Journal of Heredity Advance Access originally published online on January 13, 2005
Journal of Heredity 2005 96(3):261-278; doi:10.1093/jhered/esi030
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© 2005 The American Genetic Association
Interaction of Rearing Environment and Reproductive Tactic on Gene Expression Profiles in Atlantic Salmon
From the Bauer Center for Genomics Research, Harvard University, 7 Divinity Avenue, Cambridge, MA (02138) (Aubin-Horth and Hofmann); and the S. O. Conte Anadromous Fish Research Center, Leetwon Science Center, USGS/BRD, 1 Migratory Way, Turners Falls, MA 01376, and Department of Natural Resources Conservation, University of Massachusetts, Amherst, MA 01003 (Letcher)
Address correspondence to N. Aubin-Horth at the address above, or e-mail: NAubin-Horth{at}cgr.harvard.edu.
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Abstract |
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Organisms that share the same genotype can develop into divergent phenotypes, depending on environmental conditions. In Atlantic salmon, young males of the same age can be found either as sneakers or immature males that are future anadromous fish. Just as the organism-level phenotype varies between divergent male developmental trajectories, brain gene expression is expected to vary as well. We hypothesized that rearing environment can also have an important effect on gene expression in the brain and possibly interact with the reproductive tactic adopted. We tested this hypothesis by comparing brain gene expression profiles of the two male tactics in fish from the same population that were reared in either a natural stream or under laboratory conditions. We found that expression of certain genes was affected by rearing environment only, while others varied between male reproductive tactics independent of rearing environment. Finally, more than half of all genes that showed variable expression varied between the two male tactics only in one environment. Thus, in these fish, very different molecular pathways can give rise to similar macro-phenotypes depending on rearing environment. This result gives important insights into the molecular underpinnings of developmental plasticity in relationship to the environment.
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Introduction |
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Explaining the evolution of diversity in species and forms has long been a challenging problem in biology. It has become clear that trait variation observed within and among species cannot solely be due to change in protein coding genes but must also lie at the gene regulation level and in the interactions of the genes (Carroll et al. 2001; King and Wilson 1975). For example, in many species, the same genetic makeup can develop into strikingly different morphologies or behaviors (phenotypic plasticity [West-Eberhard 2003]). This has frequently confused taxonomists, as morphological differences within species can be as significant as those across species. Developmental plasticity is not solely due to developmental noise; rather, it is often the result of evolution by natural selection, enabling organisms to exploit a wider spectrum of resources and to cope with varying conditions throughout life (Pigliucci 2001). Plasticity of behavior, such as appropriate responses to seasonal changes and to reproductive opportunities, is a crucial determinant of an animal's fitness. This relationship between behavioral, morphological, and physiological traits and the ecological context results in a complex and integrated phenotype.
Identifying the proximate mechanisms of phenotypic plasticity constitutes an essential step toward an understanding of complex traits and their evolution in general (Gibson 2002; Via et al. 1995). Much research has focused on the molecular and physiological basis of plasticity during development and in the nervous system of a few model systems (Buonomano and Merzenich 1998; Lynch 2004). Conversely, studies of the evolution of plastic phenotypes have focused almost exclusively on ultimate causes and quantitative genetics (Bradshaw 1965; de Jong 1990; Hazel et al. 1990; Ostrowski et al. 2000; Roff 1996; Scheiner 1993; Van Buskirk 2002). While the study of the ultimate causes of plasticity remains largely divorced from the analysis of its mechanistic basis, a consensus is emerging that deciphering the mechanistic basis of trait variation within species may be fundamental to the understanding of the evolution of species diversity and that it is now time to merge these two branches of research (Hofmann 2003; Robinson and Ben-Shahar 2002). Indeed, the study of proximate and ultimate causes has recently been brought together in work on invertebrates (Abouheif and Wray 2002; Bochdanovits et al. 2003; Moczek and Nijhout 2002). Because complex traits are polygenic and gene interactions are a fundamental property of these traits, taking advantage of a genomic (multigene) approach to the study of gene expression improves the power to reveal the complex network of interrelated functional modules involved (Gracey et al. 2001; Ju et al. 2002; Koskinen et al. 2004; Oleksiak et al. 2002; Podrabsky and Somero 2004; Whitfield et al. 2003).
