Identification of Neurotransmitter Receptor Genes Under Significantly Relaxed Selective Constraint by Orthologous Gene Comparisons Between Humans and Rodents

Hisakazu Iwama and Takashi Gojobori

Center for Information Biology and DNA Data Bank of Japan, National Institute of Genetics, Mishima-shi


    Abstract
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Neurotransmitter receptors (neuroreceptors) are classified into two types, G protein–coupled receptors (GPCRs) and ligand-gated ion channels. The former occupies a small part of the large GPCR superfamily, whereas the latter consists of three superfamilies. In these superfamilies, humans and rodents share almost the same set of neuroreceptor genes. This neuroreceptor gene set is good material to examine the degree of selective constraint exerted on each member gene of a given superfamily. If there are any neuroreceptor genes under the degree of selective constraint that is very different from that of the other member genes, they may have influenced the functional features characteristic of human neural activities. With the aim of identifying such neuroreceptor genes, we collected sequence data of orthologous neuroreceptor genes for humans, mice, and rats by database searches. This data set included ortholog pairs for 141 kinds of neuroreceptor genes, which covered almost the whole set of neuroreceptor genes known to be expressed in the human brain. The degree of selective constraint was estimated by computing the ratio (dN/dS) of the number of nonsynonymous substitutions to that of synonymous substitutions. We found that the dN/dS ratio ranged widely and its distribution fitted a gamma distribution. In particular, we found that four neuroreceptor genes are under the significantly relaxed selective constraint. They are an ionotropic glutamate receptor subunit NMDA-2C, two GABAA receptor subunits, i.e., GABAA-{epsilon} and GABAA-{theta}, and a dopamine receptor D4. Interestingly, these neuroreceptors have been reported to be associated with cognitive abilities such as memory and learning, and responsiveness to novel stimuli. These cognitive abilities can influence the behavioral features of an individual. Thus, it suggests that the relaxed-constraint neuroreceptor genes have evolved, assuring that the nervous system responds to a variety of stimuli with proper flexibility.


    Introduction
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
According to the molecular structure and function, neurotransmitter receptors (neuroreceptors) are classified into two types: (1) G protein–coupled receptors (GPCRs) and (2) ligand-gated ion channels (LGICs). The former type occupies a small part of the large GPCR superfamily, whereas the latter consists of three LGIC superfamilies (Barnard 1996Citation ; Le Novère and Changeux 2001Citation ). In these superfamilies, almost the same set of neuroreceptor genes are shared between humans and rodents. The conserved set of neuroreceptor genes are considered to have been maintained by the selective constraint.

Among the neuroreceptor genes within each of the superfamilies, the neuroreceptors have molecular structure and functions similar to each other. Thus, the "functional constraint" at the molecular level is expected to be similar among the member genes. The functional constraint was originally considered to be dependent on the structural and functional features characteristic of an individual molecule (Dickerson 1971Citation ). Recently, Kuma, Iwabe, and Miyata (1995)Citation showed that the molecules with similar structure and functions could have very different degrees of selective constraint depending on the tissues or organs where the molecules are specifically expressed. They named this tissue-dependent constraint as an "alternative functional constraint." Even if we accept the concept of the alternative functional constraint in addition to the original one, the neuroreceptor genes are expected to be under the similar degree of selective constraint among the member genes of each superfamily because all the neuroreceptor genes are expressed in one tissue, i.e., the nervous tissue.

Thus, our aim is to examine the difference in the degree of selective constraint exerted on each of the neuroreceptor genes. In particular, if there are any neuroreceptor genes that have been under the degree of selective constraint very different from that of the other member genes, they may have influenced the functional features characteristic of human neural activities. The degree of selective constraint can be estimated by computing the ratio (dN/dS) of the number of nonsynonymous (amino acid alternating) substitutions (dN) to that of synonymous (silent) substitutions (dS) (Miyata, Yasunaga, and Nishida 1980Citation ; Kimura 1983Citation , pp. 4, 27, 29, 41, 47–49, 74, 169, 176, 310, 325, 332, 356, 358, 361, 363; Nei and Gojobori 1986Citation ). A low value of dN/dS indicates a stringent selective constraint and a high value of dN/dS indicates a relaxed one.

By database searches, we collected 380 full-length coding nucleotide sequences of orthologous neuroreceptor genes for humans, mice, and rats. From this data set, we identified 141 kinds of neuroreceptor ortholog pairs between humans and rodents (mouse or rat (or both)). These orthologs covered almost the whole set of the human neuroreceptor genes that are known to be expressed in the human brain. We then show that the degree of selective constraint exerted on the neuroreceptor genes is widely ranged. In particular, we report here that there are four neuroreceptor genes under significantly relaxed selective constraint based on the statistics of the neuroreceptor gene comparisons.


