Trillions of bacteria live in and on the human body. Most of them are harmless but some can cause serious infections. To grow in or on the body, bacteria often attach to proteins on the surface of cells that make up the lining of tissues like the gut or the throat. In some cases, bacteria use these proteins to invade the cells causing an infection. Genetic mutations in the genes encoding these proteins that protect against infection are more likely to be passed on to future generations. This may lead to rapid spread of these beneficial genes in a population.

A family of proteins called CEACAMs are frequent targets of infection-causing bacteria. These proteins have been shown to play a role in cancer progression. But they also play many helpful roles in the body, including helping transmit messages between cells, aiding cell growth, and helping the immune system recognize pathogens. Scientists are not sure if these multi-tasking CEACAM proteins can evolve to evade bacteria without affecting their other roles.

Baker et al. show that CEACAM proteins targeted by bacteria have undergone rapid evolution in primates. In the experiments, human genes encoding CEACAMs were compared with equivalent genes from 19 different primates. Baker et al. found the changes in human and primate CEACAMs often occur through a process called gene conversion. Gene conversion occurs when DNA sections are copied and pasted from one gene to another. Using laboratory experiments, they showed that some of these changes enabled CEACAM proteins to prevent certain harmful bacteria from binding.

The experiments suggest that some versions of CEACAM genes may protect humans or other primates against bacterial infections. Studies in natural populations are needed to test if this is the case. Learning more about how CEACAM proteins evolve and what they do may help scientists better understand the role they play in cancer and help improve cancer care. Studying CEACAM evolution may also help scientists understand how bacteria and other pathogens drive protein evolution in the body.

Epithelial surfaces are typically the initial point of contact between metazoans and microbes (Brown and Clarke, 2017). As such, host factors at this barrier play an important role in facilitating or deterring microbial colonization. Bacterial attachment to epithelial surfaces is often mediated by a broad class of surface proteins termed adhesins (Kline et al., 2009). In addition to permitting the growth and colonization of commensal microbes, adhesins are also key virulence factors for many pathogenic bacteria. Adhesin-mediated adherence to host cells is often required for other downstream processes including biofilm formation, epithelial invasion, and the delivery of toxic effectors into host cells (Kline et al., 2009; Sadarangani et al., 2011; Figure 1). Microbial adherence can also trigger epithelial cell signaling cascades, further shaping host responses to resident and invasive microbes. Despite the fundamental importance of epithelial adherence for bacterial colonization and infectious disease pathogenesis, the dynamics of these interactions between host surface proteins and bacterial adhesions over evolutionary timescales remain a mystery. Theory predicts that exploitation of host proteins by pathogens places a significant burden on host populations, driving selection for beneficial mutations that limit microbial invasion or virulence. From a microbial perspective, host defenses can also pose an existential threat resulting in reciprocal adaptation to enhance colonization, growth, and transmission (Aleru and Barber, 2020; Brockhurst et al., 2014; Hamilton et al., 1990; Van Valen, 1973). However, pathogens hijack many host factors not directly involved in immunity, possibly limiting their adaptive potential in response to pathogen interaction. For example, epithelial surface proteins are not only essential for interacting with the environment but also serve crucial cellular and physiological functions including barrier maintenance, cell-cell communication, as well as coordinating host physiological and developmental pathways (Kuespert et al., 2006). It therefore remains unclear the extent to which such proteins are able to adapt in the face of pathogen antagonism.

Figure 1

Download asset Open asset

Bacterial adhesins interact with a wide range of molecules present on host epithelial surfaces (Chatterjee et al., 2021). One important target of bacterial adhesins on vertebrate epithelia are the carcinoembryonic antigen-related cell adhesion molecule (CEACAM) family of proteins (Gray-Owen and Blumberg, 2006). There have been multiple independent expansions of this gene family across mammals (Kammerer and Zimmermann, 2010; Pavlopoulou and Scorilas, 2014) and the human genome encodes 12 functional CEACAM genes as well as several pseudogenes (Gray-Owen and Blumberg, 2006; Kammerer and Zimmermann, 2010). Collectively, CEACAMs are expressed on nearly all vertebrate epithelial surfaces including the microbe-rich surfaces of the urogenital, respiratory, and gastrointestinal tracts (Tchoupa et al., 2014). Epithelial CEACAMs play a variety of roles in cell adhesion as well as intra- and intercellular signaling (Gray-Owen and Blumberg, 2006; Kuespert et al., 2006; Tchoupa et al., 2014). A subset of CEACAMs are also expressed on other cell types, including T-cells and neutrophils where they play important roles in immune signaling and pathogen recognition. CEACAMs typically consist of an extracellular N-terminal IgV-like domain (also termed the N-domain), a variable number of IgC-like domains, and either a GPI anchor or a transmembrane and cytoplasmic domains. The characteristics of this cytoplasmic domain in turn influences the functional properties of CEACAM proteins. Extracellular protein-protein interactions involving CEACAMs have been shown to primarily occur through the extracellular N-domain (Kuespert et al., 2007; Markel et al., 2004).

While the functions of many CEACAM proteins remain obscure, mammalian CEACAM1, CEACAM5 (also known as CEA), and CEACAM6 have been shown to contribute to immunoregulation, cell-cycle progression, and development (Gray-Owen and Blumberg, 2006; Kuespert et al., 2006; Tchoupa et al., 2014). These CEACAMs, along with CEACAM3, are also notable as recurrent targets of bacterial adhesins (Gray-Owen and Blumberg, 2006; Tchoupa et al., 2014). A growing number of bacterial genera have been found to target these CEACAM proteins to promote epithelial adherence and host colonization, including Neisseria, Haemophilus, Escherichia, Fusobacterium, Streptococcus, and Helicobacter (Brewer et al., 2019; Gray-Owen and Blumberg, 2006; Javaheri et al., 2016; Königer et al., 2016; van Sorge et al., 2021). Additionally, several viruses have been reported to bind CEACAMs, including human cytomegalovirus, influenza A, murine coronavirus, and Middle East respiratory syndrome coronavirus (Chan et al., 2016; Hemmila et al., 2004; Macmaniman et al., 2014; Rahman et al., 2021). While many of the bacteria reported to target CEACAMs are able to colonize the host as benign commensals, they are also capable of causing serious infection especially in young children. Such infections may prove severely detrimental to host fitness even when not fatal.

Among bacteria, a number of structurally unrelated CEACAM-binding adhesin proteins have been described. This diversity of structures suggests that CEACAM recognition has arisen independently multiple times during bacterial evolution. These structurally diverse proteins include the integral outer membrane Opa proteins in Neisseria (Fox et al., 2014), the immunoglobulin-type β protein from group B Streptococcus (van Sorge et al., 2021), as well as other trimeric and globular protein domains (Bonsor et al., 2018; Conners et al., 2008).

Bacterial CEACAM recognition can lead to several distinct outcomes (Figure 1). First, adherence to epithelial CEACAMs can provide a stable habitat to support bacterial growth and proliferation. In mice, for example, expression of human CEACAM1 is sufficient to establish stable colonization by otherwise human-restricted strains of Neisseria meningitidis (Johswich et al., 2013). Second, CEACAM binding may facilitate bacterial dissemination through the host epithelium (Wang et al., 1998). Third, in the case of the bacterium Helicobacter pylori, CEACAM-adhesin interactions promote the translocation of virulence factors into host cells via the type 4 secretion system leading to severe gastritis and stomach ulcers in humans (Javaheri et al., 2016; Königer et al., 2016). Finally, bacterial adhesins can potentiate CEACAM-mediated signaling cascades to manipulate cellular functions, including preventing immune cell activation (Gur et al., 2019a; Gur et al., 2019b; Sadarangani et al., 2011), increasing cellular adhesion to prevent shedding of infected cells (Muenzner et al., 2016; Muenzner et al., 2010), and activation of apoptosis (N’Guessan et al., 2007). Interactions between adhesins and CEACAMs, particularly bacterial immunoglobulins that appear to mimic CEACAMs, are predicted to further disrupt endogenous CEACAM adhesion and signaling functions (Macmaniman et al., 2014; Moonens, 2018; van Sorge et al., 2021).

