© 2006 International Council for the Exploration of the Sea
Temporal variation in an immune response gene (MHC I) in anadromous Salmo trutta in an Irish river before and during aquaculture activities
a Department of Zoology, Ecology and Plant Science/Aquaculture and Fisheries Development Centre, Environmental Research Institute, National University of Ireland Cork, Ireland
b Marine Institute, Catchment Management and Aquaculture Services Newport, County Mayo, Ireland
c Norwegian Institute for Nature Research 7485 Trondheim, Norway
d Scottish Fish Immunology Research Centre, University of Aberdeen Zoology Building, Tillydrone Avenue, Aberdeen AB24 2TZ, Scotland, UK
*Correspondence to T. F. Cross: tel: +353 21 4904652; fax: +353 21 4904664. e-mail: t.cross{at}ucc.ie.
Several studies have documented the genetic effects of intraspecific hybridization of cultured and wild Atlantic salmon (Salmo salar L.). However, the effect of salmon aquaculture on wild congeners is not so well understood. Diseases, introduced or increased in incidence by salmon aquaculture activities, may have an impact on co-occurring wild sea trout (Salmo trutta L.), as implied by the steep decline in sea trout numbers in many Irish, Scottish, and Norwegian rivers since the late 1980s, which may be linked to sea lice infestations associated with marine salmonid farming. Our data suggest that salmon farming and ocean ranching can indirectly affect, most likely mediated by disease, the genetics of cohabiting sea trout by reducing variability at major histocompatibility class I genes. We studied samples of DNA extracted from scales of sea trout in the Burrishoole River, in the west of Ireland, before and at intervals during aquaculture activities. In these samples, allelic variation at a microsatellite marker, tightly linked to a locus critical to immune response (Satr-UBA), was compared with variation at six neutral microsatellite loci. A significant decline in allelic richness and gene diversity at the Satr-UBA marker locus, observed since aquaculture started and which may indicate a selective response, was not reflected by similar reductions at neutral loci. Subsequent recovery of variability at the Satr-UBA marker, seen among later samples, may reflect an increased contribution by resident brown trout to the remaining sea trout stock.
Keywords: MHC, microsatellites, Salmo trutta, sea trout, temporal variation
Received 11 November 2005; accepted 31 March 2006.
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Introduction |
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The major histocompatibility complex (MHC) genes of vertebrates comprise two major sub-families of MHC loci: the class I and class II MHC genes, which play a critical role in controlling immune responses via self/non-self recognition. MHC loci have been extensively studied in fish (Dixon et al., 1995; Grimholt et al., 2002; Stet et al., 2002; Consuegra et al., 2005); in salmonids as in other teleosts, the class I and class II loci are not physically linked and, therefore, are simply referred to as major histocompatibility (MH) genes (Grimholt et al., 2003). In tank experiments with Atlantic salmon (Salmo salar L.), differential survival of individuals with particular alleles and genotypes at MH class I and class II loci was observed in response to challenges with Aeromonas salmonicida, the causative agent of furunculosis, and with infectious salmon anaemia virus (Grimholt et al., 2003). That study used the presence of embedded variable number tandem repeat (VNTR) loci, a microsatellite in the 3'UTR of MH class I, and a minisatellite in the 3'UTR of MH class II to type a large number of challenged fish.
Salmonid culture in freshwater hatcheries began in Ireland during the late 1950s and expanded rapidly to provide smolts for ocean ranching and, later, for marine cage farming. These activities have a number of inherent risks for natural salmonid populations, and recent studies have demonstrated the potential for direct and indirect genetic effects on wild Atlantic salmon populations (McGinnity et al., 1997, 2003). Youngson et al. (1993) investigated levels of hybridization between cultured salmon and wild trout, which may be defined as a direct effect on a wild congener. However, the observed associations between MH alleles/genotypes and disease resistance or susceptibility (Grimholt et al., 2003) may indicate the possibility of an indirect genetic effect of cultured Atlantic salmon on wild conspecifics or congenerics, the brown trout, Salmo trutta L., in this case, where disease organisms originating or emanating above normal background levels from cultured fish or associated activity are transferred to cohabiting salmon or trout.