Atlantic salmon offer an excellent system to apply a genomic approach to the study of developmental plasticity, as males can develop very divergent reproductive phenotypes—and can do this within the same population (Aubin-Horth and Dodson 2004; Letcher and Gries 2003; Myers et al. 1986; Prévost et al. 1992; Whalen and Parrish 1999). During the first life stages in freshwater, juvenile males can either sexually mature precociously to become sneakers and reproduce without leaving freshwater; or they can migrate out to sea, only to return years later as large and mature anadromous fish to breed (Fleming 1998). Early sexual maturation of males results in gonadal growth, reduced somatic growth, changes in feeding and hormone levels, receptivity and attraction to female pheromones and adult male scent compared to immature males of the same age that will later become large anadromous males. These macroscopic changes in tactics are likely based on modifications of molecular, cellular, and physiological pathways in many tissues. The nature of the brain—in particular, as the center of the integration of environmental and endogenous cues and of control of physiology and behavior—guides initial studies to this important tissue (Hofmann 2003; Whitfield et al. 2003).
In salmon, the developmental decision that leads to divergence in male reproductive tactics is dependent on genetic and environmental factors and their interaction (Aubin-Horth and Dodson 2004; Hutchings and Myers 1994). In the present study, we analyzed expression profiles in the brains of wild-caught and laboratory-reared males (both sneaker and immature tactics) that originated from the same population. We reasoned that by comparing the neural gene expression profiles of these dichotomous phenotypes in different rearing environments, we can dissect the effects of reproductive tactic, environment, and their interactions. This way, we can determine genes that are (1) tactic-specific, independently of environment; (2) specific for a given environment, independently of tactic; and (3) regulated as a consequence of interactions between these two factors and, therefore, neither environment- nor tactic-specific.
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Material and Methods |
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Animals
We collected immature males and mature sneaker males of Atlantic salmon (Salmo salar) of age one year in the Sawmill River (42°30'N, 74°30'W), a tributary of the Connecticut River in western Massachusetts, United States. Laboratory-raised fish, members of the same population, were obtained from the S. O. Conte Anadromous Fish Research Center. Animals were euthanized in buffered MS-222 (100 mg l–1), sexed, and had their brains dissected and immediately transferred to RNAlater storage solution (Ambion); sex was determined by dissection.
Microarray Hybridization
Total RNA was extracted from brains according to a standard Trizol protocol (Invitrogen), following tissue homogenization (Tissue Tearor, Biospec Products). The RNA was analyzed for quantity and quality on the Bioanalyzer (Agilent) and on a standard spectrophotometer (Agilent). Four µg of total RNA extracted from each brain sample were labeled according to a standard amino-allyl attachment method protocol (Renn et al. 2004). Primer was annealed in a 15.5 µl reaction with one µl of primer solution (5 µg/µl each poly dT 12–18 with 5 µg/µl random hexamer oligonucleotides) at 70°C for 10 min, followed by 10 min at 4°C. A reaction solution was prepared as followed: 5.60 µl 5X first strand buffer (Invitrogen); 0.75 µl 50x amino-allyl-dUTP/dNTP mix (2.5 mM each dATP, dCTP, dGTP, 1.5 mM dTTP [Invitrogen], and 10 mM amino-allyl dUTP [Sigma]); 2.8 µl 0.1 M DTT, 2.8 µl of DEPC H2O, and 2 µl (200U/µl) of SuperScript II (Invitrogen) reverse transcription enzyme. After addition of the reaction solution to the primer and RNA mix, it was incubated at 42°C for 2 h. RNA left in solution after reverse transcription was then hydrolyzed; and the reverse transcription reaction was stopped by adding 10 µl of 1N NAOH and 10 µl of 0.5 M EDTA and then placing the solution at 65°C for 7 min. The reaction was neutralized with 25 µl of 1 M HEPES pH 7.5 (GIBCO BRL). The cDNA was then repeatedly rinsed and concentrated on a YM-30 filter (Millipore). The dye-coupling reaction required adding 1.5 µl of 1M sodium bicarbonate pH 9.0 and the appropriate Cy3 or Cy5 CyDye Post-labeling reactive dye pack (Amersham) and then placing it for 1 h at room temperature in the dark. The labeled cDNA was then purified using a Qiaquick column standard protocol (Qiagen), and two samples were then combined and concentrated to 50 µl on a YM 30 filter. Hybridization buffer consisted of adding 6 µl 20x SSC (Gibco), 3 µl poly (dA) poly(dT) (Sigma), 0.96 µl 1M HEPES, and 0.6 0.1M DTT (Invitrogen). After filtering on a 0.45 micron filter, 0.9 µl 10% SDS previously warmed to 37°C was added; and the labeled cDNA was denatured by placing at 100°C for 2 min. Twenty µl of probe sample were then immediately added onto each of the two replicates of the microarray on the same slide (Renn et al. 2004; NCBI GEO platform GPL928
[NCBI GEO]
) and hybridized under a cover slip (Corning) overnight in the dark at 65°C in a humidified chamber (Telechem) submerged in a water bath. The microarray used was constructed from a brain-specific cDNA library from Astatotilapia burtoni (Cichlidae). Previous analysis has shown this array platform to give suitable results with RNA derived from Atlantic salmon (Renn et al. 2004). Clone sequences are available from NCBI Genbank (accession numbers CN468542–CN472211; dbEST_Id 22642169–22645838) and contig information from TIGR gene indices (http://www.tigr.org/tdb/tgi/). Excess probe was washed at room temperature in a 400 ml wash solution consisting of 12 ml 20x SSC, 1ml 10% SDS, 4 ml 0.1M DTT, 383 ml Milli-Q Water; this was followed by a wash containing 395 ml Milli-Q Water, 1 ml 20x SSC, and 4 ml 0.1M DTT. Slides were then immediately centrifuged to dry before scanning. Arrays were scanned with an Axon 4000B scanner (Axon Instruments) using Genepix 5.0 software (Axon Instruments). Spots were examined individually and flagged as "bad" if irregularities occurred.
Two to four fish (biological) replicates were assayed per phenotype (see Figure 1, exceptions noted). RNA from each fish was labeled independently three to ten times (technical replicates, including dye-swaps), such that an individual of a given group (sneaker male, immature male, wild-caught, laboratory-raised) was directly compared to individuals of other phenotypes (see Figure 1), without the need for a reference sample (Churchill 2002; Townsend 2003). Using biological and technical replicates including dye-swaps has been shown to increase substantially the reliability of microarray results (Liang et al. 2003). Eighteen microarrays were used to compare thirty-six independent labeling reactions.
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Analysis |
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Gene Expression Level
Raw data (after flags filtering and removal of spots with intensities lower than the local background intensity, plus two standard deviation of this background intensity) was imported into R software v1.9 (R Development Core Team 2004) and normalized using the Linear Models for Microarray Data package (LIMMA v1.6.5 [Smyth et al. 2003]). Background-subtracted mean intensities (using the minimum method) were normalized using within-array loess normalization. Ratios of intensities were used in a bayesian analysis of gene expression levels (BAGEL v3.6 [Townsend and Hartl 2002]). Out of the 4,574 cDNA spots representing fish genes on the array, a certain number could not be reliably analyzed because of low hybridization quality for these genes, most probably due to sequence divergence (Renn et al. 2004). Therefore, 3,888 ESTs were used in the gene expression level analysis. This bayesian analysis takes advantage of additional information obtained from transitive comparisons of individuals when determining probability of differential expression among groups (Churchill 2002; Townsend 2003; Townsend and Hartl 2002). Annotation of the contigs formed by ESTs and singletons was based on TIGR gene indices for A. burtoni v1.0 (Quackenbush et al. 2000) (http://www.tigr.org/tdb/tgi/) and BLAST analysis using the Fugu genome (http://fugu.hgmp.mrc.ac.uk/).