Previous work has reported multiple instances of gene gain and loss as well as high levels of sequence divergence among a subset of CEACAM genes (Adrian et al., 2019; Rhesus Macaque Genome Sequencing and Analysis Consortium et al., 2007; Kammerer and Zimmermann, 2010; Pavlopoulou and Scorilas, 2014). These findings, coupled with the observation that many CEACAM-binding bacteria possess a narrow host range, suggest that host genetic variation may be a major determinant of bacterial colonization. In the case of CEACAM3, which is expressed exclusively in neutrophils and aids in destruction of CEACAM-binding bacteria, there is compelling evidence that residues at the interface of adhesin binding are evolving rapidly in a manner consistent with positive selection (Adrian et al., 2019). Unlike many other characterized mammalian CEACAMs, CEACAM3 appears to be a dedicated immune protein acting as a decoy receptor for CEACAM targeting bacteria (Bonsignore et al., 2019). The evolution of epithelial CEACAMs not dedicated to immune defense and the consequences of their evolution for microbial interactions remains unclear.

Our investigation of species-specific bacterial adherence to CEACAM1 revealed an unforeseen example of extreme genetic divergence within the great apes. The bonobo CEACAM1 gene could represent a rapid succession of single residue changes combined with multiple recombination events arising in bonobos under strong selection and/or a population bottleneck. Alternatively, this allele may be ancient and have been subject to balancing selection or incomplete lineage sorting in ancestral hominid populations. We also considered that the source of the bonobo CEACAM1 sequence may not be from functional CEACAM genes, but a pseudogenized CEACAM sequence or a pregnancy-specific glycoprotein, a family of proteins closely related to CEACAMs. However, a BLAST search of relevant NCBI databases (see Materials and methods) fails to identify any new genomic regions in bonobos or other primates with greater sequence identity than what had already been found. While there are multiple possible explanations for the highly divergent nature of bonobo CEACAM1, absent further evidence the origin of this particular allele remains obscure. What is clear from the example of bonobo CEACAM1, however, is the extent to which gene conversion can rapidly generate diversity between closely related species and the impact of such variation on interactions with microbes.

During the course of investigating the origin of the bonobo CEACAM1 sequence, we discovered evidence that gene conversion has shaped the evolution of many CEACAMs across primates, primarily impacting homologous proteins targeted by bacteria. While we identify several instances of likely gene conversion, results from phylogenetic analyses probably represent an underestimate of the true number of recombination events that have occurred among rapidly evolving CEACAMs in primates. Repeated episodes of gene conversion can obscure past instances of recombination and hinder their identification by gene-species tree discordance. GARD analyses and recombination detection programs in general also tend to miss many recombination events (Bay and Bielawski, 2011; Kosakovsky Pond et al., 2006). One particularly interesting example in orangutans implicates multiple conversion events impacting CEACAM1, CEACAM5, and CEACAM8 (Figure 5—figure supplement 1). Phylogenetic analyses indicate a species-specific conversion event between CEACAM1 and CEACAM5 in orangutans. Prior to the CEACAM1-CEACAM5 conversion, however, residues 29–64 in either CEACAM1 or CEACAM5 were likely replaced by the homologous sequence from CEACAM8. Evidence for this event includes not only multiple nonsynonymous substitutions shared with orangutan CEACAM8, but a shared synonymous substitution in both orangutan CEACAM1 and CEACAM5 only observed in hominid CEACAM8 homologs. Despite this evidence, neither our phylogenetic analyses nor GARD analyses suggest CEACAM8 has been involved in gene conversion. Like CEACAM7, the involvement of CEACAM8 in intra-paralog gene conversion is consistent with CEACAMs with rapid N-domain evolution participating in gene conversion events. Overall, the rapid shuffling of genetic variation among CEACAM genes that we observe could greatly augment the potential for host adaptation in the face of microbial antagonism.

It has been suggested that gene conversion between CEACAM paralogs preserves the ability of CEACAM3 to effectively mimic bacterially antagonized CEACAMs and thereby maintain its function as a decoy receptor (Zid and Drouin, 2013; Zimmermann, 2019). Indeed, our results and those of Adrian et al., 2019, support the importance of maintaining the similarity of CEACAM3 to other adhesin-binding CEACAMs in apes and Old World monkeys. However, our findings suggest that gene conversion does not only serve to maintain CEACAM3’s mimicry function. There is no evidence that New World monkeys encode a CEACAM3 homolog, yet within this group gene conversion appears to be rampant between CEACAM1, CEACAM5, and CEACAM6 (Figure 4C). Additionally, we observe multiple conversion events in hominids that do not involve CEACAM3 (Figure 4C, Figure 5—figure supplement 1, and Figure 6—figure supplement 4). Though we cannot rule out the possibility that New World monkey CEACAM1, CEACAM5, or CEACAM6 may have evolved a mimicry function similar to CEACAM3, our observations in hominids suggest additional drivers for pervasive gene conversion within the CCM₁₃₅₆ group.

High sequence similarity and close chromosomal proximity, such as that seen for the N-domains of the CCM₁₃₅₆ group (Figure 2—figure supplement 1B, Figure 4—figure supplement 3), could result in periodic interconversion of paralogous sequences, which in many cases may be functionally neutral (Bittihn and Tsimring, 2017; Zid and Drouin, 2013). However, we show that the common CEACAM1 variant, Q89H, which matches CEACAM5 at this position, abolishes binding to at least two Opa proteins (Figure 6C). Human CEACAM1 variant A49V, which reduces binding to Opa74, also matches the equivalent residue in CEACAM5 as well as CEACAM3. These examples illustrate the potential benefit of gene conversion between epithelial CEACAM paralogs beyond CEACAM3 mimicry. Given that gene conversion within this family appears to be enriched specifically among CEACAMs recognized by bacteria, we propose that gene conversion among epithelial CEACAMs reflects a general mechanism of pathogen evasion (Figure 7). Such a mechanism allows beneficial sets of mutations to spread, whether between decoys and targets or among antagonized epithelial CEACAMs, more rapidly than conversion of residues through independent mutational events (Bittihn and Tsimring, 2017). In addition, recombination provides decoys the ability to gain binding to CEACAM antagonists through exchanges from epithelial CEACAMs as observed for CEACAM3 (Adrian et al., 2019). Finally, the interchangeability of CEACAM domains could further accelerate the exchange and spread of beneficial mutations in host genomes (Bittihn and Tsimring, 2017). In these ways gene conversion provides an important mechanism by which the host can keep pace with rapidly evolving pathogenic microbes.

Figure 7

Download asset Open asset

Our study examined the influence of CEACAM1 diversity on bacterial binding using adhesins from H. pylori and N. gonorrhoeae as models. However, a diverse range of pathogenic and commensal microbes are known to bind host CEACAMs, and it is unlikely that natural selection across primates has been driven by any single pathogen. What is clear from these results is the major influence that CEACAM divergence and polymorphisms can have on recognition by structurally unrelated bacterial adhesins, and moreover the impact that such diversity could have on shaping future disease outbreaks. It is also notable that recombination between paralogs contributes to the immense diversity of Opa genes Neisseria (Sadarangani et al., 2011). In this regard CEACAM-Opa interactions could reflect a general mechanism by which gene conversion between paralogs contributes to reciprocal adaptation in both hosts and microbes.

