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Ainali et al. BMC Evolutionary Biology 2011, 11:142
http://www.biomedcentral.com/1471-2148/11/142

RESEARCH ARTICLE

Open Access

Protein coalitions in a core mammalian
biochemical network linked by rapidly evolving
proteins
Chrysanthi Ainali1, Michelle Simon2, Shiri Freilich3, Octavio Espinosa2,4,5, Lee Hazelwood2, Sophia Tsoka1,
Christos A Ouzounis1,6* and John M Hancock2*

Abstract
Background: Cellular ATP levels are generated by glucose-stimulated mitochondrial metabolism and determine
metabolic responses, such as glucose-stimulated insulin secretion (GSIS) from the b-cells of pancreatic islets. We
describe an analysis of the evolutionary processes affecting the core enzymes involved in glucose-stimulated
insulin secretion in mammals. The proteins involved in this system belong to ancient enzymatic pathways:
glycolysis, the TCA cycle and oxidative phosphorylation.
Results: We identify two sets of proteins, or protein coalitions, in this group of 77 enzymes with distinct
evolutionary patterns. Members of the glycolysis, TCA cycle, metabolite transport, pyruvate and NADH shuttles have
low rates of protein sequence evolution, as inferred from a human-mouse comparison, and relatively high rates of
evolutionary gene duplication. Respiratory chain and glutathione pathway proteins evolve faster, exhibiting lower
rates of gene duplication. A small number of proteins in the system evolve significantly faster than co-pathway
members and may serve as rapidly evolving adapters, linking groups of co-evolving genes.
Conclusions: Our results provide insights into the evolution of the involved proteins. We find evidence for two
coalitions of proteins and the role of co-adaptation in protein evolution is identified and could be used in future
research within a functional context.

Background
Eukaryotic organisms make use of complex biochemical
pathways to maintain homeostatic processes in response
to changes in their environment. These responses can be
broadly divided into gene regulatory responses, whereby
changes in an environmental condition give rise to
changes in gene expression, and metabolic responses,
which result from the responses of metabolic networks to
environmental changes. Many metabolic adaptations to
environmental changes are mediated by genetic regulation.
Pancreatic b-cells are the cells responsible for insulin
secretion in vertebrates and therefore play a key role in
* Correspondence: ouzounis@certh.gr; j.hancock@har.mrc.ac.uk
1
Centre for Bioinformatics, Department of Informatics, School of Natural and
Mathematical Sciences, King’s College London, Strand, London WC2R 2LS,
UK
2
Bioinformatics Group, MRC Harwell, Harwell Science and Innovation
Campus, Oxfordshire OX11 0RD, UK
Full list of author information is available at the end of the article

glucose homeostasis. It has been suggested that glucose
homeostasis evolved to protect brains from hypo- and
hyperglycaemic effects [1]. However, Diptera secrete insulin-like proteins and there is evidence for conservation of
insulin signalling pathways [2] and a role in regulating
glucose and trehalose levels in the haemolymph [3,4], suggesting that the insulin secretion and signalling pathways
pre-date the origin of chordates and their complex, glucose-dependent brain physiology. The evolution of the
pancreas and its b-cells is well studied and has been
reviewed elsewhere [5]. Hagfish and lampreys have a pancreas made up almost entirely of b-cells and the cell type
itself is present in Amphioxus, a Cephalochordate, where it
is associated with the intestinal tissue in a dispersed
manner.
Glucose-stimulated insulin secretion (GSIS) from
the b-cells of pancreatic islets is a metabolic response
that is critically dependent on cellular ATP levels generated by glucose-stimulated mitochondrial metabolism. In

© 2011 Ainali et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.

