ( lnL = −104,813.732, np = 18) was a significantly better fit than the M7 ( lnL = −105,063.284, np = 16) model ( P < 10 −10), suggesting that some AA substitutions were favored by positive Darwinian selection (but not necessarily in the SARS-CoV-2 lineage). Under the M8 model, 98.48% () of the nonsynonymous substitutions were estimated under neutral evolution or purifying selection (0 ≤ ω ≤1), and 1.52% () of the nonsynonymous substitutions were under positive selection (ω = 1.50). A Bayes Empirical Bayes (BEB) analysis suggested that 10 AA sites showed strong signals of positive selection, and, interestingly, three of these were located in the RBD of spike, including at one critical site (Fig. 1 C and Fig. S4). Thus, although these coronaviruses were generally under very strong negative selection, positive selection was also responsible for the evolution of protein sequences. These putatively positively-selected sites deserve further functional studies.

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 are shown in Fig. 2.

Most derived mutations were singletons (65.1% (28/43) of synonymous mutations and 84.3% (70/83) of nonsynonymous mutations), indicating either a recent origin [ 32] or population growth [ 33]. 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)) mutation had a derived frequency of ≥ 70% across the SARS-CoV-2 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).

Two major lineages of SARS-CoV-2 defined by two linked SNPs

To detect the possible recombination among SARS-CoV-2 viruses, we used Haploview [ 34] 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 or LOD value (Fig. 3B and C). Consistent with a recent report [ 33], we did not find evidence of recombination between the SARS-CoV-2 strains.

However, we found that SNPs at location 8,782 ( orf1ab: T8517C, synonymous) and 28,144 ( ORF8: C251T, S84L) showed significant linkage, with an value of 0.954 (Fig. 3B, red) and a LOD value of 50.13 (Fig. 3 C, red). Among the 103 SARS-CoV-2 virus strains, 101 of them exhibited complete linkage between the two SNPs: 72 strains exhibited a 'CT' haplotype (defined as 'L' lineage because T28,144 is in the codon of Leucine) and 29 strains exhibited a 'TC' haplotype (defined as 'S' lineage because C28,144 is in the codon of Serine) at these two sites. Thus, we categorized the SARS-CoV-2 viruses into two major lineages with L being the major (∼70%) and S being the minor (∼30%).

The evolutionary history of L and S lineages

Although we defined the L and S lineages based on two tightly linked SNPs, strikingly, the separation between the L (blue) and S (red) lineages was maintained when we reconstructed the haplotype networks using all the SNPs in the SARS-CoV-2 genomes (Fig. 4 A; the number of mutations between two neighboring haplotypes was 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 lineages of SARS-CoV-2.

To determine the evolutionary changes associated with L and S lineages, we examined the genomic alignment of SARS-CoV-2 and other highly related viruses. Strikingly, nucleotides of the S lineage at sites 8,782 and 28,144 were identical to the orthologous sites in the most closely related viruses (Fig. 4 B). Remarkably, both sites were highly conserved in other viruses as well. Hence, although the L lineage (∼70%) was more prevalent than the S lineage (∼30%) in the SARS-CoV-2 viruses we examined, the S lineage was evolutionarily more related to animal coronaviruses.

To further examine the relationship among the strains in the L and S lineages, we reconstructed a phylogenetic tree of all the 103 SARS-CoV-2 viruses based on their whole-genome sequences. Our phylogenetic tree also clearly shows the separation of the two lineages (Fig. 5). Viruses of the L lineage (blue) clustered together, and likewise, viruses of the S lineage (red) were also more closely related to each other. Therefore, our whole-genome comparisons further confirm the separation of the L and S lineages.

Furthermore, our mutational load analysis indicated that the L lineage had accumulated a significantly higher number of derived mutations than S lineage ( P < 0.0001, Wilcoxon rank-sum test; Fig. S5). Whether the two lineages might have different rates in transmission or replication needs to be investigated in future studies.

These results support notions that two lineages of SARS-CoV-2 viruses may have experienced different selective pressures. Of note, the above analyses were based on limited SARS-CoV-2 genomes that were collected from various locations with different time points. More comprehensive genomic data is required for further testing of our hypothesis.

Heteroplasmy of SARS-CoV-2 viruses in patients

We found that the sequence of viruses isolated from one patient that lived in the United

States on January 21 (USA_2020/01/21.a, GISAID ID: EPI_ISL_404253) had the genotype Y (C or T) at both positions 8,782 and 28,144, differing from the general trend of having either C or T. Although novel mutations could lead to this result, the most parsimonious explanation is that this patient may have been infected by both the L and S lineages (Fig. 6). The sample of USA_2020/01/21.a was collected from a 63-year-old female patient living in Chicago (from GISAID). Based on the report from the United States Centers for Disease Control and Prevention ( https://www.cdc.gov/media/releases/2020/p0124-second-travel-coronavirus.html).

