DISCUSSION
The urban sprawl is a worldwide phenomenon deeply affecting the
environment and thus requiring fast adaptive responses in city dwellers.
While a large body of literature already describes a myriad of
phenotypic shifts in urban populations of numerous species (Chamberlain
et al., 2009; Lowry, Lill, & Wong, 2013; Sepp, Mcgraw, Kaasik, &
Giraudeau, 2017), the molecular bases of these shifts and their
evolutionary implications remain yet to be documented and understood.
This study uses genomic and epigenomic analyses to decipher the
potential molecular bases implicated in phenotypic shifts and adaptation
in several urban populations of a passerine bird, the Great tit. Note
that this species shows largely parallel phenotypic shifts across its
range in terms of morphology and life history (Biard et al., 2017,
Thompson et al., sub ). Genomic analyses revealed weak yet
significant average differentiation between urban and forest
populations, suggesting ongoing gene flow and limited drift in urban
populations. These analyses also identified a limited number of loci
putatively under strong selection, non-repeated between pairs, and
numerous loci supposedly under weaker selection, compatible with a
polygenic model of evolution. On the epigenomic side, we found weak
average differentiation of the methylome between urban and forest birds,
suggesting an absence of genome-wide epigenetic deregulations, which is
notably in line with an absence of strong genetic differentiation. In
turn, we identified several strongly differentially methylated regions
between urban and forest birds, mostly non-repeated between pairs and
hence potentially implicated with local evolution of urban populations.
Genes associated with either genomic footprints of divergent selection
or differentially methylated regions had relatively similar functions,
related to the nervous system, metabolism, immunity and behaviour, that
have been repeatedly convicted in other studies (Riyahi, Björklund,
Mateos-Gonzalez, & Senar, 2017). Hence, by identifying non-repeated
genetic and epigenetic responses among replicated forest-urban
population pairs, our findings support the hypothesis of mostly
non-parallel rapid de novo adaptation to similar environments via
both genetic and epigenetic mechanisms. Our results are in line with
accumulating evidence that polygenic adaptation and epigenetic
reprogramming may be involved in quick phenotypic shifts in response to
rapidly emerging constraints such as urbanization.
Overall genetic differentiation between populations was relatively low
(FST ranging from 0.009 to 0.034), although higher than
what has been found at a much larger scale across the species
distribution (e.g. FST around 0.01 between UK and
Spanish or between French and Spanish populations (Laine et al., 2016)).
Low but significant differentiation levels are in line with previously
documented genetic divergences between city and forest great tit
populations (Perrier et al., 2018; Senar & Björklund, 2020), and
altogether suggests important gene flow, large effective population
sizes and limited genetic drift at multiple spatial scales (Kvist et
al., 2003). This overall genetic context is particularly suitable to
search for genomic footprints of divergent selection between urban and
forest populations, which would easily be identifiable above the neutral
level of genetic differentiation.
We found a limited number of strong footprints of divergent selection,
which is in line with previous results in Montpellier (Perrier et al.,
2018), and across nine European cities (Salmón et al., 2020). Similarly
to low levels of parallelism in allele frequency changes between cities
observed by previous studies (Reid et al., 2016; Salmón et al., 2020),
and despite similarities in phenotypic shifts, none of these outliers
were shared between cities. This result suggests limited parallel
evolution, supporting a scenario of independent de novo evolution
between cities and/or different selection pressures between cities.
Indeed, there may be multiple evolutionary solutions to the same
environmental challenges (Losos, 2011) and multiple traits are linked to
the same functional outcome (Thompson et al., 2017). Besides, the
identification of numerous outliers by the multivariate framework
applied at the scale of all six sampling sites supports a model of
polygenic urban adaptation implicating multiple genes, biological
pathways and phenotypic traits (Boyle, Li, & Pritchard, 2017).
Polygenic adaptation is a reasonable expectation in urban habitats since
the multiple new environmental conditions in cities most probably result
in many novel selective pressures acting on a multitude of functional
traits (Shochat, Warren, Faeth, McIntyre, & Hope, 2006), and because
many of these traits may be quantitative, and genetically correlated
(Lande, 1979). Further polygenic analyses on more samples and more
markers (i.e. whole genome data) are however required in order to
estimate the potential effect of each genetic variant implicated in the
adaptation to life in the city (Robinson, Wray, & Visscher, 2014; Zhou
& Stephens, 2012).
