Identifying the molecular mechanisms contributing to
fitness-associated phenotypic variation in natural populations is a
major goal of molecular ecology. However, the multiple regulatory steps
between genotype and phenotype mean that many potential regulatory
mechanisms can evolve to influence trait divergence. To date, the role
of transcriptional regulation in local adaptation has received the most
focus, as we can readily measure mRNA quantity and have a reasonable
grasp of how variation in the expression of many protein-coding genes
might influence phenotype. Thus, studying the evolution of protein
coding gene mRNA abundance in candidate tissues has led to some
successes in detecting the molecular mechanisms underlying local
adaptation (reviewed by Hill et al. 2021). However, the contribution of
differential splicing of precursor mRNA (pre-mRNA) to adaptive
differentiation, as well as the loci controlling this variation, remains
largely unexplored in wild populations. In their “From the Cover”’
article in this issue of Molecular Ecology, Jacobs and Elmer (2021)
re-analzye muscle RNA sequencing (RNA-seq) data to quantify the relative
contributions of variation in mRNA quantity (differentially expressed
“DE” genes) and splice variant identity (differentially spliced “DS”
genes) to parallel ecotypic divergence of wild “benthic” and
“pelagic” Arctic charr (Salvelinus alpinus). They found little
overlap in the identity and biological functions of DE and DS genes,
suggesting that these two regulatory mechanisms act on different
cellular traits to complementarily alter organismal phenotype.
Furthermore, many DE and DS genes could be mapped to cis-acting
QTL, arguing that some of this regulatory divergence is genetically
based. DE and DS genes were also more likely to be “hub genes” than
their non-divergent counterparts, hinting that this regulatory variation
may have a variety of meaningful phenotypic effects. The comparison of
three independently evolved pairs of benthic and pelagic charr uncovered
greater than expected parallelism in both expression and splicing
between ecotypes across different lakes, supporting a role for these
molecular phenotypes in adaptive divergence. Overall, Jacobs and Elmer’s
(2021) findings highlight the importance of DS as a potential mechanism
underlying local adaptation and provide a framework for others hoping to
make the most of their RNA-seq data.
Species that have repeatedly evolved comparable phenotypes in response
to similar selective pressures provide a unique opportunity to
investigate the mechanisms and predictability of evolution (Elmer and
Meyer 2011). The great phenotypic diversity and recent, repeated
post-glacial evolution of Arctic charr ecotypes in many locations across
this species’ Holarctic distribution makes them a good model for linking
genotypes to ecologically-relevant phenotypes (Klemetsen 2010). Two
commonly occurring ecotypes are the “benthic” form that feeds in the
littoral zone and the “pelagic” form that feeds on plankton in the
limnetic zone. Living in these different habitats has led to adaptive
divergence in feeding morphology and swimming activity (Klemetsen 2010).
In prior work, Jacobs et al. (2020) found that the evolution of this
ecotype-specific feeding morphology is associated with significant
parallelism in both genetic and gene expression divergence. However, as
in most transcriptomic studies, the potential contributions of splicing
was not directly assessed (Fig. 1). In contrast to differential gene
expression, which results in a quantitative difference in the number of
mRNA transcripts (a balance between transcription and degradation),
alternative splicing leads to a qualitative difference in the
transcriptome, based on which exons are included in the final mRNA
transcript prior to translation (Fig.1). If alternatively spliced
transcripts vary in their stability or function, splice variation can
also influence the quantity of specific protein isoforms. A major goal
of Jacobs and Elmer (2021) was to test for ecotype-associated variation
in mRNA splicing and contrast this form of regulatory variation to
divergence in mRNA quantity. To accomplish this, they employed a novel
suite of analyses on RNA-seq data collected from white muscle samples (a
functionally-relevant tissue given swimming differences between
ecotypes) of wild benthic and pelagic fish in three Scottish lakes (Awe,
Tay, Dughaill; Jacobs et al., 2020).
