Differential gene expression in small and large rainbow trout derived from two seasonal spawning groups
© Kocmarek et al.; licensee BioMed Central Ltd. 2014
Received: 8 July 2013
Accepted: 17 January 2014
Published: 22 January 2014
Growth in fishes is regulated via many environmental and physiological factors and is shaped by the genetic background of each individual. Previous microarray studies of salmonid growth have examined fish experiencing either muscle wastage or accelerated growth patterns following refeeding, or the influence of growth hormone and transgenesis. This study determines the gene expression profiles of genetically unmanipulated large and small fish from a domesticated salmonid strain reared on a typical feeding regime. Gene expression profiles of white muscle and liver from rainbow trout (Oncorhynchus mykiss) from two seasonal spawning groups (September and December lots) within a single strain were examined when the fish were 15 months of age to assess the influence of season (late fall vs. onset of spring) and body size (large vs. small).
Although IGFBP1 gene expression was up-regulated in the livers of small fish in both seasonal lots, few expression differences were detected in the liver overall. Faster growing Dec. fish showed a greater number of differences in white muscle expression compared to Sept. fish. Significant differences in the GO Generic Level 3 categories ‘response to external stimulus’, ‘establishment of localization’, and ‘response to stress’ were detected in white muscle tissue between large and small fish. Larger fish showed up-regulation of cytoskeletal component genes while many genes related to myofibril components of muscle tissue were up-regulated in small fish. Most of the genes up-regulated in large fish within the ‘response to stress’ category are involved in immunity while in small fish most of these gene functions are related to apoptosis.
A higher proportion of genes in white muscle compared to liver showed similar patterns of up- or down-regulation within the same size class across seasons supporting their utility as biomarkers for growth in rainbow trout. Differences between large and small Sept. fish in the ‘response to stress’ and ‘response to external stimulus’ categories for white muscle tissue, suggests that smaller fish have a greater inability to handle stress compared to the large fish. Sampling season had a significant impact on the expression of genes related to the growth process in rainbow trout.
KeywordsGrowth Microarray Rainbow trout Liver White muscle Season
Growth in fishes is a complex trait that impacts many components of fitness. Large body size provides an advantage when competing for mates and resources and can provide energy reserves during times of famine or reproduction [1, 2]. The initiation of sexual maturation in certain species can also be coupled to variation in growth rate and is therefore, a size-dependent phenomenon. Unlike mammals, most species of fish continue to grow throughout their lives . As skeletal muscle composes a large portion of body mass, fish growth is primarily dependent on an increase in muscle mass . Skeletal muscle can be divided into two types; red muscle is used for slow continuous swimming while white muscle is used for rapid bursts of speed . White muscle is utilized preferentially over red muscle as an energy reserve and exhibits wide changes in hypertrophic and hyperplastic states dependent on the physiological condition of the fish [6–8].
Fish growth is controlled by a complex array of extrinsic and intrinsic factors and their interactions. Extrinsic factors such as variation in circannual photoperiod , and changes in water temperature [10, 11] have largely predictable effects on growth rates. Other factors are more complex and relate to interactions between extrinsic and intrinsic factors that regulate growth, such as individual responses to stress and disease , population density , social status , and feeding regime . Variation in growth in response to these factors has a strong genetic basis as indicated by the detection of moderate to high heritability values [16–18] and chromosomal regions (quantitative trait loci, QTL) associated with variation in body size [19, 20]. While it is likely that all chromosomes have genes that contribute to, or interact with growth regulation, some of these will have stronger effects than others. For example, 15 of the 29 linkage groups in rainbow trout (Oncorhynchus mykiss) have QTL for body weight and/or condition factor with genome-wide effects, with an additional 8 linkage groups housing QTL with chromosome-wide effects . This indicates that perhaps three quarters of rainbow trout linkage groups contain genomic regions with major influences on growth. However, it is important to emphasize that our knowledge of growth-regulating regions is incomplete as genetically-based responses to environmental inputs are varied and can often produce conflicting results. For instance, microarray studies have examined fish experiencing muscle wastage [6–8] with the premise that genes down-regulated during muscle wastage will be up-regulated during muscle growth and vice versa. These studies have identified many candidate genes for growth including cytoskeletal, transcription/translation, protein turnover, and metabolic genes but with some conflicting results. For instance, myosin genes showed down-regulation during muscle wastage  and during GH-stimulated growth  in rainbow trout.
One of the main intrinsic regulators of growth is the somatotrophic axis, which is composed of growth hormone (GH), GH regulating factors (GH releasing factor and somatostatin), and the products GH releases from the liver by binding to GH receptors, including insulin and insulin-like growth factors (IGFs) . Indeed, many of these genes (GH, GHR2, GHR1, IGF2, and IGF1) are up-regulated in a variety of tissues (e.g., skeletal muscle, brain, and liver) from faster growing strains or individuals within several species including Nile tilapia, Oreochromis niloticus, smooth tongued sole, Cynoglossus semilaevis, and channel catfish, Ictalurus punctatus. Studies with GH-transgenic rainbow trout have also shown that liver-specific gene expression patterns are more similar to those observed in faster growing domesticated rainbow trout, compared to slower growing wild-type fish [26–28]. In fact, the IGF2 gene was highly up-regulated in the faster growing GH transgenic/domestic fish compared to wild-type fish  suggesting that increased growth is in part regulated by changes in the GH axis. Genes with differential expression were assigned to 16 Gene Ontology (GO) categories, with those comprising response to stimulus, lipid metabolism, precursor metabolites and energy, and cell/tissue structure and development having the greatest number of differences. Thus, many of the differentially expressed genes may be interconnected in regulatory pathways stimulated by those in the somatotrophic axis.
Studies of liver and muscle gene expression in salmonids indicate that genes central to energy metabolism, carbohydrate and lipid metabolism, and cytoskeletal components are major GO categories associated with growth differences [7, 27–29]. A number of muscle genes showing major expression differences between a fast (directionally selected) and a normally growing line of rainbow trout have been identified with RNAseq, and these have been verified by screening additional families with SNP variants identified within these genes . Significant differences in the distribution of SNP alleles were reported for genes such as glucose phosphate isomerase, enolase, ATP2A1, and several structural genes including myosin binding proteins, fast myotomal muscle actin 2, troponin C, and troponin T-2 between fast and slow growing rainbow trout. A large number of mitochondrial specific genes including several NADH dehydrogenase subunit genes, cytochrome b, and cytochrome c oxidase subunits, and different ATPase genes were also differentially expressed. Although it was only reported that muscle tissue was used, the finding that a large proportion of mitochondrial-specific genes were highly representative of differences between fast and slow growing trout is suggestive that a high proportion of red muscle tissue was analyzed in combination with white muscle tissue.
We utilized a 44 K feature Atlantic salmon (Salmo salar) microarray to examine differential mRNA levels in fast- and slow-growing rainbow trout from the same commercial strain but produced in two seasons (September and December). Previous studies show that Atlantic salmon microarrays are suitable for use in gene expression studies of other salmonid species [31–33]. Our goal was to identify genes that show similar patterns of differential expression between large and small rainbow trout in both liver and white muscle regardless of season for use as biomarkers of growth in selective breeding programs. The analysis of global gene expression in large and small fish reared under standard conditions also provides an understanding of typical growth patterns and complements previous analyses where growth in rainbow trout was altered by adjusting feeding regimes , domestication, or by the addition of GH [26, 27]. If differences in gene expression explain a large portion of body size variation in fishes, then many of the genes identified in studies of accelerated growth or muscle wastage are also expected to show differential expression in normal growing large and small fish. Based upon previous studies we predicted that: (1) genes related to the metabolism of carbohydrates and lipids, energy production, and members of the somatotrophic axis, including GH receptors, insulin, and IGFs should be down-regulated in the liver and white muscle of small fish compared to large fish; (2) genes for cytoskeleton components including, actin, myosin, and troponin are also expected to be differentially expressed in both tissues. Specifically, genes involved in cytoskeletal structuring will be down-regulated while myostatin will be up-regulated in small fish compared to larger fish, and (3) genes for liver-specific, lipid binding, cytoplasmic components, signaling, and transcription will be up-regulated in the livers of small fish compared to large fish.
Growth and gender
Genotyping with the sex-specific marker indicated that the microarray analysis was based on 9 males (5 = large, 4 = small) and 11 females (5 = large, 6 = small) in the Sept. lot. In the Dec. lot there were 10 males (6 = large, 4 = small) and 10 females (4 = large, 6 = small). Thus, there was no gender bias in the pools of large and small fish analyzed in either lot.
