Discussion
The aim of the present study was to investigate genome-wide patterns of
adaptive variation associated with climate in european bank vole
populations. Searching for potentially adaptive loci in a multivariate
framework is a powerful approach, especially because many adaptive
traits are likely to be polygenic in nature (Barghi et al., 2020;
Wellenreuther & Hansson, 2016). We used ddRAD sequencing and a
combination of landscape genomic approaches to uncover
environment-related evolutionary processes in the bank vole. We
characterized adaptive genetic variation by using univariate GEA methods
to detect outlier loci correlated with climate. We identified 74 genes
of interest and functional annotation suggested that energy homeostasis
and response to pathogen infection are important targets of spatially
varying selection in the bank vole. In addition, we have shown that both
population structure and climate play important (and common) roles in
explaining neutral genetic differentiation across the bank vole range.
Genetic variation among candidate loci was mainly associated with
variation in annual mean temperature, highlighting the importance of
this climatic variable in bank vole adaptation.
Loci with true selection signals must be distinguished from loci that
exhibit genetic differentiation between populations caused by neutral
forces. Correcting for these effects is an important concern when
identifying candidate loci subject to selection. Proper correction can
help to avoid possible spurious detection of candidate loci whose allele
frequencies resemble signals of selection but are the result of neutral
processes due to the shared history of populations (de Villemereuil et
al., 2014). We therefore used GEA methods that correct for such
confounding effects by accounting for population genetic structure in
the bank vole.
Agreement between the different univariate methods was reasonable
(14.9% and 15.1% of SNPs were detected by both methods for PC1 and
PC2, respectively) and was consistent with results observed in other
empirical studies using univariate methods (Harrison et al., 2017;
Prates et al., 2018; Pritchard et al., 2018). The overlap between
univariate methods and RDA was relatively small, with only 5.1% of the
loci detected by RDA being also detected by LFMM or Bayenv2. A
simulation-based study that tested the performance of univariate and
multivariate GEA methods showed that the performance of these approaches
varied depending on the strength of selection (Forester et al., 2018)
and that RDA may be more robust to our sampling design that does not
maximize environmental differentiation. The same study also suggests
that combining results from univariate and multivariate approaches may
help to increase power and to reduce false-positive rates. Our study
supports these findings and also provides a strong argument for using
multiple approaches when searching for signals of local adaptation in
highly structured populations.
Population structure explained a large part of genomic variation,
resulting in a strong pattern of isolation by distance. Even after
taking into account the effects of climate variation, population
structure still accounted for 33% of the total genomic variation
explained by the RDA. These results are not surprising, as bank vole
populations experience recurring population crashes and effective gene
flow between populations is generally low, which results in isolation by
distance across both smaller and larger geographic scales (Aars et al.,
1998; Gerlach & Musolf, 2000; Guivier et al., 2011; Redeker et al.,
2006), and although populations have strongly diverged in space since
the last glaciation (for review Kotlik et al., 2022)). On the other
hand, climate explained 18.3% of total genomic variation when taking
population structure into account, indicating association between bank
vole genetic variation and environmental gradients. A large proportion
of genetic variation (48.7%) could not be attributed either to the
effects of climate or to spatial population structure alone, indicating
that a large proportion of genomic variation associated with climate is
geographically structured. The phylogeography of C. glareolus is
marked by distinct genetic lineages, which resulted from survival within
glacial refugia and recolonization of Europe at the end of the last
glaciation (Filipi et al., 2015; Horniková et al., 2021; Kotlik et al.,
2006; Marková et al., 2020). Post-glacial expansion from glacial refugia
may result in clines of neutral allele frequencies coinciding with
climate variables related to geography (Lotterhos & Whitlock, 2014;
Rellstab et al., 2015), possibly explaining the large proportion of
genomic variation. Our results suggest that annual mean temperature is
an important driver of adaptive genomic variation and thus may be an
important selection pressure influencing adaptation in the bank vole
populations (Tiffin & Ross-Ibarra, 2014) as reflected by the strong
association between temperature and polygenic scores. The latter implies
that different alleles are maintained in different thermal environments,
suggesting the presence of spatially varying selection pressure.
Temperature is one of the most important environmental factors affecting
physiological processes such as the aerobic scope (Pörtner, 2001) and
metabolism (Lovegrove, 2003), which in turn affect a variety of life
history traits (Simons et al., 2011; Tökölyi et al., 2014). Numerous
studies have associated clinal temperature variation and genome scans
and found signals of selection in genes related to energy homeostasis
and metabolism in endotherms (e.g., Andrew et al., 2018; Fumagalli et
al., 2015; Hancock et al., 2011; S. E. Harris & Munshi-South, 2017;
Harrison et al., 2017; Lv et al., 2014). This suggests that temperature
is one of the most important environmental variables driving local
adaptation. Indeed, temperature has been linked to adaptive genetic
variation in other small mammals, such as populations of the recently
introduced house mouse (M. musculus ) along a latitudinal cline in
eastern North America (Phifer-Rixey et al., 2018) and populations of
the climate-sensitive American pika (Ochotona princeps ) along an
altitudinal cline (Waterhouse et al., 2018). The distribution and
abundance of C. glareolus from the Eastern lineage in a contact
zone with the Carpathian lineages correlated negatively with July
temperature, suggesting that these individuals are better adapted to
cooler conditions (Tarnowska et al., 2016), supporting temperature as a
driver of adaptive genetic variation in the bank vole.