Several genomic footprints of divergent selection between urban and
forest environments were in, or in the vicinity of, genes that have
already been described as playing a role in neuronal development,
behaviour or cognitive abilities. In particular, the NR4A2 gene plays an
important role in recognition of novel objects and memory in mice
(McNulty et al., 2012). Reaction to novel objects and novel food is
known as one of the main factors determining the capacity of a species
to thrive in an urban environment (Lowry et al., 2013). The DCX gene is
related to neuronal plasticity (Kim, Peregrine, & Arnold, 2006) and
experimental approaches revealed that artificial light at night induces
an overexpression of this gene linked to a change in behaviour and
expression of depressive-like behaviour in crows (Taufique, Prabhat, &
Kumar, 2018). Finally, the CHRNA1 gene is associated with aggressive
behaviour in chicken (Buitenhuis, Hedegaard, Janss, & Sørensen, 2009),
and higher aggressiveness is commonly observed and hypothesized as
adaptive in urban bird populations (Sprau & Dingemanse, 2017). Besides,
the gene ontology enrichment analysis, performed on the entire set of
genes identified via the outlier genome scan, reinforced these findings
since multiple enriched GO terms were associated with the nervous system
and stress response as well as hormonal response (Table 2). These
results are informative on the type of traits involved in avian urban
adaptation in cities and corroborate previous results from (Salmón et
al., 2020; Sih & Del Giudice, 2012; Sol, Lapiedra, & González-Lagos,
2013) suggesting that natural selection repeatedly acted on neuronal,
behavioural and cognitive traits that could contribute to the phenotypic
shifts described in urban great tits (i.e. more aggressive and
exploratory birds, with higher breath rate (Senar et al., 2017;
Torné-Noguera, Pagani-Núñez, & Senar, 2014 & Caizergues et al.
in prep ).
Contrary to the common prediction that living in cities is likely to
influence epigenomes (McNew et al., 2017; Watson et al., 2021), no
genome wide pattern of differentiation in methylation between urban and
forest great tits was detected. However, we observed a difference in
mean methylation level between birds from Warsaw and Barcelona on their
autosomes, as well as between males and females on the Z chromosome,
showing that methylation differences were identifiable. In addition, we
found no mean difference in methylation level between habitats,
revealing that urbanization did not strongly affect overall methylation
levels in a specific direction. This overall low differentiated
methylation context is perfectly suitable to investigate more localized
zones that could differ in their levels of methylation. Note that the
strong methylation contrast between males and females on sex chromosomes
(Figure 2), is in line with previous reports in vertebrates (Teranishi
et al., 2001; Waters, Capraro, McIntyre, Graves, & Waters, 2018)
showing that methylation plays a major role in sex differentiation via
regulation of gene expression and genetic imprinting.
Despite the non-significant effect of habitat on overall methylation
levels, we found a large number of DMRs between pairs of forest and
urban populations, suggesting that urbanization did affect particular
regions of the genome. DMR were less likely to occur within a gene body
than by chance, but it was not the case for promoter or TSS regions.
This latter result contrasts with Watson and collaborators (Watson et
al., 2021) who recently found an under-representation of DMR in both
gene body and regulatory regions in urban great tits from Malmö
(Sweden). Across the three cities, 35.3% of DMR were directly localized
in gene bodies, and 47.3% in TSS or promoter regions, suggesting a
potential role in gene expression modulation. Direction of methylation
patterns did not follow any consistent pattern (no over-representation
of hypo- or hypermethylated DMR in urban birds, Figure 5), in line with
Watson and collaborators’ analyses on blood sample. However, birds in
Barcelona presented significantly more hypomethylated DMR than
hypermethylated ones, a result that warrants further investigation.
Only a limited number of urbanization-linked DMR were shared between two
or more locations. In contrast, three times more sex linked DMRs were
found in two locations or more. This comparison suggests that
urbanization-linked epigenetic modifications most probably do not occur
in a parallel way across cities, but rather that each city might have
its particular epigenetic response. Indeed, in the emerging field of
urban evolutionary biology, cities are often regarded as valuable
replicates of human-altered habitats (Donihue & Lambert, 2015;
Santangelo et al., 2020), and it is often expected that parallel
adaptive responses to similar selective pressures will occur. This
expectation is particularly strong when phenotypes show parallel
changes, as is the case for the Great tit, which is consistently smaller
and lays earlier and smaller broods in the various cities where it has
been studied, compared to forest habitats (Caizergues, Grégoire, &
Charmantier, 2018; Seress, Sándor, Evans, & Liker, 2020). However, as
discussed above, parallel adaptation to similar environmental conditions
should not be expected in the case of independent evolution, especially
for multilocus traits. Additionally, cities are different from each
other because of a wide array of climatic, cultural, historical and
socioeconomic factors (Szulkin et al., 2020). In fact, besides the
obvious differences in cities’ climatic conditions depending on their
position on the globe, land use, fragmentation and pollutants levels can
also largely vary across cities (Cárdenas Rodríguez, Dupont-Courtade, &
Oueslati, 2016). In a general way, pollutants are known to affect DNA
methylation and result in both hypo and hypermethylation, but the
patterns of change observed largely rely on the pollutant involved
(Head, 2014). Hence differences in cohorts of pollutants present in
cities could be responsible for differences in patterns of methylations.