    Materials and Methods
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Collection of Neuroreceptor Orthologous Gene Pairs
We first listed the neuroreceptors which have been generally considered to be expressed in the human brain by surveying published reports. In particular, we adopted the criteria on the neurotransmitters as follows: (1) the neurotransmitter is synthesized in the neuron, (2) it is present in the presynaptic terminal and is released in amount sufficient to exert a defined action on the postsynaptic neuron or effecter organ, (3) when administered exogenously (as a drug) in reasonable concentration, it mimics the action of the endogenously released transmitter exactly, and (4) a specific mechanism exists for removing it from its site of action, the synaptic cleft (Schwartz 2000Citation ). The receptors to the neurotransmitters that meet the criteria were chosen as neuroreceptors.

Then, we performed keyword-orientated searches against the annotation data of the human LocusLink (LL) (http://www.ncbi.nlm.nih.gov/LocusLink/) (Pruitt and Maglott 2001Citation ) for the genes encoding those neuroreceptors as of the 4th of April 2001. Out of the hit entries, we selected only those which had been verified by experimental data. We selected the longest sequence that includes the full-length coding region for the neuroreceptor gene to minimize the effect of alternative splicing on ortholog-pair comparison. We used the model sequence named RefSeq (http://www.ncbi.nlm.nih.gov/LocusLink/refseq.html) (Pruitt and Maglott 2001Citation ), if the RefSeq was available ("Reviewed" or "Provisional" only) and if its annotation had no confliction with the known properties of the particular neuroreceptor. This series of retrieval yielded the initial list of human neuroreceptor genes.

Second, we performed protein BLAST (blastp) (Altschul et al. 1997Citation ) searches against the GenBank nonredundant protein database (http://www.ncbi.nlm.nih.gov/blast/html/blastcgihelp.html#protein_databases), using the amino acid sequences within the initial human neuroreceptor gene list as queries. Out of the hits for mouse and rat protein sequences, we selected orthologs according to the following criteria: (1) the expectation (E) value is to be below the level of 10-100; (2) the matched sequence should be a full-length protein sequence; (3) the matched sequence should be the longest one among the matched full-length sequences; (4) it should be adequately verified by experimental data and not conflicting with the known properties of the neuroreceptor; (5) preferential adoption of RefSeqs if the E values were the same or very close to the top hit; and (6) merely predicted or inferred protein sequences should be excluded.

Third, according to the same criteria as above, we performed blastp searches in the reverse direction, from mice or rats to humans. These searches rendered some human model sequences which had not been described in the corresponding LL entries. We added them to our list of human neuroreceptor genes. Again, using the newly obtained protein sequences of human neuroreceptors as queries, we performed blastp searches for mouse and rat protein sequences. Then, the newly matched protein sequences were added to the mouse and rat neuroreceptor gene lists, according to the same criteria as above.

Computation of Numbers of Synonymous and Nonsynonymous Substitutions
The amino acid sequences of orthologous gene pairs of human-mouse, human-rat, and mouse-rat were aligned by CLUSTAL W version 1.7 (Thompson, Higgins, and Gibson 1994Citation ). The alignments were visually inspected and modified according to knowledge-based adjustment. Knowledge-based adjustment could be exemplified by the following two cases. First, mouse and rat GABA {epsilon} neuroreceptor protein sequences were reported to have long repeat insertion sequences. These repeat sequences are conserved between mice and rats but absent in humans (Sinkkonen et al. 2000Citation ). Accordingly, after the rodent-specific repeat insertions were removed, the rodent sequences were aligned with the human sequence. This adjustment rendered appropriate alignments. Second, the human dopamine D4 receptor has an extra 16 amino acid direct-repeat sequence in the third intracellular loop which is polymorphic in repeat number of two to seven times (Van Tol et al. 1992Citation ). On the other hand, the repeat number of rodents is nonpolymorphic, i.e., two times. Therefore, we used the two-time-repeat human sequence to appropriately align with the rodent sequences.

To obtain better nucleotide alignments, we first aligned the amino acid sequences, then the nucleotide alignments were reconstructed by concatenating the triplets according to the amino acid sequences. For these nucleotide alignments, the numbers of synonymous and nonsynonymous substitutions were computed by Nei and Gojobori's method (Nei and Gojobori 1986Citation ). For computation, we used "dists" command in the ODEN package version 1.2 (Ina 1994Citation ). The standard deviations of the numbers of the synonymous and nonsynonymous substitutions were also given by the dists. The gaps in alignments were excluded from the calculation.

