Skip to main content
PLOS Biology logoLink to PLOS Biology
. 2021 Dec 21;19(12):e3001510. doi: 10.1371/journal.pbio.3001510

Evolutionary pathways to SARS-CoV-2 resistance are opened and closed by epistasis acting on ACE2

Gianni M Castiglione 1, Lingli Zhou 1, Zhenhua Xu 1, Zachary Neiman 1, Chien-Fu Hung 2, Elia J Duh 1,*
Editor: Andreas Hejnol3
PMCID: PMC8730403  PMID: 34932561

Abstract

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infects a broader range of mammalian species than previously predicted, binding a diversity of angiotensin converting enzyme 2 (ACE2) orthologs despite extensive sequence divergence. Within this sequence degeneracy, we identify a rare sequence combination capable of conferring SARS-CoV-2 resistance. We demonstrate that this sequence was likely unattainable during human evolution due to deleterious effects on ACE2 carboxypeptidase activity, which has vasodilatory and cardioprotective functions in vivo. Across the 25 ACE2 sites implicated in viral binding, we identify 6 amino acid substitutions unique to mouse—one of the only known mammalian species resistant to SARS-CoV-2. Substituting human variants at these positions is sufficient to confer binding of the SARS-CoV-2 S protein to mouse ACE2, facilitating cellular infection. Conversely, substituting mouse variants into either human or dog ACE2 abolishes viral binding, diminishing cellular infection. However, these same substitutions decrease human ACE2 activity by 50% and are predicted as pathogenic, consistent with the extreme rarity of human polymorphisms at these sites. This trade-off can be avoided, however, depending on genetic background; if substituted simultaneously, these same mutations have no deleterious effect on dog ACE2 nor that of the rodent ancestor estimated to exist 70 million years ago. This genetic contingency (epistasis) may have therefore opened the road to resistance for some species, while making humans susceptible to viruses that use these ACE2 surfaces for binding, as does SARS-CoV-2.


This study suggests that ancient events in mammalian cardiovascular evolution determined the host range of SARS-CoV-2 millions of years before the current pandemic. These physiological constraints are so inflexible that escape from SARS-CoV-2 susceptibility would likely have required significant alterations to the human cardiovascular system.

Introduction

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is closely related to a virus (RaTG13) isolated from the Chinese horseshoe bat (Rhinolophus affinis) [1], with circulation of related viruses in bat populations [24] and potential spillover into other species susceptible to infection by SARS-CoV-2–related coronaviruses (e.g., pangolin; Manis javanica) [5]. These zoonotic origins ultimately led to the evolution of a virus that is highly transmissible among humans [2], causing an unprecedented public health emergency [6,7]. A wide phylogenetic range of mammalian species have been demonstrated to be susceptible to SARS-CoV-2 infection, including nonhuman primates, dogs, cats, ferrets, hamsters, and minks [815]. Characterizing the entire host range of SARS-CoV-2 is important for identifying the risks of anthroponosis and zoonosis between humans and other species, which can pose a major health risk by forming novel viral reservoirs where new mutations can evolve [16,17]. A recent example is mink farms, where infection by humans led to the evolution of novel viral strains, which have since reinfected human populations [14]. Attempts to predict the host range of SARS-CoV-2 have greatly underestimated the extent to which SARS-CoV-2 can infect certain nonprimate species, especially nonfelid carnivores including mink, dog, and ferret [814,18]. These predictive methods depend on comparative sequence analyses of angiotensin converting enzyme 2 (ACE2)—the cellular receptor for SARS-CoV-2—and scoring based on sequence and structural homology to the human ACE2 viral binding interface [1820]. The difficulty of predicting the SARS-CoV-2 host range through these methods demonstrates that the virus can bind a wide range of ACE2 orthologs despite extensive sequence divergence.

Infection by SARS-CoV-2 is mediated by the binding of the viral S protein receptor-binding domain (RBD) to ACE2 [1,21], displaying a nanomolar affinity higher than that of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-1) [22]. Structural analyses have identified that the SARS-CoV-2 RBD targets multiple binding “hotspots” within the ACE2 ectodomain, presenting a diffuse and multifaceted binding strategy [2325]. Thus, it remains unclear why so few species are resistant to infection, since there is extensive sequence diversity within the viral binding domain of ACE2 [18]. The degeneracy of ACE2 sequence requirements for SARS-CoV-2 binding raises the question as to why sequences capable of blocking viral binding have not evolved more frequently by chance; mice (Mus musculus) are one of the only mammalian species known to be resistant to SARS-CoV-2 infection [1,21,26]. This exception is striking, as mice do not have naturally circulating SARS-related viruses that may be expected to drive the evolution of ACE2-mediated viral resistance, as observed during sarbecovirus arms races within bat ACE2 [27,28]. One explanation may be that other physiological constraints exist, potentially limiting this amino acid combination to be functionally tolerable only in mice. Genetic context or starting point can determine which sequence combinations ultimately evolve [29], where intramolecular epistasis, a form of context dependence, enables amino acids to have different functional effects depending on residues at other sites [30]. Epistasis can permit compensatory interactions to evolve following an “original” mutation [3134], ultimately generating completely different amino acid combinations converging on the same structural/functional “solution” [29,3538]. Conversely, intramolecular epistasis can limit the number of possible amino acids combinations that will confer a given function [39,40]. The opening and closing of evolutionary trajectories by epistasis and pleiotropy could potentially reconcile the dearth of sequences capable of blocking SARS-CoV-2 binding with the wide diversity of ACE2 sequences.

In mammals, ACE2 evolved to serve an essential reno- and cardioprotective role in vivo, mediated through its regulation of the renin–angiotensin–aldosterone system (RAAS) in conjunction with ACE. The signaling peptide Angiotensin-II (Ang-II) generated by ACE—a major clinical target for hypertension—stimulates vasoconstriction, inflammation, and fibrosis responses through the Ang-II/AT1R axis [41,42]. ACE2 carboxypeptidase activity counteracts these effects through conversion of Ang-II to Ang-(1–7), a peptide that induces vasodilatory, anti-inflammatory, and antifibrotic effects through MAS signaling [42,43]. Loss of ACE2 in mice worsens cardiac dysfunction in obesity, increases diabetic kidney dysfunction, increases mortality rates after myocardial infarction, and can have severe effects on cardiac contractility [41,4345]. This protective role of ACE2 is largely mediated through its enzymatic processing of Ang II to Ang-(1–7), where exogenous delivery of either ACE2 or Ang-(1–7) can protect against pathogenic features of multiple cardiovascular and kidney diseases [41,45,46]. Given the myriad protective roles of ACE2 enzymatic activity, it may be expected that ACE2 function is highly conserved across species. However, mice display approximately 50% higher ACE2 activity relative to humans [47] and are insensitive to the vasodilatory effects of Ang-(1–7) [43], suggesting that increased ACE2 activity in rodents may have evolved to serve nonvasodilatory protective functions. However, rodents depend on ACE2-mediated degradation of Ang II to main normal blood pressure and cardiovascular homeostasis [44,4852]. These observations suggest that major physiological differences between species can drive differences in ACE2 function, as seen in the evolution of other protein systems [34,40,53]. Organismal sensitivity to mutations affecting ACE2 activity may be especially pronounced due to the X-chromosome location of the ACE2 gene [54], where even heterozygous ACE2 knockout females (−/x) display increased susceptibility to heart and kidney injury [43].

If natural variation across species has evolved to shape ACE2 function, then the evolution of ACE2 sequences due to differences in physiology may alter the latent capacity for viral receptor usage and susceptibility. We therefore investigated whether SARS-CoV-2 binding to ACE2 could be abolished without disrupting ACE2 enzyme function. To expedite this, we took an evolutionary approach that leveraged the natural sequence variation found in mouse ACE2, which SARS-CoV-2 is unable to bind and gain infection [1,21,26]. Here, we identify a specific combination of mutations unique to rodents which fully abolishes RBD binding when inserted into human and dog ACE2, but which in isolation, significantly decreases ACE2 enzyme activity. These detrimental intermediates would likely severely compromise the cardio- and renoprotective functions of ACE2 activity, explaining why these mutations are rare across mammalian species.

Results

We investigated ACE2–RBD binding as well as ACE2 enzymatic function across a range of boreoeutherian mammals either susceptible or resistant to SARS-CoV-2 infection [human (Homo sapiens; XM_017650263.1), dog (Canis lupus familiaris; XM_019746337.1), and pangolin (M. javanica; XM_017650263.1) versus mouse (M. musculus; XM_017650263.1) and Chinese horseshoe bat (Rhinolophus sinicus; XM_019746337.1)] [1,10,12,21]. Using flow cytometry, we found a trend of significantly stronger association of the SARS-CoV-2 RBD with both human and pangolin ACE2 relative to that of SARS-CoV-1, consistent with previous studies [22] (Fig 1A–1D). Notably, SARS-CoV-1 and SARS-CoV-2 RBD association was strongest with human ACE2. We also found evidence that the RBD of both SARS-CoV-1 and SARS-CoV-2 S protein could not bind mouse nor bat (R. sinicus) ACE2 (Fig 1B–1D), consistent with previous studies [5,10,12,21,55]. To complement these binding assays, we next characterized the ability of ACE2 orthologs to enable SARS-CoV-2 S pseudovirus entry into cells. We transfected ACE2 orthologs into human cells (HEK293T) and exposed these cells to pseudotyped murine leukemia virus (MLV) particles containing the SARS-CoV-2 S protein. Consistent with our flow cytometry data and with previous studies, this assay largely recapitulated the host range of wild-type (WT) SARS-CoV-2 [21], displaying significant infection of cells expressing human, dog, and pangolin ACE2, but not that of mouse or R. sinicus bat (Fig 1E). To investigate whether this variation in ACE2–SARS-CoV-2 S binding is mirrored by functional variation in ACE2 enzyme activity, we measured the carboxypeptidase activity of ACE2 orthologs in vitro using a well-characterized fluorometric biochemical assay [47] (Materials and methods; S1 Fig). Importantly, the Ang-II peptide substrate of ACE2 is conserved among all species investigated here (S2 Fig). Furthermore, all ACE2 protein orthologs displayed highly similar expression levels in HEK293T cells (S3 Fig). Strikingly, we found significant variation in ACE2 hydrolysis rates across all mammalian species, suggesting tuning of enzymatic function across evolutionary history (Fig 1F and 1G), perhaps in response to interspecies RAAS variation. Interesting, dog, R. sinicus bat, and pangolin all displayed low ACE2 hydrolysis relative to human and mouse. Mice displayed the highest ACE2 hydrolysis rates, consistent with previous reports [47]. This demonstrates that ACE2 function varies considerably across species and suggests that ACE2 sequence variation could potentially reflect diversification of ACE2 enzymatic function.

Fig 1. Natural variation in SARS-CoV-2 binding is mirrored by diversity in ACE2 enzyme activities.

Fig 1

(A–D) Flow cytometry was used to quantify SARS-CoV-1 and SARS-CoV-2 RBD-Fc association with human cells (HEK293T) expressing ACE2–eGFP orthologs from various mammalian species (MFI [22]). N = 3 biological replicates. Standard deviation is shown. (E) SARS-CoV-2 S pseudovirus infection of HEK293T cells expressing ACE2 orthologs was quantified using a luciferase reporter system. N = 4 biological replicates. Standard deviation is shown. (F) The carboxypeptidase activity of ACE2 was quantified using a fluorometric peptide incubated with solubilized HEK293T cells transfected with ACE2 orthologs. N = 4 biological replicates. (G) Hydrolysis rate of ACE2 orthologs (fluorescence units/minute). N = 3 to 5 biological replicates. Standard error is shown. All data are available in S1 Data. ACE2, angiotensin converting enzyme 2; MFI, mean fluorescence intensity; RBD, receptor-binding domain; SARS-CoV-1, Severe Acute Respiratory Syndrome Coronavirus; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2.

To test this, we first took a bioinformatic approach. We reasoned that since ACE2 has evolved under pressures related to its enzymatic processing of Ang-II/Ang-(1–7), signatures of natural selection at sites within the viral binding interface may reflect a potential role of those sites in mediating ACE2 catalytic activity [56]. We therefore searched for shifts in ACE2 mutational rates (dN/dS) beyond what may be expected from neutral evolutionary pressures alone [57,58]. To do this, we constructed a phylogenetic dataset representing full-length mammalian ACE2 sequences encompassing residues 22 to 742 of human ACE2 (S4 Fig, S1 Table). This dataset represents all major mammalian lineages [59], with a high sampling of bats in particular (Chioptera) since they are known to display high ACE2 sequence diversity [27,28]. We conducted a codon-based statistical phylogenetic analysis on the entire tree (PAML, HyPhy) and found significant evidence that mammalian ACE2 is under positive selection, outperforming models assuming neutral molecular evolution (S2 Table). Since synonymous variation can confound tests of positive selection, we ran a test that explicitly accounts for it in model parameters [60]. This analysis also detected significant evidence of positive selection across ACE2 (S3 Table). As a further control, we excluded bat ACE2 from the analysis, since they are known to display positive selection reflecting an arms race with sarbecovirus binding and infection [27,28]. Even when bats were excluded from the analysis, we still detected evidence of positive selection in mammalian ACE2 (S4 Table). This provides evidence that mammalian ACE2 function may be a target of natural selection that shifts in response to physiological constraints unrelated to sarbecovirus arms races.

To focus our analysis on ACE2 sites responsible for mediating SARS-CoV-2 binding, we took an evolutionary approach leveraging natural sequence variation found in mouse ACE2, which SARS-CoV-2 is unable to bind [1,21]. We identified a set of 6 residues unique to mouse ACE2 that were of particular interest due to their proximity to viral RBD residues implicated in the ACE2–RBD structure (Fig 2A). These sites contact opposite ends of the viral RBD, facilitating binding through a combination of hydrogen bonds (Q24 and K353), as well as van der Waals forces within a hydrophobic pocket of ACE2 (L79, M82, Y83, and P84) [23,25]. These ACE2 residues are located well outside the ACE2 active site mediating catalysis of Ang-II/Ang-(1–7) (Fig 2A; yellow), suggesting that any role of these sites in mediating ACE2 function could be through indirect effects modulating the protein structure, as seen in other protein systems [56,61]. Phylogenetic analysis of evolutionary rates across the ACE2 gene with or without the inclusion of bat ACE2 sequences (S2 and S4 Tables, respectively) identified several of these sites deviating significantly from neutral expectations (Fig 2C; sites 24, 79, dN/dS>1; site 353, dN/dS<1; M8, Bayes empirical Bayes; S5 Table). This finding held even when synonymous rate variation was incorporated into the model (FUBAR; Fig 2C). Interestingly, these sites are less variable in primates relative to other mammalian lineages [19], displaying significantly decreased ACE2 evolutionary rates relative to bats (Chiroptera) and rodents (Fig 2D, S6 Table) (CmD; dN/dS). Interestingly, 2 of these sites (24 and 79) were previously identified as being under positive selection in bats, likely due to evolutionary arms race between bat ACE2 and sarbecovirus binding and infection [27,28]. The fact that these sites remain under positive selection even after bats are excluded from the analysis (S5 Table) strongly suggests that these sites are evolving in response to physiological variables directly related to ACE2 function, rather than viral binding alone.

Fig 2. Interacting amino acids mediating ACE2 activity also govern SARS-CoV-2 binding and cellular infection.

Fig 2

(A) SARS-CoV-2 gains cellular entry through the viral spike protein RBD (red), which targets binding hotspots on the ACE2 receptor (blue) distal to the ACE2 active site (yellow) [6M17; [25]]. (B) Mouse ACE2 displays unique amino acid residues at positions within the RBD binding interface relative to other mammals (S1 Table). (C) Gene-wide statistical phylogenetic analyses (dN/dS averages [dots] and ranges [gray]; PAML, HyPhy) of an alignment of mammalian ACE2 coding sequences (residues 22 to 742, human ACE2 numbering; uniport ID Q9BYF1) reveals positive (*) and negative selection (θ) on RBD binding hotspots. Alignment of ACE2 residues across boreoeutherian mammals is shown. (D) Evolutionary rates of the 6 ACE2 sites that display rare variants in mice. (E) Flow cytometry was used to quantify RBD association with human cells (HEK293T) expressing WT human and mouse ACE2, as well as mutant mouse ACE2 containing all 6 human substitutions. N = 3 biological replicates; standard deviation is shown. (F) Infection of HEK293T cells transfected with either WT or mutant ACE2 exposed to VSV-G pseudotyped with SARS-CoV-2 S protein. Cellular infection was measured as a function of luciferase luminescence. N = 4 biological replicates. Standard error is shown. (G) The effect of ACE2 mutations on ACE2 hydrolysis rates was measured using a fluorometric peptide. ACE2 activity was measured as fluorescence units per minute. N = 5 biological replicates. Standard error is shown. (H) Flow cytometry analysis of RBD association with human cells (HEK293T) expressing WT and mutant human ACE2. N = 3 biological replicates. Standard error is shown. (I) The effect of human ACE2 mutations on ACE2 hydrolysis rates. N = 3 to 5 biological replicates. Standard error is shown. (J, K) Flow cytometry analysis of RBD association with human cells (HEK293T) expressing WT and mutant human and dog ACE2. N = 2 to 3 biological replicates. Standard error is shown. (L, M) Pseudovirus infection of HEK293T cells transfected with either (L) human or (M) dog ACE2, containing the indicated mutations. N = 4 biological replicates. Standard deviation is shown. (N, O) ACE2 hydrolysis activity of dog ACE2 with indicated mutations. N = 3 to 5 biological replicates. Standard error is shown. All data are available in S1 Data. ACE2, angiotensin converting enzyme 2; LRT, likelihood ratio test; RBD, receptor-binding domain; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2; WT, wild-type.

Although these bioinformatic signatures suggest that these sites are functionally important, these predictions could have been influenced by the presence of multinucleotide substitutions among ACE2 orthologs [62]. Thus, we experimentally tested whether ACE2 sequence variation at these sites plays a role in the diversification of ACE2 function. We also investigated whether these sites mediate viral binding and infection. First, we substituted the rare residues found in mouse ACE2 with the residues found in human ACE2 (Fig 2B). Using flow cytometry, we found that these mutations were sufficient to confer mouse ACE2 with binding to SARS-CoV-2 S RBD, at nearly 80% of that of human WT ACE2 (Fig 2E). Using the MLV-SARS-CoV-2 pseudovirus system, we found that mutant mouse ACE2 containing these 6 substitutions was sufficient to confer pseudovirus infection of HEK293T cells, even beyond that conferred by WT human ACE2 (Fig 2F). This demonstrates that these rare mouse mutations likely play a key role in the resistance of mice to SARS-CoV-2 [1,21]. Interestingly, we find that these 6 mutations altogether also significantly decreased the catalytic activity of mouse ACE2 by over 25% (Fig 2G), suggesting that at least some of these sites are functionally important. Next, we investigated whether these sites also mediate the enzymatic activity and the binding of SARS-CoV-1/2 S RBD to human ACE2. Using co-immunoprecipitation and flow cytometry, we systematically characterized the effect of these rare variants from mouse on the binding of SARS-CoV-1/2 S RBD to human ACE2. The single mutation with the largest effect on the RBD binding of both SARS-CoV-1 (S5 Fig) and SARS-CoV-2 to ACE2 was K353H (Fig 2H). The other single mutations had only minor effects on SARS-CoV-2 RBD binding (Fig 2H). However, each of the 6 single mutations displayed considerable effects on human ACE2 hydrolysis activity (Fig 2I). This demonstrates that ACE2 residues within the RBD binding interface can indirectly modulate ACE2 activity.

