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J Evol Biol. 2023;00:1–27.
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1wileyonlinelibrary.com/journal/jeb
Received: 23 September 2022
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Revised: 23 April 2023
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Accepted: 26 April 2023
DOI : 10.1111/jeb.1418 1
REVIEW
Understanding the evolution of immune genes in jawed
vertebrates
Michal Vinkler1 | Steven R. Fiddaman2 | Martin Těšický1 | Emily A. O'Connor3 |
Anna E. Savage4 | Tobias L. Lenz5 | Adrian L. Smith2 | Jim Kaufman6,7 |
Daniel I. Bolnick8 | Charli S. Davies9 | Neira Dedić10 | Andrew S. Flies11 |
M. Mercedes Gómez Samblás1,12 | Amberleigh E. Henschen13 | Karel Novák14 |
Gemma Palomar15 | Nynke Raven16 | Kalifa Samaké17 | Joel Slade18 |
Nithya Kuttiyarthu Veetil1 | Eleni Voukali1 | Jacob Höglund19 |
David S. Richardson9 | Helena Westerdahl3
1Department of Zoology, Faculty of Science, Charles University, Prague, Czech Republic
2Department of Biology, University of Ox ford, Oxford, UK
3Department of Biology, Lund University, Lund, Sweden
4Department of Biology, University of Central Florida, Florida, Orlando, USA
5Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
6Institute for Immunology and Infection Research, University of Edinburgh, Edinburgh, UK
7Department of Veterinar y Medicine, University of Cambridge, Cambridge, UK
8Department of Ecology and Evolutionar y Biology, University of Connec ticut, Storrs, Connecticut, USA
9School of Biological Sciences, University of East Anglia, Nor wich, UK
10Department of Botany and Zoology, Masaryk University, Brno, Czech Republic
11Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
12Department of Parasitology, University of Granada, Granada, Spain
13Depar tment of Biological Sciences, University of Memphis, Memphis, Tennessee, USA
14Department of Genetics and Breeding, Institute of Animal Science, Prague, Uhříněves, Czech Republic
15Faculty of Biolog y, Institute of Environmental Sciences, Jagiellonian Universit y, Kraków, Poland
16Depar tment of Science, Engineering and Build Environment, Deakin Universit y, Victoria, Waurn Ponds, Australia
17Depar tment of Genetics and Microbiology, Facult y of Science, Charles University, Prague, Czech Republic
18Depar tment of Biology, California State University, Fresno, California, USA
19Depar tment of Ecology and Genetics, Uppsala Universitet, Uppsala, Sweden
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2023 The Authors. Journal of Evolutio nary Biology published by John Wiley & Sons Ltd on behalf of European Society for Evolutionary Biology.
Correspondence
Michal Vinkler, Department of Zoology,
Faculty of Science, Charles University,
Viničná 7, 128 43 Prague, Czech Republic.
Email: michal.vinkler@natur.cuni.cz
Funding information
Biotechnology and Biological Sciences
Research Council, Grant/Award Number:
BB/K004468/1, BB/M011224/1, BB/
N023803/1 and BB/V000756/1;
Abstract
Driven by co- evolution with pathogens, host immunity continuously adapts to opti-
mize defence against pathogens within a given environment. Recent advances in ge-
netics, genomics and transcriptomics have enabled a more detailed investigation into
how immunogenetic variation shapes the diversity of immune responses seen across
domestic and wild animal species. However, a deeper understanding of the diverse
2
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VINKLER et a l.
1 | INTRODUCTION
Evolutionary immunology represents an important branch of in-
fection biology that synergizes immunology and evolutionary stud-
ies across diverse taxa (Vinkler et al., 2022). Using the conceptual
framework of evolutionary biology to research biomedically rele-
vant issues (Stearns & Koella, 2008), evolutionary immunology ad-
dresses fundamental questions on the origins and consequences of
inter- and intraspecific variation in immunity and how these have
resulted in variation in disease resistance. Based on comparative ap-
proaches, it aims to provide a foundation for understanding the di-
versity of evolutionary adaptations seen across immunity. However,
to meet this aim, extensive development in the field is still needed.
The central focus in evolutionary immunology is to understand host
adaptations to either combat or tolerate pathogens. Such adapta-
tion is context dependent: pathogenic microorganisms are only part
of the immunobiome (the set of organisms— including commensal
and mutualist symbionts— that can live in or on a host; Horrocks
et al., 2011). The complex interactions between hosts and these co-
evolving organisms create variation in the selective pressures acting
on host immune systems, ultimately leading to the emergence of
extensive diversity in immune defence strategies (Buchmann, 2014;
Danilova, 2006). Additionally, life history variation and evolution-
ary history, including genetic background, can constrain immu-
nogenomic variation due to trade- offs and pleiotropy (Norris &
Evans, 2000; Schwenke et al., 2016), generating complex selective
landscapes beyond those driven only by pathogens.
While classical model species are invaluable for gaining insight
into mechanistic features of the complex jawed vertebrate immune
system (Mestas & Hughes, 2004), comparative research is needed
to reveal functional immunological variation at both the inter- and
intraspecific level. Unlike most morphological and physiological sys-
tems, where traits may show relatively straightforward adaptations,
immunity evolves as a regulatory network integrating numerous in-
dependently acting defence cells and mechanisms to control patho-
gens. Through arms races consistent with Red Queen dynamics
(Woolhouse et al., 2002), host– pathogen coevolution in favour of
the host leads to optimal pathogen control, or minimal host dam-
age, through the complex regulation of the immune system (Ashley
et al., 2012). As a co nseque nce , in cer tai n case s, ada ptat ion to pa tho-
gens does not lead to increased disease resistance but, instead, pro-
motes tolerance to infection (resilience) (Råberg et al., 20 07).
Immune genes are among the most rapidly evolving gene classes
within animals, with remarkable levels of polymorphism main-
tained in certain immune genes, even beyond speciation events
(Bustamante et al., 2005; Figueroa et al., 198 8; Fumagalli et al., 2011;
Hillier et al., 2004; Lenz et al., 2013; Těšický & Vinkler, 2015). Guided
by these observations, intensive evolutionary research focusing on
the Major Histocompatibility Complex (MHC) has occurred during
the past decades (reviewed in Apanius et al., 1997; Kaufman, 2018;
O'Connor et al., 2019; Radwan et al., 2020; Sommer, 2005; Spurgin
& Richardson, 2010). However, it has become clear that while the
MHC is essential for host adaptation to diseases, it alone is not
enough to explain variation in host resistance to infection (Acevedo-
Whitehouse & Cunningham, 2006). Consequently, other immune
genes have started to receive considerable attention, including
pattern- recognition receptors (PRRs) (Alcaide & Edwards, 2 011;
Davies et al., 2021; Krchlíková et al., 2021; Smirnova et al., 2000;
Department for Environment, Food and
Rural Affairs, UK Government, Grant/
Award Number: OD0221; Deutsche
Forschungsgemeinschaft, Grant/
Award Number: 437857095; Grantová
Agentura České Republiky, Grant/Award
Number: 19- 20152Y; Grantová Agentura,
Univerzita Karlova, Grant/Award Number:
646119; H2020 European Research
Council, Grant/Award Number: ERC- 2019-
StG- 853272- PALAEOFARM; John Fell
Fund, University of Oxford, Grant/Award
Number: 0005172; Ministerstvo Školství,
Mládeže a Tělovýchovy, Grant/Award
Number: SVV 260684/2023; Ministerstvo
Zemědělství, Grant/Award Number: MZE-
RO0723; National Institutes of Health,
Grant/Award Number: 1R01AI123659-
01A1; Univerzita Karlova v Praze,
Grant/Award Number: START/SCI/113
with reg. no. CZ.02.2.69/0.0/0.0/19_;
Vetenskapsrådet, Grant/Award Number:
2020- 04285
molecular mechanisms that shape immunity within and among species is still needed
to gain insight into— and generate evolutionary hypotheses on— the ultimate drivers of
immunological differences. Here, we discuss current advances in our understanding
of molecular evolution underpinning jawed vertebrate immunity. First, we introduce
the immunome concept, a framework for characterizing genes involved in immune de-
fence from a comparative perspective, then we outline how immune genes of interest
can be identified. Second, we focus on how different selection modes are observed
acting across groups of immune genes and propose hypotheses to explain these dif-
ferences. We then provide an overview of the approaches used so far to study the
evolutionary heterogeneity of immune genes on macro and microevolutionary scales.
Finally, we discuss some of the current evidence as to how specific pathogens af-
fect the evolution of different groups of immune genes. This review results from the
collective discussion on the current key challenges in evolutionary immunology con-
ducted at the ESEB 2021 Online Satellite Symposium: Molecular evolution of the ver-
tebrate immune system, from the lab to natural populations.
KEYWORDS
adaptation, adaptive immunity, evolutionary immunology, genomics, host- parasite
interactions, immunogenetics, innate immunity, MHC, molecular evolution, vertebrates
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VINKLER et al.
Tschirren et al., 2012; Velová et al., 2018; Vinkler & Albrecht, 2009),
antimicrobial peptides (AMPs) (Chapman et al., 2016) and signalling
pathway components (Downing et al., 2010; Hyland et al., 2021).
About 10% of genes in the jawed vertebrate genomes are
currently considered as involved in immune function (the Gene
Ontology annotation category ‘immune system process’, filtered
on 2021- 08- 27 based on Uniprot IDs, human: 2857 genes, mouse:
1586, chicken: 1576). The type and strength of selection that act
on these genes may vary considerably. Multiple evolutionary forces
modulate immunogenetic diversity and a key current challenge is to
understand how these different evolutionary forces and resulting
mechanisms influence the diverse array of immune genes in verte-
brate genomes. Recent advances in immunogenomics provide meth-
ods to describe this variation (Holt, 2015), but the availability of
genomic data alone does not provide all the insight needed. To fur-
ther our understanding, we need to formulate detailed hypotheses
and testable predictions that can explain this immunogenetic diver-
sity. Developing such a scientific framework requires collaboration
between researchers with diverse expertise and methodological
backgrounds, interlinking evolutionary biology and eco- immunology
with classical immunology and pathogen biology at molecular and
cellular levels (Seed, 199 3; Adamo, 2004; Vinkler & Albrecht, 2 011).
We used the ESEB 2021 Satellite Symposium: ‘Molecular evolution
of the vertebrate immune system, from the lab to natural popula-
tions’ to initiate an interdisciplinary discussion into the molecular
evolution of the vertebrate immune system. Based on the ground-
breaking research presented, and discussions throughout this sym-
posium, we review some of the key current concepts and advances
in evolutionary immunology. We hope to collectively contribute to
a conceptual framework that can better explain the evolution of the
immunogenetic diversity observed in jawed vertebrates.
2 | WHAT ARE IMMUNE GENES?
Hosts use a very broad array of defences against pathogens (micro-
organisms and multicellular parasites causing disease to their host)
including both classical immune mechanisms as well as non- immune
defences, for example, behavioural strategies to avoid infection,
physical barriers and general physiological processes (Schmid-
Hempel, 2011). To rationally study the evolution of the immune sys-
tem, a definition of what an immune gene is must be considered. Is
an immune gene one which is expressed in immune cells and tissues?
Or a gene whose expression is modulated in response to an infec-
tion? Or only one where the gene product directly interacts with a
pathogen? Answering these questions is not easy, given that roughly
two thirds of the vertebrate genome is active in at least one immune
cell type (Heng et al., 2008). We may call the entire genetic basis of
immunity the ‘immunome’, a term originally coined to describe the
totality of rearranged antigen- binding capability in the adaptive im-
mune system of all individuals of a species (Pederson, 1999). More
recently this term has been adopted to conform to other widely
used ‘- omes’ to mean all immunology- related genes in the genome
(Ortutay et al., 2007). However, a precise definition as to which
specific genes are included is still lacking, and this problem is fur-
ther compounded when we consider that immune genes may differ
across animal taxa.
To provide a framework upon which immune genes can be dis-
cussed, we propose a hierarchical set of definitions that relate to
three layers of the ‘immunome’ (Figure 1). (i) the core immunome:
genes whose primary (and in many cases, only) physiological func-
tion is in the recognition of, and/or response to, pathogens; (ii) the
peripheral immunome: genes with a clear immunological role, but
which also contribute to non- immune physiological function and
(iii) non- immune resistance genes (NIRGs; which could be called
the accessory immunome or even the resistome). However, Beutler
et al. (2005) proposed a broader definition for this last category, that
is, genes (or alleles of genes) that are not normally involved in im-
munity, but are nevertheless associated with resistance to a partic-
ular (class of) pathogen because they become targets for pathogen
molecules or can interfere with some phase of infection. Therefore,
NIRGs functionally contribute to immunity in the sense that they
affect the interaction between pathogen and host, but often the
resistance- conferring alleles of NIRGs are highly pathogen- specific.
Core immunome genes are undeniably part of the immune sys-
tem. Examples include genes required for the recognition of patho-
gens (e.g. PRRs, T- cell receptor [TCR] and B- cell receptor [BCR]
genes), genes involved in the antigen presentation pathways (e.g.
MHC class I and II), or genes involved in anti- pathogen responses
(e.g. AMPs and interferons). However, for many genes, involvement
in the immune response may be less obvious, or part of a wider suite
of physiological activities. These peripheral immunome genes could
include, for example, those that code for iron- binding proteins, such
as transferrin which plays a key role in iron homeostasis. While this
role may, at first glance, seem far removed from immunity, trans-
ferrin is important to sequester iron away from blood- borne patho-
gens and thus acts as an anti- microbial protein (Sridhar et al., 2000;
Watanabe et al., 1997 ). Similarly, the apoptotic (controlled cell death)
pathway is central to development and in responses to cellular stress
but is also involved in immunity (Jorgensen et al., 2017 ). This is per-
haps exemplified best by the efforts pathogens make to subvert host
cell death pathways (Robinson & Aw, 2016). Further pleiotropy can
be observed in developmental genes such as the Notch signalling
pathway (Laky & Fowlkes, 2008), which has a role in both embryo-
genesis and the function of immune cells (Radtke et al., 2010), and
transforming growth factor beta, which is involved in regulating im-
mune responses but also has several other physiological roles (Travis
& Sheppard, 2014).
The third group of genes, NIRGs, is less clearly defined but forms
part of a host's resistance phenotype to pathogens. These NIRGs
would not typically be considered part of the immune system per se
but are genes whose product variants can interfere with pathogen
cell entry and other pathogen life- cycle processes. A classic example
is the haemoglobin genes, where some alleles (sickle cell anaemia
and thalassaemia) confer protection against malaria and are main-
tained in the human population despite considerable fitness costs
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VINKLER et a l.
to the host (reviewed in Luzzatto, 2012). Alleles encoding variants
of the host cell receptors usually used by pathogens to enter a cell
may confer resistance to infection. For example, the partial deletion
allele of the chemokine receptor CCR5, alleles of CD81 and alleles of
human angiotensin- converting enzyme (ACE)- 2 provide resistance
to human immunodeficiency virus (HIV), hepatitis C virus (HCV) and
SARS- CoV- 2, respectively (McLaren et al., 2015; Sun et al., 2015;
Suryamohan et al., 2021). Indeed, interspecies sequence variation in
NIRGs is likely to contribute to host range specificity in pathogens
(Clark et al., 2022; Wei et al., 2021).
3 | RETRIEVING IMMUNE GENE SETS
FROM PUBLIC REPOSITORIES
When working with genomic data (BOX 1), immune gene lists
are usually generated based on annotations obtained from
public repositories. Current genome browsers, such as Ensembl
(Cunningham et al., 2022), offer curated annotations (derived
from, e.g. UniProtKB/Swiss- Prot; UniProt Consortium, 2019) as
well as unreviewed automatic annotations for proteins translated
from the nucleotide sequences deposited in the International
Nucleotide Sequence Database Consortium (INSDC) synchro-
nizing European Nucleotide Archive (ENA), National Center for
Biotechnology Information (NCBI) GenBank and DNA Data Bank
of Japan (DDBJ). These universal annotations are based on generic
structural, expression and functional gene characteristics. Among
the most commonly used ones are the taxon- specific annota-
tions provided by the Kyoto Encyclopedia of Genes and Genomes
(KEGG; https://www.genome.jp/kegg/; Kanehisa & Goto, 2000)
and the Gene Ontology (GO) term database (entry e.g. via AmiGO;
http://amigo.geneo ntolo gy.org/amigo; Carbon et al., 2009). While
widely used in evolutionary genomics (see e.g. Feigin et al., 2018;
Figueiró et al., 2017; Huang et al., 2017; Khan et al., 2019; Shultz
FIGURE 1 A proposed system for the hierarchical classification of immune genes. A gene considered relevant to the immune system will
fall into one of three categories: (i) the core immunome, (ii) the peripheral immunome, and (iii) non- immune resistance genes. Core immunome
components are unequivocally (and in many cases, exclusively) related to immune function. Peripheral immune genes contribute to aspects
of the immune response, despite also having additional roles in the physiological functioning of other body systems. Non- immune resistance
genes (NIRGs) are genes that do not form part of the immune system but can functionally confer resistance to a pathogen and thus are
important to consider as part of an immune phenotype. Examples of such genes (or gene classes/families) are given in each compartment.
The dashed line between core and peripheral immune components indicates that there is likely a continuum of genes whose function ranges
from exclusively immune to almost entirely for another physiological function, and that some genes may lie ambiguously between the two
categories. Abbreviations: ACE- 2, angiotensin converting enzyme- 2; MHC, major histocompatibility complex; AMPs, anti- microbial peptides;
TCR, T- cell receptor; BCR, B- cell receptor; IFNs, interferons; PRRs, pattern- recognition receptors.
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VINKLER et al.
BOX 1 Studying the functional consequences of immune gene variation
High- throughput sequencing allows for powerful comparative analyses of immune genes. Alignment of protein sequences, for a
species of interest against human and/or other model organisms, can quickly lead to hypotheses about conserved function. To
circumvent the lack of reference genomes and knowledge of splicing variants (i.e. isoforms) in many non- model species, de novo
transcriptome assemblies can be used to identify protein- coding sequences, which can then be aligned against validated sequences
from well- characterized species (Flies et al., 2017; Sun et al., 2012).
While Ensembl serves as the tool for initial gene analysis (Cunningham et al., 2022), UniProt remains a first- stop resource for under-
standing functional domains of proteins (UniProt Consortium, 2019). Key functional regions can also be interrogated using publicly
available software programs. For example, the Eukaryotic Linear Motif resource can predict functional motifs associated with signal
transduction, protein trafficking and protein binding domains (Dinkel et al., 2014). These analyses can be further expanded by the
application of in silico modelling to reveal variations in protein physicochemical properties (Follin et al., 2013; Těšický et al., 2020). In
addition, machine learning technology such as AlphaFold is fundamentally changing our ability to predict protein structure (Jumper
et al., 2021). Validation of the predicted structure and function of proteins can then be done via the expression of recombinant pro-
teins (see below).
Signatures of selection in protein- coding DNA can be detected by the rate of non- synonymous to synonymous substitutions (dN/dS
method; reviewed in Sackton, 2020). Prior to any selection analysis, recombination should be assessed (e.g. by GARD; Kosakovsky
Pond et al., 2006), especially when working with closely related species or population data. If it is found, positive selection should
be tested separately across different non- recombined DNA fragments. Several popular packages incorporating multiple methods
to detect selection have been developed, such as paml (with CODEML models; Yang, 2007 ) or Hyphy package (Kosakovsky Pond
et al., 2005), embedded within a user- friendly Datamonkey webpage interface (https://www.datam onkey.org/analyses). The stand-
ard codon- based models can be divided into three classes differing in assumptions about ω variation among branches and sites
(Hölzer & Marz, 2021): (i) Site models assuming that ω can vary across sites (COD EML or FUBAR , REL or FEL from Hy phy; Kosa kovsk y
et al., 2005; Murrell et al ., 2013) detect long- lasting pervasive selection. (ii) Branch- site models assume that ω can vary not only acro ss
sites but also among lineages and detect episodic selection (MEME, BUSTED, aBS- REL or CODEML; Murrell et al., 2012, 2015; Smith
et al., 2015). (iii) To test for lineage- specific rate of evolution, branch models may assume that selection varies across lineages while
keeping constant ω estimates across whole genes. Recently, to automate positive selection scans in large- scale datasets, compre-
hensive pipelines, such as Poseidon (Hölzer & Marz, 2021), or highly customizable DIGGN pipeline (Picard et al., 2020) requiring only
nucleotide sequences as an input and performing all steps (alignment and its cleaning, gene tree reconstruction, recombination and
positive selection screening) have been developed. As some methods might be prone to false positive results (Nozawa et al., 2009),
and results from different methods do not always correspond (Areal et al., 2011), comparing results from multiple methods is always
beneficial (Těšický et al., 2020). Furthermore, codon- usage analysis can be used to explore convergent evolution among species and
distinguish it from trans- species polymorphism (Lenz et al., 2013).
To reveal the actual immunological effect of the adaptive variation, functional testing is necessary. Recombinant protein production
has seen major advances and new proteins can be produced in a few weeks once the coding sequence is determined. DNA cod-
ing sequences can be amplified via PCR from cDNA or ordered synthetically from commercial suppliers. Plasmid DNA vectors for
prokaryotic and eukaryotic expression systems can be used, where DNA is inserted into plasmid expression vectors via restriction
digest and ligation, or via methods with improved flexibility such as Gibson assembly (Gibson et al., 2009). Studies using recombinant
proteins, for example, have helped to reveal functional variation in mammalian and avian TLRs and validate bioinformatic predictions
(Fiddaman et al., 2022; Keestra & van Putten, 2008; Lev y et al., 2020; Walsh et al., 2008).
Expression vectors can be also used to produce soluble recombinant proteins for functional assays. Most cytokines are readily se-
creted from eukaryotic cell lines and can be used directly from supernatant or in a purified form in functional assays. For example,
IFNG should upregulate MHC class I on host cells and IL2 should drive the activation and proliferation of T cells. Nevertheless, pos-
sible interspecific variation in the immunological activity of various regulators needs to be anticipated, as documented, for example,
by the variation in IFNG regulation of NO signalling in mammals (Bilham et al., 2017). The interaction of surface proteins can be tested
using fluorescent fusion proteins that allow binding interactions to be rapidly determined via fluorescent microscopy or flow cytom-
etry (Flies et al., 2020). Additionally, the production of recombinant MHC proteins has been used to identify key pathogen peptides
that can be used to understand disease pathology and support vaccine development (Halabi et al., 2021; Wang, Yue, et al., 2021).