Estimation of Standard Deviation of dN/dS
The standard deviation of the dN/dS ratio was estimated by the bootstrap method. Bootstrap data set of resampled alignments was generated by random sampling with replacement from the original nucleotide alignments. Resampling was done by the unit of a codon, keeping the codon correspondence of the original pairwise alignment. The length of the bootstrap resample alignment was set to be identical to that of original alignment without gaps. This resampling was reiterated 10,000 times for every original pairwise alignment. The dN/dS ratio was computed for every bootstrap resample alignment by dists described above. Then we calculated the standard deviation of the dN/dS ratio based on the 10,000 dN/dS ratios of the bootstrap data set for every ortholog pair.


    Results
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
Orthologous Gene Pairs Collected and Their Properties
The collected data set included 380 full-length coding sequences of neuroreceptor ortholog genes: 141 neuroreceptor genes for humans, 116 for mice, and 123 for rats (see Appendix). This data set yielded a total of 331 orthologous gene pairs among the three species: 114 pairs for human-mouse, 121 pairs for human-rat, and 96 for mouse-rat (fig. 1 ). The properties of the neuroreceptor ortholog pairs are summarized in table 1 . For 94 neuroreceptor genes, we could compare the sequences among the three species. For all the 141 human neuroreceptor genes, we were able to obtain at least one ortholog for mouse or rat.



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Fig. 1.—The numbers of neuroreceptor genes under analysis, and the numbers of neuroreceptor orthologous gene pairs. For 94 neuroreceptors, orthologous pairs of three-species comparisons (TRIOs) were available (shown in a circle)

 

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Table 1 Summary of Properties for 331 Neuroreceptor Otrholog Gene Pairs Among Humans, Mice, and Rats

 
Because GPCRs and LGICs are distinct in the molecular structure and function, they are also summarized separately (table 1 ). With regard to the comparisons between human-mouse and human-rat, no statistically significant difference was found in any category. For the data set of 94 neuroreceptor ortholog pairs which we could compare among the three species (TRIOs), the values of dS, dN and dN/dS between human-mouse and human-rat showed good correlations: the correlation coefficients were 0.91, 0.96, and 0.95, respectively. The good correlations could support the appropriate ortholog selection. The correlations of dS and dN/dS are shown by scatter plots in figures 2 and 3 , respectively.



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Fig. 2.—Correlation of synonymous distances between human-mouse and human-rat is shown by scatter plot. Vertical bars indicate the standard deviation of synonymous distance of human-rat and horizontal bars indicate that of human-mouse

 


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Fig. 3.—Correlation of dN/dS ratios between human-mouse and human-rat is shown by scatter plot. Vertical bars indicate the standard deviations of dN/dS ratio for human-rat comparison and horizontal bars indicate that for human-mouse comparison. The standard deviations were estimated by the bootstrap method with 10,000 iterations

 
Lower dN/dS Ratios for LGICs than for GPCRs
Because all the neuroreceptors can be classified into either of GPCRs or LGICs, we first examined the difference of selective constraint between the two types. The mean dN/dS ratio for GPCRs is 0.11 and that for LGICs is 0.07 between humans and mice/rats. To test if the dN/dS ratios for LGICs are lower than those for GPCRs as a whole, the rank-sum test (Wilcoxon-Mann-Whitney test) was performed. This test requires no parametric distribution pattern. By the rank-sum test, the dN/dS ratios for LGICs are significantly lower than those for GPCRs as a whole (P < 9.6x10-7).

Furthermore, the t-test was also performed because the sample number is large enough to apply the t-test to this data set. By the one-tailed t-test, assuming unequal variances for two populations, the mean dN/dS of LGICs is significantly lower than that of GPCRs (P < 2.2x10-5). These tests strongly support the lower dN/dS ratios for LGICs than for GPCRs, suggesting that more stringent selective constraint has been exerted on LGICs than on GPCRs along the evolutionary history after the divergence of humans and rodents.