Brown trout coexist with Atlantic salmon in many western European rivers, and in many parts of its range, two different life history strategies occur: first, an anadromous form that undergoes smolting and can spend a number of months to several years at sea before returning to its natal river to spawn; second, a resident form that spends its entire life in fresh water. The species is characterized by high levels of genetic differentiation among populations (Altukhov et al., 2000; Youngson et al., 2003), but the relative proportions of sea trout and resident brown trout in a freshwater system appear river-specific and may be related to primary productivity (Jonsson, 1985). There appears to be a lack of genetic differentiation between the two forms where they coexist in the same river (Hindar et al., 1991; Cross et al., 1992; Skaala, 1992), suggesting that sea trout and fresh-water-resident S. trutta may constitute parts of the same population within individual river catchments. However, mtDNA studies have revealed that there may be at least some assortative mating (Ferguson et al., 1995).
In Ireland, sea trout stock collapses occurred in many rivers in the mid-western area between 1989 and 1990 (Gargan et al., 2002). For example, the Connemara district rod catch of sea trout, which constitutes a large proportion of the mid-western region catch, fell from an annual average of 9570 fish in the period 1974–1988 to 646 in 1989 and to 240 in 1990. Similar declines of sea trout also occurred in salmon farming areas on the west coast of Scotland in the early 1990s (Butler, 2002) and have also been observed in Norway (Holst et al., 2002). It is possible that these collapses are caused generally by marine salmon farming and, specifically, by infestations of the sea louse, Lepeophtheirus salmonis (Butler, 2002; Gargan et al., 2002; Holst et al., 2002).
Substantial demographic and life history changes have been observed in the Burrishoole sea trout population, a river catchment in the west of Ireland, as salmon farming has expanded (Poole et al., in press). These changes may have occurred as a direct result of a steep decline in sea trout stock, which began in the late 1980s. However, hatchery rearing and the ranching of Atlantic salmon have continued in the lower reaches of this river system since the late 1950s. This study investigates changes in the molecular or qualitative genetic composition of the Burrishoole sea trout population, which may have occurred parallel with the quantitative changes observed by Poole et al. (in press) or as a result of earlier aquaculture activities.
The indirect effects of salmon aquaculture on wild congeneric sea trout (particularly in relation to introduction of new parasites, increased the probability of disease transmission, and the potential effect on immune response genes) were assessed by analysis of samples taken before and after the establishment of aquaculture in the Burrishoole River. Genetic variation, revealed by a microsatellite locus embedded in the 3'UTR of the MH class I gene (Satr-UBA), was compared with that at six presumed-neutral microsatellite loci. In addition, the potential genetic effects of the steep stock decline on the Burrishoole sea trout population are assessed by comparing genetic variation at these loci in samples taken before, during, and after the collapse.
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Material and methods |
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Sea trout scales from the Burrishoole River system, located in County Mayo in the west of Ireland, were used as a source of DNA for this study. The Burrishoole River system drains an area of approximately 100 km2 and is dominated by the freshwater Lough Feeagh (410 ha), which is fed by rivers in which salmonids spawn (Figure 1). There are two outlets from Lough Feeagh (the Salmon Leap and the Mill Race) into the tidal, semi-saline Lough Furnace, which discharges into Clew Bay. Both outlets have permanent adult (upstream) and smolt (downstream) trapping facilities. Partial census data are available from 1959, and complete census data of descending smolts and ascending adults since 1970.