Effects of body size
Identified genes up-regulated ≥2.4-fold in the liver
Large Sept. fish
Complement C1q-like protein 4 precursor
Small Sept. fish
Glutathione s-transferase p
Terf1 -interacting nuclear factor 2
Hemagglutinin/amebocyte aggregation factor precursor
Proteasome activator complex subunit 3
Large Dec. fish
Polycystic kidney disease protein 1-like 3 precursor
Zinc transporter ZIP3
Small Dec. fish
Insulin-like growth factor binding protein 1
e3 ubiquitin-protein ligase neurl3-like
The biological process level 2 GO category with the largest number of up-regulated sequences in both the large and small Sept. fish and the small Dec. fish was ‘cellular process’ (GO:0009987) (Additional file 5: Table S5). A list of the Agilent probe IDs that correspond to the individual GO Level 2 and Level 3 categories is provided in Additional file 6: Table S6 and Additional file 7: Table S7. Cellular process was one of three GO categories to contain identified up-regulated sequences in the large Dec. fish at biological process level 2. At biological process level 3, the ‘cellular metabolic process’ category (GO:0044237), a subcategory of the cellular process category, contained the largest number of up-regulated sequences in the both the large and small Sept. fish and the small Dec. fish (Additional file 8: Table S8). However, no significant differences in the observed number of genes assigned to either the level 2 or level 3 Blast2GO categories were detected between the large and small fish within either lot (Additional file 5: Table S5 and Additional file 8: Table S8).
Effects of season (lot)
Effects of body size
Named genes up-regulated in ≥2.4-fold the white muscle
Large Sept. fish
DNA-damage-inducible transcript 4-like protein
ADP/ATP translocase 2
Complement C1q-like protein 4 precursor
HIG1 domain family member 2a
Polycystic kidney disease protein 1-like 3 precursor
Fibrinogen gamma chain
Fibrinogen gamma chain
40s ribosomal protein s30
Cathepsin m precursor
Fibrinogen alpha chain
Beta-enolase-like isoform 1
Retinol-binding protein 2
Fibrinogen gamma chain
Complement C1q-like protein 2 precursor
Small Sept. fish
Calcium-binding and coiled-coil domain-containing protein 1 isoform 2
Calcium binding and coiled-coil domain like
Myosin heavy chain
Large Dec. fish
Fibrinogen gamma polypeptide
HIG1 domain family member 2a
Transcription factor jun-b
Butyrate response factor 1
Iron zinc purple acid phosphatase-like
Lysosome membrane protein 2-like
Lactate dehydrogenase b
Eukaryotic translation elongation factor 1 alpha 1
Acidic mammalian chitinase-like
Growth arrest and dna-damage- beta
Neutral alpha-glucosidase ab-like
Delta-6 fatty acyl desaturase
Eukaryotic translation elongation factor 1 alpha 1
Liver-expressed antimicrobial peptide 2
BolA-like protein 2
Purine nucleoside phosphorylase
Eukaryotic translation elongation factor 1 alpha 1
Heat shock protein 90
Uncharacterized protein C8orf4 homolog
Small Dec. fish
rRNA promoter binding protein
Myosin light chain 3-like
Myosin heavy chain
rRNA promoter binding protein
Troponin T, cardiac muscle
Troponin slow skeletal muscle
Eukaryotic translation elongation factor 1 epsilon 1
Myosin binding protein cardiac
Keratin-associated protein 10-4
The biological process level 2 GO category with the largest number of up-regulated sequences in the large Sept. and Dec. fish was ‘metabolic process’ (GO:0008152) while ‘cellular process’ (GO:0009987) contained the highest number of up-regulated sequences in both the small Sept. and Dec. fish (Additional file 13: Table S13). A list of the Agilent probe IDs that correspond to the individual GO Level 2 and Level 3 categories is provided in Additional file 14: Table S14 and Additional file 15: Table S15. However, the category with the second largest number of up-regulated sequences in the large Sept and Dec. fish was ‘cellular process’ and in the small Sept. and Dec. fish was ‘metabolic process’ indicating similarity in function across both size groups. At biological process level 3, the two categories with the greatest number of up-regulated sequences in the large Dec. fish and the large and small Sept. fish were ‘primary metabolic process’ (GO:0044238) and ‘regulation of biological process’ (GO:0050789), respectively (Additional file 16: Table S16). The ‘regulation of biological process’ and ‘primary metabolic process’ categories contained the largest and second largest number of up-regulated sequences in the small Dec. fish.
Effects of season (lot)
The distribution of differentially expressed genes in GO category, ‘response to external stimulus’ differed significantly between the large Dec. and Sept. fish. Genes in this category made up 8.2% of the total genes in the large Sept. fish and 2.9% of the total genes in the large Dec. fish. No differences in the observed distribution of differentially regulated genes were detected between the small Sept. and Dec. fish. A list of the specific gene probe IDs and their associated gene functions for the GO level 3 categories that show significant differences between large and small Sept. fish and with the largest number of gene differences (i.e., ‘response to external stimulus’, ‘establishment of localization’, and ‘response to stress’), are shown in Additional file 17: Table S17, Additional file 18: Table S18 and Additional file 19: Table S19, respectively. Differences between large fish in Dec. and Sept. lots for ‘response to external stimulus’ are shown in Additional file 17: Table S17. Significant differences in the relative percentage of counts between level 2 and 3 GO categories at the 5% level of differentiation are shown in Figure 5.
Validation of microarray results with qPCR
The patterns of differential gene expression between small and large rainbow trout derived from two seasonal spawning groups were more consistent in white muscle than in liver. As a result, the following discussion largely focuses on patterns seen in white muscle. Our initial prediction that genes related to carbohydrate and lipid metabolism would be more highly up-regulated in larger rainbow trout compared to smaller fish sampled in two seasons was not universally observed. Both carbohydrate and lipid metabolic processes are child terms within the primary metabolic process level 3 GO category. This category had a fairly even distribution of up-regulated genes assigned to it, across both size groups and both sampling seasons. Nonetheless, certain white muscle genes such as lactate dehydrogenase, were highly up-regulated in large fish from both seasons, as was pyruvate kinase expression in Sept. fish. Many lipid regulating genes were also more highly expressed in large fish compared to small fish and these included several apolipoproteins, fatty acid binding proteins, adipose differentiation related protein, lipoprotein lipase, and retinol binding protein genes. This latter observation is consistent with the finding that retinoic acid activates myogenesis in zebrafish .
Genes for cytoskeleton and myofibrillar components were often differentially expressed between large and small fish but the patterns did not conform to simple expectations. The proportion of differentially expressed genes assigned to cytoskeletal structure, anatomical structure development, biosynthetic process, cellular development process, and organelle organization did not differ significantly between size groupings. In fact, there was a tendency for smaller fish to have a greater proportion of white muscle genes assigned to these categories compared to large fish. The finding that myofibrillar component genes such as titin, myosin light chain 3, several actins, xin-actin-binding repeat containing protein c2, actinin, several tropomyosins, myosin heavy chain and troponin genes were highly up-regulated in smaller fish was unexpected. In contrast, we observed up-regulation for different copies of a myosin heavy chain, alpha actin, troponin I, and myosin light chain 1 in large fish. Previous studies in fish have not determined a clear pattern in actin or myosin expression relative to growth. Both actin and myosin genes have shown down-regulation in rainbow trout treated with GH  and experiencing muscle wastage . Connective tissue components such as keratins, collagens, fibronectins, amyloids, and ictacalcins were also more highly expressed in the larger fish. Thus, the finding that some cytoskeletal organization and assembly genes (e.g. telethonin) and connective tissue genes were up-regulated in large fish is consistent with predictions. However, it was also found that different members of the same gene family such as the t-complex assembly proteins were either up- or down-regulated in small fish. Hence, while the expression of individual genes demonstrated relationships with body size, the overlapping nature of their assignments within GO categories restricted the detection of significant differences between the size classes examined.
Effect of body size across seasons
Contrary to predictions, we did not observe major differences in the expression of the somatotropin axis genes in liver with the singular exception that IGFBP1 was up-regulated in small fish from both the Sept. and Dec. lots. As IGF-I stimulates muscle growth , the binding of a greater portion of the IGF by IGFBP1 in the small fish may have contributed to decreased growth. Indeed, overexpression of IGFBP in grass carp, Ctenopharyngodon idella, embryos causes phenotypes that resemble hypoxia-induced developmental delay and growth retardation . In addition, the increased expression of IGF binding proteins might reflect increased protein catabolism as shown in Atlantic salmon, Salmo salar, which could also be a contributing factor in decreased growth in the small fish. While other studies have identified expression differences in the liver for genes in a variety of categories, including lipid and carbohydrate metabolism, immunity, transcription, and growth regulation [21, 26], they compared GH-treated fish to control or GH-transgenic and domestic populations to wildtype fish. Thus, our results may not be directly comparable to these previous studies given that we only examined growth in domesticated rainbow trout reared in typical and unaltered culture conditions.