Distribution of dN/dS Ratios Fits Gamma Distribution
Out of 141 kinds of neuroreceptor genes, three-species comparisons were available for 94 genes (TRIOs). For the remaining 47 neuroreceptor genes, either human-mouse or human-rat ortholog pairs are lacking (DUOs). For example, in the case of nicotinic cholinergic Rc, human-rat ortholog pairs were available for all the fifteen genes in the family, whereas human-mouse pairs were available only for nine genes. Because this difference in the number of ortholog pairs can affect the detailed analysis of distribution patterns, we inferred the dN/dS ratios of the lacking ortholog pairs; 27 DUOs (human-rat ortholog pairs only) and 20 DUOs (human-mouse ortholog pairs only) based on the regression equations (fig. 3 ), i.e., the following two equations:


This procedure can be guaranteed by the high correlation coefficient.

Adding these inferred dN/dS ratios, we obtained the data set (all-TRIO-data-set), which included the dN/dS ratios of both human-mouse and human-rat ortholog pairs for 141 neuroreceptor genes. This data set covered almost all the neuroreceptor genes known to be expressed in the human brain. We further analyzed the distribution patterns, using this all-TRIO-data-set. In figure 4 , the histogram shows the relative frequencies of the number of GPCR ortholog pairs for each range of dN/dS ratios. Each area of histogram bars (rectangles), but not its height, corresponds to its relative frequency. Because the distribution is skewed to the high range and is long-tailing, we tested if the observed distribution fits a gamma distribution. A gamma distribution probability density function (Ga) is given by


where


The parameters {alpha} and ß for a gamma distribution were estimated to fit to the observed dN/dS distribution, by the following equations based on the moment method,


where E is the mean and V is the variance of all the dN/dS ratios for GPCR ortholog pairs in all-TRIO-data-set. Using these formulae, we obtained the parameter values {alpha} = 2.868 and ß = 0.0376. The fitted probability density curve of gamma distribution is overlaid in the histogram (fig. 4 ).



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Fig. 4.—Histogram of relative frequencies of the all 85 GPCR-type neuroreceptor ortholog pairs for dN/dS ratios. The fitted gamma distribution probability density curve (shape parameter {alpha} = 2.868, scale parameter ß = 0.0376) is overlaid. The fitting is statistically significant (P < 0.05, {chi}2 test for goodness of fit)

 
To estimate the goodness of fit of the gamma distribution given above to the observed distribution, {chi}2 test for goodness of fit was performed as follows:


where fobs stands for the observed frequency for each range, fexp stands for the expected frequency by the gamma distribution for each range, and n is the total number of the samples. In this test, the freedom ({nu}) was evaluated as, {nu} = (number of ranges) - 1 - (number of parameters, i.e., {alpha} and ß) = 13-1-2 = 10.

As a result, the hypothesis that a gamma distribution, Ga ({alpha} = 2.868, ß = 0.0376), represents the distribution of the observed dN/dS for GPCRs was not rejected by the {chi}2 test for goodness of fit (P < 0.05). In the same way, for the observed distribution of the dN/dS ratios for LGICs, the gamma distribution parameters were estimated as {alpha} = 1.263 and ß = 0.0510 (fig. 5 ). The fitting of this gamma distribution to the observed one was not rejected by the {chi}2 test for goodness of fit (P < 0.05) under the freedom of eight in this case. Therefore, we could assume that the distribution pattern of the dN/dS ratios of neuroreceptor orthologs in the GPCR superfamily fits the gamma distribution. Similarly, the dN/dS ratios of neuroreceptor orthologs in the LGIC superfamilies could be assumed to follow the gamma distribution. We will use this distribution pattern to set the threshold value for detecting the significant relaxation of selective constraint.



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Fig. 5.—Histogram of relative frequencies of the all 56 LGIC-type neuroreceptor ortholog pairs for dN/dS ratios. The fitted gamma distribution probability density curve (shape parameter {alpha} = 1.263, scale parameter ß = 0.0510) is overlaid. The fitting is statistically significant (P < 0.05, {chi}2 test for goodness of fit)

 
Distributions of dN/dS Ratios for 35 Neuroreceptor Families
So far, we viewed the distribution pattern of dN/dS ratios by dividing all the neuroreceptors into two types, i.e., GPCRs and LGICs. Next, we classified the 141 kinds of neuroreceptor genes into 35 neuroreceptor gene families based on the receptor types (GPCRs or LGICs) and their ligand molecules (neurotransmitters) (fig. 6 ). By this two-step classification, we can expect that the receptor molecules within each family have the more similar structure and function to one another because the ligand is the same within the family. Therefore, the degree of functional constraint at the molecular level is expected to be similar among the member genes within each family.