Artificial selection experiments for higher aerobic exercise performance
in bank voles resulted in an increase in resting metabolic rate and thus
resulted in the development of increased cold tolerance (Sadowska et
al., 2015; Stawski et al., 2017). This argues for a genetic basis for
thermal adaptation in bank voles that may allow individuals under
natural conditions to adapt to colder environment by having more energy
available for thermogenesis (Stawski et al., 2017). Although the
selection regime increased cold tolerance, it also decreased the ability
to thermoregulate at higher temperatures (Grosiak et al., 2020). This
suggests that warmer temperatures may also be difficult for small
mammals to cope with, as this can easily lead to overheating (Rezende et
al., 2004). This in turn could also lead to specific metabolic
adaptations in populations at warmer climates due to increased selection
pressure. In this study, AMT differed between -1.4°C (NE3.fi) and 12.4°C
(S.it) among populations. Thus, bank vole populations in Europe are
exposed to different environmental temperatures, likely resulting in
different energetic requirements and adaptive genetic divergence in
metabolic traits throughout the species’ range.
We have identified a number of promising candidate genes that could be
considered for future research aimed at linking phenotypic and genotypic
variation. The function of these candidate genes provides insight into
the physiological processes that may have experienced selection across
climatic gradients. Different populations are exposed to different local
climatic conditions that determine the energy requirements, diet and
different pathogen or predator communities. In this context, we have
identified a number of candidate genes related to lipid metabolism and
the immune system that appear to be subject to temperature-related
selection.
Adipose tissue plays an important role in energy homeostasis and
accounts for a large portion of the energy reserves of small mammals
(Birsoy et al., 2013; Sethi & Vidal-Puig, 2007). In particular, brown
adipose tissue is important for metabolic heat production through
non-shivering thermogenesis under cold conditions (Cannon & Nedergaard,
2004; Klaus et al., 1988). Two candidate genes associated with lipid
metabolism are therefore of particular interest : the PRIP gene
encodes an enzyme that modulates lipid metabolism and serves as a
signalling molecule for non-shivering thermogenesis (Kanematsu et al.,
2019; Oue et al., 2016), and LRRC8C which encodes a structural
component of the volume-regulated anion channel in adipocytes and is
associated with the early phase of adipocyte differentiation and
diet-induced obesity (Hayashi et al., 2011; Tominaga et al., 2004).
Other candidate genes with functions related to energy homeostasis
include NTRK2 , which encodes the TrkB-receptor critical for
maintaining energy homeostasis by controlling food intake and body
weight and is responsible for regulating adaptive thermogenesis (Houtz
et al., 2021; Xu & Xie, 2016). Finally, the product of IGF1 has
wide ranging effects on metabolism by coordinating protein,
carbohydrate, and lipid metabolism in a variety of different cell types
(Baker et al., 1993; Laron, 2001). Several of the candidate genes are
associated with obesity in humans including DNAH8 (Söhle et al.,
2012), IGF1 (Berryman et al., 2013), KCNH1 (Vasconcelos
et al., 2016), LRRC8C (Hayashi et al., 2011), NTRK2 (Gray
et al., 2007) and PRIP (Yamawaki et al., 2017), suggesting that
they play a role in controlling energy homeostasis.
The results showed significant enrichment of genes related to the
regulation of respiratory burst. The respiratory burst plays an
important role in the immune system. It is a crucial reaction that
occurs in phagocytes to degrade internalised pathogens after
phagocytosis (Iles & Forman, 2002). In this context, we have identified
a number of candidate genes that play important roles in the immune
system. For example, the product of DUSP10 , which was associated
with this significant GO term, plays an important role in regulating
both innate and adaptive immune responses through its regulatory
influence on the MAPK pathway (Arthur & Ley, 2013; Seternes et al.,
2019). Two other candidate genes, BATF3 and BACH2 , both
encode transcription factors that regulate T helper cell function.
Interestingly, they also interact with each other to bind to regulatory
regions of cytokine gene loci and prevent excessive T helper response
(Kuwahara et al., 2016; Yamashita & Kuwahara, 2018). Another candidate
gene of interest is STAT4 . This gene encodes a transcription
factor responsible for the differentiation of T helper cells (Kaplan,
2005)and is part of the JAK-STAT signalling pathway that controls the
immune response to viral infections (Villarino et al., 2017). JAK-STAT
is one of the significantly enriched signaling pathways associated with
Puumala hantavirus infection in the bank vole (Rohfritsch et al.,
2018). It has also been found to play a role in the immune response to
Sin Nombre hantavirus in deer mice (Peromyscus maniculatus )
(Schountz et al., 2012, 2014). This suggests that alterations in this
gene may be related to Puumala hantavirus infections in the bank vole
populations we studied. Similar evidence for differential selection on
immune-related genes has been observed in bank vole populations along
environmental gradients at both broad and local scales, using candidate
genes (Dubois et al., 2017; Guivier et al., 2014) and more exploratory
genome-wide approaches (Rohfritsch et al., 2018; White et al., 2013).
For humans, the diversity of the local pathogenic environment is the
predominant driver of local adaptation (Fumagalli et al., 2011) and we
may speculate that pathogen environment and pathogen pressure is closely
associated to climate variation, with rapid adaptations expected due to
climate change. Taken together, the selection signals in the most
promising candidate genes (Table 2, full overview in Table S5) suggest
that the energy balance and immune system in the bank vole are important
targets of temperature-mediated, spatially varying selection.