Abstract
By re-analzying public metagenomic data from 101 patients infected with influenza A virus during the 2007-2012 H1N1 flu seasons in France, we identified 22 samples with SARS-CoV sequences. In 3 of them, the SARS genome sequences could be fully assembled out of each. These sequences are highly similar (99.99% and 99.7%) to the artificially constructed recombinant SARS-CoV (SARSr-CoV) strains generated by the J. Craig Venter Institute in the USA. Moreover, samples from different flu seasons have different SARS-CoV strains, and the divergence between these strains cannot be explained by natural evolution. Our study also shows that retrospective studies using public metagenomic data from past major epidemic outbreaks serve as a genomic strategy for researching the origins or spread of infectious diseases.
Keywords: Metagenomics, SARS-CoV, Influenza A Virus, Retrospective study
1. Introduction
Genome sequencing has been used to identify the pathogen, trace virus origin, and provide outbreak surveillance for infectious disease studies. For example, the availability of the complete genome of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in January 2020 sped up the identification of the pathogen and facilitated the development of effective vaccines. With the accumulation of extensive metagenomic sequence data in the past years, one potential application is to carry out genome-based retrospective studies on major historical outbreaks to understand the occurrence and development of viral epidemics.
2. Materials and Methods
2.1. Data collection
The metagenomic data of 101 patients infected by the influenza A virus were downloaded from the NCBI SRA database (project ID: PRJEB11406). These samples were collected by the National Influenza Center near Paris, France, between 2007 and 2012, spanning five consecutive flu seasons. Sequencing data of these samples were submitted by Institute Pasteur, France, in 2018 (Table S1) [1].
2.2. Variant calling and consensus sequence generating
After removing sequencing adapters and trimming consecutive low-quality bases from the 5' and 3' read ends, cutadapt [2], clean reads were mapped to the SARS-CoV genome (NC_004718.3) using BWA (V0.7.12) [3] with default parameters. Next, the Picard program (http://picard.sourceforge.net) was used to sort mapping results to BAM format and mark duplicates of PCR amplification. Then GATK (V4.1.6.0) [4] was used for SNP and indel calling. Finally, consensus sequences were generated by applying VCF variants to the reference sequence using bcftools (v1.9) [5].
2.3. Sequence alignment, phylogenetic and network analysis
Two hundred fifty genomes of SARS-CoV with genome lengths larger than 29,0000 bases were downloaded from the NCBI Nucleotide database (https://www.ncbi.nlm.nih.gov/nuccore). We then constructed a multiple sequence alignment of 253 genomes, using the MAFFT v7.453 with parameter “--auto” [6], and the final alignment contains 30,327 nucleotides.
Neighbor-joining (NJ) phylogenetic trees of the 253 genome sequences were constructed using MEGA X 10.1.8 with a maximum composite likelihood model and default parameters [7]. In addition, phylogenetic relationships and mutations that occurred among unique genomes were further inspected from 253 genomes through median-joining networks [8], using the Network 10 (http://www.fluxus-engineering.com/) to examine changes of genetic variations across places and through times. For network analysis, an 81-bp block at the 5’-end including gaps and a 77-bp block at the 3’-end including gaps and the poly-A tails in the alignment was trimmed out, and the final alignment contains 30,169 nucleotides.
2.4. Pairwise sequence alignment
We used the BLAST online tools with default parameters (https://blast.ncbi.nlm.nih.gov/Blast.cgi) to align two sequences.
3. Results
We re-analyzed public metagenomic data from 101 patients infected with influenza A virus (H1N1), collected by the Pasteur Institute in France between 2007 and 2012, spanning five consecutive flu seasons [1]. By mapping to the SARS-CoV reference genome (NC_004718.3), 22 (21.78%) out of the 101 patient samples were identified containing SARS-CoV fragments with different proportions (0.0003% ∼ 0.6127%) (Table S1). Among them, three samples were deeply sequenced with sequencing depth >30 and genome coverage >99% (ERR1091908, ERR1091910, and ERR1091914), which complete genomes were generated from the consensus sequences using variant identification and substitution methods (Table S1, Figure S1). These 22 samples have different numbers of mutations ranging from 10 to 116 compared with the SARS-CoV reference genome (NC_004718.3, see Table S1). In addition, the samples collected during the same flu season share similar mutations, while samples from different flu seasons possess different sets of mutations (Fig. S1, Table 1 ), suggesting that distinct SARS-CoV strains existed in different flu seasons in France.