Ainali et al. BMC Evolutionary Biology 2011, 11:142
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pancreatic b-cells, ATP production is proportional to glucose uptake by the cell, which in turn is proportional to
the glucose concentration in the surrounding body fluids
[6]. A complex network of reactions in both the cytoplasm
and the mitochondrion modulates ATP production. We
recently described a kinetic model of the biochemical processes leading from glucose uptake to ATP production in
this cell type [7]. Many of the components in this biochemical network are ancient enzymes with evolutionary
origins in bacteria and therefore predate the invention of
pancreatic b-cells. Here we consider the evolution of the
components of this system as an example of an element of
core cellular biochemistry that is also used for a cell-typespecific function.
There have been evolutionary studies of certain individual components of the GSIS system. Phylogenetic analysis of metabolic evolution [8,9] suggests that the TCA
cycle may have evolved in at least two steps: an initial
phase, in which the synthesis of oxaloacetate from oxoglutarate occurred but in which the cycle was not
closed, followed by closure of the cycle after the accumulation of atmospheric oxygen. Linkage of glycolysis
to the TCA cycle took place at approximately the same
time as completion of the cycle itself. It has been suggested that many bacterial genomes lack a complete
TCA cycle, which in turn might suggest the use of alternative pathways in different lineages [10]. Glycolysis has
been well studied, most recently in the context of whole
genome duplications [11]. In that context, duplications
were identified at the root of the vertebrate tree for
most of the glycolytic enzymes. The exceptions to this
were triosephosphate isomerase and phosphoglucose
isomerase, which only showed evidence of duplication in
fish [11]. Another study has considered the evolution of
78 nuclearly encoded oxidative phosphorylation genes
and demonstrated that gene duplicates were relatively
rare in these gene families [12]. Despite these studies,
no integrated study of this system has been carried out.
Numerous forces can act on the evolution of metabolic pathways such as the GSIS network, including
pressures affecting evolutionary rate in a systematic
manner, gene duplication, positive and purifying selection, or gene expression [13]. Additionally, genes can be
categorized by phylogenetic age, corresponding to the
evolutionary distance over which homologues can be
detected [14]. Here, we consider the effects of these various influences on the recent evolution of the entire
GSIS system, allowing us to consider differences and
similarities between its component parts. We find evidence for two coalitions of proteins, corresponding to
groups of biochemical pathways, which appear to have
coevolved both in divergence rate and duplication history. We also identify some rapidly evolving proteins
which appear to form the interfaces between the

Page 2 of 13

detected protein coalitions, perhaps acting as evolutionary “adapters”.

Results
The 48 reactions identified in the GSIS model of core pancreatic b-cell biochemistry are catalysed by a minimum of
77 protein components based on current biochemical
knowledge [7]. This biochemical network includes a number of large macromolecular complexes including the
cytochrome c oxidase complex (complex IV; 10 components) and the NADH:ubiquinone oxidoreductase complex (complex I; 9 components). On the other hand, for
some of the reactions in the network we were unable to
identify corresponding proteins. Most of these unidentified
proteins are involved in metabolite transport across the
mitochondrial membrane and are likely to be unidentified
membrane proteins. In total, we were able to identify 69
GSIS protein components whose evolution we could
study.
Antiquity of GSIS core genes

To characterise the evolutionary dynamics of the GSIS
system, we first considered the antiquity of the individual
genes that comprise it. Antiquity is defined as the taxonomic distance over which homologues of the mammalian
genes could be detected searching the COGENT database
[15]. Using this process, we assigned each enzyme gene to
one of four phylogenetic groups: Universal (U), Eukaryotespecific (E), Metazoan-specific (M) and Vertebrate-specific
(V) [16]. Of the 69 enzyme genes assigned to phylogenetic
categories (Figure 1), 77% were classified as Universal, 17%
as Eukaryotic, 3% as Metazoan and 3% as Vertebrate.

Figure 1 Phylogenetic classification of GSIS genes. The
proportions of genes in individual sub-pathways of the GSIS model
are compared with the proportions seen for all human proteins.
Blue = Universal; Red = Eukaryotic; Green = Metazoan; Purple =
Vertebrate.

Ainali et al. BMC Evolutionary Biology 2011, 11:142
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Comparison of the relative proportions of U, E, M and V
genes in the GSIS network to the whole metabolic complement of the human genome (56%, 20%, 5%, 19%
respectively - [17], Table 1) indicates that the GSIS system
contains a higher proportion of U genes and fewer V
genes. This difference is statistically significant, with a Pvalue < 0.002 (chi-squared, df = 3). When the assignment
of proteins to pathways is considered, there appear to be
clear differences for different pathways. Most pathways are
predominantly Universal. However the respiratory chain
contains a mix of genes of different phylogenetic categories and relatively few Universal ones (Figure 1).

Page 3 of 13

The construction of phylogenetic profiles also allowed
the identification of human proteins that share homologues in other genomes, indicating a history of ancient
gene duplication. This analysis identified six protein clusters. The largest of these comprised the four isocitrate
dehydrogenases (IDHa, IDHg, IDHcp, IDHcc), which
shared 31 homologues in other genomes. Other groups
also consisted of functionally related proteins: pyruvate
dehydrogenase complex subunits (PDCc & PDCa; 207
shared homologues), other dehydrogenases (MDH,
LDHa, LDHb; 89 shared homologues), and superoxide
dismutases (SOD2 m & SOD2e; 80 shared homologues).