Highly conserved structures can often be tested via cross- species binding assays. For example, the amino acids of the receptor bind-
ing domains (MYPPPY) and protein trafficking domains (Y VKM) of key T cell co- signalling proteins CD28 and CTLA4 are conserved
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VINKLER et a l.
& Sackton, 2019), the KEGG pathways are available for specific
immune responses, grouping all genes revealed as differentially
expressed in annotation studies even when they do not directly
contribute to immunity. Similarly, the general GO terms (e.g. im-
mune system process: GO: 0002376) define wide ranges of im-
mune response- related genes from the core immunome to NIRGs,
some of which are only weakly associated with the actual immune
defence.
The use of immune gene databases will differ depending on the
specific research question and study organism. While some data-
bases provide only simplistic immune gene lists with assignments
of genes in functional categories (e.g. ImmPort for human genes,
where a user must retrieve all other information from third- party
databases, Bhattacharya et al., 2018), other databases are more
comprehensive (AmiGO, Reactome, Gillespie et al., 2022; KEGG and
InnateDB, Breuer et al., 2013) enabling retrieval of additional infor-
mation (e.g. gene function, cellular localisation and alternative gene
identifiers (IDs), thus allowing easy crosslinking with other special-
ized databases— Uniprot, PDB, etc.). Gene annotations in these da-
tabases are only properly curated for a few selected species, namely
humans or mice, whereas most annotations (even for some model
species) rely on automatic annotations. While it might be less prob-
lematic for core immune genes whose function is likely to be well
conserved across jawed vertebrates, caution is needed when consid-
ering genes of the peripheral immunome or NIRGs. Unfortunately,
these different categories of immune genes cannot be automati-
cally retrieved from the current immune databases, which would be
convenient when working with large datasets. On the other hand,
this problem could be partially overcome by focusing on the over-
lap of immune gene lists between several species or several immune
gene databases. Unlike core immune genes, peripheral immune
genes and (especially) NIRGs are less likely to overlap between spe-
cies and/or databases. To this end, continued endeavour to create
more specialized immune databases— such as the Avian Immunome
Database (AVIMM, Mueller et al., 2020), the Immunome Database
for Marsupials and Monotremes (IDMM, Wong et al., 2011) and the
porcine immunome (Dawson et al., 2013)— may increase the reliabil-
ity with which immunome genes can be identified. Nonetheless, the
problem with these resources is that their maintenance is demand-
ing, and they may end up out of date. In addition, these databases
have been curated using various levels of inclusivity and detection
methods, such as literature searches, gene ontology terms, con-
served protein domains, orthology searches or a combination of
approaches (e.g. Wong et al., 2011) and, therefore, differ in content .
Although current immune gene databases allow the filtering
of immune gene sets based on multiple criteria, such as molecular
function, as far as we know, their vocabularies do not allow easy
extraction of general immune gene categories (such as surface re-
ceptors, adaptor molecules, signalling molecules, etc.). Rather, they
group immune genes into hierarchical functionally interlinked path-
ways being involved in a particular molecular mechanism, for ex-
ample, in MHC class I antigen presentation or MyD88- dependent
pathways. Thus, they group together many functionally unrelated
genes ranging from surface receptors to transcription factors.
Clustering genes in this way is often subject- specific, hindering di-
rect comparison among the multiple terms. Depending on the re-
search question, manual curation of such immune gene categories
might be necessary but may be unfeasible in large- scale evolutionary
studies. In addition, in the case of manual curation, some commonly
used analyses such as gene set enrichment analysis (Subramanian
et al., 2005) may be difficult to apply.
Using gene annotation based on orthology inference is another
aspect that requires particular caution. The level of true orthology
assignment decreases with increasing genetic distance (Gabaldón
& Koonin, 2013). In such cases, the orthology relationship can be
inferred from multiple species and then compared, for example, as
in Shultz and Sackton (2019). While for most immune genes one- to-
one (one2one) ortholog relationships prevail (Těšický et. al, unpub-
lished), caution is needed in dynamically evolving multi- gene families,
for example, in MHC genes, chemokines or beta- defensins (Bean &
Lowenthal, 2022; Machado & Ottolini, 2015; Nei & Rooney, 2005;
Nomiyama et al., 2013) with more complicated relationships, such as
one2many or many2many orthology. This issue is compounded by a
lack of harmonization of gene names and their identifiers between
different species and/or different databases. For instance, in differ-
ent databases different gene symbols are given to the same gene,
or the same gene symbol is given to different but similar genes (see
e.g. AVIMM database, Mueller et al., 2020, and gProfiler, Reimand
et al., 20 07). Given these limitations of gene classification systems,
it remains critically important for a given study to consider both
across mammals, birds, reptiles and amphibians. Development of additional expression vec tors that swa p amino acids, such as chang-
ing tyrosines to alanines in the protein trafficking domain (Y VKM to AVKM) can be used in function assays to validate structure and
function (Wong et al., 2021).
Final validation of protein structure and interactions can be done through protein crystallization studies. This has previously been out
of range for most evolutionary immunologists and ecologists, limiting this application to variation between model organisms (Halabi
& Kaufman, 2022; Koch et al., 2007). However, structural biology studies have begun showing interest in non- model species and
making exciting discoveries. For example, a novel type of TCR has been recently discovered in marsupials (Morrissey et al., 2021).
Although most of the above- mentioned approaches have been applied to few species so far, wider application of these methodologi-
cal improvements could level the field for evolutionary immunology in terms of resource availability and knowledge gaps.
BOX 1 (Continued)
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VINKLER et al.
species- specific immune processes and potential selective forces
that might be acting on the ‘genes of interest’.
4 | HALLMARKS OF IMMUNE GENE
EVOLUTION
Given their crucial role in rapid host– pathogen coevolution, im-
mune genes (especially the core immunome, Figure 1) have been
hypothesised to undergo more dynamic evolution than other gene
groups, leading to high levels of diversification— interspecific as
well as intraspecific variation (Woolhouse et al., 2002). This has
been confirmed in genomic studies of humans and other organ-
isms (Ekblom et al., 2010; Hillier et al., 2004; Nielsen et al., 2005;
Sackton et al., 2007). Several underlying evolutionary mechanisms
have been suggested to increase and maintain this immunogenetic
variability. Diversity in host immune genes increases through re-
peated episodes of pathogen- driven selective sweeps (as part of a
reciprocal coevolutionary arms race) when the direction of positive
selection differs between species, between and within populations
and between generations. Adaptations usually involve only short
functional parts of the genes, such as peptide- binding region in
MHC or few functional single- nucleotide polymorphic sites (SNPs)
around the PRR ligand- binding region (Hughes & Nei, 198 8; Lenz
et al., 2013; Velová et al., 2018), with rest of the sequence being
functionally constrained and under negative selection.
Within populations balancing selection can maintain high poly-
morphism through heterozygote advantage, negative frequency-
dependent selection or fluctuating selection (Klein & Ohuigin, 1994;
Minias & Vinkler, 2022; Spurgin & Richardson, 2010; Vinkler
et al., 2022; Westerdahl et al., 2004; Woolhouse et al., 2002).
None of these three mechanisms are mutually exclusive and they
may also interact with sexual selection to maintain high polymor-
phism (Ejsmond et al., 2014). However, even alleles that are main-
tained for long time periods by balancing selection initially diversify
through reciprocal adaptations to specific pathogens driven by pos-
itive selection (Těšický & Vinkler, 2015). Moreover, immune gene
polymorphism within a species may also increase through introgres-
sion, resulting from hybridization between closely related species
(Hedrick, 2013). Despite increasing evidence of introgression in dif-
ferent taxa and immune gene types (Fijarczyk et al., 2018; Grossen
et al., 2014; Jagoda et al., 2018; Nadachowska- Brzyska et al., 2012),
how common this mechanism is remains unclear. This is, at least
partly, because introgressed alleles can subsequently be main-
tained by balancing selection, complicating our ability to distinguish
them from shared standing variation (trans- species polymorphism;
Těšický & Vinkler, 2015).
Genes that belong to the core immunome have frequently been
investigated also for other important evolutionary phenomena, such
as convergent (Storz, 2016; Yeager & Hughes, 1999) and parallel/
concerted evolution (Pavlovich et al., 2018; Świderská et al., 2018;
Těšický et al., 2020; Wutzler et al., 2012), allowing independent evo-
lution of functionally related immune variants, or gene conversion
(Högstrand & Böhme, 1999; Huang et al., 2011; Velová et al., 2018;
Yeager & Hughes, 1999), homogenizing sequences across gene loci.
While these phenomena could play a role in immune gene evolu-
tion more frequently than in other gene groups, such patterns are
typically obser ved only in specific functional domains or structural
motives (Hughes & Nei, 198 8, 1989; Smirnova et al., 2000; Velová
et al., 2018) and, since evolution is stochastic, evolve non- universally
only in specific taxa (Vlček et al., 2022). Future systematic research
wi ll be ne ede d to re vea l fr equency of th eir o ccu rre nce in ver tebrates .
We can also ask if there are any hallmarks of molecular adapta-
tion that differ between traditionally recognized adaptive immune
genes and innate immune genes. Such differences can be expected if
all parts of innate immunity are ancient (e.g. billions of years old), as
has been argued, while adaptive immunity (at least TCR/BCR- based)
emerged relatively recently in jawed vertebrates (~450 million years
ago; Marchalonis et al., 2002). However, it is important to stress that
innate and adaptive immunity do not form distinct functional sys-
tems, but rather refer to interlinked layers of regulatory and effec-
tor mechanisms that are defined based on the principal mechanism
of the origin of their receptor variation: germ- line encoded (innate)
versus somatically rearranged (adaptive: BCR and TCR). Clonal so-
matic rearrangement in BCR/TCR allows B- and T- cells to detect an
enormous variety of antigens, making these receptors functionally
unique. Throughout the evolution of the immune system, the origi-
nal defence mechanisms have been supplemented, but typically not
replaced by new ones (Danilova, 2006; Marchalonis et al., 2002).
Thus, highly redundant, carefully regulated, immune mechanisms
emerged in vertebrate hosts, crosslinking the two broad sections of
immunity.
The most conspicuous hallmark of many immune genes is their
rapid evolution. This involves high interspecific variation, that is, di-
versification of genes among species, as well as high polymorphism,
that is, the maintenance of many alleles per gene within a species.
While molecular variability is particularly marked in the adaptive
immune genes, as seen with MHC genes (Piertney & Oliver, 2006;
Schlesinger et al., 2014), single- copy innate immune genes have been
suggested to be rather conserved (e.g. Eisen & Chakraborty, 2010;
Roach et al., 2005) and even invariant at a microevolutionary scale
(although multigene families of innate immune genes can be quite
diverse, see BOX 2). Yet, adaptive immunity, understood as a path-
way, groups many genes with diverse functional roles, including, for
example, co- receptors and cytokines regulating T- cell and B- cell de-
velopment. As adaptive immune genes beyond the MHC are rarely
investigated, we do not know if these components also exhibit signa-
tures of increased adaptive variability. In parallel, the immunological
mechanisms grouped under the term ‘innate immunity’ often have
little in common except for being dependent on germline- encoded
recognition of microbes or host damage. It has recently been shown
that while some innate immune genes appear truly conserved, for
example, some AMPs (Chapman et al., 2016), others exhibit reason-
ably high inter- and intraspecific variation as well as signatures of
positive selection (Vivier & Malissen, 2005), for example, some other
AMPs (Lazzaro et al., 2020) and PRRs (Davies et al., 2021; Świderská
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BOX 2 Multicopy innate immune genes can have complex evolutionary paths
Multigene families present a range of difficulties in evolutionary analyses, due to expansion and contraction leading to copy number
variation (CNV), sequence and expression polymorphisms leading to functional differences, potential convergent evolution of par-
alogous gene family members, and changes in gene location that can change the rate of sequence exchange. Overall, the difficulty is
knowing which gene copies can be considered orthologous for comparison between individuals and between species in evolutionary
analyses.
Although there are many single- copy genes of innate immunity, multigene families based on sequence similarity are not rare.
Multigene families involved in innate immunity include natural killer cell receptors (NKRs) and their homologues that primarily rec-
ognize MHC molecules but also pathogen- encoded peptides and decoys (Djaoud & Parham, 2020), as well as PRRs that recognize
pathogen molecules (pathogen- associated molecular patterns, PAMPs) but also self- molecules located in the wrong context indicat-
ing stress (danger- or damage- associated molecular patterns, DAMPs; Gong et al., 2020). The NKRs and their homologues include
the multigene families of KIRs, LILRs, FcRs, NKG2A/CD94, NKG2D, NKR- P1 and CD300 among others, while the PRRs include TLRs,
NLRs, CLRs, scavenger receptors, AIMs, Siglecs and butyrophilins among others.
Focusing on NKRs as an example, there are molecules based on several structural families used to a greater or lesser extent in dif-
ferent vertebrates, with one explanation being wholesale replacement of one multigene family by another due to strong pathogen
pressure (so- called receptor switch). Even within one NKR multigene family, there can be significant variation between species and
also CNV within species.
In humans, closely related multigene families of tandemly duplicated paralogs that encode cell surface proteins with immunoglobulin-
like (Ig- like) ex tracellular domains are found in a cluster called the leukocyte receptor complex (LRC), including the killer Ig- like recep-
tors (KIRs) and leukocyte Ig- like receptors (LILRs), as well as a single Fc receptor (FcR) and the natural cytotoxicity receptor NKp46
(Barrow & Trowsdale, 2008). Close by is the less closely- related family of sialic acid- binding Ig- type lectin receptors (Siglecs; Pillai
et al., 2012). KIRs are primarily expressed on NK cells, LILRs primarily on myeloid cells, the FcR on B cells, and different Siglecs on
a wide variety of cells. In chickens, a similar genomic region has hundreds of chicken Ig- like receptors (ChIR) genes, many of which
encode Fc receptors, with others thought (but not proven) to be NKRs (Viertlboeck et al., 2009; Viertlboeck & Göbel, 2 011). The
expression of KIR genes is variegated, with clones of NK cells bearing different combinations of KIRs on their surface; along with the
licensing and trained immunity of such clones which are considered analogous to education and memory of adaptive immunity (Elliott
& Yokoyama, 2011; Freud et al., 2017; Netea et al., 2016). Different lineages of KIR genes are used among primates and various other
mammals but are not found at all in rodents, and similar genes with very different lineages of Ig- like domains are presumed to have
the same function in other vertebrates including some fish (Guethlein et al., 2015; Wang, Belosevic, et al., 2021).
Similarly, but on another chromosome, there are multigene families of receptors with C- type lectin extracellular domains, including
Ly49, NKG2 and NKR- P1 families which in well- characterized mammals are found as clusters in the so- called natural killer complex
(NKC), along with others less well- characterized (Kirkham & Carlyle, 2014). Functional Ly49 genes are not found in humans but are
highly expanded in rodents in the so- called killer cell lectin- like receptor subfamily A (KLRA) locus, encoding cell surface proteins that
bind MHC class I molecules and fulfil roughly the same functions as KIRs in humans. No KIRs are reported for rodents, perhaps an
example of ‘receptor switch’, selected when an immune system becomes sufficiently poor at dealing with current pathogens. Indeed,
little or no NK receptor diversity (either Ig- like or lectin- like) is found for some vertebrates, including naked mole rats and marine
mammals (Hammond et al., 2009; Hilton et al., 2 019). Most members of the NKG2 family encode cell surface proteins (NKG2A, B,
C, etc) in complex with the signalling molecule CD94, recoginising non- classical MHC class I molecules (like HLA- E in humans and
Qa1 in mice) presenting conserved signal peptides from classical MHC class I molecules as part of a molecular arms race with certain
viruses; they are also as important as KIRs in licensing human NK cells during ontogeny (Guethlein et al., 2015). NKR- P1 (also known
as NK1.1, KLRB1 and CD161) genes are paired with their lectin- like ligand genes (also known generally as CLEC2D) in the NKC of
humans, mice and rats. However, a polymorphic BNK and monomorphic Blec ligand pair is found in the MHC of chickens and a similar
pair on the Z sex chromosome of passerine birds, so such genes can move around in the genome, frustrating attempts to use synteny
to establish orthology (Rogers & Kaufman, 2016). Although a single NKR- P1/ligand gene pair is found in humans and chickens, the
family of pairs is highly expanded in mice and rats, and the co- evolution of these genes with decoys encoded by rat cytomegalovirus
has been reported (Kirkham & Carlyle, 2014).
A similar level of complexity of structure, function, number and location of genes can be found for PRRs. However, Toll- like receptor
(TLR) genes with similar functional features can be traced through the vertebrates, in part because there are typically only around
10 genes, although some genes are duplicated or deleted in par ticular taxa (Liu et al., 2020). Some researchers would view such gene
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et al., 2018; Velová et al., 2018; Walsh et al., 2008). Therefore, di-
viding genes simply based on their assumed attribution to ‘innate’
or ‘adaptive’ immunity is unlikely to fully explain the variable evo-
lutionary patterns we observe across immune genes. Moreover,
as distinguishing alleles from different loci is difficult, MHC stud-
ies in non- model organisms often report all variants observed per
individual— so- called MHC diversity (Eizaguirre & Lenz, 2010; Minias
et al., 2018; O'Connor et al., 2016; Piertney & Oliver, 2006; Radwan
et al., 2020; Richardson & Westerdahl, 2003). If different MHC gene
copies are considered together, the total MHC diversity observed
per population becomes markedly higher across MHC genes than
for single copy genes. This makes comparisons between single- copy
genes and multilocus MHC difficult.
Evolutionary distinctions are also likely to exist between dif-
ferent functional and structural categories of immune genes. One
approach could be to categorize immune genes by whether the pro-
teins they encode function in ‘recognition’, ‘signalling’ or ‘effector’
roles (Sackton et al., 2007; Zhong et al., 2021) as clear differences
can be predicted between these categories. However, multiple dis-
tinct evolutionary pressures may act even within these functional
gene groups. For example, pathogen recognition can occur either di-
rectly via pathogen- derived molecules or indirectly via host- derived
damage signals (Uematsu & Akira, 2006), leading to distinct types
of evolutionary interactions acting in these two subgroups. Here
we suggest combining immune genes into groups with predicted
similarities in their evolutionary patterns based on their functional
interactions with other molecules, forming more of a continuum of
patterns, rather than a few distinct classes (Figure 2):
4.1 | Proteins involved in direct interactions with
microbial structures versus those interacting only
with host self molecules
We hypothesise that host proteins that directly interface with mi-
crobial molecules will be more intensively involved in host– pathogen
coevolution, thus selecting for increased functional genetic varia-
tion. This principle has been suggested for self/non- self discrimi-
nation in immunity (Spottiswoode & Busch, 2019) and underpins
investigations into MHC (Bernatchez & Landry, 2003; Hughes &
Nei, 1988 , 1989; Radwan et al., 2020; Spurgin & Richardson, 2010)
and TLR variability (e.g. Alcaide & Edwards, 2011; Lev y et al., 2020;
Świderská et al., 2018). In molecules interacting only with invari-
ant molecules derived from the host, lower variation stabilized by
negative selection would be adaptive. Thus, molecules interacting
with invasive pathogens should be functionally more variable, while
those recognizing invariant self- components should be conserved
(Marchalonis et al., 2002). While direct support for this assumption
is generally still lacking, indirect support can be taken from the well-
evidenced studies on synchronized receptor- ligand co- evolution
(Grandchamp & Monget, 2018).
4.2 | Proteins involved in interactions with a high
number of structurally distinct ligands versus proteins
involved in structurally limited interactions
Interacting with a high number of variable structures should select
for broader variability (allelic diversification). Receptors interacting
with their ligands often show complex structural coevolutionary
patterns (Chakrabarti & Panchenko, 2010). Immune systems should
evolve to optimize the balance between broad responses to hetero-
geneous stimulation and restricted responses only to specific stimu-
lation. Thus, in ligand- binding molecules, function may depend on
the balance between degeneracy (i.e. the ability of one receptor to
elicit a cellular response following interaction with any one of a set
of structurally distinct ligands) and specificity i.e. the ability to inter-
act productively with a single or a few ligands related in structure
(Vivier & Malissen, 2005). A cl ear exam ple is the hier arc hy of pro mis-
cuous to fastidious MHC class I molecules in chickens and humans
(Kaufman, 2018). Assuming single domains are involved, more com-
plex structural coevolution may be required in cases of structurally
families (as determined by sequence and structural similarity) as behaving more like single- copy genes in evolution, significantly sim-
plifying their analyses (Velová et al., 2018). In contrast, there are hundreds of TLR, NLR and scavenger receptor genes found in sea
urchins (Buckley & Rast, 2012), and large differences in gene copy number are found within vertebrates for the NLRs, with 20 or so
in humans but thousands in zebrafish (Howe et al., 2016). With many genes that can more- or- less freely exchange sequence features,
tracing the evolution can be difficult.
How do these changes occur? Among the many mechanisms (including mutation, gene conversion and genetic translocation), an in-
fluential concept has been the birth- and- death model (Nei et al., 1997 ), which depends on unequal crossing- over between members
of a multigene family through homologous recombination. A guiding principle is that changing from one to two copies of a gene will
generally depend on non- homologous recombination, making this a difficult and infrequent process, but once there are two genes in
the same transcriptional orientation, homologous recombination allows rapid expansion to many copies but also contraction down to
as few as one copy by unequal crossing- over between chromosomes and/or deletion within a chromosome. One strategy to reduce
the potential for contraction is to organize homologous genes in opposite transcriptional organization (as can be seen within the
chicken MHC, Afrache et al., 2020), for which homologous recombination would lead to inversion rather than deletion.
BOX 2 (Continued)
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variable ligands (entirely distinct molecules, Uematsu & Akira, 2006,
or even heterogeneous structures of the same molecule, such as
differently acylated LPS variants, Wang et al., 2015). Such patterns
have been described in avian TLRs, where genes encoding recep-
tors recognizing simple structurally invariant ligands (e.g. nucleic
acids, such as in TLR3) show lower levels of positive selection than
those specifically binding complex ligands (e.g. TLR2 or TLR4; Velová
et al., 2018). Yet, in contrast to the co- evolutionary patterns sug-
gested for pathogen- derived structures, interacting with a high
number of invariant host- self pathway components may lead to
strong negative selection depleting variation (Wang et al., 2019).
We predict higher adaptive genetic variation in immune recognition
mechanisms based on detection/presentation of non- self (MHC and
some PRRs) than those recognizing disruption of normal integrity
(PRRs) or missing self (inhibitory NK cell receptors).