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Full-scale and continuous ocean ranching of Atlantic salmon in the Burrishoole River began with the release of 1440 smolts in 1960, although there had been some small-scale hatchery rearing activity from 1956. The highest number of smolts released was 77 955 in 2002 (Annual Reports of the Salmon Research Agency and the Marine Institute, 1955–2005), and the site currently produces approximately 150 000 smolts for both farming and smolt release programmes (Annual Reports of the Salmon Research Agency and the Marine Institute, 1955–2005). Marine cage salmon and rainbow trout farming began in Clew Bay in 1986, and the numbers of fish on-grown have ranged from 42 000 to 612 000 for Atlantic salmon and from 35 000 to 250 000 for rainbow trout. Fish count data from the Burrishoole since 1959 indicate that, historically, the annual numbers of sea trout entering the system have varied from approximately 1000 to 3500 adult returns. However, numbers have been in severe decline since the late 1980s, and currently, only a few hundred or fewer sea trout are recorded annually. Figure 2 summarizes the numbers of sea trout counted annually since 1958.
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Samples of sea trout scales from the Marine Institute's archive scale collection were used to measure the genetic variability in the population during potentially critical periods in its history, i.e. a baseline sample before fish culture began in the area, a sample before the sea trout stock collapse in 1989, and samples after the collapse. Thus, scales were taken from 1958 (n = 50), 1980 (n = 41), 1989 (n = 50), 1990 (n = 91), and 1995 (n = 50). To avoid the inclusion of sea trout from other catchments, which may occur in the lower brackish Lough Furnace (Cross et al., 1992), samples were obtained from rod-caught sea trout from the upstream lake (Lough Feeagh) or from sea trout trapped while entering this lake.
DNA was extracted from scale samples following a proteinase-K digestion/phenol–chloroform method (Sambrook et al., 1989). All samples were screened for variation at six presumed selectively neutral microsatellite loci, hereafter referred to as neutral loci: Ssa2216 (Paterson et al., 2004), SsOSL417 (Slettan et al., 1995), Ssa197 and Ssa85 (O'Reilly et al., 1996), F43 (Sanchez et al., 1996), and Str73 (Estoup et al., 1993), and a microsatellite locus embedded in the MH class I locus of brown trout (Satr-UBA) (sensu Sasa-UBA, Grimholt et al., 2002). Polymerase Chain Reaction (PCR) amplifications were carried out in a total volume of 10 µl, consisting of 1 µl DNA extract, Reaction Buffer IV (Advanced Biotechnologies) (0.075 M Tris, pH 9.0, 20 mM (NH4)SO4, 0.01% Tween20), 250 µM dNTPs, 0.5 U Taq polymerase, 2.0 mM MgCl2, and 1 µM of forward and reverse primers, with the forward primer labelled with either IRD800 or IRD700 to permit fragment detection on a LiCor DNA sequencer. Amplification involved an initial denaturation period of 3 min at 95°C, followed by 30–35 cycles of 95°C for 30 s, 56°C for 30 s, and 72°C for 30 s. Alleles were resolved on 18 or 25 cm 6% polyacrylamide gels, using a LiCor 4200 DNA sequencer, and allele sizes were determined by reference to a 50–350 bp size ladder. Additionally, allele size standards, constructed in the laboratory and containing the size range of alleles seen at each locus, were included on each gel to ensure consistent scoring between gels and samples. Approximately 50% of all samples, including all the oldest samples, were re-extracted and re-amplified to ensure correct and reliable genotyping.
Allele frequencies, observed and expected heterozygosities, and probabilities of departures from Hardy–Weinberg equilibrium were calculated using GENEPOP 3.0 (Raymond and Rousset, 1995). Allelic richness for each locus in each sample was calculated using FSTAT 2.9.3 (Goudet, 2001). Tests for linkage disequilibrium between loci and allele frequency heterogeneity between samples (analysed using Fisher's exact tests) were also performed with GENEPOP 3.0. Overall and pairwise FST values (Wright, 1951) estimated across populations (Weir and Cockerham, 1984) were calculated using F-STAT 1.2 (Goudet, 1995). The FST significance values were determined by bootstrapping over loci. To analyse changes in variability at each locus within each sample, a bootstrap method was used to test for significant differences in allele frequency distributions between pairs of samples. The method corrects for sampling variance and differences in sample size, and under the null model of no difference between two samples, an allele frequency distribution is estimated from pooled pairs of samples. Equivalent samples, the same number as in the original samples, are drawn, with replacement, from this distribution to create a pair of simulated samples. The allelic richness and gene diversity statistics used are sensitive to loss or gain of rare alleles and changes in the frequency of common alleles, respectively. If the difference between the statistics from the original pair of samples is larger than that indicated by the simulated null distribution of differences in statistics, the two samples have significantly different allele frequency distributions.
