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Published in final edited form as: J Neurogenet. 2010 Jul;24(2):75–82. doi: 10.3109/01677061003770614

Differential Evolutionary Rates of Neuronal Transcriptome in Aplysia kurodai and Aplysia californica as a Tool for Gene Mining

Sun-Lim Choi 1, Yong-Seok Lee 1, Young-Soo Rim 1, Tae-Hyung Kim 3, Leonid L Moroz 4, Eric R Kandel 5, Jong Bhak 3, Bong-Kiun Kaang 1,2
PMCID: PMC8212809  NIHMSID: NIHMS578057  PMID: 20536287

Abstract

The marine mollusk Aplysia is a fascinating model organism for studying molecular mechanisms underlying learning and memory. However, evolutionary studies about Aplysia have been limited by the lack of its genomic information. Recently, large-scale expressed sequence tag (EST) databases have been acquired by sequencing cDNA libraries from A. californica and A. kurodai. The closeness between the two species allowed us to investigate rapidly evolving genes by comparing their orthologs. Using this method, we found that a subset of signal transduction genes in neurons showed rates of protein evolution higher than those of housekeeping genes. Moreover, we were also able to find several candidate genes that may be involved in learning and memory or synaptic plasticity among genes showing relatively higher Ka/Ks ratios. We also investigated the relationship between evolutionary rates and tissue distribution of Aplysia genes. They propose that the estimation of evolutionary rates cannot be a good marker to assess neuronal expression; however, it still can be an efficient way to narrow down the pool of candidate genes involved in neuronal functions for the further studies.

Keywords: Aplysia, EST, Ka/Ks, neuron, transcriptome

INTRODUCTION

The marine mollusk Aplysia is the one of the most important model organisms for studying learning and memory because its nervous system offers many advantages that could facilitate the discovery of molecular mechanisms, such as large cell size and simple nervous system (Bailey et al., 1996; Carew & Sahley, 1986; Kandel, 2001; Lee et al., 2008a). Because of these advantages, a large number of key molecules and molecular pathways that are important in learning and memory have been discovered extensively in Aplysia (Kandel, 1976; Lim et al., 1997).

Unlike the molecular and cellular approaches, however, few evolutionary approaches about neuronal genes of Aplysia have been taken until now. Lack of genomic information about Aplysia might be one of the important limiting factors that restrict this type of approach. Evolutionary approaches may provide useful information to find candidate genes for the studies of neuronal functions such as learning and memory or synaptic plasticity. Recently, two large-scale expressed sequence tag (EST) analyses for closely the related species A. californica and A. kurodai were completed (Moroz et al., 2006). Moroz, Kandel, and their colleagues sequenced over 200,000 ESTs and identified more than 65,000 nonredundant sequences from A. californica (Moroz et al., 2006). Kaang and his colleagues also sequenced 11,493 ESTs, which represented over 4859 nonredundant sequences from A. kurodai cDNA libraries derived from the central nervous system (CNS) (Lee et al., 2008b). These data from closely related species of Aplysia can provide additional insights into the biology and evolution of the molluskan nervous system and perhaps into neuronal-derived genes of vertebrate animals (Lee et al., 2008b; Moroz et al., 2006).

Here, we subjected these two ESTs to a series of analyses to compare and evaluate the evolutionary rates of orthologous genes using the Ka/Ks ratio. First, we compared the rates of protein evolution between two subsets of selected neuronal derived genes in Aplysia. Based on these data, we also tried to find candidate genes for functional studies without prior knowledge. In addition, we investigated the tissue distributions of some genes to examine the relationship between the Ka/Ks ratio and differential neuronal expression.