4.3 | Cell- surface proteins and soluble proteins
(outer) versus cytosolic (inner) proteins
While the inner environment of cells is strictly regulated to maintain
normal physiology (Casey et al., 2010), the extracellular space often
provides more changeable conditions, for example, pH (Erra Díaz
et al., 2018) or heterogeneity of interacting molecules. Therefore,
cytosolic components may be facing selection by less diverse stimuli
than extracellular components. While little evidence is presently
available to support this hypothesis, human immunogenetic research
has shown that cytosolic sensors of infection, such as NOD- like re-
ceptors (NLRs), RIG- I- like receptors (RLRs), Cytosolic DNA sensors
(CDSs), and cytoplasmic signalling pathway members, are far less
frequently associated with resistance to infectious diseases than the
membrane- bound molecular sensors, such as C- type lectin recep-
tors (CLRs) and TLRs (Pothlichet & Quintana- Murci, 2013).
4.4 | Constitutively expressed proteins versus
proteins with inducible expression
In immunity, the constitutively expressed proteins typically serve
in broad- range effector mechanisms— for example, some AMPs
(Zasloff, 2002)— that interfere with the growth of pathogens through
mostly invariant mechanisms. This is in contrast to the proteins in-
volved in inducible defence, where binding specificity determines
the efficiency of the immune response (Eisen & Chakraborty, 2010 ;
Marchalonis et al., 2002; Vivier & Malissen, 20 05). Therefore, one
could predict higher adaptive genetic variation in proteins with in-
ducible expression compared with those constitutively expressed.
Rapid evolution is especially likely in host genes that parasites might
use to interfere with inducible host responses, either by blocking
induction or by activating inhibitory pathways. Preliminary evidence
FIGURE 2 Differences in selective
pressures in different immune gene
sets. Current evidence suggests that
groups of immune genes tend to have
differing levels of genetic variation as
well as strength of selection acting on
them. We predict increased pathogen-
mediated natural selection (range
from yellow = low to red = high) that
drives increased interspecific genetic
variation and intraspecific population
polymorphism especially in the genes that
encode proteins: (I) directly involved with
pathogen structures rather than host- self
molecules; (II) involved with a high number
of structurally distinct ligands rather than
with structurally limited interactions; (III)
expressed on the cell- surface rather than
to cytoplasm and (IV) with inducible rather
than constitutive expression.
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VINKLER et al.
for this has been reported by Shultz and Sackton (2019) who used a
large vertebrate transcriptomic dataset to show that genes upregu-
lated in response to pathogens are enriched for signals of positive
selection. However, further research is needed to elucidate whether
this pattern is unique or driven by the combination of other overlap-
ping factors.
To effectively defend the host organisms, immune systems re-
quire a carefully optimized balance of detection and effector mech-
anisms exhibiting evolutionarily dynamic and conservative features.
While supported by evidence from specific genes and gene families,
testing the above- mentioned predictions on whole- genome ver-
tebrate data is still problematic. The current structures of genome
annotation databases are not suitably organized to allow automatic
differentiating between the immune gene groups as defined above.
Genes belonging to the same groups from the perspective of their
predicted evolutionary clustering are mostly combined under dif-
ferent and overlapping GO terms (Kosiol et al., 2008), Reactome
(Gillespie et al., 2022) and KEGG pathways (Shultz & Sackton, 2019).
5 | HOW TO APPROACH IMMUNE GENE
HETEROGENEITY IN EVOLUTIONARY
STUDIES?
Studies in non- model organisms have mainly adopted the candi-
date gene approach, with MHC and TLR genes being popular in a
broad range of jawed vertebrates, from fish to amphibians, birds
and mammals (Bagheri & Zahmatkesh, 2018; Brouwer et al., 2010;
Minias & Vinkler, 2022; O'Connor et al., 2019; Wilson, 2017).
Although all jawed vertebrates possess mostly overlapping core im-
munomes, it is evident that the final composition of immune genes
and gene copy numbers can differ considerably between vertebrate
classes and orders, and even within orders and families (Fiddaman
et al., 2022; Meyer- Lucht et al., 2016; Minias et al., 2019; O'Connor
& Westerdahl, 2021; Těšický & Vinkler, 2015; Velová et al., 2018;
Wang et al., 2012). This raises the question of how bes t to study pat-
terns of selection in immune genes in non- model organisms.
An important consideration in choosing an appropriate method-
ological approach is timescales. Studies that measure immune gene
variation within a species are by definition done on a microevolu-
tionary scale (Reznick & Ricklefs, 2009). Analyses of the variation
that exists between species afford a macroevolutionary perspective
and provide insights into large- scale processes that shape immune
gene diversity (Kolora et al., 2021; Velová et al., 2018). Past selec-
tion can be traced as genetic footprints in immune genes and the
selective patterns in orthologous genes (see BOX 1) can be targeted
on both micro and macroevolutionary scales (Cortázar- Chinarro
et al., 2018; Ebert & Fields, 2020; Nielsen et al., 2005; O'Connor
et al., 2018; Scherman et al., 2 014; Tschirren et al., 2011).
Micro and macro perspectives in evolution pinpoint slightly dif-
ferent evolutionary questions and have different challenges when
executed. In many respects, studies conducted within species are
easier than between species, because within species it is likely that
all targeted immune genes will occur in all individuals. Moreover,
single- copy immune genes are much easier to compare between
species than duplicated genes (paralogs), thus such innate immune
genes (excluding multigene families, see BOX 2) are often easier to
study on a macroevolutionary scale than those of the adaptive im-
mune system, which are often highly duplicated (e.g. MHC class I and
II genes; Fijarczyk et al., 2018; Malmstrøm et al., 2016; Westerdahl
et al., 2022) or even undergo somatic recombination (e.g. TCRs and
BCRs). For this reason, a focus on single- copy genes may be appro-
priate to provide initial insights into a broad range of genes across
species. Furthermore, evolution by loss- of- function should not be
neglected, and gene losses have been reported in both innate and
adaptive immunity (Albalat & Cañestro, 2016; Fiddaman et al., 2022;
Hilton et al., 2019 ; Malmstrøm et al., 2016; Roth et al., 2020).
An othe r impo r t ant po int is that al thou gh ofte n buil t with a si m-
ilar immunome, the immune system is inherently flexible, and even
closely related species may react differently to the same patho-
gen (Tschirren et al., 2012). This flexibility is further reflected in
the ‘tolerance to infection’ strategy which some species use to
reduce the negative impact of disease on host fitness (Medzhitov
et al., 2012; Råberg et al., 2009), whereas others exhibit strong
responses aimed at clearing the infection (resistance) despite
immunopathological effects (Graham et al., 2005). Tolerance to
infection has been evidenced in natural populations and verified
in experimental set- ups (Råberg et al., 2007; Savage et al., 2020;
Savage & Zamudio, 2016). One example is the response to viral
infec tion observed in some bats, whereby high basal levels of ty pe
I interferon (IFN) are partnered with low levels of inflammation,
setting their response to viruses apart from many other verte-
brates (Banerjee et al., 2020). Persistent virus titres reported in
apparently healthy bats suggest they employ a tolerance strategy
to many viruses (Irving et al., 2021). The genetic footprints ob-
served in immune genes with respect to resistance versus toler-
ance to infection can be expected to differ, which should be taken
into consideration in studies of immune gene evolution.
5.1 | The study of immune genes on a
microevolutionary scale (within species)
A microevolutionary perspective gives insights into how natu-
ral selection shapes immune gene variation over generations.
Consequently, such studies often use population genetic approaches
to understand immune gene variation within and across popula-
tions, such as examining changes in alleles frequencies over time
(Charbonnel & Pemberton, 2005; Westerdahl et al., 2004) and space
(Cortázar- Chinarro et al., 2018; Gonzalez- Quevedo et al., 2015;
Strand et al., 2013). Particularly valuable insights come from long-
term studies of natural populations. For example, the study of Soay
sheep (Ovis aries) suggests that MHC class II genes are under bal-
ancing selection, consistent with spatial and temporal heterogene-
ity in pathogen pressure (Huang et al., 2022). Likewise, work on the
great reed warbler (Acrocephalus arundinaceus) reports evidence of
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VINKLER et a l.
balancing selection and high MHC class I diversity correlated with
both disease resistance (Westerdahl et al., 2004, 2005, 2012) and
fitness (Roved et al., 2018). Moreover, research on the Seychelles
warbler (Acrocephalus sechellensis), shows evidence of both bal-
ancing selection at the MHC (Brouwer et al., 2010; Richardson &
Westerdahl, 2003) and positive selection at the innate TLR3 gene
(Davies et al., 2021), although this latter pattern may be part of neg-
ative frequency- dependent selection spanning decades. Balancing
selection has also been documented in MHC and TLRs of wild ungu-
lates in cross- sectional studies (Quéméré et al., 2015, 2021).
Alleles subject to balancing selection are maintained for longer
time- intervals than alleles subject to neutral evolution. Therefore,
population differentiation is expected to be less evident for im-
mune genes subject to balancing selection than neutral markers
(Borg et al., 2011; Schut et al., 2011). However, local adaptation to
population- specific pathogens can result in selective sweeps for cer-
tain advantageous immune alleles and then the population differen-
tiation will instead be larger for certain immune genes than neutral
markers (Nunes et al., 2021). Immune gene evolution must be seen in
the context of population structure, immigration/emigration and en-
vironmental components, such as pathogens. Therefore we probably
cannot expect to observe a consistent pattern among studies com-
paring population differentiation using immune genes and neutral
markers unless environmental components and other constraints
are taken into account (Eizaguirre & Lenz, 2 010; Gonzalez- Quevedo
et al., 2015; Muirhead, 2001; Spurgin & Richardson, 2010).
5.2 | The study of immune genes on a
macroevolutionary scale (between species)
Evolutionary processes that shape immune genes within species
scale up to shape the immunogenetic variation we see on a mac-
roevolutionary level (Simons, 20 02). The sequences of orthologous
immune genes across species can be used to infer the strength and
type of historical selection on immune genes (BOX 1); which are
often found to be under positive selection (Sackton, 2020; Shultz &
Sackton, 2019). Interestingly, even across closely related species or-
thologous immune genes may be subject to different selection pres-
sure. A study of TLR2 genes of two sympatric rodent populations
showed that these genes are under balancing selection in bank voles,
(Myodes glareolus), but not yellow- necked mice (Apodemus flavicollis)
(Tschirren et al., 2011), possibly as a result of different strategies to
combat Borrelia infections in the two species (Zhong et al., 2021).
Studying immune gene variation across species can un-
cover trans- species polymorphisms (Figueroa et al., 1988; Lenz
et al., 2013). This is commonly seen in immune genes under balanc-
ing selection, with one explanatory factor being the maintenance
and sharing of favourable alleles conferring protection from partic-
ular pathogens (Těšický & Vinkler, 2015). Structural genetic varia-
tion can also be revealed by studying immune genes across species
(Burri et al., 2010 ; Minias et al., 2019; O'Connor et al., 2016, 2018;
Shiina & Blancher, 2019; Velová et al., 2018; Vinkler et al., 2014).
The presence or absence of particular immune genes, or even the
expansion and contraction of whole immune gene families, can be
indicative of their functional relevance in different species (Bainová
et al., 2014; Colgan et al., 2021; Roth et al., 2020; Sackton, 2 019;
Sharma et al., 2020). A recent comparative study of rockfish (genus
Sebastes) showed that particularly long- lived species have an ex-
panded number of immune modulatory butyrophilin genes and
proposed an adaptive role for the immunosuppressive function of
these genes in the long lifespans of these fish (Kolora et al., 2021).
The immune system is remarkably flexible in terms of gene gain
and loss (Chattopadhyay et al., 2020; Colgan et al., 2021; Divín
et al., 2022; Krchlíková et al., 2023; Parham & Moffett, 2013;
Solbakken et al., 2016; Velová et al., 2018). For example, gadiform
fish have lost the MHC class II genes, a loss which seems to have
been subsequently compensated for by an expansion of MHC
class I gene copy number (Malmstrøm et al., 2016). Based on tran-
scriptomic evidence, naked mole rats (Heterocephalus glaber) lack
natural killer (NK) cells and show restricted diversity in the genes
regulating NK cell function (Hilton et al., 2019). This is, perhaps, a
consequence of their subterranean lifestyle where they potentially
encounter few viruses. Furthermore, some gene losses can be com-
pensated by evolutionary shifts in the function of the remaining
genes, as observed in the mouse (Mus musculus) CD1 gene family,
where gene- loss compensation in ligand- binding based on pH was
observed (Dascher & Brenner, 2003).
6 | CURRENT EVIDENCE FOR SPECIFIC
PATHOGENS DRIVING IMMUNE GENE
EVOLUTION
A primary challenge in the study of pathogen- mediated immune
gene evolution lies in detecting the signatures of evolution gener-
ated by specific pathogens (Spurgin & Richardson, 2 010). Some im-
mune genes are clearly hotspots of positive selection within the
jawed vertebrate genome (Piasecka et al., 2018), but the specific
(groups of) pathogens imposing selection on each gene or regula-
tory element remain largely unknown. In certain cases, alleles have
been linked to disease resis tance, for ex ample, specif ic MHC class II
alleles in Atlantic salmon (Salmo salar) and three- spined stickleback
(Gasterosteus aculeatus) confer reduced risk of bac t e r i a l or ne m a t o d e
infection (Bolnick et al., 2014; Dionne et al., 2007, 2009; Eizaguirre
et al., 2012; Grimholt et al., 2003). How ever, the range of pathoge ns
that previously interacted with these MHC genes to generate the
currently observed diversity patterns is likely to have been plen-
tiful. Although technically challenging, on- going selection and/or
recent evolution in immune genes can be measured in populations
over time; such long- term sampling approaches have been executed
in some studies (Davies et al., 2021; Huang et al., 2022; Westerdahl
et al., 2004). An alternative approach is screening genetic footprints
of previous selection in genomes, but this may not be able to pro-
vide direct evidence for the role of any particular pathogen (Ebert
& Fields, 2020). Nonetheless, see for example Souilmi et al. (2021),
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VINKLER et al.
for putative direct connections. Genomic information can then be
interpreted with bioinformatic tools to infer selection within genes
an d als o evolu t ion rel ate d to regu latory mutat ion s (gene exp res sion;
Enard et al., 2 014). Sequencing of genomes from historical museum
samples (museomics) is also a way to view how patterns of varia-
tion at immune genes change over time (Alves et al., 2019; Irestedt
et al., 2022; Krause- Kyora et al., 2018), although again identifying
the specific pathogens responsible may be difficult. In BOX 3, we
highlight examples of host– pathogen interactions involving differ-
ent classes of pathogens and vertebrate taxa to draw inferences
about which genes and selective mechanisms may be invoked by
different types of infections.
6.1 | Complex patterns of pathogen- mediated
natural selection acting on immune genes
The examples in BOX 3, along with many others in the literature,
highlight that disease resistance is often a polygenic trait, including
genes of the core and peripheral immunome as well as NIRGs. While
a single pathogen can have a strong effect on host fitness and pro-
duce detectable host evolution, an array of pathogens will contribute
to the total selective pressure experienced by a host. Simultaneous
exposure to multiple pathogens (probably the norm in wild popula-
tions) can lead to complex patterns of selection that might be diffi-
cult to disentangle (Alizon & van Baalen, 2008; Cattadori et al., 20 08;
Garcia- Longoria et al., 2022; Schmid- Hempel, 2011). Furthermore,
different pathogens may act on different spatial– temporal scales or
illicit different mechanisms of selection, again complicating signa-
tures of immunogenetic evolution (Spurgin & Richardson, 2010). One
aspect in which pathogens differ from each other is the composition
of the ir immunit y agon ist s, from PRR liga nds to an tigens pre sen ted by
MHC molecules. A recent analysis showed that most human patho-
gens share few antigen peptides, suggesting that dif ferent pathogens
will select for different MHC variants with distinct peptide binding
properties, providing a possible explanation for high allelic diversity
at the MHC genes (Özer & Lenz, 2021). Yet, some MHC alleles bind
a wide variety of peptides and confer resistance to many unrelated
pathogens (Kaufman, 2018). Differences among pathogens (both
species and strains) and the modes of recognition by host receptors
are the basis of any hypothesis for pathogen- mediated balancing se-
lection, and a better understanding of these differences will be a key
to understanding the evolution of immune genes at large.
7 | FUTURE DIRECTIONS
Until recently, evolutionary patterns in immune genes in non- model
organisms were predominantly investigated in a limited number of
gene sets, that is, MHCs and TLRs. However, genomic research has
found evidence that a broader range of immune genes involved in the
immunome experience rapid evolution, including other components
of adaptive immunity, inflammatory pathways and complement
(Kosiol et al., 2008). Future research needs to take a broader scope
and include the peripheral immunome and NIRGs. With improved
sequencing technology and dedicated analysis pipelines it now be-
comes feasible to investigate ultra- diverse immune receptor (TCR,
BCR) repertoires in non- model species (Migalska et al., 2018) as well
as gene expression in different tissues and different cell types (Scalf
et al., 2019; Sudmant et al., 2015).
With the pace at which high- quality genomes are being produced
through key consortia (e.g. Tree of Life and Vertebrate Genome
Project), the availability of species- specific reference genomes should
not be a limitation for much longer. However, research using long-
read sequencing techniques shows that classical short- read refer-
ence genomes alone are not sufficient to grasp the structural genetic
variation in complex immune gene families (Vekemans et al., 2021).
Recent methodological advances allow large population screenings,
comparative analyses of populations or even investigation of allele
frequency shifts based on historical samples. Yet, availability of the
genomic information alone is not enough to promote our understand-
ing of the evolutionary processes diversifying immune responses in
animals. Improved genome annotations and a better understanding of
gene function across species boundaries are also imperative. In most
non- model animal species evolutionary researchers have struggled
with a lack of substantial genome annotations. This challenge can be
partially overcome using annotations from related model organisms,
but to a certain extent experimental functional annotation may still be
required in the species of interest. The need for such testing depends
heavily on the gene investigated. While in gene orthologs functional
conser vatism can be predicted, in cases of frequent paralogous genes,
such as MHC , only limited predictions can be made. Moreover, careful
considerations must be also taken when comparing MHC genes and
MHC genetic diversity among distantly related genera/families since
both phylogenetic history of MHC class I and MHC class II genes and
their expression differ considerably among species (Burri et al., 2010;
Chen et al., 2015; Kaufman et al., 1999; Shiina et al., 2 017; Westerdahl
et al., 2022). These limitations have to be considered when interpret-
ing results and conclusions should be correspondingly cautious.
A greater level of functional testing, consistent with that under-
taken by immunologists in classical model species, will be needed to
verify the effects of individual adaptive variation that has emerged
through host- pathogen coevolution. In silico predictions (Follin
et al., 2013; Králová et al., 2018; Těšický et al., 2020) and struc-
tural analysis (Chappell et al., 2015; Koch et al., 2007) can guide this
functional testing (Fiddaman et al., 2022; Hyland et al., 2021; Levy
et al., 2020) to make these efforts feasible. Although such functional
approaches are beyond the scope of the present review, we argue
they require future attention.
8 | CONCLUSION
Evolutionary immunology is a field that emerged from com-
parative immunology (Flies & Wild Comparative Immunology
Consortium, 2020) and evolutionary ecology (Sheldon &
14
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VINKLER et a l.
BOX 3 Examples of host– pathogen systems revealing immune gene evolution
Human immunodeficiency virus
In humans, HIV is arguably the most thoroughly studied pathogen related to host– pathogen interactions (International HIV
Controllers Study et al., 2010). In genome- wide association studies (GWAS), HIV viral load, a proxy for disease progression, has
been associated with variation in two key genomic regions, the CCR5, used by the virus for cell entry, and the MHC region (McLaren
et al., 2015). The latter signal could be mapped to a handful of amino acid residues in the peptide binding grooves of the MHC class
I genes (International HIV Controllers Study et al., 2010; McLaren et al., 2015). Overall genomic markers account for 19– 25% of
the variation in viral load. Further work has shown that the MHC- HIV association is based on both qualitative (i.e. specific peptide
binding) and quantitative (i.e. number of peptides bound) aspects of peptide binding by different MHC variants (Arora et al., 2020;
Chappell et al., 2015; Košmrlj et al., 2010). This recent genomic work aligns nicely with earlier work reporting MHC heterozygote
advantage in slow disease progressors (Carrington et al., 1999). A number of genetic mechanisms have been shown to impact HIV
disease progression, including epistasis with NK and similar myeloid receptors (Bashirova et al., 2020; Goulder & Walker, 2012;
Martin et al., 2018). While this obser ved selection may contribute to the maintenance of high levels of MHC polymorphism in natural
populations (Radwan et al., 2020), it needs to be pointed out that mortality due to the HIV infection is delayed in the lifetime, which
weakens the selection efficiency of the pathogen.
Avian viruses
Selection of avian immune genes by viral diseases is exemplified by strong associations between the chicken MHC class I region
and Marek's disease virus (Briles et al., 1977). Presence and expression of specific MHC class I haplotypes at the B locus, such as
B21, confer resistance, while other haplotypes (B13 or B19) are associated with susceptibility. In the case of avian influenza A viruses,
chickens with homozygous B21 haplotype had 100% survival rate, while those homozygous for the B12 or B13 haplotypes suffered
100% mortality (Boonyanuwat et al., 2006). This evidence documents the importance of MHC alleles binding broad ranges of dif-
ferent peptides (B21) in resistance to viral infections, compared with specialists binding more narrow peptide repertoires (Chappell
et al., 2015). On the other hand, specialized alleles (B12) may provide advantage to specific pathogens, such as the Rous sarcoma virus
(Hofmann et al., 2003; Wallny et al., 2006). Genomic evidence from one of the key natural reservoirs for influenza A, the Mallard
duck (Anas platyrhynchos) indicates that β- defensin and butyrophilin- like gene families also play key roles in host responses to avian
influenza (Huang et al., 2013). Likewise, using genomic approaches novel genes associated with Avipoxvirus, involved in cellular
stress signalling and immune responses, were identified across divergent island populations of Berthelot's pipit (Anthus berthelotii;
Sheppard et al., 2022).