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Fig. 6.—Scatter plot of the dN/dS ratio distributions for the 35 neuroreceptor families. A black dot represents the dN/dS ratio for each human-mouse and human-rat ortholog pair: a blue bar, the median of dN/dS ratios within each family; a red dot, the mean dN/dS ratio within each family. A red bar represents the threshold value for each family. The threshold value was determined for each family as equivalent to the 95% point on the gamma distribution of the whole GPCRs or LGICs. Note that for each neuroreceptor gene, two black dots, i.e., one for human-mouse and the other for human-rat, were plotted. The abbreviation of neuroreceptor families are as follows: GABA, gamma-aminobutyric acid; TRH, thyrotropin releasing hormone; CRH, corticotropin releasing hormone; GRH, gonadotropin releasing hormone; VIP, vasoactive intestinal peptide; and GHRH, growth hormone-releasing hormone

 
But different from the expectation, we found that a few member genes tend to show very high dN/dS ratios relative to the other member genes within a family, whereas most member genes were clustered densely in the low value range. This feature is particularly apparent for the families comprising many member genes. The skewed distribution within a family was consistent with the result that the observed distributions of the whole GPCRs and the whole LGICs showed long-tailing distributions in the high ranges and fitted gamma distributions. This consistency allows us to assume that the dN/dS ratios within a family would follow a gamma distribution.

Identification of Four Neuroreceptor Genes Under Significantly Relaxed Selective Constraint
To identify the neuroreceptor genes that have been under the significantly relaxed selective constraint (i.e., high dN/dS), we set the statistical threshold on the assumption of gamma distribution within each family. The threshold level was determined for each family as equivalent to the 95% point (P95%) on the gamma distribution of the whole GPCRs or LGICs. P95% of the gamma distribution for GPCRs was applied to the gene families belonging to GPCRs, and P95% of the gamma distribution for LGICs was applied to the gene families belonging to LGICs (see fig. 6 ).

In this analysis, the median is adopted to represent the within-family distribution because in the case of a small number of samples, the median is robust against outliers relative to the mean. As a result, the P95% corresponds to 2.43-fold of its median on the gamma distribution fitted to the observed dN/dS ratios for the whole GPCRs. Similarly, on the gamma distribution fitted to the observed dN/dS ratios for the whole LGICs, the P95% corresponds to 3.58-fold of its median. Consequently, the 2.43-fold value of the median for each family that belongs to the LGICs was set as the threshold value for the LGIC-type family, and the 3.58-fold value of the median for each family that belongs to GPCRs was set as the threshold value for the GPCR-type family. The thresholds are shown for the families containing more than three member genes in figure 6 .

On the basis of the thresholds mentioned above, we found that four neuroreceptor genes have significantly high dN/dS ratios. They were an ionotropic glutamate receptor subunit NMDA-2C (GRIN2C), two GABAA receptor subunits, i.e., GABAA-{epsilon} (GABRE) and GABAA-{theta} (GABRQ), and a dopamine receptor D4 (DRD4) (table 2 ). These genes were considered to have been under highly relaxed selective constraint.


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Table 2 List of Neuroreceptor Genes with Significantly High dN/dS Ratio Within Each Family

 

    Discussion
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
More Stringent Selective Constraint on LGICs than on GPCRs
Because all the neuroreceptors consist of two types of receptors, GPCRs and LGICs, we first analyzed the difference of the degree of selective constraint between the two types of neuroreceptor genes. The dN/dS ratio of LGICs was significantly lower than that of GPCRs as a whole. This suggests that more stringent selective constraint has been exerted on LGIC genes than on GPCR genes. The LGIC molecules are regarded as composite receptors. A LGIC neuroreceptor acts not only as a receptor but also as an effecter, i.e., an ion channel. In contrast, GPCR molecules do not have effecter activities by themselves. Because LGICs bear two major functions, more stringent selective constraint would have been exerted on LGIC genes. On the other hand, GPCRs specialize in ligand binding. The effecter activities are separated from GPCRs by means of the G protein mediation. Because GPCRs are released from the selective constraint of the effecter functions, they could have a freedom in mutational changes relative to LGICs. This freedom may be a key factor to have allowed GPCRs to expand their receptor functions not only as neuroreceptors but also as the molecules sensing various kinds of substances, e.g., olfactory receptors.