MATERIALS AND METHODS

Orthologs Alignment and Calculation of Ka/Ks

For all experiments, two Aplysia EST databases were used as resources (Lee et al., 2008b; Moroz et al., 2006). A. kurodai contig_IDs are available at http://seahare.org, and A. californica contig_IDs are available at http://aplysia.uf-genome.org and http://aplysia.cu-genome.org. The orthologs alignment pipeline uses ClustalW sequence alignment tool (Thompson et al., 1994). As an input, it requires two orthologous mRNA sequences. It translates these mRNA sequences into protein sequences and chooses the longest open reading frame (ORF) with a start codon by a standard genetic code table. Because we performed the analysis using ESTs as resources (Lee et al., 2008b; Moroz et al., 2006), only well-aligned protein-coding sequences, which were manually inspected and verified to be translated in correct frames, were used for the analysis. Moreover, because PAML cannot perform well with short coding regions (Tzeng et al., 2004), we used only long singletons or contigs to overcome the limitation (cutoff threshold was 99 nucleotides encoding 33 amino acids including the start codon).

The Ka/Ks values were calculated by codeml program, implementing the method of Nei and Gojobori in PAML package (Yang, 1997). Orthologs were removed for Ka/Ks values over 10 due to many alignment errors or gaps.

RT-PCR Analysis

For all experiments, mRNAs were isolated from five different kinds of tissues—central nervous system (CNS), buccal mass (BM), stomach (ST), gill (GL), and ovotestis (OT)—using TRIzol Reagent (Invitrogen) following the manufacturer’s manual. Samples were then treated with RNase-free DNase I (Ambion) for 40 min to remove residual genomic DNA. cDNA was synthesized as described previously (Yim et al., 2006). The cDNA was amplified using specific primer sets (see Supplementary Table 4; online version only). PCR reaction consisted of one cycle of 95°C for 5 min, followed by 30 or 35 cycles of 95°C for 15 s, 60°C for 15 s, and 72°C for 30 s. The final extension reaction was carried out at 72°C for 1 min.

Real-Time PCR

The cDNA described above was used for quantitative real-time PCR. PCR reactions were performed in the Thermal Cycler Dice Real Time System, TP800 (Takara) using SYBR Premix Ex Taq™ (Takara) and gene specific primer sets (see Supplementary Table 4; online version only). Amplification reaction consisted of one cycle of 95°C for 5 min, followed by 60 cycles of 95°C for 15 s, 60°C for 15 s, and 72°C for 30 s. Data were collected during the extention phase at 72°C. S4 was used as an internal control. For the relative comparison of each mRNA, we analyzed CT value using the 2−ΔΔCT method (Livak & Schmittgen, 2001).

Statistical Analysis

Data collected by real-time PCR were analyzed using a one-way analysis of variance (ANOVA) test. When significant differences in gene expression levels were found, post hoc comparisons were executed by Tukey’s multiple comparison test.

RESULTS AND DISCUSSION

Differential Evolution of Neuronal Genes in Aplysia

In primates and rodents, the evolution of genes involved in various aspects of nervous system function is known to be faster than that of housekeeping genes that are involved in basic metabolic functions. Moreover, the evolutionary rates of these genes are significantly higher in primates than in rodents, consistent with the rapid evolution of brain structure and function (Dorus et al., 2004). However, it is not known that signal transduction genes in neurons show faster evolutionary rate in Aplysia, which has a relatively primitive nervous system. To investigate the rates of protein evolution based on sequences derived from the CNSs of two closely related Aplysia species, we compiled lists of molluskan homologs of mammalian genes involved in neuronal signaling as well as those of putative housekeeping genes. In the previous study, Dorus et al. selected genes that (i) play important roles in the nervous system, (ii) enriched in nervous system, or (iii) involved in nervous system disorders as “nervous system” genes (Dorus et al., 2004). As the first step to obtain the lists of signal transduction genes in neurons or housekeeping genes in Aplysia, we simply collected Aplysia EST sequences homologous to those used in Dorus et al’s study (Dorus et al., 2004). In addition, we used the Gen-Bank databases to obtain homologous and orthologous sequences, which were previously cloned and characterized in Aplysia californica but do not exist either in A. kurodai or A. californica EST database. Finally, we excluded the homologous sequences that are not functionally characterized (e.g., unnamed protein product). In this way we obtained from both species of Aplysia 44 signal transduction genes in neurons as well as 31 putative housekeeping or basic metabolic genes (Supplementary Tables 1 and 2; online version only). It should be noted that we took a biased approach in the selection process of these two classes of genes and all the signal transduction genes in neurons are not preferentially expressed in the nervous system. The selection of two groups of genes was largely based on prior knowledge on gene functions in vertebrate homologs.