In addition to exploring the role of CEACAM gene conversion among primates, we provide evidence that this process continues to shape CEACAM diversity within human populations. The three human CEACAM1 variants we test in our adhesin-binding assay are part of a group of related CEACAM1 haplotypes that increase sequence similarity to CEACAM3 and/or CEACAM5 (Figure 6—figure supplement 1). Extended haplotypes that increase similarly to CEACAM1 at both synonymous and nonsynonymous positions in the N-domain are also found for CEACAM3 and CEACAM5 in humans (Figure 6—figure supplement 3 and Figure 6—figure supplement 4). Indeed, haplotypes consisting of variants of putative recombination events are the most common non-reference alleles for CEACAM1, CEACAM3, and CEACAM5 (Figure 6, Figure 6—figure supplements 1–4). Variant sites in these proteins tend to lie along the protein-binding interface of the N-domain and often impact residues known to influence adhesin recognition. The relationships between these different CEACAM haplotypes appears to be complex, as many different combinations of partial variant haplotypes exist for each CEACAM paralog. The haplotype structures we observe suggest these CEACAM variants are the result of one or more conversion events between paralogous sequences, likely followed by further recombination with the major CEACAM allele.

Important questions remain regarding the rapid evolution of a subset of primate CEACAM proteins. Among these questions is why CEACAM7 and CEACAM8 show similar patterns of evolution to bacterially antagonized CEACAMs despite no known instances of bacterial antagonism. The simplest explanation is that CEACAM7 and CEACAM8 are themselves the targets of as yet unidentified pathogen antagonists (Sintsova et al., 2015). Alternatively, their rapid evolution may reflect pressure to maintain binding with rapidly evolving bacterially antagonized CEACAMs (Gray-Owen and Blumberg, 2006; Skubitz and Skubitz, 2008) could merely be a result of their genomic proximity to rapidly evolving CEACAMs prone to gene conversion (Zid and Drouin, 2013) or could be the result of some as yet unknown evolutionary pressures. Another intriguing aspect of rapid CEACAM evolution is the impact rapid divergence might have beyond interactions with pathogenic microbes. Given the extensive overlap of CEACAM-binding sites among unrelated bacterial adhesins, the ramifications of rapid CEACAM evolution likely extend beyond the adhesins of pathogens to those of commensal and beneficial microbes as well. For commensal microbes that rely on these interaction surfaces, CEACAM evolution could significantly alter their ability to colonize the host. The impact of CEACAM divergence on composition of the host microbiome and/or the evolution of commensal strains warrants further investigation.

Studies of other ‘housekeeping’ proteins targeted by pathogens have found that sites under positive selection typically do not overlap with sites involved in essential host functions (Barber and Elde, 2014; Demogines et al., 2013). This is clearly not the case for CEACAMs, where we observed extensive overlap between sites involved in host protein interactions, sites targeted by bacterial adhesins, and sites undergoing rapid evolution (Figure 2B). How CEACAMs are able to rapidly evolve while maintaining their other essential host protein interactions remains a mystery. Future studies on CEACAM protein functions, interaction networks, and pathogen antagonism will likely clarify these outstanding questions regarding rapidly evolving CEACAMs.

Collectively our study provides evidence that repeated adaptation among primate CEACAMs has shaped host-specific cell adherence by diverse pathogenic bacteria. We find that over half of the CEACAM paralogs found in humans display signatures of positive selection across the primate lineage, localized primarily to the extracellular N-domain. We further discovered that rapid evolution of CEACAM N-domains has been facilitated by extensive ‘shuffling’ of sequences between a subset of CEACAM paralogs likely through repeated gene conversion. The diversification of primate CEACAM N-domain sequences has likely had significant consequences for interactions between primates and bacteria. Consistent with observations across other primate species, we also provide evidence that gene conversion events may impact bacterial pathogen recognition of CEACAMs in contemporary human populations. Together this work reveals how dynamic evolutionary processes have shaped bacterial-host cell associations with consequences for infectious disease susceptibility.

The following files contain the images, data and/or code used to perform analyses and generate figures for this work, Figure 3 - Source data 1, Figure 3 - SuppFig 1 - source data 1, Figure 5 - Source data 1, Figure 6 - Source data 1, Figure 6 - Source data 2, Figure 6 - SuppFig 3 - source data 1, Figure 6 - SuppFig 4 - source data 1.

1. 1. Genome Reference Consortium

   (2017) NCBI Assembly

   ID GCA_000001405.27. GRCh38.p12.

   https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.38
2. 1. Max-Planck Institute for Evolutionary Anthropology

   (2015) NCBI Assembly

   ID GCA_000258655.2. panpan1.1.

   https://www.ncbi.nlm.nih.gov/assembly/GCF_000258655.2
3. 1. University of Washington

   (2021) NCBI Assembly

   ID GCA_013052645.3. Mhudiblu_PPA_v2.

   https://www.ncbi.nlm.nih.gov/assembly/GCA_013052645.3
4. 1. University of Washington

   (2018) NCBI Assembly

   ID GCA_002880755.3. Clint_PTRv2.

   https://www.ncbi.nlm.nih.gov/assembly/GCF_002880755.1
5. 1. Wellcome Trust Sanger Institute

   (2014) NCBI Assembly

   ID GCA_000151905.3. gorGor4.

   https://www.ncbi.nlm.nih.gov/assembly/GCF_000151905.2
6. 1. University of Washington

   (2018) NCBI Assembly

   ID GCA_002880775.3. Susie_PABv2.

   https://www.ncbi.nlm.nih.gov/assembly/GCF_002880775.1
7. 1. Vervet Genomics Consortium

   (2014) NCBI Assembly

   ID GCF_000409795.2. Chlorocebus_sabeus 1.1.

   https://www.ncbi.nlm.nih.gov/assembly/GCF_000409795.2
8. 1. Human Genome Sequencing Center

   (2017) NCBI Assembly

   ID GCF_000264685.3. Panu_3.0.

   https://www.ncbi.nlm.nih.gov/assembly/GCF_000264685.3
9. 1. Baylor College of Medicine

   (2015) NCBI Assembly

   ID GCF_000951035.1. Cang.pa_1.0.

   https://www.ncbi.nlm.nih.gov/assembly/GCF_000951035.1
10. 1. Laboratory for conservation and utilization of Bio-resource

    (2016) NCBI Assembly

    ID GCF_001698545.1. ASM169854v1.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_001698545.1
11. 1. Washington University (WashU)

    (2013) NCBI Assembly

    ID GCF_000364345.1. Macaca_fascicularis_5.0.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_000364345.1
12. 1. Baylor College of Medicine

    (2015) NCBI Assembly

    ID GCF_000956065.1. Mnem_1.0.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_000956065.1
13. 1. Novogene

    (2014) NCBI Assembly

    ID GCF_000769185.1. Rrox_v1.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_000769185.1
14. 1. The Genome Institute at Washington University School of Medicine

    (2019) NCBI Assembly

    ID GCF_003339765.1. Mmul_10.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_003339765.1
15. 1. Human Genome Sequencing Center - BCM

    (2015) NCBI Assembly

    ID GCF_000955945.1. Caty_1.0.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_000955945.1
16. 1. Broad Institute

    (2011) NCBI Assembly

    ID GCF_000235385.1. SaiBol1.0.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_000235385.1
17. 1. Washington University (WashU)

    (2010) NCBI Assembly

    ID GCF_000004665.1. Callithrix jacchus-3.2.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_000004665.1
18. 1. Baylor College of Medicine

    (2017) NCBI Assembly

    ID GCF_000952055.2. Anan_2.0.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_000952055.2
19. 1. McDonnell Genome Institute - Washington University

    (2016) NCBI Assembly

    ID GCF_001604975.1. Cebus_imitator-1.0.

    https://www.ncbi.nlm.nih.gov/assembly/GCF_001604975.1
20. 1. IBE(CSIC-Universitat Pompeu Fabra)

    (2013) NCBI BioProject

    ID PRJNA189439. Whole genome sequences for a set of 79 great ape individuals. Genome sequencing.

    https://www.ncbi.nlm.nih.gov/bioproject?LinkName=biosample_bioproject&from_uid=1920513
21. 1. Great Ape Genome Project