Table 1.
Summary of three SARS-CoV sequences and their closest sequences.
| Cluster | Sample ID | Sample collection location | Collection date | Organism | Submitter | Identity with SARS-CoV sequences (%) |
|---|---|---|---|---|---|---|
| 1 | ERR1091914 | Haute Normandie, France | 2008/12/23 | Influenza A virus | Institute Pasteur | 99.99 |
| FJ882938* | Tennessee, USA | 2007/9/22 | SARS-CoV wtic-MB | J. Craig Venter Institute | ||
| 2 | ERR1091908 | Lorraine, France | 2008/1/15 | Influenza A virus | Institute Pasteur | 99.70 |
| ERR1091910 | Picardie, France | 2008/1/16 | Influenza A virus | |||
| FJ882941 | Nashville, Tennessee, USA | 2008/3/28 | SARS-CoV ExoN1 | J. Craig Venter Institute |
*One sequence closed to ERR1091914 is shown and the remaining 18 sequences closed to ERR1091914 are presented in Table S2.
SARS-CoV, a life-threatening respiratory infectious disease [9], once caused an outbreak of SARS in 2002 but disappeared in human populations after 2003. To investigate the origin of these SARS-CoV sequences detected in the patients infected with influenza A virus between 2007 and 2012, we noticed the three assembled SARS-CoV genome sequences (ERR1091908, ERR1091910, and ERR1091914) were pooled together with 250 complete SARS-CoV genomes downloaded from the NCBI database. Therefore, we constructed a phylogeny of these sequences with the neighbor-joining approach and a haplotype network with the median-joining process, respectively (Fig. 1 ). As a result, we found that ERR1091908 and ERR1091910 are clustered with the SARS-CoV ExoN1 strain (colored in blue in Fig. 1) while ERR1091914 is clustered with the SARS-CoV wtic-MB strain (colored in black in Fig. 1), consistent with the finding of differential SARS-CoV mutations in the patient samples from different flu seasons. Note that SARS-CoV ExoN1, SARS-CoV wtic-MB, SARS-CoV MA15, and SARS-CoV MA15 ExoN1 all belong to recombinant SARSr-CoV, a group of SARS-CoV sequences artificially constructed using the exact infectious clone (ic) recombinant virus strain of SARS-CoV Urbani (AY278741) that was initially isolated from a patient with SARS-CoV [10], [11], [12]. Moreover, the three newly assembled SARS-CoV sequences along all the recombinant SARSr-CoV sequences are separated from other naturally occurring SARS-CoV sequences, including AY278741 (colored in yellow) cyan in Fig. 1) in the phylogeny and haplotype network. Therefore, the above-said results indicate that all the three SARS-CoV sequences (ERR1091908, ERR1091910, and ERR1091914) are more likely to be categorized as artificially constructed recombinant SARSr-CoV strains.
Fig. 1.
Phylogeny tree and haplotype network of 253 SARS-CoV genome sequences. a. A phylogeny of the 253 SARS-CoV genome sequences is constructed with the neighbor-joining approach. b. Haplotype network of the 253 SARS-CoV genome sequences is constructed with the median-joining approach. Size of each circle represents the number of identical sequences.
ERR1091914 is almost identical (99.99%) to SARS-CoV wtic-MB (19 identical sequences in the data), with only one base pair different (bp) (R10626A, referred to the genome position of FJ882938.1) after trimming the 20-bp at the 5’-end and 44-bp at the 3’-end of ERR1091914 (Table 1). Here, R represents A or G base. There are a total of 15 mutation differences between ERR1091914 and AY278741, the closest naturally occurring SARS-CoV, and the 15 mutations are shared by all the recombinant SARSr-CoV, indicating that ERR1091914 is indeed derived from the SARSr-CoV wtic-MB sequences instead of evolving independently from naturally occurring SARS-CoV sequences.
