Complex patterns of divergence among green- Clade model analyses

Complex patterns of divergence among green- Clade model analyses
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Open Access
Complex patterns of divergence among greensensitive (RH2a) African cichlid opsins revealed by
Clade model analyses
Cameron J Weadick1,2 and Belinda SW Chang3*
Background: Gene duplications play an important role in the evolution of functional protein diversity. Some
models of duplicate gene evolution predict complex forms of paralog divergence; orthologous proteins may
diverge as well, further complicating patterns of divergence among and within gene families. Consequently,
studying the link between protein sequence evolution and duplication requires the use of flexible substitution
models that can accommodate multiple shifts in selection across a phylogeny. Here, we employed a variety of
codon substitution models, primarily Clade models, to explore how selective constraint evolved following the
duplication of a green-sensitive (RH2a) visual pigment protein (opsin) in African cichlids. Past studies have linked
opsin divergence to ecological and sexual divergence within the African cichlid adaptive radiation. Furthermore,
biochemical and regulatory differences between the RH2aα and RH2aβ paralogs have been documented. It thus
seems likely that selection varies in complex ways throughout this gene family.
Results: Clade model analysis of African cichlid RH2a opsins revealed a large increase in the
nonsynonymous-to-synonymous substitution rate ratio (ω) following the duplication, as well as an even larger
increase, one consistent with positive selection, for Lake Tanganyikan cichlid RH2aβ opsins. Analysis using the
popular Branch-site models, by contrast, revealed no such alteration of constraint. Several amino acid sites known
to influence spectral and non-spectral aspects of opsin biochemistry were found to be evolving divergently,
suggesting that orthologous RH2a opsins may vary in terms of spectral sensitivity and response kinetics. Divergence
appears to be occurring despite intronic gene conversion among the tandemly-arranged duplicates.
Conclusions: Our findings indicate that variation in selective constraint is associated with both gene duplication
and divergence among orthologs in African cichlid RH2a opsins. At least some of this variation may reflect an
adaptive response to differences in light environment. Interestingly, these patterns only became apparent through
the use of Clade models, not through the use of the more widely employed Branch-site models; we suggest that
this difference stems from the increased flexibility associated with Clade models. Our results thus bear both on
studies of cichlid visual system evolution and on studies of gene family evolution in general.
Keywords: Codon substitution model, Visual pigment evolution, Nonsynonymous-to-synonymous substitution rate
ratio, dN/dS, Clade model, Maximum likelihood, Gene family evolution
* Correspondence: [email protected]
Department of Ecology and Evolutionary Biology, Department of Cell and
Systems Biology, and Centre for the Analysis of Genome Evolution and
Function, University of Toronto, 25 Harbord Ave, Toronto, Ontario M5S 3G5,
Full list of author information is available at the end of the article
© 2012 Weadick and Chang; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (, which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Gene duplication is known to play major roles in genomic and phenotypic evolution, and often precipitates
divergent evolution of protein structure and function
[1,2]. A number of models have been proposed to explain the retention and evolution of duplicated genes in
the face of deleterious, pseudogenizing mutations [3-5];
these models differ in the predictions they make about
post-duplication sequence evolution and, consequently,
in how amenable they are to investigation. The classic
neofunctionalization model [3], for example, predicts a
fairly simple pattern of post-duplication protein sequence evolution, with one paralog diverging while the
other retains the ancestral function under a regime of
purifying selection. However, other models, such as the
duplication-degeneration-complementation model [6]
and the escape from adaptive conflict model [7,8], predict more complex forms of divergence. Furthermore, it
is becoming increasingly recognized that divergent protein evolution can occur without duplication—that is,
among orthologs—and that this can contribute to adaptive phenotypic evolution [9-11], contrary to classical
assumptions [12]. Distinguishing among models of gene
duplication, and determining the relative roles played by
adaptive and non-adaptive processes in protein evolution, thus requires approaches that can accommodate
complex patterns of sequence evolution.
Recent advances in codon substitution models that account for variation in site-specific selective constraint
among multiple clades or lineages [13,14] provide a
promising approach for distinguishing among models of
gene duplication and evolution. Branch-site models
[15,16] are commonly used to detect the signature of
strong site-specific positive selection along a prespecified lineage after gene duplication (cf. 'conservedbut-different' or 'Type II' divergence patterns) [17,18].
Clade models [19,20], meanwhile, can be used to detect
more subtle differences in site-specific selective constraint among entire clades or partitions of a phylogeny
(c.f. 'covarion-like' or 'Type I' divergence patterns)
[17,18]. Clade models are not restricted to detecting
strict cases of positive selection and can be used to consider variation among multiple clades simultaneously
[21]. As such, Clade models may be better suited to
detecting complex forms of divergence in selective constraint across gene families than the Branch-site models
[22]. However, compared to the popular Branch-site
models, Clade models have been relatively under used.
Species derived from recent adaptive radiations are intriguing systems for studying patterns of evolution at the
molecular level, as rapid phenotypic evolution implies a
comparable degree of change in the underlying genome
[23,24]. The endemic and diverse cichlid fishes of the
Rift Valley of eastern Africa are thought to be the result
Page 2 of 17
of multiple, young, adaptive radiations [25,26]. The high
numbers of species found within the Rift Valley’s lakes
and rivers, as well as the impressive degree of phenotypic variation present among closely related species,
make these fishes ideal for studies on functional diversification and speciation. Recently, progress has been
made associating adaptive phenotypic evolution in
African cichlids with variation at the molecular level, for
example with regard to jaw morphology or colour patterning, [27-30]. Perhaps most notably, a number of
studies have linked ecological and sexual divergence
among African cichlids to divergence in colour vision
genes, the opsins [31,32]. Opsins form the protein component of visual pigments, the photosensitive compounds expressed in the rod and cone photoreceptor
cells of the retina that absorb and transduce photons of
light into the biochemical signals that ultimately underlie
the visual sense [33-35]. Amino acid substitutions that
affect the opsin’s retinal chromophore binding pocket
can alter the pigment's absorbance spectrum, generating
variation in spectral sensitivity [36,37]. Recent studies of
African cichlid opsins have linked variation in opsin protein sequences and expression patterns to ecologicallyand sexually-selected divergence among closely related
populations and species [38,39] and, as a result, African
cichlids have emerged as model systems for study of the
molecular biology, evolution, and ecology of opsins
Compared to most vertebrates, African cichlids possess a large number of opsin genes, with seven cone
opsins and one rod opsin [40]. The resulting visual pigments vary in spectral sensitivity, with the wavelength of
maximal absorbance (λmax) ranging from the ultraviolet
(UV) to the yellow. Most of these opsins are evolutionarily ancient, with orthologs present in most teleosts, if
not most vertebrates. However, the green-sensitive
RH2aα and RH2aβ opsins are relatively young [41,42],
and descend from a duplication event specific to the
African cichlid clade [43]. Spectrophotometric study of
RH2aα and RH2aβ pigments from Oreochromis niloticus
(Nile tilapia) and Metriaclima zebra (Lake Malawi zebra
mbuna), expressed in vitro, revealed functional divergence between the paralogs, with the RH2aα pigments
red-shifted (λmax ≈ 528 nm) compared to the RH2aβ
pigments (λmax ≈ 518 nm) [41,42]. However, it has
proven difficult to broadly survey for variation in
RH2aα/β λmax via microspectrophotometry (MSP) due
to their fairly close λmax values and the noise inherent in
MSP [32]. Regulatory differences are also apparent
[42,44] but the high level of sequence similarity between
these paralogs (~95% identical at the nucleotide level)
makes quantitative PCR studies challenging. As a result
of these methodological difficulties, it is sometimes simply assumed that the RH2aα and RH2aβ paralogs are
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
sufficiently similar to justify treating them equivalently
[45,46]. However, there is reason to believe that comparably small differences in λmax can be ecologically and
evolutionarily important in African cichlids [38], and
whether or not these opsins are functionally equivalent
from the perspective of African cichlid visual biology
and fitness is not clear. Comparative sequence analysis
may be able to provide useful insights into this system.
Given the important role vision plays in the African
cichlid adaptive radiation [31,32] as well as the important role gene duplication plays in functional diversification in general [3], we set out to explore patterns of
sequence evolution associated with the RH2aα-RH2aβ
gene duplication event using both Branch-site and Clade
codon-substitution model approaches. We document
complex patterns of divergence among duplicated
African cichlid RH2aα and RH2aβ opsins, reflecting both
paralog-specific and species-specific processes. Importantly, positive selection was documented not using
Branch-site models, but the less widely employed Clade
models. We discuss the implications of our findings in
light of gene duplication theory, cichlid visual ecology,
and opsin structure and function.
Phylogenetic analyses were carried out on a data set
of 48 fish RH2 opsin sequences from 29 species; species
names and accession numbers are provided in
Additional file 1: Figure S1. Translated amino acid
sequences were assembled in MEGA 4 [47] and aligned
using ClustalW [48], after which the extreme N- and Ctermini of the opsin sequences were trimmed, leaving an
alignment 343 codons in length. Bayesian phylogenetic
analysis was carried out using MrBayes 3.2 [49] using
the GTR+I+Γ nucleotide substitution model, which was
selected based on AIC rank, as calculated by MrModeltest 2.2 [50]. Four runs, each consisting of four chains
(three heated and one cold), were run for 5 x 106 generations, sampling every 100 generations. The first 25%
of the samples were considered ‘burn-in’ and discarded.
Adequate sampling and convergence were assured by
ensuring that (1) the standard-deviation of split frequencies was less than 0.01 by the end of the analysis, (2)
post-scale reduction factors were approximately 1.000
for all parameter estimates and topological partitions, (3)
parameter estimate-by-generation plots were stationary,
and (4) effective sample sizes were greater than 100.