The limited number of instances where large fish showed up-regulation of genes compared to small fish in liver restricted our ability to find consistent patterns across seasons. In fact, our search for size-specific consistency in liver expression profiles across seasons was largely limited to patterns observed in smaller fish. In addition to IGFBP1, levels of zinc finger protein 568 and glutamine synthetase (GS) expression were elevated in small fish sampled from both seasons. This supports our prediction that genes for signaling and transcriptional activity would be up-regulated in small fish. Although GS does not directly modulate RNA polymerase II activity in a fashion similar to zinc finger proteins, GS modulates glutamate to glutamine pools in muscle and liver. Glutamate:glutamine ratios can influence muscle homeostasis, via the regulation of metabolism in response to intracellular energy states, and these ratios can vary in response to stress and nutritional levels .
Classification of gene expression profiles in muscle also revealed associations between blood and energy production, calcium metabolism, and stress and immune function between large and small fish. Genes involved in blood and energy production and immune function were up-regulated in large fish relative to small fish while the opposite was true for genes related to calcium metabolism. Genes involved in the clotting process (fibrinogen gamma polypeptide; 4 fibrinogen sequences, C1 inhibitor) and energy production (HIG1 domain family member 2a, ADP/ATP translocase 2)[40, 41] were up-regulated in large fish from at least one of the lots. Salem et al.  also reported that the expression patterns of several metabolic genes including, ATP and ATPase were associated with faster growth in rainbow trout muscle. The observation that a number of genes related to immune function were up-regulated in the large fish suggests that enhanced growth may be associated with enhanced immune function. Liver-expressed antimicrobial peptide 2, which is involved in innate immunity , flavin reductase, a broad specificity oxidoreductase, and fucolectin-4 precursor, were all highly up-regulated in the large Dec. fish. C-type lectin was highly-up-regulated in the large Sept. fish and belongs to a super-family of proteins with a variety of functions including involvement in cell adhesion, receptor binding, and immunity . The suggestion that immune function plays an important role in growth is supported by the concordance of expression profiles of such genes between GH-transgenic and domestic rainbow trout (fast growth) when compared to wildtype fish (slower growth) .
In contrast, many genes related to calcium metabolism were up-regulated in small fish relative to large fish. For example, parvalbumin 2 and parvalbumin beta-2, which bind calcium ions and are involved in muscle relaxation after contraction, were up-regulated in the small fish suggesting that genes controlling calcium homeostasis also regulate growth in fish. Overexpression of parvalbumins (PARV) can lead to faster quick-twitch muscle relaxation times, but paradoxically, have also been coupled to lower mitochondrial densities in slow-twitch muscle groups [46, 47]. Lower PARV levels also indicate enhanced mitochondrial and metabolic flux capabilities as evidenced by inverse associations between SIRT1/PGG-1α activities and PARV levels . Therefore, it is possible that the larger fish in our study have higher mitochondrial densities and metabolic capabilities than smaller fish. Increased expression of certain IGFBP genes also appear to directly elevate levels of intracellular calcium and are linked to repressed cell growth . This suggests a more direct link between the elevated IGFBP levels we observed in liver with the elevated PARV gene expression levels detected in muscle.
Our data provide interesting insights into metabolic functional links related to some of our initial predictions. The observation that smaller fish have enhanced levels of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression suggests that mTOR activity might be suppressed in smaller fish as observed in other species . GAPDH is a key regulator of mTOR expression through its regulatory affinities to Rheb, an activator of the mTOR cascade . Buller et al.  observed that GLUT1 activation can regulate glycolytic flux and noted that enhanced GAPDH expression depresses mTOR activation, presumably by diminishing the binding interactions between Rheb/mTOR when GAPDH levels are elevated. mTOR levels are also known to be lower in dwarf Icelandic Arctic charr (Salvelinus alpinus) compared to fish with more typical growth patterns . However, the current findings are also somewhat paradoxical, as GAPDH levels may also be expected to be elevated in large fish given that carbohydrate metabolic flux is expected to be greater since type II glycolytic muscle mass (i.e., white muscle tissue) represent a greater proportion of body mass in larger fish . Since GAPDH is the rate-limiting enzyme in glycolytic flux , enhanced carbohydrate based metabolism should be reflected in higher GAPDH levels in larger fish. GAPDH activity was observed to be significantly higher in gilthead sea bream (Sparus aurata) under normal growing conditions and was considered a signature gene of altered metabolism following environmental perturbations with feeding regime . As mentioned previously, we did detect significantly enhanced lactate dehydrogenase and pyruvate kinase expression levels in larger fish supporting the prediction that glycolytic flux derived energy acquisition is enhanced in larger fish. It is possible that duplicated forms of GAPDH, not detected with the probes available on the current microarray, may show enhanced expression in larger fish and therefore more research is required.
Heterogeneity among GO categories
The two seasonal lots of fish did not show significant shifts in gene ontology categories for liver tissue. While this does not mean that all genes are uniformly expressed in the two seasonal lots, it does suggest that functionally equivalent classes of liver genes are similarly regulated during times of varying photoperiod, at least within the experimental families we surveyed. This may be a consequence, in part, of temperature-induced maintenance of metabolic rates, as the fish were kept at water temperatures that fluctuated very little throughout the year. More dramatic differences would possibly be observed in nature, especially at the juvenile stage when young salmonids inhabiting a fluvial environment may experience more dramatic fluctuations in temperature.
Differential white muscle gene counts were evident between large and small fish within 4 different GO categories. Small and large Sept. fish showed significant differences in the level 2 ‘localization’ category. At level 3 the number of genes assigned to GO categories within the ‘response to external stimulus’, ‘establishment of localization’ and ‘response to stress’ level 3 GO groupings showed the greatest number of significant differences between large and small fish in the Sept. lot. Significant differences between large fish from the two seasonal lots were also evident within the ‘response to external stimulus’ category. These findings suggest that differences in expression levels between large and small fish may be more pronounced during periods of growth retardation compared to periods of acceleration, given that most of the significant GO categories were evident in the Sept. lot. The fish from the Sept. lot were sampled during a period of low specific growth rate at the winter solstice time period (Figure 3). The following discussion will be restricted to these level 3 categories.
Response to external stimulus - level 3
As predicted, large fish in the Sept. lot can be characterized as having a greater number of up-regulated genes related to cytoskeletal structure compared to small Sept. fish and these include two telethonin sequences, fibronectin precursor, and beta-enolase. Telethonin is a muscle regulating factor , beta-enolase has a function in striated muscle development , and fibronectin is involved in muscle growth . Large Sept. fish also had a greater number of genes related to angiogenesis and haemopoeisis up-regulated in this category (e.g., fibrinogen alpha chain, fibrinogen gamma chain (3 probe IDs), coagulation factor ii precursor, albumin precursor, and antithrombin-iii precursor (2 probe IDs), compared to small Sept. fish (platelet glycoprotein 4).
Establishment of localization – level 3
We also observed differential expression patterns of calmodulin and the calmodulin induced kinases that were contrary to expectations. Calmodulin, calcium calmodulin-dependent protein kinase ii delta 2, and calcium calmodulin-dependent protein kinase iv were all up-regulated in the small Sept. fish. Typically, calmodulin genes and their associated kinases are related to increased growth and anabolic functions and thus their expression levels are expected to be higher in faster growing organisms. For instance, calcium calmodulin-dependent protein kinase i delta is highly expressed in the skeletal muscle of zebrafish embryos  and calcium calmodulin-dependent protein kinase ii delta 2 is involved in skeletal muscle regeneration  and the regulation of vascular smooth muscle migration [61, 62] in mammalian models. Moreover, calcium calmodulin-dependent protein kinase iv has been reported to stimulate hypertrophic muscle growth through the regulation of transcription factors associated with the expression of red muscle and the biogenesis of skeletal muscle mitochondria [63, 64]. The finding that the calcium calmodulin dependent kinases are also more highly up-regulated in small fish is in accordance with the observation of increased calcium-dependent metabolism in small fish as evidenced by their elevated levels of PARV.
Expression of calcium calmodulin-dependent protein kinase iv within the skeletal muscle remains controversial. During zebrafish embryogenesis calcium calmodulin-dependent protein kinase iv was not expressed in the skeletal muscle but was instead involved in the development of the brain and neurons . In humans, calcium calmodulin-dependent protein kinase iv is undetectable  but it is detectable in mice . Strict anabolic functions for this complex are also controversial as calmodulin has also been shown to be a regulator of apoptosis , with calcium calmodulin-dependent protein kinase ii identified as being important for apoptosis maintenance . Seales et al. have also shown that calmodulin signaling also regulates increased osteoclastogenesis. Current evidence does not suggest that calcium calmodulin-dependent protein kinase iv is linked to apoptotic processes. Nevertheless, the pleiotropic nature of this complex clearly requires more research, and linking the activity of the genes to reduced growth may indicate coupling to increased apoptotic activation.