Table 1 Proteins considered in this analysis
Protein (Abbreviation)

PID (Hs)1

Path-way2

Class3

H-M4

Glucokinase (GK)

P35557

G

U

98.3

6-phosphofructokinase (F6P)

P17858

G

U

97.0

fructose-bisphosphate aldolase (A) (FBa)

P04075

G

U

98.9

fructose-bisphosphate aldolase (B) (FBb)
fructose-bisphosphate aldolase (C) (FBc)

P05062
P09972

G
G

U
U

99.7
98.6

glyceraldehyde 3-phosphate dehydrogenase (GAPD)

P04406

G

U

97.0

bisphosphoglycerate phosphatase (1) (PGP1)

P18669

G

U

99.6

bisphosphoglycerate phosphatase (2) (PGP2)

P15259

G

U

95.2

Pyruvate kinase (PK)

P30613

G

U

95.5

Lactate dehydrogenase A (LDHa)

P00338

G

U

97.6

Lactate dehydrogenase B (LDHb)

P07195

G

U

99.1

Pyruvate Dehydrogenase Complex A (PDCa)
Pyruvate Dehydrogenase Complex B (PDCb)

P08559
P11177

T
T

U
U

99.5
97.5

Pyruvate Dehydrogenase Complex C (PDCc)

P29803

T

U

87.2

Citrate Synthase (CS)

O75390

T, P

U

97.4

Aconitase (ACO)

Q99798

T, P

U

98.8

Isocitrate Dehydrogenase (NAD+) (A) (IDHa)

P50213

T, P

U

98.6

Isocitrate Dehydrogenase (NAD+) (G) (IDHg)

P51553

T, P

U

97.2

Oxoglutarate Dehydrogenase Complex (OGDC)

Q02218

T

U

94.1

Succinyl-CoA synthetase A (SCSa)
Succinyl-CoA synthetase B (SCSb)

P53597
Q96I99

T
T

U
U

97.0
96.1
94.0

Succinate Dehydrogenase A (SDHa)

P31040

T

U

Succinate Dehydrogenase B (SDHb)

P21912

T

U

97.0

Fumarase (FM)

P07954

T, N

U

95.3

Malate Dehydrogenase (mitochondrion & cytosol) (MDH)

P40926

T, N

U

97.6

Alanine Transaminase (AlaTA)

P24298

N

U

92.5

Aspartate Transaminase (AspTA)

P00505

N

U

98.4

Nucleoside Diphosphate Kinase (NDK)
NADH: Ubiquinone oxidoreductase 1 (MT-ND1)

O00746
O15239

T
R

U
E

87.8
92.9

NADH: Ubiquinone oxidoreductase 2 (MT-ND2)

O43678

R

E

93.9

NADH: Ubiquinone oxidoreductase 3 (MT-ND3)

O95167

R

E

88.1

NADH: Ubiquinone oxidoreductase 4 (MT-ND4)

O00483

R

E

92.7

NADH: Ubiquinone oxidoreductase 5 (MT-ND5)

Q16718

R

U

91.3
90.8

NADH: Ubiquinone oxidoreductase 6 (MT-ND6)

P56556

R

U

NADH: Ubiquinone oxidoreductase 7 (MT-ND7)

O95182

R

E

95.5

NADH: Ubiquinone oxidoreductase 8 (MT-ND8)
NADH: Ubiquinone oxidoreductase 9 (MT-ND9)

P51970
Q16795

R
R

U
U

94.7
87.3

Ubiquinol: Cytochrome c Oxidoreductase H (UQCRH)

P08574

R

U

92.3

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Table 1 Proteins considered in this analysis (Continued)
Ubiquinol: Cytochrome c Oxidoreductase I (UQCRI)

P47985

R

U

Ubiquinol: Cytochrome c Oxidoreductase Q (UQCRQ)

O14949

R

E

94.5
85.2

Ubiquinol: Cytochrome c Oxidoreductase R1 (UQCRR1)

O14957

R

M

92.7

Ubiquinol: Cytochrome c Oxidoreductase C2 (UQCRC2)
Cytochrome c Oxidase 6B1 (Cox6B1)

P22695
P14854

R
R

U
E

93.2
96.5

Cytochrome c Oxidase 2* (Cox2)

P00403

R

U

86.8

Cytochrome c Oxidase 8a (Cox8a)

P10176

R

V

85.5
88.0

Cytochrome c Oxidase 5A (Cox5a)

P20674

R

U

Cytochrome c Oxidase 4A (Cox4a)

P13073

R

E

89.9

Cytochrome c Oxidase 6C (Cox6c)

P09669

R

M

86.7

Cytochrome c Oxidase 7B (Cox7b)