These checks were carried out through direct examination of the MrBayes output file and using Tracer 1.5
[51]. A codon-partitioned approach was employed as
well, assuming separate GTR+I+Γ substitution models
for each of the three codon positions. Maximum likelihood (ML) phylogenetic analyses were carried out using
PhyML 3.0 [52] assuming the GTR+I+Γ nucleotide
Page 3 of 17
substitution model. Ten random trees plus a tree inferred using the BIONJ algorithm were used as starting
trees, and both nearest neighbour interchange (NNI)
and subtree-pruning and regrafting (SPR) tree-search
approaches were used to explore tree space. Node support for the ML tree was evaluated using the SH-like approximate likelihood ratio test approach [52]. For both
Bayesian and ML analyses, four rate categories were
assumed for the +Γ distribution used to described
among-site rate variation [53].
After estimating the fish RH2 phylogeny, we focused
on the RH2a opsin clade for subsequent molecular evolutionary analyses. Specifically, we extracted and analyzed the monophyletic sub-tree containing the
duplicated African cichlid RH2aα and RH2aβ opsins plus
five outgroup RH2a sequences; species names and accession numbers for these sequences can be found in
Figure 1 and Additional file 1: Figure S1. Due to uneven
taxonomic coverage in online genetic databases, the
RH2aβ clade possesses opsin sequences from three additional species compared to the RH2aα clade. The three
extra sequences in the RH2aβ clade are all derived from
Lake Tanganyikan cichlids, which were surveyed prior to
the discovery of the RH2a duplication event [54];
whether these species have retained or lost their RH2aα
opsins is currently not known, though current phylogenetic hypotheses for Lake Tanganyikan cichlids [55] suggest that multiple deletions would be required for all
three species to truly lack RH2aα opsins in their respective genomes (see Additional file 1: Figure S2). Note that
the RH2aβ sequences from Ophthalmotilapia ventralis
and Neolamprologous brichardi were obtained from
cDNA even though relative RH2a gene expression is
skewed towards the RH2aα paralog in Oreochromis niloticus and Lake Malawi haplochromine cichlids [42,44].
RH2a opsin sequences are available for many other African cichlids, but often differ from one another at just
one or two positions; because including all such
sequences would greatly increase computational time
without adding much information, we chose to focus on
key sequences representing well-studied species and key
cichlid lineages.
We explored patterns of selective constraint across
this RH2a data set through the use of codon substitution
models that include the nonsynonymous to synonymous
substitution rate ratio (ω or dN/dS) as a parameter
[56,57]. The ω ratio speaks to the form and strength of
selective constraint operating on protein-coding DNA:
0 < ω < 1 is consistent with purifying selection (nonsynonymous substitutions are accumulating more slowly
than synonymous substitutions), ω = 1 suggests neutrality (nonsynonymous and synonymous substitutions are
accumulating at equivalent rates), and ω > 1 indicates
positive selection (nonsynonymous substitutions are
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Page 4 of 17
Figure 1 RH2a opsin gene tree showing the African cichlid-specific duplication event that produced the RH2aα (thick, dotted, blue
branches) and RH2aβ (thick, solid/dashed, red branches) opsins. Dashed red branches indicate RH2aβ lineages from Lake Tanganyikan
cichlids. Numbers adjacent to nodes indicate clade posterior probability, and branch lengths indicate expected number of substitutions per
nucleotide site. Branch model analyses (inset, upper left) revealed that ω was significantly elevated along the Lake Tanganyikan branches
compared to the rest of the RH2aβ clade. Species code abbreviations indicate the first letter of the genus and first two letters of each species: Cfr,
Crenicichla frenata; Lgo, Lucania goodei; Mau, Melanochromis auratus; Mze, Metriaclima zebra; Nbr, Neolamprologus brichardi; Ola, Oryzias latipes;
Oni, Oreochromis niloticus; Ove, Ophthalmotilapia ventralis; Ppu, Pundamilia pundamilia; Pre, Poecilia reticulata; Tdu, Tropheus duboisi; Tin,
Tramitichromis intermedius. Genbank accession numbers are included alongside the species codes.
accumulating faster than synonymous substitutions).
Three different approaches were used, each of which
makes different assumptions about how ω varies across
the alignment and/or across the phylogeny: (1) Branch
models [58], (2) Branch-site models [16], and (3) Clade
models [20]. Models were fit to the data using the
codeml program of the PAML 4.2 software package
[59]. Within each of these model classes, likelihood
ratio tests (LRTs) were used to compare the fit of complex models against simpler, nested models [60,61].
LRTs were carried out by comparing twice the difference in ln likelihood scores of nested models against a
χ2 distribution with the degrees of freedom equal to the
number of extra parameters estimated by the more
complex model. However, as LRTs can only be used to
compare nested models, we also used AIC scores [62]
to help convey the relative fit of the different Clade
models applied to our fish RH2a data set. In addition to
the selection pressure parameters (dN/dS, ω; proportions, p), the transition-to-transversion substitution rate
ratio (κ) and branch lengths were optimized as well.
Codon frequencies were approximated using the F3x4
calculation. Each model was fit to the data multiple
times from different starting parameter values to help
ensure local optima were avoided, with either ω or κ
perturbed, as needed, depending on the particular
model. As these methods assume that the aligned
sequences are related by a phylogenetic tree, not by a
reticulating network, the signature of gene conversion
within this data set was searched for using Phi [63], as
implemented in PhiPack. Significance was assessed either assuming a normally approximated null distribution or via a permutation approach (with 1000
permutations), and analyses were run with the window
size left at the recommended default value of w = 100
and at w = 50. The results were qualitatively equivalent
in spite of these changes; as such, only results derived
using the normal approximation and w = 100 are
shown. Dot plots were created using eBioX 1.5.1 [64].
Branch models [65] assume that the ω ratio varies
across branches of the phylogeny (specified a priori) but
that it is invariant across sites of the alignment; comparing complex Branch models (i.e., ones with multiple ω
ratios) against simpler, nested models tests whether ω
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
varies significantly between sections of the phylogeny.
Since Branch models make the unrealistic assumption of
among-site homogeneity, they often lack power to detect
subtle patterns of divergence across phylogenies, and we
conducted post-hoc Branch model analysis simply to
help demonstrate our Clade model partitioning schemes
(described below). Branch-site models and Clade models
similarly allow for variation in ω among pre-specified
branches of the phylogeny, but, unlike Branch models,
also incorporate among-site variation in selective constraint. The signature of positive selection (ω > 1) along
pre-specified lineages was tested for using the Branchsite approach of Zhang, Nielsen, and Yang [16]. This
model assumes that there are four classes of sites and
that the phylogeny can be divided into ‘background’ and
‘foreground’ lineages based on an a priori hypothesis
about when positive selection may have occurred. The
first two classes of sites correspond to codons that experience selection consistently across the entire phylogeny, experiencing either purifying selection (0 < ω0 <
1) or neutral pressure (ω1 = 1), respectively. The final
two classes of sites correspond to codons that experience
purifying or neutral selection on the background
lineages, but positive selection (ω2 > 1) on the foreground
lineage. These four site classes comprise proportions p0
(universally-purifying site class), p1 (universally-neutral
site class), p0*p2/(1 – p2) (purifying-to-positive selection
site class), and p1*p2/(1 – p2) (neutral-to-positive selection
site class) of the total data set (where p2 = 1 – p0 – p1).
The goodness-of-fit of this Branch-site model is established by comparing it via a LRT against a constrained null
model where ω2 = 1; this LRT thus tests for the presence
of positively selected sites.
The signature of divergent selective constraint across
the phylogeny was tested for using the Clade model C
(CmC) approach of Bielawski and Yang [20] as modified
by Yang, Wong, and Nielsen [66]. In its simplest form,
CmC assumes that the branches of the phylogeny can be
divided into two partitions, the ‘background’ branches
and the ‘foreground’ branches. CmC accommodates
among-site variation in the substitution process by assuming three site classes. As with the Branch-site approach described above, the first two classes of sites
correspond to codons that experience selection consistently across the entire phylogeny, experiencing either
purifying selection (0 < ω0 < 1) or neutral pressure (ω1 =
1). The third site class accounts for codons that experience divergent selection pressures in different, predefined partitions (i.e., ω2 > 0 for the background
branches and ω3 > 0 for the foreground branches). These
site classes correspond to proportions p0, p1, and p2 of
the total data set (where p2 = 1 – p0 – p1). Models M1a
and M2a_rel, neither of which incorporates amonglineage variation in ω, were used as null models to test
Page 5 of 17
for the presence of divergently selected sites. M1a is the
standard null model for CmC analyses [66]; M1a possesses only two site classes: one for sites subject to purifying selection (0 < ω0 < 1), and one for neutral sites
(ω1 = 1). However, our previous analyses of simulated
data sets revealed that the CmC versus M1a LRT is
prone to false positive test results when faced with
moderate among-site variation in selective constraint.
We therefore also employed our newly proposed
M2a_rel null model for CmC analyses [67]; M2a_rel
possesses purifying and neutral site classes, like the M1a
model, but also possesses a third site class under which
a single ω ratio is estimated for all branches of the phylogeny (ω2 > 0). We checked the robustness of our
results to slight changes in model framework by reanalyzing the data using the Clade model D (CmD) framework [20]; like CmC, CmD assumes three site classes
with the final class modeling divergent selection among
clades but, unlike CmC, no constraints are placed on the
ω estimates for any of the site classes.