To measure the rates of protein evolution, we applied a common method (Hurst, 2002) in which we calculated the ratio (Ka/Ks) of the number of nonsynonymous substitutions per nonsynonymous site (Ka) to the number of synonymous substitutions per synonymous site (Ks). Although it is known that evolutionary rates of brain-specific genes are slower than those of other tissue-specific genes, they are still faster than those of housekeeping genes (Zhang & Li, 2004). We found that the average Ka/Ks ratio of selected signal transduction genes in neurons is significantly higher, by a factor of 2, than that of putative housekeeping genes (0.111 ± 0.019, n = 44; and 0.062 ± 0.017, n = 31, respectively, mean ± SEM; p < .05, Kolmogorov-Smirnov test; Figure 1A and B). The average Ka and Ks values of the signal transduction genes in neurons are 0.019 ± 0.004 and 0.209 ± 0.025, respectively. Because Ka/Ks values of signal transduction genes in neurons were all <1.0, we can assume that positive selection pressure on these genes was not the major driving force of evolution (Hurst, 2002). Therefore, in large part these differences might be a result of stronger purifying selection constraints on the housekeeping genes than on the signal transduction genes in neurons in Aplysia. These values are also comparable with those presented in previous reports on humans and rodents (Duret & Mouchiroud, 2000; Zhang & Li, 2004). These data suggest that the comparative rates of protein evolution of the two subsets of neuronal-derived genes in Aplysia appear different. However, it should be noted that we examined only a limited number of genes that were selected by their predicted molecular functions and that the definition of neuronal-signaling-related gene can be argued as arbitrary.

Figure 1.

Figure 1.

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Faster evolution rate of the mammalian homologs of signal transduction genes in Aplysia neurons (see text for details). (A) Evolutionary rates of signal transduction genes and housekeeping genes in Aplysia. (B) The Ka/Ks distribution of the same two subsets of brain derived genes in Aplysia.

Unbiased Evaluation of the Evolutionary Rates of Aplysia Orthologs

We next subjected the total collection of Aplysia orthologs without any categorization to the Ka/Ks analysis. We only used well-aligned, BLASTX-matched 410 orthologous sequences for this analysis (Supplementary Table 3; online version only). To calculate the Ka/Ks value of total Aplysia ESTs, we used the same method as we used in Figure 1 to measure the Ka/Ks values of signal transduction genes in neurons and housekeeping genes. The average Ka/Ks of these genes was 0.093 ± 0.005, which was between the values of the selected signal transduction genes in neurons (0.111 ± 0.019) and the housekeeping metabolism-related genes (0.062 ± 0.017). Because A. kurodai ESTs were originally collected from the central ganglia, all the orthologs can be considered to have derived from neurons, glia, and components of connective tissues and circulatory systems (Lee et al., 2008b).