    (2019) greatape

    ID greatape. greatape/data.

    https://eichlerlab.gs.washington.edu/greatape/data/

1. 1. Adrian J
   2. Bonsignore P
   3. Hammer S
   4. Frickey T
   5. Hauck CR

   (2019) Adaptation to Host-Specific Bacterial Pathogens Drives Rapid Evolution of a Human Innate Immune Receptor

   Current Biology 29:616–630.

   https://doi.org/10.1016/j.cub.2019.01.058

   • PubMed
   • Google Scholar
2. 1. Aleru O
   2. Barber MF

   (2020) Battlefronts of evolutionary conflict between bacteria and animal hosts

   PLOS Pathogens 16:e1008797.

   https://doi.org/10.1371/journal.ppat.1008797

   • PubMed
   • Google Scholar
3. 1. Alm RA
   2. Ling LS
   3. Moir DT
   4. King BL
   5. Brown ED
   6. Doig PC
   7. Smith DR
   8. Noonan B
   9. Guild BC
   10. deJonge BL
   11. Carmel G
   12. Tummino PJ
   13. Caruso A
   14. Uria-Nickelsen M
   15. Mills DM
   16. Ives C
   17. Gibson R
   18. Merberg D
   19. Mills SD
   20. Jiang Q
   21. Taylor DE
   22. Vovis GF
   23. Trust TJ

   (1999) Genomic-sequence comparison of two unrelated isolates of the human gastric pathogen Helicobacter pylori

   Nature 397:176–180.

   https://doi.org/10.1038/16495

   • PubMed
   • Google Scholar
4. 1. Anisimova M
   2. Nielsen R
   3. Yang Z

   (2003) Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites

   Genetics 164:1229–1236.

   https://doi.org/10.1093/genetics/164.3.1229

   • PubMed
   • Google Scholar
5. 1. Baltrus DA
   2. Amieva MR
   3. Covacci A
   4. Lowe TM
   5. Merrell DS
   6. Ottemann KM
   7. Stein M
   8. Salama NR
   9. Guillemin K

   (2009) The complete genome sequence of Helicobacter pylori strain G27

   Journal of Bacteriology 191:447–448.

   https://doi.org/10.1128/JB.01416-08

   • PubMed
   • Google Scholar
6. 1. Barber MF
   2. Elde NC

   (2014) Escape from bacterial iron piracy through rapid evolution of transferrin

   Science (New York, N.Y.) 346:1362–1366.

   https://doi.org/10.1126/science.1259329

   • PubMed
   • Google Scholar
7. 1. Bay RA
   2. Bielawski JP

   (2011) Recombination detection under evolutionary scenarios relevant to functional divergence

   Journal of Molecular Evolution 73:273–286.

   https://doi.org/10.1007/s00239-011-9473-0

   • PubMed
   • Google Scholar
8. 1. Bielawski JP

   (2013) Detecting the signatures of adaptive evolution in protein-coding genes

   Current Protocols in Molecular Biology Chapter 19:Unit.

   https://doi.org/10.1002/0471142727.mb1901s101

   • PubMed
   • Google Scholar
9. 1. Bittihn P
   2. Tsimring LS

   (2017) Gene Conversion Facilitates Adaptive Evolution on Rugged Fitness Landscapes

   Genetics 207:1577–1589.

   https://doi.org/10.1534/genetics.117.300350

   • PubMed
   • Google Scholar
10. Software

    1. Blackmon H
    2. Adams RH

    (2015)

    evobiR: Comparative and population genetic analyses

    EvobiR.
11. 1. Bonsignore P
    2. Kuiper JWP
    3. Adrian J
    4. Goob G
    5. Hauck CR

    (2019) CEACAM3-A Prim(at)e Invention for Opsonin-Independent Phagocytosis of Bacteria

    Frontiers in Immunology 10:3160.

    https://doi.org/10.3389/fimmu.2019.03160

    • PubMed
    • Google Scholar
12. 1. Bonsor DA
    2. Zhao Q
    3. Schmidinger B
    4. Weiss E
    5. Wang J
    6. Deredge D
    7. Beadenkopf R
    8. Dow B
    9. Fischer W
    10. Beckett D
    11. Wintrode PL
    12. Haas R
    13. Sundberg EJ

    (2018) The Helicobacter pylori adhesin protein HopQ exploits the dimer interface of human CEACAMs to facilitate translocation of the oncoprotein CagA

    The EMBO Journal 37:e98664.

    https://doi.org/10.15252/embj.201798664

    • PubMed
    • Google Scholar
13. 1. Boratyn GM
    2. Camacho C
    3. Cooper PS
    4. Coulouris G
    5. Fong A
    6. Ma N
    7. Madden TL
    8. Matten WT
    9. McGinnis SD
    10. Merezhuk Y
    11. Raytselis Y
    12. Sayers EW
    13. Tao T
    14. Ye J
    15. Zaretskaya I

    (2013) BLAST: a more efficient report with usability improvements

    Nucleic Acids Research 41:W29–W33.

    https://doi.org/10.1093/nar/gkt282

    • PubMed
    • Google Scholar
14. 1. Brewer ML
    2. Dymock D
    3. Brady RL
    4. Singer BB
    5. Virji M
    6. Hill DJ

    (2019) Fusobacterium spp. target human CEACAM1 via the trimeric autotransporter adhesin CbpF

    Journal of Oral Microbiology 11:1–16.

    https://doi.org/10.1080/20002297.2018.1565043

    • PubMed
    • Google Scholar
15. 1. Brockhurst MA
    2. Chapman T
    3. King KC
    4. Mank JE
    5. Paterson S
    6. Hurst GDD

    (2014) Running with the Red Queen: the role of biotic conflicts in evolution

    Proceedings. Biological Sciences 281:20141382.

    https://doi.org/10.1098/rspb.2014.1382

    • PubMed
    • Google Scholar
16. 1. Brown RL
    2. Clarke TB

    (2017) The regulation of host defences to infection by the microbiota

    Immunology 150:1–6.

    https://doi.org/10.1111/imm.12634

    • PubMed
    • Google Scholar
17. 1. Cao P
    2. Cover TL

    (2002) Two different families of hopQ alleles in Helicobacter pylori

    Journal of Clinical Microbiology 40:4504–4511.

    https://doi.org/10.1128/JCM.40.12.4504-4511.2002

    • PubMed
    • Google Scholar
18. 1. Chan CM
    2. Chu H
    3. Wang Y
    4. Wong BHY
    5. Zhao X
    6. Zhou J
    7. Yang D
    8. Leung SP
    9. Chan JFW
    10. Yeung ML
    11. Yan J
    12. Lu G
    13. Gao GF
    14. Yuen KY

    (2016) Carcinoembryonic Antigen-Related Cell Adhesion Molecule 5 Is an Important Surface Attachment Factor That Facilitates Entry of Middle East Respiratory Syndrome Coronavirus

    Journal of Virology 90:9114–9127.

    https://doi.org/10.1128/JVI.01133-16

    • PubMed
    • Google Scholar
19. 1. Chatterjee S
    2. Basak AJ
    3. Nair AV
    4. Duraivelan K
    5. Samanta D

    (2021) Immunoglobulin-fold containing bacterial adhesins: molecular and structural perspectives in host tissue colonization and infection

    FEMS Microbiology Letters 368:fnaa220.

    https://doi.org/10.1093/femsle/fnaa220

    • PubMed
    • Google Scholar
20. 1. Chen JM
    2. Cooper DN
    3. Chuzhanova N
    4. Férec C
    5. Patrinos GP

    (2007) Gene conversion: mechanisms, evolution and human disease

    Nature Reviews. Genetics 8:762–775.

    https://doi.org/10.1038/nrg2193

    • PubMed
    • Google Scholar
21. 1. Conners R
    2. Hill DJ
    3. Borodina E
    4. Agnew C
    5. Daniell SJ
    6. Burton NM
    7. Sessions RB
    8. Clarke AR
    9. Catto LE
    10. Lammie D
    11. Wess T
    12. Brady RL
    13. Virji M