Yoshida et al. [21] recently extended CmC to allow for
more than two tree partitions, each with a separately estimated ω ratio. We refer to this as a ‘multi-clade’ approach, and we used this approach to examine complex
patterns of divergence in selection across the phylogeny
by comparing such models against simpler, nested models with fewer tree partitions. Assuming a phylogeny can
be partitioned into three clades (X, Y, and Z), the multiclade approach could be used to estimate three separate
ω ratios for the three tree partitions (ω2 > 0 for clade X,
ω3 > 0 for clade Y, and ω4 > 0 for clade Z). Comparing
this model against a simpler, null model with only two
tree partitions (say, ω2 > 0 for clade X, and ω3 > 0 for
both clade Y and clade Z) would constitute a test of
whether selective constraint is equivalent in clades Y and
Z (i.e., whether or not ω3 ≠ ω4). This LRT’s null model is
formed by imposing a single, non-boundary constraint
on the alternative model (i.e., the constraint that ω3 =
ω4), reducing model size by one estimated parameter. As
a result, the null distribution for this LRT should follow a
χ2 distribution with one degree of freedom. We therefore
generated simulated data sets assuming a CmC framework (with two tree partitions), and used these data sets
to evaluate this multi-clade LRT’s false-positive rate.
Simulated data sets were generated using the evolver
program of the PAML 4.2 software package [68]. Following our earlier simulation study of CmC LRTs [67], 100
data sets of 10 taxa and 500 codons were simulated
under CmC assuming the topology, branch lengths, and
partitioning shown in Figure 2a. Half of the codons of
each simulated data set (p0 = 0.5) were generated assuming strong purifying selection (ω = 0.0), p1 = 0.20 were
generated assuming neutrality (ω1 = 1.0), and the
remaining codons (p2 = 0.3) were simulated assuming
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Page 6 of 17
Figure 2 Simulation-based analyses were used to establish the false-positive rate of the LRT comparing CmC assuming three tree
partitions against a null version of CmC assuming only two tree partitions. (a) The two-partition tree used for simulating null data sets. (b)
The three-partition tree used as the LRT’s ‘alternative’ model. (c) Histogram showing the distribution of LRT test-statistics from analysis of 100 data
sets simulated under the null model. (d) The same data as in (c), but here plotted as an empirical cumulative density function. For both (c) and
(d), the solid, curved line shows the expected χ21 distribution.
divergent selection pressure between the solid (ω2 =
0.15) and dashed (ω3 = 0.65) tree partitions. Additional
parameters for the simulations were set as follows: the
transition to transversion substitution rate ratio (κ) was
set to κ = 2.0; the total tree length (TL) was set to TL =
3.0 substitutions per codon; and the equilibrium frequency for each sense codon was set to 1/61. For null
model analyses, we ran CmC assuming the two partitions shown in Figure 2a (i.e., correct partitioning), while
for alternative model analyses, we ran CmC assuming
the three partitions shown in Figure 2b (i.e., overly complex partitioning). Branch lengths were freely estimated,
but κ and the equilibrium codon frequencies were fixed
at their simulated values. Analyses were run multiple
times from different starting ω values to help detect and
avoid local optima in the likelihood surface, as described
in Weadick and Chang [67]. The LRT test statistics for
each of the 100 simulated data sets—twice the difference
in ln likelihood scores from the alternative and null
model analyses—were calculated, compiled, and compared against the expected χ21 null distribution. Observed
and expected cumulative density functions were compared via a one-sided Kolmogorov-Smirnov test and a
one-sided binomial test. We carried out additional analyses on the 100 simulated data sets where the phylogenetic partitioning of the alternative and null models was
misspecified, with branch 10 of the tree shown in
Figure 2a,b included in the ‘dashed’ partition, rather
than the correct ‘solid’ partition.
Following Clade model analysis, a Bayes empirical
Bayes (BEB) approach was used to identify specific
codons with high posterior probability (PP) of being in
the ‘divergent selection’ site class [66]. BEB-identified
sites were then mapped on to the three-dimensional
crystal structure of bovine rhodopsin (PDB accession
1u19) [69] using MacPyMol (Delano Scientific). The
phylogenetic location of specific amino acid substitutions was inferred by using ancestral reconstruction
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
methods [70] to estimate the most probable residue at
each node under the WAG+F+Γ amino acid substitution
model [53,71]. Site numbering is based on alignment
against bovine RH1 opsin (rhodopsin).
Phylogenetic analyses recovered reciprocally monophyletic clades of African cichlid RH2aα and RH2aβ opsins
and a sister relationship between the African cichlid
RH2a opsin clade and the RH2a opsin of the Neotropical
cichlid Crenicichla frenata (Figure 1). Trees estimated
via the codon-partitioned Bayesian approach and the
ML approach were highly similar, and only differed with
respect to the arrangement of a few of the highly similar
haplochromine cichlid RH2a opsin sequences; the full
Bayesian (codon-partitioned) and ML trees are provided
in Additional file 1: Figure S1. The non-partitioned and
codon-partitioned Bayesian methods yielded trees with
equivalent branching patterns (result not shown). For
molecular evolutionary analyses, we focused on the subtree corresponding to the RH2a opsins of cichlids and
those of closely related atherinomorph fishes (guppy,
Poecilia reticulata; bluefin killifish, Lucania goodei; and
medaka, Oryzias latipes) from the codon-partitioned
Bayesian phylogeny (Figure 1). Fitting the simple M0
codon substitution model to this data set provided an
overall ω estimate of 0.1476, indicating that purifying selection is the predominant force shaping the evolution
of these RH2a opsin sequences. Estimates of dS, calculated for each branch given the branch length, the number of nonsynonymous and synonymous sites in the
sequence, and the overall ω estimate calculated under
the M0 model, were always well below one, indicating
that saturation of synonymous substitutions is unlikely
to adversely affect our analyses.
Gene conversion between paralogs violates the
assumptions of ML methods for estimating ω, and can
even cause spurious signatures of positive selection
under some conditions [72]. It has previously been suggested that gene conversion is unlikely to affect the
African cichlid RH2a opsins because the paralogs are
arranged in a head-to-head manner [42]. However, recent work on rodent genomes has shown that duplicates
oriented in such a fashion are as prone to gene conversion as those oriented in the typical head-to-tail manner
[73]. We therefore used Phi to test for local correlations
in phylogenetic incompatibility across the data set (i.e.,
across the alignment). While we did detect a significant
signature of recombination (P = 0.028), this reflected
gene conversion between the distantly related RH2B and
RH2C paralogs of the medaka (Oryzies latipes), not gene
conversion between the African cichlid RH2a opsins.
Unlike the African cichlid RH2a paralogs, these duplicated medaka opsins are oriented in a head-to-tail
Page 7 of 17
manner, indicating an independent duplication event
[40]. Visual inspection revealed that the third and fourth
introns of the medaka’s RH2B and RH2C paralogs are
highly similar, while the first and second introns are divergent (Additional file 1: Figure S3), and removing either (or both) of the medaka RH2B/C paralogs from the
data set eliminated the signature of conversion (P > 0.50
in all cases). Furthermore, strong gene conversion
should result in sequences clustering by species, not by
paralog, and this pattern is not observed within the African cichlid RH2a opsin portion of the estimated phylogeny (Figure 1). These results suggest that gene
conversion has not had a notable impact on the evolutionary trajectories of the coding sequences of the duplicated African cichlid RH2a opsins and thus should not
have adverse effects on our analyses. Visual inspection
of the RH2aα and RH2aβ opsins of Oreochromis niloticus [74], however, revealed that introns one and four are
highly similar, while introns two and three are relatively
divergent (Additional file 1: Figure S3). This may indicate that natural selection is maintaining distinct opsin
coding sequences in spite of at least some intronic gene
conversion, as occurs in human red and green opsins
[75]. Alternatively, the fact that these introns are highly
similar may reflect strong purifying selection on noncoding motifs with roles in gene regulation or splicing
control; more intronic RH2a sequence data, obtained
from numerous species, will be needed to address these
Several amino acid substitutions occurred along
the RH2aα and RH2aβ post-duplication branches
(Additional file 1: Table S1), but in neither case did we
obtain evidence indicative of adaptive divergence following gene duplication using Branch-sites methods
(Table 1). The Branch-site LRT for positive selection [16]
was applied four times, with different branches set as the
foreground partition: (1) the RH2aα post-duplication
branch; (2) the RH2aβ post-duplication branch; (3) both
post-duplication branches, combined; and (4) the branch
joining the paralogous clades in a reduced data set for
which outgroup sequences were excluded. Our Branchsite tests remained non-significant even when a more liberal null distribution (a 50:50 mixture of 0 and χ21) was
employed instead. We do note, however, that the substitutions inferred along the RH2aβ post-duplication
branch were densely clustered (substitutions occurred at
six sites in a 13 site stretch: sites 27–39; Additional file 1:
Table S1). This region of the protein has recently been
proposed to serve as the entry channel for the retinal
chromophore into the protein’s binding pocket [76].
One of these substitutions (I36F) involved the replacement of a non-aromatic isoleucine residue with an aromatic phenylalanine residue at a site located at the base
of the proposed entry channel; aromatic residues are
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Page 8 of 17
Table 1 Parameter estimates, log-likelihood scores, and likelihood ratio test (LRT) P values obtained from Branch-site
analyses of the RH2a data set
Model (n.p.)