Next, we attempted to abolish RBD binding to both human and dog ACE2 by making multiple substitutions at these ACE2 sites. Consistent with the reciprocal experiment in mice described above (Fig 2E), RBD binding to the ACE2 of both human and dog was abolished with just these 6 mouse substitutions (Fig 2J and 2K). This is surprising given the numerous other sites implicated in mediating human and dog ACE2 interactions with the viral RBD [23,25,63]. In both human and dog ACE2, abolition of RBD binding depended on mutating sites 24 and 82 (Fig 2J and 2K), despite displaying different residues in human (Q24; M82) and dog (L24; T82) ACE2 (Fig 2C). This demonstrates that SARS-CoV-2 utilizes the same combination of ACE2 positions to bind both human and dog ACE2 despite amino acid variation at these sites. This may explain why bioinformatic predictions of SARS-CoV-2 host range based on human ACE2 sequence homology have tended to underestimate the infection risk of species such as dogs, ferrets, and minks [9,10,1214,18]. Using the MLV-SARS-CoV-2 pseudovirus system, we found that these 6 mouse substitutions also significantly reduced pseudovirus infection of HEK293T cells expressing WT and sextuple mutant human and dog ACE2 (Fig 2L and 2M). Although infection was not completely abolished in mutant human and dog ACE2, the reciprocal human substitutions into mouse ACE2 were sufficient to confer infection (Fig 2E). This altogether demonstrates that these rare mouse mutations are likely sufficient for resistance to SARS-CoV-2.

We had observed that these rare mouse substitutions are partly responsible for the high enzymatic activity of mouse ACE2 relative to that of human (Fig 2G). Yet, when substituted into human ACE2, single mutations drive down ACE2 activity (Fig 2I). The dependence of mutational effects on genetic background implies epistasis in ACE2 function. Consistent with this, we observed in both human and dog ACE2 a context dependence (epistasis) of mutational effects between the distal domains of the RBD binding interface. Specifically, we found that the detrimental functional effects of the quadruple mutant in both human and dog ACE2 (L79T; T/M82S; Y83F; P84S) as well as the K353H single mutant were significantly reversed when combined—a phenomenon known as sign epistasis—displaying partial compensation for each other’s detrimental effects on ACE2 hydrolysis rates (Fig 2N and 2O). Further evidence of epistasis in this ACE2 domain is seen in the sextuple mutant, where introduction of L24N in dog ACE2 fully rescued ACE2 activity, whereas Q24N decreased activity in human ACE2 relative to the quintuple mutant (Fig 2N and 2O). This discrepancy is likely attributable to amino acid interactions between site 24 and other sites not investigated. These results demonstrate that SARS-CoV-2 binding and infection depend on functionally important ACE2 sites that are highly sensitive to background effects (intramolecular epistasis).

Since normal ACE2 activity is essential for blood pressure regulation and cardiac homeostasis (Fig 3A and 3B; [43]), the deleterious functional effects of these mutations may have closed this human ACE2 evolutionary trajectory leading to SARS-CoV-2 resistance [31,40]. Indeed, we found extremely low human allele frequencies for missense polymorphisms at these ACE2 sites (gnomAD), with most at zero (Fig 3C). It is notable that the 2 sites with polymorphisms (82 and 84) are the only 2/6 where we detected no evidence of positive or negative selection (Fig 2C). We investigated the predicted pathogenicity of these polymorphisms and compared them to the hypothetical polymorphism K353H (Fig 3D)—a substitution required to abolish SARS-CoV-2 binding at the cost of large detrimental effects on human ACE2 activity. We employed PolyPhen—a machine learning–based Bayesian method for estimating pathogenicity of nonsynonymous human mutations ([64]). Consistent with its relatively high allele frequency (2.44 × 10−5), the M82I polymorphism had a zero PolyPhen score, indicating it as benign (Fig 3D). The nearly 5-fold less common polymorphism P84T (5.47 × 10−6) had a slightly higher pathogenicity score but was still predicted as benign (Fig 3D). By comparison, the hypothetical human mutation (K353H) produced a “possibly damaging” PolyPhen score (Fig 3D), consistent with the 2-fold decrease in human ACE2 activity caused by the K353H mutation (Fig 2H), the absence of human polymorphisms at this site (Fig 3C), and strong purifying selection across mammals (Fig 2C). Interestingly, a human ACE2 polymorphism directly adjacent to site 353 (G352V) produced a “probably damaging” PolyPhen score (Fig 3D), consistent with its low allele frequency (5.75 × 10−6). This analysis suggests that polymorphisms at sites 352 to 353 may be unlikely to evolve in human populations due to functionally deleterious effects. This functional constraint likely blocks evolutionary escape of human ACE2 from SARS-CoV-2 binding, which depends on K353H (Fig 3E). In dog ACE2, many single and combinatorial mutations at these sites are also functionally detrimental, including K353H (Fig 3F). Unlike human ACE2, however, in the dog ACE2 background, the sign epistasis of K353H induces fully compensatory effects, such that simultaneously substituting all 6 mutations becomes functionally nearly neutral (Fig 3F, dashed yellow arrow). This suggests that unlike humans and other primates, evolutionary escape from CoV-2 binding may be possible in canines along future evolutionary trajectories.

Fig 3. Human polymorphisms sufficient for SARS-CoV-2 resistance are blocked by deleterious functional effects related to cardiovascular constraints.

Fig 3

(A, B) Hydrolysis of ANG-II by ACE2 generates ANG-(1–7), a vasodilatory peptide with protective effects against cardiac hypertrophy [43]. (C) Allele frequencies for human missense polymorphisms at these positions are lower than the protein-wide average frequency at a given ACE2 site (black line). Site 352 is included for comparison to site 353, which had no polymorphisms. (D) Predicted pathogenicity of human ACE2 polymorphisms and the hypothetical substitution (K353H) sufficient for resistance to SARS-CoV-2 (PolyPhen). (E) Substitution of mouse residues into human ACE2 abolishes ACE2–RBD binding, but with large trade-offs on ACE2 hydrolytic activity due to K353H. (F) Unlike human ACE2, dog ACE2 can theoretically reach an alternative sequence that abolishes viral binding without deleterious trade-offs on ACE2 activity. For comparison, each species ACE2 hydrolysis rates are normalized to respective WT values. All data are available in S1 Data. ACE2, angiotensin converting enzyme 2; RBD, receptor-binding domain; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2; WT, wild-type.

We hypothesized that the situation may have been analogous in the rodent ancestor 70 MYA; these 6 mutations may have represented a viable alternative sequence–function optimum, thus facilitating their eventual evolution in the mus genus. To test this, we constructed a large jawed vertebrate ACE2 phylogenetic dataset (Fig 4A, S6 Fig) and reconstructed the ancestral rodent (RodAnc) ACE2 using likelihood methods [65]. Since the carboxyl terminus of vertebrate ACE2 was too divergent to align, we constructed the RodAnc ACE2 coding-sequence with either mouse (-M) or human (-H) carboxyl termini and measured ACE2-specific hydrolysis activity. We found that RodAnc-M displayed high ACE2 hydrolysis activity indistinguishable from that of mouse (Fig 4B). Moreover, the RodAnc with the human carboxyl terminus displayed ACE2 activity significantly lower than mouse ACE2, but significantly higher than that of human, suggesting that the carboxyl terminus is necessary, but not sufficient to explain the high ACE2 activity seen in the RodAnc. We therefore continued our mutagenesis investigation using RodAnc-M (hereafter referred to as RodAnc). RodAnc ACE2 had none of the rare sequence variants unique to the RBD binding domain of mouse ACE2 (Fig 4C). This strongly suggests that these mutations conferring mice with resistance to SARS-CoV-2 infection appeared relatively recently in rodent ACE2 evolution. Consistent with this, RodAnc ACE2 bound SARS-CoV-2 S RBD at a level nearly identical to that of human ACE2, with viral binding abolished after introduction of the 6 mouse mutations (Fig 4D). Consistent with our hypothesis, we found that RodAnc could tolerate the introduction of all 6 mouse mutations without deleterious effects on ACE2 enzymatic activity (Fig 4E). The functional viability of this alternative sequence combination in the genetic background of RodAnc ACE2 may therefore explain why these rare variants were ultimately permitted to evolve in mice.

Fig 4. Alternative sequence–function optima in rodent ancestor ACE2 serendipitously confers resistance to SARS-CoV-2 in extant mice.

Fig 4

(A) Phylogeny of jawed vertebrates used in the ancestral reconstruction of ACE2 from the last common ancestor of rodents (RodAnc). (B) Evolution of high ACE2 activity in the rodent ancestor. The carboxyl terminus of mouse (M) or human (H) was used to determine hydrolysis rates of a fluorometric peptide. N = 4 to 5 biological replicates. Standard error is shown. (C) RodAnc ACE2 displays none of the rare sequence variants unique to the RBD binding domain of mice. (D) Flow cytometry analysis of SARS-CoV-2 S RBD binding to RodAnc-M and human ACE2. Sextuple mutants of each contain the 6 mouse variants. N = 3 biological replicates. Standard deviation is shown. (E) Hydrolysis rates of WT and mutant ACE2. N = 5 biological replicates. Standard error is shown. (F) Residues conferring mice with resistance to SARS-CoV-2 are unlikely to evolve in humans due to antagonistic pleiotropy with human ACE2 hydrolysis rates. This antagonistic pleiotropy is absent from dogs and the rodent ancestor. All data points relative to human WT, except dog, for which RBD binding is shown relative to dog WT. (G) Systolic blood pressure divergence between rodents (red) and primates (blue) is correlated with differences in body size (p = 0.001; phylogenetically independent least squares linear regression). All data are available in S1 Data. ACE2, angiotensin converting enzyme 2; PIC, phylogenetic independent contrast; RBD, receptor-binding domain; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2; WT, wild-type.

If the functional viability of this alternative sequence explains the traversal of this trajectory by mice, then why was this alternative sequence combination never realized in dog ACE2, where it is, a priori, equally viable? We hypothesized that mammalian differences in physiological constraints related to ACE2 function may influence these evolutionary trajectories. Although ACE2 activity plays a critical role in cardioprotection and prevention of high blood pressure in both mice and humans [43,66], a key physiological difference in rodents is small body size, which is known to result in a lower homeostatic blood pressure set point relative to larger body size mammals [67]. We conducted a phylogenetic statistical analysis on systolic blood pressure values across mammalian species and observed a significant correlation with body size (r2 = 0.26; p = 0.001; phylogenetic independent contrast least squares linear regression) (Figs 4G and S7). Blood pressures were lowest in rodent and bat species and much higher in carnivores, including dog, as well as primates (Fig 4G). Since ACE2 is critical for degrading ANG-II and preventing ANG-II-mediated vasoconstriction in rodents [44,4852], we propose that high ACE2 activity may have evolved in the rodent ancestor to maintain the lower homeostatic blood pressure set point in rodents relative to large mammals, such as primates and carnivores. This is consistent with fossorial data suggesting that the RodAnc was among the smallest ancestral mammal 70 MYA [68]. Although preliminary, these observations suggest that low blood pressure, and by extension, high ACE2 activity, may be a prerequisite to traverse this evolutionary trajectory, even if it ultimately leads to a sequence conferring equivalent activity (Fig 4F). By contrast, high ACE2 activity may be unlikely to evolve in carnivores and primates due to blood pressure constraints, therefore closing this trajectory. Although speculative, we discuss below how these genetic and physiological contingencies may have indirectly influenced the evolution of resistance to SARS-CoV-2.

Discussion

We have shown that the ACE2 evolutionary trajectory leading to SARS-CoV-2 resistance in mice [26] was likely unattainable during human evolution. This is caused by the fact that the 6 mutations abolishing SARS-CoV-2 S protein RBD–ACE2 binding also individually disrupt ACE2 hydrolysis activity by up to 50% to 60%. These ACE2 mutations are fully compensatory in combination, yet only in the genetic background of dog and the rodent ancestor, not human. Although it is possible that these sites directly modulate substrate binding, it is known that angiotensin substrates bind within a deep cleft housing the active site [69], whereas RBD binding occurs on the outer surface of ACE2 [25]. Interestingly, RBD binding is known to increase ACE2 activity [70], suggesting that RBD binding does not impede substrate recognition. Our results therefore suggest that like other proteins, the catalytic activity of ACE2 can be mediated by the indirect structural effects of residues outside the active site, which may be modulated by RBD binding [40,56,61]. Although previous work demonstrated that inactivation of the ACE2 active site had no effect on SARS-CoV-1 and SARS-CoV-2 binding, these mutations were directly within the active site far from the viral binding interface [71,72]. The mutations investigated here are directly within the viral binding interface of SARS-CoV-2 and from species (M. musculus) naturally resistant to SARS-CoV-2 that still maintain ACE2 activity. While it is conceivable that human ACE2 could perhaps have evolved reduced viral binding affinity through other mutations that do not affect enzymatic activity, a previous study showed that human ACE2 engineered to increase viral binding (T27Y, L79T, and N330Y) also displays reduced ACE2 activity [73]. Here, we discuss the implications of epistasis and antagonistic pleiotropy for the evolution of ACE2 function and potential impacts on therapeutic design.

Our work demonstrates the essential importance of the ACE2 viral binding domain on catalytic function. The consequent constraints on the ACE2 amino acid sequence in humans and other primates strongly indicate that protective polymorphisms in the human population are unlikely, in contrast to examples such as CCR5, the co-receptor for HIV [74]. Our analysis shows that allele frequencies for ACE2 missense polymorphisms at 6 sites necessary for SARS-CoV-2 binding are lower than the protein-wide average, reflecting the physiological importance of these positions in mediating ACE2 catalytic activity. Although potentially protective ACE2 polymorphisms have been predicted at other sites in the RBD–ACE2 interface [75,76], we show that statistically unlikely combinations of mutations are sufficient to disrupt SARS-CoV-2 binding, each of which have severe effects on ACE2 activity. Given the importance of ACE2 enzymatic processing of Ang II to Ang-(1–7) in protection against pathogenic features of multiple cardiovascular and kidney diseases [41,45,46], it is possible that that the extant mutational combinations observed in nature (e.g., human, bat, dog, mouse, and pangolin) may all represent alternative sequence “solutions” [29,77] each uniquely required for an animal’s physiology, thereby representing fitness “peaks” in the sequence landscape [29,31,34,7881]. Alternatively, the lack of evolutionary conservation at these functionally relevant sites may suggest that mutational effects on ACE2 activity are not physiological relevant. Other RAAS components may be more important, such as renin activity levels, which are a major factor in determining circulating Ang-II levels in humans [43,82,83]. Yet, there is a known association of human ACE2 polymorphisms with hypertension [84,85], and our analysis suggests that at least 1 of the 6 mutations sufficient for resistance to SARS-CoV-2 may be pathogenic. Selective processes may have therefore partly influence ACE2 evolution, which was likely complicated by the effects of epistasis and pleiotropy, as discussed below.

The effect of mutations on protein function is often complicated by pleiotropy and epistasis [31,86]. Both phenomena can constrain evolutionary trajectories by forcing a dependence on background genotype and phenotype [31,34,40,81,8790]. Here, we find that these 2 phenomena interact in a complex way to block humans from a trajectory that led to SARS-CoV-2 resistance in mice. Specifically, we find that sign epistasis determines the occurrence of antagonistic pleiotropy: In dog ACE2, all 6 mouse mutations are fully compensatory, whereas in humans, no such alternative sequence–function optima exist. In the rodent ancestor ACE2, the alternative optima also existed as it currently does in dog, suggesting that dog ACE2 could eventually converge on this sequence combination. However, evolution can take different pathways depending on the genetic starting point [29], and ostensibly accessible alternative optima can be blocked by environmental and physiological constraints [34,91,92]. The occurrence of this sequence combination only a few times in evolutionary history despite widespread sequence degeneracy suggests that constraints exist, which limit the evolvability of this sequence. For instance, the existence of functionally detrimental intermediates may explain why these sequences did not evolve more frequently in evolutionary history [29,31,34,7880]. Notably, the fitness effects of pleiotropy can change with genetic and environmental background [87,88], suggesting the detrimental intermediates may have had more minimal fitness effects in the rodent ancestor, potentially due to higher basal levels of ACE2 activity in rodents. Although preliminary, our evidence suggests that unique cardiovascular constraints in rodents may have opened the available pool of sequence variation, allowing sequence diversification in the rodent ancestor.

Our study also has relevance for therapeutic design. Human recombinant soluble ACE2 (hrsACE2) is in active development as a strategy to neutralize SARS-CoV-2 by binding the viral spike protein [83,93]. In human Coronavirus Disease 2019 (COVID-19) patients, the catalytic activity of hrsACE2 can help reduce angiotensin II levels as well as inflammation associated with COVID-19, likely through elevating Ang-(1–7) levels [83]. hrsACE2 may therefore have the added benefit of minimizing the injury to multiple organs caused by viral-induced downregulation of ACE2 expression and renin–angiotensin hyperactivation [44,9498]. However, under normal physiological conditions, higher sACE2 plasma activity has been associated with increased pulmonary artery systolic pressure and ventricular systolic dysfunction [99]. In these instances where patients have preexisting conditions that can be exacerbated by increasing sACE2 activity, it may be beneficial to make use of catalytically inactive hrsACE2 that binds RBD with similar efficiency [71]. There is likely to be a spectrum of scenarios where variable levels of ACE2 activity is desirable, in which case recombinant nonhuman ACE2 can serve a key role.

It is important to note the caveats of our interpretations. In addition to its main role in Ang-II processing, ACE2 carboxypeptidase activity can also process other peptide mediators of RAAS signaling, including Ang-I and Angiotensin A [43]. ACE2 also plays an important role in the intestine as a trafficking adaptor for the large amino acid transporter B(O)AT1, which is essential for regulating tryptophan levels in blood [100,101]. This suggests that natural variation in mammalian ACE2 may represent adaptation to a diversity of physiological processes mediated by these important ACE2 functions. Second, our experimental characterizations have limited ACE2 sampling. For instance, we did not investigate other bat ACE2 orthologs, which vary in their ability to facilitate SARS-CoV-2 infection [102] and may have other receptors permitting entry. Similarly, we did not investigate other ancestral intermediates between the rodent ancestor and mouse, which may reveal additional mutational combinations at these sites with varying degrees of epistasis and pleiotropy. Last, we only explored a subset of sequence–function space in human ACE2, raising the possibility that other evolutionary pathways to SARS-CoV-2 resistance may exist without deleterious trade-offs on ACE2 function. Even considering these caveats, our results provide evidence that ACE2 likely evolves in response to functional constraints, which limit the accessibility of evolutionary trajectories, one of which may have led to resistance to SARS-CoV-2.