Taken together, the results of the present study highlight the
importance of questioning the assumption that cities are replicated
environments that can be considered similar, and parallel evolution
across cities may be the exception rather than the norm.
As mentioned earlier, increasing evidence suggests that DNA methylation
can be associated with environmental and stress factors (env: (Foust et
al., 2016), stress: (Sun et al., 2018)) especially during early
development (Meaney & Szyf, 2005). Here, we found four genes (POMC,
ADAMTS3, PAPD4 et GCC1), associated with DMR that were previously
described in great tits as undergoing major changes in methylation
levels in case of exposure to higher levels of pollutants (Mäkinen, van
Oers, Eeva, Laine, & Ruuskanen, 2019). Notably, the functions of these
genes remain to be determined, and they could thus be interesting to
target in future studies. In addition, the past literature has
repeatedly found SERT and DRD4 as two major genes involved in
urban-specific avian human avoidance (or wariness) behaviours (see for
example in the Great tit (Riyahi et al., 2015), in the blackbird
(Garroway & Sheldon, 2013), in the black swan (Van Dongen, Robinson,
Weston, Mulder, & Guay, 2015) & in the burrowing owl (Mueller et al.,
2020) ; see SI Figure S7 to S11 to see patterns of methylation
associated with 6 classical great tit linked candidate genes). In this
study, while urban great tits show higher levels of aggressiveness in at
least two of the cities (Riyahi et al., 2017 & Caizergues et al.
in prep ) we found no DMR associated with these two genes in either of
the three forest-city pairs. However, we found a significant
urban-related change in methylation linked to the DRD3 gene, belonging
to the same gene family as DRD4 and known to be similarly involved in
chicken aggressive behaviour (Z. Li et al., 2016). In line with these
results, GO analyses revealed enrichment in genes associated with
neuronal functions, behaviour, but also blood, immune and endocrine
systems (Table 2, Figure S5), revealing the potential need of
physiological adjustments in urban habitats. Surprisingly, a recent
study on great tit differences of methylation between city and forest
habitats in another European city found no GO enrichment in blood, while
some in liver tissue (Watson et al., 2021) (note that they investigated
DMSs, Differentially Methylated Sites, which differs from DMRs
identified here). These contrasted results highlight the fact that
methylation patterns highly depend on the analysed tissues (Derks et
al., 2016), and show, once more, that urban linked methylation might not
be similar from one city to another. In addition, it has been
demonstrated that DNA methylation can undergo seasonal variation
(Viitaniemi et al., 2019). Hence, analyses on multiple tissues and
life-stages replicated in multiple pairs of urban and forest populations
are required, to draw a broader view of the impact of urbanization on
global methylation patterns and to understand replicated parallel
occurrence across cities. However, tissue-specific and age-specific
analyses in multiple individuals across several pairs of urban and
forest environments poses major technical, budget and ethical
limitations and should be coordinated very carefully. Additionally,
specific drivers of shifts in methylation remain to be disentangled to
understand which environmental factors are responsible for which change
in methylation. To do so, experimental settings manipulating
environmental factors such as performed by Mäkinen and collaborators
(Mäkinen et al., 2019) would be particularly useful. More integratively,
information on how shifts in methylation patterns affect phenotype,
fitness, and adaptation, often remain elusive (Sepers et al., 2019). To
our knowledge, a limited number of studies attempted to link methylation
and expression levels in natural population contexts (Derks et al.,
2016; Laine et al., 2016), and even fewer in urban habitats (but see
e.g. McNew et al., 2017; Watson et al., 2020). Hence, future work might
need to tackle the question of the origin and adaptive significance of
these variations in a controlled framework.
This study identified both genomic footprints of selection and
differentially methylated regions between urban and forest populations,
suggesting that both genetic and epigenetic processes could play an
important role in rapid adaptation to urban habitat. To our knowledge,
our study is the first to use replicated pairs of cities and forest
populations to study modifications of methylation in urban habitats.
This study design revealed limited evidence for parallelism between
cities both at the genetic and epigenetic levels, suggesting that cities
might not present exactly similar environmental conditions or that
different genetic and epigenetic pathways are involved in adaptation to
urban environmental conditions, although possibly associated with
similar biological functions. This study finally highlights the need to
unravel both environmental origins and evolutionary implications of
methylation shifts, in order to understand to which extent environmental
induced methylation can contribute to adaptation.