Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates

doi:10.1073/pnas.2010146117

Comparative Study

. 2020 Sep 8;117(36):22311-22322.

doi: 10.1073/pnas.2010146117. Epub 2020 Aug 21.

Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates

Joana Damas¹, Graham M Hughes², Kathleen C Keough^{3

4}, Corrie A Painter⁵, Nicole S Persky⁶, Marco Corbo¹, Michael Hiller^{7

8

9}, Klaus-Peter Koepfli¹⁰, Andreas R Pfenning¹¹, Huabin Zhao^{12

13}, Diane P Genereux¹⁴, Ross Swofford¹⁴, Katherine S Pollard^{4

15

16}, Oliver A Ryder^{17

18}, Martin T Nweeia^{19

20

21}, Kerstin Lindblad-Toh^{14

22}, Emma C Teeling², Elinor K Karlsson^{14

23

24}, Harris A Lewin^{25

26

27}

Affiliations

¹ The Genome Center, University of California, Davis, CA 95616.
² School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland.
³ Graduate Program in Pharmaceutical Sciences and Pharmacogenomics, Quantitative Biosciences Consortium, University of California, San Francisco, CA 94117.
⁴ Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158.
⁵ Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142.
⁶ Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142.
⁷ Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany.
⁸ Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany.
⁹ Center for Systems Biology Dresden, 01307 Dresden, Germany.
¹⁰ Center for Species Survival, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA 22630.
¹¹ Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.
¹² Department of Ecology, Tibetan Centre for Ecology and Conservation at WHU-TU, Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan 430072, China.
¹³ College of Science, Tibet University, Lhasa 850000, China.
¹⁴ Broad Institute of MIT and Harvard, Cambridge, MA 02142.
¹⁵ Department of Epidemiology & Biostatistics, Institute for Computational Health Sciences, and Institute for Human Genetics, University of California, San Francisco, CA 94158.
¹⁶ Chan Zuckerberg Biohub, San Francisco, CA 94158.
¹⁷ San Diego Zoo Institute for Conservation Research, Escondido, CA 92027.
¹⁸ Department of Evolution, Behavior, and Ecology, Division of Biology, University of California San Diego, La Jolla, CA 92093.
¹⁹ Department of Restorative Dentistry and Biomaterials Sciences, Harvard School of Dental Medicine, Boston, MA 02115.
²⁰ School of Dental Medicine, Case Western Reserve University, Cleveland, OH 44106.
²¹ Marine Mammal Program, Department of Vertebrate Zoology, Smithsonian Institution, Washington, DC 20002.
²² Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, 751 23 Uppsala, Sweden.
²³ Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01655.
²⁴ Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655.
²⁵ The Genome Center, University of California, Davis, CA 95616; Lewin@ucdavis.edu.
²⁶ Department of Evolution and Ecology, University of California, Davis, CA 95616.
²⁷ John Muir Institute for the Environment, University of California, Davis, CA 95616.

PMID: 32826334
PMCID: PMC7486773
DOI: 10.1073/pnas.2010146117

Comparative Study

Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates

Joana Damas et al. Proc Natl Acad Sci U S A. 2020.

. 2020 Sep 8;117(36):22311-22322.

doi: 10.1073/pnas.2010146117. Epub 2020 Aug 21.