Materials and methods

ACE2 and SARS-CoV-2 constructs

WT hACE2 with a 1D4 (C9) carboxyl-terminal tag (TETSQVAPA) was a gift from Hyeryun Choe (Addgene plasmid # 1786; http://n2t.net/addgene:1786; RRID:Addgene_1786) [103]. Site-directed mutagenesis primers were designed to induce single amino acid substitutions via PCR (QuickChange II, Agilent, Santa Clara, California, USA). Mouse ACE2 was cloned from cDNA synthesized from an RNA extraction of ileum tissue from a C57/BL6 mouse sacrificed in compliance with all regulations of the Johns Hopkins University Institutional Animal Care and Use committee. ACE2 coding sequences from dog (XM_014111329.2), pangolin (M. javanica; XM_017650263.1), bat (R. sinicus; XM_019746337.1), and the rodent ancestor were synthesized as gblocks (Integrated DNA Technologies, Coralville, Iowa, USA). All animal ACE2 inserts were cloned into a pEGFP-N1 vector, with eGFP carboxyl-terminal tag. For interspecies comparisons, hACE2 was also cloned into pEGFP-N1. The RBD of the SARS-CoV-2 S protein was obtained as a pcDNA3-SARS-CoV-2-S-RBD-Fc plasmid, as a gift from Erik Procko [73] (Addgene plasmid # 141183; http://n2t.net/addgene:141183; RRID:Addgene_141183). pcDNA3.1-SARS-Spike was a gift from Fang Li (Addgene plasmid # 145031; http://n2t.net/addgene:145031; RRID:Addgene_145031) [23]. We used this plasmid as a template to clone the RBD of SARS-CoV-1 Spike protein into pcDNA3 with a Fc carboxyl-terminal tag. This RBD consisted of residues 318 to 510, as previously described (numbering as per AAP13441.1) [104].

Flow cytometry and immunoprecipitation

HEK293T cells were transfected with ACE2-1D4, or SARS-CoV-2 S protein RBD-Fc constructs using TransIT-X2 (Mirus, Madison, Wisconsin, USA). Twenty-four hours after transfection, the media of RBD-transfected cells was replaced with OptiPRO SFM (Thermo Fisher, Waltham, Massachusetts, USA). Seventy-two hours after transfection, the media from RBD-transfected cells was concentrated using an Amicon Ultra-15 centrifugal filter unit with a 3,000-kDa molecular mass cutoff. In parallel, ACE2-transfected cells were harvested and washed with PBS. For flow cytometry, 5.0 × 105 cells were resuspended in 1-mL incubation buffer [Dulbecco’s PBS containing 0.02% EDTA (Sigma-Aldrich, St. Louis, Missouri, USA), 50 μg/mL DNase I (Worthington), and 5 mM MgCl2] and incubated with 20 ug/mL of RBD-Fc for 30 minutes at room temperature as previously described [22]. Cells were then washed with buffer (5% FBS, 0.1% sodium azide in PBS) and incubated with human ACE2 Alexa Fluro 647-conjugated antibody (1 μg/106 cell, #FAB9332R, R&D Systems Minneapolis, Minnesota, USA), human IgG Fc PE-conjugated antibody (10μg/106 cell, #FAB110P, R&D Systems), or human IgG Fc APC-conjugated antibody (10μg/106 cell, #FAB110A, R&D Systems) for 30 minutes at room temperature. LSR II (BD Bioscience, Franklin lakes, New Jersey, USA) was used to collect the data, and Flowjo (Flowjo, Ashland, Oregon, USA) was used for analysis, conducted as previously described [22]. For immunoprecipitation, ACE2-transfected cells were lysed in a PBS buffer containing 1% CHASPO (Sigma-Aldrich) and incubated with Dynabeads Protein G (Thermo Fisher) and 2 μg of RBD-Fc concentrate. Dynabeads were washed with PBS (0.5% CHAPSO), and elutions were immunoblotted using antibodies against 1D4 (Abcam; ab5417) and Human IgG Fc (Abcam, Cambridge, UK; ab97225).

Pseudovirus assay

SARS-CoV-2 spike pseudotyped MLV were generated based on a published protocol [105]. Briefly, HEK293T cells were transfected with 3 plasmids, which express MLV Gag and Pol, firefly luciferase reporter and SARS-CoV-2 S protein. Forty-eight hours after transfection, culture medium containing pseudotyped particles were centrifuged and then filtered through a sterile 0.45-μm pore-sized filter to remove cell debris. For transduction, HEK293T cells were first transfected with ACE2 orthologs using lipofectamine 3000. Twenty-four hours later, 200 μL of SARS-CoV-2 Spike pseudotyped MLV were added to each well and incubated for 2 days. The transduction efficiency was quantified by measuring the activity of luciferase using luciferase assay system (Promega, Madison, Wisconsin, USA) and GloMax 20/20 luminometer (Promega).

ACE2 hydrolysis assay

HEK293T cells were transfected with ACE2 constructs using TransIT-X2 (Mirus). Seventy-two hours later, cells were washed in PBS and lysed in ACE2 reaction buffer pH 6.5 (1 M NaCl, 0.5% Triton X-100, 0.5 mM ZnCl2, 75 mM Tris-HCl). Cell lysates were diluted in reaction buffer to 0.5-μg protein, incubated ± the ACE2-specific inhibitor 10μM DX600 (Cayman Chemical, Ann Arbor, Michigan, USA) for 20 minutes at room temperature, followed by addition of 100 μM Mca-YVADAPK(Dnp) (R&D Systems) and incubation at 37°C. Fluorescence emission at 405 nm was measured at 10, 60, and 120 minutes using a microplate reader (BMG Labtech, Ortenberg, Germany) after excitation at 320 nm. Hydrolysis rates were quantified as fluorescence units per minute, using the slope of fluorescence development between 10 and 120 minutes, as previously described [47]. An ANOVA general linear model with fluorescence as the response variable, mutation as the factor, and assay time as the covariate was fit to fluorescence data generated in the ACE2 hydrolysis assay. Statistical differences in ACE2 hydrolysis rates were determined using a cross factor between the mutation factor and time covariate.

Phylogenetic comparative methods

Human ACE and ACE2 coding sequences were used as blast queries to identify mammalian ACE2 orthologs. After removing low-quality sequences with gaps and ambiguous characters, this resulted in 107 species representing all major jawed vertebrate lineages were obtained from GenBank (S1 Table). This encompassed nearly the entire ACE2 coding sequence (residues 22 to 742, human ACE2 numbering; uniport ID Q9BYF1). These sequences were aligned using PRANK followed by manual adjustment [106]. This alignment was used to estimate a gene tree using PhyML 3.1 [107] (S5 Fig), with GTR selected using automatic model selection based on AIC values [108], and aLRT SH-like branch support. This ML tree was rooted using ACE and recapitulated all major phylogenetic relationships (S4 Fig) [109]. Ancestral sequences and posterior probability distributions were inferred using the best fitting models in the codeml package of PAML 4.9 (S8 Table) [58]. For estimation of dN/dS values using random sites models in the codeml package [58] and HyPhy [60,110], ACE sequences were pruned from the dataset, nonmammalian ACE2 was also pruned, and additional mammalian ACE2 sequences added to increase sampling. This alignment represented 89 species (S1 Table) and was used to infer a ML gene tree using IQ-Tree (S2 Fig), with the substitution model auto selected, followed by ultrafast bootstrap analysis and SH-aLRT branch tests [111]. PAML random sites models were used to investigate evidence of positive selection (S2 Table). To test for dN/dS differences among branches in the phylogeny, clade model D (CmD) [112] was used to analyze an ACE2 alignment containing only those sites of interest in the ACE2 viral binding interface (S6 Table). M3 with 3 site classes was used as the null model for CmD. All random sites and clade model PAML model pairs were statistically evaluated for significance by likelihood ratio tests (LRTs) with a χ2 distribution.

We conducted a phylogenetic comparative analysis on systolic blood pressure dataset by combining bat data with mammalian data compiled from a previous study [67,113]. We pruned this dataset to include only species with fossil calibrated divergence times [109] (S6 Fig). Body size values (grams) were obtained from the Ageing Genomic Resources AnAge database [114]. We conducted a phylogenetically independent correlation analysis (least squares linear regression; S7 Fig) by using the PDTREE program of the PDAP module of MESQUITE [115,116] to calculate phylogenetically independent contrasts of log10 body mass (grams) with systolic blood pressures (mm Hg) as the dependent variable. Before independent contrasts were calculated, branch lengths reflecting divergence times were ln transformed to meet the assumptions of independent contrast analysis [115,116]. To produce dN/dS estimates for branches in the untransformed phylogeny, we pruned the mammalian ACE2 alignment to match the species represented in the blood pressure dataset and subjected the phylogeny and the alignment to analysis by aBSREL [117].

Supporting information

S1 Fig. Recombinant ACE2 hydrolysis activity in solubilized transfected HEK293T cells.

ACE2 hydrolysis activity was measured using a fluorogenic peptide substrate (Mca-YVADAPK(Dnp)-OH) incubated with lysates of HEK293T cells for 2 hours. Cells were either untransfected or were transfected with a human ACE2 construct. Incubation of lysate–peptide mixture with a ACE2-specific inhibitor (DX600) reduced fluorescence attributable to ACE2 hydrolysis activity. N = 5 to 6 biological replicates. Standard error is shown. All data are available in S1 Data. ACE2, angiotensin converting enzyme 2.

(DOCX)

S2 Fig. Conservation of angiotensin peptide sequences across mammalian species investigated in this study.

Renin produces angiotensin 1 by cleaving Angiotensinogen (AGT gene). Angiotensin 1 is subsequently cleaved by ACE, followed by ACE2. ACE2, angiotensin converting enzyme 2.

(DOCX)

S3 Fig. Expression of ACE2–gfp orthologs in transfected HEK293T cells was assessed by flow cytometry (% of GFP-positive cells).

These expressed ACE2 proteins were used in hydrolysis assays. Human ACE2 served as an internal control in each separate assay. ACE2, angiotensin converting enzyme 2.

(DOCX)

S4 Fig. Maximum likelihood phylogeny used in PAML analyses.

aLRT-SH like branch support values (IQ-Tree) are shown. Note that a basal trichotomy was artificially induced to accommodate input file requirements. All data are available in S1 Data.

(DOCX)

S5 Fig. Targeted mutations to human ACE2 disrupt binding to the RBD of SARS-CoV-1 and SARS-CoV-2.

Western blots of immunopreciptations and cell lysates of HEK293T cells co-transfected with an Fc-tagged SARS-CoV-2 S protein RBD and 1D4-tagged (C9) human ACE2 construct. ACE2, angiotensin converting enzyme 2; RBD, receptor-binding domain; SARS-CoV-1, Severe Acute Respiratory Syndrome Coronavirus; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2.

(DOCX)

S6 Fig. Maximum likelihood phylogeny used in ancestral reconstruction of Rodent ACE2.

aLRT-SH like branch support values (PhyML) are shown. All data are available in S1 Data. ACE2, angiotensin converting enzyme 2.

(DOCX)

S7 Fig. Species phylogeny and least squares linear regression using phylogenetically independent contrasts of systolic blood pressure and body mass.

All data are available in S1 Data.

(DOCX)

S1 Table. ACE2 accession numbers used in dN/dS estimates.

ACE2, angiotensin converting enzyme 2.

(DOCX)

S2 Table. Analyses of selection on mammalian ACE2 using PAML random sites models.

ACE2, angiotensin converting enzyme 2.

(DOCX)

S3 Table. Results of BUSTED (HyPhy) analyses of mammalian ACE2.

This model accounts for synonymous rate variation (SRV). Log L values demonstrate that the unconstrained model performs better than the constrained, specifically due to the inclusion of a positive selection omega site category (ω3). ACE2, angiotensin converting enzyme 2.

(DOCX)

S4 Table. Analyses of selection on mammalian ACE2 without bat (Chioptera) sequences using PAML random sites models.

ACE2, angiotensin converting enzyme 2.

(DOCX)

S5 Table. Positively selected sites in mammalian ACE2.

ACE2, angiotensin converting enzyme 2.

(DOCX)

S6 Table. Results of CmD analyses of mammalian ACE2 under various partitions.

ACE2, angiotensin converting enzyme 2; CmD, clade model D.

(DOCX)

S7 Table. ACE and ACE2 accession numbers used in ancestral reconstruction.

ACE, angiotensin converting enzyme; ACE2, angiotensin converting enzyme 2.

(DOCX)

S8 Table. Results of random sites analyses of vertebrate ACE2, with the best fitting mode (M8) used for the ancestral reconstruction of mammalian ACE2.

ACE2, angiotensin converting enzyme 2.

(DOCX)

S1 Data. Contains all individual data points used to derive the means and errors represented throughout this study.

(XLSX)

Acknowledgments

The authors thank the Johns Hopkins Genetic Resources Core Facility (RRID:SCR_018669) for DNA sequencing.