Differential gene expression between ecotypes was re-assessed using
Deseq2; this analysis is based on a gene-level count, so is not entirely
independent of splicing variation. Differential splicing was assessed
using two techniques: differential exon usage (using Dexseq) and
differential intron excision (using Leafcutter; Li et al. 2018). These
analyses revealed that DE genes differed from those which were DS and
were associated with different GO and regulatory processes (Fig.2a).
However, both DE and DS genes were enriched for GO terms related to DNA
regulation. In addition, DE and DS genes both occupied more central
positions in gene co-expression networks than non-differentiated genes
(Fig. 2b), suggesting increased pleiotropy. DS, but not DE, genes were
also associated with more GO terms, than non-differentiated genes,
suggesting that DS genes may have greater pleiotropy than DE genes.
These data match findings from tissue-specific splicing QTLs (sQTLs) in
humans, which were found to be more pleiotropic and have a stronger
effect on phenotype than expression QTLs (eQTLs) (Garrido-Martín et al.
2021). Thus, studies of sQTL may be a critical addition to our toolkit
used to find the genetic basis of adaptation.
As these tissue samples were collected from wild fish populations living
in diverse environments, the relative contributions of genetically based
variation versus pure phenotypic plasticity to the observed
transcriptional variation is unclear. Given Arctic charr’s known high
capacity for plasticity (Klemetsen 2010), Jacobs and Elmer (2021) tested
if transcriptomic variation has evolved by conducting QTL mapping ofcis- variation associated with differential expression (eQTL) and
splicing (sQTL). Despite limited power (n=4 per ecotype per lake), they
were able to find many such loci, suggesting that at least some of the
observed molecular divergence has a genetic basis. cis- eQTL and
sQTL mapping also found that expression and splicing variation across
ecotypes and lakes were controlled by distinct loci (Fig. 2c), but few
of these loci were outliers. Since trans -eQTL/sQTL , which could
not be located in this study, are predicted to co-regulate the
expression and splicing of multiple genes (reviewed by Hill et al.
2015), further studies are needed to conclusively test the extent to
which these molecular phenotypes are controlled by different loci.
When studying the mechanisms of adaptation, a key question is the level
of repeatability, and thus evolutionary predictability, we see in
populations independently adapting to a given environment. Parallelism
can indicate that phenotypes of interest are the result of adaptation,
and is predicted to increase when selective pressures are similar,
populations have high levels of shared genetic variation, and there are
strong genetic, developmental or physiological constraints (reviewed by
Rosenblum et al. 2014). Both alternative splicing and expression showed
significant parallelism among ecotypes (Fig. 2d). While little is known
about the constraints on ecotypic adaptation, the effects of shared
genetic variation are likely at play, as all lakes were recently
colonized by a common glacial lineage (Atlantic lineage) after
Scotland’s deglaciation ~10 - 15 000 years ago.
Interestingly, Jacobs and Elmer (2021) found that in some instances
where DS genes were identical across lakes, the specific way these genes
were differentially spliced was non-identical across lakes. This finding
matches prior work suggesting that the extent of convergence or
parallelism may increase with increasing levels of biological
organization, due to the hierarchical nature of biological traits. For
example, many different mutations could lead to convergent differences
in mRNA expression (Jacobs et al. 2020). It will be interesting to see
if non-parallel transcriptional regulatory changes found in replicate
charr populations might lead to convergent changes in swimming muscle
size or function among ecotypes, as found in benthic and limnetic Lake
Whitefish (Coregonus clupeaformis ) populations (Dalziel et al.
2017).
In sum, the work of Jacobs and Elmer (2021) suggests that
post-transcriptional processes may regulate adaptive differentiation and
therefore deserve further attention as potential mechanisms contributing
to ecological divergence. Additionally, this study provides a blueprint
for those interested in measuring variation in pre-mRNA splicing in
their own RNA-seq datasets; Leafcutter’s ability to investigate
differential intron excision from just a genomic reference, without the
need for an annotated genome, will be especially useful for molecular
ecologists studying non-genomic model organisms (Li et al. 2018).