The Bonferroni procedure (Rice, 1989) was implemented to correct for multiple tests. In testing for Hardy–Weinberg deficits, each locus and each sample was considered to have an equal chance of displaying significant deviation; therefore, the global probability level was adjusted from 0.05 to 0.0014 (0.05/35). Tests for differentiation were carried out only between each pair of temporally consecutive samples and between the earliest and latest samples; therefore, comparisons were considered to have been carried out independently, and no correction factor was applied to counteract the number of sample comparisons. Furthermore, anticipating differing patterns of variation at the MHC locus and the neutral microsatellites, correction was applied only within the six neutral microsatellite comparisons by adjusting the global probability level from 0.05 to 0.008 (0.05/6).
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Results |
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Most individual scale samples produced small quantities of good quality DNA, except some of the oldest samples. A number of samples from the 1958 scales failed to produce amplifiable DNA, probably because of the deterioration of the DNA in the scales. However, repeated samples always produced identical results to original scoring, indicating a high degree of confidence in the genotypes obtained. The number of individuals successfully screened, the number of alleles scored, allelic richness, and observed and expected heterozygosities for each sample are presented in Table 1. Seven single locus departures from Hardy–Weinberg equilibrium (p < 0.05) were observed, although none remained significant after correction for multiple tests. These were a mixture of apparent heterozygote excesses and deficits and departures from expected genotype proportions (in the absence of differences in observed and expected heterozygosities). Two samples appeared to be out of Hardy–Weinberg equilibrium when all neutral loci were considered: 1958 (p = 0.0185) and 1990 (p = 0.020).
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Exact test p-values for allele frequency differentiation between consecutive temporal samples are presented in Table 2. Single locus differences were observed between the 1958 and 1980 samples at Satr-UBA (p = 0.0217) and Ssa2216 (p = 0.0046), although only differences at the latter locus can be considered statistically significant following correction for multiple tests. There were slight (non-significant) fluctuations in allele frequencies at Ssa197 between 1980 and 1989, but most highly significant differences were seen in the comparison of the 1958 and 1995 samples. In this case, three loci had significantly different allele frequencies (SsOSL417, p = 0.0009; Str73, p = 0.0060; Ssa2216, p = 0.0001). The overall FST value among the samples for the neutral loci was low but significantly different from zero (FST = 0.008, p < 0.001). The overall FST value for the Satr-UBA marker was also low but marginally significantly different from zero (Satr-UBA marker; FST = 0.006, p < 0.042). Pairwise FST values between consecutive temporal samples showed evidence of significant differences between 1958 and 1980 at both neutral loci and the Satr-UBA marker. Although the FST value for the Satr-UBA marker (FST = 0.019, p = 0.013) was slightly higher than that revealed by the neutral loci (FST = 0.010, p = 0.008), the difference between the two marker types was not statistically significant. Pairwise FST values for the other temporally consecutive tests were not significantly different from zero, although comparison of the 1958 and 1995 samples revealed a significant value for the neutral loci (FST = 0.025, p = 0.001) but not for the Satr-UBA locus. Among other possible pairwise comparisons, only the comparison of 1989 and 1995 showed a significant FST value for the Satr-UBA marker (FST = 0.011, p = 0.049).
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Probability values for differences or changes in variability, as measured by allelic richness and gene diversity, between temporally consecutive samples are presented in Table 2. For the Satr-UBA marker, a significant reduction in allelic richness, equalling a loss of five alleles, was observed between the 1958 and 1980 samples. There were no significant reductions in allele richness at any of the neutral loci during this period. There was also evidence of a loss of alleles at SsOSL417 when the 1958 and 1995 samples were compared, although this was not considered statistically significant following Bonferroni correction. In terms of gene diversity, differences were significant at the Satr-UBA locus between 1958 and 1980, which resulted from a reduction in expected heterozygosity of 8.1%. Some differences between other samples at different loci were also apparent (Table 2), but only a reduction in gene diversity at SsOSL417 between 1958 and 1995 remained significant following correction for multiple tests.