Abbreviations

ACE2

angiotensin converting enzyme 2

Ang-II

Angiotensin-II

CmD

clade model D

COVID-19

Coronavirus Disease 2019

hrsACE2

human recombinant soluble ACE2

LRT

likelihood ratio test

MLV

murine leukemia virus

RAAS

renin–angiotensin–aldosterone system

RBD

receptor-binding domain

SARS-CoV-1

Severe Acute Respiratory Syndrome Coronavirus

SARS-CoV-2

Severe Acute Respiratory Syndrome Coronavirus 2

WT

wild-type

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was supported by research grants from the National Institutes of Health (EY022383 and EY022683; to E.J.D.) and Core Grant P30EY001765, Imaging and Microscopy Core Module. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579(7798):270–3. Epub 2020/02/06. doi: 10.1038/s41586-020-2012-7 ; PubMed Central PMCID: PMC7095418. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.MacLean OA, Lytras S, Weaver S, Singer JB, Boni MF, Lemey P, et al. Natural selection in the evolution of SARS-CoV-2 in bats created a generalist virus and highly capable human pathogen. PLoS Biol. 2021;19(3):e3001115. Epub 2021/03/13. doi: 10.1371/journal.pbio.3001115 ; PubMed Central PMCID: PMC7990310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Boni MF, Lemey P, Jiang X, Lam TT, Perry BW, Castoe TA, et al. Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic. Nat Microbiol. 2020;5(11):1408–17. Epub 2020/07/30. doi: 10.1038/s41564-020-0771-4 . [DOI] [PubMed] [Google Scholar]
  • 4.Andersen KG, Rambaut A, Lipkin WI, Holmes EC, Garry RF. The proximal origin of SARS-CoV-2. Nat Med. 2020;26(4):450–2. Epub 2020/04/15. doi: 10.1038/s41591-020-0820-9 ; PubMed Central PMCID: PMC7095063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Xiao K, Zhai J, Feng Y, Zhou N, Zhang X, Zou JJ, et al. Isolation of SARS-CoV-2-related coronavirus from Malayan pangolins. Nature. 2020;583(7815):286–9. Epub 2020/05/08. doi: 10.1038/s41586-020-2313-x . [DOI] [PubMed] [Google Scholar]
  • 6.Chu DK, Akl EA, Duda S, Solo K, Yaacoub S, Schünemann HJ, et al. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020;395(10242):1973–87. doi: 10.1016/S0140-6736(20)31142-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Randolph HE, Barreiro LB. Herd Immunity: Understanding COVID-19. Immunity. 2020;52(5):737–41. Epub 2020/05/21. doi: 10.1016/j.immuni.2020.04.012 ; PubMed Central PMCID: PMC7236739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Halfmann PJ, Hatta M, Chiba S, Maemura T, Fan S, Takeda M, et al. Transmission of SARS-CoV-2 in Domestic Cats. N Engl J Med. 2020. Epub 2020/05/14. doi: 10.1056/NEJMc2013400 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Kim YI, Kim SG, Kim SM, Kim EH, Park SJ, Yu KM, et al. Infection and Rapid Transmission of SARS-CoV-2 in Ferrets. Cell Host Microbe. 2020;27(5):704–9 e2. Epub 2020/04/08. doi: 10.1016/j.chom.2020.03.023 ; PubMed Central PMCID: PMC7144857. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Shi J, Wen Z, Zhong G, Yang H, Wang C, Huang B, et al. Susceptibility of ferrets, cats, dogs, and other domesticated animals to SARS-coronavirus 2. Science. 2020;368(6494):1016–20. Epub 2020/04/10. doi: 10.1126/science.abb7015 ; PubMed Central PMCID: PMC7164390. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Sia SF, Yan LM, Chin AWH, Fung K, Choy KT, Wong AYL, et al. Pathogenesis and transmission of SARS-CoV-2 in golden hamsters. Nature. 2020;583(7818):834–8. Epub 2020/05/15. doi: 10.1038/s41586-020-2342-5 ; PubMed Central PMCID: PMC7394720. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Sit THC, Brackman CJ, Ip SM, Tam KWS, Law PYT, To EMW, et al. Infection of dogs with SARS-CoV-2. Nature. 2020. Epub 2020/05/15. doi: 10.1038/s41586-020-2334-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Oreshkova N, Molenaar R-J, Vreman S, Harders F, Munnink BBO, Hakze R, et al. SARS-CoV2 infection in farmed mink, Netherlands, April 2020. bioRxiv. 2020. doi: 10.1101/2020.05.18.101493 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Oude Munnink BB, Sikkema RS, Nieuwenhuijse DF, Molenaar RJ, Munger E, Molenkamp R, et al. Jumping back and forth: anthropozoonotic and zoonotic transmission of SARS-CoV-2 on mink farms. bioRxiv. 2020. doi: 10.1101/2020.09.01.277152 [DOI] [Google Scholar]
  • 15.Munster VJ, Feldmann F, Williamson BN, van Doremalen N, Pérez-Pérez L, Schulz J, et al. Respiratory disease in rhesus macaques inoculated with SARS-CoV-2. Nature. 2020;585(7824):268–72. doi: 10.1038/s41586-020-2324-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Olival KJ, Cryan PM, Amman BR, Baric RS, Blehert DS, Brook CE, et al. Possibility for reverse zoonotic transmission of SARS-CoV-2 to free-ranging wildlife: A case study of bats. PLoS Pathog. 2020;16(9):e1008758. Epub 2020/09/04. doi: 10.1371/journal.ppat.1008758 ; PubMed Central PMCID: PMC7470399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Santini JM, Edwards SJL. Host range of SARS-CoV-2 and implications for public health. Lancet Microbe. 2020;1(4):e141–e2. doi: 10.1016/S2666-5247(20)30069-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Damas J, Hughes GM, Keough KC, Painter CA, Persky NS, Corbo M, et al. Broad host range of SARS-CoV-2 predicted by comparative and structural analysis of ACE2 in vertebrates. Proc Natl Acad Sci U S A. 2020;117(36):22311–22. Epub 2020/08/23. doi: 10.1073/pnas.2010146117 ; PubMed Central PMCID: PMC7486773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Melin AD, Janiak MC, Marrone F 3rd, Arora PS, Higham JP. Comparative ACE2 variation and primate COVID-19 risk. Commun Biol. 2020;3(1):641. Epub 2020/10/29. doi: 10.1038/s42003-020-01370-w ; PubMed Central PMCID: PMC7591510. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Zhai X, Sun J, Yan Z, Zhang J, Zhao J, Zhao Z, et al. Comparison of Severe Acute Respiratory Syndrome Coronavirus 2 Spike Protein Binding to ACE2 Receptors from Human, Pets, Farm Animals, and Putative Intermediate Hosts. J Virol. 2020;94(15). Epub 2020/05/15. doi: 10.1128/JVI.00831-20 ; PubMed Central PMCID: PMC7375388. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Hoffmann M, Kleine-Weber H, Schroeder S, Kruger N, Herrler T, Erichsen S, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell. 2020;181(2):271–80 e8. Epub 2020/03/07. doi: 10.1016/j.cell.2020.02.052 ; PubMed Central PMCID: PMC7102627. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Tai W, He L, Zhang X, Pu J, Voronin D, Jiang S, et al. Characterization of the receptor-binding domain (RBD) of 2019 novel coronavirus: implication for development of RBD protein as a viral attachment inhibitor and vaccine. Cell Mol Immunol. 2020;17(6):613–20. Epub 2020/03/24. doi: 10.1038/s41423-020-0400-4 ; PubMed Central PMCID: PMC7091888. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Shang J, Ye G, Shi K, Wan Y, Luo C, Aihara H, et al. Structural basis of receptor recognition by SARS-CoV-2. Nature. 2020;581(7807):221–4. Epub 2020/04/01. doi: 10.1038/s41586-020-2179-y ; PubMed Central PMCID: PMC7328981. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Walls AC, Park YJ, Tortorici MA, Wall A, McGuire AT, Veesler D. Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein. Cell. 2020;181(2):281–92 e6. Epub 2020/03/11. doi: 10.1016/j.cell.2020.02.058 ; PubMed Central PMCID: PMC7102599. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Yan R, Zhang Y, Li Y, Xia L, Guo Y, Zhou Q. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science. 2020;367(6485):1444–8. Epub 2020/03/07. doi: 10.1126/science.abb2762 ; PubMed Central PMCID: PMC7164635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Winkler ES, Bailey AL, Kafai NM, Nair S, McCune BT, Yu J, et al. SARS-CoV-2 infection of human ACE2-transgenic mice causes severe lung inflammation and impaired function. Nat Immunol. 2020;21(11):1327–35. Epub 2020/08/26. doi: 10.1038/s41590-020-0778-2 ; PubMed Central PMCID: PMC7578095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Demogines A, Farzan M, Sawyer SL. Evidence for ACE2-utilizing coronaviruses (CoVs) related to severe acute respiratory syndrome CoV in bats. J Virol. 2012;86(11):6350–3. Epub 2012/03/23. doi: 10.1128/JVI.00311-12 ; PubMed Central PMCID: PMC3372174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Guo H, Hu BJ, Yang XL, Zeng LP, Li B, Ouyang SY, et al. Evolutionary Arms Race between Virus and Host Drives Genetic Diversity in Bat Severe Acute Respiratory Syndrome-Related Coronavirus Spike Genes. J Virol. 2020;94(20). ARTN e00902-20 doi: 10.1128/JVI.00902-20 WOS:000579827700012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Starr TN, Picton LK, Thornton JW. Alternative evolutionary histories in the sequence space of an ancient protein. Nature. 2017;549(7672):409–13. Epub 2017/09/14. doi: 10.1038/nature23902 ; PubMed Central PMCID: PMC6214350. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Harms MJ, Thornton JW. Evolutionary biochemistry: revealing the historical and physical causes of protein properties. Nat Rev Genet. 2013;14(8):559–71. Epub 2013/07/19. doi: 10.1038/nrg3540 ; PubMed Central PMCID: PMC4418793. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.DePristo MA, Weinreich DM, Hartl DL. Missense meanderings in sequence space: a biophysical view of protein evolution. Nat Rev Genet. 2005;6(9):678–87. Epub 2005/08/03. doi: 10.1038/nrg1672 . [DOI] [PubMed] [Google Scholar]
  • 32.Ivankov DN, Finkelstein AV, Kondrashov FA. A structural perspective of compensatory evolution. Curr Opin Struct Biol. 2014;26:104–12. Epub 2014/07/02. doi: 10.1016/j.sbi.2014.05.004 ; PubMed Central PMCID: PMC4141909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Tokuriki N, Tawfik DS. Stability effects of mutations and protein evolvability. Curr Opin Struct Biol. 2009;19(5):596–604. Epub 2009/09/22. doi: 10.1016/j.sbi.2009.08.003 . [DOI] [PubMed] [Google Scholar]
  • 34.Pal C, Papp B. Evolution of complex adaptations in molecular systems. Nat Ecol Evol. 2017;1(8):1084–92. Epub 2017/08/07. doi: 10.1038/s41559-017-0228-1 ; PubMed Central PMCID: PMC5540182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Palmer AC, Toprak E, Baym M, Kim S, Veres A, Bershtein S, et al. Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes. Nat Commun. 2015;6:7385. Epub 2015/06/11. doi: 10.1038/ncomms8385 ; PubMed Central PMCID: PMC4548896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Shah P, McCandlish DM, Plotkin JB. Contingency and entrenchment in protein evolution under purifying selection. Proc Natl Acad Sci U S A. 2015;112(25):E3226–35. Epub 2015/06/10. doi: 10.1073/pnas.1412933112 ; PubMed Central PMCID: PMC4485141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Wu NC, Dai L, Olson CA, Lloyd-Smith JO, Sun R. Adaptation in protein fitness landscapes is facilitated by indirect paths. Elife. 2016;5. Epub 2016/07/09. doi: 10.7554/eLife.16965 ; PubMed Central PMCID: PMC4985287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Sailer ZR, Harms MJ. Molecular ensembles make evolution unpredictable. Proc Natl Acad Sci U S A. 2017;114(45):11938–43. Epub 2017/10/29. doi: 10.1073/pnas.1711927114 ; PubMed Central PMCID: PMC5691298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Stern DL. The genetic causes of convergent evolution. Nat Rev Genet. 2013;14(11):751–64. Epub 2013/10/10. doi: 10.1038/nrg3483 . [DOI] [PubMed] [Google Scholar]
  • 40.Storz JF. Causes of molecular convergence and parallelism in protein evolution. Nat Rev Genet. 2016;17(4):239–50. Epub 2016/03/15. doi: 10.1038/nrg.2016.11 ; PubMed Central PMCID: PMC5482790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Clarke NE, Turner AJ. Angiotensin-converting enzyme 2: the first decade. Int J Hypertens. 2012;2012:307315. Epub 2011/11/29. doi: 10.1155/2012/307315 ; PubMed Central PMCID: PMC3216391. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.South AM, Diz DI, Chappell MC. COVID-19, ACE2, and the cardiovascular consequences. Am J Physiol Heart Circ Physiol. 2020;318(5):H1084–H90. Epub 2020/04/02. doi: 10.1152/ajpheart.00217.2020 ; PubMed Central PMCID: PMC7191628. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Santos RAS, Sampaio WO, Alzamora AC, Motta-Santos D, Alenina N, Bader M, et al. The ACE2/Angiotensin-(1–7)/MAS Axis of the Renin-Angiotensin System: Focus on Angiotensin-(1–7). Physiol Rev. 2018;98(1):505–53. Epub 2018/01/20. doi: 10.1152/physrev.00023.2016 ; PubMed Central PMCID: PMC7203574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Crackower MA, Sarao R, Oudit GY, Yagil C, Kozieradzki I, Scanga SE, et al. Angiotensin-converting enzyme 2 is an essential regulator of heart function. Nature. 2002;417(6891):822–8. doi: 10.1038/nature00786 [DOI] [PubMed] [Google Scholar]
  • 45.Patel VB, Mori J, McLean BA, Basu R, Das SK, Ramprasath T, et al. ACE2 Deficiency Worsens Epicardial Adipose Tissue Inflammation and Cardiac Dysfunction in Response to Diet-Induced Obesity. Diabetes. 2016;65(1):85–95. Epub 2015/08/01. doi: 10.2337/db15-0399 ; PubMed Central PMCID: PMC4686955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Zhang C, Zhao YX, Zhang YH, Zhu L, Deng BP, Zhou ZL, et al. Angiotensin-converting enzyme 2 attenuates atherosclerotic lesions by targeting vascular cells. Proc Natl Acad Sci U S A. 2010;107(36):15886–91. Epub 2010/08/28. doi: 10.1073/pnas.1001253107 ; PubMed Central PMCID: PMC2936602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Pedersen KB, Sriramula S, Chhabra KH, Xia H, Lazartigues E. Species-specific inhibitor sensitivity of angiotensin-converting enzyme 2 (ACE2) and its implication for ACE2 activity assays. Am J Physiol Regul Integr Comp Physiol. 2011;301(5):R1293–9. Epub 2011/09/02. doi: 10.1152/ajpregu.00339.2011 ; PubMed Central PMCID: PMC3213941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Crowley SD, Gurley SB, Herrera MJ, Ruiz P, Griffiths R, Kumar AP, et al. Angiotensin II causes hypertension and cardiac hypertrophy through its receptors in the kidney. Proc Natl Acad Sci U S A. 2006;103(47):17985–90. doi: 10.1073/pnas.0605545103 WOS:170906784900073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Gletsu N, Doan TN, Cole J, Sutliff RL, Bernstein KE. Angiotensin II-induced hypertension in mice caused an increase in insulin secretion. Vascul Pharmacol. 2005;42(3):83–92. Epub 2005/03/29. doi: 10.1016/j.vph.2005.01.006 . [DOI] [PubMed] [Google Scholar]
  • 50.Gurley SB, Allred A, Le TH, Griffiths R, Mao L, Philip N, et al. Altered blood pressure responses and normal cardiac phenotype in ACE2-null mice. J Clin Invest. 2006;116(8):2218–25. Epub 2006/08/01. doi: 10.1172/JCI16980 ; PubMed Central PMCID: PMC1518789. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Liu Z, Huang XR, Chen HY, Fung E, Liu J, Lan HY. Deletion of Angiotensin-Converting Enzyme-2 Promotes Hypertensive Nephropathy by Targeting Smad7 for Ubiquitin Degradation. Hypertension. 2017;70(4):822–30. Epub 2017/08/16. doi: 10.1161/HYPERTENSIONAHA.117.09600 . [DOI] [PubMed] [Google Scholar]
  • 52.Wang Q, Wang H, Wang J, Venugopal J, Kleiman K, Guo C, et al. Angiotensin II-induced Hypertension is Reduced by Deficiency of P-selectin Glycoprotein Ligand-1. Sci Rep. 2018;8(1):3223. Epub 2018/02/21. doi: 10.1038/s41598-018-21588-3 ; PubMed Central PMCID: PMC5818646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Hauser FE, Chang BS. Insights into visual pigment adaptation and diversity from model ecological and evolutionary systems. Curr Opin Genet Dev. 2017;47:110–20. Epub 2017/11/06. doi: 10.1016/j.gde.2017.09.005 . [DOI] [PubMed] [Google Scholar]
  • 54.Tipnis SR, Hooper NM, Hyde R, Karran E, Christie G, Turner AJ. A human homolog of angiotensin-converting enzyme. Cloning and functional expression as a captopril-insensitive carboxypeptidase. J Biol Chem. 2000;275(43):33238–43. Epub 2000/08/05. doi: 10.1074/jbc.M002615200 . [DOI] [PubMed] [Google Scholar]
  • 55.Li Y, Wang H, Tang X, Fang S, Ma D, Du C, et al. SARS-CoV-2 and Three Related Coronaviruses Utilize Multiple ACE2 Orthologs and Are Potently Blocked by an Improved ACE2-Ig. J Virol. 2020;94(22). Epub 2020/08/28. doi: 10.1128/JVI.01283-20 ; PubMed Central PMCID: PMC7592233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Echave J, Spielman SJ, Wilke CO. Causes of evolutionary rate variation among protein sites. Nat Rev Genet. 2016;17(2):109–21. Epub 2016/01/20. doi: 10.1038/nrg.2015.18 ; PubMed Central PMCID: PMC4724262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Pond SL, Frost SD, Muse SV. HyPhy: hypothesis testing using phylogenies. Bioinformatics. 2005;21(5):676–9. Epub 2004/10/29. doi: 10.1093/bioinformatics/bti079 . [DOI] [PubMed] [Google Scholar]
  • 58.Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24(8):1586–91. Epub 2007/05/08. doi: 10.1093/molbev/msm088 . [DOI] [PubMed] [Google Scholar]
  • 59.Foley NM, Springer MS, Teeling EC. Mammal madness: is the mammal tree of life not yet resolved? Philos Trans R Soc Lond B Biol Sci. 2016;371(1699). Epub 2016/06/22. doi: 10.1098/rstb.2015.0140 ; PubMed Central PMCID: PMC4920340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Wisotsky SR, Kosakovsky Pond SL, Shank SD, Muse SV. Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril. Mol Biol Evol. 2020;37(8):2430–9. Epub 2020/02/19. doi: 10.1093/molbev/msaa037 ; PubMed Central PMCID: PMC7403620. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Siddiqui KS, Cavicchioli R. Cold-adapted enzymes. Annu Rev Biochem. 2006;75:403–33. Epub 2006/06/08. doi: 10.1146/annurev.biochem.75.103004.142723 . [DOI] [PubMed] [Google Scholar]
  • 62.Venkat A, Hahn MW, Thornton JW. Multinucleotide mutations cause false inferences of lineage-specific positive selection. Nat Ecol Evol. 2018;2(8):1280–8. doi: 10.1038/s41559-018-0584-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Zhang Z, Zhang Y, Liu K, Li Y, Lu Q, Wang Q, et al. The molecular basis for SARS-CoV-2 binding to dog ACE2. Nat Commun. 2021;12(1):4195. Epub 2021/07/09. doi: 10.1038/s41467-021-24326-y . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. 2013;Chapter 7:Unit7 20. Epub 2013/01/15. doi: 10.1002/0471142905.hg0720s76 ; PubMed Central PMCID: PMC4480630. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Chang BSW, Jönsson K, Kazmi MA, Donoghue MJ, Sakmar TP. Recreating a Functional Ancestral Archosaur Visual Pigment. Mol Biol Evol. 2002;19(9):1483–9. doi: 10.1093/oxfordjournals.molbev.a004211 [DOI] [PubMed] [Google Scholar]
  • 66.Patel VB, Zhong JC, Grant MB, Oudit GY. Role of the ACE2/Angiotensin 1–7 Axis of the Renin-Angiotensin System in Heart Failure. Circ Res. 2016;118(8):1313–26. Epub 2016/04/16. doi: 10.1161/CIRCRESAHA.116.307708 ; PubMed Central PMCID: PMC4939482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.White CR, Seymour RS. The role of gravity in the evolution of mammalian blood pressure. Evolution. 2014;68(3):901–8. Epub 2013/10/25. doi: 10.1111/evo.12298 . [DOI] [PubMed] [Google Scholar]
  • 68.Baker J, Meade A, Pagel M, Venditti C. Adaptive evolution toward larger size in mammals. Proc Natl Acad Sci U S A. 2015;112(16):5093–8. Epub 2015/04/08. doi: 10.1073/pnas.1419823112 ; PubMed Central PMCID: PMC4413265. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Towler P, Staker B, Prasad SG, Menon S, Tang J, Parsons T, et al. ACE2 X-ray structures reveal a large hinge-bending motion important for inhibitor binding and catalysis. J Biol Chem. 2004;279(17):17996–8007. Epub 2004/02/03. doi: 10.1074/jbc.M311191200 ; PubMed Central PMCID: PMC7980034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Lu J, Sun PD. High affinity binding of SARS-CoV-2 spike protein enhances ACE2 carboxypeptidase activity. J Biol Chem. 2020;295(52):18579–88. Epub 2020/10/31. doi: 10.1074/jbc.RA120.015303 ; PubMed Central PMCID: PMC7833600. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Liu P, Xie X, Gao L, Jin J. Designed variants of ACE2-Fc that decouple anti-SARS-CoV-2 activities from unwanted cardiovascular effects. Int J Biol Macromol. 2020;165(Pt B):1626–33. Epub 2020/10/21. doi: 10.1016/j.ijbiomac.2020.10.120 ; PubMed Central PMCID: PMC7568492. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Moore MJ, Dorfman T, Li W, Wong SK, Li Y, Kuhn JH, et al. Retroviruses pseudotyped with the severe acute respiratory syndrome coronavirus spike protein efficiently infect cells expressing angiotensin-converting enzyme 2. J Virol. 2004;78(19):10628–35. Epub 2004/09/16. doi: 10.1128/JVI.78.19.10628-10635.2004 ; PubMed Central PMCID: PMC516384. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Chan KK, Dorosky D, Sharma P, Abbasi SA, Dye JM, Kranz DM, et al. Engineering human ACE2 to optimize binding to the spike protein of SARS coronavirus 2. Science. 2020. Epub 2020/08/06. doi: 10.1126/science.abc0870 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Hütter G, Nowak D, Mossner M, Ganepola S, Müßig A, Allers K, et al. Long-Term Control of HIV by CCR5 Delta32/Delta32 Stem-Cell Transplantation. N Engl J Med. 2009;360(7):692–8. doi: 10.1056/NEJMoa0802905 [DOI] [PubMed] [Google Scholar]
  • 75.Devaux CA, Rolain J-M, Raoult D. ACE2 receptor polymorphism: Susceptibility to SARS-CoV-2, hypertension, multi-organ failure, and COVID-19 disease outcome. J Microbiol Immunol Infect. 2020;53(3):425–35. doi: 10.1016/j.jmii.2020.04.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Stawiski EW, Diwanji D, Suryamohan K, Gupta R, Fellouse FA, Sathirapongsasuti JF, et al. Human ACE2 receptor polymorphisms predict SARS-CoV-2 susceptibility. bioRxiv. 2020:2020.04.07.024752. doi: 10.1101/2020.04.07.024752 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Castiglione GM, Chang BS. Functional trade-offs and environmental variation shaped ancient trajectories in the evolution of dim-light vision. Elife. 2018;7. Epub 2018/10/27. doi: 10.7554/eLife.35957 ; PubMed Central PMCID: PMC6203435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.de Visser JA, Krug J. Empirical fitness landscapes and the predictability of evolution. Nat Rev Genet. 2014;15(7):480–90. Epub 2014/06/11. doi: 10.1038/nrg3744 . [DOI] [PubMed] [Google Scholar]
  • 79.Poelwijk FJ, Kiviet DJ, Weinreich DM, Tans SJ. Empirical fitness landscapes reveal accessible evolutionary paths. Nature. 2007;445(7126):383–6. Epub 2007/01/26. doi: 10.1038/nature05451 . [DOI] [PubMed] [Google Scholar]
  • 80.Steinberg B, Ostermeier M. Environmental changes bridge evolutionary valleys. Sci Adv. 2016;2(1):e1500921. Epub 2016/02/05. doi: 10.1126/sciadv.1500921 ; PubMed Central PMCID: PMC4737206. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Starr TN, Thornton JW. Exploring protein sequence-function landscapes. Nat Biotechnol. 2017;35(2):125–6. Epub 2017/02/09. doi: 10.1038/nbt.3786 ; PubMed Central PMCID: PMC6086343. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Gheblawi M, Wang K, Viveiros A, Nguyen Q, Zhong JC, Turner AJ, et al. Angiotensin-Converting Enzyme 2: SARS-CoV-2 Receptor and Regulator of the Renin-Angiotensin System: Celebrating the 20th Anniversary of the Discovery of ACE2. Circ Res. 2020;126(10):1456–74. Epub 2020/04/09. doi: 10.1161/CIRCRESAHA.120.317015 ; PubMed Central PMCID: PMC7188049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Zoufaly A, Poglitsch M, Aberle JH, Hoepler W, Seitz T, Traugott M, et al. Human recombinant soluble ACE2 in severe COVID-19. Lancet Respir Med. 2020;8(11):1154–8. doi: 10.1016/S2213-2600(20)30418-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Lu N, Yang Y, Wang Y, Liu Y, Fu G, Chen D, et al. ACE2 gene polymorphism and essential hypertension: an updated meta-analysis involving 11,051 subjects. Mol Biol Rep. 2012;39(6):6581–9. doi: 10.1007/s11033-012-1487-1 [DOI] [PubMed] [Google Scholar]
  • 85.Luo Y, Liu C, Guan T, Li Y, Lai Y, Li F, et al. Association of ACE2 genetic polymorphisms withhypertension-related target organ damages in south Xinjiang. Hypertens Res. 2019;42(5):681–9. doi: 10.1038/s41440-018-0166-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Barrett RD, Hoekstra HE. Molecular spandrels: tests of adaptation at the genetic level. Nat Rev Genet. 2011;12(11):767–80. Epub 2011/10/19. doi: 10.1038/nrg3015 . [DOI] [PubMed] [Google Scholar]
  • 87.Pavličev M, Cheverud JM. Constraints Evolve: Context Dependency of Gene Effects Allows Evolution of Pleiotropy. Annu Rev Ecol Evol Syst. 2015;46(1):413–34. doi: 10.1146/annurev-ecolsys-120213-091721 [DOI] [Google Scholar]
  • 88.Soskine M, Tawfik DS. Mutational effects and the evolution of new protein functions. Nat Rev Genet. 2010;11(8):572–82. Epub 2010/07/17. doi: 10.1038/nrg2808 . [DOI] [PubMed] [Google Scholar]
  • 89.Losos JB. Convergence, adaptation, and constraint. Evolution. 2011;65(7):1827–40. Epub 2011/07/07. doi: 10.1111/j.1558-5646.2011.01289.x . [DOI] [PubMed] [Google Scholar]
  • 90.Weinreich DM, Delaney NF, DePristo MA, Hartl DL. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science. 2006;312(5770):111–4. doi: 10.1126/science.1123539 WOS:166011934400043. [DOI] [PubMed] [Google Scholar]
  • 91.Fraebel DT, Mickalide H, Schnitkey D, Merritt J, Kuhlman TE, Kuehn S. Environment determines evolutionary trajectory in a constrained phenotypic space. Elife. 2017;6. Epub 2017/03/28. doi: 10.7554/eLife.24669 ; PubMed Central PMCID: PMC5441876. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92.Ogbunugafor CB, Wylie CS, Diakite I, Weinreich DM, Hartl DL. Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance. PLoS Comput Biol. 2016;12(1):e1004710. Epub 2016/01/26. doi: 10.1371/journal.pcbi.1004710 ; PubMed Central PMCID: PMC4726534. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Monteil V, Kwon H, Prado P, Hagelkruys A, Wimmer RA, Stahl M, et al. Inhibition of SARS-CoV-2 Infections in Engineered Human Tissues Using Clinical-Grade Soluble Human ACE2. Cell. 2020;181(4):905–13 e7. Epub 2020/04/26. doi: 10.1016/j.cell.2020.04.004 ; PubMed Central PMCID: PMC7181998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Guo J, Huang Z, Lin L, Lv J. Coronavirus Disease 2019 (COVID-19) and Cardiovascular Disease: A Viewpoint on the Potential Influence of Angiotensin-Converting Enzyme Inhibitors/Angiotensin Receptor Blockers on Onset and Severity of Severe Acute Respiratory Syndrome Coronavirus 2 Infection. J Am Heart Assoc. 2020;9(7):e016219. Epub 2020/04/03. doi: 10.1161/JAHA.120.016219 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Inciardi RM, Lupi L, Zaccone G, Italia L, Raffo M, Tomasoni D, et al. Cardiac Involvement in a Patient With Coronavirus Disease 2019 (COVID-19). JAMA Cardiol. 2020. Epub 2020/03/29. doi: 10.1001/jamacardio.2020.1096 ; PubMed Central PMCID: PMC7364333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96.Siripanthong B, Nazarian S, Muser D, Deo R, Santangeli P, Khanji MY, et al. Recognizing COVID-19-related myocarditis: The possible pathophysiology and proposed guideline for diagnosis and management. Heart Rhythm. 2020. Epub 2020/05/11. doi: 10.1016/j.hrthm.2020.05.001 ; PubMed Central PMCID: PMC7199677. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Li W, Shi Z, Yu M, Ren W, Smith C, Epstein JH, et al. Bats are natural reservoirs of SARS-like coronaviruses. Science. 2005;310(5748):676–9. Epub 2005/10/01. doi: 10.1126/science.1118391 . [DOI] [PubMed] [Google Scholar]
  • 98.Wong DW, Oudit GY, Reich H, Kassiri Z, Zhou J, Liu QC, et al. Loss of Angiotensin-Converting Enzyme-2 (Ace2) Accelerates Diabetic Kidney Injury. Am J Pathol. 2007;171(2):438–51. doi: 10.2353/ajpath.2007.060977 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Epelman S, Shrestha K, Troughton RW, Francis GS, Sen S, Klein AL, et al. Soluble angiotensin-converting enzyme 2 in human heart failure: relation with myocardial function and clinical outcomes. J Card Fail. 2009;15(7):565–71. Epub 2009/08/25. doi: 10.1016/j.cardfail.2009.01.014 ; PubMed Central PMCID: PMC3179261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Hashimoto T, Perlot T, Rehman A, Trichereau J, Ishiguro H, Paolino M, et al. ACE2 links amino acid malnutrition to microbial ecology and intestinal inflammation. Nature. 2012;487(7408):477–81. Epub 2012/07/28. doi: 10.1038/nature11228 ; PubMed Central PMCID: PMC7095315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 101.Kowalczuk S, Broer A, Tietze N, Vanslambrouck JM, Rasko JE, Broer S. A protein complex in the brush-border membrane explains a Hartnup disorder allele. FASEB J. 2008;22(8):2880–7. Epub 2008/04/22. doi: 10.1096/fj.08-107300 . [DOI] [PubMed] [Google Scholar]
  • 102.Yan H, Jiao H, Liu Q, Zhang Z, Xiong Q, Wang BJ, et al. ACE2 receptor usage reveals variation in susceptibility to SARS-CoV and SARS-CoV-2 infection among bat species. Nat Ecol Evol. 2021;5(5):600–8. Epub 2021/03/03. doi: 10.1038/s41559-021-01407-1 . [DOI] [PubMed] [Google Scholar]
  • 103.Li WH, Moore MJ, Vasilieva N, Sui JH, Wong SK, Berne MA, et al. Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature. 2003;426(6965):450–4. doi: 10.1038/nature02145 WOS:000186800800040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.Wong SK, Li W, Moore MJ, Choe H, Farzan M. A 193-amino acid fragment of the SARS coronavirus S protein efficiently binds angiotensin-converting enzyme 2. J Biol Chem. 2004;279(5):3197–201. Epub 2003/12/13. doi: 10.1074/jbc.C300520200 . [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 105.Millet JK, Whittaker GR. Murine Leukemia Virus (MLV)-based Coronavirus Spike-pseudotyped Particle Production and Infection. Bio Protoc. 2016;6(23):e2035. doi: 10.21769/BioProtoc.2035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106.Loytynoja A, Goldman N. webPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser. BMC Bioinformatics. 2010;11:579. Epub 2010/11/30. doi: 10.1186/1471-2105-11-579 ; PubMed Central PMCID: PMC3009689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.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(3):307–21. Epub 2010/06/09. doi: 10.1093/sysbio/syq010 . [DOI] [PubMed] [Google Scholar]
  • 108.Lefort V, Longueville J-E, Gascuel O. SMS: Smart Model Selection in PhyML. Mol Biol Evol. 2017;34(9):2422–4. doi: 10.1093/molbev/msx149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Hedges SB, Marin J, Suleski M, Paymer M, Kumar S. Tree of life reveals clock-like speciation and diversification. Mol Biol Evol. 2015;32(4):835–45. Epub 2015/03/06. doi: 10.1093/molbev/msv037 ; PubMed Central PMCID: PMC4379413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 110.Murrell B, Moola S, Mabona A, Weighill T, Sheward D, Kosakovsky Pond SL, et al. FUBAR: a fast, unconstrained bayesian approximation for inferring selection. Mol Biol Evol. 2013;30(5):1196–205. Epub 2013/02/20. doi: 10.1093/molbev/mst030 ; PubMed Central PMCID: PMC3670733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 111.Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44(W1):W232–W5. doi: 10.1093/nar/gkw256 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112.Bielawski JP, Yang Z. A maximum likelihood method for detecting functional divergence at individual codon sites, with application to gene family evolution. J Mol Evol. 2004;59(1):121–32. Epub 2004/09/24. doi: 10.1007/s00239-004-2597-8 . [DOI] [PubMed] [Google Scholar]
  • 113.Neuweiler G. The Biology of Bats: Oxford University Press; 2000. 310 p. [Google Scholar]
  • 114.Tacutu R, Thornton D, Johnson E, Budovsky A, Barardo D, Craig T, et al. Human Ageing Genomic Resources: new and updated databases. Nucleic Acids Res. 2018;46(D1):D1083–D90. Epub 2017/11/10. doi: 10.1093/nar/gkx1042 ; PubMed Central PMCID: PMC5753192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 115.Maddison WP, Maddison DR. Mesquite: a modular system for evolutionary analysis. 3.51 ed. http://www.mesquiteproject.org2018.
  • 116.Garland T, Ives AR. Using the past to predict the present: Confidence intervals for regression equations in phylogenetic comparative methods. Am Nat. 2000;155(3):346–64. WOS:000086444100005. doi: 10.1086/303327 [DOI] [PubMed] [Google Scholar]
  • 117.Smith MD, Wertheim JO, Weaver S, Murrell B, Scheffler K, Kosakovsky Pond SL. Less Is More: An Adaptive Branch-Site Random Effects Model for Efficient Detection of Episodic Diversifying Selection. Mol Biol Evol. 2015;32(5):1342–53. doi: 10.1093/molbev/msv022 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Roland G Roberts