P24311

R

V

88.8

Cytochrome c Oxidase 1* (Cox1)
Cytochrome c Oxidase 7R (Cox7r)

P00395
O14548

R
R

U
E

95.5
93.0
88.9

Cytochrome c Oxidase 7C (Cox7c)

P15954

R

E

Oxoglutarate Carrier (OGC)

Q02978

N, P

E

97.8

Citrate Carrier (CIC)

P53007

M

E

96.5

Nicotinamide nucleotide transhydrogenase (NNT)

Q13423

S

U

96.2

Glutathione reductase (GSSGR)

P00390

S

U

89.3

Glutathione peroxidase (GSSGP)

P07203

S

U

90.6

Glycerol-3-phosphate dehydrogenase (FAD dependent) (GUT2P)
Glycerol-3-phosphate dehydrogenase (NAD+) (G3PD)

P43304
P21695

N
N

U
U

97.0
96.8

Malate Dehydrogenase (oxaloacetate-decarboxylating) (NADP+) X (MEx)

P48163

P

U

94.9

Malate Dehydrogenase (oxaloacetate-decarboxylating)(NADP+) N (Men)

Q16798

P

U

96.4

ATP/ADP Carrier 2 (AAC)

P05141

M

U

99.3

cytosolic Isocitrate Dehydrogenase (NADP+) P (IDHcp)

P48735

P

U

82.0

cytosolic Isocitrate Dehydrogenase (NADP+) C (IDHcc)

O75874

P

U

97.8

Pyruvate Carboxylase (PC)

P11498

T, P

U

98.6

ETF:Q oxidoreductase (ETF-QO)
Manganese-dependent superoxide dismutase 2 C (SOD2c)

Q16134
P00441

N
S

U
U

95.8
88.9

Manganese-dependent superoxide dismutase 2 M (SOD2 m)

P04179

S

U

91.9

Manganese-dependent superoxide dismutase 2 E (SOD2e)

P08294

S

U

75.2

1

Uniprot Accession ID for the human protein.
Pathway designation: G = glycolysis, T = TCA cycle, R = respiratory chain, N = NADH shuttle, P = pyruvate cycle, M = metabolite transport, S = glutathione
pathway.
3
Phylogenetic class.
4
Human-mouse sequence similarity (%).
2

Two weaker linkages were also detected between less
obviously related proteins: ETF:Q oxidoreductase (ETFQO) and glutathione reductase (GSSGR) (7 shared
homologues) and pyruvate dehydrogenase subunit B
(PDCb) and cytochrome c oxidase subunit 5A (Cox5A)
(2 shared homologues). Searches of the Pfam database
[18] identified common domain compositions underlying
most of these relationships. IDHs all contain the Iso_dh
domain (PF00180), PDCs (a and c) contain the E1_dh
domain (PF00676), MDH and LDHs share two domains,
Ldh_1_N (PF00056) and Ldh_1_C (PF02866) and the
SODs share the Sod_Cu domain (PF00080). Structural
relationships between the more weakly linked proteins
were less clear. ETF-QO and GSSGR both contain
domains classified as part of the FAD/NAD(P)-binding
Rossmann fold superfamily (CL0063) but no shared

domains were identified between PDCb and Cox5A,
most likely indicating a false positive observation.
Sequence conservation

The preceding analysis indicates that a high proportion
of GSIS proteins have been conserved over long periods
of evolutionary time. We therefore investigated whether
this was reflected in their level of sequence conservation.
We did this by measuring conservation between human
and mouse orthologues, as a proxy for the overall level of
evolutionary conservation. We used this pair of species
because of the quality of their genome sequences and
because their divergence time (c. 90 MYA) is short
enough that they will show relatively little mutational
saturation. Of 77 enzyme genes for which we searched
for human and mouse orthologues, we could identify 69

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Figure 2 Schematic representation of protein conservation in the GSIS biochemical network. Proteins are represented as circles with
shading representing their level of sequence similarity between human and mouse (see Table 1). Darkly shaded proteins are most similar and
lightly shaded proteins least similar. Five shades of grey are used to represent the five quintiles of the sequence similarity distribution. Proteins
are separated between cytoplasm, mitochondrial membrane and mitochondrial matrix, grouped together into sub-pathways and co-localised to
give an approximate representation of their functional relationships.