Similarity of Gene Expression Profiles Among Individual Males
A clustering analysis of gene expression patterns of each individual was performed using the heatmap function of the stats package (R software v1.9 [R Development Core Team 2004]) to determine similarity across brains of each male phenotype. Hierarchical clustering of males' transcription profiles was based on the dissimilarity between expression levels for a given gene using the "average link" agglomeration method. Euclidian distance—which integrates effects of amplitude of ratios, as well as direction (correlation) in patterns—was used to calculate the dissimilarity matrix. Genes whose expression levels were significantly affected by tactic and rearing environment were used for clustering.
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Results |
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In the analysis, 3,888 ESTs (85% of spots on the array) were included, confirming the utility of heterologous hybridization (i.e., hybridizing RNA samples to an array constructed for a different species) when sufficient replication is used, as in the present study (for a systematic analysis, see Renn et al. 2004). Overall, 10.5% (n=409) of genes surveyed showed differential expression, depending on rearing environments and/or male tactics (P <.05).
Rearing Environment and Male Tactic Independent Effects
Some gene expression profiles varied between rearing environments, with no difference in transcription between the brains of sneaker males and immature males sharing the same environment (Figure 2a). These "rearing environment effect" genes (n=72) included haemoglobin, several ribosomal proteins, ATP synthase, 14–3–3G2 protein, and heat-shock proteins (see supplementary Table 1 for complete list). A more complicated picture emerged for some other genes (n=6): for example, one clone representing pentraxin (TIGR contig TC193) was upregulated in wild fish (both sneakers and immature males), compared with laboratory-reared fish; however, for other clones belonging to this contig, immature males showed higher expression than sneakers in the laboratory only. Similarly, depending on the clone, the TIGR contig TC330 was upregulated in laboratory-reared fish with a phenotype effect detectable only in the laboratory. Finally, TIGR contig TC69 was found to be overexpressed not only in wild fish versus hatchery fish, but also in immature males in both environments (supplementary Table 1, bold entries).
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For 113 genes, expression varied only with the male tactic, without a significant effect of the environment, such that sneakers in both environments exhibited upregulation or downregulation for these genes (Figure 2b). Examples include Na+/K+ ATPase ß subunit isoform 2, stathmin 2 (superior cervical ganglion-10 protein), sorting nexin 10, N-acetylglucosaminyltransferase V, semaphorin, neuroligin 3, and ribosomal proteins (see supplementary Table 2 for complete list). Only one gene showed an effect of environment overlaid on the tactic effect: elongation factor 1-alpha was overexpressed in immature males compared to sneaker males, and it also showed a significantly higher expression in wild immature fish compared with those from the laboratory fish.
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Interaction of Rearing Environment and Male Tactic
As hypothesized, we found considerable interaction effects of tactic and environment on gene expression (n=225 genes; Figure 2c). More than half of all the genes showing differential expression had an expression pattern of divergence between sneaker males and immature males that was found only in one environment. For instance, a male tactic effect on gene expression was found only in wild salmon for 138 ESTs (supplementary Table 3), while 78 EST showed an effect of male tactic solely in fish reared in laboratory settings (supplementary Table 4). Reassuringly, genes showing opposite patterns of expression in a tactic between environments were rare (n=8; supplementary Table 5), even at P <.05, and are most likely false positives in one or both comparisons.
