This study provides key evidence for two critical genetic architecture component effects on the gut microbiome composition of Chinook salmon reared in a common environment. The first component is characterized by significant hybrid cross effects acting among populations at bacterial community and ASV-levels, reflective of strong inter-population genetic divergence effects. Based on the published literature for fish, we expected to find among-population gut microbiome differences, indicative of previously reported host genetic divergence effects and the known role of the microbiome in assisting the hosts to cope with their environment (Sullam et al., 2015; Webster et al., 2018). The second component constitutes cross-specific significant sire effects, indicative of the first report of additive genetics effects acting within populations on fish microbiome composition at the community level. The sire effects observed in the microbiome composition were surprising: Although there is no literature on the effects of additive genetics in fish, studies in humans show they contribute minimally in humans (Yatsunenko et al., 2012; Kurilshikov, Wijmenga, Fu & Zhernakova, 2017; Rothschild et al., 2018; but see Goodrich et al., 2014b). While we did not find hybrid-cross or sire effects on alpha-diversity, we did find significant, but small and rare, pen effects on alpha-diversity reflective of environmental effects. Pen effects were not expected as the replicate pens were designed to be as similar as possible (size, water quality, feeding regime, etc.); however, these differences are likely due to the generally reported high magnitude of environmental drivers on the microbiome (Wu et al., 2013; Goodrich et al., 2014a; Sullam et al., 2015; Rothschild et al., 2018). Overall, this study presents a rare but much needed approach to studying genetic architecture and putative host-genotype effects on the fish microbiome.
Despite rearing fish in a common environment, we found significant and consistent hybrid-cross effects on the composition of the microbiome, which reflect, primarily, additive among-population genetic architecture effects. Indeed, microbiome composition is known to vary in response to host origin (i.e. wild vs. domesticated) and among populations from wild and domesticated origins in Atlantic salmon (Webster et al., 2018). However, in contrast to previous studies (Webster et al., 2018), we found no differences in microbiome community spread among populations, further supporting that the observed differences in microbiome composition are due to average compositional differences. The patterns of microbiome divergence observed among populations here, and in other studies, are critical to determining the evolutionary history of the host and microbiome interaction. Specifically, such population-level effects are consistent with host-microbiome co-divergence (Sullam et al., 2015) and have been proposed to contribute to host local adaptation (Sullam et al., 2015; Webster et al., 2018). Despite this, environmental contributions to microbiome diversity often complicate the interpretation of microbiome divergence among locally sourced populations (Sullam et al., 2015). For example, diet was previously suspected to be the major factor driving microbiome composition differences among populations of Atlantic salmon (Webster et al., 2018), and differences were observed in microbiome composition among various habitats of Trinidian guppies (Sullam et al., 2015). These environmental contributions may be strong in the wild, as these environments offer more diverse feeding options (Smith, Snowberg, Caporaso, Knight & Bolnick, 2015; Sullam et al., 2015), ecosystem-specific ecological interactions (Sullam et al., 2015) and variation across different life-stages and their associated habitats (Llewellyn et al., 2015). As such, since all our fish were reared in a common hatchery/netpen environment, our alpha diversity indices across all population-crosses matched those of salmonids sampled from hatchery environments (Webster et al., 2018) more closely to those caught in the wild (Llewellyn et al., 2014 Webster et al., 2018), in spite of the wild-hybrid status of the majority of the hybrid crosses. Furthermore, some genetic architecture components, such as maternal effects, include both environmental and dam effects (Aykanat, Bryden & Heath 2012a). Such effects are known to contribute substantially to among-population phenotypic variation in Chinook salmon for life history and fitness-related traits (Aykanat et al., 2012a; Aykanat, Heath, Dixon & Heath 2012b). Thus, we emphasised studying the contribution of cross and sire effects while limiting the potential for maternal effects by using a common dam in our breeding design (Semeniuk et al., 2019).
Three broad patterns consistently emerged across our analyses: 1) YIAL, exhibited the most divergent microbiome composition at the ASV- and community levels; 2) CHILL possesses an intermediate microbiome community composition similar to YIAL but less divergent from other crosses; 3) The remaining crosses clustered away from YIAL and CHILL. As expected, the fully-domesticated cross, YIAL, showed the most divergent microbiome. It is likely that these differences derive from strong domestication selective pressures experienced within the YIAL production stock, perhaps driving rapid divergence in both the host and gut microbiome community. Despite YIAL having an outlier microbiome, it was not significantly different from CHILL. Additionally, pairwise comparisons showed that CHILL was different from BQ (Jaccard distances), and CAP (Jaccard and Bray-Curtis). Taken together, these patterns indicate that CHILL possesses an intermediate microbiome community composition similar to YIAL but less divergent from other populations. Interestingly, using the same study system and hybrid crosses described here, CHILL was shown to vary from the other hybrid crosses in related studies. For example, in a study designed to detect gene expression differences among and within the hybrid crosses, CHILL exhibited a marked difference in gene transcription profile relative to the other hybrid cross stocks (including YIAL) consistent with the divergence observed pattern in this study (Toews, Wellband, Dixon & Heath, 2019). Furthermore, over the entire production period, CHILL was found to exhibit the lowest survival relative to the other crosses (Semeniuk et al., 2019). If this difference represents population additive genetic variance specific to CHILL, then these differences could be explained by selection, including those of anthropogenic stressors, or genetic drift (Yeaman & Otto, 2012). Here, we consider the potential of those processes in contributing to CHILL's intermediate microbiome. It is known that Chilliwack River (CHILL) channels have experienced two main anthropogenic stressors in the forms of extensive forest harvesting (Boyle, Lavkulich, Schreier & Kiss, 1997) and road building (Blackwell, Picard & Foy, 1999), introducing woody debris and sediments into the river, respectively. Furthermore, large floods were experienced in the CHILL between the years of 1952 and 1980 (Ham, 1996), but the impact of those floods on Chilliwack River Chinook salmon stocks is unknown (Bradford, 1995). While we cannot rule out potential genetic drift (Whitehead, 2012), we consider evidence from patterns to support selection (Kawecki & Ebert, 2004) among hybrid-crosses at putatively functional sequence (ASV) level. For instance, CHILL, showed the most extreme counts of the lactic acid bacteria (LABs), frequently showing significantly higher relative abundances in pairwise contrasts to other crosses. Lactic acid bacteria are known contribute favourably to host health in fish (Ingerslev et al., 2014; He, Chaganti & Heath, 2018). If higher LAB abundance is indeed adaptive, this may explain the why CHILL had the survival observed at the sampling (saltwater) incubation phase (Semeniuk et al., 2019). Moreover, YIAL and CHILL harboured higher counts of ASVs known to exhibit biochemical and ecological versatility such as Comamonadaceae (Willems, 2014). While not conclusive, these results point towards non-neutral co-divergence in host genetic architecture and microbiome community structure. Further work is needed to characterize the effects culminating in the formation of divergent microbiome community compositions among population crosses.