5 Mar 2021

Dear Dr Duh,

Thank you for submitting your manuscript entitled "Ancient epistasis gave rise to the modern-day SARS-CoV-2 pandemic" for consideration as a Research Article by PLOS Biology.

Your manuscript has now been evaluated by the PLOS Biology editorial staff, as well as by an academic editor with relevant expertise, and I'm writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by Mar 09 2021 11:59PM.

Login to Editorial Manager here: https://www.editorialmanager.com/pbiology

During resubmission, you will be invited to opt-in to posting your pre-review manuscript as a bioRxiv preprint. Visit http://journals.plos.org/plosbiology/s/preprints for full details. If you consent to posting your current manuscript as a preprint, please upload a single Preprint PDF when you re-submit.

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Given the disruptions resulting from the ongoing COVID-19 pandemic, please expect delays in the editorial process. We apologise in advance for any inconvenience caused and will do our best to minimize impact as far as possible.

Feel free to email us at plosbiology@plos.org if you have any queries relating to your submission.

Kind regards,

Roli Roberts

Roland G Roberts, PhD,

Senior Editor

PLOS Biology

Decision Letter 1

Roland G Roberts

11 May 2021

Dear Dr Duh,

Thank you very much for submitting your manuscript "Ancient epistasis gave rise to the modern-day SARS-CoV-2 pandemic" as a Research Article for review by PLOS Biology. As with all papers reviewed by the journal, yours was assessed and discussed by the PLOS Biology editors, by an academic editor with relevant expertise, and in this case by three independent reviewers. We had recruited a fourth reviewer, but they are very overdue and are not responding to communications, so we reached a decision using the three reviews received. Based on the reviews, I regret that we will not be pursuing this manuscript for publication in the journal.

You'll see while all three reviewers are positive about the research question, reviewer #3 raises a number of very serious concerns about the support for your central claims. Reviewer #1's assessment is more positive, but overlaps somewhat, with concerns about the logic underlying some of your arguments. All three reviewers feel that the claims would need to be tempered (to varying degrees) before publication. Unfortunately we were strongly persuaded by the careful analysis from reviewer #3, and will therefore not be considering your manuscript further.

The reviews are attached, and we hope they may help you should you decide to revise the manuscript for submission elsewhere. I am sorry that we cannot be more positive on this occasion.

I hope you appreciate the reasons for this decision and will consider PLOS Biology for other submissions in the future. Thank you for your support of PLOS and of Open Access publishing.

Sincerely,

Roli Roberts

Roland Roberts

Senior Editor

PLOS Biology

rroberts@plos.org

------------------------------------------------------------------------

REVIEWERS' COMMENTS:

Reviewer #1:

In this paper, Castiglione et al. use computational evolutionary and functional experiments to understand how sequence variation in mammalian ACE2 impacts this proteins enzymatic function in addition to its latent capacity to enable SARS-related coronavirus entry. The manuscript is quite nice, I like the figure graphics, and there are some interesting analyses. I really liked the work on epistasis, and also appreciated the conclusion that because of epistasis, simple predictions of what ACE2s a virus may or may not bind are not easy to predict from knowledge within a single ACE2 alone. I do not completely follow some of the logical links between different experiments, and I detail some minor concerns below. Nonetheless, there are some very interesting components in the manuscript that I find compelling and of broad potential interest.

Major points:

1. Title: I don't find the language of "gave rise to" the SARS-CoV-2 pandemic as appropriately describing the findings here. This title suggests a much more proximal role in this ancient genetic variation and the origin of the pandemic. A better title might use language more like "enables susceptibility to SARS-CoV-2 binding"

2. Some inaccuracies or statements in describing the state of the field on SARS-CoV-2 evolution that could use correcting or more description:

a. Line 50: RaTG13 is not an "ancestor," but simply another tip on the phylogeny that has been sampled within the same sarbecovirus clade as SARS-CoV-2

b. Line 130-131: simply observing that pangolin ACE2 can bind SARS-CoV-2 does not "affirm" any intermediate host status for the direct SARS-CoV-2 line of transmission, which will end up being as much about ecology and contingency as much as anything else. (Though of course, binding/infectivity is a pre-requisitie, observing this binding/infectivity does not mean any individual species is an "intermediate" in the zoonotic sense). There is not yet widespread understanding or evidence that pangolins are direct intermediates in the spillover of SARS-CoV-2 itself, let alone being a result to be "reaffirmed" as in the language here

c. Line 125: there are multiple alleles of R. sinicus ACE2 (e.g. PMID 32699095) that differ in their SARS1 binding capacity - which sequence is being used here should be explicitly stated when introducing the sequence in the main text. And in general, making it clear you tested "a" bat/Rs ACE2 sequence across all language seems important to appropriately represent that there are many different bat ACE2s, including many different alleles even within this one host species

3. I have a couple important questions about the ACE2 hydrolysis activity assays:

a. Is the ACE2 peptide that is being hydrolyzed 100% conserved across the species being tested? If not, that would raise a major caveat about the assays as currently performed and their physiological relevance

b. Is the raw expression of ACE2 among variants within the cell lysates used for activity assays a potential confounder of the measured activities? If so, it may be necessary to quantify and normalize activity by relative expression levels in order to make conclusions about differences in activity of these orthologs.

4. Fig. 2F-G, lines 225-227: I do not interpret the difference between the effect of the K353H mutation as measured individually versus on top of the 79/82/83/84 mutations as exhibiting a "more muted effect" in the background of the additional mutations as stated in the manuscript. I think this may be a consequence of me not understanding the proper "scale" that mutations should combine on if additive/non-epistatic. For example, K353H alone has a 50% reduction in the relative RBD association metric - which could also be described as a 2-fold loss of binding relative to WT. When K353H is introduced on top of the 79/82/83/84 mutant background, it has only a 20-30% "raw" decrease in RBD binding (my understanding is this is what the authors are describing as "more muted"), but in fact, this 20-30% decrease in RBD association is >2-fold loss of binding relative to the 79/82/83/84 mutant in both the human and dog ACE2 backgrounds - which would argue the opposite of the authors conclusion, with K353H having a larger relative effect in this combinatorial background. I think in either case, given this assay is not a quantitative binding assay with e.g. thermodynamic measurements, we can't truly know how mutations would additively combine w.r.t. this metric, and so probably making any conclusiosn about potential magnitude epistasis is not fruitful. (This does not impact the sign epistasis in regards to the ACE2 activity assays, as sign epistasis is not subject to this 'uncertain scale of additivity' conern.)

5. Lines 163-166; 177-179; 182-184 : for the tests of positive selection, are you detecting positive selection within specific branches on the tree, or is it just saying "there is postivie selection somewhere on this tree"? Several publications have illustrated positive selection in bats, especially within the Rhinolophus bat reservoirs of sarbecoviruses, which is presumably due to selection on ACE2 sequence specifically to evade sarbecovirus binding and infection. (PMID 32699095, 22438550). In the current manuscript, it is unclear to me whether this positive selection within bats specifically due to viral pressures is giving rise to the positive selection signal, which is then being interpreted across all mammalian orders as evidence for selection related to intrinsic ACE2 function. This should be clarified, and probably the statements implying that this positive selection is specifically due to ACE2 physiological enzymatic activity might need to be tempered. (And these two papers are probably worth discussing specifically in the context of this work including the ACE2 residues they identify as positively selected in these host-virus arms' races.)

6. Line 280: it is unclear to me why having a lower native blood pressure would relax selection on ACE2 function. Regardless of the homeostatic 'set point', presumably the enzymatic activity of ACE2 is needed anyway to maintain that homeostatic set point. It seems like changes in global blood pressure are probably instead modulated by e.g. the upstream regulation of the peptide itself, or response pathways to the cleaved product, or some other pathway - not the actual catalytic activity of ACE2 itself. And it's further unclear why a correlation in between body mass and blood pressure establishes any relaxation in constraint - it's actually almost the opposite, in that it argues that there is some overarching 'reason' why smaller animals have lower blood pressure and therefore in fact it is an attuned process, not a relaxation of constraint. This analysis is interesting, but I think the authors might need to more carefully consider how to link it into their overall 'thesis'. The further linkage of all of this to effective population size in lines 369-371 extends this all way too far in my opinion, and the statements on lines 369-371 should just be left out.

7. The setup argued for starting in line 294 does not seem to be 'followed through' with the ASR that was actually performed. Simple measurement of the activity of the reconstructed sequence does not clearly illustrate whether these mutations were differentially tolerated in this ancestor without the loss of activity. It seems this analysis should involve not only reconstructing the ancestor, but also introducing the same mutations as illustrated in Figs. 2F/G into this ancestral sequence to identify whether the 'valley' is absent in this ancestral rodent sequence. I understand that's asking for substantial additional experiment, but it would really increase the interest added by this ASR component to the story - without it, the ASR component doesn't seem to add much additional insight.