    (2008) The Moraxella adhesin UspA1 binds to its human CEACAM1 receptor by a deformable trimeric coiled-coil

    The EMBO Journal 27:1779–1789.

    https://doi.org/10.1038/emboj.2008.101

    • PubMed
    • Google Scholar
22. 1. Daugherty MD
    2. Zanders SE

    (2019) Gene conversion generates evolutionary novelty that fuels genetic conflicts

    Current Opinion in Genetics & Development 58–59:49–54.

    https://doi.org/10.1016/j.gde.2019.07.011

    • PubMed
    • Google Scholar
23. 1. Delport W
    2. Poon AFY
    3. Frost SDW
    4. Kosakovsky Pond SL

    (2010) Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology

    Bioinformatics (Oxford, England) 26:2455–2457.

    https://doi.org/10.1093/bioinformatics/btq429

    • PubMed
    • Google Scholar
24. 1. Demogines A
    2. Abraham J
    3. Choe H
    4. Farzan M
    5. Sawyer SL

    (2013) Dual host-virus arms races shape an essential housekeeping protein

    PLOS Biology 11:1001571.

    https://doi.org/10.1371/journal.pbio.1001571

    • PubMed
    • Google Scholar
25. 1. Edgar RC

    (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput

    Nucleic Acids Research 32:1792–1797.

    https://doi.org/10.1093/nar/gkh340

    • PubMed
    • Google Scholar
26. 1. Elde NC
    2. Child SJ
    3. Geballe AP
    4. Malik HS

    (2009) Protein kinase R reveals an evolutionary model for defeating viral mimicry

    Nature 457:485–489.

    https://doi.org/10.1038/nature07529

    • PubMed
    • Google Scholar
27. 1. Fox DA
    2. Larsson P
    3. Lo RH
    4. Kroncke BM
    5. Kasson PM
    6. Columbus L

    (2014) Structure of the Neisserial outer membrane protein Opa₆₀: loop flexibility essential to receptor recognition and bacterial engulfment

    Journal of the American Chemical Society 136:9938–9946.

    https://doi.org/10.1021/ja503093y

    • PubMed
    • Google Scholar
28. 1. Gendreau KL
    2. Hornsby AD
    3. Hague MTJ
    4. McGlothlin JW

    (2021) Gene Conversion Facilitates the Adaptive Evolution of Self-Resistance in Highly Toxic Newts

    Molecular Biology and Evolution 38:4077–4094.

    https://doi.org/10.1093/molbev/msab182

    • PubMed
    • Google Scholar
29. 1. Gray-Owen SD
    2. Blumberg RS

    (2006) CEACAM1: contact-dependent control of immunity

    Nature Reviews. Immunology 6:433–446.

    https://doi.org/10.1038/nri1864

    • PubMed
    • Google Scholar
30. 1. Guindon S
    2. Dufayard JF
    3. Lefort V
    4. Anisimova M
    5. Hordijk W
    6. Gascuel O

    (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0

    Systematic Biology 59:307–321.

    https://doi.org/10.1093/sysbio/syq010

    • PubMed
    • Google Scholar
31. 1. Gur C
    2. Maalouf N
    3. Gerhard M
    4. Singer BB
    5. Emgård J
    6. Temper V
    7. Neuman T
    8. Mandelboim O
    9. Bachrach G

    (2019a) The Helicobacter pylori HopQ outermembrane protein inhibits immune cell activities

    Oncoimmunology 8:e1553487.

    https://doi.org/10.1080/2162402X.2018.1553487

    • PubMed
    • Google Scholar
32. 1. Gur C
    2. Maalouf N
    3. Shhadeh A
    4. Berhani O
    5. Singer BB
    6. Bachrach G
    7. Mandelboim O

    (2019b) Fusobacterium nucleatum supresses anti-tumor immunity by activating CEACAM1

    Oncoimmunology 8:1–6.

    https://doi.org/10.1080/2162402X.2019.1581531

    • PubMed
    • Google Scholar
33. 1. Hamilton WD
    2. Axelrod R
    3. Tanese R

    (1990) Sexual reproduction as an adaptation to resist parasites (a review)

    PNAS 87:3566–3573.

    https://doi.org/10.1073/pnas.87.9.3566

    • PubMed
    • Google Scholar
34. 1. Hemmila E
    2. Turbide C
    3. Olson M
    4. Jothy S
    5. Holmes KV
    6. Beauchemin N

    (2004) Ceacam1a-/- mice are completely resistant to infection by murine coronavirus mouse hepatitis virus A59

    Journal of Virology 78:10156–10165.

    https://doi.org/10.1128/JVI.78.18.10156-10165.2004

    • PubMed
    • Google Scholar
35. 1. Javaheri A
    2. Kruse T
    3. Moonens K
    4. Mejías-Luque R
    5. Debraekeleer A
    6. Asche CI
    7. Tegtmeyer N
    8. Kalali B
    9. Bach NC
    10. Sieber SA
    11. Hill DJ
    12. Königer V
    13. Hauck CR
    14. Moskalenko R
    15. Haas R
    16. Busch DH
    17. Klaile E
    18. Slevogt H
    19. Schmidt A
    20. Backert S
    21. Remaut H
    22. Singer BB
    23. Gerhard M

    (2016) Helicobacter pylori adhesin HopQ engages in a virulence-enhancing interaction with human CEACAMs

    Nature Microbiology 2:16189.

    https://doi.org/10.1038/nmicrobiol.2016.189

    • PubMed
    • Google Scholar
36. 1. Johswich KO
    2. McCaw SE
    3. Islam E
    4. Sintsova A
    5. Gu A
    6. Shively JE
    7. Gray-Owen SD

    (2013) In vivo adaptation and persistence of Neisseria meningitidis within the nasopharyngeal mucosa

    PLOS Pathogens 9:e1003509.

    https://doi.org/10.1371/journal.ppat.1003509

    • PubMed
    • Google Scholar
37. 1. Käll L
    2. Krogh A
    3. Sonnhammer ELL

    (2004) A combined transmembrane topology and signal peptide prediction method

    Journal of Molecular Biology 338:1027–1036.

    https://doi.org/10.1016/j.jmb.2004.03.016

    • PubMed
    • Google Scholar
38. 1. Kammerer R
    2. Zimmermann W

    (2010) Coevolution of activating and inhibitory receptors within mammalian carcinoembryonic antigen families

    BMC Biology 8:12.

    https://doi.org/10.1186/1741-7007-8-12

    • PubMed
    • Google Scholar
39. 1. Katoh K
    2. Standley DM

    (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability

    Molecular Biology and Evolution 30:772–780.

    https://doi.org/10.1093/molbev/mst010

    • Google Scholar
40. 1. Kline KA
    2. Fälker S
    3. Dahlberg S
    4. Normark S
    5. Henriques-Normark B

    (2009) Bacterial adhesins in host-microbe interactions

    Cell Host & Microbe 5:580–592.

    https://doi.org/10.1016/j.chom.2009.05.011

    • Google Scholar
41. 1. Königer V
    2. Holsten L
    3. Harrison U
    4. Busch B
    5. Loell E
    6. Zhao Q
    7. Bonsor DA
    8. Roth A
    9. Kengmo-Tchoupa A
    10. Smith SI
    11. Mueller S
    12. Sundberg EJ
    13. Zimmermann W
    14. Fischer W
    15. Hauck CR
    16. Haas R

    (2016) Helicobacter pylori exploits human CEACAMs via HopQ for adherence and translocation of CagA

    Nature Microbiology 2:188.

    https://doi.org/10.1038/nmicrobiol.2016.188

    • Google Scholar
42. 1. Kosakovsky Pond SL
    2. Frost SDW

    (2005) Not so different after all: A comparison of methods for detecting amino acid sites under selection