Ranavirus in ectotherms
The genus Ranavirus includes eight species of viruses that infect many ectothermic vertebrates including, to date, 34 reptile, 27 fish
and 63 amphibian genera (Brunner et al., 2021). While host species and developmental stages vary in susceptibility, ranaviruses have
played a major role in mass mortalities of multiple species (e.g. Geng et al., 2010; Johnson et al., 2008; Price et al., 2014 ). Although still
poorly understood, the ranavirus infection triggers broad- scale immune responses (reviewed in Grayfer et al., 2015) involving type
I and III interferon (IFN), tumour necrosis factor- alpha (TNFA), interleukin 1- beta (IL1B), myxovirus resistance protein 1 (Mx1) and
TLRs. Humoral and cellular immunity are also activated with ranavirus- specific immunoglobulins (IgY) and T cells ranging from cyto-
toxic CD8 T cells to an invariant T cell population (CD8 /CD4 ) dependent on XNC10 (a nonclassical MHC class Ib gene). However,
although the repertoire of potential targets of ranavirus- mediated selection among the immune genes is large, solid evidence is still
scarce. Some immune genes seem to be differentially expressed between frog populations with and without ranaviral disease his-
tory (Campbell et al., 2018) and some MHC class I and II supertypes may be associated with resistance to ranavirus infection (Savage
et al., 2019; Teacher et al., 2009).
Bacterial infection in mammals
Mycobacterium avium subsp. paratuberculosis (MAP) is the agent of Johne's disease in ruminants. Shared susceptibility to MAP in
domesticated and wild populations makes ungulates an excellent model for studying the adaptive evolution of immune genes (Chebii
et al., 2021; Griffin, 2 014; Jolles et al., 2015; Ren et al., 2021). The role of TLR2 and other TLRs in ungulate defence against MAP has
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VINKLER et al.
been documented (Fisher et al., 2011), and a GWAS identified a number of candidate genes for MAP resistance, including NLRP3, IFi 47,
TRIM41, TNFRS F18 and TNFRSF4 (Mallikarjunappa et al., 2018). Wh ile in total more than 20 0 bov ine genes were su gge ste d to contrib-
ute to MAP resistance, functional validation is still mostly missing (Mallikarjunappa et al., 2021). Analogous genetic mapping efforts in
humans, focusing on M. tuberculosis, identified a number of resistance factors against the disease, including PRRs (Dubé et al., 2021).
Wild rodents are the main reservoir of many bacteria (Ostfeld, 2012), and in a wild populat ion of bank voles (Myodes glareolus), one of
the main TLR2 haplotype clusters was associated with resistance to tick- transmitted Borrelia afzelii (Tschirren et al., 2013). Given the
patterns of distribution of the TLR2 polymorphism in vole populations across Europe, the TLR2 variation appears to be maintained
long- term (Morger et al., 2015). Furthermore, recent research in MHC class II alleles (DQB) unravelled complex host genotype- by-
pathogen genotype interactions between the voles and Borrelia strains (Råberg et al., 2022; Scherman et al., 2021).
Mycoplasma gallisepticum in birds
In the 1990s, the bacterial pathogen Mycoplasma gallisepticum (MG) spilled over from domestic poultry into the house finch
(Haemorhous mexicanus) (Ley et al., 1996; Luttrell et al., 1998). In two decades, MG spread across North America (Hochachka
et al., 2013; Staley et al., 2018), causing up to a 60% population decrease (Hochachka & Dhondt, 2000) and inducing strong selection.
Comparing an MG- naïve population of house finches to one with a history of MG endemism, Zhang et al. (2014) identified two genes
that showed positive selection: the HSPBAP1 gene associated with heat- shock and a T- cell precursor gene THYMIS. Moreover, low
MHC class II diversity was associated with increased disease severity (conjunctivitis) during MG infection (Hawley & Fleischer, 2012).
Furthermore, birds with a particular HSP90- alpha genotype had lower pathogen loads (Backstrom et al., 2013). Selection driven
by MG also shapes expression patterns in various immune genes, including MHC class II- associated invariant chain and neutrophil
cytosolic factor 4 (NCF4; Bonneaud et al., 2011). MG evolves to promote conjunctival inflammation, possibly to facilitate its trans-
mission, upregulating expression of pro- inflammatory cytokines, including IL1B and IL6 (Vinkler et al., 2018). Interestingly, the house
finches evolve to counteradapt, as evidenced by IL1B expression only being elevated during MG infection in birds from an MG- naïve
population, but not those with a long history of MG endemism (Adelman et al., 2013). This suggests that there may be selection for
tolerance to MG infection in house finches.
Batrachochytrium in amphibians
Chytridiomycosis is a fungal skin infection caused by Batrachochytrium dedrobatidis (Bd) that emerged in amphibian hosts world-
wide over the past half- century. Bd infection has caused the greatest pathogen- driven loss of biodiversity ever recorded (Scheele
et al., 2019), suggesting that Bd imposes potent selective pressure on host immune systems. Indeed, positive selection has been
revealed in AMP and MHC genes in affected hosts (Kosch et al., 2016, 2017; Woodhams et al., 2010), transcriptome- wide gene ex-
pression changes in response to Bd infection have been described (Ellison, Savage, et al., 2 014; Ellison, Tunstall, et al., 2014 Eskew
et al., 2018; Poorten & Rosenblum, 2016; Ribas et al., 2009; Rosenblum et al., 2012; Savage et al., 2020), and AMP and MHC immune
variation associated with host Bd susceptibility has been identified (Bataille et al., 2015; Savage et al., 2018; Woodhams et al., 2007 ).
While most of these studies are unable to show that Bd is the definitive cause of immunogenetic change, extensive work on MHC
evolution provides the most compelling evidence that Bd has prompted rapid immunogenetic adaptation. For example, studies of
the lowland leopard frog (Rana yavapaiensis) have demonstrated that an MHC class II variant (allele Q) arose under recent posi-
tive selection, is associated with Bd survival and only occurs in high frequency in natural populations that are Bd tolerant (Savage
& Zamudio, 2011, 2016). Likewise, alpine tree frogs (Litoria verreauxii alpina) from a population with a long history of Bd exposure
significantly upregulated multiple immune genes (including MHC) relative to one that was only recently exposed to Bd (Grogan
et al., 2018), indicating evolved gene expression shifts after multiple generations of Bd selective pressure. Recently MHC class II al-
leles in the common toad (Bufo bufo) have been shown to associate with strain- specific differences in survival after experimental Bd
infection (Cortázar- Chinarro et al., 2022).
Trypanosomes in cattle
Evidence of Trypanosoma- induced immune adaptation comes from studying the migration of cattle across Africa, estimated to start
at 5000 BP (Gautier et al., 2009; Mwai et al., 2015). Cattle were subject to new selection factors, including trypanosomiasis, as they
spread across novel tropical environments, which drove changes in the gene pools of domestic cattle (Smetko et al., 2015). Whole
genome Bayesian scans of cattle populations in west Africa have identified footprints of adaptive selection comprising 53 genomic
BOX 3 (Continued)
16
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VINKLER et a l.
Verhulst, 1996). Yet, advances in this discipline are hindered by a lack
of conceptual unity between researchers with backgrounds in dif-
ferent fields, making it challenging to form collaborative networks.
The interdisciplinary discussion opened at the ESEB 2021 Online
Satellite Symposium: ‘Molecular evolution of the vertebrate immune
system, from the lab to natural populations’ took the community be-
yond reviewing the state- of- the- art in evolutionary immunology. To
help expose, understand and reconcile the differing evidence and
insights gained from different disciplines, we discussed key concepts
an d maj or shi fts in researc h top ics an d appro ache s. One mai n con clu -
sion is that evolutionary genetics needs to be integrated with other
types of data explaining the immunological mechanisms involved
in the evolution of host immune defence. Improved interpretation
of results through such integrated analyses is key to advancing this
field. This is best achieved through the collaborative involvement of
scientists with different backgrounds, skills and perspectives; essen-
tially developing a true interdisciplinary basis for the field. As a com-
munity, we feel the initiated strategy of interdisciplinary meetings
between evolutionary biologists, immunologists, bioinformaticians,
ecologists, zoologists, microbiologists, parasitologists and veteri-
nary scientists is highly conducive to develop evolutionary immunol-
ogy as an interdisciplinary research field. We hope this collaborative
review stimulates discussion and may form the foundation for novel
studies and collaborations in the future.
regions and 42 candidate genes, including strong balancing selection on the MHC (Gautier et al., 2009). Another identified candidate
gene, TICAM1 codes for a TLR adapter and thus plays a key role in innate immunity. Similarly, natural selection appears to have shaped
the gene encoding chemokine receptor CXCR4, a modulator of T- cell responses, which coincides with the previously identified QTL
for trypanosome tolerance (Hanotte et al., 2003). Finally, the receptor for anthrax toxin (ANTRXR2) has been identified among the
positively selected genes (Gautier et al., 2009). This example illustrates the importance of reconstructing the historical population
genetic structure for evidencing the contribution of genes to host adaptation against newly encountered pathogens.
Transmissible cancer in Tasmanian devils
Transmissible cancers are a rare type of pathogen where a single cancer evolves to become infectious and spreads through a popula-
tion. Devil facial tumour disease (DFTD), a transmissible cancer of the Tasmanian devil (Sarcophilus harrisii), has had a mortality rate
close to 100% in adult devils (Margres et al., 2020; Wright et al., 2017), which has stimulated strong selection within the popula-
tion. Selection can occur despite such a high mortality rate as DFTD can take up to 1 to 2 years to kill the host. So devils that breed
younger, invest more into a single breeding season or tolerate the tumours for longer periods are able to contribute more to the next
generation (Lachish et al., 2009; Wells et al., 2017 ). Within a few generations there have been allele frequency changes (detected
using SNPs) in genomic areas surrounding immune- and cancer- related genes (including CD146, THY1, CRBN; Epstein et al., 2016;
Hubert et al., 2018; Stahlke et al., 2021), a reduction in alleles associated with abiotic local adaptation (Fraik et al., 2020) and even
changes in gene expression with DFTD infection (immunoglobulins, NKG2D, CD16; Raven et al., 2022; Ujvari et al., 2016). The PAX3
gene has been associated with documented tumour regressions, possibly involved in a regulatory pathway, slowing tumour growth
(Wright et al., 2017). In addition, the expression of RAS L11 A in the tumours, stimulating the Rat sarcoma pathway, has been con-
nected to documented tumour regressions (Margres et al., 2020). However, more recently the rapid evolution has been questioned
due to allele frequencies at putative DFTD- associate loci being similar in wild devils exposed to DFTD and those from the insurance
population which have had little or no exposure to DFTD (Farquharson et al., 2022).
Cestodes in three- spined sticklebacks
Three- spined sticklebacks (Gasterosteus aculeatus) are frequently infected by a cestode, Schistocephalus solidus. This tapeworm is
known to affect various host traits, including behaviour (Berger et al., 2021), and impact host fitness (Barber et al., 2008; Heins &
Baker, 2008). Stickleback populations have been shown to exhibit varying resistance against S. solidus, possibly in response to dif-
ferential prevalence of the parasite in their natural habitats (Piecyk et al., 2019; Weber et al., 2 017). In some populations, resistant
fish mount an intensive fibrosis response that traps the parasite in a cyst. An experimental cross between a high- prevalence (tolerant)
and low- prevalence (resistant) stickleback population identified two major quantitative trait loci (QTL) (Weber et al., 2022). Selection
screens within these QTL identified that the SPI1 gene, a major regulatory switch controlling fibroblast polarization to generate
fibrosis (Watt et al., 2021; Wohlfahrt et al., 2019), is up- regulated in more fibrotic fish and that knock- outs of SPI1B exhibit altered fi-
brosis (Fuess et al., 2021). The other major QTL contributes to both inflammation (respiratory burst) and cestode growth suppression
above and beyond the effect of fibrosis, possibly involving the immune genes STAT6 and HNF4A. Thus, evidence points to selection
acting on multiple genes (NIRGs as well as core immunome genes), contributing to the suppression of cestode growth and viability in
sticklebacks. A specific role for MHC variability in S. solidus infection remains debated (Kurtz et al., 2004; Natsopoulou et al., 2012;
Peng et al., 2021; Stutz & Bolnick, 2017).
BOX 3 (Continued)
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VINKLER et al.
AUTHOR CONTRIBUTIONS
Michal Vinkler: Conceptualization (lead); visualization (equal); writ-
ing – original draft (lead); writing – review and editing (lead). Steven
R. Fiddaman: Conceptualization (equal); visualization (equal); writ-
ing – original draft (equal); writing – review and editing (equal).
Martin Těšický: Conceptualization (equal); writing – original draft
(equal); writing – review and editing (equal). Emily A. O'Connor:
Conceptualization (equal); writing – original draft (equal); writ-
ing – review and editing (equal). Anna Savage: Conceptualization
(equal); writing – original draft (equal); writing – review and editing
(equal). Tobias L. Lenz: Conceptualization (equal); writing – original
draft (equal); writing – review and editing (equal). Adrian L. Smith:
Conceptualization (equal); writing – original draft (equal); writ-
ing – review and editing (equal). Jim Kaufman: Conceptualization
(equal); writing – original draft (equal); writing – review and edit-
ing (equal). Daniel I. Bolnick: Conceptualization (equal); writing
– original draft (equal); writing – review and editing (supporting).
Charli Davies: Conceptualization (equal); writing – original draft
(equal); writing – review and editing (supporting). Neira Dedić:
Conceptualization (equal); writing – original draft (equal). Andrew
S. Flies: Conceptualization (equal); writing – original draft (equal);
writing – review and editing (supporting). Mercedes Gómez
Samblás: Conceptualization (equal); writing – original draft (equal).
Amberleigh E. Henschen: Conceptualization (equal); writing – origi-
nal draft (equal). Karel Novák: Conceptualization (equal); writing
– original draft (equal); writing – review and editing (supporting).
Gemma Palomar: Conceptualization (equal); writing – original draft
(equal). Nynke Raven: Conceptualization (equal); writing – original
draft (equal). Kalifa Samaké: Conceptualization (equal); writing –
original draft (equal). Joel Slade: Conceptualization (equal); writing
– original draft (equal). Nithya KuttiyarthuVeetil: Conceptualization
(equal); writing – original draft (equal); writing – review and editing
(supporting). Eleni Voukali: Conceptualization (equal); wr iting – orig-
inal draft (equal). Jacob Höglund: Conceptualization (equal); writing
– review and editing (equal). David Richardson: Conceptualization
(equal); writing – original draft (equal); writing – review and editing
(equal). Helena Westerdahl: Conceptualization (lead); writing – orig-
inal draft (lead); writing – review and editing (lead).
ACKNO WLE DGE MENTS
We thank the European Society for Evolutionary Biology (ESEB) and
organizers of the ESEB 2021/2022 congress for the opportunity to
meet and share our results and views. We are grateful the funders
supporting the research outlined in this article. M.V. was funded
by grant 19- 20152Y from the Czech Science Foundation. M.T. and
N.K.V. were supported through the projects START/SCI/113 with
reg. no. CZ.02.2.69/0.0/0.0/19_073/0016935 and GAUK 646119
awarded by the Charles University and SVV 260684/2023 awarded
by the Czech Ministry of Education, Youth and Sports. A.L.S and
S.R.F were supported by Biotechnology and Biological Sciences
Research Council (BBSRC) (grant nos.: BB/M011224/1; BB/
N023803/1 and BB/K004468/1), Department for Environment,
Food & Rural Affairs (DEFRA; grant no. OD0221), the John Fell
Fund (grant no. 0005172) and European Research Council (ERC;
grant no. ERC- 2019- StG- 853272- PALAEOFARM). T.L.L. was funded
by the Deutsche Forschungsgemeinschaft (DFG, German Research
Foundation) grant No. 437857095. J.K. was supported by the
Biotechnology and Biological Sciences Research Council (BBSRC)
of United Kingdom Research and Innovation (UKRI) project grant
BB/V000756/1. D.B. was supported from the National Institutes of
Health grant 1R01AI123659- 01A1. K.N. and K.S. were supported
by the Ministry of Agriculture of the Czech Republic, institutional
support MZE- RO0723. H.W. was funded by the Swedish Research
Council 2020- 04285.
CONFLICT OF INTEREST STATEMENT
The authors have no conflict of interest to declare.
PEER REVIEW
The peer review history for this article is available at ht t p s : //
www.webof scien ce.com/api/gatew ay/wos/peer- revie w/10.1111/
jeb.14181.
DATA AVAIL AB ILI T Y STAT E MEN T
N/A
ORCID
Michal Vinkler https://orcid.org/0000-0003-3572-9494
Steven R. Fiddaman https://orcid.org/0000-0001-8222-7687
Martin Těšický https://orcid.org/0000-0001-8097-5331
Emily A. O’Connor https://orcid.org/0000-0001-8702-773X
Anna E. Savage https://orcid.org/0000-0002-4917-8358
Tobias L. Lenz https://orcid.org/0000-0002-7203-0044
Adrian L. Smith https://orcid.org/0000-0002-7657-6191
Jim Kaufman https://orcid.org/0000-0002-7216-8422
Daniel I. Bolnick https://orcid.org/0000-0003-3148-6296
Charli S. Davies https://orcid.org/0000-0001-9030-7820
Neira Dedić https://orcid.org/0000-0002-8674-6203
Andrew S. Flies https://orcid.org/0000-0002-4550-1859
M. Mercedes Gómez Samblás https://orcid.
org/0000-0003-4801-528X
Amberleigh E. Henschen https://orcid.
org/0000-0002-6288-5575
Karel Novák https://orcid.org/0000-0002-9366-3764
Gemma Palomar https://orcid.org/0000-0002-0659-8766
Nynke Raven https://orcid.org/0000-0001-8039-1264
Kalifa Samaké https://orcid.org/0009-0008-0972-6373
Joel Slade https://orcid.org/0000-0002-4587-5033
Nithya Kuttiyarthu Veetil https://orcid.
org/0000-0002-1202-4902
Eleni Voukali https://orcid.org/0000-0001-6029-6171
Jacob Höglund https://orcid.org/0000-0002-5840-779X
David S. Richardson https://orcid.org/0000-0001-7226-9074
Helena Westerdahl https://orcid.org/0000-0001-7167-9805
18
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VINKLER et a l.
REFERENCES
Acevedo- Whitehouse, K ., & Cunningham, A. A. (2006). Is MHC enough
for understanding wildlife immunogenetics? Trends in Ecology
Evolution, 21, 433– 438.
Adamo, S. A. (2004). How should behavioural ecologists interpret mea-
surements of immunity? Animal Behaviour, 68, 1443– 1449.
Adelman, J. S., Kirkpatrick, L., Grodio, J. L., & Hawley, D. M. (2013).
House finch populations differ in early inflammatory signaling and
pathogen tolerance at the peak of mycoplasma gallisepticum infec-
tion. The American Naturalist, 181, 674– 689.
Afrache, H., Tregaskes, C. A., & Kaufman, J. (2020). A potential nomen-
clature for the Immuno polymorphism database (IPD) of chicken
MHC genes: Progress and problems. Immunogenetics, 72, 9– 24 .
Albalat, R., & Cañestro, C. (2016). Evolution by gene loss. Nature Reviews
Genetics, 17, 379– 391.
Alcaide, M., & Edwards, S. V. (2011). Molecular evolution of the toll- like
receptor multigene family in birds. Molecular Biology and Evolution,
28, 1703– 1715.
Alizon, S., & van Baalen, M. (2008). Multiple infections, immune dynam-
ics, and the evolution of virulence. The American Naturalist, 172,
E150– E168.
Alves, J. M., Carneiro, M., Cheng, J. Y., de Matos, A. L., Rahman, M. M.,
Loog, L., Campos, P. F., Wales, N., Eriksson, A., Manica, A., Strive,
T., Graham, S. C., Afonso, S., Bell, D. J., Belmont, L., Day, J. P., Fuller,
S. J., Marchandeau, S., Palmer, W. J., … Jiggins, F. M. (2019). Parallel
adaptation of rabbit populations to myxoma virus. Science, 363,
1319– 1326.
Apanius, V., Penn , D., Slev, P. R., Ruff, L . R., & Potts, W. K. (1997). The na-
ture of selection on the major histocompatibility complex. Critical
Reviews in Immunology, 17, 1 79 – 22 4.
Areal, H., Abrantes, J., & Esteves, P. J. (2011). Signatures of pos-
itive selection in Tolllike receptor (TLR) genes in mammals.
BMC Evolutionary Biology, 11, 368 http://www.biome dcent ral.
com/1471-2148/11/368
Arora, J., Pierini, F., McLaren, P. J., Carrington, M., Fellay, J., & Lenz, T.
L. (2020). HLA heterozygote advantage against HIV- 1 is driven by
quantitative and qualitative differences in HLA allele- specific pep-
tide presentation. Molecular Biology and Evolution, 37, 639– 650.
Ashley, N. T., Weil, Z. M., & Nelson, R. J. (2012). Inflammation:
Mechanisms, costs, and natural variation. Annual Review of Ecolog y,
Evolution, and Systematics, 43, 385– 406.
Backstrom, N., Shipilina, D., Blom, M. P., & Edwards, S. V. (2013). Cis-
regulatory sequence variation and association with mycoplasma
load in natural populations of the house finch (Carpodacus mexi-
canus). Ecology and Evolution, 3, 655– 666.
Bagheri, M., & Zahmatkesh, A. (2018). Evolution and species- specific
conservation of toll- like receptors in terrestrial vertebrates.
International Reviews of Immunology, 37, 217– 2 2 8 .
Bainová, H., Králová, T., Bryjová, A., Albrecht, T., Bryja, J., & Vinkler, M.
(2014). First evidence of independent pseudogenization of toll-
like receptor 5 in passerine birds. Developmental and Comparative
Immunology, 45, 151– 155.
Banerjee, A., Baker, M. L., Kulcsar, K., Misra, V., Plowright, R., & Mossman,
K. (2020). Novel insights into immune Systems of Bats. Frontier s in
Immunology, 11, 26.
Barber, I., Wright, H. A., Arnott, S. A., & Wootton, R. J. (2008). Growth
and energetics in the stickleback– Schistocephalus host– parasite
system: A review of experimental infection studies. Behaviour, 145,
647– 668 Brill.
Barrow, A. D., & Trowsdale, J. (2008). The extended human leukocyte
receptor complex: Diverse ways of modulating immune responses.
Immunological Reviews, 2 24, 98– 123.
Bashirova, A. A., Viard, M., Naranbhai, V., Grifoni, A., Garcia- Beltran, W.,
Akdag, M., Yuki, Y., Gao, X., O’hUigin, C., Raghavan, M., Wolinsky,
S., Bream, J. H., Duggal, P., Martinson, J., Michael, N. L., Kirk, G. D.,
Buchbinder, S. P., Haas, D., Goedert, J. J., … Carrington, M. (2020).
HLA tapasin independence: Broader peptide repertoire and HIV
control. Proceeding s of the National Acad emy of Sciences of the U nited
States of America, 117, 28232– 28238.