Minor points:

1. Line 51: "fusing" with isn't clear what that means,

2. Not sure if the use of "pleiotropic" on line 110 is necessarily wrong, but it is sort of tripping me up. Maybe the directionality is reversed? The argument is not that this surface has evolved as a SARS-CoV-2 interaction interface and has pleiotropic consequences for ACE2 activity, but rather sort of the reverse - that evolution of ACE2 sequences due to differences in physiology alters the latent capacity for sarbecovirus receptor usage and susceptibility. Somehow describing this more specifically might enable the authors to avoid using a somewhat contentious word of 'pleiotropy' loosely

3. The disconcordance between Fig. 2F/G and Fig. S4 seems important for proper assessment of results. It suggests that the dynamic range of the RBD binding assay is lower than for actual viral entry. It might be worth simply including Fig. S4 within Fig. 2

Reviewer #2:

In this fascinating article, the authors undertake a series of investigations into the broad host tropism exhibited by SARS-CoV-2, beyond that predicted by studies focused exclusively on comparative sequence analyses of the ACE2 homology to the human ACE2 viral binding interface. In particular, the authors undertake the following major analyses:

1.They investigate ACE2 to SARS-CoV-1 (hereafter SC1) and SARS-CoV-2 (hereafter SC2) RBD binding across a suite of mammalian ACE2 orthologs transfected into 293T cells: human, mouse, bat, dog, pangolin. They show stronger association of SC2-RBD with both human and pangolin ACE2 relative to SC1 and show that the SC1 and SC2 proteins cannot bind mouse not bat ACE2

2.They follow this up in a pseudotype virus system, showing cell entry patterns that recapitulate those described in #1 across these same 5 transfected cell lines.

3.They then measured functional variation in ACE2 enzymatic activity across these same cell lines and found a wide range of hydrolysis activity. Dog, bat, and pangolin displayed low activity compared to human and mouse.

4.They next investigated dN/sS shifts in ACE2 mutational rates across these ACE2 orthologs in a suite of mammals and found evidence of ACE2 under positive selection.

5.They next identified six residues unique to mouse ACE2 and adjacent to the viral RBD to explore the effects of these on virus binding. They took the same approach as in #4 to show that "several" of the sites were under either positive or purifying selection and that primates demonstrated decreased evolutionary rates relative to bats or rodents.

6.They then used site-directed mutagenesis to substitute these six mouse sites in human and dog ACE2. They demonstrate that single mutations had limited effects on SC2 binding and huge deleterious effects on ACE2 hydrolysis activity but when introduced altogether both abolished AC2 binding and rescued the hydrolysis activity of ACE2 due to epistatic activity ('sign epistasis').

7.Now, the authors reasoned that species with higher blood pressure would have a greater need for ACE2 blood pressure regulation and therefore have greater constraints on ACE2 sequence space, so they attempted to correlate ACE2 evolutionary rate with blood pressure. They found that rodents and bats with higher ACE2 evolutionary rates also had lower blood pressure, possibly a mechanism for a less constrained ACE2 fitness landscape in these taxa

8.Finally, in order to explore the hypothesis that rodents may simply have evolved these six mutations by traversing a landscape of permissive mutations, the authors reconstructed ancestral rodent ACE2 and discovered it lacked the six residues of interest (suggesting these mutations evolved recently) but likely had high activity which could have compensated for deleterious mutations on the path to the modern receptor.

The paper is a tour-de-force of intriguing ideas and analyses. There are a few places where I have questions and where the discussion goes a bit too far but on the whole, it is in excellent shape:

1.For #4 above, why were the 107 chosen species selected (or the 89 included in the pruned analyses)? It seems likely that there may be extensive variation in dN/dS across bat species for instance. Some justification for the selected subset is needed

2.Following on above, the authors say that "several" of the six residues of interest for mouse ACE2 show evidence of positive or purifying selection across many taxa, with much higher rates in bats and rodents and constraints in humans. Can these findings be summarized in an accessible way, maybe in the supplement?

3.Can you make the y-axis on a log scale in Figure 3E? It's unclear whether there is any correlation at all or simply whether rodents and bats just show unusually high evolutionary rates for ACE2 compared with other taxa.

4.The authors talk a lot about bats and mice being "immune" to WT SC2. This is not known for bats. The authors would need to demonstrate lack in infectivity in a live animal model to show this, and in fact, they only show lack of virus entry into a transfected 293T cell line with a single bat species ACE2 ortholog. Since the bat chosen (R. sinicus) is not even the host for the closest known CoV to SC2 (R. affinis), this is a stretch. Additionally, Zhou et al 2020 shows some SC2 virus entry in HeLa cells expressing R. sinicus ACE2. Most of the work in this paper is focused on mouse immunity to SC2 and the relevant residues driving this interaction. I suggest the authors keep most of their speculations limited to mice and not try to extrapolate too far into bats.

5.Following on above, I would like to see a 'caveats and limitations' paragraph that mentions how we cannot really determine host range with a limited tool kit in this way. No mention is made of the fact that bats, for instance, might permit SC2 virus entry in some tissues (e.g. GIT tissues) and not others and might have other receptors permitting entry.

6.Additionally, the pangolin fusion hypotheses should be dropped. While pangolin CoV may effectively invade human cells, this paper provides no evidence that it is an intermediate host between bat CoV and WT SC2. It is largely believed that a closer genotype to SC2 than has yet been described is probably circulating in wild bats somewhere still. See MacLeean et al 2021, Boni et al 2020, Andersen et al 2020

7.Finally, the bit about genome engineering of minks or other domestic mammals should be dropped, as this is a big step, of questionable ethics, and moreover, this paper does not demonstrate that it would even work.

Reviewer #3:

This paper seeks to identify amino acid sequence states in the ACE2 protein that confer differences in susceptibility to SARS-Cov2 infection between species and to explain the evolution of these different states in terms of epistasis, natural selection, and pleiotropic effects on ACE2's endogenous enzyme activity.

Understanding the sequence-function relationships underlying ACE2 interactions with SARS-Cov2 is a worthy goal, as is understanding the evolutionary causes of differences in these interactions among species. The subject matter of the paper is therefore of significant potential interest. However, many of the claims are not supported by the experiments and analyses presented. I'm sorry to say that the claims that have sufficient support after a careful reading are of rather narrow scientific impact and seem best suited for a specialist audience.

1. The authors' approach is to transfer 6 amino acid states that exist in mouse ACE2 into human and dog and measure their effects on molecular function. They choose these species and states because: 1) these residues are at sites on the surface of ACE2 that binds the SARS-Cov2 spike protein, 2) human and dog ACE2 have higher relative affinity for SARS-Cov2 than mouse ACE2 does, and 3) mouse is less susceptible to SARS-Cov2 infection than human and dog. With several nice experiments in Figs 1A-E, the authors provide evidence that reinforces premises 2 and 3: mouse ACE2 binds the SARS-Cov2 less efficiently than human and dog ACE2, and cultured cells transfected with mouse ACE2 are less susceptible to pseudovirus infection.

2. But the paper does not show that the six residues are sufficient causes for the difference in affinity and infectivity between the species' ACE2 proteins. The major experiments transfer the 6 mouse states into human and dog ACE2 proteins. However, these "chimeric" ACE2 proteins do not confer cellular resistance to infection nearly as well as the mouse ACE2 protein itself does. Thus, the 6 mouse amino acids can reduce to some extent but not nearly recapitulate the fully resistant cellular phenotype conferred by mouse ACE2 (compare Fig 1E to Fig S4). Further, the reciprocal experiment, where human or dog amino acids at these sites are introduced into the mouse ACE2 was not performed; we therefore do not know the extent to which the 6 states account for the resistance exhibited by the mouse ACE2. Moreover, the historical substitutions from ancestral states to any derived state is never assayed in any of the species' proteins, as would be required to support the contention that these substitutions played a causal role in the evolution of increased or reduced affinity/susceptibility. The evidence therefore establishes that a small number of residues in mouse ACE2 can partially reduce affinity and infectivity when introduced into human or dog. But it does not support a causally sufficient role for the mouse states in resistance by the mouse ACE2. This observation is interesting in terms of ACE2 sequence-function relationships, but it does not have clearly interpretable implications for genetic/biochemical causality that underlies differences between species or the evolution of those differences.

3. A central claim of the paper is that the 6 states interact epistatically in producing the reduced affinity of ACE2 for SARS-Cov2 and the reduced enzyme activity. These claimed epistatic interactions are then said to explain why susceptible species have not evolved genotypes resistant to SARS-Cov2. The evidence for epistasis with respect to affinity is not convincing, because detecting epistasis requires a significant deviation from a well-founded expectation for a quantitative phenotype that would be observed in the absence of epistasis. For example, in the absence of epistasis, one would expect the effect of a combination of mutations on the free energy of binding to be the sum of the energetic effects of each mutation introduced singly; the effect on Kd is expected to be multiplicative. But no such expectation or test is provided here to show that the effects of combinations are different from the effects that would arise if there were no epistasis. For binding, the assay is a complex one that does not directly quantify affinity, occupancy of the bound state, etc.

reasons.

The paper claims an epistatic interaction for binding between mutation at site 353 and those at the other sites, but there is no apparent epistasis at all on this case: site 353 and the set of the other mutations each reduce affinity, and they reduce affinity to a greater extent when combined, precisely as expected with no epistasis. The data do show that 5 of the 6 mouse states produce no clear effect on binding when introduced singly, and they do reduce binding when combined with each other or with the sixth state which affects affinity on its own. This does not necessarily indicate epistasis. Suppose the assay has an intrinsically nonlinear dose-response relationship (such as a hyperbolic or sigmoidal relationship, as is expected in any saturable assay); in such a case, single mutations that each have a moderate effect on affinity may produce no detectable reduction in the signal of binding, if introduced singly into a high-affinity protein, but when introduced into a protein whose affinity has already been weakened by other mutations, that effect will become apparent. Further, there is no evidence for epistasis in the infectivity assay shown in Fig S4, where progressively including more mutations progressively reduces infectivity. These observations do not rule out the possibility of some kind of relatively subtle epistasis with respect to the magnitude of mutations' effects, but they do not establish it. The paper therefore provides no persuasive evidence of epistasis for the phenotype of ACE2 affinity for SARS-Cov2 or susceptibility.

In the absence of quantitative knowledge concerning the expected phenotype when nonepistatic mutations are combined, one could still provide some evidence of epistasis if the sign of the effect of a mutation differs when introduced into different genetic backgrounds; the only way sign epistasis can arise without epistasis is for the underlying relationship between the measured phenotype and underlying biochemical effects to be nonmonotonic, and that is unlikely in the case of apparent binding in these assays. The authors do observe sign epistasis for the catalytic phenotype, because the mouse state at site 353 reduces activity on its own when introduced into human ACE2 but increases it when introduced into the context of four other mouse states. Thus, the authors should make no claim for epistasis with respect to binding or infectivity, but they can make a limited claim for epistasis of this mutational combination with respect to catalysis.

4. The paper's central evolutionary narrative is that the lack of evolved resistance in humans and dog is attributable to a claimed pleiotropic cost incurred by reducing ACE2's affinity for SARS-Cov2. Mice are claimed to be free of this pleiotropic constraint, because the function of ACE2 in their cardiovascular system is different from that in humans and many other mammals. The data do not coherently support this premise, for several reasons.

a. Fig. 1 shows that the mouse ACE2 actually has higher peptidase activity than human and dog, not lower, as would be required for the peptidase-versus-affinity tradeoff to explain the evolution of viral resistance in mouse but not in humans.

reasons.

b. The authors observe reduced ACE2 enzyme activity when the 6 mouse states are introduced into the human ACE2, but no such reduction is observed in dog. This means that there is no intrinsic association between the two phenotypes, as is required to claim that humans and dogs have not evolved resistance to SARS-Cov2 because of antagonistic pleiotropy related to ACE2 activity.

c. The observation that the 6 mouse states decrease SARS-Cov2 affinity and reduce peptidase activity in human ACE2 would at best imply only that humans may be unlikely to evolve reduced affinity by acquiring these particular six mouse states. This does not establish that they could not do so via other mutations that may not affect peptidase activity.

reasons.

d. The fact that the six mouse states do not have the deleterious effects on dog ACE2 indicates that the states at other sites in the protein can prevent the deleterious effect of the six mouse states, indicating that human ACE2 might be able to acquire the 6 mouse states if it also acquired other residues that have a similar modifying or buffering effect. Thus, the authors' data establish only that human ACE2 could not reduce its SARS-Cov2 affinity without incurring pleiotropic effects on affinity by acquiring only the 6 mouse sites. A general statement about acquisition of resistance per se therefore cannot be justified.

5. The authors claim that evolution of the ACE2 protein and several of the six states in particular has been driven by positive selection. They base this claim on the results of two kinds of model-based likelihood ratio test, the branch-sites test and the sites test. However, both of these tests have been shown in the literature to be unreliable, with very high propensities to return false positive conclusions under realistic conditions. These methods therefore do not provide reliable evidence for the claims about selection. It is true that these methods have been widely used in the past as evidence for positive selection; given the recent findings, however, they should no longer be used. See Witosky et al, Synonymous site-to-site substitution rate variation dramatically inflates false positive rates of selection analyses: ignore at your own peril, MBE 2020; Venkat et al, Multinucleotide mutations cause false inferences of lineage-specific positive selection, Nature Evol Evol 2018.

6. The authors claim that the 6 mouse states could have been selectively accessible in rodents because rodents have lower systolic blood pressure than other mammals, which could result in lower selective pressure to maintain ACE2 function, thereby reducing the deleterious costs of the 6 states. The authors provide as evidence of this relaxed constraint hypothesis a higher ratio of nonsynonymous to synonymous rates of evolution at these sites in rodents and bats compared to other mammals. In addition to concerns about these tests as discussed above, the analysis appears to have been performed only on the subset of sites that differ in amino acid state between mouse and other mammals, which are tautologically expected to have higher rates of nonsynonymous substitution in rodents.

A second problem is that the authors also state that unlike other mammals, mice do not exhibit a vasodilatory response to Ang-(1-7), the product of ACE2 hydrolysis. This observation seems to contradict the low blood pressure hypothesis for putative relaxed selective constraint: if ACE2 does not mediate vasodilation, then it is unclear why low blood pressure should produce any relaxed constraint at all.

7. Based on the observation that the 6 mouse states that reduce the peptidase activity of human ACE2 are not located at the protein's catalytic active site, the authors state that this effect must be mediated by indirect structural effects. However, the most plausible mechanism by which mutations would have this effect would be by impairing substrate binding, which takes place at the portion of the ACE2 surface where SARS-Cov2 binding occurs. This would not reflect a surprising mechanism, and it would not be indirect, as it would involve mutations at the protein's surface directly compromising interactions with the substrate at that surface.

8. The authors state that it is surprising that the 6 mouse residues reduce SARS-Cov2 affinity when introduced into either human or dog, because human and dog have different states at most of these sites. They say that this indicates that homology-based reasoning is a poor predictor of proteins' affinity. But the observation that the human and dog states are different is not at all surprising - all it means is that there are multiple amino acid states per residue that are compatible will affinity higher than that conferred by the mouse states. It is very common in multiple sequence alignments to observe some sequence variability at functionally important sites in a protein, such as exchanges between hydrophobic states in a protein's core, or between polar states on a protein's surface, or between donor states (or acceptors) in hydrogen-bonding residues. No selective or epistatic explanation is required to account for this variation - some sequence degeneracy of the functional property is all that is required. The authors' results do show that a search for strict conservation of a single state between proteins with similar affinity is not a reliable guide to identifying sequence sites that contribute to that phenotype, but it would be very naïve to think that it would be. A further consideration related to sequence variability at these sites is that affinity for SARS-Cov2 could not possibly have been a source of long-term constraints that affect sequence variation among species, because the virus did not emerge until 2019. There is no reason that we should expect sites that contribute to affinity to be strictly conserved over evolutionary time.

9. The authors refer to a "functional convergence" between dog and human in their shared susceptibility to SARS-Cov2. But the paper suggests that susceptibility and high ACE2 affinity is ancestral, with a reduction in these phenotypes in the lineage leading to mouse. Susceptibility is therefore not convergent but a retained ancestral state.

I am sorry to say that when these issues are all considered, many of the paper's claims turn out not to be sufficiently supported by the evidence. The paper does establish that six states in mouse can contribute to reducing both affinity and peptidase activity when introduced into human ACE2; further, these states reduce affinity but not activity when introduced into dog ACE2. That is interesting from a sequence-function perspective and should be of interest to scientists whose studies are focused in detail on ACE2 binding and catalysis. But the current paper does not establish whether those states are sufficient to confer resistance in mouse, nor they establish why those states evolved in any of the taxa of interest. Further, there is virtually no evidence for epistasis or an effect on evolutionary processes. The idea of pleiotropic constraints contributing to susceptibility in many lineages is plausible, but the evidence presented does not support it. I do appreciate this paper's effort to connect genetic experiments in ACE2 to evolutionary processes, but the paper's claims on this subject are not justified by the evidence. In my opinion, then, the work reported here should be reported to specialists in the field, but in doing so the claims should be dramatically narrowed.

Decision Letter 2

Roland G Roberts

28 Sep 2021

Dear Dr Duh,

Thank you for your Appeal regarding our previous decision to reject your manuscript "Ancient epistasis gave rise to the modern-day SARS-CoV-2 pandemic" after peer-review. We appreciated the points that you raised, and your proposed revisions based on novel experimental data, and after discussion among the team and with the Academic Editor, we have decided to give you an opportunity to submit a much-revised version that takes into account the reviewers' comments.

However, we warn you that we cannot make any further decision until we have seen the revised manuscript and your response to the reviewers' comments. Your responses will need to satisfy the current reviewers, and it is possible that we may need to solicit further expert input in order to reach a firm decision.

We expect to receive your revised manuscript within 3 months.

Please email us (plosbiology@plos.org) if you have any questions or concerns, or would like to request an extension. At this stage, your manuscript remains formally under active consideration at our journal; please notify us by email if you do not intend to submit a revision so that we may end consideration of the manuscript at PLOS Biology.

**IMPORTANT - SUBMITTING YOUR REVISION**

Your revisions should address the specific points made by each reviewer. Please submit the following files along with your revised manuscript:

1. A 'Response to Reviewers' file - this should detail your responses to the editorial requests, present a point-by-point response to all of the reviewers' comments, and indicate the changes made to the manuscript.

*NOTE: In your point by point response to the reviewers, please provide the full context of each review. Do not selectively quote paragraphs or sentences to reply to. The entire set of reviewer comments should be present in full and each specific point should be responded to individually, point by point.

You should also cite any additional relevant literature that has been published since the original submission and mention any additional citations in your response.

2. In addition to a clean copy of the manuscript, please also upload a 'track-changes' version of your manuscript that specifies the edits made. This should be uploaded as a "Related" file type.

*Re-submission Checklist*

When you are ready to resubmit your revised manuscript, please refer to this re-submission checklist: https://plos.io/Biology_Checklist

To submit a revised version of your manuscript, please go to https://www.editorialmanager.com/pbiology/ and log in as an Author. Click the link labelled 'Submissions Needing Revision' where you will find your submission record.

Please make sure to read the following important policies and guidelines while preparing your revision:

*Published Peer Review*

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:

https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/

*PLOS Data Policy*

Please note that as a condition of publication PLOS' data policy (http://journals.plos.org/plosbiology/s/data-availability) requires that you make available all data used to draw the conclusions arrived at in your manuscript. If you have not already done so, you must include any data used in your manuscript either in appropriate repositories, within the body of the manuscript, or as supporting information (N.B. this includes any numerical values that were used to generate graphs, histograms etc.). For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5

*Blot and Gel Data Policy*

We require the original, uncropped and minimally adjusted images supporting all blot and gel results reported in an article's figures or Supporting Information files. We will require these files before a manuscript can be accepted so please prepare them now, if you have not already uploaded them. Please carefully read our guidelines for how to prepare and upload this data: https://journals.plos.org/plosbiology/s/figures#loc-blot-and-gel-reporting-requirements

*Protocols deposition*

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for your submission to our journal. We hope that our editorial process has been constructive thus far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Roli Roberts

Roland Roberts

Senior Editor

PLOS Biology

rroberts@plos.org

*****************************************************

REVIEWERS' COMMENTS:

Reviewer #1:

In this paper, Castiglione et al. use computational evolutionary and functional experiments to understand how sequence variation in mammalian ACE2 impacts this proteins enzymatic function in addition to its latent capacity to enable SARS-related coronavirus entry. The manuscript is quite nice, I like the figure graphics, and there are some interesting analyses. I really liked the work on epistasis, and also appreciated the conclusion that because of epistasis, simple predictions of what ACE2s a virus may or may not bind are not easy to predict from knowledge within a single ACE2 alone. I do not completely follow some of the logical links between different experiments, and I detail some minor concerns below. Nonetheless, there are some very interesting components in the manuscript that I find compelling and of broad potential interest.