    Molecular Biology and Evolution 22:1208–1222.

    https://doi.org/10.1093/molbev/msi105

    • Google Scholar
43. 1. Kosakovsky Pond SL
    2. Posada D
    3. Gravenor MB
    4. Woelk CH
    5. Frost SDW

    (2006) Automated phylogenetic detection of recombination using a genetic algorithm

    Molecular Biology and Evolution 23:1891–1901.

    https://doi.org/10.1093/molbev/msl051

    • Google Scholar
44. 1. Kuespert K
    2. Pils S
    3. Hauck CR

    (2006) CEACAMs: their role in physiology and pathophysiology

    Current Opinion in Cell Biology 18:565–571.

    https://doi.org/10.1016/j.ceb.2006.08.008

    • PubMed
    • Google Scholar
45. 1. Kuespert K
    2. Weibel S
    3. Hauck CR

    (2007) Profiling of bacterial adhesin--host receptor recognition by soluble immunoglobulin superfamily domains

    Journal of Microbiological Methods 68:478–485.

    https://doi.org/10.1016/j.mimet.2006.10.003

    • PubMed
    • Google Scholar
46. 1. Kupsch EM
    2. Knepper B
    3. Kuroki T
    4. Heuer I
    5. Meyer TF

    (1993) Variable opacity (Opa) outer membrane proteins account for the cell tropisms displayed by Neisseria gonorrhoeae for human leukocytes and epithelial cells

    The EMBO Journal 12:641–650.

    https://doi.org/10.1002/j.1460-2075.1993.tb05697.x

    • PubMed
    • Google Scholar
47. 1. Macmaniman JD
    2. Meuser A
    3. Botto S
    4. Smith PP
    5. Liu F
    6. Jarvis MA
    7. Nelson JA

    (2014) Human Cytomegalovirus-Encoded pUL7 Is a Novel CEACAM1-Like Molecule Responsible for Promotion of Angiogenesis

    MBio 5:e02035.

    https://doi.org/10.1128/mBio.02035-14

    • Google Scholar
48. 1. Mao Y
    2. Catacchio CR
    3. Hillier LW
    4. Porubsky D
    5. Li R
    6. Sulovari A
    7. Fernandes JD
    8. Montinaro F
    9. Gordon DS
    10. Storer JM
    11. Haukness M
    12. Fiddes IT
    13. Murali SC
    14. Dishuck PC
    15. Hsieh P
    16. Harvey WT
    17. Audano PA
    18. Mercuri L
    19. Piccolo I
    20. Antonacci F
    21. Munson KM
    22. Lewis AP
    23. Baker C
    24. Underwood JG
    25. Hoekzema K
    26. Huang T-H
    27. Sorensen M
    28. Walker JA
    29. Hoffman J
    30. Thibaud-Nissen F
    31. Salama SR
    32. Pang AWC
    33. Lee J
    34. Hastie AR
    35. Paten B
    36. Batzer MA
    37. Diekhans M
    38. Ventura M
    39. Eichler EE

    (2021) A high-quality bonobo genome refines the analysis of hominid evolution

    Nature 594:77–81.

    https://doi.org/10.1038/s41586-021-03519-x

    • PubMed
    • Google Scholar
49. 1. Markel G
    2. Gruda R
    3. Achdout H
    4. Katz G
    5. Nechama M
    6. Blumberg RS
    7. Kammerer R
    8. Zimmermann W
    9. Mandelboim O

    (2004) The critical role of residues 43R and 44Q of carcinoembryonic antigen cell adhesion molecules-1 in the protection from killing by human NK cells

    Journal of Immunology (Baltimore, Md 173:3732–3739.

    https://doi.org/10.4049/jimmunol.173.6.3732

    • PubMed
    • Google Scholar
50. 1. Meyer D
    2. C Aguiar VR
    3. Bitarello BD
    4. C Brandt DY
    5. Nunes K

    (2018) A genomic perspective on HLA evolution

    Immunogenetics 70:5–27.

    https://doi.org/10.1007/s00251-017-1017-3

    • PubMed
    • Google Scholar
51. 1. Moonens K

    (2018) Helicobacter pylori adhesin HopQ disrupts trans dimerization in human CEACAMs

    The EMBO Journal 37:e98665.

    https://doi.org/10.15252/embj.201798665

    • Google Scholar
52. Software

    1. Morgan M

    (2019)

    BiocManager: Access the Bioconductor Project Package Repository

    BiocManager.
53. 1. Muenzner P
    2. Bachmann V
    3. Zimmermann W
    4. Hentschel J
    5. Hauck CR

    (2010) Human-Restricted Bacterial Pathogens Block Shedding of Epithelial Cells by Stimulating Integrin Activation

    Science 329:1197–1201.

    https://doi.org/10.1126/science.1190892

    • PubMed
    • Google Scholar
54. 1. Muenzner P
    2. Kengmo Tchoupa A
    3. Klauser B
    4. Brunner T
    5. Putze J
    6. Dobrindt U
    7. Hauck CR

    (2016) Uropathogenic E. coli Exploit CEA to Promote Colonization of the Urogenital Tract Mucosa

    PLOS Pathogens 12:e1005608.

    https://doi.org/10.1371/journal.ppat.1005608

    • Google Scholar
55. 1. Murrell B
    2. Wertheim JO
    3. Moola S
    4. Weighill T
    5. Scheffler K
    6. Kosakovsky Pond SL

    (2012) Detecting individual sites subject to episodic diversifying selection

    PLOS Genetics 8:e1002764.

    https://doi.org/10.1371/journal.pgen.1002764

    • PubMed
    • Google Scholar
56. 1. Murrell B
    2. Moola S
    3. Mabona A
    4. Weighill T
    5. Sheward D
    6. Kosakovsky Pond SL
    7. Scheffler K

    (2013) FUBAR: a fast, unconstrained bayesian approximation for inferring selection

    Molecular Biology and Evolution 30:1196–1205.

    https://doi.org/10.1093/molbev/mst030

    • PubMed
    • Google Scholar
57. 1. N’Guessan PD
    2. Vigelahn M
    3. Bachmann S
    4. Zabel S
    5. Opitz B
    6. Schmeck B
    7. Hippenstiel S
    8. Zweigner J
    9. Riesbeck K
    10. Singer BB
    11. Suttorp N
    12. Slevogt H

    (2007) The UspA1 protein of Moraxella catarrhalis induces CEACAM-1-dependent apoptosis in alveolar epithelial cells

    The Journal of Infectious Diseases 195:1651–1660.

    https://doi.org/10.1086/514820

    • PubMed
    • Google Scholar
58. 1. Pavlopoulou A
    2. Scorilas A

    (2014) A comprehensive phylogenetic and structural analysis of the carcinoembryonic antigen (CEA) gene family

    Genome Biology and Evolution 6:1314–1326.

    https://doi.org/10.1093/gbe/evu103

    • PubMed
    • Google Scholar
59. 1. Pecon-Slattery J

    (2014) Recent advances in primate phylogenomics

    Annual Review of Animal Biosciences 2:41–63.

    https://doi.org/10.1146/annurev-animal-022513-114217

    • PubMed
    • Google Scholar
60. Software

    1. Pedersen TL

    (2021)

    ggforce: Accelerating “ggplot2.”