Bataille, A., Cashins, S. D., Grogan, L., Skerratt, L. F., Hunter, D.,
McFadden, M., Scheele, B., Brannelly, L . A., Macris, A., Harlow, P.
S., Bell, S., Berger, L., & Waldman, B. (2015). Susceptibilit y of am-
phibians to chytridio mycosis is ass ociated with MH C class II confor-
mation. Proceedings of the Biological Sciences, 282, 2 0 14 312 7.
Bean, A. G. D., & Lowenthal, J. W. (2022). Chapter 9 - Avian cytokines
and their receptors. In B. Kaspers, K. A. Schat, T. W. Göbel, & L.
Vervelde (Eds.), Avian Immunology (3rd ed., pp. 249– 276). Academic
Press.
Berger, C. S., Laroche, J., Maaroufi, H., Martin, H., Moon, K.- M.,
Landr y, C. R., Foster, L. J., & Aubin- Horth, N. (2021). The parasite
Schistocephalus solidus secretes proteins with putative host ma-
nipulation functions. Parasites & Vectors, 14, 436.
Bernatchez, L., & Landry, C. (2003). MHC studies in nonmodel verte-
brates: What have we learned about natural selection in 15 years?
Journal of Evolutionary Biology, 16, 363– 377.
Beutler, B., Crozat, K., Koziol, J. A., & Georgel, P. (2005). Genetic dissec-
tion of innate immunity to infection: The mouse cytomegalovirus
model. Current Opinion in Immunology, 17, 36– 43.
Bhattacharya, S., Dunn, P., Thomas, C. G., Smith, B., Schaefer, H., Chen,
J., Hu, Z., Zalocusky, K. A., Shankar, R. D., Shen- Orr, S. S., Thomson,
E., Wiser, J., & Butte, A. J. (2018). ImmPort, toward repurposing of
open access immunological assay data for translational and clinical
research. Scientific Data, 5, 180015.
Bilham, K., Boyd, A. C., Preston, S. G., Buesching, C. D., Newman, C.,
Macdonald, D. W., & Smith, A. L. (2017). Badger macrophages fail
to produce nitric oxide, a key anti- mycobacterial effector molecule.
Scientific Reports, 7, 45470.
Bolnick, D. I., Snowberg, L. K., Caporaso, J. G., Lauber, C., Knight, R.,
& Stutz, W. E. (2014). Major histocompatibility complex class IIb
polymorphism influences gut microbiota composition and diversity.
Molecular Ecology, 23, 4831– 4845.
Bonneaud, C., Balenger, S. L., Russell, A. F., Zhang, J. W., Hill, G. E., &
Edwards, S. V. (2011). Rapid evolution of disease resistance is ac-
companied by functional changes in gene expression in a wild bird.
Proceedings of the National Academy of Sciences of the United States
of America, 108, 7866– 7871.
Boonyanuwat, K., Thummabutra, S., Sookmanee, N., Vatchavalkhu, V., &
Siripholvat, V. (2006). Influences of major histocompatibility com-
plex class I haplotypes on avian influenza virus disease traits in Thai
indigenous chickens. Animal Science Journal, 77, 28 5– 28 9.
Borg, Å. A., Pedersen, S. A., Jensen, H., & Westerdahl, H. (2011).
Variation in MHC genotypes in two populations of house sparrow
(Passer domesticus) with different population histories. Ecology and
Evolution, 1, 145– 159.
Breuer, K., Foroushani, A. K., Laird, M. R., Chen, C., Sribnaia, A., Lo, R.,
Winsor, G. L., Hancock, R. E. W., Brinkman, F. S. L., & Lynn, D. J.
(2013). InnateDB: Systems biology of innate immunity and beyond-
- recent updates and continuing curation. Nucleic Acids Research, 41,
D1228– D1233.
Briles, W. E., Stone, H. A., & Cole, R. K. (1977). Marek's disease: Effects of
B histocompatibility alloalleles in resistant and susceptible chicken
lines. Science, 195, 193– 195.
Brouwer, L., Barr, I., van de Pol, M., Burke, T., Komdeur, J., & Richardson,
D. S. (2010). MHC- dependent survival in a wild population:
Evidence for hidden genetic benefits gained through extra- pair fer-
tilizations. Molecular Ecology, 19, 3444– 3455.
Brunner, J. L., Olson, D. H., Gray, M. J., Miller, D. L., & Duffus, A. L. J.
(2021). Global patterns of ranavirus detections. FACETS, 6, 912– 924.
Buchmann, K . (2014). Evolution of innate immunity: clues from inverte-
brates via fish to mammals. Frontiers in Immunology, 5, 459. https://
doi.org/10.3389/fimmu.2014.00459
|
19
VINKLER et al.
Buckley, K. M., & Rast, J. P. (2012). Dynamic evolution of toll- like recep-
tor multigene families in echinoderms. Frontiers in Immunology, 3,
136. https://doi.org/10.3389/fimmu.2012.00136
Burri, R., Salamin, N., Studer, R. A., Roulin, A., & Fumagalli, L. (2010).
Adaptive divergence of ancient gene duplicates in the avian MHC
class II beta. Molecular Biology and Evolution, 27, 2360– 2374.
Bustamante, C. D., Fledel- Alon, A., Williamson, S., Nielsen, R., Hubisz,
M. T., Glanowski, S., Tanenbaum, D. M., White, T. J., Sninsky, J. J.,
Hernandez, R. D., Civello, D., Adams, M. D., Cargill, M., & Clark, A.
G. (2005). Natural selection on protein- coding genes in the human
genome. Nature, 437, 1153– 1157.
Campbell, L. J., Hammond, S. A., Price, S. J., Sharma, M. D., Garner,
T. W. J., Birol, I., Helbing, C. C., Wilfert, L., & Griffiths, A. G. F.
(2018). A novel approach to wildlife transcriptomics provides evi-
dence of disease- mediated differential expression and changes to
the microbiome of amphibian populations. Molecular Ecology, 27,
1413– 1427.
Carbon, S., Ireland, A., Mungall, C. J., Shu, S., Marshall, B., Lewis, S., the
AmiGO Hub, & the Web Presence Working Group. (2009). AmiGO:
Online access to ontology and annotation data. Bioinformatics, 25,
28 8– 28 9.
Carrington, M., Nelson, G. W., Martin, M. P., Kissner, T., Vlahov, D.,
Goedert, J. J., Kaslow, R., Buchbinder, S., Hoots, K., & O'Brien, S. J.
(1999). HLA and HIV- 1: Heterozygote advantage and B*35- Cw*04
disadvantage. Science, 283, 1748– 1752.
Casey, J. R ., Grinstein, S., & Orlowski, J. (2010). Sensors and regulators
of intracellular pH. Nature Reviews Molecular Cell Biology, 11, 5 0 – 6 1 .
Cattadori, I. M., Boag, B., & Hudson, P. J. (2008). Parasite co- infection
and interaction as drivers of host heterogeneity. International
Journal for Parasitology, 38, 371– 380.
Chakrabarti, S., & Panchenko, A. R. (2010). Structural and functional
roles of coevolved sites in proteins. PLoS One, 5, e8591.
Chapman, J. R ., Hellgren, O., Helin, A . S., Kraus, R. H. S., Cromie, R. L.,
& Waldenstrom, J. (2016). The evolution of innate immune genes:
Purifying and balancing selection on beta- defensins in waterfowl.
Molecular Biolog y and Evolution, 33, 3075– 3087.
Chappell, P. E., Meziane, E. K., Harrison, M., Magiera, Ł., Hermann,
C., Mears, L., Wrobel, A. G., Durant, C., Nielsen, L. L., Buus, S.,
Ternette, N., Mwangi, W., Butter, C., Nair, V., Ahyee, T., Duggleby,
R., Madrigal, A., Roversi, P., Lea, S. M., & Kaufman, J. (2015).
Expression levels of MHC class I molecules are inversely correlated
with promiscuity of peptide binding. eLife, 4, e05345.
Charbonnel, N., & Pemberton, J. (2005). A long- term genetic survey of
an ungulate population reveals balancing selection acting on MHC
through spatial and temporal fluctuations in selection. Heredity, 95,
37 7– 3 8 8 .
Chattopadhyay, B., Garg, K. M., Ray, R., Mendenhall, I. H., & Rheindt, F.
E. (2020). Novel de novo genome of Cynopterus brachyotis reveals
evolutionarily abrupt shifts in gene family composition across fruit
bats. Genome Biology and Evolution, 12, 259– 272.
Chebii, V. J., Mpolya, E. A., Muchadeyi, F. C., & Domelevo Entfellner, J.- B.
(2021). Genomics of Adaptations in Ungulates. Animals (Basel), 11,
1617.
Chen, L.- C., Lan, H., Sun, L., Deng, Y.- L., Tang, K.- Y., & Wan, Q.- H. (2015).
Genomic organization of the crested ibis MHC provides new in-
sight into ancestral avian MHC structure. Scientific Reports, 5, 7963
Nature Publishing Group.
Clark, L. E., Clark, S. A., Lin, C., Liu, J., Coscia, A., Nabel, K. G., Yang, P.,
Neel, D. V., Lee, H., Brusic, V., Stryapunina, I., Plante, K. S., Ahmed,
A. A., Catteruccia, F., Young- Pearse, T. L., Chiu, I. M., Llopis, P. M.,
Weaver, S. C., & Abraham, J. (2022). VLDLR and ApoER 2 are recep-
tors for multiple alphaviruses. Nature, 602, 475– 480.
Colgan, T. J., Moran, P. A., Archer, L. C., Wynne, R., Hutton, S. A.,
McGinnity, P., & Reed, T. E. (2021). Evolution and expression of the
immune system of a Facultatively anadromous salmonid. Frontie rs
in Immunology, 12, 1– 17.
Cortázar- Chinarro, M., Meurling, S., Schroyens, L., Siljestam, M., Richter-
Boix, A., Laurila, A ., & Höglund, J. (2022). Major histocompatibility
complex variation and haplotype associated survival in response
to experimental infection of two Bd- GPL strains along a latitudinal
gradient. Frontiers in Ecolog y and Evolution, 10, 915271.
Cortázar- Chinarro, M., Meyer- Lucht, Y., Laurila, A., & Höglund, J. (2018).
Signatures of historical selection on MHC reveal different selection
patte rns in th e moo r fr og (Rana arvalis). Immunogenetics, 70, 47 7– 484.
Cunningham, F., Allen, J. E., Allen, J., Alvarez- Jarreta, J., Amode, M.
R., Armean, I. M., Austine- Orimoloye, O., Azov, A. G., Barnes,
I., Bennett, R., Berry, A., Bhai, J., Bignell, A., Billis, K., Boddu, S.,
Brooks, L., Charkhchi, M., Cummins, C., da Rin Fioretto, L., … Flicek,
P. (2022). Ensembl 2022. Nucleic Acids Research, 50, D988– D995.
Danilova, N. (2006). The evolution of immune mechanisms. Journal of
Experimental Zoology Par t B- Molecular and Developmental Evolution,
306B, 496– 520.
Dascher, C. C., & Brenner, M. B. (2003). Evolutionary constraints on CD1
structure: Insights from comparative genomic analysis. Trends in
Immunology, 24, 41 2– 418 .
Davies, C. S., Taylor, M., Hammers, M., Burke, T., Komdeur, J., Dugdale,
H. L., & Richardson, D. S. (2021). Contemporary evolution of the
innate immune receptor gene TLR3 in an isolated vertebrate popu-
lation. Molecular Ecology, 30, 2528– 2542 Wiley, Hoboken.
Dawson, H. D., Loveland, J. E., Pascal, G., Gilbert, J. G. R., Uenishi, H.,
Mann, K. M., Sang, Y., Zhang, J., Carvalho- Silva, D., Hunt, T., Hardy,
M., Hu, Z., Zhao, S. H., Anselmo, A., Shinkai, H., Chen, C., Badaoui,
B., Berman, D., Amid, C., … Tuggle, C. K. (2013). Structural and
functional annotation of the porcine immunome. BMC Genomics,
14, 332.
Dinkel, H., Van Roey, K., Michael, S., Davey, N. E., Weatherit t, R. J., Born,
D., Speck, T., Krüger, D., Grebnev, G., Kuban, M., Strumillo, M., Uyar,
B., Budd, A., Altenberg, B., Seiler, M., Chemes, L. B., Glavina, J.,
Sánchez, I. E., Diella, F., & Gibson, T. J. (2014). The eukaryotic linear
motif resource ELM: 10 years and counting. Nucleic Acids Research,
42, D259– D266.
Dionne, M., Miller, K. M., Dodson, J. J., & Bernatchez, L. (2009). MHC
standing genetic variation and pathogen resistance in wild Atlantic
salmon. Philosophical Transactions of the Royal Society of London.
Series B, Biological Sciences, 364, 1555– 1565.
Dionne, M., Miller, K. M., Dodson, J. J., Caron, F., & Bernatchez, L. (2007).
Clinal variation in MHC diversity with temperature: Evidence for
the role of host- pathogen interaction on local adaptation in Atlantic
salmon. Evolution, 61, 2154– 2164.
Divín, D., Goméz Samblas, M., Kuttiyarthu Veetil, N., Voukali, E.,
Świderská, Z., Krajzingrová, T., Těšický, M., Beneš, V., Elleder, D.,
Bartoš, O., & Vinkler, M. (2022). Cannabinoid receptor 2 evolu-
tionary gene loss makes parrots more susceptible to neuroinflam-
mation. Proceedings of the Royal Society B: Biological Sciences, 289,
20221941.
Djaoud, Z., & Parham, P. (2020). HLAs, TCRs, and KIRs, a triumvirate of
human cell- mediated immunity. Annual Review of Biochemistry, 89,
71 7– 7 3 9.
Downing, T., Lloyd, A. T., O'Farrelly, C ., & Bradley, D. G. (2010). The dif-
ferential evolutionary dynamics of avian cytokine and TLR gene
classes. Journal of Immunology, 184, 6993– 7000.
Dubé, J.- Y., Fava, V. M., Schurr, E., & Behr, M. A. (2021). Underwhelming
or misunders to od? Genetic var ia bility of pat te rn recognition recep-
tors in immune responses and resistance to mycobacterium tuber-
culosis. Frontiers in Immunology, 12, 714808.
Ebert, D., & Fields, P. D. (2020). Host- parasite co- evolution and its ge-
nomic signature. Nature Reviews Genetics, 21, 754– 768.
Eisen, H. N., & Chakraborty, A. K. (2010). Evolving concepts of specificity
in immune reactions. PNAS, 107, 22373– 22380.
Eizaguirre, C., & Lenz, T. L. (2010). Major histocompatibility complex
polymorphism: Dynamics and consequences of parasite- mediated
local adaptation in fishes. Journal of Fish Biolog y, 77, 2023– 2047.
20
|
VINKLER et a l.
Eizaguirre, C., Lenz, T. L., Kalbe, M., & Milinski, M. (2012). Rapid and
adaptive evolution of MHC genes under parasite selection in ex-
perimental vertebrate populations. Nature Communications, 3, 621.
Ejsmond, M. J., Radwan, J., & Wilson, A. B. (2014). Sexual selection and
the evolutionary dynamics of the major histocompatibility com-
plex. Proceedings of the Royal Society B: Biological Science s, 281,
20141662.
Ekblom, R., French, L., Slate, J., & Burke, T. (2010). Evolutionary analy-
sis and expression profiling of zebra finch immune genes. Genome
Biology and Evolution, 2, 781– 790.
Elliott, J. M., & Yokoyama, W. M. (2011). Unifying concepts of MHC-
dependent natural killer cell education. Trends in Immunology, 32,
3 6 4 – 3 7 2 .
Ellison, A. R., Savage, A . E., DiRenzo, G. V., Langhammer, P., Lips, K. R.,
& Zamudio, K. R. (2014). Fighting a losing battle: Vigorous immune
response countered by pathogen suppression of host defenses in
the chytridiomycosis- susceptible frog Atelopus zeteki. G3: Genes
Genomes, Genetics, 4, 1275– 1289.
Ellison, A. R., Tunstall, T., DiRenzo, G. V., Hughey, M. C., Rebollar, E. A.,
Belden, L. K., Harris, R. N., Ibáñez, R., Lips, K. R., & Zamudio, K. R.
(2014). More than skin deep: Functional genomic basis for resis-
tance to amphibian chytridiomycosis. Genome Biolog y and Evolution,
7, 286– 298.
Enard, D., Messer, P. W., & Petrov, D. A. (2014). Genome- wide signals
of positive selection in human evolution. Genome Research, 24,
885– 895.
Epstein, B., Jones, M., Hamede, R., Hendricks, S., McCallum, H.,
Murchison, E. P., Schönfeld, B., Wiench, C., Hohenlohe, P., &
Storfer, A. (2016). Rapid evolutionary response to a transmissible
cancer in Tasmanian devils. Nature Communications, 7, 12684.
Erra Díaz, F., Dantas, E., & Geffner, J. (2018). Unravelling the interplay
between extracellular acidosis and immune cells. Mediators of
Inflammation, 2018, e12 182 97.
Eskew, E. A ., Shock, B. C., LaDouceur, E. E. B., Keel, K., Miller, M. R.,
Foley, J. E., & Todd, B. D. (2018). Gene expression differs in suscep-
tible and resistant amphibians exposed to Batrachochytrium dend-
robatidis. Royal Society Open Science, 5, 170910 .
Farquharson, K. A., McLennan, E. A., Cheng, Y., Alexander, L., Fox, S.,
Lee, A. V., Belov, K., & Hogg, C. J. (2022). Restoring faith in con-
servation action: Maintaining wild genetic diversity through the
Tasmanian devil insurance program. iScience, 25, 10 4474.
Feigin, C. Y., Newton, A. H., Doronina, L., Schmit z, J., Hipsley, C. A.,
Mitchell, K. J., Gower, G., Llamas, B., Soubrier, J., Heider, T. N.,
Menzies, B. R., Cooper, A., O'Neill, R. J., & Pask, A. J. (2018).
Genome of the Tasmanian tiger provides insights into the evolution
and demography of an extinct marsupial carnivore. Nature Ecology
and Evolution, 2, 18 2– 1 9 2 .
Fiddaman, S. R., Vinkler, M., Spiro, S. G., Levy, H., Emerling, C. A., Boyd,
A. C., Dimopoulos, E. A., Vianna, J. A., Cole, T. L., Pan, H., Fang, M.,
Zhang, G., Hart, T., Frantz, L. A. F., & Smith, A. L. (2022). Adaptation
and cryptic Pseudogenization in penguin toll- like receptors.
Molecular Biolog y and Evolution, 39, msab354.
Figueiró, H. V., Li, G., Trindade, F. J., Assis, J., Pais, F., Fernandes, G.,
Santos, S. H. D., Hughes, G. M., Komissarov, A., Antunes, A.,
Trinca, C. S., Rodrigues, M. R ., Linderoth, T., Bi, K., Silveira, L.,
Azevedo, F. C. C., Kantek, D., Ramalho, E., Brassaloti, R. A., …
Eizirik, E. (2017). Genome- wide signatures of complex introgres-
sion and adaptive evolution in the big cats. Science Advances, 3,
e17 0 029 9.
Figueroa, F., Günther, E., & Klein, J. (1988). MHC polymorphism pre-
dating speciation. Nature, 335, 265– 267.
Fijarczyk, A., Dudek, K., Niedzicka, M., & Babik, W. (2018). Balancing
selection and introgression of newt immune- response genes.
Proceedi ngs of the Royal Societ y B: Biological S ciences, 285, 20180819
Roy al So ci et y.
Fisher, C. A., Bhattarai, E. K., Osterstock, J. B., Dowd, S. E., Seabury, P. M.,
Vikram, M., Whitlock, R. H., Schukken, Y. H., Schnabel, R. D., Taylor,
J. F., Womack, J. E., & Seabury, C. M. (2011). Evolution of the bovine
TLR gene family and member associations with Mycobacterium
avium subspecies paratuberculosis infection. PLoS One, 6, e2 7744 .
Flies, A. S., Blackburn, N. B., Lyons, A. B., Hayball, J. D., & Woods, G. M.
(2017). Comparative analysis of immune checkpoint molecules and
their potential role in the transmissible Tasmanian devil facial tumor
disease. Frontiers in Immunology, 8, 513 .
Flies, A . S., Darby, J. M., Lennard, P. R., Murphy, P. R., Ong, C. E., Pinfold,
T. L., De Luca, A., Lyons, A. B., Woods, G. M., & Patchett, A. L.
(2020). A novel system to map protein interactions reveals evo-
lutionarily conserved immune evasion pathways on transmissible
cancers. Science Advances, 6, eaba5031.
Flies, A. S., & Wild Comparative Immunology Consortium. (2020).
Rewilding immunology. Science, 369, 37– 3 8 .
Follin, E., Karlsson, M., Lundegaard, C., Nielsen, M., Wallin, S., Paulsson,
K., & Westerdahl, H. (2013). In silico peptide- binding predictions
of passerine MHC class I reveal similarities across distantly related
species, suggesting convergence on the level of protein function.
Immunogenetics, 65, 299– 311.
Fraik, A. K., Margres, M. J., Epstein, B., Barbosa, S., Jones, M., Hendricks,
S., Schönfeld, B., Stahlke, A. R., Veillet, A., Hamede, R., McCallum,
H., Lopez- Contreras, E., Kallinen, S. J., Hohenlohe, P. A., Kelley, J.
L., & Storfer, A. (2020). Disease swamps molecular signatures of
genetic- environmental associations to abiotic factors in Tasmanian
devil (Sarcophilus harrisii) populations. Evolution, 74, 1392– 1408.
Freud, A. G., Mundy- Bosse, B. L., Yu, J., & Caligiuri, M. A. (2017). The
broad Spectrum of human natural killer cell diversity. Immunity, 47,
820– 833.
Fuess, L. E., Weber, J. N., den Haan, S., Steinel, N. C., Shim, K. C., &
Bolnick, D. I. (2021). Between- population differences in constitu-
tive and infection- induced gene expression in threespine stickle-
back. Molecular Ecology, 30, 6791– 6805.
Fumagalli, M., Sironi, M., Pozzoli, U., Ferrer- Admettla, A., Pattini, L., &
Nielsen, R. (2011). Signatures of environmental genetic adaptation
pinpoint pathogens as the Main selective pressure through human
evolution. PLoS Genetics, 7, e1002355.
Gabaldón, T., & Koonin, E. V. (2013). Functional and evolutionary impli-
cations of gene orthology. Nature Reviews Genetics, 14, 360– 366.