Distribution of dN/dS Ratios Fits Gamma Distribution
We observed that the distributions of the dN/dS ratio fitted significantly (P < 0.05; {chi}2 test for goodness of fit) distinct gamma distributions for GPCRs and LGICs, separately. Gamma distributions have been reported to fit well the observed rate variation of nucleotide substitutions at different sites for coding regions of nuclear genes (Golding 1983Citation ; Holmquist et al. 1983Citation ) and also for several different regions of human mitochondrial DNA (Golding 1983Citation ; Kocher and Wilson 1991Citation ; Tamura and Nei 1993Citation ; Wakeley 1994Citation ). The substitution rate at each nucleotide site can be considered to be the inverse of the degree of the selective constraint exerted on the particular nucleotide site. Similarly, the dN/dS ratio of each gene also can be considered to be the inverse of the degree of selective constraint exerted on the particular gene. Thus, the degree of variability of genetic elements (such as nucleotide sites of a gene, and member genes of a gene family) may follow a gamma distribution.

Interpretation of the Wide Difference of Selective Constraint
When we classified the neuroreceptors into 35 families based on the receptor type (i.e., GPCR or LGIC) and the ligand molecule (i.e., neurotransmitter), 30 families out of the 35 families belong to GPCRs, and five families belong to LGICs. Out of the five families, three families, i.e., GABAA receptor (Rc), glycine Rc, and ionotropic serotonin Rc constitute the cys-loop superfamily. The rest of the two families, glutamate Rc and ionotropic purinergic Rc, constitute the glutamate-activated cation channel superfamily and the ATP-gated channel superfamily, respectively. Within each of these four neuroreceptor superfamilies (i.e., three LGIC superfamilies and one GPCR superfamily), the neuroreceptors share similar molecular structure and functions. Furthermore, because the ligand molecule is identical within each family, the structural and functional feature of the neuroreceptors within the family is considered to be closely similar. Therefore, within each family, the degree of functional constraint at the molecular level is expected to be similar among the member genes.

But we found that the degree of selective constraint was distributed in a wide range, despite the closely similar molecular feature. Even if the alternative functional constraint that is derived from the tissue/organ level is taken into consideration, this wide difference is yet unexplained because all the neuroreceptors of interest exist in one tissue, i.e., the nervous tissue. Therefore, we can assume the other level of selective constraint, in addition to the molecular-level and the tissue/organ-level selective constraints.

The brain has a highly compartmentalized organization. Each of the compartmentalized regions of the brain bears distinctive neural functions, such as perception, motion, cognition, and maintenance of the life-supporting physiological homeostasis. In many cases, these functions are finely localized in the brain, and the neuroreceptors also have distinctive region-specific expression pattern in the brain. From the viewpoint of the functional localization of the brain, it is reasonable to suppose that the selective constraint operates on the neuroreceptor genes, depending on the brain region or the neural circuit in which the neuroreceptor gene is specifically expressed. Therefore, we suggest that the wide difference of the degree of selective constraint among the member genes of a given neuroreceptor family is attributable to the difference of the neural function in which the neuroreceptor is involved.

The Neurobiological Features of the Neuroreceptor Genes Under Relaxed Selective Constraint
In this section, we will show the neurobiological features characteristic of the four relaxed-constraint neuroreceptors that we detected.

First, the subunit NMDA-2C ({epsilon}3) belongs to the NR2 subunit group ({epsilon}1–{epsilon}4). This group of subunits contribute to form the distinctive gated ion channel. This channel responds to two modes of stimuli: ligand and postsynaptic potential. This associative process causes long-term potentiation that is now known to be the key mechanism of memory and learning (Kandel 2000Citation ).

Second, the localization of GABAA-{epsilon} and GABAA-{theta} in the primate brain is different from that in the rat brain. The {theta} subunit is expressed in the cerebral cortex and hippocampus of primates, whereas it is not expressed in those brain regions of rats. Furthermore, the {epsilon} subunit is expressed in the hippocampus CA3 region of primates, which is important for LTP, but not in the region of rats (Whiting et al. 1997Citation ). There is a possibility that the change of the expression pattern of these genes influences cognitive abilities of primates. In addition, both GABAA-{epsilon} and GABAA-{theta} are strongly expressed in locus ceruleus (Sinkkonen et al. 2000Citation ). The locus ceruleus plays a crucial role in maintaining the vigilance and responsiveness to the novel environmental stimuli by extensive projections to every major region of the brain and spinal cord (Saper 2000Citation ). Therefore, it is plausible that GABAA-{epsilon} and GABAA-{theta} molecules influence the cognitive abilities of the organism.