Authors

Affiliations

¹ The Genome Center, University of California, Davis, CA 95616.
² School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland.
³ Graduate Program in Pharmaceutical Sciences and Pharmacogenomics, Quantitative Biosciences Consortium, University of California, San Francisco, CA 94117.
⁴ Gladstone Institute of Data Science and Biotechnology, San Francisco, CA 94158.
⁵ Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142.
⁶ Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142.
⁷ Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany.
⁸ Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany.
⁹ Center for Systems Biology Dresden, 01307 Dresden, Germany.
¹⁰ Center for Species Survival, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA 22630.
¹¹ Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.
¹² Department of Ecology, Tibetan Centre for Ecology and Conservation at WHU-TU, Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan 430072, China.
¹³ College of Science, Tibet University, Lhasa 850000, China.
¹⁴ Broad Institute of MIT and Harvard, Cambridge, MA 02142.
¹⁵ Department of Epidemiology & Biostatistics, Institute for Computational Health Sciences, and Institute for Human Genetics, University of California, San Francisco, CA 94158.
¹⁶ Chan Zuckerberg Biohub, San Francisco, CA 94158.
¹⁷ San Diego Zoo Institute for Conservation Research, Escondido, CA 92027.
¹⁸ Department of Evolution, Behavior, and Ecology, Division of Biology, University of California San Diego, La Jolla, CA 92093.
¹⁹ Department of Restorative Dentistry and Biomaterials Sciences, Harvard School of Dental Medicine, Boston, MA 02115.
²⁰ School of Dental Medicine, Case Western Reserve University, Cleveland, OH 44106.
²¹ Marine Mammal Program, Department of Vertebrate Zoology, Smithsonian Institution, Washington, DC 20002.
²² Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, 751 23 Uppsala, Sweden.
²³ Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01655.
²⁴ Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655.
²⁵ The Genome Center, University of California, Davis, CA 95616; Lewin@ucdavis.edu.
²⁶ Department of Evolution and Ecology, University of California, Davis, CA 95616.
²⁷ John Muir Institute for the Environment, University of California, Davis, CA 95616.

PMID: 32826334
PMCID: PMC7486773
DOI: 10.1073/pnas.2010146117

Abstract

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of COVID-19. The main receptor of SARS-CoV-2, angiotensin I converting enzyme 2 (ACE2), is now undergoing extensive scrutiny to understand the routes of transmission and sensitivity in different species. Here, we utilized a unique dataset of ACE2 sequences from 410 vertebrate species, including 252 mammals, to study the conservation of ACE2 and its potential to be used as a receptor by SARS-CoV-2. We designed a five-category binding score based on the conservation properties of 25 amino acids important for the binding between ACE2 and the SARS-CoV-2 spike protein. Only mammals fell into the medium to very high categories and only catarrhine primates into the very high category, suggesting that they are at high risk for SARS-CoV-2 infection. We employed a protein structural analysis to qualitatively assess whether amino acid changes at variable residues would be likely to disrupt ACE2/SARS-CoV-2 spike protein binding and found the number of predicted unfavorable changes significantly correlated with the binding score. Extending this analysis to human population data, we found only rare (frequency <0.001) variants in 10/25 binding sites. In addition, we found significant signals of selection and accelerated evolution in the ACE2 coding sequence across all mammals, and specific to the bat lineage. Our results, if confirmed by additional experimental data, may lead to the identification of intermediate host species for SARS-CoV-2, guide the selection of animal models of COVID-19, and assist the conservation of animals both in native habitats and in human care.

Keywords: ACE2; COVID-19; SARS-CoV-2; comparative genomics; species conservation.

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Conflict of interest statement

The authors declare no competing interest.

Figures

**Fig. 1.**
Cross-species conservation of ACE2 at the known binding residues and predictions of SARS-CoV-2 S-binding propensity. Species are sorted by binding scores. The ID column depicts the number of amino acids identical to human binding residues. Bold amino acid positions (also labeled with asterisks) represent residues at binding hot spots and constrained in the scoring scheme. Each amino acid substitution is colored according to its classification as nonconservative (orange), semiconservative (yellow), or conservative (blue), as compared to the human residue. Bold species names depict species with threatened IUCN risk status. The 410 vertebrate species dataset is available in Dataset S1.

**Fig. 2.**
Cross-species conservation of ACE2 at the known binding residues and predictions of SARS-CoV-2 S-binding propensity. Species are sorted by binding scores. The ID column depicts the number of amino acids identical to human binding residues. Bold amino acid positions (also labeled with asterisks) represent residues at binding hot spots and constrained in the scoring scheme. Each amino acid substitution is colored according to its classification as nonconservative (orange), semiconservative (yellow), or conservative (blue), as compared to the human residue. Bold species names depict species with threatened IUCN risk status. The 410 vertebrate species dataset is available in Dataset S1.