Site class 0
Site class 1
Site class 2
BrS-N αβ (36)
BrS-A αβ reduced (27)
BrS-N αβ reduced (26)
BrS-A α (37)
BrS-N α (36)
BrS-A β (37)
BrS-N β (36)
BrS-A αβ (37)
NOTE—n.p number of parameters.
thought to assist in retinal uptake [76], suggesting that
this change may enhance visual pigment regeneration
Given our initial findings using Branch-site methods,
we explored alternative signatures of divergence in selective constraint using the Clade model C (CmC) approach [20,21,66]. First, we built on our earlier
simulation-based study of CmC LRTs [67] in order to
evaluate the appropriateness of the χ21 null distribution
for the multi-clade LRT comparing CmC with three tree
partitions against a simpler, nested version of CmC with
only two tree partitions [21]. The empirical and expected
distributions of LRT test statistics from our simulation
analyses are plotted in Figures 2c and 2d, and it can be
seen that the two distributions follow one another quite
closely. Parameter estimates under the alternative and
null model are summarized in Additional file 1: Figure
S4. Although eight of the 100 tests indicated positive test
results, this value is not significantly different from the
expected 5% (one-sided binomial test: P = 0.1280). Furthermore, a Kolmogorov-Smirnov test comparing the
observed and expected cumulative density functions was
non-significant, indicating a good fit to the expected null
distribution (one-sided test for an empirical distribution
falling below the χ21 distribution: D = 0.0609; P =
0.4759). Our simulation results therefore suggest that
the LRT comparing CmC with three tree partitions
against a simpler, nested version of CmC with two tree
partitions can be evaluated using a χ21 null distribution,
though we note that further analyses are needed to fully
evaluate the reliability and power of this approach. Recently, Gossmann and Schmid [77] carried out similar
simulation-based analyses of this LRT, concluding that it
has fair-to-good power and a relatively low false positive
rate; however, these analyses were carried out on smaller
data sets than we employed here.
Additional analyses of the simulated data sets were
carried out using an incorrect phylogenetic partitioning
strategy. Given the tree shown in Figure 2b, we treated
the branch leading to tip #10 as part of the ‘dashed’ partition, rather than the correct ‘solid’ partition, and we
then tested for divergence (i.e., ω3 ≠ ω4) by comparing
the ‘dotted’ partition against the expanded, heterogeneous ‘dashed’ partition. Based on the simulated branch
lengths and ω parameter values, misspecifying the phylogenetic partitioning in this way should reduce the ω3 estimate (to ω3 ≈ 0.52) compared to the ω4 estimate (ω4 ≈
0.65), generating a signature of divergence. We found
that 23 of the 100 LRTs were significant, suggesting
weak power for this test under the given conditions.
Maximum likelihood estimates of the parameter values
appeared to be accurate for most of the parameters (ω0,
ω2, ω3, p0, p2), though slightly upwardly biased for the
ω4 estimate (Additional file 1: Figure S4). Given these
results, we conclude that the properly specified multiclade LRT is statistically sound, but caution that care
must be taken when designating partitions for Clade
model analyses. Specifically, we recommend that partitioning choices be carefully based on external considerations, such as gene duplication theory, taxonomic
sampling, or phylogenetic patterns of niche variation. Of
course, additional simulation-based studies of this LRT’s
properties will be beneficial, and future work should address the performance of this test using larger data sets
and assuming more complex evolutionary scenarios
designed to challenge the assumptions of the alternative
and null models.
We first applied CmC with either the entire RH2aα
clade (‘CmC α’) or the entire RH2aβ clade (‘CmC β’) set
as the foreground partition (Figure 1); all other branches
comprised the background partition. In both cases, the
ω ratio for the divergent selection site class changed
from less than one on the background lineages (ω2 ≈
0.55–0.65) to greater than one on the foreground
lineages (ω3 ≈ 1.15–1.53), with approximately 21% of the
data set assigned to the divergent selection site class
(Table 2). These estimates suggest that selective constraint was relaxed following duplication, and may
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Page 9 of 17
Table 2 Parameter estimates, log-likelihood scores, and AIC weights obtained from Clade model analyses of the RH2a
data set
Model (n.p.)
CmC αβMVR & βT (39)
SC 0
SC 1
SC 2
ω2, ω3, ω4
ω2: 0.4988
AIC weight
ω3: 1.0919
ω4: 2.6122
CmC αβ (38)
ω2: 0.5062
ω3: 1.3889
CmC α & β (39)
ω2: 0.5037
ω3: 1.1308
ω4: 1.5420
CmC β (38)
ω2: 0.5471
CmC βT (38)
ω2: 0.5882
CmC α (38)
ω2: 0.6458
M2a_rel (37)
ω2: 0.5401
M1a (35)
ω3: 1.5315
ω3: 2.5296
ω3: 1.1498
NOTE—n.p. = number of parameters; SC = site class.
Models are ranked according to AIC score.
indicate the action of weak positive selection as well.
Both models fit the data significantly better than the
M1a null model, but ‘CmC α’ did not fit the data significantly better than the more reliable M2a_rel null model
(Table 3). However, this ‘CmC α’ model includes the
RH2aβ clade as part of the ‘background’ partition alongside the outgroup orthologs of non-African cichlids. This
may be inappropriate, as the ‘CmC β’ vs. M2a_rel LRT
suggested a large increase in ω for the RH2aβ clade. To
evaluate this possibility, we employed a multi-clade
CmC approach assuming three partitions: the RH2aα
branches, the RH2aβ branches, and the outgroup orthologous branches. Using this model, which we call the
‘CmC α & β’ model, it was estimated that approximately
21% of the data set evolved under divergent selective
constraint across the three partitions, with a ω ratio less
than one along the outgroup branches (ω2 = 0.50),
slightly above one along the RH2aα branches (ω3 =
1.13), and somewhat higher still along the RH2aβ
branches (ω4 = 1.54) (Table 2). Comparison against the
‘CmC β’ model yielded a significant LRT result (Table 3),
indicating that selective constraint did indeed change
after duplication in the RH2aα clade; this difference only
became statistically significant once the elevated ω ratio
for the RH2aβ clade was accounted for in the model.
Comparison against a different null model, one where all
African cichlid RH2a branches were considered as a single foreground partition (termed the ‘CmC αβ’ model),
revealed that the ω ratios estimated for the RH2aα and
RH2aβ partitions under the ‘CmC α & β’ model were
not significantly different from one another (Table 3),
with a common ω ratio estimate of ω3 = 1.39 (Table 2).
Our RH2a data set was taxonomically unbalanced,
possessing three Lake Tanganyikan cichlid RH2aβ
sequences without corresponding RH2aα paralogs, and
our Branch model analyses revealed that selective constraint was significantly different between the branches
corresponding to these Lake Tanganyikan cichlid opsin
RH2aβ lineages and the remaining RH2aβ branches
(Figure 1, inset). It is therefore possible that the increased
ω ratio observed for the RH2aβ clade in our CmC analyses
was driven by these Lake Tanganyikan RH2aβ sequences.
In lieu of removing these taxonomically-unbalancing
sequences from the alignment, which would represent an
unfortunate loss of data, we carried out further analyses
using the recently developed multi-Clade approach [21].
Specifically, we added a third partition to the ‘CmC αβ’
model that separated the Lake Tanganyikan RH2aβ
branches (βΤ) from the Lake Malawi, Lake Victoria, and
riverine RH2aα and RH2aβ branches (αβMVR). Using this
model, which we call the ‘CmC αβMVR & βΤ’ model, it was
estimated that approximately 21% of the data set evolved
under divergent selective constraint, with a ω ratio less
than one along the outgroup branches (ω2 = 0.50), slightly
above one along the αβMVR branches (ω3 = 1.09), and substantially higher along the βT branches (ω4 = 2.61)
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Page 10 of 17
Table 3 Likelihood ratio test (LRT) P values for nested Clade model C (CmC) comparisons
Null model
Alternative model
CmC αβMVR & βT
< 0.0001 (4)
CmC α
< 0.0001 (2)
CmC β
CmC αβ
CmC βT
0.0408 (1)
0.0022 (1)
CmC α & β
< 0.0001 (4)
< 0.0001 (2)
CmC αβ
< 0.0001 (3)
< 0.0001 (1)
CmC α
0.0124 (3)
0.0925 (1)
CmC β
< 0.0001 (3)
0.0001 (1)
CmC βT
< 0.0001 (3)
0.0001 (1)
< 0.0001 (1)
0.0226 (1)
0.4251 (1)
Degrees of freedom for each LRT are indicated in parentheses.
(Table 2). Out of the Clade models considered, the ‘CmC
αβMVR & βT’ model was the best fitting according to AIC
scores, and comparison against the ‘CmC αβ’ model
yielded a significant LRT result (Table 3), indicating that
the ω ratio differs significantly between the Lake Tanganyikan cichlid RH2aβ opsins (the βT partition) and the
RH2aα and RH2aβ opsins of other African cichlids (the
αβMVR partition). Importantly, partitioning the tree in this
manner did not eliminate the ω ratio increase observed for
African cichlid RH2a opsin clade compared to the outgroup
orthologs (LRT against ‘CmC βT’; Tables 2, 3). Finally, the
divergent ω ratio estimate for the Lake Tanganyikan RH2aβ
branches (the βT partition) was significantly greater than
ω = 1 (Table 4), indicating that the increase in ω seen for
the βT partition is due to site-specific positive selection and
not simply relaxed functional constraint. Conversely, the divergent ω ratio estimated for the αβMVR partition, though
elevated, was not found to significantly exceed ω = 1
(Table 4).
Reanalyzing the data under the CmD framework gave
broadly similar results (compare Tables 2 and 3 with
Additional file 1: Tables S2 and S3). First, parameter estimates were qualitatively similar between CmC and CmD
analogues. Second, the rank order of CmC and CmD analogues was almost completely equivalent, with the only
difference being the relative position of the two and
three site-class null models (M1a and M2a_rel for CmC;
M3 (K = 2) and M3 (K = 3) for CmD), which in both cases
were very poor fits to the data. And, finally, P values for the
various LRTs were generally similar, with only three tests
providing qualitatively different results under the two
model frameworks (one test achieved significance under
CmD but not under CmC, and two others achieved significance under CmC but not CmD); notably, non-significant
P values were still less than P = 0.10 for these each of these
three cases. Notwithstanding these few differences, the
broad similarity of AIC ranks, the majority of the LRTs,
and the qualitative agreement in parameter estimates suggest that our CmC analyses have provided reliable inferences on cichlid RH2a opsin evolution.