Garcia- Longoria, L., Magallanes, S., Huang, X., Drews, A., Råberg, L.,
Marzal, A., Bensch, S., & Westerdahl, H. (2022). Reciprocal positive
effects on parasitemia between coinfecting haemosporidian para-
sites in house sparrows. BMC Ecology and Evolution, 22, 73.
Gautier, M., Flori, L., Riebler, A., Jaffrézic, F., Laloé, D., Gut, I., Moazami-
Goudarzi, K., & Foulley, J. L. (2009). A whole genome Bayesian
scan for adaptive genetic divergence in west African cattle. BMC
Genomics, 10, 550.
Geng, Y., Wang, K., Li, C., Wang, J., Liao, Y., Huang, J., & Zhou, Z. (2010).
PCR detection and electron microscopic observation of bred
Chinese giant salamander infected with ranavirus associated with
mass mortality. Chinese Veterinary Science/Zhongguo Shouyi Kexue,
40(8), 817– 821.
Gibson, D. G., Young, L., Chuang, R.- Y., Venter, J. C., Hutchison, C. A ., &
Smith, H. O. (2009). Enzymatic assembly of DNA molecules up to
several hundred kilobases. Nature Methods, 6, 343– 345.
Gillespie, M., Jassal, B., Stephan, R., Milacic, M., Rothfels, K., Senff-
Ribeiro, A., Griss, J., Sevilla, C., Matthews, L., Gong, C., Deng,
C., Varusai, T., Ragueneau, E., Haider, Y., May, B., Shamovsky, V.,
Weiser, J., Brunson, T., Sanati, N., … D’Eustachio, P. (2022). The re-
actome pathway knowledgebase 2022. Nucleic Acids Research, 50,
D687– D692.
Gong, T., Liu, L., Jiang, W., & Zhou, R. (2020). DAMP- sensing receptors
in sterile inflammation and inflammatory diseases. Nature Reviews
Immunology, 20, 95– 112.
|
21
VINKLER et al.
Gonzalez- Quevedo, C., Spurgin, L. G., Illera, J. C., & Richardson, D. S.
(2015). Drift, not selection, shapes toll- like receptor variation among
oceanic Island populations. Molecular Ecology, 24, 5852– 5863.
Goulder, P. J. R., & Walker, B. D. (2012). HIV and HLA class I: An evolving
relationship. Immunity, 37, 426– 440.
Graham, A. L., Allen, J. E., & Read, A. F. (2005). Evolutionary causes
and consequences of immunopathology. Annual Review of Ecology,
Evolution, and Systematics, 36, 373 – 397.
Grandchamp, A., & Monget, P. (2018). Synchronous birth is a dominant
pattern in receptor- ligand evolution. BMC Genomics, 19, 611.
Grayfer, L., Edholm, E.- S., De Jesús Andino, F., Chinchar, V. G., & Robert,
J. (2015). Ranavirus host immunity and immune evasion. In M. J.
Gray & V. G. Chinchar (Eds.), Ranaviruses (pp. 141– 170). Springer
International Publishing.
Griffin, F. (2014). Farmed deer: A veterinary model for chronic myco-
bacterial diseases that is accessible, appropriate and cost- effective.
Journal of Pharmacy & Bioallied Sciences, 6, 10– 15 .
Grimholt, U., Larsen, S., Nordmo, R., Midtlyng, P., Kjoeglum, S., Storset,
A., Saeb, S., & Stet, R. J. M. (2003). MHC polymorphism and disease
resistance in Atlantic salmon (Salmo salar); facing pathogens with
single expressed major histocompatibility class I and class II loci.
Immunogenetics, 55, 2 10 – 219.
Grogan, L. F., Cashins, S. D., Skerratt, L. F., Berger, L., McFadden, M. S.,
Harlow, P., Hunter, D. A., Scheele, B. C., & Mulvenna, J. (2018).
Evolution of resistance to chytridiomycosis is associated with a ro-
bust early immune response. Molecular Ecology, 27, 919– 93 4.
Grossen, C., Keller, L., Biebach, I., International Goat Genome
Consortium, & Croll, D. (2014). Introgression from domestic goat
generated variation at the major histocompatibility complex of al-
pine ibex. PLoS Genetics, 10, e1004 43 8.
Guethlein, L. A ., Norman, P. J., Hilton, H. G., & Parham, P. (2015). Co-
evolution of MHC class I and variable NK cell receptors in placental
mammals. Immunological Reviews, 267, 259– 282.
Halabi, S., Ghosh, M., Stevanović, S., Rammensee, H.- G., Bertzbach, L. D.,
Kaufer, B. B., Moncrieffe, M. C., Kaspers, B., Härtle, S., & Kaufman,
J. (2021). The dominantly expressed class II molecule from a re-
sistant MHC haplotype presents only a few Marek's disease virus
peptides by using an unprecedented binding motif. PLoS Bi ology, 19,
e3 0 0 10 5 7.
Halabi, S., & Kaufman, J. (2022). New vistas unfold: Chicken MHC mol-
ecules reveal unexpected ways to present peptides to the im-
mune system. Frontiers in Immunology, 13, 886672. ht t p s : //d o i .
org/10.3389/fimmu.2022.886672
Hammond, J. A., Guethlein, L. A ., Abi- Rached, L., Moesta, A . K., &
Parham, P. (2009). Evolution and survival of marine carnivores did
not require a diversity of killer cell Ig- like receptors or Ly49 NK cell
receptors. The Journal of Immunology, 182, 3618– 3627.
Hanotte, O., Ronin, Y., Agaba, M., Nilsson, P., Gelhaus, A., Horstmann,
R., Sugimoto, Y., Kemp, S., Gibson, J., Korol, A., Soller, M., & Teale,
A. (2003). Mapping of quantitative trait loci controlling trypanotol-
erance in a cross of tolerant west African N'Dama and suscepti-
ble east African Boran cattle. Proceedings. National Academy of
Sciences. United States of America, 100 , 7443– 7448 .
Hawley, D. M., & Fleischer, R. C. (2012). Contrasting epidemic histories
reveal pathogen- mediated balancing selection on class II MHC di-
versity in a wild songbird. PLoS One, 7, e30222.
Hedrick, P. W. (2013). Adaptive introgression in animals: Examples and
comparison to new mutation and standing variation as sources of
adaptive variation. Molecular Ecology, 22, 4606– 4618.
Heins, D.C. & Baker, J.A. 2008. The stickleback- Schistocephalus host-
parasite system as a model for understanding the effect of a mac-
roparasite on host reproduction. Behaviour 145: 625– 645.
Heng, T. S. P., Painter, M. W., Elpek, K., Lukacs- Kornek, V., Mauermann,
N., Turley, S. J., Bork, P., Burt, D. W., Groenen, M. A. M., Delany,
M. E., & Dodgson, J. B. (2008). The immunological genome project:
Networks of gene expression in immune cells. Nature Immunology,
9, 10 91– 1094.
Hillier, L. W., Miller, W., Birney, E., Warren, W., Hardison, R. C., & Ponting,
C. P. (2004). Sequence and comparative analysis of the chicken ge-
nome provide unique perspectives on vertebrate evolution. Nature,
432, 695– 716.
Hilton, H. G., Rubinstein, N. D., Janki, P., Ireland, A. T., Bernstein, N.,
Wright, K. M., Smith, M., Finkle, D., Martin- McNulty, B., Roy, M.,
Imai, D. M., Jojic, V., & Buffenstein, R. (2019). Single- cell transcrip-
tomics of the naked mole- rat reveals unexpected features of mam-
malian immunity. PLoS Biology, 17, e3000528.
Hochachka, W. M., & Dhondt, A. A. (2000). Density- dependent de-
cline of host abundance resulting from a new infectious disease.
Proceedings of the National Academy of Sciences of the United States
of America, 97, 5303– 5306.
Hochachka, W. M., Dhondt, A. A., Dobson, A., Hawley, D. M., Ley, D. H.,
& Lovette , I. J. (20 13 ). Mult iple host tr ans fer s, bu t only one su cces s-
ful lineage in a continent- spanning emergent pathogen. Proceedings
Biological Sciences, 280, 20131068. https://doi.org /10.1098/
rspb.2013.1068
Hofmann, A., Plachy, J., Hunt, L., Kaufman, J., & Hala, K. (2003). v- src
oncogene- specific carboxy- terminal peptide is immunoprotective
against Rous sarcoma growth in chickens with MHC class I allele
B- F12. Vaccine, 21, 4694– 4699.
Högstrand, K., & Böhme, J. (1999). Gene conversion can create new
MHC alleles. Immunological Reviews, 167, 305– 317.
Holt, R. A. (2015). Immunogenomics: A foundation for intelligent immune
design. Genome Medicine, 7, 116.
Hölzer, M., & Marz, M. (2021). PoSeiDon: A Nextflow pipeline for the
detection of evolutionary recombination events and positive selec-
tion. Bioinformatics, 37, 1018– 1020.
Horrocks, N. P. C., Matson, K. D., & Tieleman, B. I. (2011). Pathogen
pressure puts immune defense into perspective. Integrative and
Comparative Biology, 51, 563– 576.
Howe, K., Schiffer, P. H., Zielinski, J., Wiehe, T., Laird, G. K., Marioni, J.
C., Soylemez, O., Kondrashov, F., & Leptin, M. (2016). Structure and
evolutionary history of a large family of NLR proteins in the zebraf-
ish. Open Biology, 6, 160009.
Huang, W., Dicks, K. L ., Hadfield, J. D., Johnston, S. E., Ballingall, K.
T., & Pemberton, J. M. (2022). Contemporary selection on MHC
genes in a free- living ruminant population. Ecology Letters, 25,
828– 838.
Huang, W., Guo, Y., Du, W., Zhang, X., Li, A., & Miao, X. (2017). Global
transcriptome analysis identifies differentially expressed genes
related to lipid metabolism in wagyu and Holstein cattle. Scientific
Reports, 7, 5278.
Huang, Y. H., Li, Y. R., Burt, D. W., Chen, H. L., Zhang, Y., Qian, W. B.,
Kim, H., Gan, S., Zhao, Y., Li, J., Yi, K ., Feng, H., Zhu, P., Li, B., Liu,
Q., Fairley, S., Magor, K. E., Du, Z., Hu, X., … Li, N. (2013). The duck
genome and transcriptome provide insight into an avian influenza
virus reservoir species. Nature Genetics, 45, 776– 783.
Huang, Y., Temperley, N. D., Ren, L., Smith, J., Li, N., & Burt, D. W. (2011).
Molecular evolution of the vertebrate TLR1 gene family - a complex
history of gene duplication, gene conversion, positive selection and
co- evolution. BMC Evolutionary Biology, 11, 14 9.
Hubert, J.- N., Zerjal, T., & Hospital, F. (2018). Cancer- and behavior-
related genes are targeted by selection in the Tasmanian devil
(Sarcophilus harrisii). PLoS One, 13, e0201838.
Hughes, A. L., & Nei, M. (1988). Pattern of nucleotide substitution at
major histocompatibility complex class I loci reveals overdominant
selection. Nature, 335, 167– 170.
Hughes, A. L ., & Nei, M. (1989). Nucleotide substitution at major histo-
compatibility complex class II loci: Evidence for overdominant se-
lection. Proceed ings of the National A cademy of Sciences of t he United
States of America, 86, 958– 962.
22
|
VINKLER et a l.
Hyland, E. M., Webb, A. E., Kennedy, K. F., Gerek Ince, Z. N., Loscher, C.
E., & O'Connell, M. J. (2021). Adaptive evolution in TRIF leads to
discordance between human and mouse innate immune signaling.
Genome Biology and Evolution, 13, evab268.
International HIV Controllers Study, Pereyra, F., Jia, X., McLaren, P. J.,
Telenti, A., de Bakker, P. I. W., Walker, B. D., Ripke, S., Brumme, C. J.,
Pulit, S. L., Carrington, M., Kadie, C. M., Carlson, J. M., Heckerman,
D., Graham, R. R., Plenge, R. M., Deeks, S. G., Gianniny, L.,
Crawford, G., … Lemay, P. (2010). The major genetic determinants
of HIV- 1 control affect HLA class I peptide presentation. Science,
330, 1551– 1557.
Irestedt, M., Thörn, F., Müller, I. A., Jønsson, K. A., Ericson, P. G. P., &
Blom, M. P. K. (2022). A guide to avian museomics: Insights gained
from resequencing hundreds of avian study skins. Molecular Ecology
Resources, 22, 2672– 2684.
Irving, A. T., Ahn, M., Goh, G., Anderson, D. E., & Wang, L. (2021). Lessons
from the host defences of bats, a unique vira l re servoir. Nature, 589,
363– 370 Springer US.
Jagoda, E., Lawson, D. J., Wall, J. D., Lamber t, D., Muller, C., Westaway,
M., Leavesley, M., Capellini, T. D., Mirazón Lahr, M., Gerbault, P.,
Thomas, M. G., Migliano, A. B., Willerslev, E., Metspalu, M., &
Pagani, L. (2018). Disentangling immediate adaptive introgres-
sion from selection on standing Introgressed variation in humans.
Molecular Biolog y and Evolution, 35, 623– 630.
Johnson, A. J., Pessier, A. P., Wellehan, J. F. X., Childress, A ., Norton, T.
M., Stedman, N. L., Bloom, D. C., Belzer, W., Titus, V. R., Wagner,
R., Brooks, J. W., Spratt, J., & Jacobson, E. R. (2008). Ranavirus in-
fection of free- ranging and captive box turtles and tortoises in the
United States. Journal of Wildlife Diseases, 44, 851– 863.
Jolles, A. E., Beechler, B. R., & Dolan, B. P. (2015). Beyond mice and men:
Environmental change, immunity and infections in wild ungulates.
Parasite Immunology, 37, 255– 266.
Jorgensen, I., Rayamajhi, M., & Miao, E. A. (2017). Programmed cell
death as a defence against infection. Nature Reviews. Immunology,
17, 151– 164.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O.,
Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland,
A., Meyer, C., SA A, K., Ballard, A. J., Cowie, A., Romera- Paredes, B.,
Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate
protein structure prediction with AlphaFold. Nature, 596, 583– 589.
Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and
genomes. Nucleic Acids Research, 28, 27– 3 0 .
Kaufman, J. (2018). Generalists and specialists: A new view of how MHC
class I molecules fight infectious pathogens. Trends in Immunology,
39, 367– 379.
Kaufman, J., Milne, S., Gobel, T. W. F., Walker, B. A., Jacob, J. P.,
Auffray, C., Zoorob, R., & Beck, S. (1999). The chicken B locus
is a minimal essential major histocompatibility complex. Nature,
401, 923– 925.
Keestra, A. M., & van Putten, J. P. M. (2008). Unique properties of the
chicken TLR4/MD- 2 complex: Selective lipopolysaccharide activa-
tion of the MyD88- dependent pathway. Journal of Immunology, 181,
4354– 4362.
Khan, N., Bano, A., Rahman, M. A., Guo, J., Kang, Z., & Babar, M. A.
(2019). Comparative physiological and metabolic analysis reveals
a complex mechanism involved in drought tolerance in chickpea
(Cicer arietinum L .) induced by PGPR and PGRs. Scientific Reports,
9, 20 97.
Kirkham, C. L., & Carlyle, J. R. (2014). Complexity and diversity of
the NKR- P1: Clr (Klrb1:Clec2) recognition systems. Frontiers in
Immunology, 5, 214. https://doi.org/10.3389/fimmu.2014.00214
Klein, J., & Ohuigin, C. (1994). Mhc polymorphism and parasites.
Philosophical Transac tions of the Royal Society of London Series B-
Biological Sciences, 346, 351– 357.
Koch, M., Camp, S., Collen, T., Avila, D., Salomonsen, J., Wallny, H. J., van
Hateren, A., Hunt, L., Jacob, J. P., Johnston, F., Marston, D. A., Shaw,
I., Dunbar, P. R., Cerundolo, V., Jones, E. Y., & Kaufman, J. (2007).
Structures of an MHC class I molecule from B21 chickens illustrate
promiscuous peptide binding. Immunity, 27, 885– 899.
Kolora, S. R. R., Owens, G. L., Vazquez, J. M., Stubbs, A., Chatla, K.,
Jainese, C., Seeto, K., McCrea, M., Sandel, M. W., Vianna, J. A.,
Maslenikov, K., Bachtrog, D., Orr, J. W., Love, M., & Sudmant, P. H.
(2021). Origins and evolution of extreme lifespan in Pacific Ocean
rockfishes. Science, 374 , 842– 847.
Kosakovsky Pond, S. L., & Frost, S. D. W. (2005). Not so different after
all: A comparison of methods for detecting amino acid sites under
selection. Molecular Biolog y and Evolution, 22, 1208– 1222.
Kosakovsky Pond, S. L., Frost, S. D. W., & Muse, S. V. (2005). HyPhy:
Hypothesis testing using phylogenies. Bioinformatics, 21, 676– 679.
Kosakovsky Pond, S. L ., Posada, D., Gravenor, M. B., Woelk, C. H., &
Fros t, S. D. W. (20 06). Automated phyl ogene tic dete cti on of re com-
bination using a genetic algorithm. Molecular Biology and Evolution,
23, 1 891– 19 0 1 .
Kosch, T. A ., Bataille, A., Didinger, C., Eimes, J. A ., Rodríguez- Brenes, S.,
Ryan, M. J., & Waldman, B. (2016). Major histocompatibility complex
selection dynamics in pathogen- infected túngara frog (Physalaemus
pustulosus) populations. Biology Letters, 12, 20160345.
Kosch, T. A., Eimes, J. A., Didinger, C., Brannelly, L. A., Waldman, B.,
Berger, L., & Skerratt, L. F. (2017). Characterization of MHC class IA
in the endangered southern corroboree frog. Immunogenetics, 69,
1 6 5 – 1 7 4 .
Kosiol, C., Vinar, T., da Fonseca, R. R., Hubisz, M. J., Bustamante, C. D.,
Nielsen, R., & Siepel, A. (2008). Patterns of positive selection in six
mammalian genomes. PLoS Genetics, 4, e100014 4.
Košmrlj, A., Read, E. L., Qi, Y., Allen, T. M., Altfeld, M., Deeks, S. G.,
Pereyra, F., Carrington, M., Walker, B. D., & Chakraborty, A. K.
(2010). Effects of thymic selection of the T- cell repertoire on HL A
class I- associated control of HIV infection. Nature, 465, 350– 354.
https://doi.org/10.1038/natur e0 8997
Králová, T., Albrecht, T., Bryja, J., Horak, D., Johnsen, A., Lifjeld, J. T.,
Novotný, M., Sedláček, O., Velová, H., & Vinkler, M. (2018).
Signatures of diversifying selection and convergence acting on
passerine toll- like receptor 4 in an evolutionary context. Molecular
Ecology, 27, 2871– 2883.
Krause- Kyora, B., Nutsua, M., Boehme, L., Pierini, F., Pedersen, D. D.,
Kornell, S.- C., Drichel, D., Bonazzi, M., Möbus, L., Tarp, P., Susat,
J., Bosse, E., Willburger, B., Schmidt, A. H., Sauter, J., Franke, A.,
Wittig, M., Caliebe, A., Nothnagel, M., … Nebel, A. (2018). Ancient
DNA study reveals HLA susceptibility locus for leprosy in medieval
Europeans. Nature Communications, 9, 1569.
Krchlíková, V., Hron, T., Těšický, M., Li, T., Hejnar, J., Vinkler, M., & Elleder,
D. (2021). Repeated MDA5 gene loss in birds: An evolutionary per-
spective. Viruses, 13, 2131.
Krchlíková, V., Hron, T., Těšický, M., Li, T., Ungrová, L., Hejnar, J., Vinkler,
M., & Elleder, D. (2023). Dynamic evolution of avian RNA virus sen-
sors: Repeated loss of RIG- I and RIPLET. Viruses, 15, 3.
Kurtz, J., Kalbe, M., Aeschlimann, P. B., Häberli, M. A., Wegner, K. M.,
Reusch, T. B. H., & Milinski , M. (20 04). Major hi stocompatibilit y com-
plex diversity influences parasite resistance and innate immunity in
sticklebacks. Proceedings of the Biological Sciences, 271, 197– 204.
Lachish, S., McCallum, H., & Jones, M. (2009). Demography, disease and
the devil: Life- histor y changes in a disease- affected population of
Tasmanian devils (Sarcophilus harrisii). The Journal of Animal Ecolog y,
78, 427– 436.
Laky, K., & Fowlkes, B. J. (2008). Notch signaling in CD4 and CD8 T cell
development. Current Opinion in Immunology, 20, 197– 202.
Lazzaro, B. P., Zasloff, M., & Rolff, J. (2020). Antimicrobial peptides:
Application informed by evolution. Science, 368, eaau54 80.
Lenz, T. L., Eizaguirre, C., Kalbe, M., & Milinski, M. (2013). Evaluating pat-
terns of convergent evolution and trans- species polymorphism at
MHC immunogenes in two sympatric stickleback species. Evolution,
67, 24 00– 2412.
|
23
VINKLER et al.
Levy, H., Fiddaman, S. R., Vianna, J. A ., Noll, D., Clucas, G., Sidhu, J. K.
H., Polito, M. J., Bost, C. A ., Phillips, R. A., Crofts, S., Miller, G. D.,
Pistorius, P., Bonnadonna, F., Le Bohec, C., Barbosa, A., Trathan,
P., Raya Rey, A., LAF, F., Hart, T., & Smith, A. L. (2020). Evidence of
pathogen- induced Immunogenetic selection across the large geo-
graphic range of a wild seabird. Molecular Biolog y and Evolution, 37,
1708– 1726.
Ley, D. H., Berkhoff, J. E., & McLaren, J. M. (1996). Mycoplasma galli-
septicum isolated from house finches (Carpodacus mexicanus) with
conjunctivitis. Avian Diseases, 40, 480– 483.
Liu, G., Zhang, H., Zhao, C., & Zhang, H. (2020). Evolutionary history
of the toll- like receptor gene family across vertebrates. Genome
Biology and Evolution, 12, 3615– 3634.
Luttrell, M. P., Stallknecht, D. E., Fischer, J. R., Sewell, C. T., & Kleven, S.
H. (1998). Natural Mycoplasma gallisepticum infection in a captive
flock of house finches. Journal of Wildlife Diseases, 34, 289– 296.
Luzzatto, L. (2012). Sickle cell anaemia and malaria. Mediterranean Journal
of Hematology and Infectious Diseases, 4, e2012065.
Machado, L. R., & Ottolini, B. (2015). An evolutionary history of defen-
sins: A role for copy number variation in maximizing host innate and
adaptive immune responses. Frontiers in Immunology, 6, 115.
