Relationships between environmental variables and mucus
transcriptomic profiles
As the transcriptome responds to environmental changes, differences and
similarities in environmental variables may explain mucus transcriptome
variation among lake trout populations (Oomen & Hutchings, 2017). The
high degree of transcriptomic similarity among L224 and L373 populations
could potentially be explained by the fact that both lakes were fairly
oligotrophic whereas L260 and L223, while still oligotrophic, tended to
be more productive. However, comparison of results from the water
quality PCA and transcriptome PCA suggests there are environmental
factors not accounted for in the study. For example, while L260 and L373
were most similar in their water quality characteristics (Figure 6),
lake trout from those lakes deviated from each other in terms of their
transcriptomic profiles (Figure 2). Therefore, it is likely that there
were other abiotic and biotic variables influencing gene expression
changes other than those taken into account in the water quality PCA,
such as pathogen loading, epigenetics, or food availability.
While PCA was used to characterize similarity in water quality profiles
among lakes, a linear mixed model was used to quantify the percent
variance in transcriptome-wide gene expression explained by those water
quality variables. As the linear mixed model accounted for 68.1% of
transcriptional variance, results suggest that variation in water
quality contributes to transcriptional differences among lake trout
populations (Figure 7). Conductivity, chlorophyll a, alkalinity, and
dissolved oxygen explained the greatest proportion of variance within
the transcriptome, suggesting these variables had the greatest influence
on gene expression differences among lakes (Figure 7). Variables
identified by the linear mixed model as accounting for the most
transcriptomic variation among lake trout populations were also among
the variables identified in the PCA as driving inter-lake water quality
differences. However, a median of 31.9% of transcriptomic variance was
not explained by the water quality variables in the model. Therefore,
both the water quality PCA and linear mixed model suggest that the
variables considered in the present study did not fully explain
transcriptional variance and, thus, other variables could be driving
transcriptomic differences among populations albeit similar water
quality conditions. As suggested by differential gene expression data,
an additional source of transcriptional variance among populations may
stem from presence of pathogens. For example, despite their fairly
similar water quality profiles, the divergence in transcriptomic
profiles of L223 and L260 lake trout may be partially explained by the
strong immune response in L223. Overall, it is challenging to robustly
quantify the effects of environmental variables on gene expression
patterns as transcription may be affected by even small changes in
environment (Alvarez et al., 2015).
Aside from environmental factors, epigenetic modifications are another
potential source of gene expression variation among populations, with
studies showing that genes may be transgenerationally dysregulated
following exposure to stressors in prior generations. Transgenerational
alterations in the transcriptome are well-illustrated in a study which
examined responses to increased temperature across generations in common
reef fish (Acanthochromis polyacanthus ) (Veilleux et al., 2015).
Compared to controls, fish that were transgenerationally exposed to
higher temperatures showed upregulation of metabolic, immune, and
stress-responsive genes, likely an adaptive mechanism to maintain
performance and cope with higher temperatures. Epigenetic modifications
are highly possible in lake trout populations at IISD-ELA as L260 and
L223 have been used for whole-lake experiments in the past. In an effort
to understand population-level impacts of environmental estrogens, L260
was subject to whole-lake ethynylestradiol additions for three years
starting in 2001, resulting in feminization and a near extirpation of
fathead minnow (Pimephales promelas ) as well as population
declines of lake trout (Kidd et al., 2007; Palace et al., 2009). To
simulate the effects of acid precipitation on freshwater lakes, sulfuric
acid was added to L223 over a three year period from 1976 to 1978
(Schindler, Wagemann, Cook, Ruszczynski, & Prokopowich, 1980),
resulting in disappearance of fathead minnow, increased lake trout
embryonic mortality and deformities, and decreased survival of lake
trout due to emaciation (Kennedy, 1980; Mills, Chalanchuk, Mohr, &
Davies, 1987; Nero & Schindler, 1983; Schindler & Turner, 1982).
Interestingly, L224 and L373, which have not been subject to
experimental modifications, had highly similar transcriptomic profiles,
whereas the experimentally-modified L260 and L223 diverge from all other
lakes (Figure 2); these observations suggest that epigenetic
modifications in response to past stressful conditions may contribute to
the observed transcriptional variations.
As the transcriptome reflects both short-term plastic responses and
transgenerational plasticity (Oomen & Hutchings, 2017), it is difficult
to adequately discern whether transcriptional variation among
populations stems from environmental changes or transgenerational
alterations. Although concrete identification of explanatory variables
requires further development, RNA sequencing of mucus provides
meaningful information on transcriptomic variation among fish
populations and can be used to identify molecular differences driving
variation in a nonlethal manner.