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Discussion |
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Molecular studies of wild salmonid populations have generally shown that these populations remain genetically stable for long periods (Nielsen et al., 1997; Hansen et al., 2002). However, Garant et al. (2000) demonstrated that an ecological change within a river system could result in the disruption of temporal stability in Atlantic salmon populations, suggesting that external challenges (natural or anthropogenic) may influence or disturb genetic stability of wild salmonid populations. Østergaard et al. (2003) also demonstrated a high degree of temporal variation in brown trout populations inhabiting unstable environments. The current study provides evidence of significant temporal instability of allele frequencies and allelic variation, as measured by allelic richness and gene diversity for both the Satr-UBA marker and some neutral markers in the Burrishoole sea trout stock. However, significant differences in both allelic richness and gene diversity were found only at the Satr-UBA locus and only between the 1958 and 1980 samples. These differences appear to be the result of a loss of five alleles and an 8.1% reduction in gene diversity. Observed heterozygosity at this locus in the 1980 sample was also much smaller than in the 1958 sample (reduced by 13.1%; Table 1). Among the neutral loci, some reductions in allelic richness and gene diversity were also observed between these two samples, although these differences were not statistically significant.
The significant differences observed in both allelic richness and gene diversity between the 1958 and 1980 samples at the Satr-UBA marker, which were not apparent across the neutral loci, are compelling evidence for significant genetic loss at a locus, which is likely to be under selection in the Burrishoole sea trout population. Additionally, this change appears associated with the development of salmon aquaculture in the system. Detailed records (Annual Reports of the Salmon Research Agency of Ireland, 1955–1999) catalogue the continuing chronic and episodic acute outbreaks of diseases, e.g. furunculosis, vibriosis, myxobacterial gill disease, and ulcerative dermal necrosis (UDN) in the hatchery rearing programme from 1960 to the beginning of the 1980s. Therefore, our findings appear to confirm disease-mediated directional selection at this locus, as has been shown previously in tank experiments with Atlantic salmon (Grimholt et al., 2003).
Recent sequencing of UBA from 30 S. trutta individuals from the Burrishoole system uncovered 21 novel Satr-UBA alleles (unpublished data), although the same sample yielded only 13 microsatellite alleles. However, overall linkage between Satr-UBA and microsatellite alleles was highly significant (p = 0.0001), but only the common microsatellite alleles were always associated with a particular Satr-UBA allele (unpublished data). Recombination within the Satr-UBA locus (Shum et al., 2001) seems to complicate relationships and, in most instances, microsatellite alleles were linked to two or more, often highly divergent, Satr-UBA alleles. This implies that, for any selective pressure to be detected using changes in allelic richness, in particular, or gene diversity at the microsatellite marker, such pressure must affect all Satr-UBA alleles linked to particular microsatellite alleles similarly. Because even minor changes in the peptide-binding properties of two related Satr-UBA alleles might lead to radically different selective regimes operating on both, we contend that the evidence of selective pressure presented here, given the potential confounding factors outlined above, indicates a potentially more profound impact on underlying variation at the Satr-UBA locus itself.
If this is true, the apparent loss of marker alleles seen between 1958 and 1980 is associated with a loss of functional alleles. During this period, substantial outbreaks of disease, particularly furunculosis, were observed in the hatchery, and one might assume that there was cross-infection to cohabiting wild salmon and sea trout. It is likely, therefore, that this had a consequent impact on the immune response genes of these wild populations (Peeler and Murray, 2004). It is not known whether the disease outbreaks in the hatchery were a consequence of pathogens introduced into the system by the coincidental rearing of non-indigenous stocks or whether the pathogens were endemic at low levels in the wild population. As significant differences in allele frequencies at this locus in disease-susceptible and resistant Atlantic salmon were seen in tank experiments when fish were challenged with disease (Grimholt et al., 2003), a similar response in brown trout, as is apparent here in a wild situation, is not unexpected. The lack of significant (following Bonferroni correction) changes in allelic richness and gene diversity at neutral loci appears to preclude this change being caused by other population genetic effects, such as temporary stock declines (Figure 2).