Major points:

1. Title: I don't find the language of "gave rise to" the SARS-CoV-2 pandemic as appropriately describing the findings here. This title suggests a much more proximal role in this ancient genetic variation and the origin of the pandemic. A better title might use language more like "enables susceptibility to SARS-CoV-2 binding"

2. Some inaccuracies or statements in describing the state of the field on SARS-CoV-2 evolution that could use correcting or more description:

a. Line 50: RaTG13 is not an "ancestor," but simply another tip on the phylogeny that has been sampled within the same sarbecovirus clade as SARS-CoV-2

b. Line 130-131: simply observing that pangolin ACE2 can bind SARS-CoV-2 does not "affirm" any intermediate host status for the direct SARS-CoV-2 line of transmission, which will end up being as much about ecology and contingency as much as anything else. (Though of course, binding/infectivity is a pre-requisitie, observing this binding/infectivity does not mean any individual species is an "intermediate" in the zoonotic sense). There is not yet widespread understanding or evidence that pangolins are direct intermediates in the spillover of SARS-CoV-2 itself, let alone being a result to be "reaffirmed" as in the language here

c. Line 125: there are multiple alleles of R. sinicus ACE2 (e.g. PMID 32699095) that differ in their SARS1 binding capacity - which sequence is being used here should be explicitly stated when introducing the sequence in the main text. And in general, making it clear you tested "a" bat/Rs ACE2 sequence across all language seems important to appropriately represent that there are many different bat ACE2s, including many different alleles even within this one host species

3. I have a couple important questions about the ACE2 hydrolysis activity assays:

a. Is the ACE2 peptide that is being hydrolyzed 100% conserved across the species being tested? If not, that would raise a major caveat about the assays as currently performed and their physiological relevance

b. Is the raw expression of ACE2 among variants within the cell lysates used for activity assays a potential confounder of the measured activities? If so, it may be necessary to quantify and normalize activity by relative expression levels in order to make conclusions about differences in activity of these orthologs.

4. Fig. 2F-G, lines 225-227: I do not interpret the difference between the effect of the K353H mutation as measured individually versus on top of the 79/82/83/84 mutations as exhibiting a "more muted effect" in the background of the additional mutations as stated in the manuscript. I think this may be a consequence of me not understanding the proper "scale" that mutations should combine on if additive/non-epistatic. For example, K353H alone has a 50% reduction in the relative RBD association metric - which could also be described as a 2-fold loss of binding relative to WT. When K353H is introduced on top of the 79/82/83/84 mutant background, it has only a 20-30% "raw" decrease in RBD binding (my understanding is this is what the authors are describing as "more muted"), but in fact, this 20-30% decrease in RBD association is >2-fold loss of binding relative to the 79/82/83/84 mutant in both the human and dog ACE2 backgrounds - which would argue the opposite of the authors conclusion, with K353H having a larger relative effect in this combinatorial background. I think in either case, given this assay is not a quantitative binding assay with e.g. thermodynamic measurements, we can't truly know how mutations would additively combine w.r.t. this metric, and so probably making any conclusiosn about potential magnitude epistasis is not fruitful. (This does not impact the sign epistasis in regards to the ACE2 activity assays, as sign epistasis is not subject to this 'uncertain scale of additivity' conern.)

5. Lines 163-166; 177-179; 182-184 : for the tests of positive selection, are you detecting positive selection within specific branches on the tree, or is it just saying "there is postivie selection somewhere on this tree"? Several publications have illustrated positive selection in bats, especially within the Rhinolophus bat reservoirs of sarbecoviruses, which is presumably due to selection on ACE2 sequence specifically to evade sarbecovirus binding and infection. (PMID 32699095, 22438550). In the current manuscript, it is unclear to me whether this positive selection within bats specifically due to viral pressures is giving rise to the positive selection signal, which is then being interpreted across all mammalian orders as evidence for selection related to intrinsic ACE2 function. This should be clarified, and probably the statements implying that this positive selection is specifically due to ACE2 physiological enzymatic activity might need to be tempered. (And these two papers are probably worth discussing specifically in the context of this work including the ACE2 residues they identify as positively selected in these host-virus arms' races.)

6. Line 280: it is unclear to me why having a lower native blood pressure would relax selection on ACE2 function. Regardless of the homeostatic 'set point', presumably the enzymatic activity of ACE2 is needed anyway to maintain that homeostatic set point. It seems like changes in global blood pressure are probably instead modulated by e.g. the upstream regulation of the peptide itself, or response pathways to the cleaved product, or some other pathway - not the actual catalytic activity of ACE2 itself. And it's further unclear why a correlation in between body mass and blood pressure establishes any relaxation in constraint - it's actually almost the opposite, in that it argues that there is some overarching 'reason' why smaller animals have lower blood pressure and therefore in fact it is an attuned process, not a relaxation of constraint. This analysis is interesting, but I think the authors might need to more carefully consider how to link it into their overall 'thesis'. The further linkage of all of this to effective population size in lines 369-371 extends this all way too far in my opinion, and the statements on lines 369-371 should just be left out.

7. The setup argued for starting in line 294 does not seem to be 'followed through' with the ASR that was actually performed. Simple measurement of the activity of the reconstructed sequence does not clearly illustrate whether these mutations were differentially tolerated in this ancestor without the loss of activity. It seems this analysis should involve not only reconstructing the ancestor, but also introducing the same mutations as illustrated in Figs. 2F/G into this ancestral sequence to identify whether the 'valley' is absent in this ancestral rodent sequence. I understand that's asking for substantial additional experiment, but it would really increase the interest added by this ASR component to the story - without it, the ASR component doesn't seem to add much additional insight.

Minor points:

1. Line 51: "fusing" with isn't clear what that means,

2. Not sure if the use of "pleiotropic" on line 110 is necessarily wrong, but it is sort of tripping me up. Maybe the directionality is reversed? The argument is not that this surface has evolved as a SARS-CoV-2 interaction interface and has pleiotropic consequences for ACE2 activity, but rather sort of the reverse - that evolution of ACE2 sequences due to differences in physiology alters the latent capacity for sarbecovirus receptor usage and susceptibility. Somehow describing this more specifically might enable the authors to avoid using a somewhat contentious word of 'pleiotropy' loosely

3. The disconcordance between Fig. 2F/G and Fig. S4 seems important for proper assessment of results. It suggests that the dynamic range of the RBD binding assay is lower than for actual viral entry. It might be worth simply including Fig. S4 within Fig. 2

Reviewer #2:

In this fascinating article, the authors undertake a series of investigations into the broad host tropism exhibited by SARS-CoV-2, beyond that predicted by studies focused exclusively on comparative sequence analyses of the ACE2 homology to the human ACE2 viral binding interface. In particular, the authors undertake the following major analyses:

1.They investigate ACE2 to SARS-CoV-1 (hereafter SC1) and SARS-CoV-2 (hereafter SC2) RBD binding across a suite of mammalian ACE2 orthologs transfected into 293T cells: human, mouse, bat, dog, pangolin. They show stronger association of SC2-RBD with both human and pangolin ACE2 relative to SC1 and show that the SC1 and SC2 proteins cannot bind mouse not bat ACE2

2.They follow this up in a pseudotype virus system, showing cell entry patterns that recapitulate those described in #1 across these same 5 transfected cell lines.

3.They then measured functional variation in ACE2 enzymatic activity across these same cell lines and found a wide range of hydrolysis activity. Dog, bat, and pangolin displayed low activity compared to human and mouse.

4.They next investigated dN/sS shifts in ACE2 mutational rates across these ACE2 orthologs in a suite of mammals and found evidence of ACE2 under positive selection.

5.They next identified six residues unique to mouse ACE2 and adjacent to the viral RBD to explore the effects of these on virus binding. They took the same approach as in #4 to show that "several" of the sites were under either positive or purifying selection and that primates demonstrated decreased evolutionary rates relative to bats or rodents.

6.They then used site-directed mutagenesis to substitute these six mouse sites in human and dog ACE2. They demonstrate that single mutations had limited effects on SC2 binding and huge deleterious effects on ACE2 hydrolysis activity but when introduced altogether both abolished AC2 binding and rescued the hydrolysis activity of ACE2 due to epistatic activity ('sign epistasis').

7.Now, the authors reasoned that species with higher blood pressure would have a greater need for ACE2 blood pressure regulation and therefore have greater constraints on ACE2 sequence space, so they attempted to correlate ACE2 evolutionary rate with blood pressure. They found that rodents and bats with higher ACE2 evolutionary rates also had lower blood pressure, possibly a mechanism for a less constrained ACE2 fitness landscape in these taxa

8.Finally, in order to explore the hypothesis that rodents may simply have evolved these six mutations by traversing a landscape of permissive mutations, the authors reconstructed ancestral rodent ACE2 and discovered it lacked the six residues of interest (suggesting these mutations evolved recently) but likely had high activity which could have compensated for deleterious mutations on the path to the modern receptor.

The paper is a tour-de-force of intriguing ideas and analyses. There are a few places where I have questions and where the discussion goes a bit too far but on the whole, it is in excellent shape:

1.For #4 above, why were the 107 chosen species selected (or the 89 included in the pruned analyses)? It seems likely that there may be extensive variation in dN/dS across bat species for instance. Some justification for the selected subset is needed

2.Following on above, the authors say that "several" of the six residues of interest for mouse ACE2 show evidence of positive or purifying selection across many taxa, with much higher rates in bats and rodents and constraints in humans. Can these findings be summarized in an accessible way, maybe in the supplement?

3.Can you make the y-axis on a log scale in Figure 3E? It's unclear whether there is any correlation at all or simply whether rodents and bats just show unusually high evolutionary rates for ACE2 compared with other taxa.

4.The authors talk a lot about bats and mice being "immune" to WT SC2. This is not known for bats. The authors would need to demonstrate lack in infectivity in a live animal model to show this, and in fact, they only show lack of virus entry into a transfected 293T cell line with a single bat species ACE2 ortholog. Since the bat chosen (R. sinicus) is not even the host for the closest known CoV to SC2 (R. affinis), this is a stretch. Additionally, Zhou et al 2020 shows some SC2 virus entry in HeLa cells expressing R. sinicus ACE2. Most of the work in this paper is focused on mouse immunity to SC2 and the relevant residues driving this interaction. I suggest the authors keep most of their speculations limited to mice and not try to extrapolate too far into bats.

5.Following on above, I would like to see a 'caveats and limitations' paragraph that mentions how we cannot really determine host range with a limited tool kit in this way. No mention is made of the fact that bats, for instance, might permit SC2 virus entry in some tissues (e.g. GIT tissues) and not others and might have other receptors permitting entry.

6.Additionally, the pangolin fusion hypotheses should be dropped. While pangolin CoV may effectively invade human cells, this paper provides no evidence that it is an intermediate host between bat CoV and WT SC2. It is largely believed that a closer genotype to SC2 than has yet been described is probably circulating in wild bats somewhere still. See MacLeean et al 2021, Boni et al 2020, Andersen et al 2020

7.Finally, the bit about genome engineering of minks or other domestic mammals should be dropped, as this is a big step, of questionable ethics, and moreover, this paper does not demonstrate that it would even work.

Reviewer #3:

This paper seeks to identify amino acid sequence states in the ACE2 protein that confer differences in susceptibility to SARS-Cov2 infection between species and to explain the evolution of these different states in terms of epistasis, natural selection, and pleiotropic effects on ACE2's endogenous enzyme activity.

Understanding the sequence-function relationships underlying ACE2 interactions with SARS-Cov2 is a worthy goal, as is understanding the evolutionary causes of differences in these interactions among species. The subject matter of the paper is therefore of significant potential interest. However, many of the claims are not supported by the experiments and analyses presented. I'm sorry to say that the claims that have sufficient support after a careful reading are of rather narrow scientific impact and seem best suited for a specialist audience.

1. The authors' approach is to transfer 6 amino acid states that exist in mouse ACE2 into human and dog and measure their effects on molecular function. They choose these species and states because: 1) these residues are at sites on the surface of ACE2 that binds the SARS-Cov2 spike protein, 2) human and dog ACE2 have higher relative affinity for SARS-Cov2 than mouse ACE2 does, and 3) mouse is less susceptible to SARS-Cov2 infection than human and dog. With several nice experiments in Figs 1A-E, the authors provide evidence that reinforces premises 2 and 3: mouse ACE2 binds the SARS-Cov2 less efficiently than human and dog ACE2, and cultured cells transfected with mouse ACE2 are less susceptible to pseudovirus infection.

2. But the paper does not show that the six residues are sufficient causes for the difference in affinity and infectivity between the species' ACE2 proteins. The major experiments transfer the 6 mouse states into human and dog ACE2 proteins. However, these "chimeric" ACE2 proteins do not confer cellular resistance to infection nearly as well as the mouse ACE2 protein itself does. Thus, the 6 mouse amino acids can reduce to some extent but not nearly recapitulate the fully resistant cellular phenotype conferred by mouse ACE2 (compare Fig 1E to Fig S4). Further, the reciprocal experiment, where human or dog amino acids at these sites are introduced into the mouse ACE2 was not performed; we therefore do not know the extent to which the 6 states account for the resistance exhibited by the mouse ACE2. Moreover, the historical substitutions from ancestral states to any derived state is never assayed in any of the species' proteins, as would be required to support the contention that these substitutions played a causal role in the evolution of increased or reduced affinity/susceptibility. The evidence therefore establishes that a small number of residues in mouse ACE2 can partially reduce affinity and infectivity when introduced into human or dog. But it does not support a causally sufficient role for the mouse states in resistance by the mouse ACE2. This observation is interesting in terms of ACE2 sequence-function relationships, but it does not have clearly interpretable implications for genetic/biochemical causality that underlies differences between species or the evolution of those differences.

3. A central claim of the paper is that the 6 states interact epistatically in producing the reduced affinity of ACE2 for SARS-Cov2 and the reduced enzyme activity. These claimed epistatic interactions are then said to explain why susceptible species have not evolved genotypes resistant to SARS-Cov2. The evidence for epistasis with respect to affinity is not convincing, because detecting epistasis requires a significant deviation from a well-founded expectation for a quantitative phenotype that would be observed in the absence of epistasis. For example, in the absence of epistasis, one would expect the effect of a combination of mutations on the free energy of binding to be the sum of the energetic effects of each mutation introduced singly; the effect on Kd is expected to be multiplicative. But no such expectation or test is provided here to show that the effects of combinations are different from the effects that would arise if there were no epistasis. For binding, the assay is a complex one that does not directly quantify affinity, occupancy of the bound state, etc.

reasons.

The paper claims an epistatic interaction for binding between mutation at site 353 and those at the other sites, but there is no apparent epistasis at all on this case: site 353 and the set of the other mutations each reduce affinity, and they reduce affinity to a greater extent when combined, precisely as expected with no epistasis. The data do show that 5 of the 6 mouse states produce no clear effect on binding when introduced singly, and they do reduce binding when combined with each other or with the sixth state which affects affinity on its own. This does not necessarily indicate epistasis. Suppose the assay has an intrinsically nonlinear dose-response relationship (such as a hyperbolic or sigmoidal relationship, as is expected in any saturable assay); in such a case, single mutations that each have a moderate effect on affinity may produce no detectable reduction in the signal of binding, if introduced singly into a high-affinity protein, but when introduced into a protein whose affinity has already been weakened by other mutations, that effect will become apparent. Further, there is no evidence for epistasis in the infectivity assay shown in Fig S4, where progressively including more mutations progressively reduces infectivity. These observations do not rule out the possibility of some kind of relatively subtle epistasis with respect to the magnitude of mutations' effects, but they do not establish it. The paper therefore provides no persuasive evidence of epistasis for the phenotype of ACE2 affinity for SARS-Cov2 or susceptibility.

In the absence of quantitative knowledge concerning the expected phenotype when nonepistatic mutations are combined, one could still provide some evidence of epistasis if the sign of the effect of a mutation differs when introduced into different genetic backgrounds; the only way sign epistasis can arise without epistasis is for the underlying relationship between the measured phenotype and underlying biochemical effects to be nonmonotonic, and that is unlikely in the case of apparent binding in these assays. The authors do observe sign epistasis for the catalytic phenotype, because the mouse state at site 353 reduces activity on its own when introduced into human ACE2 but increases it when introduced into the context of four other mouse states. Thus, the authors should make no claim for epistasis with respect to binding or infectivity, but they can make a limited claim for epistasis of this mutational combination with respect to catalysis.

4. The paper's central evolutionary narrative is that the lack of evolved resistance in humans and dog is attributable to a claimed pleiotropic cost incurred by reducing ACE2's affinity for SARS-Cov2. Mice are claimed to be free of this pleiotropic constraint, because the function of ACE2 in their cardiovascular system is different from that in humans and many other mammals. The data do not coherently support this premise, for several reasons.

a. Fig. 1 shows that the mouse ACE2 actually has higher peptidase activity than human and dog, not lower, as would be required for the peptidase-versus-affinity tradeoff to explain the evolution of viral resistance in mouse but not in humans.

reasons.

b. The authors observe reduced ACE2 enzyme activity when the 6 mouse states are introduced into the human ACE2, but no such reduction is observed in dog. This means that there is no intrinsic association between the two phenotypes, as is required to claim that humans and dogs have not evolved resistance to SARS-Cov2 because of antagonistic pleiotropy related to ACE2 activity.

c. The observation that the 6 mouse states decrease SARS-Cov2 affinity and reduce peptidase activity in human ACE2 would at best imply only that humans may be unlikely to evolve reduced affinity by acquiring these particular six mouse states. This does not establish that they could not do so via other mutations that may not affect peptidase activity.

reasons.

d. The fact that the six mouse states do not have the deleterious effects on dog ACE2 indicates that the states at other sites in the protein can prevent the deleterious effect of the six mouse states, indicating that human ACE2 might be able to acquire the 6 mouse states if it also acquired other residues that have a similar modifying or buffering effect. Thus, the authors' data establish only that human ACE2 could not reduce its SARS-Cov2 affinity without incurring pleiotropic effects on affinity by acquiring only the 6 mouse sites. A general statement about acquisition of resistance per se therefore cannot be justified.

5. The authors claim that evolution of the ACE2 protein and several of the six states in particular has been driven by positive selection. They base this claim on the results of two kinds of model-based likelihood ratio test, the branch-sites test and the sites test. However, both of these tests have been shown in the literature to be unreliable, with very high propensities to return false positive conclusions under realistic conditions. These methods therefore do not provide reliable evidence for the claims about selection. It is true that these methods have been widely used in the past as evidence for positive selection; given the recent findings, however, they should no longer be used. See Witosky et al, Synonymous site-to-site substitution rate variation dramatically inflates false positive rates of selection analyses: ignore at your own peril, MBE 2020; Venkat et al, Multinucleotide mutations cause false inferences of lineage-specific positive selection, Nature Evol Evol 2018.