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Because we performed heterologous hybridizations to a non-salmon fish array platform, we examined the level of concordance shown by spots belonging to the same contigs (and likely the same genes). As supplementary Tables 3 and 4 show, many ESTs from the same TIGR contig were upregulated concordantly. This is an important result, as it validates the utility of this array platform for heterologous hybridizations with salmon RNA (see also Figure 5 in Renn et al. 2004).
Similarity of Gene Expression Profiles Among Individual Males
Hierarchical clustering showed that similarity in transcription profiles among the brains of each individual male was both related to the reproductive tactic and the environment the fish was reared in (Figure 3). Importantly, wild sneaker males clustered together separately from all other fish. Among immature males, those obtained from the wild also clustered together.
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Discussion |
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TopAbstractIntroductionMaterial and MethodsAnalysisResultsDiscussionConclusionReferences |
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In the present study, we used a microarray-based approach to examine how rearing environment affects neural expression profiles that underlie the dramatic divergence between distinct male reproductive tactics of the same population. We showed that environment and reproductive tactic, as well as tactic-by-environment effects, give rise to specific gene expression patterns in the brain of male Atlantic salmon.
Rearing Environment and Male Tactic Independent Effects
We found an effect of rearing environment on gene expression such that neural expression profiles of laboratory and wild fish differed independent of male tactic. Both laboratory and wild-caught fish were derived from the same population. This result implies that the environmental cues and surrounding conditions (e.g., population density, feeding resources, and temperature and light regimes) can lead to profound differences in the molecular makeup of the brains of animals whose macro-phenotypes nevertheless are considered to be the same. It is important to note that no genes known for their role in reproductive maturation showed a rearing environment effect.
We do not know whether laboratory conditions can be considered extreme or "unnatural" with respect to their molecular consequences on the brain. However, we can hypothesize that similar large-scale differences may arise after transplanting wild fish of the same genetic population into different rivers (or different locations along the same river). Our results therefore have implications for the study of gene expression variation among populations and their interpretation as examples of local adaptations. Indeed, our findings suggest that gene expression profiles can vary significantly as the result of environmental variation only, even with similar genetic background, suggesting that experimental manipulations such as reciprocal transplants and common garden experiments would be necessary to partition the genetic and environmental components underlying variation in gene expression and to determine the relative importance of these factors (Falconer and Mackay 1996).
Organisms (or at least their brains) reared in different environments appear to implement the same reproductive tactics (e.g., sneaker) at least in part by different molecular mechanisms. This remarkable ability may allow organisms to integrate, at the molecular and physiological level, variations in external as well as internal cues and to "canalize" them into one or a few macro-phenotypes. In other words, there is more than one way to "make" a sneaker brain.
While we are not yet able to assign a biological function to all genes whose activities differ between laboratory and wild fish, some intriguing interpretations are already possible. For example, extraretinal opsins were upregulated in the brains of laboratory-reared fish, possibly indicating a different regulation of photoperiod entrainment (Alvarez-Viejo et al. 2004; Kojima et al. 2000). Similarly, HSP-90 and other chaperones upregulated in laboratory fish independent of male tactic may indicate increased growth and/or a stress response. In fish, HSP-90 supports various components of the cytoskeleton and of steroid hormone receptors (Basu et al. 2002). Gobies acclimatized to summer temperature had higher levels of HSP-90 in the brain (as well as a higher induction threshold) than fish acclimatized to winter temperature (Dietz and Somero 1992). HSP-70 were significantly raised in the brains of goldfish that were reared in the presence of a predator, an effect that is likely mediated by circulating cortisol levels (Kagawa and Mugiya 2002). Also, increased HSP expression shuts down protein synthesis (Rose et al. 1989), which is consistent with our finding that several ribosomal proteins were among the genes underexpressed in the brains of laboratory fish, potentially indicating lower levels of protein synthesis activity.
