Toll-related immune genes are evolving recurrently in D. innubila
likely due to strong pathogen pressures
We next sought to identify genes and functional categories showing
strong signatures of adaptive evolution, suggesting recurrent evolution
as opposed to recent local adaptation. We reasoned that if the
population differentiation seen in antifungal genes and cuticle
development proteins (Figure 2 & 3, Supplementary Figure 4) was due to
local adaptation also acting over longer time periods, we would expect
to see signatures of adaptation in those categories. Furthermore, Hillet al. used dN/dS-based statistics to show that genes involved in
some immune defense pathways were among the fastest evolving genes in
the D. innubila genome (Hill et al. 2019). We
also sought to identify what genes are evolving due to recurrent
positive selection in D. innubila in one or all populations, and
if this is associated with environmental factors. To this end we
calculated the McDonald-Kreitman based statistic direction of selection
(DoS) (Stoletzki and Eyre-Walker, 2011) and SnIPRE selection effect
(Eilertson et al., 2012) to identify an excess of selection. We then fit
a linear model to identify gene ontology groups with significantly
higher DoS or selection effect than expected. In this survey we find
cuticle genes and antifungal genes did have some signatures of adaptive
evolution (DoS > 0 and selection effect > 0
for 80% of genes in these categories) but as a group showed no
significant differences from the background (GLM t-value = 1.128,p -value = 0.259, Supplementary Table 4). In fact, we only found
two functional groups significantly higher than the background, Toll
signaling proteins (GLM t-value = 2.581 p -value = 0.00986,
Supplementary Table 3) and antimicrobial peptides (AMPs, GLM t-value =
3.66 p -value = 0.00025, Supplementary Table 3). In a previous
survey we found that these categories were also the only functional
groups to have significantly elevated rates of amino acid divergence
(Hill et al. 2019). These results suggest that this
divergence is indeed adaptive.
Interestingly, D. innubila is burdened by Drosophila innubila
nudivirus (DiNV), a Nudivirus that infects 40-50% of individuals in the
wild (Unckless 2011a; Hill and Unckless 2020). A close
relative of the virus suppresses Toll-regulated AMPs in D.
melanogaster (Palmer et al. 2018; Hill and
Unckless 2020), which might explain why the Toll pathway and AMPs are
fast evolving in D. innubila. Five AMPs showed consistently
positive DoS and selection effect values (which are also among the
highest in the genome): four Bomanins and Listericin . All
are AMPs regulated by Toll signaling (and additionally JAK-STAT in the
case of Listericin ) (Hoffmann 2003; Takeda and
Akira 2005). Listericin has been implicated in the response to
viral infection due to its expression upon viral infection
(Dostert et al. 2005; Zambon et al.2005; Imler and Elftherianos 2009; Merkling and van
Rij 2013). For all immune categories, as well as cuticle proteins and
antifungal proteins, we find no significant differences between
populations for either MK-based statistics, and no significant
differences in the distribution of these statistics between populations
(GLM t-value < 0.211, p-value > 0.34 for all
populations, Supplementary Table 3). Thus, perhaps selection at these
loci is ubiquitous and genes flow between populations homogenizes that
signature.
Mutation rates, efficacy of selection and population structure can vary
across the genome, which can confound scans for selection
(Charlesworth et al. 2003; Stajich and Hahn
2005).To work around this, we employed a control-gene resampling
approach to identify the average difference from the background for each
immune category (Chapman et al. 2019). Consistent with
our results previous results, we find no signatures of recurrent
positive selection in antifungal genes (Supplementary Figure 10, 61%
resamples > 0) or cuticle genes (Figure 6, 54% resamples
> 0) but do again find extremely high levels of positive
selection in AMPs (Figure 6, 100% resamples > 0) and Toll
signaling genes (Figure 6, 99.1% resamples > 0).
Segregating slightly deleterious mutations can bias inference of
selection using McDonald-Kreitman based tests (Messer and
Petrov 2012). To account for this bias, we also calculated asymptotic α
for all functional categories across the genome (Haller and
Messer 2017). To this end we calculated the asymptotic α for all
functional categories across the genome (Haller and Messer
2017). As before, while we find signals for adaptation in antifungal and
cuticle proteins (asymptotic α > 0), we find no evidence of
higher rates of adaptation than the background (Supplementary Figure 10,
permutation test Antifungal p -value = 0.243, Cuticlep -value = 0.137). Again, the only categories significantly higher
than the background are Toll signaling genes (Permutation testp -value = 0.033) and AMPs (Permutation test p -value =
0.035). Together these results suggest that while genes involved in
antifungal resistance and cuticle development are evolving adaptively,
it is not recurrent across the whole functional category, instead only
occurring in one or two specific genes. Alternatively, the adaptation
may be too recent to detect signal using these metrics. Long-term
recurrent adaptation appears to be driven by host-pathogen interactions
(likely with DiNV (Hill et al. 2019)) as opposed to
local adaptation.
Figure 6: McDonald-Kreitman based statistics for immune
categories in D. innubila and cuticle development. The
left two plots show estimated statistics (Direction of Selection and
Selection Effect) for each gene. The right two plots show the difference
in average statistic (Direction of Selection and Selection Effect) for
each gene and a randomly sampled nearby gene.