Eighty-one of the 343 total codons in the alignment
were variable at the amino acid level and, of these, 27
were identified by BEB analysis as members of the divergently evolving site class under the best fitting ‘CmC
αβMVR & βT’ model (PP > 0.75 for each site) (Table 5).
Included among these identified sites are seven of the 10
sites that substituted along the RH2aα and RH2aβ postduplication branches (Additional file 1: Table S1). The
position of these sites within the opsin protein's secondary and tertiary structure are shown in Figure 3. Most of
these sites are situated within the opsin protein’s extracellular half (Figure 3) and several are, or are adjacent
to, known RH2 pigment spectral tuning sites (Table 5).
Most prominent among these is site 122; all African
cichlid species in our data set possess a glutamate residue (E122) at this site except for the Lake Tanganyikan
species Ophthalmotilapia ventralis, which instead possesses a glutamine residue (Q122). The E122Q substitution is known to have a large effect on RH2 pigment
spectral sensitivity, shifting λmax to shorter wavelengths
by ~12-16 nm [78-80]. Moreover, this substitution is
known to dramatically affect non-spectral properties of
visual pigments (discussed below). Two other notable
substitutions occurred along this same branch: F213V
and G273V. Mutating site 273 has been shown to affect
retinal uptake [76,81], while substitutions at site 213 are
known to have slight effects on λmax in zebrafish (Danio
rerio) RH2 opsins [80]. Furthermore, substitutions at site
Table 4 Likelihood ratio tests (LRTs) to determine whether foreground ω estimates from the ‘CmC αβMVR & βT’ model
significantly differ from ω = 1
Unconstrained ω estimate
lnL with constraint ω = 1
αβMVR (RH2aα and non-Tanganyikan RH2aβ branches)
βT (Tanganyikan RH2aβ branches)
Foreground partition
Both LRTs have 1 degree of freedom.
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Table 5 Sites identified as divergently evolving by Bayes
empirical Bayes inference under the ‘CmC αβMVR & βT’
Clade model
N-term / TM1
Adjacent to RH2 spectral tuning site
(site 108) [80].
E1 / TM3
Adjacent to RH2 spectral tuning site
(site 108) [80]. Adjacent to cysteine bond
site (site 110) [82].
RH2 spectral tuning site [80]. Adjacent to
opsin counterion (site 113) [83].
Major RH2 spectral tuning site [78]. Also
influences G protein activation efficiency,
active-state decay rate, and visual pigment
regeneration rate [84].
C2 / TM4
Possible phosphorylation site [85,86].
RH2 spectral tuning site [80].
Adjacent to RH2 spectral tuning site
(site 213) [80].
RH2 spectral tuning site [80].
TM6 / E3
Notes on Opsin Structure–Function
RH2 spectral tuning site [80]. Situated on
edge of retinal uptake/release channel [76].
Possible RH2 spectral tuning site [80].
Role in retinal uptake [76,81].
Possible phosphorylation site [85,86].
NOTE— Site numbering follows bovine RH1 opsin. Approximate location
follows Sakmar et al. [34]. Abbreviations: N-term N-terminal tail, TM
Transmembrane helix, E, Extracellular loop, C Cytoplasmic loop, C-term
C-terminal tail.
Only sites with posterior probability (PP) > 0.75 are shown. Underlined sites
are those which substituted along branches within the Lake Tanganyikan
cichlid RH2aβ partition of the phylogeny.
213 and 273 have been experimentally shown to influence the decay rate of the activated visual pigment in
zebrafish RH1 opsins (J.M. Morrow and B.S.W. Chang,
unpublished data). Interestingly, these three sites (122,
213, and 273) all substituted independently along the
branch terminating with the Oryzies latipes (medaka)
Page 11 of 17
RH2C sequence, which suggests that some non-spectral
opsin properties may be evolving convergently.
A total of 178.5 amino acid changes were estimated to
have occurred across the entire tree (given an alignment
of 343 codons and a total tree length of 0.52032 amino
acid substitutions per site, as estimated under the WAG
+F+Γ amino acid substitution model). By reconstructing
the ancestral amino acid residues at each node, we infer
that 86 nonsynonymous changes, at minimum, occurred
at the 27 BEB identified sites (mean ± SD = 3.19 ± 1.11
amino acid changes per BEB site; range = 2–6). For 18
of these 27 BEB sites, substitutions occurred along
branches within the Lake Tanganyikan cichlid RH2aβ
partition. Examining the changes at these sites in the
context of the phylogeny reveals a few interesting patterns (Figure 4, Additional file 1: Table S4). First, BEB
identified sites 273 and 277 both substituted along the
branch terminating in the Ophthalmotilapia ventralis
RH2aβ opsin sequence; these two sites are situated approximately one α helical turn apart in the opsin’s sixth
transmembrane α helix, suggesting coevolutionary
change. Possibly, this pattern reflects compensatory evolution, as one of the substitutions (G273V) introduced a
larger amino acid while the other (M277L) introduced a
smaller one. Similarly, BEB identified sites 158 and 162
are both situated one α helical turn apart in the fourth
transmembrane α helix, and both substituted along the
branch terminating in the Tropheus duboisi RH2aβ opsin
sequence. Again, we see one substitution introduce a larger reside (L158F) and the other introduce a smaller
residue (I162V). These two sites both project outwards
into a proposed opsin dimerization interface [87], and
position 162 has been experimentally shown to contribute to dimerization in the homologous dopamine D2 receptor proteins [88]. Changes at these sites may thus
influence dimerization strength, which, in turn, can influence G protein binding [87]. Interestingly, these
changes also occurred along the RH2aα post-duplication
branch, suggesting not simply coevolution, but convergence as well. Finally, a cluster of BEB identified sites
(sites 107, 109, and 112) found at the boundary between
the opsin’s first extracellular loop and third transmembrane α helix all substituted along the branch leading to
the last common ancestor of the Ophthalmotilapia ventralis and Neolamprologus brichardi RH2aβ opsin
sequences. Site 112 is a known RH2 pigment spectral
tuning site [80], while sites 107 and 109 surround another known RH2 spectral tuning site (site 108) [80].
The substitution at site 109 is particularly interesting, as
the inferred change (F109S) is quite physicochemically
severe, replacing a bulky, hydrophobic phenylalanine
residue with a smaller, hydroxyl-bearing serine residue.
More broadly, it can be seen that more sites known to
affect spectral sensitivity (or that are adjacent to such
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Page 12 of 17
Figure 3 Opsin snake plot (a) and 3D model (b) showing sites inferred to be evolving under divergent selection pressure according to
Bayes empirical Bayes analysis with the CmC αβMVR & βT Clade model. Only sites with PP > 0.75 are shown. (a) The seven transmembrane α
helixes (TM1-TM7), helix 8 (H8), and the extracellular (E1-E3) and cytoplasmic (C1-C3) loops are labeled. Every 10th amino acid (starting with AA
10) is shaded grey and drawn in thicker stroke. (b) The opsin backbone is shown as a gray ribbon, while the retinal chromophore is shown in
black stick format. The 3D model is viewed with the extracellular side on top. For both (a) and (b) the protein structure and numbering follow
bovine rhodopsin.
sites) substitute within the RH2aβ clade compared to the
RH2aα clade. Most of this difference stems from
changes along the branch ancestral to Ophthalmotilapia
ventralis and Neolamprologus brichardi, and along the
branch terminating in Ophthalmotilapia ventralis.
Evolution after gene duplication is often characterized
by some combination of relaxed selective constraint and
the positively selected fixation of advantageous mutations, ultimately resulting in functional divergence
among paralogs [3-5,89]. Consistent with these expectations, our results (1) revealed a dramatic change in selective constraint following the African cichlid RH2a
opsin duplication event and (2) identified the signature
of divergent evolution at several amino acid sites of
known functional importance in RH2 visual pigments.
Clade model analyses revealed that, after duplication, the
selective regime experienced by many alignment sites
changed from weak purifying selection to either neutral
evolution or weak positive selection. Interestingly, this
switch in constraint applied to both duplicated clades
relative to the outgroup orthologs, and this pattern was
only detected once divergence among entire clades was
considered but not when just the branches immediately
following the duplication event were considered.
While the patterns of evolution we observed in the fish
RH2a opsin data set are consistent with a dramatic
change in selective constraint following the African cichlid RH2a opsin duplication event, it is not obvious which
models of gene duplication are operating here, as the
observed patterns do not neatly fit the predictions of
most models. The adaptive and non-adaptive (DykhuizenHartl) neofunctionalization models [3,90] both posit
that one copy accumulates previously deleterious substitutions, potentially leading to the evolution of a new function, while the other retains the ancestral function under
a regime of purifying selection. These neofunctionalization models thus predict asymmetrical rates of evolution
after duplication between paralogs, but our results indicate approximately equal shifts in selective constraint
after duplication in both duplicates (with ω changing
from ω ≈ 0.5 before duplication to ω ≈ 1.1–1.5, afterwards). The segregation avoidance model [3,91] proposes
that duplication may beneficially fix both alleles at loci
harbouring balanced polymorphisms, thus eliminating
costs associated with segregation load. This model
predicts that functional divergence occurs among alleles
before duplication, not long after the duplicate loci have
fixed as we have found. The dosage model operates
when possessing multiple loci provides a beneficial increase in gene product [3,92]; this model seems inappropriate as well, as RH2aβ opsin expression is generally
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
Page 13 of 17
Figure 4 RH2a cladogram showing the inferred location of amino acid substitutions at the 27 ‘divergently evolving’ sites listed in
Table 5. Posterior probabilities of inferred ancestral amino acids are provided in Additional file 1: Table S4. Lines are drawn to indicate the RH2aα
(dotted blue branches), RH2aβ (thick red branches), and Lake Tanganyikan tree RH2aβ partitions (dashed red branches), as in Figure 1.
quite low in both Oreochromis niloticus and Lake Malawi
haplochromine cichlids [42,44], and as the paralogs are
known to be functionally divergent, contrary to model
predictions. The popular duplication-degeneration-complementation model applies to multifunctional proteins,
and describes a scenario by which each daughter protein
neutrally loses one or more of the multiple functions
such that both copies are then needed to perform all
tasks [6]. This model could explain increases in ω seen
in both daughter clades, but this model seems unlikely
to apply to opsins, at least at the protein coding level, as
it is difficult to conceive of a way opsin protein biochemistry could be subfunctionalized. Opsin proteins
have several measurable biochemical phenotypes (including λmax, active state stability, and regeneration rate), but
proper visual pigment functioning requires an integrated
protein for successful phototransduction. Subfunctionalization could occur at the regulatory level, however [93].