Another laboratory-specific gene encodes a laminin (beta-1 chain) precursor, a major component of basement membranes that has numerous biological activities (Meiners and Mercado 2003), including promotion of cell adhesion, migration, growth, and differentiation (e.g., neurite outgrowth). Interestingly, the DMY gene, which has been implicated in sex differentiation of males (Matsuda 2003; Winkler et al. 2004), is also upregulated in the brains of laboratory-reared fish, although its function here can only be guessed. Another gene upregulated in laboratory fish is Ras-related protein Rab-1A, which belongs to a family of small GTP-binding proteins relevant in regulating intracellular vesicle trafficking (Wright and Harding 2004).
Additionally, a number of genes involved in basic metabolic processes were upregulated in the brains of wild animals, which may indicate increased metabolic demands in a natural stream, compared with controlled laboratory conditions. One example is ATP synthase, which is of crucial importance in ATP production. Glutamic acid decarboxylase (GAD67), a GABA-synthesizing enzyme, is also up in wild fish, which may be related to differences in the water temperatures (Fraser et al. 2002).
We found that expression of some genes was affected by male reproductive tactic independent of the environment the fish were reared in, such that core sneaker genes could be determined. For example, superior cervical ganglia neural-specific protein (stathmin), a small regulatory protein integrating diverse intracellular signaling pathways involved in the control of cell proliferation and differentiation (Curmi et al. 1999), was overexpressed in sneakers in two spots from the same contig annotated as this gene.
Interaction of Rearing Environment and Male Tactic
More than half of all genes that showed variable expression exhibited an interacting effect of male tactic and rearing environment, such that differences between the two male tactics was found in only one environment. In this context, it is interesting that the number of genes found to be upregulated in sneakers in only one environment is 39 (wild) + 18 (laboratory) = 57, while the number of genes found upregulated in immature males in only one environment is 57 (wild) + 45 (laboratory) = 102 (i.e., almost twice as high). Whether this result means that neural transcription profiles in sneakers are less susceptible to a variable environment (or that immature males are more susceptible), though intriguing as a hypothesis, cannot yet be decided.
Similarity of Gene Expression Profiles Among Individual Males
We used a clustering analysis to determine if gene expression profiles were more similar between individuals raised in the same environment regardless of tactic or between the same male tactics in different environments. It is notable that the environmental effect seems as important as the tactic adopted. Laboratory fish clustered together independent of tactic, while wild fish tended to cluster by tactic rather than together. This may be indicative of the homogenization of gene expression in fish raised in controlled (and more stable) conditions and of the wider variation between male tactics when faced with larger range or more realistic ecological factors. The way laboratory fish clustered together may also suggest that the numerous molecular and biochemical modules, which are integrated into a reproductive tactic, are triggered by various (environmental) cues. Thus, when only some cues are present, only certain pathways become activated in the brain.
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Conclusion |
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There is a growing consensus that knowledge of the proximate mechanisms underlying organismal diversity is necessary for a thorough understanding of the evolution of complex phenotypes. The work on Atlantic salmon presented here highlights the extent of transcriptional plasticity in the face of environmental variation and the surprising insights that can be derived from a genomic dissection of phenotypic plasticity. We expect our results to be broadly applicable to other systems and to facilitate the integration of knowledge on molecular and cellular pathways with data on physiological, behavioral, and ecological processes toward an understanding of organismal plasticity in the natural environment.
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Acknowledgments |
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We thank Kitty Griswold, Sarah Annis, Keith Nislow, and Christian Landry for their assistance during sampling and Yehoda Martei for laboratory assistance. We are grateful to Christian Landry and Suzy Renn for comments on the manuscript. N. A.-H. was supported by a postdoctoral fellowship from Fonds Québécois de la Recherche sur la Nature et les Technologies (FQRNT). Funding of this project was provided by the Bauer Center for Genomics Research. This paper is based on a presentation given at the symposium entitled "Genomes and Evolution 2004," cosponsered by the American Genetic Association and the International Society of Molecular Biology and Evolution, at The Pennsylvania State University, State College, PA, USA, June 17–20, 2004.
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Footnotes |
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Corresponding Editor: Shozo Yokoyama
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