Mallikarjunappa, S., Brito, L. F., Pant, S. D., Schenkel, F. S., Meade, K.
G., & Karrow, N. A. (2021). Johne's disease in dairy cattle: An
Immunogenetic perspective. Frontiers in Veterinary Science, 8, 71 89 87.
Mallikarjunappa, S., Sargolzaei, M., Brito, L. F., Meade, K. G., Karrow, N.
A., & Pant, S. D. (2018). Short communication: Uncovering quanti-
tative trait loci associated with resistance to Mycobacterium avium
ssp. paratuberculosis infection in Holstein cattle using a high-
density single nucleotide polymorphism panel. Journal of Dairy
Science, 101, 7280– 7286.
Malmstrøm, M., Matschiner, M., Tørresen, O. K., Star, B., Snipen, L. G.,
Hansen, T. F., Baalsrud, H. T., Nederbragt, A. J., Hanel, R., Salzburger,
W., Stenseth, N. C., Jakobsen, K. S., & Jentoft, S. (2016). Evolution
of the immune system influences speciation rates in teleost fishes.
Nature Genetics, 48, 1204– 1210.
Marchalonis, J. J., Kaveri, S., Lacroix- Desmazes, S., & Kazatchkine, M. D.
(2002). Natural recognition repertoire and the evolutionary emer-
gence of the combinatorial immune system. The FASEB Journal, 16,
842– 848..
Margres, M. J., Ruiz- Aravena, M., Hamede, R., Chawla, K., Patton, A. H.,
Lawrance, M. F., Fraik, A. K., St ahlke, A. R., Davis, B. W., Ostrander,
E. A., Jones, M. E., McCallum, H., Paddison, P. J., Hohenlohe, P.
A., Hockenbery, D., & Storfer, A. (2020). Spontaneous tumor re-
gression in Tasmanian devils associated with RASL11A activation.
Genetics, 215, 114 3– 1152.
Martin, M. P., Naranbhai, V., Shea, P. R., Qi, Y., Ramsuran, V., Vince,
N., Gao, X., Thomas, R., Brumme, Z. L., Carlson, J. M., Wolinsky,
S. M., Goedert, J. J., Walker, B. D., Segal, F. P., Deeks, S. G., Haas,
D. W., Migueles, S. A ., Connors, M., Michael, N., … Carrington, M.
(2018). Killer cell immunoglobulin- like receptor 3DL1 variation
modifies HLA- B*57 protection against HIV- 1. The Journal of Clinical
Investigation, 128, 1903– 1912.
McLaren, P. J., Coulonges, C., Bartha, I., Lenz, T. L., Deutsch, A. J.,
Bashirova, A., Buchbinder, S., Carrington, M. N., Cossarizza, A.,
Dalmau, J., de Luca, A., Goedert, J. J., Gurdasani, D., Haas, D. W.,
Herbeck, J. T., Johnson, E. O., Kirk, G. D., Lambotte, O., Luo, M., …
Fellay, J. (2015). Polymorphisms of large effect explain the majority
of the host genetic contribution to variation of HIV- 1 virus load.
Proceedings of the National Academy of Sciences of the United States
of America, 112, 14658– 14663.
Medzhitov, R., Schneider, D. S., & Soares, M. P. (2012). Disease tolerance
as a defense strategy. Science, 335, 936– 941.
Mestas, J., & Hughes, C. C. W. (2004). Of mice and not men: Differences
between mouse and human immunology. The Journal of Immunology,
172, 2731– 2738.
Meyer- Lucht, Y., Mulder, K. P., James, M. C., McMahon, B. J., Buckley, K.,
Piertney, S. B., & Höglund, J. (2016). Adaptive and neutral genetic
differentiation among Scottish and endangered Irish red grouse
(Lagopus lagopus scotica). Conservation Genetics, 17, 615– 630.
Migalska, M., Sebastian, A., & Radwan, J. (2018). Profiling of the TCRβ
repertoire in non- model species using high- throughput sequencing.
Scientific Reports, 8, 11613.
Minias, P., Pikus, E., Whittingham, L. A., & Dunn, P. O. (2018). A global
analysis of selection at the avian MHC. Evolution, 72, 1278– 1293.
Minias, P., Pikus, E., Whittingham, L. A ., & Dunn, P. O. (2019). Evolution
of copy number at the MHC varies across the avian tree of life.
Genome Biology and Evolution, 11, 17– 28 .
Minias, P., & Vinkler, M. (2022). Selection balancing at innate immune
genes: Adaptive polymorphism maintenance in toll- like receptors.
Molecular Biolog y and Evolution, 39, msac102.
Morger, J., Råberg, L., Hille, S. M., Helsen, S., Štefka, J., Al- Sabi, M. M.,
Kapel, C. M. O., Mappes, T., Essbauer, S., Ulrich, R. G., Bartolommei,
P., Mortelliti, A., Balčiauskas, L., van den Brink, N. W., Rémy, A.,
Bajer, A., Cheprakov, M., Korva, M., García- Pérez, A. L., … Tschirren,
B. (201 5). Dist inct hapl ot ype st ruc tu re at the innate immune recep-
tor toll- like receptor 2 across bank vole populations and lineages
in Europe. Biological Journal of the Linnean Society, 116, 124– 133.
Morrissey, K. A., Wegrecki, M., Praveena, T., Hansen, V. L., Bu, L.,
Sivaraman, K. K., Darko, S., Douek, D. C., Rossjohn, J., Miller, R. D.,
& le Nours, J. (2021). The molecular assembly of the marsupial γμ T
cell receptor defines a third T cell lineage. Science, 371, 1383– 1388.
Mueller, R. C., Mallig, N., Smith, J., Eöry, L., Kuo, R. I., & Kraus, R. H.
S. (2020). Avian Immunome DB: An example of a user- friendly in-
terface for extracting genetic information. BMC Bioinformatics, 21,
502.
Muirhead, C. A. (20 01). Consequences of population structure on genes
under balancing selection. Evolution, 55, 1532– 1541.
Murrell, B., Moola, S., Mabona, A., Weighill, T., Sheward, D., Kosakovsky
Pond, S. L., & Schef fler, K. (2013). FUBAR: A fast, unconstrained
Bayesian AppRoximation for inferring selection. Molecular Biology
and Evolution, 30, 1196– 1205.
Murrell, B., Weaver, S., Smith, M. D., Wertheim, J. O., Murrell, S., Aylward,
A., Eren, K., Pollner, T., Martin, D. P., Smith, D. M., Scheffler, K., &
Kosakovsky Pond, S. L. (2015). Gene- wide identification of episodic
selection. Molecular Biolog y and Evolution, 32, 1365– 1371.
Murrell, B., Wertheim, J. O., Moola, S., Weighill, T., Scheffler, K., &
Kosakovsky Pond, S. L. (2012). Detecting individual sites subject to
episodic diversifying selection. PLoS Genetics, 8, e1002764.
Mwai, O., Hanotte, O., Kwon, Y.- J., & Cho, S. (2015). African indigenous
cattle: Unique genetic resources in a rapidly changing world. Asian-
Australasian Journal of Animal Sciences, 28, 9 11– 92 1 .
Nadachowska- Brzyska, K., Zieliński, P., Radwan, J., & Babik, W. (2012).
Interspecific hybridization increases MHC class II diversity in two
sister species of newts. Molecular Ecology, 21, 887– 906.
Natsopoulou, M. E., Pálsson, S., & Ólafsdóttir, G. Á. (2012). Parasites
and parallel divergence of the number of individual MHC alleles
between sympatric three- spined stickleback Gasterosteus aculeatus
morphs in Iceland. Journal of Fish Biology, 81, 1696– 1714.
Nei, M., Gu, X., & Sitnikova, T. (1997). Evolution by the birth- and- death
process in multigene families of the vertebrate immune system.
Proceedings of the National Academy of Sciences, 94, 7799– 7806.
Nei, M., & Rooney, A. P. (2005). Concerted and birth- and- death evolution
of multigene families. Annual Review of Genetics, 39, 121– 1 52 .
Netea, M. G., Joosten, L. A. B., Latz, E., Mills, K. H. G., Natoli, G.,
Stunnenberg, H. G., O’Neill, L. A . J., & Xavier, R. J. (2016). Trained
immunity: A program of innate immune memory in health and dis-
ease. Science, 352, aa f1098.
Nielsen, R., Bustamante, C., Clark, A. G., Glanowski, S., Sackton, T. B.,
Hubisz, M. J., Fledel- Alon, A., Tanenbaum, D. M., Civello, D., White,
T. J., J. Sninsky, J., Adams, M. D., & Cargill, M. (2005). A scan for
24
|
VINKLER et a l.
positively selected genes in the genomes of humans and chimpan-
zees. PLoS Biology, 3, 976– 985.
Nomiyama, H., Osada, N., & Yoshie, O. (2013). Systematic classification
of vertebrate chemokines based on conserved synteny and evolu-
tionary history. Genes to Cells, 18, 1– 16.
Norris, K., & Evans, M. R. (2000). Ecological immunology: Life histor y
trade- offs and immune defense in birds. Behavioral Ecology, 11,
1 9 – 2 6 .
Nozawa, M., Suzuki, Y., & Nei, M. (2009). Reliabilities of identifying pos-
itive selection by the branch- site and the site- prediction methods.
Proceedings of the National Academy of Sciences, 10 6, 6700– 6705.
Nunes, K., Maia, M. H. T., dos Santos, E. J. M., dos Santos, S. E. B.,
Guerreiro, J. F., Petzl- Erler, M. L., Bedoya, G., Gallo, C., Poletti, G.,
Llop, E., Tsuneto, L., Bortolini, M. C., Rothhammer, F., Single, R.,
Ruiz- Linares, A., Rocha, J., & Meyer, D. (2021). How natural selec-
tion shapes genetic differentiation in the MHC region: A case study
with native Americans. Human Immunology, 82, 523– 531 .
O'Connor, E. A., Cornwallis, C. K., Hasselquist, D., Nilsson, J.- A., &
Westerdahl, H. (2018). The evolution of immunit y in relation to col-
onization and migration. Nature Ecology and Evolution, 2, 841– 849.
O'Connor, E. A., Strandh, M., Hasselquist, D., Nilsson, J.- A., & Westerdahl,
H. (2016). The evolution of highly variable immunity genes across a
passerine bird radiation. Molecular Ecology, 25, 9 77– 989.
O'Connor, E. A., & Westerdahl, H. (2021). Tradeoffs in expressed major
histocompatibility complex diversity seen on a macro- evolutionary
scale among songbirds. Evolution, 75, 1– 9.
O'Connor, E. A., Westerdahl, H., Burri, R., & Edwards, S. V. (2019). Avian
MHC evolution in the era of genomics: Phase 1.0. Cell, 8, E1152.
Ortutay, C., Siermala, M., & Vihinen, M. (2007). Molecular characteriza-
tion of the immune system: Emergence of proteins, processes, and
domains. Immunogenetics, 59, 333– 348.
Ostfeld, R. (2012). Lyme disease: The ecolog y of a complex system,
Illustrated edition. Oxford University Press.
Özer, O., & Lenz, T. L . (2021). Unique pathogen Peptidomes facilitate
pathogen- specific selection and specialization of MHC alleles.
Molecular Biolog y and Evolution, 38, 4376– 4387.
Parham, P., & Moffett, A. (2013). Variable NK cell receptors and their
MHC class I ligands in immunity, reproduction and human evolu-
tion. Nature Reviews Immunology, 13, 1 3 3 – 14 4 .
Pavlovich, S. S., Lovett, S. P., Koroleva, G., Guito, J. C., Arnold, C. E.,
Nagle, E. R., Kulcsar, K., Lee, A., Thibaud- Nissen, F., Hume, A.
J., Mühlberger, E., Uebelhoer, L. S., Towner, J. S., Rabadan, R.,
Sanchez- Lockhart, M., Kepler, T. B., & Palacios, G. (2018). The
Egyptian Rousette genome reveals unexpected features of bat an-
tiviral immunity. Cell, 173, 1098– 1110.e18.
Pederson, T. (1999). The immunome. Molecular Immunology, 36,
1127– 1128.
Peng, F., Ballare, K. M., Hollis Woodard, S., den Haan, S., & Bolnick, D. I.
(2021). What evolutionary processes maintain MHC IIβ diversity
within and among populations of stickleback? Molecular Ecology, 30,
1659– 1671.
Piasecka, B., Duffy, D., Urrutia, A ., Quach, H., Patin, E., Posseme, C.,
Bergstedt, J., Charbit, B., Rouilly, V., MacPherson, C., Hasan, M.,
Albaud, B., Gentien, D., Fellay, J., Albert, M. L., Quintana- Murci,
L., & Mili eu Intér ie ur Consor tium . (2018). Distinct ive roles of age,
sex, and genetics in shaping transcriptional variation of human
immune responses to microbial challenges. Proceedings of the
National Academy of Sciences of the United States of America, 115,
E488– E497.
Picard, L., Ganivet, Q., Allatif, O., Cimarelli, A., Guéguen, L., & Etienne, L.
(2020). DGINN, an automated and highly- flexible pipeline for the
detection of genetic innovations on protein- coding genes. Nucleic
Acids Research, 48, e103.
Piec yk, A., Roth , O. , & Kalbe , M. (2019). Spe cificit y of res is tance and geo-
graphic patterns of virulence in a vertebrate host- parasite system.
BMC Evolutionary Biology, 19, 80.
Piertney, S. B., & Oliver, M. K. (2006). The evolutionar y ecology of the
major histocompatibility complex. Heredity, 96, 7– 21 .
Pillai, S., Netravali, I. A., Cariappa, A., & Mattoo, H. (2012). Siglecs and
immune regulation. Annual Review of Immunology, 30, 357– 392.
Poorten, T. J., & Rosenblum, E. B. (2016). Comparative study of host
response to chytridiomycosis in a susceptible and a resistant toad
species. Molecular Ecology, 25, 5663– 5679.
Pothlichet, J., & Quintana- Murci, L. (2013). The genetics of innate
immunity sensors and human disease. International Reviews of
Immunology, 32, 1 5 7– 208.
Price, S. J., Garner, T. W. J., Nichols, R. A., Balloux, F., Ayres, C., Mora-
Cabello de Alba, A., & Bosch, J. (2014). Collapse of amphibian
communities due to an introduced Ranavirus. Current Biology, 24 ,
2586– 2591.
Quéméré, E., Galan, M., Cosson, J.- F., Klein, F., Aulagnier, S., Gilot-
Fromont, E., Merlet, J., Bonhomme, M., Hewison, A. J. M., &
Charbonnel, N. (2015). Immunogenetic heterogeneity in a wide-
spread ungulate: The European roe deer (Capreolu s capreolus).
Molecular Ecology, 24, 3873– 3887.
Quéméré, E., Hessenauer, P., Galan, M., Fernandez, M., Merlet, J., Chaval,
Y., Morellet, N., Verheyden, H., Gilot- Fromont, E., & Charbonnel, N.
(2021). Pathogen- mediated selection favours the maintenance of
innate immunity gene polymorphism in a widespread wild ungulate.
Journal of Evolutionary Biology, 34, 1156– 1166.
Råberg, L., Clough, D., Hagström, Å., Scherman, K., Andersson, M.,
Drews, A., Strandh, M., Tschirren, B., & Westerdahl, H. (2022).
MHC class II genotype- by- pathogen genotype interaction for in-
fection prevalence in a natural rodent- Borrelia system. Evolution,
76, 2067– 2075.
Råberg, L., Graham, A. L., & Read, A. F. (2009). Decomposing health:
Tolerance and resistance to parasites in animals. Philosophical
Transactions of the Royal Society of London. Series B, Biological
Sciences, 364, 3 7– 49.
Råberg, L., Sim, D., & Read, A. F. (2007). Disentangling genetic varia-
tion for resistance and tolerance to infectious diseases in animals.
Science, 318, 812– 814.
Radtke, F., Fasnacht, N., & Macdonald, H. R. (2010). Notch signaling in
the immune system. Immunity, 32, 14– 27.
Radwan, J., Babik, W., Kaufman, J., Lenz, T. L., & Winternitz, J. (2020).
Advances in the evolutionary understanding of MHC polymor-
phism. Trends in Genetics, 36, 298– 311.
Raven, N., Klaassen, M., Madsen, T., Thomas, F., Hamede, R. K., & Ujvari,
B. (2022). Transmissible cancer influences immune gene expression
in an endangered marsupial, the Tasmanian devil (Sarcophilus harri-
sii). Molecular Ecology, 31, 2 293 – 23 11 .
Reimand, J., Kull, M., Peterson, H., Hansen, J., & Vilo, J. (2007). g:Profil-
er— a web- based toolset for functional profiling of gene lists from
large- scale experiments. Nucleic Acids Research, 35, W193– W200.
Ren, Y., MacPhillamy, C., To, T.- H, Smith, T. P. L., Williams, J. L., & Low,
W. Y. (2021). Adaptive selection signatures in river buffalo with
emphasis on immune and major histocompatibility complex genes.
Genomics, 113, 3599– 3609.
Reznick, D. N., & Ricklefs, R. E. (2009). Darwin's bridge between micro-
evolution and macroevolution. Nature, 457, 837– 842.
Ribas, L., Li, M.- S., Doddington, B. J., Robert, J., Seidel, J. A., Kroll, J. S.,
Zimmerman, L. B., Grassly, N. C., Garner, T. W. J., & Fisher, M. C.
(2009). Expression profiling the temperature- dependent amphibian
response to infection by Batrachochytrium dendrobatidis. PLoS One,
4, e8408.
Richardson, D. S., & Westerdahl, H. (2003). MHC diversity in two
Acrocephalus species: The outbred great reed warbler and the in-
bred Seychelles warbler. Molecular Ecology, 12, 3523– 3529.
Roach, J. C., Glusman, G., Rowen, L., Kaur, A., Purcell, M. K., Smith, K.
D., Hood, L. E., & Aderem, A . (2005). The evolution of vertebrate
toll- like receptors. Proceedings National Academy of Sciences. United
States of America, 102, 9577– 9582.
|
25
VINKLER et al.
Robinson, K. S., & Aw, R. (2016). The commonalities in bacterial effector
inhibition of apoptosis. Trends in Microbiology, 24, 665– 680.
Rogers, S. L., & Kaufman, J. (2016). Location, location, location: The evo-
lutionary history of CD1 genes and the NKR- P1/ligand systems.
Immunogenetics, 68, 49 9– 51 3.
Rosenblum, E. B., Poorten, T. J., Settles, M., & Murdoch, G. K. (2012).
Only skin deep: Shared genetic response to the deadly chytrid fun-
gus in susceptible frog species. Molecular Ecology, 21, 3110– 3120 .
Roth, O., Solbakken, M. H., Tørresen, O. K., Bayer, T., Matschiner, M.,
Baalsrud, H. T., SNK, H., MSO, B., Haase, D., Hanel, R., TBH, R.,
& Jentoft, S. (2020). Evolution of male pregnancy associated with
remodeling of canonical vertebrate immunity in seahorses and
pipefishes. Proceedings of the National Academy of Sciences, 117,
9431– 9439.
Roved, J., Hansson, B., Tarka, M., Hasselquist, D., & Westerdahl, H.
(2018). Evidence for sexual conflict over major histocompatibility
complex diversity in a wild songbird. Procee dings of the Royal Socie ty
B- Biological Science, 285, 20180841.
Sackton, T. B. (2019). Comparative genomics and transcriptomics of host
– Pathogen interactions in insects: Evolutionary insights and future
directions. Current Opinion in Insec t Science, 31, 106– 113.
Sackton, T. B. (2020). Studying natural selection in the era of ubiquitous
genomes. Trends in Genetic s, 36, 792– 803.
Sackton, T. B., Lazzaro, B. P., Schlenke, T. A., Evans, J. D., Hultmark, D., &
Clark, A. G. (2007). Dynamic evolution of the innate immune sys-
tem in drosophila. Nature Genetics, 39, 1461– 1468.
Savage, A. E., Gratwicke, B., Hope, K., Bronikowski, E., & Fleischer, R.
C. (2020). Sustained immune activation is associated with sus-
ceptibility to the amphibian chytrid fungus. Molecular Ecology, 29,
28 89– 290 3 .
Savage, A. E., Mulder, K. P., Torres, T., & Wells, S. (2018). Lost but not
forgotten: MHC genotypes predict overwinter survival despite de-
pauperate MHC diversity in a declining frog. Conservation Genetics,
19, 30 9– 32 2.
Savage, A. E., Muletz- Wolz, C. R., Campbell Grant, E. H., Fleischer, R. C.,
& Mulder, K. P. (2019). Functional variation at an expressed MHC
class IIβ locus associates with Ranavirus infection intensity in larval
anuran populations. Immunogenetics, 71, 335– 346.
Savage , A . E., & Zamudi o, K. R. (2011) . MHC gen oty pes as socia te with re-
sistance to a frog- killing fungus. Proceed ings of the Nationa l Academy
of Sciences of the United States of America, 108 , 16705– 16710.
Savage, A. E., & Zamudio, K. R. (2016). Adaptive tolerance to a patho-
genic fungus drives major histocompatibility complex evolution in
natural amphibian populations. Proceedings of the Royal Society B,
283, 20153115.
Scalf, C. S., Chariker, J. H., Rouchka, E. C., & Ashley, N. T. (2019).
Transcriptomic analysis of immune response to bacterial lipopoly-
saccharide in zebra finch (Taeniopygia guttata). BMC Genomics, 20,
647.
Scheele, B. C., Pasmans, F., Skerratt, L. F., Berger, L., Martel, A.,
Beukema, W., Acevedo, A. A., Burrowes, P. A., Carvalho, T.,
Catenazzi, A., de la Riva, I., Fisher, M. C., Flechas, S. V., Foster, C.
N., Frías- Álvarez, P., Garner, T. W. J., Gratwicke, B., Guayasamin,
J. M., Hirschfeld, M., … Canessa, S. (2019). Amphibian fungal
panzootic causes catastrophic and ongoing loss of biodiversity.
Science, 363, 1459– 1463.
Scherman, K., Råberg, L., & Westerdahl, H. (2014). Positive selection on
MHC class II DRB and DQB genes in the Bank vole (Myodes glareo-
lus). Journal of Molecular Evolution, 78, 293– 305.
Scherman, K., Råberg, L., & Westerdahl, H. (2021). Borrelia infection in
Bank voles Myodes glareolus is associated with specific DQB hap-
lotypes which affect allelic divergence within individuals. Frontiers
in Immunology, 12, 703025.
Schlesinger, K .J., Stromberg, S.P. & Carlson, J.M. 2014. Coevolutionary
immune system dynamics driving pathogen speciation. PLoS One 9:
e102821. Public Library of Science.