The ‘gene sharing’ model (also referred to as the
‘specialization’ or ‘escape from adaptive conflict’ model)
[7,8] seems to be the most appropriate model for our
cichlid opsin data set. If a single-copy protein’s ability to
efficiently serve two roles is compromised due to pleiotropy then, after duplication, each copy can adaptively
specialize on one of the two roles (assuming both roles
are suboptimal in the ancestor). The post-duplication ω
increases we observed for our RH2a data set may indicate weak positive selection in both paralogs, which
would be consistent with this prediction. While this
model is typically applied to multifunctional proteins
that carry out two totally different roles (e.g., a structural
role and an enzymatic role, as in α lens crystallins), it
can also apply to proteins that perform a single biochemical task (e.g., a particular enzymatic reaction) if
there is a benefit to having copies with subtly different
rates or efficiencies [4]. With regard to opsins, this could
mean that a property such as λmax diverged in both copies compared to the ancestor, one to a longer wavelength
and one to a shorter wavelength. Similarly, structural
constraints may prevent co-optimization of different
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
aspects of the protein’s overall function. Such predictions are testable through ancestral reconstruction and
functional characterization of the single-copy ancestor.
Of course, it should be noted that many of these models
of gene duplicate retention and evolution can act in concert or subsequent to one another [94]. Indeed, the patterns of amino acid substitutions inferred along the
RH2aβ post-duplication branch are quite different than
those inferred along the RH2aα post-duplication branch,
with six highly-clustered sites substituting along the
RH2aβ branch (Additional file 1: Table S1). Changes at
these sites could conceivably influence pigment regeneration rate [76], and this may indicate that both neofunctionalization and specialization occurred following the
RH2a duplication event in this system. Furthermore,
here we have only considered the evolution of protein
biochemistry, but these models of duplicate gene evolution can also be considered with regard to gene expression patterns.
The fact that we uncovered among-lineage ω variation
when Clade models were used, but not when Branchsite models were used, opens the possibility that divergence among African cichlid RH2a opsins is also influenced by a collection of lineage-specific processes.
Consistent with this hypothesis, we documented a large
increase in ω along Lake Tanganyikan cichlid RH2aβ
opsin branches that was above and beyond the increase
already described for the RH2aβ clade. The estimated ω
ratio for this tree partition was significantly above one,
clearly indicating the action of positive selection. This
finding is of note given that our initial focus was only on
divergence associated with gene duplication, not divergence among orthologs. Our study thus serves as an example of how the evolution of duplicated genes can be
driven by both paralog-specific and species-specific processes [11]. This point has practical importance as well.
Many studies within the field of visual ecology assume
functional equivalence among ortholgous pigments and
model focal species’ perceptual abilities using data from
close relatives [95]. Our results suggest that such an approach should only be applied tentatively for studies on
cichlid visual ecology.
At this point, it is difficult to say what factors are behind the positive selection operating along the Lake Tanganyikan cichlid RH2aβ opsin lineages, as the visual
niches inhabited by the three Lake Tanganyikan species
included in our data set have surely evolved over the
time-scale captured by our phylogeny. These three species inhabit distinct visual niches, varying in colour patterning, habitat depth, and diet [96], and these
ecological differences could precipitate divergent selection on opsin biochemistry and expression; the detection
of divergent sexually selected courtship signals or food
sources may select for divergence in λmax, while vision
Page 14 of 17
under brighter or dimmer conditions could select for divergence in non-spectral, kinetic properties of visual pigments. Interestingly, some of the sites identified as
positively selected along Lake Tanganyikan RH2aβ opsin
lineages are known to effect both spectral (i.e., λmax) and
non-spectral attributes of visual pigments (Table 5).
Most notably, the E122Q substitution, which occurred
along the terminal branch leading to the Lake Tanganyikan cichlid Ophthalmotilapia ventralis, is known to increase the λmax of RH2 pigments by a large amount
(~12-16 nm) [78-80], and to affect a number of nonspectral, kinetic properties, including the photoactivated
pigment’s decay rate, the efficiency with which the activated pigment activates the downstream G protein, and
the rate of visual pigment reformation following retinal
release [84,97]. For each of these properties, experimentally adding the E122Q substitution to rod pigments
produces mutant pigments that behave in a more conelike manner (i.e., with a faster rate of active state decay
and faster pigment regeneration). Ophthalmotilapia ventralis is known to generally reside at shallower depths
than the other two Lake Tanganyikan cichlids in our
data set (approximate depth range: O. ventralis 2–10 m;
N. brichardi 5–30 m, T. duboisi 3–15 m) [96], where the
visual environment is expected to be somewhat brighter.
We thus see a compatible pattern between opsin molecular evolution and comparative visual ecology in this
system. Overall, our results suggest that the RH2aβ
opsins play an important role in vision in at least some
Lake Tanganyikan species, despite the fact that the
RH2aβ opsin has generally been found to be lowly
expressed in other African cichlids.
It is notable that we were only able to uncover amonglineage ω variation once Clade models were employed, but
not when the more popular Branch-site models were used.
A number of factors are expected to affect the power of
the Branch-site test [98], and it may be that future analyses of larger data sets will yield different results, though
we suspect otherwise, as substitutions occurred at several
amino acids along both of the post-duplication branches.
It appears that functional divergence simply occurred in a
manner undetectable by the Branch-site methods.
Branch-site models assume a very specific form of functional divergence—that is, a punctuated burst of adaptive
sequence turnover—but functional divergence can instead
manifest as variation in the overall strength of constraint
or residue conservation between clades [99]. Most of the
sites that substituted along the RH2aα and RH2aα postduplication branches also substituted along other
branches of the phylogeny, and such substitution patterns
may not fit neatly within the site classes defined by the
Branch-site models. The design of Clade models makes
them better able to detect this alternative signature of
functional divergence. Furthermore, the patterns we
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
uncovered were only detectable because the Clade model
approach was recently expanded to allow for multiple
foreground partitions [21], which allowed us to fit models
that accommodate multiple shifts in the ω ratio across the
phylogeny. To date, only three other studies have
employed this new approach: Yoshida et al. [21] uncovered variation in the strength of positive selection affecting
HIV env genes sampled from different decades; Wei and
Ge [100] documented divergent selective constraint
among duplicated MADS-box transcription factors in
grasses; and, finally, Gossmann and Schmid [77] studied
genome-wide patterns of divergence among duplicated
Arabidopsis thaliana genes. We note that our study is the
first, to our knowledge, to explicitly investigate divergence
among both orthologs and paralogs in the same data set.
Finally, it is noteworthy that several studies have used the
results of Clade model analyses to support arguments of
positive selection [101,102], but whether or not the relevant ω estimates significantly exceed ω = 1 has not been
explicitly tested, as we have done here; this point, while at
first glance technical in nature, is of large importance, as
ω estimates larger than ω = 1 may occur due to chance.
In conclusion, our results are indicative of functional
divergence among African cichlid RH2a opsins driven, at
least in part, by positive selection. Combined with the
insights of past studies, which indicate biochemical and
expression differences among paralogs and among species, our results suggest that there is much to be learned
by distinguishing among African cichlid RH2a opsin
sequences, rather than grouping them together on account of their high sequence similarity. Furthermore,
these results provide a framework for mechanistic studies of functional diversification among cichlid RH2a
opsins, and help establish African cichlid RH2a opsins as
a useful system for research on how function and linkage
shape the evolution of young tandem duplicates [103].
Finally, our study adds to a growing body of research
directed towards uncovering the molecular signature of
diversification within the rapidly speciating African cichlid clade.
Additional file
Additional file 1: Supplemental Material.
Competing interests
The authors declare no competing interests, financial or otherwise, regarding
this manuscript.
Authors’ contributions
CJW conceived of the study, performed the analyses; CJW and BSWC
designed the analyses, and wrote the paper. Both authors read and
approved the final manuscript.
Page 15 of 17
Helen Rodd provided feedback and support throughout all stages of this
project. We thank Bonnie Fraser, Asher Cutter, Karen Carleton, Marla
Sokolowski, and two anonymous reviewers for providing feedback on earlier
versions of this manuscript, David Yu, Frances Hauser, and Shannon Refvik
for proofreading, and the members of the Chang lab for informative
discussions. This work was supported by the National Sciences and
Engineering Research Council of Canada (CJW, BSWC), and a University of
Toronto Vision Science Research Program Fellowship (CJW).
Author details
Current Affiliation: Department of Evolutionary Biology, Max Planck Institute
for Developmental Biology, Spemmanstr. 37, Tuebingen 72076, Germany.
Department of Ecology and Evolutionary Biology, University of Toronto, 25
Harbord Ave, Toronto, Ontario M5S 3G5, Canada. 3Department of Ecology
and Evolutionary Biology, Department of Cell and Systems Biology, and
Centre for the Analysis of Genome Evolution and Function, University of
Toronto, 25 Harbord Ave, Toronto, Ontario M5S 3G5, Canada.