Biological population dynamic studies (Poole et al., in press) show that, historically, sea trout in the Burrishoole system constituted a significant component of the overall trout stock. Although earlier genetic studies of resident and anadromous trout in the Burrishoole have failed to demonstrate a significant genetic difference between the two forms (Cross et al., 1992), historical levels of assortative mating and self-recruitment among the Burrishoole sea trout stock are unknown. Since the late 1980s, the anadromous stock component has been virtually lost from the system (Figure 2), and the genetic propensity, if such exists, for substantial numbers of sea trout to reoccur in the system appears severely reduced (Poole et al., in press). Sea lice infestations seem to have been the cause of the 1989 stock collapse, although there is little evidence from these samples of a genetic change in the stock as a result of this collapse, apart from a significant FST value at the Satr-UBA marker (FST = 0.011, p = 0.049) between the 1989 and 1995 samples. This suggests that substantive genetic changes in the Burrishoole sea trout stock preceded the stock decline in 1989, as outlined above.
However, there has been some genetic change since the population collapse. Only three of the Satr-UBA marker alleles, apparently lost between 1958 and 1980, are also absent in the later samples, which appear to show a steady increase in the number of alleles (Table 1). In addition, three new alleles not present in the 1958 or 1980 samples were observed in the later samples (data not shown). Furthermore, five alleles among the neutral loci were also seen in the later samples that were not present in the 1958 or 1980 samples. It is not clear how the new alleles became established in the remaining sea trout stock, although migration from other rivers, mutation events, or sampling error whereby alleles may not be "lost" but are, or have become, rare and are therefore not observed, cannot be ruled out. It is also noted that the 1990 sample appeared to be out of Hardy–Weinberg equilibrium when all neutral loci were considered together (p = 0.020), a time at which the sea trout stock was in decline. Deviation from Hardy–Weinberg equilibrium was also marginal in the 1995 sample (p = 0.057). These deviations may be related to the severe sea trout stock collapse, increased outbreeding of the remaining stock with resident trout, or both. Genetic analysis of fry samples, known to be almost exclusively the offspring of resident fish, indicates that resident trout parents are now contributing significantly to the remaining sea trout in the system (unpublished data). Therefore, it is speculated that genetic changes, since the stock collapse, have occurred not exclusively as a consequence of sea lice infestations but also indirectly, through a change in the relative contributions of the surviving sea trout and resident trout to the remnant sea trout stock (Poole et al., in press).
In summary, no substantial evidence of the variability of a genetic signal for the immune response genes was observed at neutral microsatellite loci. This signal was apparent as a loss of genetic variability at an MH class I linked microsatellite, which preceded a severe sea trout stock collapse and appears to be associated with aquaculture activities. The subsequent recovery of some variability at this locus may result from the increased importance of the genetic contribution of resident fish to the sea trout component of the population since the stock collapse in the late 1980s. In other rivers, which may have significantly greater sea trout to resident trout ratios, and in which aquaculture-mediated diseases exist, the potential for non-migratory fish to refresh immune response gene allelic variability would be much reduced. The screening of further archived scale samples from the Burrishoole and from other sea trout rivers, both with and without a history of aquaculture, is recommended using these and other loci, especially for MH class II genes, where embedded markers are also present.
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Acknowledgements |
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We thank all the members of the SALIMPACT consortium for discussions and comments during the project. We also thank two anonymous referees for helpful suggestions, and the Marine Institute of Ireland for providing sea trout scale samples. This research was funded by the EC framework 5 programme SALIMPACT (Q5RS-2001-01185).
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References |
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