The majority of genes related to transcription, translation, and protein production were up-regulated in the small Sept. fish. These included transcription factor, membrane-bound transcription factor site 1, splicing arginine serine-rich 11, protein fat-free homolog, 60s ribosomal export protein nmd3, 60s ribosomal protein l11, and ras-related protein rab-2a. These findings support prediction (3), albeit based upon gene expression levels in white muscle tissue, whereas the up-regulation of signaling genes in small fish were previously based upon liver microarray findings . Genes involved in protein degradation such as selenoprotein S, der1-like domain member 2, and ubiquitin C also showed up-regulation in the small Sept. fish. This suggests that the increased protein production indicated in the small fish may not translate into finished proteins (see Discussion in following section). In contrast, genes related to protein production or assembly showed up-regulation in the large Sept. fish (e.g., Ap-1 complex subunit gamma-1, which is involved in protein sorting at the golgi , and ribosomal protein s13 which is involved in protein biosynthesis , telethonin which is involved in sarcomere assembly ), as did genes related to lipid metabolism (e.g., retinol binding proteins, fatty acid binding proteins, and apolipoprotein a-i) and blood production (e.g., hemoglobin alpha and beta units). These findings support prediction (1) and partially support prediction (2), given that the myofibrillar component genes of muscle assembly were more up-regulated in the small fish (see Discussion above) which was not expected.
Response to stress - level 3
Small Sept. fish show up-regulation for a higher number of genes related to apoptosis compared to large fish despite the fact that as a portion of total genes differentially expressed, the ‘response to stress’ category contained a higher proportion of genes in large fish. Differences were apparent in the make-up of genes contributing to this GO category between the size classes. Genes up-regulated in large fish could be categorized as enhancers of innate and acquired immune function (e.g., Complement component c3, complement c3-like, complement component c9, and Ca2+-dependent complex c1r c1s subunit), transcriptional activation, such as max protein and pyruvate kinase, and regulators of oxidative stress (e.g., glutathione peroxidase). Additionally, inspection of Additional file 9: Table S9 reveals that genes such as thioredoxin and glutathione-s-transferase were up-regulated in the large but not the small fish supporting the contention that large fish are better able to handle oxidative stress. Conversely, genes up-regulated in the small Sept. fish may be characterized as having enhanced apoptotic responses to stress (i.e., increased expression of proteasome subunit alpha type-6, proteasome subunit alpha type-5, ubiquinone biosynthesis protein coq7 homolog, 26 s proteasome non-ATPase regulatory subunit 3, ubiquitin C, apoptosis enhancing nuclease, proteasome (macropain) 26 s 6, 26 s protease regulatory subunit 8, and DNA damage-binding protein-1 [DDBP1]). However, with DDBP1 its associated substrate ligand is linked to the degradation of a cell cycle inhibitor , and thus up-regulation of this gene would be expected to increase cell growth. Inhibition of 26s proteasome promotes muscle growth in rats  and therefore higher activation in smaller fish may be coupled to decreased growth which is consistent with our findings.
Genes of mixed function, such as those related to protein degradation regulation such as heat shock protein hsp 90-alpha (hsp90a), selanoprotein s (SelS), and der1-like domain member 2, are also up-regulated in small Sept. fish. Interestingly all three of these proteins may have regulatory interactions in that hsp90a is required for SelS and general selanoprotein synthesis, and SelS interacts with der1 in shuttling mis-folded proteins across the endoplasmic reticulum membrane towards eventual ubiquination and proteasome degradation . The observation that large Sept. fish express higher levels of HIG1 domain family member 2a, glutathione peroxidase, and C1 inhibitor, which all act to prevent apoptosis and protease degradation, supports the idea that small fish are experiencing more protein turnover, possibly as a response to stress, than large fish. In this regard, however, it is also important to highlight that large fish also express much higher levels of genes related to catabolism such as cathepsins b, d, m, and l, and several genes related to chitinase, collagenase, aminopeptidase, serine protease, lysozyme, and matrix metalloproteinase activities. This indicates that catabolic activities appear to be somewhat higher in large fish and may reflect upon their increased metabolic activity. These results are similar to the up-regulation of genes related to carbohydrate, lipid, and amino acid metabolism coupled with the up-regulation of proteolysis in GH-transgenic and domestic fish compared to wildtype fish .
Effect of season (lots)
The larger Dec. fish showed differential expression of more genes in white muscle and fewer genes in the liver than the Sept. fish. While underlying genetic differences between the parents used to produce the two lots could contribute to the observed differences in gene expression, it is also important to consider environmental effects. While the lots were exposed to the same water, temperature, dissolved oxygen, and nutrition, they experienced a different photoperiod regime at the time tissues were sampled for RNA analysis. The Sept. lot was sampled at a time period approaching winter solstice, a period of declining growth, while the Dec. lot was experiencing increasing daylight approaching spring equinox, a period of increasing growth. The effect of season on the different growth rates between the lots is supported by the TGC profiles (Figure 3). As expected, the TGC values were either constant (Dec. lot) or declining (Sept. lot) at the time of the December 2009 sampling despite the fact that fish numbers had recently been reduced through culling. All previous samplings when tank densities were reduced were followed by a large increase in TGC values in the subsequent sampling period, simply because the fish had more environmental space in which to grow. This was not observed in the December sampling in either lot, and therefore photoperiod clearly had a major influence on the growth rates in these fish at that time of the year.
Photoperiod regimes have major influences on growth rates in fishes but these are not always in predictable directions and may be species-specific. Although increased light exposure produces increased growth  in rainbow trout, varied effects (increase or decrease) have been observed in Atlantic cod (Gadus morhua) [9, 78]. Our findings indicate that the induced changes in liver gene expression may be more multifarious between large and small fish and may be confounded by varying photoperiod regime. In contrast, the vast majority (93%) of gene probe IDs that demonstrated significant up-regulation in white muscle were consistent in either small or large fish across seasons. Large and small fish from the Sept. lot showed a greater number of differences in gene expression in the liver compared to the Dec. lot, while the converse was true for white muscle. This could be explained by the initiation of spring growth in the Dec. lot, as suggested by changes in the TGC at the time of sampling, leading to increased variability in white muscle expression levels between fish of different sizes. This indicates that expression levels differ on a seasonal basis with fewer differences between large and small fish during reduced growth in the fall, and greater differences coupled to increasing photoperiod in the spring.
These results indicate that sampling season can have a significant impact on the expression of genes related to the growth process in rainbow trout, with the TGC showing seasonal changes in both lots. In muscle, the large fish from the Dec. lot showed increased expression of genes related to muscle and connective tissue growth while genes related to blood production and the immune system were up-regulated in the large Sept. fish. This pattern may correspond to the differences in the growth profiles of the two lots. The Dec. fish were entering a period of increased growth while the Sept. fish were entering a phase of lower growth and possibly increased stress when they were sampled. These differences highlight the importance of sampling season in interpreting findings from gene expression studies.
A greater number of genes in white muscle demonstrated consistent associations with fish size regardless of sampling season, suggesting they may be better predictors of the growth response in rainbow trout compared to those in liver. Differences between large and small Sept. fish in the ‘response to stress’ category for white muscle tissue, indicate an up regulation of genes related to innate and acquired immunity in the large fish, while genes related to apoptosis have higher expression levels in the small fish. Depression of immune function genes and lower levels of oxidative stress genes in small fish suggest diminished stress handling capabilities. Genes within the ‘response to external stimulus’ and ‘establishment of localization’ categories also showed major expression differences between large and small Sept. fish. These categories show up-regulation of genes related to transcription, translation, and protein production in the small fish. However, since genes related to apoptosis were also up-regulated in the small fish, this suggests that higher cellular turnover rates may diminish the expected physiological effects of increased transcription, translation, and protein production. Genes related to blood and energy production showed up-regulation in the large fish compared to the small fish indicating an increased energy demand in the muscle of the large fish. In contrast to muscle tissue, IGFBP1 was the only gene with highly significant and consistent up-regulation in the liver across seasons.
where wt2 = mean weight of the fish at time t2 (days) and wt1 = mean weight of the fish at time t1 (days)
thermal growth co-efficient (TGC) , and tank densities were calculated throughout the experimental period (Figure 3) and no disease outbreaks occurred. Mortalities were minimal throughout with the largest percentage occurring during the early rearing stages. Both the Sept. and Dec. lots had similar mortality profiles with the largest values observed following the first bulk weighing period. Fish were fasted for two days before weighing. Fork length (nearest mm) and weight (nearest g) were measured at approximately 15 months of age - December 2, 2009 for the Sept. lot and February 23, 2010 for the Dec. lot. All fish were reproductively immature at the time of sampling. Gonadal inspection revealed only thin gonadal strands in the head kidney region and thus all the fish were actively growing juveniles. Weight differences among the half-sib families within each lot were compared using an ANOVA and Tukey’s post-hoc test based on the results of a Levene’s test for equality of variance.