Schmid- Hempel, P. (2011). Evolutionary parasitology: The integrated study
of infections, immunology, ecology, and genetics. Oxford University
Press.
Schut, E., Rivero- de Aguilar, J., Merino, S., Magrath, M. J. L., Komdeur,
J., & Westerdahl, H. (2011). Characterization of MHC- I in the
blue tit (Cyanistes caeruleus) reveals low levels of genetic diver-
sity and trans- population evolution across European populations.
Immunogenetics, 63, 531– 54 2 .
Schwenke, R. A., Lazzaro, B. P., & Wolfner, M. F. (2016). Reproduction-
immunity trade- offs in insects. Annual Review of Entomology, 61,
2 3 9 – 2 5 6 .
Seed, J. R. (1993). Immunoecology- origins of an idea. Journal of
Parasitology, 79, 470– 471.
Sharma, V., Walther, F., Hecker, N., Stuckas, H., & Hiller, M. (2020).
Convergent losses of TLR5 suggest altered extracellular Flagellin
detection in four mammalian lineages. Molecular Biology and
Evolution, 37, 1847– 1854.
Sheldon, B. C., & Verhulst, S. (1996). Ecological immunology: Costly par-
asite defences and trade- offs in evolutionary ecology. Trends in
Ecology & Evolution, 11, 317– 3 2 1.
Sheppard, E. C., Martin, C. A., Armstrong, C., González- Quevedo, C.,
Illera, J. C., Suh, A., Spurgin, L. G., & Richardson, D. S. (2022).
Genomic associations with poxvirus across divergent Island popu-
lations in Berthelot's pipit. Molecular Ecology, 31, 3154– 3173.
Shiina, T., & Blancher, A. (2019). The Cynomolgus macaque MHC poly-
morphism in experimental medicine. Cell, 8, E978.
Shiina, T., Blancher, A., Inoko, H., & Kulski, J. K. (2017). Comparative ge-
nomics of the human, macaque and mouse major histocompatibility
complex. Immunology, 150, 1 2 7– 1 3 8 .
Shultz, A. J., & Sackton, T. B. (2019). Immune genes are hotspots of shared
positive selection across birds and mammals. eLife, 8, e 41815.
Simons, A. M. (20 02). The continuity of microevolution and macroevolu-
tion. Journal of Evolutionar y Biolog y, 15, 688– 701.
Smetko, A., Soudre, A., Silbermayr, K., Müller, S., Brem, G., Hanotte, O.,
Boettcher, P. J., Stella, A., Mészáros, G., Wurzinger, M., Curik, I.,
Müller, M., Burgstaller, J., & Sölkner, J. (2015). Trypanosomosis:
Potential driver of selection in African cattle. Frontiers in Genetics,
6, 1 37.
Smirnova, I., Poltorak, A., Chan, E. K. L., McBride, C., & Beutler, B. (2000).
Phylogenetic variation and polymorphism at the toll- like receptor
4 locus (TLR4). Genome Biology, 1, research002.1– research002.10.
Smith, M. D., Wertheim, J. O., Weaver, S., Murrell, B., Scheffler, K., &
Kosakovsky Pond, S. L. (2015). Less is more: An adaptive branch-
site random effects model for efficient detection of episodic diver-
sifying selection. Molecular Biolog y and Evolution, 32, 1342– 1353.
Solbakken, M. H., Tørresen, O. K., Nederbragt, A. J., Seppola, M., Gregers,
T. F., Jakobsen, K. S., & Jentoft, S. (2016). Evolutionary redesign of
the Atlantic cod (Gadus morhua L.) toll- like receptor repertoire by
gene losses and expansions. Scientific Reports, 6, 25211.
Sommer, S. (2005). The importance of immune gene variability (MHC) in
evolutionary ecology and conservation. Frontiers in Zoology, 2, 16.
Souilmi, Y., Lauterbur, M. E., Tobler, R., Huber, C. D., Johar, A. S., Moradi,
S. V., Johnston, W. A., Krogan, N. J., Alexandrov, K., & Enard, D.
(2021). An ancient viral epidemic involving host coronavirus in-
teracting genes more than 20,000 years ago in East Asia. Current
Biology, 31, 3704.
Spottiswoode, C. N., & Busch, R. (2019). Vive la difference! Self/non-
self recognition and the evolution of signatures of identity in arms
races with parasites. Philosophical Transactions of the Royal Society
B- Biological Science, 374, 20180206.
Spurgin, L. G., & Richardson, D. S. (2010). How pathogens drive genetic
diversity: MHC, mechanisms and misunderstandings. Proceedings
of the Biological Sciences, 277, 979– 988 .
Sridhar, S., Ahluwalia, M., Brummer, E., & Stevens, D. A. (20 00).
Characterization of an anticryptococcal protein isolated from
human serum. Infection and Immunity, 68, 3787– 3791.
26
|
VINKLER et a l.
Stahlke, A. R., Epstein, B., Barbosa, S., Margres, M. J., Patton, A. H.,
Hendricks, S. A ., Veillet, A., Fraik, A. K., Schönfeld, B., HI, M. C.,
Hamede, R., Jones, M. E., Storfer, A., & Hohenlohe, P. A. (2021).
Contemporary and historical selection in Tasmanian devils
(Sarcophilus harrisii) support novel, polygenic response to transmis-
sible cancer. Proceedings Biological Sciences, 288, 20210577.
Staley, M., Bonneaud, C., McGraw, K. J., Vleck, C. M., & Hill, G. E.
(2018). Detection of mycoplasma gallisepticum in house finches
(Haemorhous mexicanus) from Arizona. Avian Diseases, 62, 14 – 17.
Stearns, S. C., & Koella, J. C. (2008). Evolution in health and disease (2nd
ed.). Oxford University Press.
Storz, J. F. (2016). Causes of molecular convergence and parallelism in
protein evolution. Nature Reviews Genetics, 17, 23 9– 2 5 0.
Strand, T., Wang, B., Meyer- Lucht, Y., & Höglund, J. (2013). Evolutionary
history of black grouse major histocompatibility complex class IIB
genes revealed through single locus sequence- based genotyping.
BMC Genetics, 14, 29.
Stutz, W. E., & Bolnick, D. I. (2017). Natural selec tion on MHC IIβ in para-
patric lake and stream stickleback: Balancing, divergent, both or
neither? Molecular Ecology, 26, 4772– 4786.
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L.,
Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander,
E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A
knowledge- based approach for interpreting genome- wide expres-
sion profiles. Proceedings of the National Academy of Sciences of the
United States of America, 10 2, 15545– 15550.
Sudmant, P. H., Alexis, M. S., & Burge, C. B. (2015). Meta- analysis of
RNA- seq expression data across species, tissues and studies.
Genome Biology, 16, 2 87.
Sun, S., Jin, G., & Kang, H. (2015). CD81 and CLDN1 polymorphisms and
hepatitis C virus infection susceptibility: A case control study. Gene,
567, 87– 91 .
Sun, Y., Wei, Z., Li, N., & Zhao, Y. (2012). A comparative overview of im-
munoglobulin genes and the generation of their diversity in tetra-
pods. Developmental and Comparative Immunology, 39, 103– 109.
Suryamohan, K., Diwanji, D., Stawiski, E. W., Gupta, R., Miersch, S., Liu, J.,
Chen, C., Jiang, Y. P., Fellouse, F. A., Sathirapongsasuti, J. F., Albers,
P. K., Deepak, T., Saberianfar, R., Ratan, A., Washburn, G., Mis, M.,
Santhosh, D., Somasekar, S., Hiranjith, G. H., … Seshagiri, S. (2021).
Human ACE2 receptor polymorphisms and altered susceptibility to
S A R S - C o V - 2 . Communications Biology, 4, 475.
Świderská, Z., Šmídová, A ., Buchtová, L., Bryjová, A ., Fabianová, A.,
Munclinger, P., & Vinkler, M. (2018). Avian toll- like receptor allelic
diversity far exceeds human polymorphism: An insight from do-
mestic chicken breeds. Scientific Reports, 8, 17878.
Teacher, A. G. F., Garner, T. W. J., & Nichols, R. A. (2009). Evidence for di-
rectional selection at a novel major histocompatibility class I marker
in wild common frogs (Rana temporaria) exposed to a viral pathogen
(Ranavirus). PLoS One, 4, e4616.
Těšický, M., Velová, H., Novotný, M., Kreisinger, J., Beneš, V., & Vinkler,
M. (2020). Positive selection and convergent evolution shape
molecular phenotypic traits of innate immunity receptors in tits
(Paridae). Molecular Ecology, 29, 3056– 3070.
Těšický, M., & Vinkler, M. (2015). Trans- species polymorphism in immune
genes: General pattern or MHC- restricted phenomenon? Journal of
Immunology Research, 2015, 1– 1 0 .
Travis, M. A., & Sheppard, D. (2014). TGF- β activation and function in
immunity. Annual Review of Immunology, 32, 51– 82.
Tschirren, B., Andersson, M., Scherman, K., Westerdahl, H., Mittl, P. R. E.,
& Råberg, L. (2013). Polymorphisms at the innate immune receptor
TLR2 are associated with Borrelia infection in a wild rodent popula-
tion. Proceedings of the Royal Society B, 280, 20130364.
Tschirren, B., Andersson, M., Scherman, K., Westerdahl, H., & Råberg, L.
(2011). Contrasting patterns of diversity and population differenti-
ation At the innate immunity gene toll- like receptor 2 (Tlr2) in two
sympatric rodent species. Evolution, 66, 720– 731.
Tschirren, B., Andersson, M., Scherman, K., Westerdahl, H., & Råberg, L.
(2012). Contrasting patterns of diversity and population differenti-
ation at the innate immunity gene toll- like receptor 2 (tlr2) in two
sympatric rodent species. Evolution, 66, 720– 731.
Uematsu, S., & Akira, S. (2006). Toll- like receptors and innate immunity.
Journal of Molecular Medicine, 84, 712– 725.
Ujvari, B., Hamede, R., Peck, S., Pemberton, D., Jones, M., Belov, K., &
Madsen, T. (2016). Immunoglubolin dynamics and cancer preva-
lence in Tasmanian devils (Sarcophilus harrisii). Science Reports, 6,
25093. https://doi.org/10.1038/srep2 5093
Uni Prot Consort ium. (2019). UniProt: A worldwide hub of protein knowl-
edge. Nucleic Acids Research, 47, D506– D515.
Vekemans, X., Castric, V., Hipperson, H., Müller, N. A ., Westerdahl, H., &
Cronk, Q. (2021). Whole- genome sequencing and genome regions
of special interest: Lessons from major histocompatibility complex,
sex determination, and plant self- incompatibility. Molecular Ecology,
30, 6072– 6086.
Velová, H., Gutowska- Ding, M. W., Burt, D. W., & Vinkler, M. (2018). Toll-
like receptor evolution in birds: Gene duplication, pseudogeniza-
tion, and diversifying selection. Molecular Biology and Evolution, 35,
2170– 2184.
Viertlboeck, B. C., & Göbel, T. W. (2011). The chicken leukocyte receptor
clus te r. Veterinary Immunology and Immunopathology, 144, 1– 1 0 .
Viertlboeck, B. C., Schweinsberg, S., Schmitt, R., Herberg, F. W., & Göbel,
T. W. (2009). The chicken leukocyte receptor complex encodes a
family of different affinity FcY receptors. The Journal of Immunology,
182, 6985– 6992.
Vinkler, M., Adelman, J. S., & Ardia, D. R. (2022). Evolutionary and eco-
logical immunology. In B. Kaspers, K. A. Schat, T. W. Göbel, & L.
Vervelde (Eds.), Avian Immunology (pp. 519– 557). Academic Press.
Vinkler, M., & Albrecht, T. (2009). The question waiting to be asked:
Innate immunity receptors in the perspective of zoological re-
search. Folia Zoologica, 58, 15– 28.
Vinkler, M., & Albrecht, T. (2011). Handling immunocompetence’ in eco-
logical studies: Do we operate with confused terms? Journal of
Avian Biology, 42, 490– 493.
Vinkler, M., Bainová, H., & Bryja, J. (2014). Protein evolution of toll- like
receptors 4, 5 and 7 within Galloanserae birds. Genetics, Selection,
Evolution, 46, 72.
Vinkler, M., Leon, A. E., Kirkpatrick, L., Dalloul, R. A., & Hawley, D. M.
(2018). Differing house finch cytokine expression responses to
original and evolved isolates of Mycoplasma gallisepticum. Frontiers
in Immunology, 9, 13.
Vivier, E., & Malissen, B. (20 05). Innate and adaptive immunity:
Specificities and signaling hierarchies revisited. Nature Immunology,
6, 17– 21 .
Vlček, J., Miláček, M., Vinkler, M., & Štefka, J. (2022). Effect of popu-
lation size and selection on toll- like receptor diversity in popula-
tions of Galápagos mockingbirds. Journal of Evolutionar y Biology, 36,
10 9 – 1 2 0 .
Wallny, H. J., Avila, D., Hunt, L. G., Powell, T. J., Rieger t, P., Salomonsen,
J., Skjødt, K., Vainio, O., Vilbois, F., Wiles, M. V., & Kaufman, J.
(2006). Peptide motifs of the single dominantly expressed class I
molecule explain the striking MHC- determined response to Rous
sarcoma virus in chickens. Proceedings of the National Academy of
Sciences of the United States of America, 103, 14 34– 1439.
Walsh, C., Gangloff, M., Monie, T., Smyth, T., Wei, B., McKinley, T.
J., Maskell, D., Gay, N., & Bryant, C. (2008). Elucidation of the
MD- 2/TLR4 interface required for signaling by lipid IVa. Journal of
Immunology, 181, 1245– 1254.
Wang, B., Ekblom, R., Strand, T. M., Portela- Bens, S., & Höglund, J. (2012).
Sequencing of the core MHC region of black grouse (Tetrao tetrix)
and comparative genomics of the galliform MHC. BMC Genomics,
13, 553.
Wang, J., Belosevic, M., & Stafford, J. L. (2021). Identification of distinct
LRC- and fc receptor complex- like chromosomal regions in fish
|
27
VINKLER et al.
supports that teleost leukocy te immune- type receptors are distant
relatives of mammalian fc receptor- like molecules. Immunogenetics,
73, 9 3 – 10 9.
Wang, P., Yue, C., Liu, K., Lu, D., Liu, S., Yao, S., Li, X., Su, X., Ren, K., Chai,
Y., Qi, J., Zhao, Y., Lou, Y., Sun, Z., Gao, G. F., & Liu, W. J. (2021).
Peptide presentations of marsupial MHC class I visualize immune
features of lower mammals paralleled with bats. The Journal of
Immunology, 207, 2167– 2178.
Wang, X., Branciamore, S., Gogoshin, G., & Rodin, A. S. (2019).
Dependency between protein– protein interactions and protein
variability and evolutionary rates in vertebrates: Observed rela-
tionships and stochastic modeling. Journal of Molecular Evolution,
87, 1 8 4 – 198.
Wang, X., Quinn, P. J., & Yan, A. (2015). Kdo 2 - lipid a: Structural diver-
sity and impact on immunopharmacology: Kdo 2 - lipid a. Biological
Reviews, 90, 40 8– 427.
Watanabe, T., Tanaka, H., Nakao, N., Mikami, T., Suzuki, M., & Matsumoto,
T. (1997). Anti Ca ndi da ac tivit y of induce d trans ferri n in mice immu -
nized with inactivated Candida albicans. Biological & Pharmaceutical
Bulletin, 20, 637– 640.
Watt, S., Vasquez, L., Walter, K., Mann, A . L., Kundu, K., Chen, L., Sims,
Y., Ecker, S., Burden, F., Farrow, S., Farr, B., Iotchkova, V., Elding, H.,
Mead, D., Tardaguila, M., Ponstingl, H., Richardson, D., Datta, A.,
Flicek, P., … Soranzo, N. (2021). Genetic perturbation of PU.1 bind-
ing and chromatin looping at neutrophil enhancers associates with
autoimmune disease. Nature Communications, 12, 2298.
Weber, J. N., Kalbe, M., Shim, K. C., Erin, N. I., Steinel, N. C., Ma, L., &
Bolnick, D. I. (2017). Resist globally, infect locally: A transcontinen-
tal test of adaptation by stickleback and their tapeworm parasite.
The American Naturalist, 189, 4 3 – 57.
Weber, J. N., Steinel, N. C., Peng, F., Shim, K. C., Lohman, B. K., Fuess,
L. E., Subramanian, S., Lisle, S. P. D., & Bolnick, D. I. (2022).
Evolutionary gain and loss of a pathological immune response to
parasitism. Science, 377, 1206– 1211.
Wei, Y., Aris, P., Farookhi, H., & Xia, X. (2021). Predicting mammalian
species at risk of being infected by SARS- CoV- 2 from an ACE2 per-
spective. Scientific Reports, 11, 170 2.
Wells, K., Hamede, R. K., Kerlin, D. H., Storfer, A., Hohenlohe, P. A.,
Jones, M. E., & McCallum, H. I. (2017). Infection of the fittest: Devil
facial tumour disease has greatest effect on individuals with high-
est reproductive output. Ecology Letters, 20, 770– 778.
Westerdahl, H., Asghar, M., Hasselquist, D., & Bensch, S. (2012).
Quantitative disease resistance: To better understand parasite-
mediated selection on major histocompatibility complex.
Proceedings of the Royal Society B: Biological Sciences, 279, 577– 58 4.
Westerdahl, H., Hansson, B., Bensch, S., & Hasselquist, D. (2004).
Between- year variation of MHC allele frequencies in great reed
warblers: Selection or drift? Journal of Evolutionary Biology, 17,
485– 492 .
Westerdahl, H., Mellinger, S., Sigeman, H., Kutschera, V. E., Proux- Wéra,
E., Lundberg, M., Weissensteiner, M., Churcher, A., Bunikis, I.,
Hansson, B., Wolf, J. B. W., & Strandh, M. (2022). The genomic ar-
chitecture of the passerine MHC region: High repeat content and
contrasting evolutionary histories of single copy and tandemly du-
plicated MHC genes. Molecular Ecology Resources, 22, 2379– 2395.
Westerdahl, H., Waldenstrom, J., Hansson, B., Hasselquist, D., von
Schantz, T., & Bensch, S. (20 05). Associations between malaria and
MHC genes in a migratory songbird. Proceedings of the Royal Socie ty
B- Biological Science, 272, 1511– 1518 .
Wilson, A. B. (2017). MHC and adaptive immunity in teleost fishes.
Immunogenetics, 69, 521– 528.
Wohlfahrt, T., Rauber, S., Uebe, S., Luber, M., Soare, A., Ekici, A., Weber,
S., Matei, A . E., Chen, C. W., Maier, C., Karouzakis, E., Kiener, H.
P., Pachera, E., Dees, C., Beyer, C., Daniel, C., Gelse, K., Kremer, A.
E., Naschberger, E., … Ramming, A. (2019). PU.1 controls fibroblast
polarization and tissue fibrosis. Nature, 566, 344– 349.
Wong, C., Darby, J. M., Murphy, P. R., Pinfold, T. L., Lennard, P. R., Woods,
G. M., Lyons, A. B., & Flies, A . S. (2021). Tasmanian devil CD28 and
CTLA4 capture CD80 and CD86 from adjacent cells. Developmental
and Comparative Immunology, 115, 103882.
Wong, E. S. W., Papenfuss, A. T., & Belov, K. (2011). Immunome database
for marsupials and monotremes. BMC Immunology, 12, 48.
Woodhams, D. C., Ardipradja, K., Alford, R. A., Marantelli, G., Reinert,
L. K., & Rollins- Smith, L. A . (2007). Resistance to chytridiomycosis
varies among amphibian species and is correlated with skin peptide
defenses. Animal Conservation, 10, 40 9 – 417.
Woodhams, D. C., Kenyon, N., Bell, S. C., Alford, R. A., Chen, S.,
Billheimer, D., Shyr, Y., & Rollins- Smith, L. A. (2010). Adaptations
of skin peptide defences and possible response to the amphibian
chytrid fungus in populations of Australian green- eyed treefrogs,
Litoria genimaculata. Diversity and Distributions, 16, 70 3 – 71 2 .
Woolhouse, M. E. J., Webster, J. P., Domingo, E., Charlesworth, B., &
Levin, B. R. (2002). Biological and biomedical implications of the
co- evolution of pathogens and their hosts. Nature Genetics, 32,
5 6 9 – 5 7 7 .
Wright, B., Willet, C.E., Hamede, R., Jones, M., Belov, K. & Wade, C.M.
2017. Variants in the host genome may inhibit tumour growth in
devil facial tumours: Evidence from genome- wide association.
Scientific Reports 7: 423.
Wutzler, R., Foerster, K., & Kempenaers, B. (2012). MHC class I variation
in a natural blue tit population (Cyanistes caeruleus). Genetica, 140 ,
349– 364.
Yang, Z. (20 07). PAML 4: Phylogenetic analysis by maximum likelihood.
Molecular Biolog y and Evolution, 24, 1586– 1591.
Yeager, M., & Hughes, A. L. (1999). Evolution of the mammalian MHC:
Natural selection, recombination, and convergent evolution.
Immunological Reviews, 167, 45– 58.
Zasloff, M. (2002). Antimicrobial peptides of multicellular organisms.
Nature, 415, 389– 395 Nature Publishing Group.
Zhang, Q., Hill, G. E., Edwards, S. V., & Backström, N. (2014). A house
finch (Haemorhous mexicanus) spleen transcriptome reveals intra-
and interspecific patterns of gene expression, alternative splicing
and genetic diversity in passerines. BMC Genomics, 15, 305.
Zhong, X., Lundberg, M., & Råberg, L . (2021). Divergence in coding se-
quence and expression of different functional categories of im-
mune genes between two wild rodent species. Genome Biology and
Evolution, 3(3), evab023. https://doi.org/10.1093/gbe/evab023
How to cite this article: Vinkler, M., Fiddaman, S. R., Těšický,
M., O’Connor, E. A., Savage, A. E., Lenz, T. L., Smith, A. L.,
Kaufman, J., Bolnick, D. I., Davies, C. S., Dedić, N., Flies, A. S.,
Samblás, M. M. G., Henschen, A. E., Novák, K., Palomar, G.,
Raven, N., Samaké, K., Slade, J. … Westerdahl, H. (2023).
Understanding the evolution of immune genes in jawed
vertebrates. Journal of Evolutionary Biology, 00, 1–27. ht tps://
doi .org /10.1111/j eb.14181