Because our biased approach revealed that signal transduction genes in neurons show faster evolutionary rates, we tried to find candidate genes for the studies of learning and memory or synaptic plasticity simply by measuring the evolutionary rates without a priori knowledge about those genes (Supplementary Table 3; online version only). Table 1 demonstrates the top 15 Aplysia genes that showed the highest Ka/Ks rates. Some of them (RAB2, soluble acetylcholine receptor, and ApCREB2) had already been used for the Ka/Ks calculation as signal transduction genes in neurons (Supplementary Table 3; online version only). From this list, we could find some candidate genes for functional studies about learning and memory or modulation of the neuronal function. Of these, a small guanosine triphosphate (GTP)-binding protein RAB2 is known to play a role in neuronal adhesion and neurite growth in dissociated rat midbrain neurons (Ayala et al., 1990). The vacuolar adenosine triphosphate (ATP) synthase (v-ATPase), which we identified in Aplysia, is an important proton pump that acidifies a wide variety of intracellular and some extracellular compartments (Nishi & Forgac, 2002). In the nervous system, v-ATPase is involved in vesicle exocytosis (Hiesinger et al., 2005) and in loading synaptic vesicles with neurotransmitters (Amara & Kuhar, 1993). However, v-ATPase expression is not limited to the nervous system. It is abundant in all secretory tissues such as salivary glands and components of the digestive tract as well as in epithelial structures (Nelson & Harvey, 1999). Another example of gene mining in this study was proline 4-hydroxylase. This enzyme catalyzes the formation of 4-hydroxyproline in collagens and more than 10 additional proteins with collagen-like sequences. It also negatively regulates the stability of several proteins that have critical roles in adaptation to hypoxic or oxidative stress (Kivirikko & Myllyharju, 1998; Siddiq et al., 2005). The transcript of proline 4-hydroxylase can be specific for connective tissues that support molluskan ganglia. Interestingly, this protein homolog has been suggested as a target for neuroprotection in the central nervous system of mammals (Siddiq et al., 2005). As the final example, MIP-related peptide precursor has been identified in Aplysia and has been reported to operate in the neural circuits that initiate feeding (Fujisawa et al., 1999) and gill-siphon withdrawal (Moroz et al., 2006). Furthermore, we also looked at the bottom 15 Aplysia genes showing low Ka/Ks rates (Table 2). We found that there were four ribosomal proteins that could be considered as housekeeping genes involved in basic protein synthesis. Moreover, we could not find any interesting candidate gene for functional studies on learning and memory or modulation of the neuronal function based on gene description among bottom 15 genes. These bioinformatical analyses suggest that trimming out the genes that have relatively low Ka/Ks ratio can be an effective way to narrow down the pool of candidate genes for the functional studies of learning and memory or modulation of the neuronal function in Aplysia.

Table 1.

Top 15 Aplysia genes showing the highest Ka/Ks rate

Gene description GI E-value Ka Ks Ka/Ks HSP length+
Vacuolar ATP synthase subunit e 68065343 5E-16 0.013 0.014 0.937 76
RAB2 288938 6E-62 0.068 0.140 0.488 141
Soluble acetylcholine receptor 17225107 5E-113 0.091 0.196 0.467 236
Hemocyanin 62679967 3E-81 0.063 0.136 0.466 158
Heart-type fatty acid-binding protein 17530523 2E-16 0.170 0.374 0.456 132
Glutathione S-transferase 8917596 6E-28 0.098 0.221 0.443 203
Cyclophylin isoform 94468464 5E-49 0.032 0.077 0.411 179
Dehydrogenases, short-chain family member (dhs-14) 17562906 1E-14 0.111 0.275 0.405 142
ATP synthase, mitochondrial F1 complex, alpha subunit 127798841 3E-106 0.006 0.015 0.400 266
ApCREB2 1123037 2E-120 0.027 0.070 0.386 228
Proline 4-hydroxylase 48735337 7E-80 0.032 0.087 0.366 260
MIP-related peptide precursor 8886135 2E-71 0.067 0.185 0.361 140
Zinc finger, HIT type 3 17389844 2E-19 0.063 0.183 0.346 148
Cct7-prov protein 50418287 2E-26 0.056 0.161 0.346 201
Translation initiation factor 5A 47085971 8E-58 0.017 0.054 0.322 151
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Note.

+

High score pairing length of translated amino acids sequences.

Table 2.