**Fig. 3.**
Congruence between binding score and the structural homology analysis. Species predicted with very high (red) or high binding scores (orange) have significantly fewer amino acid substitutions rated as potentially altering the binding interface between ACE2 and SARS-CoV-2 using protein structural analysis when compared to species with low (green) or very low (blue) binding scores. The more severe unfavorable variants are counted on the y axis and less severe weaken variants on the x axis. Black numerical labels indicate species count.

**Fig. 4.**
Residues at the binding interface between ACE2 and SARS-CoV-2 S are under positive selection (CodeML analysis). In the SARS-CoV-2 spike protein RBD (light teal), this includes three positively selected residues (green, labeled with two asterisks). In ACE2 (wheat-colored, with binding interface residues in yellow), selected residues occur both outside the binding interface (dark blue) and inside the binding interface (red, labeled with one asterisk). (A) Positively selected residues in all mammals, including two at the binding interface. (B) A with 90° rotation. (C) Positively selected residues in the Chiroptera lineage, including five at the binding interface. (D) C with 90° rotation.

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Update of

Broad Host Range of SARS-CoV-2 Predicted by Comparative and Structural Analysis of ACE2 in Vertebrates.
Damas J, Hughes GM, Keough KC, Painter CA, Persky NS, Corbo M, Hiller M, Koepfli KP, Pfenning AR, Zhao H, Genereux DP, Swofford R, Pollard KS, Ryder OA, Nweeia MT, Lindblad-Toh K, Teeling EC, Karlsson EK, Lewin HA. Damas J, et al. bioRxiv [Preprint]. 2020 Apr 18:2020.04.16.045302. doi: 10.1101/2020.04.16.045302. bioRxiv. 2020. Update in: Proc Natl Acad Sci U S A. 2020 Sep 8;117(36):22311-22322. doi: 10.1073/pnas.2010146117 PMID: 32511356 Free PMC article. Updated. Preprint.

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References

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[1] Zhou P. et al. ., A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020). - PMC - PubMed

[2] Zhou P. et al. ., A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273 (2020). - PMC - PubMed

[3] Lu G., Wang Q., Gao G. F., Bat-to-human: Spike features determining “host jump” of coronaviruses SARS-CoV, MERS-CoV, and beyond. Trends Microbiol. 23, 468–478 (2015). - PMC - PubMed

[4] Lu G., Wang Q., Gao G. F., Bat-to-human: Spike features determining “host jump” of coronaviruses SARS-CoV, MERS-CoV, and beyond. Trends Microbiol. 23, 468–478 (2015). - PMC - PubMed

[5] Shan C. et al. ., Infection with novel coronavirus (SARS-CoV-2) causes pneumonia in the Rhesus macaques. Cell Res. 30, 670–677 (2020). - PMC - PubMed

[6] Shan C. et al. ., Infection with novel coronavirus (SARS-CoV-2) causes pneumonia in the Rhesus macaques. Cell Res. 30, 670–677 (2020). - PMC - PubMed

[7] Laude H., Van Reeth K., Pensaert M., Porcine respiratory coronavirus: Molecular features and virus-host interactions. Vet. Res. 24, 125–150 (1993). - PubMed

[8] Laude H., Van Reeth K., Pensaert M., Porcine respiratory coronavirus: Molecular features and virus-host interactions. Vet. Res. 24, 125–150 (1993). - PubMed

[9] Saif L. J., Bovine respiratory coronavirus. Vet. Clin. North Am. Food Anim. Pract. 26, 349–364 (2010). - PMC - PubMed

[10] Saif L. J., Bovine respiratory coronavirus. Vet. Clin. North Am. Food Anim. Pract. 26, 349–364 (2010). - PMC - PubMed

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Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates

Affiliations

Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates

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Abstract

Conflict of interest statement

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