The other two newly identified SARS-CoV sequences (ERR1091908 and ERR1091910) are almost identical with only one base difference (99.99%). Both lines are highly similar (99.70%) to the SARS-CoV ExoN1 sequence FJ882941.1, with 88 base differences after trimming 21-bp at the 5’-end and 65-bp at 3’-end (Table 1). Annotation results showed that 84 of the 88 bases were located at coding regions of SARS-CoV and resulted in 45 amino acid (AA) substitutions. Most of these mutations are in the coding regions of open reading frame 1a (ORF1a) (13 substitutions), ORF1ab (7 substitutions), and the spike (S) glycoprotein (9 substitutions, Table 2 ), among which ORF1ab and S glycoprotein are functionally related to the viral replication, transmission and pathogenicity [13], [14], suggesting potential gain-function effects of these mutations in ERR1091908 and ERR1091910. SARS-CoV ExoN1 strains are known to have a 21-fold increase in mutation rate during replication in previous research [12], which is consistent with the fact that the sequences within the SARS-CoV ExoN1 clades of the haplotype network are highly divergent compared with other recombinant SARSr-CoV clades (Fig. S2). Assuming a mutation rate of 1.0 × 10-3 per site per year for naturally evolving SARS-CoV and the SARS-CoV genome length of 29,751 bp, only 1.69 months (50.7 days) are needed to generate 88 site differences between the two SARS-CoV ExoN1 sequences, indicating a recent divergence time between the two sequences (ERR1091908 and ERR1091910) and FJ882941.1.
Table 2.
The positions of 84 base differences between ERR1091908 and FJ882941.1
| ORF | CDS (position in NC_004718.3) | Mutations found in ERR1091908 compared to FJ882941.1 | Function [ref] | |
|---|---|---|---|---|
| Positions of nucleotide change (number) | Positions of amino acid change in SARS-CoV protein (number) | |||
| ORF 1a | 265-13413 | 654,707,1771,1905,2976,3229,3491,3603,3845,4731,4808,5015,5061,5236,5412,6087,6265,6459,6476,7484,8004,8922,10119,10658,12411,13149 (26) | 148,503,989,1076,1194,1489,1515,1584,1658,2001,2071,2407,3465 (13) | Involved in viral replication and transcription, and virus pathogenesis [13], [15] |
| ORF 1ab | 265-21485 | 13874,13925,14178,14630,14876,15497,15605,15740,15821,15905,16356,16386,17269,17602,18238,18239,18244,18245,18749,18860,19082,19814,19917,20528,20555,20789,21038 (27) | 987,997,1291,1402,1614,1616,2174 (7) | |
| S | 21492-25259 | 21860,22206,22352,22423,23243,23374,23468,23518,23823,24249,24873,24910,24957 (13) | 239,311,628,676,778,920,1128,1140,1156 (9) | Associated with cell entry of SARS-CoV and viral transmission [14], [16] |
| ORF 3a | 25268-26092 | 25550,25626,25783,25800,26049 (5) | 120,178,261 (3) | Playing roles in virus uptake and release, viral-related apoptosis, and formation of viral envelope [17] |
| ORF 3b | 25689-26153 | 25783,25800,26049,26121 (4) | 32,38,121,145 (4) | Involving in immunomodulation, and acting as interferon antagonist [17] |
| E | 26117-26347 | 26121,26226,26241,26335 (4) | 2,37,42 (3) | A small integral membrane proteins with roles in virus morphogenesis, assembly, budding, and replication [15] |
| M | 26398-27063 | NA | NA | NA |
| ORF 6 | 27074-27265 | 27167,27248 (2) | 32,59 (2) | Acting as a β-interferon antagonist and contribute to virulence [17] |
| ORF 7a | 27273-27641 | 27290,27639 (2) | 123 (1) | Involving in virus-host interaction and contribute to SARS-CoV pathogenesis [17] |
| ORF 7b | 27638-27772 | 27639,27648 (2) | 1,4 (2) | A potential attenuating factor [17] |
| ORF 8a | 27779-27898 | NA | NA | Potential roles in the host ubiquitin–proteasome system [17] |
| ORF 8b | 27864-28118 | 27917 (1) | NA | |
| N | 28120-29388 | 28557,29271,29324 (3) | 402 (1) | Playing role in virus replication and transcription, and acting as an interferon antagonist [15], [16] |
| ORF 9b | 28130-28426 | NA | NA | Inducing caspase-dependent apoptosis [17] |
| Total number | NA | 84 | 45 | NA |
The sequencing data of 101 patients were submitted by the Pasteur Institute in France, which established a laboratory with a level-three biosafety standard and conducted research on SARS-CoV [18], [19], [20]. All the 116 recombinant SARSr-CoV sequences were submitted by the J. Craig Venter Institute (JCVI) from Tennessee, USA. The two institutes have collaborated and published their work on viral genome sequence [21].