6. The authors claim that the 6 mouse states could have been selectively accessible in rodents because rodents have lower systolic blood pressure than other mammals, which could result in lower selective pressure to maintain ACE2 function, thereby reducing the deleterious costs of the 6 states. The authors provide as evidence of this relaxed constraint hypothesis a higher ratio of nonsynonymous to synonymous rates of evolution at these sites in rodents and bats compared to other mammals. In addition to concerns about these tests as discussed above, the analysis appears to have been performed only on the subset of sites that differ in amino acid state between mouse and other mammals, which are tautologically expected to have higher rates of nonsynonymous substitution in rodents.

A second problem is that the authors also state that unlike other mammals, mice do not exhibit a vasodilatory response to Ang-(1-7), the product of ACE2 hydrolysis. This observation seems to contradict the low blood pressure hypothesis for putative relaxed selective constraint: if ACE2 does not mediate vasodilation, then it is unclear why low blood pressure should produce any relaxed constraint at all.

7. Based on the observation that the 6 mouse states that reduce the peptidase activity of human ACE2 are not located at the protein's catalytic active site, the authors state that this effect must be mediated by indirect structural effects. However, the most plausible mechanism by which mutations would have this effect would be by impairing substrate binding, which takes place at the portion of the ACE2 surface where SARS-Cov2 binding occurs. This would not reflect a surprising mechanism, and it would not be indirect, as it would involve mutations at the protein's surface directly compromising interactions with the substrate at that surface.

8. The authors state that it is surprising that the 6 mouse residues reduce SARS-Cov2 affinity when introduced into either human or dog, because human and dog have different states at most of these sites. They say that this indicates that homology-based reasoning is a poor predictor of proteins' affinity. But the observation that the human and dog states are different is not at all surprising - all it means is that there are multiple amino acid states per residue that are compatible will affinity higher than that conferred by the mouse states. It is very common in multiple sequence alignments to observe some sequence variability at functionally important sites in a protein, such as exchanges between hydrophobic states in a protein's core, or between polar states on a protein's surface, or between donor states (or acceptors) in hydrogen-bonding residues. No selective or epistatic explanation is required to account for this variation - some sequence degeneracy of the functional property is all that is required. The authors' results do show that a search for strict conservation of a single state between proteins with similar affinity is not a reliable guide to identifying sequence sites that contribute to that phenotype, but it would be very naïve to think that it would be. A further consideration related to sequence variability at these sites is that affinity for SARS-Cov2 could not possibly have been a source of long-term constraints that affect sequence variation among species, because the virus did not emerge until 2019. There is no reason that we should expect sites that contribute to affinity to be strictly conserved over evolutionary time.

9. The authors refer to a "functional convergence" between dog and human in their shared susceptibility to SARS-Cov2. But the paper suggests that susceptibility and high ACE2 affinity is ancestral, with a reduction in these phenotypes in the lineage leading to mouse. Susceptibility is therefore not convergent but a retained ancestral state.

I am sorry to say that when these issues are all considered, many of the paper's claims turn out not to be sufficiently supported by the evidence. The paper does establish that six states in mouse can contribute to reducing both affinity and peptidase activity when introduced into human ACE2; further, these states reduce affinity but not activity when introduced into dog ACE2. That is interesting from a sequence-function perspective and should be of interest to scientists whose studies are focused in detail on ACE2 binding and catalysis. But the current paper does not establish whether those states are sufficient to confer resistance in mouse, nor they establish why those states evolved in any of the taxa of interest. Further, there is virtually no evidence for epistasis or an effect on evolutionary processes. The idea of pleiotropic constraints contributing to susceptibility in many lineages is plausible, but the evidence presented does not support it. I do appreciate this paper's effort to connect genetic experiments in ACE2 to evolutionary processes, but the paper's claims on this subject are not justified by the evidence. In my opinion, then, the work reported here should be reported to specialists in the field, but in doing so the claims should be dramatically narrowed.

Decision Letter 3

Roland G Roberts

29 Nov 2021

Dear Dr Duh,

Thank you for submitting your revised Research Article entitled "Evolutionary pathways to SARS-CoV-2 immunity are opened and closed by epistasis" for publication in PLOS Biology. I have now obtained advice from one of the original reviewers and have discussed their comments with the Academic Editor. 

Based on the reviews, we will probably accept this manuscript for publication, provided you satisfactorily address the remaining points raised by the reviewers. Please also make sure to address the following data and other policy-related requests.

IMPORTANT:

a) We agree with reviewer #1 regarding the use of the word "immunity," which we feel is potentially misleading for the reasons that the reviewer identifies. This should be changed throughout, including in the Title. We would favour the terms "resistance" or "lack of susceptibility." Thus the Title should be changed to "Evolutionary pathways to SARS-CoV-2 resistance are opened and closed by epistasis." Additionally it might be helpful to mention ACE2 in the title; perhaps "Evolutionary pathways to SARS-CoV-2 resistance are opened and closed by epistasis acting on ACE2"?

b) We also think that the doom-laden fatalism should be avoided where it occurs - one example is the last sentence of the Abstract, where "..opened the road to immunity for some species, while dooming humans to SARS-CoV-2 susceptibility millions of years before the pandemic started" should perhaps be changed to something like "...opened the road to resistance for some species, while making humans susceptible to viruses that use these ACE2 surfaces for binding, as does SARS-CoV-2."

c) We think that you paper, which is succinct, and has a relatively straightforward message, would be better published as a Short Report. This does not have any formatting implications, but please can you select "Short Reports" as the article type when you resubmit?

d) Please attend to all of the remaining points raised by reviewer #1.

e) Please address my Data Policy requests below; specifically, we need you to supply the numerical values underlying Figs 1ABCDEFG, 2CDEFGHIJKLM, 3CDEF, 4BDEFG, S1, and tree files for 4A, S4, S6, S7. Please cite the location of the data clearly in each relevant main and supplementary Fig legend.

As you address these items, please take this last chance to review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the cover letter that accompanies your revised manuscript.

We expect to receive your revised manuscript within two weeks.

To submit your revision, please go to https://www.editorialmanager.com/pbiology/ and log in as an Author. Click the link labelled 'Submissions Needing Revision' to find your submission record. Your revised submission must include the following:

-  a cover letter that should detail your responses to any editorial requests, if applicable, and whether changes have been made to the reference list

-  a Response to Reviewers file that provides a detailed response to the reviewers' comments (if applicable)

-  a track-changes file indicating any changes that you have made to the manuscript. 

NOTE: If Supporting Information files are included with your article, note that these are not copyedited and will be published as they are submitted. Please ensure that these files are legible and of high quality (at least 300 dpi) in an easily accessible file format. For this reason, please be aware that any references listed in an SI file will not be indexed. For more information, see our Supporting Information guidelines:

https://journals.plos.org/plosbiology/s/supporting-information  

*Published Peer Review History*

Please note that you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:

https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/

*Early Version*

Please note that an uncorrected proof of your manuscript will be published online ahead of the final version, unless you opted out when submitting your manuscript. If, for any reason, you do not want an earlier version of your manuscript published online, uncheck the box. Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us as soon as possible if you or your institution is planning to press release the article.

*Protocols deposition*

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please do not hesitate to contact me should you have any questions.

Sincerely,

Roli Roberts

Roland G Roberts, PhD,

Senior Editor,

rroberts@plos.org,

PLOS Biology

------------------------------------------------------------------------

DATA POLICY:

You may be aware of the PLOS Data Policy, which requires that all data be made available without restriction: http://journals.plos.org/plosbiology/s/data-availability. For more information, please also see this editorial: http://dx.doi.org/10.1371/journal.pbio.1001797 

Note that we do not require all raw data. Rather, we ask that all individual quantitative observations that underlie the data summarized in the figures and results of your paper be made available in one of the following forms:

1) Supplementary files (e.g., excel). Please ensure that all data files are uploaded as 'Supporting Information' and are invariably referred to (in the manuscript, figure legends, and the Description field when uploading your files) using the following format verbatim: S1 Data, S2 Data, etc. Multiple panels of a single or even several figures can be included as multiple sheets in one excel file that is saved using exactly the following convention: S1_Data.xlsx (using an underscore).

2) Deposition in a publicly available repository. Please also provide the accession code or a reviewer link so that we may view your data before publication. 

Regardless of the method selected, please ensure that you provide the individual numerical values that underlie the summary data displayed in the following figure panels as they are essential for readers to assess your analysis and to reproduce it: Figs 1ABCDEFG, 2CDEFGHIJKLM, 3CDEF, 4BDEFG, S1; also tree files for 4A, S4, S6, S7. NOTE: the numerical data provided should include all replicates AND the way in which the plotted mean and errors were derived (it should not present only the mean/average values).

IMPORTANT: Please also ensure that figure legends in your manuscript include information on where the underlying data can be found, and ensure your supplemental data file/s has a legend.

Please ensure that your Data Statement in the submission system accurately describes where your data can be found.

 ------------------------------------------------------------------------

BLOT AND GEL REPORTING REQUIREMENTS:

We require the original, uncropped and minimally adjusted images supporting all blot and gel results reported in an article's figures or Supporting Information files. We will require these files before a manuscript can be accepted so please prepare and upload them now. Please carefully read our guidelines for how to prepare and upload this data: https://journals.plos.org/plosbiology/s/figures#loc-blot-and-gel-reporting-requirements 

------------------------------------------------------------------------

DATA NOT SHOWN?

- Please note that per journal policy, we do not allow the mention of "data not shown", "personal communication", "manuscript in preparation" or other references to data that is not publicly available or contained within this manuscript. Please either remove mention of these data or provide figures presenting the results and the data underlying the figure(s).

------------------------------------------------------------------------

REVIEWER'S COMMENTS:

Reviewer #1:

The authors have performed considerable work to streamline the manuscript in light of reviewer comments. I still have some various nitpicky details, of minor to modest importance, as I still feel there are some errant claims and comments in the manuscript. In addition to the specific calls below, I feel as a general tonal comment, speculations made beyond conclusions across the manuscript stray a bit far and could be reined in for a tighter interpretation. However, I am better able to appreciate the logical flow of this manuscript than in its prior form. My additional comments are below - all should be achievable via changes in writing with no further experiments requested from my reading. Comments are in order of reading, not importance.

1. Most dramatically in the title and abstract, but also used throughout the manuscript: I dislike the use of "immunity" to describe resistance to SARS-CoV-2 infection in ACE2 variants that resist binding by SARS-CoV-2. In the virology and immunology field, the suggestion of "immunity" would immediately invoke something about innate or adaptive immune system conferring "immunity" to infection by the virus. I believe instead of "immunity", phrasing about "susceptibility' versus perhaps "resistance" (though there may be a better word still than resistance) would be much preferred over "immunity"

2. Lines 43-46: RaTG13 is from R. affinis, not R. sinicus. There is no supported role for recombination with a pangolin virus nor support for pangolin as an intermediate source at this time. To establish zoonotic origin, should be fine to just leave it as referencing circulation of related viruses in several Rhinolophus bat species (at this point, can cite other sarbecoviruses more closely related than RaTG13 as well, and probably don't need to name specific isolates as much as generally point out closely related bat viruses). If needing to discuss why pangolin is included, I would summarize pangolins at this point as another species susceptible to SARS-CoV-2-related CoV infection and spillover, though the routes of exposure and transmission in pangolin communities (e.g. natural circulation, animal trafficking, etc.) are at present unclear.

3. Line 64-67: interaction interface of SARS1 and SARS2 is homologous, and this dichotomy between SARS1 susceptibility to a single mutation and SARS2 targeting multiple hotspots is not a supported claim to my knowledge. SARS1 structure papers also discuss "hotspot" ACE2 residues for interaction, and citations 24-26 don't establish that the single mutation in citation 23 that reduces SARS1 binding does not also reduce SARS2 binding. This differentiation between SARS1 and SARS2 structural mechanisms of ACE2 binding is not well supported and is not necessary to the paper.

4. Line 72-74: somehow missing in this section is consideration that for most species, apart from natural bat reservoirs, there might not be selective pressure to evade binding by SARS-related coronaviruses because these viruses do not naturally circulate in these other species. The current description around antagonistic pleiotropy seems to assume that there naturally would be selective pressure in all of these species to evade binding by SARS-related coronaviruses, thereby requiring some additional explanation, but there need not be any reason in species beyond bats why resistance has evolved that is proximally related to coronavirus susceptibility

5. Line 126 and on: Shortening of SARS-CoV-2 and -1 to simply "CoV-2" is not standard. Ok in the figure legends for conciseness, but I would avoid it in the written text.

6. Fig. 1: the first time pangolin's and what I have to assume is a bat's silhouettes appear, there is no written label to clarify what ACE2 is being shown. Silhouettes are not sufficient

7. Line 238-240: result seems overstated - K353H does still have a noticeable effect on RBD association in the flow-based assay. Given likely nonlinear relationship between true RBD:ACE2 affinity and labeling in this flow-based assay, this difference in complete versus partial abolishment by K353H could be more about starting affinity of SARS-CoV-1 versus SARS-CoV-2 more than an underlying difference of sensitivities of these viral RBDs to this mutation. True affinity measurements would be needed to support this claim.

8. What error bars represent, the number of replicates, and whether they represent technical versus biological replicates should be clarified in all figure legends

9. The orange circle in the Fig. 4B cartoon is indicative of the primate-rodent ancestor (lateral movement is irrelevant on the tree, so it is equivalent in position to the node of the human/mouse common ancestor). Assuming the black wedge is assumed to represent proliferation of rodents, the orange circle should be moved up to the apex of this black wedge.

10. In the paragraph starting 357 - this hypothesis doesn't "explain" why these six substitutions occurred, because the data suggests that although they maintain the higher catalytic rate, they don't further improve it - is that correct? If so, this paragraph currently is written as though explaining why these mutations occurred in the mouse lineage but not the dog - but in either case, as shown in Fig. 4F, in both backgrounds the six mutations have no net change on the base activity - so this is insufficient to explain their occurrence in mouse but not in dog which in my current reading is the 'purpose' of this paragraph

11. In places such as line 421, the assumption is that the mouse combination is the only route to resistance to SARS2 binding. This is assuredly not the case, as bat ACE2 alleles that abolish SARS-CoV-2 binding use different ACE2 substitutions. Therefore, words such as "required" are incorrect (implying necessity), but rather language here and elsewhere should indicate the mouse solution is one "sufficient" way to restrict binding. I now see this is caveated as a Discussion point, but that doesn't offset the need to use appropriately cautious language when discussing the results.

Decision Letter 4

Roland G Roberts

8 Dec 2021

Dear Dr Duh,

On behalf of my colleagues and the Academic Editor, Andreas Hejnol, I'm pleased to say that we can in principle accept your Short Report "Evolutionary pathways to SARS-CoV-2 resistance are opened and closed by epistasis acting on ACE2" for publication in PLOS Biology, provided you address any remaining formatting and reporting issues. These will be detailed in an email that will follow this letter and that you will usually receive within 2-3 business days, during which time no action is required from you. Please note that we will not be able to formally accept your manuscript and schedule it for publication until you have any requested changes.

Please take a minute to log into Editorial Manager at http://www.editorialmanager.com/pbiology/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS: We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with biologypress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for choosing PLOS Biology for publication and supporting Open Access publishing. We look forward to publishing your study. 

Sincerely, 

Roli Roberts

Roland G Roberts, PhD 

Senior Editor 

PLOS Biology

rroberts@plos.org

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Recombinant ACE2 hydrolysis activity in solubilized transfected HEK293T cells.

    ACE2 hydrolysis activity was measured using a fluorogenic peptide substrate (Mca-YVADAPK(Dnp)-OH) incubated with lysates of HEK293T cells for 2 hours. Cells were either untransfected or were transfected with a human ACE2 construct. Incubation of lysate–peptide mixture with a ACE2-specific inhibitor (DX600) reduced fluorescence attributable to ACE2 hydrolysis activity. N = 5 to 6 biological replicates. Standard error is shown. All data are available in S1 Data. ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S2 Fig. Conservation of angiotensin peptide sequences across mammalian species investigated in this study.

    Renin produces angiotensin 1 by cleaving Angiotensinogen (AGT gene). Angiotensin 1 is subsequently cleaved by ACE, followed by ACE2. ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S3 Fig. Expression of ACE2–gfp orthologs in transfected HEK293T cells was assessed by flow cytometry (% of GFP-positive cells).

    These expressed ACE2 proteins were used in hydrolysis assays. Human ACE2 served as an internal control in each separate assay. ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S4 Fig. Maximum likelihood phylogeny used in PAML analyses.

    aLRT-SH like branch support values (IQ-Tree) are shown. Note that a basal trichotomy was artificially induced to accommodate input file requirements. All data are available in S1 Data.

    (DOCX)

    S5 Fig. Targeted mutations to human ACE2 disrupt binding to the RBD of SARS-CoV-1 and SARS-CoV-2.

    Western blots of immunopreciptations and cell lysates of HEK293T cells co-transfected with an Fc-tagged SARS-CoV-2 S protein RBD and 1D4-tagged (C9) human ACE2 construct. ACE2, angiotensin converting enzyme 2; RBD, receptor-binding domain; SARS-CoV-1, Severe Acute Respiratory Syndrome Coronavirus; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2.

    (DOCX)

    S6 Fig. Maximum likelihood phylogeny used in ancestral reconstruction of Rodent ACE2.

    aLRT-SH like branch support values (PhyML) are shown. All data are available in S1 Data. ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S7 Fig. Species phylogeny and least squares linear regression using phylogenetically independent contrasts of systolic blood pressure and body mass.

    All data are available in S1 Data.

    (DOCX)

    S1 Table. ACE2 accession numbers used in dN/dS estimates.

    ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S2 Table. Analyses of selection on mammalian ACE2 using PAML random sites models.

    ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S3 Table. Results of BUSTED (HyPhy) analyses of mammalian ACE2.

    This model accounts for synonymous rate variation (SRV). Log L values demonstrate that the unconstrained model performs better than the constrained, specifically due to the inclusion of a positive selection omega site category (ω3). ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S4 Table. Analyses of selection on mammalian ACE2 without bat (Chioptera) sequences using PAML random sites models.

    ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S5 Table. Positively selected sites in mammalian ACE2.

    ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S6 Table. Results of CmD analyses of mammalian ACE2 under various partitions.

    ACE2, angiotensin converting enzyme 2; CmD, clade model D.

    (DOCX)

    S7 Table. ACE and ACE2 accession numbers used in ancestral reconstruction.

    ACE, angiotensin converting enzyme; ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S8 Table. Results of random sites analyses of vertebrate ACE2, with the best fitting mode (M8) used for the ancestral reconstruction of mammalian ACE2.

    ACE2, angiotensin converting enzyme 2.

    (DOCX)

    S1 Data. Contains all individual data points used to derive the means and errors represented throughout this study.

    (XLSX)

    Attachment

    Submitted filename: Castiglione et al_Response to Reviewers.pdf

    Attachment

    Submitted filename: Response to Reviewers.pdf

    Data Availability Statement

    All relevant data are within the paper and its Supporting Information files.


    Articles from PLoS Biology are provided here courtesy of PLOS

    RESOURCES