    Ggplot2.
61. 1. Pettersen EF
    2. Goddard TD
    3. Huang CC
    4. Couch GS
    5. Greenblatt DM
    6. Meng EC
    7. Ferrin TE

    (2004) UCSF Chimera--a visualization system for exploratory research and analysis

    Journal of Computational Chemistry 25:1605–1612.

    https://doi.org/10.1002/jcc.20084

    • PubMed
    • Google Scholar
62. 1. Pond SLK
    2. Frost SDW
    3. Muse SV

    (2005) HyPhy: hypothesis testing using phylogenies

    Bioinformatics (Oxford, England) 21:676–679.

    https://doi.org/10.1093/bioinformatics/bti079

    • PubMed
    • Google Scholar
63. 1. Prado-Martinez J
    2. Sudmant PH
    3. Kidd JM
    4. Li H
    5. Kelley JL
    6. Lorente-Galdos B
    7. Veeramah KR
    8. Woerner AE
    9. O’Connor TD
    10. Santpere G
    11. Cagan A
    12. Theunert C
    13. Casals F
    14. Laayouni H
    15. Munch K
    16. Hobolth A
    17. Halager AE
    18. Malig M
    19. Hernandez-Rodriguez J
    20. Hernando-Herraez I
    21. Prüfer K
    22. Pybus M
    23. Johnstone L
    24. Lachmann M
    25. Alkan C
    26. Twigg D
    27. Petit N
    28. Baker C
    29. Hormozdiari F
    30. Fernandez-Callejo M
    31. Dabad M
    32. Wilson ML
    33. Stevison L
    34. Camprubí C
    35. Carvalho T
    36. Ruiz-Herrera A
    37. Vives L
    38. Mele M
    39. Abello T
    40. Kondova I
    41. Bontrop RE
    42. Pusey A
    43. Lankester F
    44. Kiyang JA
    45. Bergl RA
    46. Lonsdorf E
    47. Myers S
    48. Ventura M
    49. Gagneux P
    50. Comas D
    51. Siegismund H
    52. Blanc J
    53. Agueda-Calpena L
    54. Gut M
    55. Fulton L
    56. Tishkoff SA
    57. Mullikin JC
    58. Wilson RK
    59. Gut IG
    60. Gonder MK
    61. Ryder OA
    62. Hahn BH
    63. Navarro A
    64. Akey JM
    65. Bertranpetit J
    66. Reich D
    67. Mailund T
    68. Schierup MH
    69. Hvilsom C
    70. Andrés AM
    71. Wall JD
    72. Bustamante CD
    73. Hammer MF
    74. Eichler EE
    75. Marques-Bonet T

    (2013) Great ape genetic diversity and population history

    Nature 499:471–475.

    https://doi.org/10.1038/nature12228

    • PubMed
    • Google Scholar
64. 1. Quevillon E
    2. Silventoinen V
    3. Pillai S
    4. Harte N
    5. Mulder N
    6. Apweiler R
    7. Lopez R

    (2005) InterProScan: protein domains identifier

    Nucleic Acids Research 33:W116–W120.

    https://doi.org/10.1093/nar/gki442

    • PubMed
    • Google Scholar
65. Software

    1. R Development Core Team

    (2019) R: A Language and Environment for Statistical Computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
66. 1. Rahman SK
    2. Ansari MA
    3. Gaur P
    4. Ahmad I
    5. Chakravarty C
    6. Verma DK
    7. Sharma A
    8. Chhibber S
    9. Nehal N
    10. Wirth D
    11. Lal SK

    (2021) The Immunomodulatory CEA Cell Adhesion Molecule 6 (CEACAM6/CD66c) Is a Protein Receptor for the Influenza a Virus

    Viruses 13:726.

    https://doi.org/10.3390/v13050726

    • PubMed
    • Google Scholar
67. 1. Rhesus Macaque Genome Sequencing and Analysis Consortium
    2. Gibbs RA
    3. Rogers J
    4. Katze MG
    5. Bumgarner R
    6. Weinstock GM
    7. Mardis ER
    8. Remington KA
    9. Strausberg RL
    10. Venter JC
    11. Wilson RK
    12. Batzer MA
    13. Bustamante CD
    14. Eichler EE
    15. Hahn MW
    16. Hardison RC
    17. Makova KD
    18. Miller W
    19. Milosavljevic A
    20. Palermo RE
    21. Siepel A
    22. Sikela JM
    23. Attaway T
    24. Bell S
    25. Bernard KE
    26. Buhay CJ
    27. Chandrabose MN
    28. Dao M
    29. Davis C
    30. Delehaunty KD
    31. Ding Y
    32. Dinh HH
    33. Dugan-Rocha S
    34. Fulton LA
    35. Gabisi RA
    36. Garner TT
    37. Godfrey J
    38. Hawes AC
    39. Hernandez J
    40. Hines S
    41. Holder M
    42. Hume J
    43. Jhangiani SN
    44. Joshi V
    45. Khan ZM
    46. Kirkness EF
    47. Cree A
    48. Fowler RG
    49. Lee S
    50. Lewis LR
    51. Li Z
    52. Liu Y-S
    53. Moore SM
    54. Muzny D
    55. Nazareth LV
    56. Ngo DN
    57. Okwuonu GO
    58. Pai G
    59. Parker D
    60. Paul HA
    61. Pfannkoch C
    62. Pohl CS
    63. Rogers Y-H
    64. Ruiz SJ
    65. Sabo A
    66. Santibanez J
    67. Schneider BW
    68. Smith SM
    69. Sodergren E
    70. Svatek AF
    71. Utterback TR
    72. Vattathil S
    73. Warren W
    74. White CS
    75. Chinwalla AT
    76. Feng Y
    77. Halpern AL
    78. Hillier LW
    79. Huang X
    80. Minx P
    81. Nelson JO
    82. Pepin KH
    83. Qin X
    84. Sutton GG
    85. Venter E
    86. Walenz BP
    87. Wallis JW
    88. Worley KC
    89. Yang S-P
    90. Jones SM
    91. Marra MA
    92. Rocchi M
    93. Schein JE
    94. Baertsch R
    95. Clarke L
    96. Csürös M
    97. Glasscock J
    98. Harris RA
    99. Havlak P
    100. Jackson AR
    101. Jiang H
    102. Liu Y
    103. Messina DN
    104. Shen Y
    105. Song HX-Z
    106. Wylie T
    107. Zhang L
    108. Birney E
    109. Han K
    110. Konkel MK
    111. Lee J
    112. Smit AFA
    113. Ullmer B
    114. Wang H
    115. Xing J
    116. Burhans R
    117. Cheng Z
    118. Karro JE
    119. Ma J
    120. Raney B
    121. She X
    122. Cox MJ
    123. Demuth JP
    124. Dumas LJ
    125. Han S-G
    126. Hopkins J
    127. Karimpour-Fard A
    128. Kim YH
    129. Pollack JR
    130. Vinar T
    131. Addo-Quaye C
    132. Degenhardt J
    133. Denby A
    134. Hubisz MJ
    135. Indap A
    136. Kosiol C
    137. Lahn BT
    138. Lawson HA
    139. Marklein A
    140. Nielsen R
    141. Vallender EJ
    142. Clark AG
    143. Ferguson B
    144. Hernandez RD
    145. Hirani K
    146. Kehrer-Sawatzki H
    147. Kolb J
    148. Patil S
    149. Pu L-L
    150. Ren Y
    151. Smith DG
    152. Wheeler DA
    153. Schenck I
    154. Ball EV
    155. Chen R
    156. Cooper DN
    157. Giardine B
    158. Hsu F
    159. Kent WJ
    160. Lesk A
    161. Nelson DL
    162. O’brien WE
    163. Prüfer K
    164. Stenson PD
    165. Wallace JC
    166. Ke H
    167. Liu X-M
    168. Wang P
    169. Xiang AP
    170. Yang F
    171. Barber GP
    172. Haussler D
    173. Karolchik D
    174. Kern AD
    175. Kuhn RM
    176. Smith KE
    177. Zwieg AS

    (2007) Evolutionary and biomedical insights from the rhesus macaque genome

    Science (New York, N.Y.) 316:222–234.

    https://doi.org/10.1126/science.1139247

    • PubMed
    • Google Scholar
68. 1. Roth A
    2. Mattheis C
    3. Muenzner P
    4. Unemo M
    5. Hauck CR