To further investigate the heteroplasmy of SARS-CoV-2 viruses in patients, we searched 12 deep-sequencing libraries of SARS-CoV-2 genomes that were deposited in the Sequence Read Archive (SRA) (Table S3, Materials and Methods). We found 17 genomic sites that showed evidence of heteroplasmy of SARS-CoV-2 virus in five patients, but we did not find any other instances of the co-existence of L and S lineages in any patient (Table 2). These findings point to the complexity of SARS-CoV-2 evolution. Further studies investigating how the different alleles of SARS-CoV-2 viruses compete with one and another will be of significant value.

DISCUSSION

In this study, we investigated the patterns of molecular divergence between SARS-CoV-2 and other related coronaviruses. Although the genomic analyses suggested that SARS-CoV-2 was closest to RaTG13, their difference at neutral sites was much higher than previously realized. Our results provide novel insights for tracing the intermediate natural host of SARS-CoV-2. With population genetic analyses of 103 genomes of SARS-CoV-2, we found that SARS-CoV-2 viruses had two major lineages (L and S lineages), and the two lineages were well defined by just two SNPs that show complete linkage across SARS-CoV-2 strains. The L lineage (∼70%) was found to be more prevalent than the S lineage (∼30%) in the SARS-CoV-2 viruses we examined, our evolutionary analyses suggested the S appeared to be more related to coronaviruses in animals.

Since nonsynonymous sites are usually under stronger negative selection than synonymous sites, calculating sequence differences without separating these two classes of sites could lead to a potentially significant underestimate of the degree of molecular divergence. For example, although the overall nucleotides only differed by ∼4% between SARS-CoV-2 and RaTG13, the genomic average dS value, which is usually a neutral proxy, was 0.17 between these two viruses (Table 1). Of note, the genome-wide dS value is 0.012 between humans and chimpanzees [ 35], and 0.08 between humans and rhesus macaques [ 36]. Thus, the neutral molecular divergence between SARS-CoV-2 and RaTG13 is 14 times larger than that between humans and chimpanzees, and twice as large as that between humans and macaques. The genomic average dS value between SARS-CoV-2 and GD Pangolin-CoV is 0.469, which is comparable to that between humans and mice (0.5) [ 37], and the dS value between SARS-CoV-2 and GX Pangolin-Cov is even larger (0.722). The scale of these measures suggests that we should perhaps consider the difference in the neutral evolving site rather than the difference in all nucleotide sequences when tracing the origin and natural intermediate host of SARS-CoV-2.

In this work, we propose that SARS-CoV-2 can be divided into two major lineages (L and S). Intriguingly, the S and L lineages can be clearly defined by just two tightly linked SNPs at positions 8,782 ( orf1ab: T8517C, synonymous) and 28,144 ( ORF8: C251T, S84L). orf1ab, which encodes replicase/transcriptase, is required for viral genome replication and might also be important for viral pathogenesis [ 38]. Although the T8517C mutation in orf1ab does not change the protein sequence (it changes the codon AGT (Ser) to AGC (Ser)), it may affect orf1ab translation since AGT is preferred while AGC is unpreferred (Table S2). ORF8 promotes the expression of ATF6, the ER unfolded protein response factor, in human cells [ 39]. Thus, it will be interesting to investigate the function of the S84L AA change in ORF8, as well as the combinatory effect of these two mutations in SARS-CoV-2 pathogenesis.

As previously noted [ 19], the data examined in this study are still very limited, and follow-up analyses of a larger set of data are needed to have a better understanding of the evolution and epidemiology of SARS-CoV-2.

MATERIALS AND METHODS

Molecular evolution of SARS-CoV-2 and other related viruses

The set of 103 complete genome sequences were downloaded from GISAID (Global Initiative on Sharing All Influenza Data; https://www.gisaid.org/) with acknowledgment, GenBank ( https://www.ncbi.nlm.nih.gov/genbank), and NMDC ( http://nmdc.cn/#/nCoV). Sequences and annotations of the reference genome of SARS-CoV-2 (NC_045512) and other related viruses were downloaded from GenBank, GISAID or Genome Warehouse. The two genomes of coronavirus from Guangdong Pangolins were downloaded from GISAID (EPI_ISL_410544) and Genome Warehouse ( GWHABKW00000000; see Table S1 for acknowledgement). We merged them to build the consensus sequence. The genomic sequences of SARS-CoV-2 were aligned using MUSCLE v3.8.31 [ 40].