Bottom 15 Aplysia genes showing the lowest Ka/Ks rates

Gene description GI E-value Ka Ks Ka/Ks HSP length+
Ribosomal protein S14 12083607 7E-65 0 0.0497 0 139
GTP-binding protein alpha-o subunit 9633 2E-83 0 0.0495 0 156
RHO_APLCA RAS-like GTP-binding protein RHO 132545 2E-95 0 0.0481 0 193
Unnamed protein product 67969593 1E-75 0 0.0478 0 245
40S ribosomal protein S15 20069100 8E-39 0 0.0474 0 78
Unnamed protein product 47230461 8E-21 0 0.0418 0 56
AGAP010957-PA 158287848 2E-79 0 0.0371 0 149
Guanine nucleotide regulatory protein beta subunit 312632 9E-103 0 0.0369 0 186
Ribosomal protein L12 22758902 4E-69 0 0.0325 0 158
Cnot4-prov protein 28278582 2E-43 0 0.0197 0 101
Y-box factor homolog (APY1) 1175568 5E-53 0 0.0192 0 165
Unnamed protein product 47228202 1E-41 0.0001 0.1492 0.0007 162
Splicing factor-like protein 51105084 2E-32 0.0001 0.1484 0.0007 73
Similar to Rps16 protein 50728374 1E-71 0.0001 0.1441 0.0007 146
Similar to glucocorticoid-induced gene 1 109464606 2E-14 0.0001 0.1404 0.0007 151
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+

High score pairing length of translated amino acids sequences.

Tissue Distribution of Fast- and Slow-Evolving Genes

To further explore the implications of this observation, we investigated the tissue distributions of these genes. It is known that there is a correlation between gene expression and amino acid sequence divergence at least in humans and rodents (Zhang & Li, 2004). And we can speculate that there is a higher probability that the gene may play a significant biological role in a specific tissue where it is highly expressed, although the tissue-specific enrichment of a gene does not necessitate an important function within that tissue. To examine the tissue distributions of the 13 genes showing the highest Ka/Ks, reverse transcriptase–polymerase chain reaction (RT-PCR) analysis was performed using total RNAs from five different kinds of tissues as templates: central nervous system (CNS), buccal mass (BM), stomach (ST), gill (GL), and ovotestis (OT). Among these 13 genes, 4 (soluble acetylcholine receptor, hemocyanin, heart-type fatty acid–binding protein, and Mytilus inhibitory peptide (MIP) showed higher expression levels in the central nervous system than in the other tissues. The expression levels of three genes (soluble acetylcholine receptor, hemocyanin, and heart-type fatty acid–binding protein) were confirmed by real-time PCR (Figure 2A and B). Similarly, RT-PCR analysis was also performed on the 14 genes that showed the lowest Ka/Ks values. Unlike the 13 genes with the highest Ka/Ks values, these genes with low Ka/Ks values tended to show less variable expression levels across the five tissues. This might be because some housekeeping genes such as ribosomal proteins were contained in the list of genes with low Ka/Ks values. However, we could not find any strong relationship between Ka/Ks values and differential tissue expressions. Compared to the genes that exhibited the highest Ka/Ks values, the four specific genes enriched in the CNS, two genes (ENSANGP00000012700 and GTP-binding protein alpha-o subunit) showed higher expression levels in the CNS among the 14 putative low rated genes. These results were also confirmed by real-time PCR (Figure 3A and B). These data suggest that evolutionary rate of some genes cannot be an effective marker to estimate the neuronal expression in Aplysia.

Figure 2.

Figure 2.

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Differential expression levels of the genes showing the highest Ka/Ks values. (A) RT-PCR results on the 13 Aplysia genes showing highest evolutionary rates. Soluble acetylcholine receptor, hemocyanin, heart-type fatty acid–binding protein, and MIP were highly expressed in the central nervous system. One of these genes (MIP) was expressed only in CNS. Ka/Ks ratio for each gene is indicated in parenthesis (B) Expression levels of three genes (soluble acetylcholine receptor, hemocyanin, and heart-type fatty acid–binding protein) were confirmed by real-time PCR. Two genes (soluble acetylcholine receptor and heart-type fatty acid–binding protein) showed significantly higher expression levels in the CNS when compared to the other tissues (p < .05, ANOVA and Tukey’s multiple comparison test). Hemocyanin was also significantly enriched in the CNS except when compared to the OT (p > .05, ANOVA and Tukey’s multiple comparison test). CNS, central nervous system; BM, buccal mass; ST, stomach; GL, gill; OT, ovotestis; vATP, vacuolar ATP synthase subunit e; sAChR, soluble acetylcholine receptor; Haemo, hemocyanin; FABP, heart-type fatty acid–binding protein; CycloA, cyclophylin isoform; dhs14, dehydrogenase, short-chain family member; ATPsyn, ATP synthase, mitochondrial F1 cmplex alpha subunit; CREB2, ApCREB2; P4H, proline 4-hydroxylase; MIP, MIP-related protein precursor; Zn, zinc finger, HIT type 3; cct7, cct7-prov protein; TIF5, transcription initiation factor 5A.