    (2013) Innate recognition by neutrophil granulocytes differs between Neisseria gonorrhoeae strains causing local or disseminating infections

    Infection and Immunity 81:2358–2370.

    https://doi.org/10.1128/IAI.00128-13

    • PubMed
    • Google Scholar
69. 1. Sadarangani M
    2. Pollard AJ
    3. Gray-Owen SD

    (2011) Opa proteins and CEACAMs: pathways of immune engagement for pathogenic Neisseria

    FEMS Microbiology Reviews 35:498–514.

    https://doi.org/10.1111/j.1574-6976.2010.00260.x

    • PubMed
    • Google Scholar
70. 1. Sintsova A
    2. Wong H
    3. MacDonald KS
    4. Kaul R
    5. Virji M
    6. Gray-Owen SD

    (2015) Selection for a CEACAM receptor-specific binding phenotype during Neisseria gonorrhoeae infection of the human genital tract

    Infection and Immunity 83:1372–1383.

    https://doi.org/10.1128/IAI.03123-14

    • PubMed
    • Google Scholar
71. 1. Skubitz KM
    2. Skubitz APN

    (2008) Interdependency of CEACAM-1, -3, -6, and -8 induced human neutrophil adhesion to endothelial cells

    Journal of Translational Medicine 6:78.

    https://doi.org/10.1186/1479-5876-6-78

    • PubMed
    • Google Scholar
72. 1. Tchoupa AK
    2. Schuhmacher T
    3. Hauck CR

    (2014) Signaling by epithelial members of the CEACAM family - mucosal docking sites for pathogenic bacteria

    Cell Communication and Signaling 12:27.

    https://doi.org/10.1186/1478-811X-12-27

    • PubMed
    • Google Scholar
73. 1. van Sorge NM
    2. Bonsor DA
    3. Deng L
    4. Lindahl E
    5. Schmitt V
    6. Lyndin M
    7. Schmidt A
    8. Nilsson OR
    9. Brizuela J
    10. Boero E
    11. Sundberg EJ
    12. van Strijp JAG
    13. Doran KS
    14. Singer BB
    15. Lindahl G
    16. McCarthy AJ

    (2021) Bacterial protein domains with a novel Ig-like fold target human CEACAM receptors

    The EMBO Journal 40:e106103.

    https://doi.org/10.15252/embj.2020106103

    • PubMed
    • Google Scholar
74. 1. Van Valen L

    (1973)

    A new evolutionary law

    Evolutionary Theory 1:1–30.

    • Google Scholar
75. 1. Wang J
    2. Gray-Owen SD
    3. Knorre A
    4. Meyer TF
    5. Dehio C

    (1998) Opa binding to cellular CD66 receptors mediates the transcellular traversal of Neisseria gonorrhoeae across polarized T84 epithelial cell monolayers

    Molecular Microbiology 30:657–671.

    https://doi.org/10.1046/j.1365-2958.1998.01102.x

    • PubMed
    • Google Scholar
76. 1. Wang LG
    2. Lam TTY
    3. Xu S
    4. Dai Z
    5. Zhou L
    6. Feng T
    7. Guo P
    8. Dunn CW
    9. Jones BR
    10. Bradley T
    11. Zhu H
    12. Guan Y
    13. Jiang Y
    14. Yu G

    (2020) Treeio: An R Package for Phylogenetic Tree Input and Output with Richly Annotated and Associated Data

    Molecular Biology and Evolution 37:599–603.

    https://doi.org/10.1093/molbev/msz240

    • PubMed
    • Google Scholar
77. 1. Weaver S
    2. Shank SD
    3. Spielman SJ
    4. Li M
    5. Muse SV
    6. Kosakovsky Pond SL

    (2018) Datamonkey 2.0: A Modern Web Application for Characterizing Selective and Other Evolutionary Processes

    Molecular Biology and Evolution 35:773–777.

    https://doi.org/10.1093/molbev/msx335

    • PubMed
    • Google Scholar
78. Book

    1. Wickham H

    (2016) Ggplot2

    springer.

    https://doi.org/10.1007/978-3-319-24277-4

    • Google Scholar
79. 1. Wilson D
    2. Pethica R
    3. Zhou Y
    4. Talbot C
    5. Vogel C
    6. Madera M
    7. Chothia C
    8. Gough J

    (2009) SUPERFAMILY--sophisticated comparative genomics, data mining, visualization and phylogeny

    Nucleic Acids Research 37:D380–D386.

    https://doi.org/10.1093/nar/gkn762

    • PubMed
    • Google Scholar
80. 1. Yang Z
    2. Wong WSW
    3. Nielsen R

    (2005) Bayes empirical bayes inference of amino acid sites under positive selection

    Molecular Biology and Evolution 22:1107–1118.

    https://doi.org/10.1093/molbev/msi097

    • PubMed
    • Google Scholar
81. 1. Yang Z

    (2007) PAML 4: phylogenetic analysis by maximum likelihood

    Molecular Biology and Evolution 24:1586–1591.

    https://doi.org/10.1093/molbev/msm088

    • PubMed
    • Google Scholar
82. 1. Yang DC
    2. Blair KM
    3. Taylor JA
    4. Petersen TW
    5. Sessler T
    6. Tull CM
    7. Leverich CK
    8. Collar AL
    9. Wyckoff TJ
    10. Biboy J
    11. Vollmer W
    12. Salama NR

    (2019) A Genome-Wide Helicobacter pylori Morphology Screen Uncovers a Membrane-Spanning Helical Cell Shape Complex

    Journal of Bacteriology 201:e00724-18.

    https://doi.org/10.1128/JB.00724-18

    • PubMed
    • Google Scholar
83. 1. Yu G
    2. Smith DK
    3. Zhu H
    4. Guan Y
    5. Lam TT

    (2017) ggtree: An r package for visualization and annotation of phylogenetic trees with their covariates and other associated data

    Methods in Ecology and Evolution 8:28–36.

    https://doi.org/10.1111/2041-210x.12628

    • Google Scholar
84. 1. Yu G
    2. Lam TTY
    3. Zhu H
    4. Guan Y

    (2018) Two Methods for Mapping and Visualizing Associated Data on Phylogeny Using Ggtree

    Molecular Biology and Evolution 35:3041–3043.

    https://doi.org/10.1093/molbev/msy194

    • PubMed
    • Google Scholar
85. 1. Zid M
    2. Drouin G

    (2013) Gene conversions are under purifying selection in the carcinoembryonic antigen immunoglobulin gene families of primates

    Genomics 102:301–309.

    https://doi.org/10.1016/j.ygeno.2013.07.003

    • PubMed
    • Google Scholar
86. 1. Zimmermann W

    (2019) Evolution: Decoy Receptors as Unique Weapons to Fight Pathogens

    Current Biology 29:R128–R130.

    https://doi.org/10.1016/j.cub.2018.12.006

    • PubMed
    • Google Scholar

Author details

1. EmilyClare P Baker

   Institute of Ecology and Evolution, University of Oregon, Eugene, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Validation, Visualization, Writing – original draft, Writing – review and editing, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7875-2144

2. Ryan Sayegh

   Institute of Ecology and Evolution, University of Oregon, Eugene, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Kristin M Kohler

   Institute of Ecology and Evolution, University of Oregon, Eugene, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Wyatt Borman

   Institute of Ecology and Evolution, University of Oregon, Eugene, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Claire K Goodfellow

   1. Institute of Ecology and Evolution, University of Oregon, Eugene, United States
   2. Department of Biology, University of Oregon, Eugene, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Eden R Brush

   Institute of Ecology and Evolution, University of Oregon, Eugene, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Matthew F Barber

   1. Institute of Ecology and Evolution, University of Oregon, Eugene, United States
   2. Department of Biology, University of Oregon, Eugene, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review and editing, Writing – original draft

   For correspondence

   mfbarber@uoregon.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2008-2165

• 2,693

  views

• 314

  downloads

• 8

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.