The annotated CDSs of other viruses were downloaded from GenBank. To avoid missing annotations in other viruses, we also annotated the ORFs using CDSs annotated in SARS-CoV-2 using Exonerate (-model protein2genome: bestfit -score 5 -g y) [ 41]. The protein sequences of SARS-CoV-2 and other related viruses were aligned with MUSCLE v3.8.31 [ 40], and the codon alignments were made based on the protein alignment with RevTrans [ 42]. The codon alignments of the conserved ORFs were further concatenated for down-stream evolutionary analysis. The phylogenetic tree was constructed by the neighbor-joining method in MEGA-X [ 43] using the parameters of Kimura 2-parameter model, and only the third positions of codons were considered. YN00 from PAML v4.9a [ 22] was used to calculate the pairwise divergence between SARS-CoV-2 and other viruses for each individual gene or for the concatenated sequences. The free-ratio model in CODEML in the PAML [ 22] package was used to calculate the dN, dS, and ω values for each branch.

Positively selected amino acids

Positive selection was detected using EasyCodeML [ 44], a recently published wrapper of CODEML [ 22]. The M7 and M8 models were compared. In the M7 model, ω follows a beta distribution such that 0 ≤ ω ≤ 1, and in the M8 model, a proportion p ₀ of sites have ω drawn from the beta distribution, and the remaining sites with proportion p ₁ are positively selected and have ω ₁ > 1. The LRTs between M7 and M8 models were conducted by comparing twice the difference in log-likelihood values (2 ln Δl) against a χ ²-distribution (df = 2). The positively selected sites were identified with the Bayes Empirical Bayes (BEB) score larger than 0.95.

Haplotype network

DnaSP v6.12.03 [ 45] was used to generate multi-sequence aligned haplotype data, and PopART v1.7 [ 46] was used to draw haplotype networks based on the haplotypes generated by DnaSP. RAxML v8.2.12 [ 47] was used to build the maximum likelihood phylogenetic tree of 103 aligned SARS-CoV-2 genomes with theparameters '-p 1234 -m GTRCAT'.

SNP calling process

We downloaded 12 SARS-CoV-2 metagenomic sequencing libraries (Table S2), and mapped the NGS reads to the reference genome of SARS-CoV-2 (NC_045512) using BWA (0.7.17-r1188) [ 48] with the default parameters. SNP calling was done using bcftools mpileup (bcftools 1.9) [ 49].

Codon usage bias analysis

We calculated the RSCU (Relative Synonymous Codon Usage) value of each codon in the SARS-CoV-2 reference genome (NC_045512). The RSCU value for each codon was the observed frequency of this codon divided by its expected frequency under equal usage among the amino acid [ 50]. The codons with RSCU > 1 were defined as preferred codons, and those with RSCU < 1 were defined as unpreferred codons. The FOP (frequency of optimal codons) value of each gene was calculated as the number of preferred codons divided by the total number of preferred and unpreferred codons.

Acknowledgements

The authors thank the researchers who generated and shared the sequencing data from GISAID ( https://www.gisaid.org/) on which this research is based. We thank Dr. Chung-I Wu, Hong Wu, Hongya Gu, Liping Wei, Xuemei Lu, Weiwei Zhai, Guodong Wang, Xiaodong Su, Keping Hu, and Leiliang Zhang for suggestive comments to this study. This work was supported by grants from the National Natural Science Foundation of China (No. 91731301) to J.L. JC is supported by CAS Pioneer Hundred Talents Program.

Conflict of interest statement. None declared.

REFERENCES

Author notes

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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On the origin and continuing evolution of SARS-CoV-2

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The SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China, and has since impacted a large portion of China and raised major global concern. Herein, we investigated the extent of molecular divergence between SARS-CoV-2 and other related coronaviruses.

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E o novo coronavírus, de onde ele surgiu?

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E o novo coronavírus, de onde ele surgiu?

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As principais teorias descritas no artigo abaixo são as seguintes:
1. Seleção natural em um hospedeiro antes da transferência zoonótica
2. Seleção natural em humanos seguido de transferência zoonótica
3. Seleção durante a passagem

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Como esses vírus surgem? Assista ao vídeo abaixo...

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Sabia que o 1º coronavírus capaz de causar infecção em seres humanos foi descrito em 1965? O novo coronavírus, SARS-CoV 2, descrito em Dez de 2019, é membro da família Coronaviridae como mostrado no esquema abaixo, causa a doença chamada COVID-19.