Received: 20 April 2012 Accepted: 9 October 2012
Published: 18 October 2012
1. Hanada K, Kuromori T, Myouga F, Toyoda T, Shinozaki K: Increased
expression and protein divergence in duplicate genes is associated with
morphological diversification. PLoS Genet 2009, 5(12):e1000781.
2. Lynch M, Conery JS: The origins of genome complexity. Science 2003,
3. Ohno S: Evolution by Gene Duplication. New York: Springer-Verlag; 1970.
4. Hahn MW: Distinguishing Among Evolutionary Models for the
Maintenance of Gene Duplicates. J Hered 2009, 100(5):605–617.
5. Conant GC, Wolfe KH: Turning a hobby into a job: How duplicated genes
find new functions. Nat Rev Gen 2008, 9(12):938–950.
6. Force A, Lynch M, Pickett FB, Amores A, Yan YL, Postlethwait J: Preservation
of duplicate genes by complementary, degenerative mutations. Genetics
1999, 151(4):1531–1545.
7. Wistow G: Lens crystallins - gene recruitment and evolutionary
dynamism. Trends Biochem Sci 1993, 18(8):301–306.
8. Hughes AL: The evolution of functionally novel proteins after gene
duplication. Proc R Soc Lond B Biol Sci 1994, 256(1346):119–124.
9. Levasseur A, Orlando L, Bailly X, Milinkovitch MC, Danchin EGJ, Pontarotti P:
Conceptual bases for quantifying the role of the environment on gene
evolution: the participation of positive selection and neutral evolution.
Biol Rev 2007, 82(4):551–572.
10. Hoekstra HE, Coyne JA: The locus of evolution: Evo devo and the genetics
of adaptation. Evolution 2007, 61(5):995–1016.
11. Studer RA, Robinson-Rechavi M: How confident can we be that orthologs
are similar, but paralogs differ? Trends Genet 2009, 25(5):210–216.
12. Kimura M, Ohta T: Some principles governing molecular evolution. Proc
Natl Acad Sci U S A 1974, 71(7):2848–2852.
13. Anisimova M, Liberles DA: The quest for natural selection in the age of
comparative genomics. Heredity 2007, 99(6):567–579.
14. Anisimova M: Parametric models of codon evolution. In Codon Evolution:
Mechanisms and Models. Edited by Cannarozii GM, Schneider A. Oxford:
Oxford University Press; 2012.
15. Yang Z, Nielsen R: Codon-substitution models for detecting molecular
adaptation at individual sites along specific lineages. Mol Biol Evol 2002,
16. Zhang JZ, Nielsen R, Yang ZH: Evaluation of an improved branch-site
likelihood method for detecting positive selection at the molecular level.
Mol Biol Evol 2005, 22(12):2472–2479.
17. Studer RA, Robinson-Rechavi M: Large-scale analysis of orthologs and
paralogs under covarion-like and constant-but-different models of
amino acid evolution. Mol Biol Evol 2010, 27(11):2618–2627.
18. Gu X: A simple statistical method for estimating type-II (cluster-specific)
functional divergence of protein sequences. Mol Biol Evol 2006,
19. Forsberg R, Christiansen FB: A codon-based model of host-specific
selection in parasites, with an application to the influenza A virus. Mol
Biol Evol 2003, 20(8):1252–1259.
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
20. Bielawski JP, Yang ZH: A maximum likelihood method for detecting
functional divergence at individual codon sites, with application to gene
family evolution. J Mol Evol 2004, 59(1):121–132.
21. Yoshida I, Sugiura W, Shibata J, Ren FR, Yang ZH, Tanaka H: Change of
Positive Selection Pressure on HIV-1 Envelope Gene Inferred by Early
and Recent Samples. PLoS One 2011, 6(4):e18630.
22. Chang BSW, Du J, Weadick CJW, Muller J, Bickelmann C, Yu DD, Morrow JM:
The Future of Codon Models in Studies of Molecular Function: Ancestral
Reconstruction, and Clade Models of Functional Divergence. In Codon
Evolution: Mechanisms and Models. Edited by Cannarozii GM, Schneider A.
Oxford: Oxford University Press; 2012.
23. Hodges SA, Derieg NJ: Adaptive radiations: from field to genomic studies.
Proc Natl Acad Sci U S A 2009, 106(Suppl 1):9947–9954.
24. Jeukens J, Bittner D, Knudsen R, Bernatchez L: Candidate genes and
adaptive radiation: insights from transcriptional adaptation to the
limnetic niche among coregonine fishes (Coregonus spp., Salmonidae).
Mol Biol Evol 2009, 26(1):155–166.
25. Kocher TD: Adaptive evolution and explosive speciation: the cichlid fish
model. Nat Rev Gen 2004, 5(4):288–298.
26. Salzburger W: The interaction of sexually and naturally selected traits in
the adaptive radiations of cichlid fishes. Mol Ecol 2009, 18(2):169–185.
27. Roberts RB, Hu Y, Albertson RC, Kocher TD: Craniofacial divergence and
ongoing adaptation via the hedgehog pathway. Proc Natl Acad Sci 2011,
28. Gunter HM, Clabaut C, Salzburger W, Meyer A: Identification and
characterization of gene expression involved in the coloration of Cichlid
fish using microarray and qRT-PCR approaches. J Mol Evol 2011,
29. Salzburger W, Braasch I, Meyer A: Adaptive sequence evolution in a color
gene involved in the formation of the characteristic egg-dummies of
male haplochromine cichlid fishes. BMC Biol 2007, 5:51.
30. Albertson RC, Streelman JT, Kocher TD, Yelick PC: Integration and evolution
of the cichlid mandible: The molecular basis of alternate feeding
strategies. Proc Natl Acad Sci U S A 2005, 102(45):16287–16292.
31. Terai Y, Okada N: Speciation of Cichlid Fishes by Sensory Drive. In From
Genes to Animal Behavior. Edited by Inoue-Murayama M, Kawamura S, Weiss
A. Tokyo: Springer Japan; 2011:311–328.
32. Carleton K: Cichlid fish visual systems: mechanisms of spectral tuning.
Integr Zool 2009, 4(1):75–86.
33. Terakita A: The opsins. Genome Biol 2005, 6(3):213.
34. Sakmar TP, Menon ST, Marin EP, Awad ES: Rhodopsin: insights from recent
structural studies. Annu Rev Biophys Biomol Struct 2002, 31:443–484.
35. Yau KW, Hardie RC: Phototransduction motifs and variations. Cell 2009,
36. Yokoyama S: Evolution of dim-light and color vision pigments. Annu Rev
Genom Hum Genet 2008, 9:259–282.
37. Bowmaker JK: Evolution of vertebrate visual pigments. Vision Res 2008,
38. Seehausen O, Terai Y, Magalhaes IS, Carleton KL, Mrosso HDJ, Miyagi R, van
der Sluijs I, Schneider MV, Maan ME, Tachida H, et al: Speciation through
sensory drive in cichlid fish. Nature 2008, 455(7213):620–U623.
39. Carleton KL, Parry JW, Bowmaker JK, Hunt DM, Seehausen O: Colour vision
and speciation in Lake Victoria cichlids of the genus Pundamilia. Mol Ecol
2005, 14(14):4341–4353.
40. Hofmann CM, Carleton KL: Gene duplication and differential gene
expression play an important role in the diversification of visual
pigments in fish. Integrative and Comparative Biology 2009, 49(6):630–643.
41. Parry JWL, Carleton KL, Spady T, Carboo A, Hunt DM, Bowmaker JK: Mix and
match color vision: Tuning spectral sensitivity by differential opsin gene
expression in Lake Malawi Cichlids. Curr Biol 2005, 15(19):1734–1739.
42. Spady TC, Parry JWL, Robinson PR, Hunt DM, Bowmaker JK, Carleton KL:
Evolution of the cichlid visual palette through ontogenetic
subfunctionalization of the opsin gene arrays. Mol Biol Evol 2006,
43. Weadick CJ, Loew E, Rodd FH, Chang BSW: Visual pigment molecular
evolution in the Trinidadian pike cichlid (Crenicichla frenata): A less
colourful world for Neotropical cichlids? Mol Biol Evol 2012,
44. Smith AR, D'Annunzio L, Smith AE, Sharma A, Hofmann CM, Marshall NJ,
Carleton KL: Intraspecific cone opsin expression variation in the cichlids
of Lake Malawi. Mol Ecol 2011, 20(2):299–310.
Page 16 of 17
45. O'Quin KE, Smith AR, Sharma A, Carleton KL: New evidence for the role of
heterochrony in the repeated evolution of cichlid opsin expression. Evol
Dev 2011, 13(2):193–203.
46. Sabbah S, Laria RL, Gray SM, Hawryshyn CW: Functional diversity in the
color vision of cichlid fishes. BMC Biol 2011, 8:133.
47. Tamura K, Dudley J, Nei M, Kumar S: MEGA4: Molecular evolutionary
genetics analysis (MEGA) software version 4.0. Mol Biol Evol 2007,
48. Thompson JD, Higgins DG, Gibson TJ: Clustal-W - Improving the
Sensitivity of Progressive Multiple Sequence Alignment through
Sequence Weighting, Position-Specific Gap Penalties and Weight Matrix
Choice. Nucleic Acids Res 1994, 22(22):4673–4680.
49. Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inference
under mixed models. Bioinformatics 2003, 19(12):1572–1574.
50. Nylander JAA: MrModeltest 2.2.
51. Rambaut A, Drummond AJ: Tracer 1.5. [].
52. Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O: New
Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies:
Assessing the Performance of PhyML 3.0. Syst Biol 2010, 59(3):307–321.