This study presents the first report of within-population additive genetic variance effects on the microbiome composition in fish. Additive genetic variation is a critical component of the overall genetic architecture for any trait, as it defines the scope for traditional evolutionary response to selection (Gjedrem, 1983; Garcia de Leaniz et al., 2007; Visscher et al., 2008; van Open, Oliver, Putnam & Gates, 2015). Although within-population microbiome variation was previously found among unrelated families of rainbow trout (Naverrete et al., 2012), estimates of additive genetic variation for fish gut microbiomes are lacking. In the breeding design used in this study, variation among sires within stocks was estimated using half-sibling families as a measure of additive genetic variance, since a common egg source (i.e. highly inbred females combined) was used for all crosses. Given this breeding design and previous reports of low additive genetic variance, we expected that no additive genetic effects would be observed in this study. Indeed, compared to additive among-crosses variance, sire effects were low, indicating that additive genetics contribute to overall microbiome composition. This finding is in agreement with studies in other vertebrates demonstrating a small influence of additive genetic variance on microbiome variation in cows (Difford et al., 2018), mice (Leamy et al., 2018), and humans (Yatsunenko et al., 2012; Kurilshikov et al., 2017; Rothschild et al., 2018; Brüssow 2020). Despite the overall low additive genetic variance contribution to microbiome variation across all study crosses, we found significant, cross-specific, additive genetics effects on the on the microbiome composition at the community level of the microbiome. This is observed for NIT (Using Bray-Curtis and Jaccard pairwise comparisons), and CHILL (Jaccard), indicating that the natal environments of sires from these population crosses select for more diverse microbiomes. A previous study showed that microbial quantitative trait loci (mbQTLs) interact with host immunity to shape the gut microbiome in humans (Kurilshikov et al., 2017). Additionally, MHC class II genotypes contribute to the regulation of the microbiome composition among hosts in a sex dependent manner in three-spine stickleback (Gasterosteus aculeatus; Bolnick et al., 2014b). Variation in underlying genetic architecture (specifically additive genetic variance) among populations is critical to predict a population’s immediate response to selection and are a requisite for artificial selection-based commercial (e.g. aquaculture) and non-commercial (e.g. conservation and restoration) breeding applications (Gjedrem, 1983; Falconer & Mackay, 1996; Visscher et al., 2008; van Open et al., 2015).
Pairs of replicate net pens for each hybrid cross were used to allow the partitioning of possible environmental effects; however, our use of common rearing environments and matched net pens made strong environmental effects on gut microbiome unlikely. Nonetheless, replicate pen effects were found for the Chao1 index, corroborating previous studies (Schmidt et al., 2016). Given that the Chao1 index gives more weight to low abundance species (Kim et al., 2017), this may indicate that microbiome alpha diversity is strongly influenced by the environment. These environmental effects may be explained by fine-scale environmental heterogeneity. Such effects can drive subtle phenotypic differences, often complicating the study of local adaptation, or genetics, in host-microbe systems (Kaltz & Shykoff, 1998, Savolainen et al., 2013). Furthermore, we suspect that uncontrollable variation in social interactions among individuals may exist within pens (Gilmour et al., 2005), and drive microbiome differences between replicates. This emphasizes the challenge in minimizing the effect of the environmental factors driving the gut microbiome, which have been shown to dominate host-related factors in humans (Wu et al., 2013; Rothschild et al., 2018).
In conclusion, our study shows a rarely reported pattern of population-level variation in the gut microbiome community in fish. Such a pattern is consistent with local adaptation, perhaps due to strong selection associated with seven generations of domestication combined with local selection forces acting to create divergent microbiome community compositions. Inter-population effects were the largest and most consistent drivers of gut microbiome variation among the hybrid cross stocks. Additive genetic variance contributed to microbiome community variation in a cross-specific manner, and insignificantly to overall microbiome composition. Although pen effects contributed insignificantly to community composition, rare significant effects were found for alpha diversity. Microbiome ASV-level effects were found to be population-specific, further supporting the role of local population effects driving microbiome structure, despite rearing in a common environment with a common dam. Our results highlight the importance of preserving genetic variation in Chinook salmon to respond to environmental heterogeneity especially in the face of oceanic climate changes and habitat degradation from urban development and anthropogenic practices.