Samples of liver and white muscle were collected from 50 to 55 fish within each half-sib lot which were purposefully size-selected to represent the fastest and slowest growing fish in each lot. The tissues were placed in an RNA preserving solution (3.75 M (NH4)2SO4, 10 mM EDTA, 25 mM Na3C6H5O7, adjusted to pH 5.2) and then stored at −80°C until use. White muscle was excised immediately below the dorsal fin and well above the lateral line to avoid contamination with red muscle. A small square plug measuring less than a 1 cm was excised and the surface skin removed. Small fragments of tissue (~100 mg sections) from below the dermal connective tissue bundle (i.e., muscle myofibrils only) were placed in the RNA preserving solution.
Parentage and sex determination
DNA was extracted from the liver samples using a standard phenol chloroform protocol . The purity and concentration of the DNA was quantified using a Nanodrop 8000 spectrophotometer (Thermo Scientific, Waltham, MA) and samples were stored at −20°C. The archived parental tissue was genotyped for variation in microsatellite loci to identify the parentage of the progeny. Markers BX887563, Omm1220, Omm5147, and Omy1212UW were used to identify the parentage of the progeny in the Sept. lot and markers Omm1054, Omm1087, Omm1088, and Omm5156 were used to identify parentage in the Dec. lot . Polymerase chain reaction (PCR) mixtures were made in 7 μL volumes (2.6 ng genomic DNA · μL-1, 1× PCR buffer, 0.125 mM dNTP, 1.5 mM MgCl2, 1.5 nM BSA, 0.3 μM of each forward or reverse marker primers labeled with tetrachloro-6-carboxy-flourescent, 0.021 U μL-1 Taq DNA polymerase). PCR conditions began with initial denaturation (95°C for 10 min), followed by 35 cycles of denaturation (95°C for 1 min), annealing (30 s), and extension (72°C for 30 s), and concluded with final extension (72°C for 5 min). An annealing temperature of 58°C was used for all primers except BX887563, which used 54°C.
The sex of the progeny was determined using the rainbow trout Y-specific marker OmyY1 . Polymerase chain reaction (PCR) mixtures were made in 20 μL volumes (3 ng genomic DNA · μL-1, 1× PCR buffer, 0.2 mM dNTP, 1.75 mM MgCl2, 0.2 μM of each forward or reverse marker primers, 0.01 U μL-1 Taq DNA polymerase). PCR conditions began with initial denaturation (95°C for 5 min), followed by 30 cycles of denaturation (95°C for 30 s), annealing (58°C for 1 min), and extension (72°C for 1 min), and concluded with final extension (72°C for 10 min).
Total RNA was isolated from the liver and white muscle of the 2 largest and 2 smallest fish by weight in each half-sib family for both the Sept. and Dec. lots. TRIzol (Invitrogen, Carlsbad, CA) extractions were performed according to the manufacturer’s instructions and the resulting RNA concentrations were determined using a Nanodrop8000 spectrophotometer. The samples were stored at −80°C until future use. The presence of distinct 18S and 28S rRNA bands after agarose gel electrophoresis was used to confirm that the RNA was not degraded. The extraction process was repeated until a minimum of 40 μg of RNA was collected from each sample. Since large and small fish are growing at different rates it is possible that rRNA makes up a different proportion of the total RNA in the small and large fish. This would result in different amounts of mRNA within a unit volume of total RNA. Therefore, we avoided this potential bias by using purified mRNA. Prior to cDNA synthesis, mRNA was purified from the total RNA using μMACS mRNA Isolation Kits – Small Scale (Miltenyi Biotec Inc, Auburn, CA) according to the manufacturer’s instructions.
Average weights of the pooled fish used for the microarray (g)
L1 vs. S2
L2 vs. S1
Gene expression was profiled using an Agilent salmonid microarray (catalog number - G2519F-020938) containing cDNAs from 43,663 genes selected from Atlantic salmon expressed sequence tag databases (Aglient Technologies, Mississauga, ON). This chip allows quantification in expression of genes involved in muscle growth such as GH, IGF-I and II, actin, and myosin. Approximately 0.2-0.3 μg of cDNA was prepared from the mRNA using a SuperScript® Plus Direct cDNA Labeling System with Alexa Fluor® aha-dUTPs (Invitrogen, Burlington, ON) and labeled with either Alexa Fluor 555 or Alexa Fluor 647. Briefly, the mRNA was reverse transcribed using an anchored oligod(T)20 primer and random hexamers in cDNA synthesis reactions that incorporated Alexa Fluor-labeled nucleotides following the manufacturer’s specifications. The concentration and dye incorporation rates of the cDNA were calculated using a Nanodrop 8000 spectrophotometer. Samples were stored at −20°C until hybridization, which occurred on either the same or the following day.
The cDNA was hybridized overnight to the microarrays according to the hybridization protocol described in Two-Color Microarray-Based Gene Expression Analysis (Agilent Technologies). Dye flips were included to compensate for any dye effects. Because air ozone levels could affect the Alexa Fluor 555 signal, we adopted the Agilent Ozone protection protocol following the manufacturers’ specifications to prevent degradation. This was followed by washing to remove any cDNA that had not hybridized to the array. The microarrays were scanned on a GenePix 4200A scanner (Axon Instruments, Weatherford Texas). Genepix Pro 6.1 software was used to process images, align the spots, integrate the Genepix Array List file with the microarray images, and quantify the spots. The arrays were subject to manual review and all unacceptable spots were flagged and excluded.
Genespring GX 7.3.1 (http://www.genomics.agilent.com) software was used to analyze the microarray data. Data were preprocessed by removing data points where the signal intensities ratio of both channels was less than a baseline threshold value of 0.1. Lowess (locally weighted scatter plot smoothing) normalization was applied to the data to normalize the spot intensity from various replicates. Spots that did not have a value for all of the replicates within a lot were removed from the analysis. Cross-array lowess normalizations were performed by the software and for each spot, t-statistic, p-value (probability), and the ratio between the two dyes were calculated by the Genespring software. Following corrections spots with a minimum of 1.2-fold change or more were considered differentially expressed (p ≤ 0.05). Microarray data were submitted to the NCBI Gene Expression Omnibus  under the submission number GSE42584.
Target gene lists obtained from the significance analysis were re-annotated using Blast2GO (http://www.blast2go.com/b2ghome) to assign GO terms to the differentially expressed microarray sequences. This software applies an automated BlastX alignment of the sequences to the NCBI non-redundant database. The BlastX expectation value threshold was set to 1.0E-6 whereas all other parameters were set to the default values. If Blast2GO was unable to assign GO terms to a sequence, manual evaluations were considered using AmiGO (http://amigo.geneontology.org/cgi-bin/amigo/go.cgi) assignments. A heterogeneity G-test (http://www.uoguelph.ca/~rdanzman with links to the software module) was used to compare the number of genes differently expressed in the GO categories at GO biological process levels 2 and 3 (GOslim generic categories) between groups using a backwards elimination procedure, using counts assigned within the Blast2GO program to the various level 2 and 3 GOslim categories. If significance was detected, the software reported the tabular category with the greatest heterogeneity. This category was removed from the analysis and the remaining categories were re-analyzed in a stepwise fashion until no significant differences remained. The GO categories removed at each step (i.e., containing significant differences in gene expression levels between the groups compared), are reported in Additional files provided. However, in several cases, the number of genes assigned to a grouping was small in number, and often with an absence of gene assignments within 1 of the 2 cells being compared. Therefore, only GO categories that differed by more than 5% (i.e. total proportion of all GO terms assigned) of all the genes assigned across all groupings were considered and discussed. The comparisons evaluated by the heterogeneity G-tests were: large vs. small fish within each seasonal lot; large vs. large fish from different seasonal lots, and small vs. small fish from different seasonal lots. This yielded 4 contrasts where two tested for the effects of size within a season and two tested for seasonal effects on fish of the same size category.
Real time PCR
Genes used for qPCR in the liver and white muscle to confirm the results of the microarray
IGF-binding protein 1 precursor (IGF1BP)
Complement C1q-like protein 4 precursor (C1QL4 pre)
Hemoglobin subunit alpha-1 (HBA1)
Max protein (MAX)
Glyceraldehyde-3-phosphate dehydrogenase (GAPDH)
ADP/ATP translocase 2 (ANT2)
This investigation was supported by an NSERC Strategic Grant. The authors wish to thank Sean Pressey and Lynn and Clarke Rieck from Lyndon Fish Hatcheries Inc., for providing the experimental fish used in this study. We also wish to thank Michael Burke and the staff of the Alma Aquaculture Research Station for their care and maintenance of the experimental fish used in the study. In addition, Xia Yue and Anne Easton helped with the collection of the phenotypic data.