53. Yang Z: Maximum-likelihood phylogenetic estimation from DNAsequences with variable rates over sites - approximate methods. J Mol
Evol 1994, 39(3):306–314.
54. Spady TC, Seehausen O, Loew ER, Jordan RC, Kocher TD, Carleton KL:
Adaptive molecular evolution in the opsin genes of rapidly speciating
cichlid species. Mol Biol Evol 2005, 22(6):1412–1422.
55. Koblmuller S, Sefc KM, Sturmbauer C: The Lake Tanganyika cichlid species
assemblage: recent advances in molecular phylogenetics. Hydrobiologia
2008, 615:5–20.
56. Anisimova M, Kosiol C: Investigating protein-coding sequence evolution
with probabilistic codon substitution models. Mol Biol Evol 2009,
57. Yang Z, Bielawski JP: Statistical methods for detecting molecular
adaptation. Trends Ecol Evol 2000, 15(12):496–503.
58. Yang Z: Likelihood ratio tests for detecting positive selection and
application to primate lysozyme evolution. Mol Biol Evol 1998,
59. Yang Z: Computational Molecular Evolution. Oxford: Oxford University Press;
60. Huelsenbeck JP, Bollback JP: Application of the Likelihood Function in
Phylogenetic Analysis. In Handbook of Statistical Genetics. Volume 1. 3rd
edition. Edited by Balding DJ, Bishop M, Cannings C. West Sussex: Wiley;
61. Huelsenbeck JP, Rannala B: Phylogenetic methods come of age: testing
hypotheses in an evolutionary context. Science 1997, 276(5310):227–232.
62. Akaike H: New look at statistical-model identification. IEEE Trans Autom
Control 1974, AC19(6):716–723.
63. Bruen TC, Philippe H, Bryant D: A simple and robust statistical test for
detecting the presence of recombination. Genetics 2006, 172(4):2665–2681.
64. Lagercrantz E: eBioX 1.5.1. [].
65. Yang Z, Nielsen R: Synonymous and nonsynonymous rate variation in
nuclear genes of mammals. J Mol Evol 1998, 46(4):409–418.
66. Yang ZH, Wong WSW, Nielsen R: Bayes empirical Bayes inference of
amino acid sites under positive selection. Mol Biol Evol 2005,
67. Weadick CJ, Chang BSW: An improved likelihood ratio test for detecting
site-specific functional divergence among clades of protein-coding
genes. Mol Biol Evol 2012, 29(5):1297–1300.
68. Yang Z: PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol
Evol 2007, 24(8):1586–1591.
69. Okada T, Sugihara M, Bondar AN, Elstner M, Entel P, Buss V: The retinal
conformation and its environment in rhodopsin in light of a new 2.2
angstrom crystal structure. J Mol Biol 2004, 342(2):571–583.
70. Yang Z, Kumar S, Nei M: A new method of inference of ancestral
nucleotide and amino-acid-sequences. Genetics 1995, 141(4):1641–1650.
71. Whelan S, Goldman N: A general empirical model of protein evolution
derived from multiple protein families using a maximum-likelihood
approach. Mol Biol Evol 2001, 18(5):691–699.
72. Casola C, Hahn MW: Gene Conversion Among Paralogs Results in
Moderate False Detection of Positive Selection Using Likelihood
Methods. J Mol Evol 2009, 68(6):679–687.
Weadick and Chang BMC Evolutionary Biology 2012, 12:206
73. Ezawa K, Oota S, Saitou N: Genome-wide search of gene conversions in
duplicated genes of mouse and rat. Mol Biol Evol 2006, 23(5):927–940.
74. O'Quin KE, Smith D, Naseer Z, Schulte J, Engel SD, Loh YHE, Streelman JT,
Boore JL, Carleton KL: Divergence in cis-regulatory sequences
surrounding the opsin gene arrays of African cichlid fishes. BMC Evol Biol
2011, 11:120.
75. Zhao ZM, Hewett-Emmett D, Li WH: Frequent gene conversion between
human red and green opsin genes. J Mol Evol 1998, 46(4):494–496.
76. Hildebrand PW, Scheerer P, Park JH, Choe HW, Piechnick R, Ernst OP,
Hofmann KP, Heck M: A ligand channel through the g protein coupled
receptor opsin. PLoS One 2009, 4(2):e4382.
77. Gossmann TI, Schmid KJ: Selection-driven divergence after gene
duplication in Arabidopsis thaliana. J Mol Evol 2011, 73(3–4):153–165.
78. Yokoyama S, Zhang H, Radlwimmer FB, Blow NS: Adaptive evolution of
color vision of the common coelacanth (Latimeria chalumnae). Proc Natl
Acad Sci U S A 1999, 96(11):6279–6284.
79. Takenaka N, Yokoyama S: Mechanisms of spectral tuning in the RH2
pigments of Tokay gecko and American chameleon. Gene 2007,
80. Chinen A, Matsumoto Y, Kawamura S: Reconstitution of ancestral green
visual pigments of zebrafish and molecular mechanism of their spectral
differentiation. Mol Biol Evol 2005, 22(4):1001–1010.
81. Piechnick R, Ritter E, Hildebrand PW, Ernst OP, Scheerer P, Hofmann KP,
Heck M: Effect of channel mutations on the uptake and release of the
retinal ligand in opsin. Proc Natl Acad Sci U S A 2012, 109(14):5247–5252.
82. Karnik SS, Khorana HG: Assembly of functional rhodopsin requires a
disulfide bond between cysteine residues 110 and 187. J Biol Chem 1990,
83. Zhukovsky EA, Oprian DD: Effect of carboxylic acid side chains on the
absorption maximum of visual pigments. Science 1989, 246(4932):928–930.
84. Imai H, Kojima D, Oura T, Tachibanaki S, Terakita A, Shichida Y: Single
amino acid residue as a functional determinant of rod and cone visual
pigments. Proc Natl Acad Sci U S A 1997, 94(6):2322–2326.
85. Zhang L, Sports CD, Osawa S, Weiss ER: Rhodopsin phosphorylation sites
and their role in arrestin binding. J Biol Chem 1997, 272(23):14762–14768.
86. Shi W, Osawa S, Dickerson CD, Weiss ER: Rhodopsin Mutants Discriminate
Sites Important for the Activation of Rhodopsin Kinase and G(T). J Biol
Chem 1995, 270(5):2112–2119.
87. Fotiadis D, Jastrzebska B, Philippsen A, Muller DJ, Palczewski K, Engel A:
Structure of the rhodopsin dimer: a working model for G-proteincoupled receptors. Curr Opin Struct Biol 2006, 16(2):252–259.
88. Guo W, Shi L, Filizola M, Weinstein H, Javitch JA: Crosstalk in G proteincoupled receptors: changes at the transmembrane homodimer interface
determine activation. Proc Natl Acad Sci U S A 2005, 102(48):17495–17500.
89. Taylor JS, Raes J: Duplication and divergence: the evolution of new genes
and old ideas. Annu Rev Genet 2004, 38:615–643.
90. Kimura M: The Neutral Theory of Molecular Evolution. Cambridge, U.K.:
Cambridge University Press; 1983.
91. Proulx SR, Phillips PC: Allelic divergence precedes and promotes gene
duplication. Evolution 2006, 60(5):881–892.
92. Innan H, Kondrashov F: The evolution of gene duplications: classifying
and distinguishing between models. Nat Rev Gen 2010, 11(2):97–108.
93. Temple SE: Why different regions of the retina have different spectral
sensitivities: a review of mechanisms and functional signicance of
intraretinal variability in spectral sensitivity in vertebrates. Vis Neurosci
2011, doi:10.1017/S0952523811000113.
94. Rastogi S, Liberles DA: Subfunctionalization of duplicated genes as a
transition state to neofunctionalization. BMC Evol Biol 2005, 5:28.
95. Renoult JP, Courtiol A, Kjellberg F: When assumptions on visual system
evolution matter: nestling colouration and parental visual performance
in birds. J Evol Biol 2010, 23(1):220–225.
96. Brichard P: Cichlids and All the Other Fishes of Lake Tanganyika. Neptune City:
TFH Publications; 1989.
97. Imai H, Kefalov V, Sakurai K, Chisaka O, Ueda Y, Onishi A, Morizumi T, Fu YB,
Ichikawa K, Nakatani K, et al: Molecular properties of rhodopsin and rod
function. J Biol Chem 2007, 282(9):6677–6684.
98. Yang Z, dos Reis M: Statistical properties of the branch-site test of
positive selection. Mol Biol Evol 2011, 28(3):1217–1228.
99. Gaucher EA, Gu X, Miyamoto MM, Benner SA: Predicting functional
divergence in protein evolution by site-specific rate shifts. Trends
Biochem Sci 2002, 27(6):315–321.
Page 17 of 17
100. Wei RX, Ge S: Evolutionary history and complementary selective
relaxation of the duplicated PI genes in grasses. J Integr Plant Biol 2011,
101. Liu Y, Cotton JA, Shen B, Han X, Rossiter SJ, Zhang S: Convergent sequence
evolution between echolocating bats and dolphins. Curr Biol 2010,
102. Li Z, Gan X, He S: Distinct evolutionary patterns between two duplicated
color vision genes within cyprinid fishes. J Mol Evol 2009, 69(4):346–359.
103. Hasselmann M, Lechner S, Schulte C, Beye M: Origin of a function by
tandem gene duplication limits the evolutionary capability of its sister
copy. Proc Natl Acad Sci U S A 2010, 107(30):13378–13383.
Cite this article as: Weadick and Chang: Complex patterns of divergence
among green-sensitive (RH2a) African cichlid opsins revealed by Clade
model analyses. BMC Evolutionary Biology 2012 12:206.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Related manuals

Download PDF