Third, the dopamine D4 receptor expression was reported to be elevated in postmortem brains of schizophrenic patients (Seeman, Guan, and Van Tol 1993Citation ). The main target of the atypical antipsychotic drug, clozapin, is considered to be the D4 receptor (Van Tol et al. 1991Citation ; Seeman and Van Tol 1994Citation ). This drug alleviates the symptoms like delusion and hallucination, and it also improves hypobulia. These facts indicate that the D4 is deeply related to cognition and volition. The D4 receptor gene contains repeat sequences. The number of the repeats is polymorphic in humans but not polymorphic in rodents. It was reported that this polymorphism is associated with the personality traits called "novelty seeking" (Benjamin et al. 1996Citation ; Ebstein et al. 1996Citation ). Furthermore, the D4 knockout mice exhibited reduced behavioral responses to novelty and a decrease in novelty-related exploration (Dulawa et al. 1999Citation ). Despite these phenotypic impairments, the D4 knockout mice were reproductive and not lethal (Rubinstein et al. 1997Citation ).

From the facts described above, we were able to show that the relaxed-constraint neuroreceptors have a common feature that they are involved in the cognitive abilities such as (1) memory and learning and (2) responsiveness to novel stimuli. The cognitive abilities can influence the behavioral features of an organism through interactions with the environment and communications among individuals. These behavioral features may not have direct effects on the basic life-supporting functions of the organism, as is exemplified by the fact that the knockout mice of the dopamine D4 receptor are not lethal. But because the environment has not remained unchanged during the evolutionary time scale, to adapt to the changing environment, it could be advantageous for individuals to have some genes that are relatively free or flexible for any changes. In fact, those flexible genes can exist in a large gene family because many other genes with similar functions can undertake the entire functions that the gene family should pursue. Flexibility and functional importance of a member gene seem to be in the relationship of trade-off within a given large gene family.

In the present study, it is of particular interest to note that such relaxed or flexible neuroreceptor genes we detected are related to behavioral features of the organism. Therefore, we suggest that the relaxed-constraint neuroreceptor genes could have allowed the nervous system to obtain the freedom for a new adaptive ability, by virtue of the other member genes' engagement in the basic life-supporting functions under the more stringent constraint.


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Appendix List of Neuroreceptor Genes used and the dN/dS Ratios Among Humans, Mice and Rats

 

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Appendix (Continued)

 

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Appendix (Continued)

 

    Acknowledgements
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 
We are grateful to Dr. Y. Niimura for helpful discussions and suggestions.


    Footnotes
 
Dan Graur, Reviewing Editor

Keywords: neurotransmitter receptor synonymous substitution G protein–coupled receptor (GPCR) ligand-gated ion channel (LGIC) selective constraint ortholog Homo sapiens Back

Address for correspondence and reprints: Takashi Gojobori, Yata 1111, Mishima, Shizuoka-ken, 411-8540 Japan. tgojobor{at}genes.nig.ac.jp . Back


    References
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Acknowledgements
 References
 

    Altschul S. F., T. L. Madden, A. A. Schäffer, J. Zhang, Z. Zhang, W. Miller, D. J. Lipman, 1997 Gapped BLAST and PSI-BLAST: a new generation of protein database search programs Nucleic Acids Res 25:3389-3402[Abstract/Free Full Text]

    Barnard E. A., 1996 The transmitter-gated channels: a range of receptor types and structures Trends Pharmacol. Sci 17:305-309[ISI][Medline]

    Benjamin J., L. Li, C. Patterson, B. D. Greenberg, D. L. Murphy, D. H. Hamer, 1996 Population and familial association between the D4 dopamine receptor gene and measures of Novelty Seeking Nat. Genet 12:81-84[ISI][Medline]

    Dickerson R. E., 1971 The structure of cytochrome c and the rates of molecular evolution J. Mol. Evol 1:26-45[Medline]

    Dulawa S. C., D. K. Grandy, M. J. Low, M. P. Paulus, M. A. Geyer, 1999 Dopamine D4 receptor-knock-out mice exhibit reduced exploration of novel stimuli J. Neurosci 19:9550-9556[Abstract/Free Full Text]

    Ebstein R. P., O. Novick, R. Umansky, B. Priel, Y. Osher, D. Blaine, E. R. Bennett, L. Nemanov, M. Katz, R. H. Belmaker, 1996 Dopamine D4 receptor (D4DR) exon III polymorphism associated with the human personality trait of novelty seeking Nat. Genet 12:78-80[ISI][Medline]

    Golding G. B., 1983 Estimates of DNA and protein sequence divergence: an examination of some assumptions Mol. Biol. Evol 1:125-142[Abstract]

    Holmquist R., M. Goodman, T. Conroy, J. Czelusniak, 1983 The spatial distribution of fixed mutations within genes coding for proteins J. Mol. Evol 19:437-448[ISI][Medline]