- Garant D, Dodson JJ, Bernatchez L: Differential reproductive success and heritability of alternative reproductive tactics in wild Atlantic salmon (Salmo salar l.). Evolution. 2003, 57: 1133-1141.PubMedGoogle Scholar
- Hutchings J: Age- and size-specific costs of reproduction within populations of brook trout, Salvelinus fontinalis. Oikos. 1994, 70: 12-20. 10.2307/3545693.View ArticleGoogle Scholar
- Levesque HM, Shears MA, Fletcher GL, Moon TW: Myogenesis and muscle metabolism in juvenile Atlantic salmon (Salmo salar) made transgenic for growth hormone. J Exp Biol. 2008, 211: 128-137. 10.1242/jeb.006890.PubMedView ArticleGoogle Scholar
- Carpio Y, Acosta J, Morales R, Santisteban Y, Sanchéz A, Estrada MP: Regulation of body mass growth through activin type IIB receptor in teleost fish. Gen Comp Endocrinol. 2009, 160: 158-167. 10.1016/j.ygcen.2008.11.009.PubMedView ArticleGoogle Scholar
- Curtin NA, Lou F, Woledge RC: Sustained performance by red and white muscle fibres from the dogfish Scyliorhinus canicula. J Exp Biol. 2010, 213: 1921-1929. 10.1242/jeb.036012.PubMedView ArticleGoogle Scholar
- Rescan P-Y, Montfort J, Rallière C, Le Cam A, Esquerré D, Hugot K: Dynamic gene expression in fish muscle during recovery growth induced by a fasting-refeeding schedule. BMC Genomics. 2007, 8: 438-455. 10.1186/1471-2164-8-438.PubMed CentralPubMedView ArticleGoogle Scholar
- Salem M, Kenney PB, Rexroad CE, Yao J: Microarray gene expression analysis in atrophying rainbow trout muscle: a unique nonmammalian muscle degradation model. Physiol Genomics. 2006, 28: 33-45. 10.1152/physiolgenomics.00114.2006.PubMedView ArticleGoogle Scholar
- Salem M, Kenney PB, Rexroad CE, Yao J: Molecular characterization of muscle atrophy and proteolysis associated with spawning in rainbow trout. Comp Biochem Physiol D Genomics Proteomics. 2006, 1: 227-237. 10.1016/j.cbd.2005.12.003.View ArticleGoogle Scholar
- Nagasawa K, Giannetto A, Fernandes JMO: Photoperiod influences growth and mll (mixed-lineage leukaemia) expression in Atlantic cod. PLoS ONE. 2012, 7: e36908-10.1371/journal.pone.0036908.PubMed CentralPubMedView ArticleGoogle Scholar
- Lefébure R, Larsson S, Byström P: A temperature-dependent growth model for the three-spined stickleback Gasterosteus aculeatus. J Fish Biol. 2011, 79: 1815-1827. 10.1111/j.1095-8649.2011.03121.x.PubMedView ArticleGoogle Scholar
- Li M, Leatherland J: Temperature and ration effects on components of the IGF system and growth performance of rainbow trout (Oncorhynchus mykiss) during the transition from late stage embryos to early stage juveniles. Gen Comp Endocrinol. 2008, 155: 668-679. 10.1016/j.ygcen.2007.08.017.PubMedView ArticleGoogle Scholar
- Vehviläinen H, Kause A, Kuukka-Anttila H, Koskinen H, Paananen T: Untangling the positive genetic correlation between rainbow trout growth and survival. Evol Appl. 2012, 5: 732-745. 10.1111/j.1752-4571.2012.00251.x.PubMed CentralPubMedView ArticleGoogle Scholar
- Leips J, Rodd FH, Travis J: The adaptive significance of population differentiation in offspring size of the least killifish, Heterandria formosa. Ecol Evol. 2013, 3: 948-960. 10.1002/ece3.509.PubMed CentralPubMedView ArticleGoogle Scholar
- Pottinger TG, Carrick TR: Stress responsiveness affects dominant-subordinate relationships in rainbow trout. Horm Behav. 2001, 40: 419-427. 10.1006/hbeh.2001.1707.PubMedView ArticleGoogle Scholar
- Zhou QB, Wu HD, Zhu CS, Yan XH: Effects of dietary lipids on tissue fatty acids profile, growth and reproductive performance of female rice field eel (Monopterus albus). Fish Physiol Biochem. 2011, 37: 433-445. 10.1007/s10695-010-9444-1.PubMedView ArticleGoogle Scholar
- Kause A, Paananen T, Ritola O, Koskinen H: Direct and indirect selection of visceral lipid weight, fillet weight, and fillet percentage in a rainbow trout breeding program. J Anim Sci. 2007, 85: 3218-3227. 10.2527/jas.2007-0332.PubMedView ArticleGoogle Scholar
- Martyniuk CJ, Perry GML, Mogahadam KH, Ferguson MM, Danzmann RG: The genetic architecture of correlations among growth-related traits and male age at maturation in rainbow trout. J Fish Biol. 2003, 63: 746-764. 10.1046/j.1095-8649.2003.00188.x.View ArticleGoogle Scholar
- Perry GML, Martyniuk CM, Ferguson MM, Danzmann RG: Genetic parameters for upper thermal tolerance and growth-related traits in rainbow trout (Oncorhynchus mykiss). Aquaculture. 2005, 250: 120-128. 10.1016/j.aquaculture.2005.04.042.View ArticleGoogle Scholar
- Baranski M, Moen T, Våge DI: Mapping of quantitative trait loci for flesh colour and growth traits in Atlantic salmon (Salmo salar). Genet Sel Evol. 2010, 42: 17-30. 10.1186/1297-9686-42-17.PubMed CentralPubMedView ArticleGoogle Scholar
- Wringe BF, Devlin RH, Ferguson MM, Moghadam HK, Sakhrani D, Danzmann RG: Growth-related quantitative trait loci in domestic and wild rainbow trout (Oncorhynchus mykiss). BMC Genet. 2010, 11: 63-476.PubMed CentralPubMedView ArticleGoogle Scholar
- Gahr SA, Vallejo RL, Weber GM, Shepherd BS, Silverstein JT, Rexroad CE: Effects of short-term growth hormone treatment on liver and muscle transcriptomes in rainbow trout (Oncorhynchus mykiss). Physiol Genomics. 2008, 32: 380-392.PubMedView ArticleGoogle Scholar
- Nordgarden U, Fjelldal PG, Hansen T, Björnsson BT, Wargelius A: Growth hormone and insulin-like growth factor-I act together and independently when regulating growth in vertebral and muscle tissue of Atlantic salmon postsmolts. Gen Comp Endocrinol. 2006, 149: 253-260. 10.1016/j.ygcen.2006.06.001.PubMedView ArticleGoogle Scholar
- Huang CW, Li YH, Hu SY, Chi JR, Lin GH, Lin CC, Gong HY, Chen JY, Chen RH, Chang SJ, Liu FG, Wu JL: Differential expression patterns of growth-related microRNAs in the skeletal muscle of Nile tilapia (Oreochromis niloticus). J Anim Sci. 2012, 90: 4266-4279. 10.2527/jas.2012-5142.PubMedView ArticleGoogle Scholar
- Ma Q, Liu SF, Zhuang ZM, Lin L, Sun ZZ, Liu CL, Su YQ, Tang QS: Genomic structure, polymorphism and expression analysis of growth hormone-releasing hormone and pituitary adenylate cyclase activating polypeptide genes in the half-smooth tongue sole (Cynoglossus semilaevis). Genet Mol Res. 2011, 10: 3828-3846. 10.4238/2011.December.12.1.PubMedView ArticleGoogle Scholar
- Peterson BC, Waldbieser GC, Bilodeau L: IGF-I and IGF-II mRNA expression in slow and fast growing families of USDA103 channel catfish (Ictalurus punctatus). Comp Biochem Physiol A Mol Integr Physiol. 2004, 139: 317-323. 10.1016/j.cbpb.2004.09.015.PubMedView ArticleGoogle Scholar
- Devlin RH, Sakhrani D, White S, Overturf K: Effects of domestication and growth hormone transgenesis on mRNA profiles in rainbow trout (Oncorhynchus mykiss). J Anim Sci. 2013, -Epub ahead of printGoogle Scholar
- Devlin RH, Sakhrani D, Tymchuck WE, Rise ML, Goh B: Domestication and growth hormone transgenesis cause similar changes in gene expression in coho salmon (Oncorhynchus kisutch). Proc Natl Acad Sci U S A. 2009, 106: 3047-3052. 10.1073/pnas.0809798106.PubMed CentralPubMedView ArticleGoogle Scholar
- Tymchuk W, Sakhrani D, Devlin R: Domestication causes large-scale effects on gene expression in rainbow trout: Analysis of muscle, liver and brain transcriptomes. Gen Comp Endocrinol. 2009, 164: 175-183. 10.1016/j.ygcen.2009.05.015.PubMedView ArticleGoogle Scholar