4. Discussion
One possible explanation of the co-existence of SARS and H1N1 sequences in the patients is that the artificially constructed recombinant SARSr-CoV caused a co-infection outside the laboratory during 2007 - 2012, but did not result in the SARS-CoV epidemic; an alternative hypothesis is a contamination of the samples in the lab since the Pasteur Institute also conducted SARS-CoV studies. Moreover, samples from different flu seasons have different strains of SARS-CoV, and the divergence between these SARS-CoV strains cannot be explained by natural evolution. This intriguing finding warrants further efforts to sleuth out the culprit. In 2014, the Pasteur Institute France once lost vials containing patient samples collected during SARS (https://www.sciencemag.org/news/2014/05/frances-institut-pasteur-under-fire-over-missing-sars-vials). It raises a serious concern about laboratory biosafety in both institutions. Our study also shows that retrospective studies using public metagenomic data from past major epidemic outbreaks serve as a proper genomic strategy for researching the origins or spread of infectious diseases.
Acknowledgments
Acknowledgments
This project was supported by the National Natural Science Foundation of China (Grant No. 31571370, 91731302, and 31772435).
Conflict of interest statement
The authors declare that there are no conflicts of interest.
References
- 1.Pelletier I, Rousset D, Enouf V et al. Highly heterogeneous temperature sensitivity of 2009 pandemic influenza A(H1N1) viral isolates, northern France. Euro Surveill 2011; 16:19999. 10.2807/ese.16.43.19999-en. [PubMed]
- 2.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17(1):10. doi: 10.14806/ej.17.110.14806/ej.17.1.200. [DOI] [Google Scholar]
- 3.Li H., Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–1760. doi: 10.1093/bioinformatics/btp324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.McKenna A., Hanna M., Banks E., Sivachenko A., Cibulskis K., Kernytsky A., Garimella K., Altshuler D., Gabriel S., Daly M., DePristo M.A. The Genome Analysis Toolkit: A MapReduce Framework for Analyzing Next-Generation DNA Sequencing Data. Genome Res. 2010;20(9):1297–1303. doi: 10.1101/gr.107524.110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Danecek P., Bonfield J.K., Liddle J., Marshall J., Ohan V., Pollard M.O., Whitwham A., Keane T., McCarthy S.A., Davies R.M., Li H. Twelve years of SAMtools and BCFtools. Gigascience. 2021 doi: 10.1093/gigascience/giab008. Feb 16;10:giab008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Katoh K., Misawa K., Kuma K.-i., Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30(3059–3066) doi: 10.1093/nar/gkf436. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol Biol Evol 2018; 35:1547-1549. 10.1093/molbev/msy096. [DOI] [PMC free article] [PubMed]
- 8.Bandelt H.J., Forster P., Rohl A. Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol. 1999;16(1):37–48. doi: 10.1093/oxfordjournals.molbev.a026036. [DOI] [PubMed] [Google Scholar]
- 9.Ksiazek T., G., et al. A Novel Coronavirus Associated with Severe Acute Respiratory Syndrome. N Engl J Med. 2003;348(1953–1966) doi: 10.1056/NEJMoa030781. [DOI] [PubMed] [Google Scholar]