    Ina Y., 1994 ODEN: a program package for molecular evolutionary analysis and database search of DNA and amino acid sequences Comput. Appl. Biosci 10:11-12[Medline]

    Kandel E. R., 2000 Explicit memory in mammals involves long-term potentiation in the hippocampus Pp.1259–1272 in E. R. Kandel, J. H. Schwartz, and T. M. Jessell, eds. Principles of neural science. 4th edition. McGraw-Hill Companies, New York

    Kimura M., 1983 The neutral theory of molecular evolution Cambridge University Press, Cambridge

    Kocher T. D., A. C. Wilson, 1991 Sequence evolution of mitochondrial DNA in human and chimpanzees: control region and protein coding region Pp. 391–413 in S. Osawa and T. Honjo, eds. Evolution of life: fossils, molecules and culture. Springer, Tokyo

    Kuma K., N. Iwabe, T. Miyata, 1995 Functional constraints against variations on molecules from the tissue level: slowly evolving brain-specific genes demonstrated by protein kinase and immunoglobulin supergene families Mol. Biol. Evol 12:123-130[Abstract]

    Miyata T., T. Yasunaga, T. Nishida, 1980 Nucleotide sequence divergence and functional constraint in mRNA evolution Proc. Natl. Acad. Sci. USA 77:7328-7332[Abstract]

    Nei M., T. Gojobori, 1986 Simple method for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions Mol. Biol. Evol 3:418-426[Abstract]

    Le Novère N., J.-P. Changeux, 2001 LGICdb: the ligand-gated ion channel database Nucleic Acids Res 29:294-295[Abstract/Free Full Text]

    Pruitt K. D., D. R. Maglott, 2001 RefSeq and LocusLink: NCBI gene-centered resources Nucleic Acids Res 29:137-140[Abstract/Free Full Text]

    Rubinstein M., T. J. Phillips, D. K. Grandy, et al. (14 co-authors) 1997 Mice lacking dopamine D4 receptors are supersensitive to ethanol, cocaine, and methamphetamine Cell 90:991-1001[ISI][Medline]

    Saper C. B., 2000 Cell groups in the brain stem with long projections can be defined by their neurotransmitters. Pp. 895–896 in E. R. Kandel, J. H. Schwartz, and T. M. Jessell, eds. Principles of neural science. 4th edition. McGraw-Hill Companies, New York.

    Schwartz J. H., 2000 Neurotransmitters Pp. 280–281 in E. R. Kandel, J. H. Schwartz, and T. M. Jessell, eds. Principles of neural science. 4th edition. McGraw-Hill Companies, New York

    Seeman P., H. C. Guan, H. H. Van Tol, 1993 Dopamine D4 receptors elevated in schizophrenia Nature 365:441-445[ISI][Medline]

    Seeman P., H. H. Van Tol, 1994 Dopamine receptor pharmacology Trends Pharmacol. Sci 15:264-270[ISI][Medline]

    Sinkkonen S. T., M. C. Hanna, E. F. Kirkness, E. R. Korpi, 2000 GABAA receptor {epsilon} and {theta} subunits display unusual structural variation between species and are enriched in rat locus ceruleus J. Neurosci 20:3588-3595[Abstract/Free Full Text]

    Tamura K., M. Nei, 1993 Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees Mol. Biol. Evol 10:512-526[Abstract]

    Thompson J. D., D. G. Higgins, T. J. Gibson, 1994 CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice Nucleic Acids Res 22:4673-4680[Abstract]

    Van Tol H. H. M., J. R. Bunzow, H. C. Guan, R. K. Sunahara, P. Seeman, H. B. Niznik, O. Civelli, 1991 Cloning of the gene for a human dopamine D4 receptor with high affinity for the antipsychotic clozapine Nature 350:610-614[ISI][Medline]

    Van Tol H. H. M., C. M. Wu, H.-G. Guan, K. Ohara, J. R. Bunzow, O. Civelli, J. Kennedy, P. Seeman, H. B. Niznik, V. Jovanovic, 1992 Multiple dopamine D4 receptor variants in the human population Nature 358:149-152[ISI][Medline]

    Wakeley J., 1994 Substitution-rate variation among sites and the estimation of transition bias Mol. Biol. Evol 11:436-442[Abstract]

    Whiting P. J., G. McAllister, D. Vasilatis, et al. (14 co-authors) 1997 Neuronally restricted RNA splicing regulates the expression of a novel GABAA receptor subunit conferring atypical functional properties J. Neurosci 17:5027-5037[Abstract/Free Full Text]

Accepted for publication June 11, 2002.