- White SL, Sakhrani D, Danzmann RG, Devlin RH: Influence of developmental stage and genotype on liver mRNA levels among wild, domesticated, and hybrid rainbow trout (Oncorhynchus mykiss). BMC Genomics. 2013, 14: 673-10.1186/1471-2164-14-673.PubMed CentralPubMedView ArticleGoogle Scholar
- Salem M, Vallejo RL, Leeds TD, Palti Y, Liu S, Sabbagh A, Rexroad CE, Yao J: RNA-Seq identifies SNP markers for growth traits in rainbow trout. PLoS One. 2012, 7: e36264-10.1371/journal.pone.0036264.PubMed CentralPubMedView ArticleGoogle Scholar
- Rise ML, Douglas SE, Sakhrani D, Williams J, Ewart KV, Rise M, Davidson WS, Koop BF, Devlin RH: Multiple microarray platforms utilized for hepatic gene expression profiling of GH transgenic coho salmon with and without ration restriction. J Mol Endocrinol. 2006, 37: 259-282. 10.1677/jme.1.02031.PubMedView ArticleGoogle Scholar
- Rise ML, von Schalburg KR, Brown GD, Mawer MA, Devlin RH, Kuipers N, Busby M, Beetz-Sargent M, Alberto R, Gibbs AR, Hunt P, Shukin R, Zeznik JA, Nelson C, Jones SRM, Smailus DE, Jones SJM, Schein JE, Marra MA, Butterfield YSN, Stott JM, Ng SHS, Davidson WS, Koop BF: Development and application of a salmonid EST database and cDNA microarray: data mining and interspecific hybridization characteristics. Genome Res. 2004, 14: 478-490. 10.1101/gr.1687304.PubMed CentralPubMedView ArticleGoogle Scholar
- von Schalburg KR, Rise ML, Cooper GA, Brown GD, Gibbs AR, Nelson CC, Davidson WS, Koop BF: Fish and chips: various methodologies demonstrate utility of a 16,006-gene salmonid microarray. BMC Genomics. 2005, 15: 126-133.View ArticleGoogle Scholar
- Hamade A, Deries M, Begemann G, Bally-Cuif L, Genêt C, Sabatier F, Bonnieu A, Cousin X: Retinoic acid activates myogenesis in vivo through Fgf8 signalling. Dev Biol. 2006, 289: 127-140. 10.1016/j.ydbio.2005.10.019.PubMedView ArticleGoogle Scholar
- Kamei H, Ding Y, Kajimura S, Wells M, Chiang P, Duan C: Role of IGF signaling in catch-up growth and accelerated temporal development in zebrafish embryos in response to oxygen availability. Development. 2011, 138: 777-786. 10.1242/dev.056853.PubMedView ArticleGoogle Scholar
- Sun CF, Tao Y, Jiang XY, Zou SM: IGF binding protein 1 is correlated with hypoxia-induced growth reduce and developmental defects in grass carp (Ctenopharyngodon idellus) embryos. Gen Comp Endocrinol. 2011, 172: 409-415. 10.1016/j.ygcen.2011.04.005.PubMedView ArticleGoogle Scholar
- Hevrøy EM, Azpeleta C, Shimizu M, Lanzén A, Kaiya H, Espe M, Olsvik PA: Effects of short-term starvation on ghrelin, GH-IGF system, and IGF-binding proteins in Atlantic salmon. Fish Physiol Biochem. 2011, 37: 217-232. 10.1007/s10695-010-9434-3.PubMedView ArticleGoogle Scholar
- Dang CV: Links between metabolism and cancer. Genes Dev. 2012, 26: 877-890. 10.1101/gad.189365.112.PubMed CentralPubMedView ArticleGoogle Scholar
- Davis AE, Lu F, Mejia P: C1 inhibitor, a multi-functional serine protease inhibitor. Thromb Haemost. 2010, 104: 886-893. 10.1160/TH10-01-0073.PubMedView ArticleGoogle Scholar
- Chen YC, Taylor EB, Dephoure N, Heo JM, Tonhato A, Papandreou I, Nath N, Denko NC, Gygi SP, Rutter J: Identification of a protein mediating respiratory supercomplex stability. Cell Metab. 2012, 15: 348-360. 10.1016/j.cmet.2012.02.006.PubMed CentralPubMedView ArticleGoogle Scholar
- Strogolova V, Furness A, Robb-McGrath M, Garlich J, Stuart RA: Rcf1 and Rcf2, members of the hypoxia-induced gene 1 protein family, are critical components of the mitochondrial cytochrome bc1-cytochrome c oxidase supercomplex. Mol Cell Biol. 2012, 32: 1363-1373. 10.1128/MCB.06369-11.PubMed CentralPubMedView ArticleGoogle Scholar
- Howard A, Townes C, Milona P, Nile CJ, Michailidis G, Hall J: Expression and functional analyses of liver expressed antimicrobial peptide-2 (LEAP-2) variant forms in human tissues. Cell Immunol. 2010, 261: 128-133. 10.1016/j.cellimm.2009.11.010.PubMedView ArticleGoogle Scholar
- Cunningham O, Gore MG, Mantle TJ: Initial-rate kinetics of the flavin reductase reaction catalysed by human biliverdin-IXbeta reductase (BVR-B). Biochem J. 2000, 345: 393-399. 10.1042/0264-6021:3450393.PubMed CentralPubMedView ArticleGoogle Scholar
- Pan S, Tang J, Gu X: Isolation and characterization of a novel fucose-binding lectin from the gill of bighead carp (Aristichthys nobilis). Vet Immunol Immunopathol. 2010, 133: 154-164. 10.1016/j.vetimm.2009.07.015.PubMedView ArticleGoogle Scholar
- Zelensky AN, Gready JE: The C-type lectin-like domain superfamily. FEBS J. 2005, 272: 6179-6217. 10.1111/j.1742-4658.2005.05031.x.PubMedView ArticleGoogle Scholar
- Racay P, Gregory P, Schwaller B: Parvalbumin deficiency in fast-twitch muscles leads to increased ‘slow-twitch type’ mitochondria, but does not affect the expression of fiber specific proteins. FEBS J. 2006, 273: 96-108. 10.1111/j.1742-4658.2005.05046.x.PubMedView ArticleGoogle Scholar
- Seebacher F, Walter I: Differences in locomotor performance between individuals: importance of parvalbumin, calcium handling and metabolism. J Exp Biol. 2012, 215: 663-670. 10.1242/jeb.066712.PubMedView ArticleGoogle Scholar
- Ducreux S, Gregory P, Schwaller B: Inverse regulation of the cytosolic Ca2+ buffer parvalbumin and mitochondrial volume in muscle cells via SIRT1/PGC-1α Axis. PLoS One. 2012, 7: e44837-10.1371/journal.pone.0044837.PubMed CentralPubMedView ArticleGoogle Scholar
- Seurin D, Lombet A, Babajko S, Godeau F, Ricort JM: Insulin-like growth factor binding proteins increase intracellular calcium levels in two different cell lines. PLoS One. 2013, 8: e59323-10.1371/journal.pone.0059323.PubMed CentralPubMedView ArticleGoogle Scholar
- Macqueen DJ, Kristjánsson BK, Paxton CG, Vieira VL, Johnston IA: The parallel evolution of dwarfism in Arctic charr is accompanied by adaptive divergence in mTOR-pathway gene expression. Mol Ecol. 2011, 20: 3167-3184. 10.1111/j.1365-294X.2011.05172.x.PubMedView ArticleGoogle Scholar
- Lee MN, Ha SH, Kim J, Koh A, Lee CS, Kim JH, Jeon H, Kim DH, Suh PG, Ryu SH: Glycolytic flux signals to mTOR through glyceraldehyde-3-phosphate dehydrogenase-mediated regulation of Rheb. Mol Cell Biol. 2009, 29: 3991-4001. 10.1128/MCB.00165-09.PubMed CentralPubMedView ArticleGoogle Scholar
- Buller CL, Heilig CW, Brosius FC: GLUT1 enhances mTOR activity independently of TSC2 and AMPK. Am J Physiol Renal Physiol. 2011, 301: F588-F596. 10.1152/ajprenal.00472.2010.PubMed CentralPubMedView ArticleGoogle Scholar
- Goolish EM: The scaling of aerobic and anaerobic muscle power in rainbow trout (Salmo gairdneri). J Exp Biol. 1989, 147: 493-505.Google Scholar
- Ochocki JD, Simon MC: Nutrient-sensing pathways and metabolic regulation in stem cells. J Cell Biol. 2013, 203: 23-33. 10.1083/jcb.201303110.PubMed CentralPubMedView ArticleGoogle Scholar
- Garcia de la Serrana D, Estévez A, Andree K, Johnston IA: Fast skeletal muscle transcriptome of the gilthead sea bream (Sparus aurata) determined by next generation sequencing. BMC Genomics. 2012, 13: 181-10.1186/1471-2164-13-181.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang R, Yang J, Zhu J, Xu X: Depletion of zebrafish Tcap leads to muscular dystrophy via disrupting sarcomere-membrane interaction, not sarcomere assembly. Hum Mol Genet. 2009, 18: 4130-4140. 10.1093/hmg/ddp362.PubMed CentralPubMedView ArticleGoogle Scholar