Figure 3.

Figure 3.

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Differential expression levels of the genes showing the lowest Ka/Ks values. (A) RT-PCR results on the 14 Aplysia genes showing the lowest evolutionary rates. ENSANGP00000012700 and GTP-binding protein alpha-o subunit were highly expressed in the central nervous system. Ka/Ks ratio for each gene is indicated in parenthesis. (B) Expression levels of four genes (AGAP010957-PA, GTP-binding protein alpha-o subunit, guanine nucleotide regulatory protein beta subunit, and RHO_APLCA RAS-like GTP-binding protein RHO) were confirmed by real-time PCR. Two genes (AGAP010957-PA and GTP-binding protein alpha-o subunit) showed significantly higher expression levels in the CNS when compared to the other tissues (p < .05, ANOVA and Tukey’s multiple comparison test). GNRPb was also significantly enriched in the CNS compared to the ST and GL, and Rho showed significantly higher expression in CNS than in BM (p < .05, ANOVA and Tukey’s multiple comparison test). AGAP, AGAP010957-PA; GBPα, GTP-binding protein alpha-o subunit; Rho, RHO_APLCA RAS-like GTP-binding protein RHO; GNRPβ, guanine nucleotide regulatory protein beta subunit; S14, ribosomal protein S14; UnPP, unnamed protein product; S15, 40S ribosomal protein S15; L12, ribosomal protein L12; Cnot4, Cnot4-prov protein; APY1, YBOXH_APLCA Y-box factor homolog; S16, similar to Rps16 protein; Gluco, similar to glucocorticoid-induced gene 1.

CONCLUSION

Among the species of Aplysia, A. california and A. kurodai are the most extensively studied. However, the comparative studies between these two species are very limited: for example, behavioral and ecological niche of A. kurodai and A. californica have not been systematically investigated. In the present study, we investigated the molecular and genetic diversity between these two species.

Comparing the Ka/Ks ratio using A. kurodai and A. californica EST databases, we discovered that the evolutionary rates of a group of selected nervous system–related and signal transduction–associated genes were higher than those of putative housekeeping genes (Lee et al., 2008b; Moroz et al., 2006). Although we examined a limited number of genes, we confirmed that signal transduction genes in neurons, basically defined by Dorus et al. (Dorus et al., 2004), showed faster evolutionary rates than those of housekeeping genes in Aplysia. We were also able to find candidates for further functional studies simply by measuring the evolutionary rates in the group of genes with higher Ka/Ks ratios without a priori knowledge about those genes. We suggest that trimming out the genes that have very low Ka/Ks ratio can be an efficient way to narrow down the pool of candidate genes involved in learning and memory or modulation of the neuronal functions. We also tested whether the evolutionary rate of some genes can be used as an effective marker to estimate neuronal expression. Although we found that evolutionary rate is not an effective marker to estimate neuronal expression, we suggested differential tissue expression profiles of some genes. These expression profiles, however, still help us identify candidate genes that have important roles in Aplysia CNS.

Supplementary Material

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Declaration of interest:

This work was supported by the National Creative Research Initiative Program of the Korean Ministry of Science and Technology and the Marine and Extreme Genome Research Center Program, Ministry of Marine Affairs and Fisheries, Republic of Korea. Korean Bio Information Center (KO-BIC) was supported by the Korean Ministry of Science and Technology under grant M10407010001-04N0701-00110 and by the Ministry of Information and Communication, Korea, under the Korea Agency Digital opportunity and Promotion support program (06-121). Y.-S.L. and S.-L.C. are supported by BK21 fellowships. S.-L.C. is supported by Seoul Science Fellowship. The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Footnotes

Supplementary material available online

Table showing collated results

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