- 10.Yount B., Curtis K.M., Fritz E.A., Hensley L.E., Jahrling P.B., Prentice E., Denison M.R., Geisbert T.W., Baric R.S. Reverse genetics with a full-length infectious cDNA of severe acute respiratory syndrome coronavirus. Proc Nat Acad Sci USA. 2003;100(22):12995–13000. doi: 10.1073/pnas.1735582100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Roberts Anjeanette, Deming Damon, Paddock Christopher D., Cheng Aaron, Yount Boyd, Vogel Leatrice, Herman Brian D., Sheahan Tim, Heise Mark, Genrich Gillian L., Zaki Sherif R., Baric Ralph, Subbarao Kanta. A Mouse-adapted SARS-coronavirus causes disease and mortality in BALB/c mice. PLoS Pathog. 2007;3(1):e5. doi: 10.1371/journal.ppat.0030005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Eckerle Lance D., Becker Michelle M., Halpin Rebecca A., Li Kelvin, Venter Eli, Lu Xiaotao, Scherbakova Sana, Graham Rachel L., Baric Ralph S., Stockwell Timothy B., Spiro David J., Denison Mark R., Emerman Michael. Infidelity of SARS-CoV Nsp14-Exonuclease Mutant Virus Replication Is Revealed by Complete Genome Sequencing. PLoS Pathog. 2010;6(5):e1000896. doi: 10.1371/journal.ppat.1000896. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Graham Rachel L., Sparks Jennifer S., Eckerle Lance D., Sims Amy C., Denison Mark R. SARS coronavirus replicase proteins in pathogenesis. Virus Res. 2008;133(1):88–100. doi: 10.1016/j.virusres.2007.02.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Bolles Meagan, Donaldson Eric, Baric Ralph. SARS-CoV and emergent coronaviruses: viral determinants of interspecies transmission. Curr Opin Virol. 2011;1(6):624–634. doi: 10.1016/j.coviro.2011.10.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Perlman S., Netland J. Coronaviruses post-SARS: update on replication and pathogenesis. Nat Rev Microbiol. 2009;7(6):439–450. doi: 10.1038/nrmicro2147. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hilgenfeld Rolf, Peiris Malik. From SARS to MERS: 10 years of research on highly pathogenic human coronaviruses. Antiviral Res. 2013;100(1):286–295. doi: 10.1016/j.antiviral.2013.08.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Liu D.X., Fung T.S., Chong K.K., Shukla A., Hilgenfeld R. Accessory proteins of SARS-CoV and other coronaviruses. Antiviral Res. 2014;109:97–109. doi: 10.1016/j.antiviral. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Mantke O.D., Schmitz H., Herve Z., Heyman P., Papa A., Niedrig M. Quality assurance for the diagnostics of viral diseases to enhance the emergency preparedness in Europe. Euro Surveill. 2005;10:102–106. doi: 10.2807/esm.10.06.00545-en. [DOI] [PubMed] [Google Scholar]
- 19.Yap YeeLeng, Zhang XueWu, Andonov Anton, He RunTao. Structural analysis of inhibition mechanisms of Aurintricarboxylic Acid on SARS-CoV polymerase and other proteins. Comput Biol Chem. 2005;29(3):212–219. doi: 10.1016/j.compbiolchem.2005.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Fontanet A. Cross-species transmission: last obstacle before pandemic. Transfus Clin Bio. 2007;14:16. doi: 10.1016/j.tracli.2007.04.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Eppinger Mark, Rosovitz M. J, Fricke Wolfgang Florian, Rasko David A, Kokorina Galina, Fayolle Corinne, Lindler Luther E, Carniel Elisabeth, Ravel Jacques, Guttman David S. The Complete Genome Sequence of Yersinia pseudotuberculosis IP31758, the Causative Agent of Far East Scarlet-Like Fever. PLoS Genet. 2007;3(8):e142. doi: 10.1371/journal.pgen.0030142. [DOI] [PMC free article] [PubMed] [Google Scholar]