- Fougerousse F, Edom-Vovard F, Merkulova T, Ott MO, Durand M, Butler-Browne G, Keller A: The muscle-specific enolase is an early marker of human myogenesis. J Muscle Res Cell Motil. 2001, 22: 535-544. 10.1023/A:1015008208007.PubMedView ArticleGoogle Scholar
- Snow CJ, Peterson MT, Khalil A, Henry CA: Muscle development is disrupted in zebrafish embryos deficient for fibronectin. Dev Dyn. 2008, 237: 2542-2553. 10.1002/dvdy.21670.PubMed CentralPubMedView ArticleGoogle Scholar
- Senga Y, Nagamine T, Kameshita I, Sueyoshi N: Knockdown of two splice variants of Ca(2+)/calmodulin-dependent protein kinase Iδ causes developmental abnormalities in zebrafish, Danio rerio. Arch Biochem Biophys. 2012, 517: 71-82. 10.1016/j.abb.2011.11.008.PubMedView ArticleGoogle Scholar
- Abraham ST, Shaw C: Increased expression of deltaCaMKII isoforms in skeletal muscle regeneration: Implications in dystrophic muscle disease. J Cell Biochem. 2006, 97: 621-632. 10.1002/jcb.20669.PubMedView ArticleGoogle Scholar
- Li W, Li H, Sanders PN, Mohler PJ, Backs J, Olson EN, Anderson ME, Grumbach IM: The multifunctional Ca2+/calmodulin-dependent kinase II delta (CaMKIIdelta) controls neointima formation after carotid ligation and vascular smooth muscle cell proliferation through cell cycle regulation by p21. J Biol Chem. 2011, 286: 7990-7999. 10.1074/jbc.M110.163006.PubMed CentralPubMedView ArticleGoogle Scholar
- Pfleiderer PJ, Lu KK, Crow MT, Keller RS, Singer HA: Modulation of vascular smooth muscle cell migration by calcium/ calmodulin-dependent protein kinase II-delta 2. Am J Physiol Cell Physiol. 2004, 286: C1238-C1245. 10.1152/ajpcell.00536.2003.PubMedView ArticleGoogle Scholar
- Guerfali I, Manissolle C, Durieux AC, Bonnefoy R, Bartegi A, Freyssenet D: Calcineurin A and CaMKIV transactivate PGC-1alpha promoter, but differentially regulate cytochrome c promoter in rat skeletal muscle. Pflugers Arch. 2007, 454: 297-305. 10.1007/s00424-007-0206-6.PubMedView ArticleGoogle Scholar
- Handschin C, Rhee J, Lin J, Tarr PT, Spiegelman BM: An autoregulatory loop controls peroxisome proliferator-activated receptor gamma coactivator 1alpha expression in muscle. Proc Natl Acad Sci U S A. 2003, 100: 7111-7116. 10.1073/pnas.1232352100.PubMed CentralPubMedView ArticleGoogle Scholar
- Nimura T, Sugiyama Y, Sueyoshi N, Shigeri Y, Ishida A, Kameshita I: A minimum size homologue of Ca2+/calmodulin-dependent protein kinase IV naturally occurring in zebrafish. J Biochem. 2010, 147: 857-865. 10.1093/jb/mvq021.PubMedView ArticleGoogle Scholar
- Rose AJ, Kiens B, Richter EA: Ca2 + −calmodulin-dependent protein kinase expression and signalling in skeletal muscle during exercise. J Physiol. 2003, 574: 889-903.View ArticleGoogle Scholar
- Wu H, Kanatous SB, Thurmond FA, Gallardo T, Isotani E, Bassel-Duby R, Williams RS: Regulation of mitochondrial biogenesis in skeletal muscle by CaMK. Science. 2002, 296: 349-352. 10.1126/science.1071163.PubMedView ArticleGoogle Scholar
- Ikeda S, He A, Kong SW, Lu J, Bejar R, Bodyak N, Lee KH, Ma Q, Kang PM, Golub TR, Pu WT: MicroRNA-1 negatively regulates expression of the hypertrophy-associated calmodulin and Mef2a genes. Mol Cell Biol. 2009, 29: 2193-2204. 10.1128/MCB.01222-08.PubMed CentralPubMedView ArticleGoogle Scholar
- Olofsson MH, Havelka AM, Brnjic S, Shoshan MC, Linder S: Charting calcium-regulated apoptosis pathways using chemical biology: role of calmodulin kinase II. BMC Chem Biol. 2008, 8: 2-10.1186/1472-6769-8-2.PubMed CentralPubMedView ArticleGoogle Scholar
- Seales EC, Micoli KJ, McDonald JM: Calmodulin is a critical regulator of osteoclastic differentiation, function, and survival. J Cell Biochem. 2006, 97: 45-55. 10.1002/jcb.20659.PubMedView ArticleGoogle Scholar
- Zizioli D, Forlanelli E, Guarienti M, Nicoli S, Fanzani A, Bresciani R, Borsani G, Preti A, Cotelli F, Schu P: Characterization of the AP-1 μ1A and μ1B adaptins in zebrafish (Danio rerio). Dev Dyn. 2010, 239: 2404-2412. 10.1002/dvdy.22372.PubMedView ArticleGoogle Scholar
- Pridgeon JW, Klesius PH: Identification and expression profile of multiple genes in Nile tilapia in response to formalin killed Streptococcus iniae vaccination. Vet Immunol Immunopathol. 2011, 142: 201-206. 10.1016/j.vetimm.2011.05.015.PubMedView ArticleGoogle Scholar
- Wang T, Secombes CJ: Complete sequencing and expression of three complement components, C1r, C4 and C1 inhibitor, of the classical activation pathway of the complement system in rainbow trout Oncorhynchus mykiss. Immunogenetics. 2003, 55: 615-628. 10.1007/s00251-003-0622-5.PubMedView ArticleGoogle Scholar
- Jiang L, Rong R, Sheikh MS, Huang Y: Cullin-4A · DNA damage-binding protein 1 E3 ligase complex targets tumor suppressor RASSF1A for degradation during mitosis. J Biol Chem. 2011, 286: 6971-6978. 10.1074/jbc.M110.186494.PubMed CentralPubMedView ArticleGoogle Scholar
- Bergantin LB, Figueiredo LB, Godinho RO: The lumbrical muscle: a novel in situ system to evaluate adult skeletal muscle proteolysis and anticatabolic drugs for therapeutic purposes. J Appl Physiol. 2011, 111: 1710-1718. 10.1152/japplphysiol.00586.2011.PubMedView ArticleGoogle Scholar
- Bellinger FP, Raman AV, Reeves MA, Berry MJ: Regulation and function of selenoproteins in human disease. Biochem J. 2009, 422: 11-22. 10.1042/BJ20090219.PubMed CentralPubMedView ArticleGoogle Scholar
- Taylor JF, Migaud H, Porter MJR, Bromage NR: Photoperiod influences growth rate and plasma insulin-like growth factor-I levels in juvenile rainbow trout, Oncorhynchus mykiss. Gen Comp Endocrinol. 2005, 142: 169-185. 10.1016/j.ygcen.2005.02.006.PubMedView ArticleGoogle Scholar
- Fülberth M, Moran D, Jarlbæk H, Støttrup J: Growth of juvenile Atlantic cod Gadus morhua in land-based recirculation systems: Effects of feeding regime, photoperiod and diet. Aquaculture. 2009, 292: 225-231. 10.1016/j.aquaculture.2009.04.028.View ArticleGoogle Scholar
- Dumas A, France J, Bureau DP: Evidence of three growth stanzas in rainbow trout (Oncorhynchus mykiss) across life stages and adaptation of the thermal-unit growth coefficient. Aquaculture. 2007, 267: 139-146. 10.1016/j.aquaculture.2007.01.041.View ArticleGoogle Scholar
- Taggart JB, Hynes RA, Prodöuhl PA, Ferguson A: A simplified protocol for routine total DNA isolation from salmonid fishes. J Fish Biol. 1992, 40: 963-965. 10.1111/j.1095-8649.1992.tb02641.x.View ArticleGoogle Scholar
- Haidle L, Janssen JE, Gharbi K, Moghadam HK, Ferguson MM, Danzmann RG: Determination of quantitative trait loci (QTL) for early maturation in rainbow trout (Oncorhynchus mykiss). Mar Biotechnol. 2008, 10: 579-592. 10.1007/s10126-008-9098-5.PubMed CentralPubMedView ArticleGoogle Scholar
- Brunelli JP, Wertzler KJ, Sundin K, Thorgaard GH: Y-specific sequences and polymorphisms in rainbow trout and Chinook salmon. Genome. 2008, 51: 739-748. 10.1139/G08-060.PubMedView ArticleGoogle Scholar
- Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30: 207-210. 10.1093/nar/30.1.207.PubMed CentralPubMedView ArticleGoogle Scholar
- Bookout AL, Mangelsdorf DJ: Quantitative real-time PCR protocol for analysis of nuclear receptor signaling pathways. Nucl Recept Signal. 2003, 1: e012-PubMed CentralPubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.