orthologue pairs in the two species. As mentioned, the
missing genes primarily reflect absence of knowledge of
the identity of some membrane transporters in the system (see also Materials & Methods).
We determined sequence similarity between these
orthologs at the protein level, as this is the most relevant
measure for identifying functional conservation. Individual
values for protein sequence similarity for each enzyme are

presented in Table 1 and summarised graphically in Figure
2. Details of the GSIS biochemical network are given in [7]
and the Additional File 1. The mean human-mouse conservation across these proteins was 93.7% (standard deviation 4.9%), which is considerably greater than the widely
accepted average of 85% [19], but there was significant
variability - the lowest conservation observed was 75.2%
for Manganese-dependent superoxide dismutase 2E, while

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Page 6 of 13

chain proteins than for members of the glycolysis and
TCA cycle groups. The glutathione pathway proteins also
showed significantly lower conservation than the pyruvate
and NADH shuttle proteins. Taken together, these results
suggest that there are two clusters of conservation level in
the data set - on the one hand glycolysis, metabolite transport, TCA cycle and pyruvate/NADH shuttles and on the
other hand the respiratory chain and the glutathione pathway. This distinction is illustrated in the Tukey’s boxplot
(Figure 3), which shows the glutathione and respiratory
chain pathways with lower and more variable conservation
values than the other sub-pathways. The distinction is also
evident in Figure 2.
Gene Duplication events and positive selection

Figure 3 Effect of sub-pathway membership on sequence
divergence of GSIS proteins. Tukey’s boxplot of the results of
ANOVA analysis on the effect of sub-pathway membership on
sequence divergence of GSIS proteins. G = glycolysis, M =
metabolite transport, R = respiratory chain, S = glutathione pathway,
TPN = TCA cycle+pyruvate cycle+NADH shuttle.

five proteins showed conservation of > 99%. Careful examination of the alignments of the less conserved proteins
confirmed that the low levels of conservation observed in
these proteins were not alignment artifacts. Three of the
five very highly conserved proteins, bisphosphoglycerate
phosphatase (1), fructose-bisphosphate aldolase (A) and
lactate dehydrogenase B, are components of the cytoplasmic glycolysis pathway, which computational analysis suggests plays a critical role in controlling the activity of the
GSIS network [7]. The other two, ATP/ADP carrier 2 and
Pyruvate Dehydrogenase Complex A, are proteins of the
metabolite transport and TCA cycle pathways, which take
place in mitochondria. The two mitochondrially encoded
oxidative phosphorylation genes analysed here (Cox 1 &
Cox 2) did not show lower conservation than the other
genes considered (mean 91.15%), consistent with previous
observations [20].
To further characterise the distribution of conservation
across the identifiable sub-pathways and phylogenetic
categories in the model we carried out an analysis of variance (ANOVA). Of five treatment variables (pathway
membership, phylogenetic classification, the number of
gene duplications observed in the Ensembl gene trees, the
presence/absence of positive selection in these trees, and
known association or otherwise with human disease), only
sub-pathway membership had a significant effect on divergence (F = 7.82; df = 5; P < 0.001). A Tukey HSD test
showed significantly lower sequence conservation
(adjusted P < 0.05) for glutathione pathway and respiratory

The cluster diagram in Figure 4 represents the similarities
and differences in the duplication histories between the
GSIS genes. Details of the individual duplication vectors
are presented in Figure 5. Figure 4 shows five clusters of
three or more gene histories (labelled A-E in the Figure).
The largest of these (A in Figure 4) is dominated by
respiratory chain genes, including the mitochondrially
encoded Cox1 and Cox2, and represents a history without
detectable duplication. The other groups are less functionally homogeneous. To investigate whether the hierarchy
shows association of proteins within subpathways of the
model or within phylogenetic groups, these subsets were
compared with all remaining pairs to identify whether any
showed a significant difference from expected values. Significant clustering was observed for respiratory chain proteins (P << 0.001; Mann-Whitney). Weaker clustering was
observed for pyruvate/NADH shuttle and TCA cycle
genes (P < 0.05) but these were not significant after
Bonferroni correction. A significant anticlustering was
observed for glycolysis genes compared to average gene
pairs. This in part reflected a high amount of duplication
observed for GAPD (glyceraldehyde 3-phosphate dehydrogenase), but was detectable even if GAPD was excluded
from the analysis. No significant clustering was observed
within phylogenetic categories but significant anticlustering was observed for the Universal class. This reflected the
presence of GAPD in this class as when it was removed no
significant anticlustering was observed.
After duplication, genes appear to continue to undergo
purifying selection with their evolutionary rates and
genomic persistence depending on their level of essentiality [21]. However, there are examples of positive selection
acting on duplicated genes [22], presumably reflecting
adaptive changes. We therefore investigated whether
there was evidence of positive selection affecting GSIS
genes. We identified seventeen possible episodes of positive selection, summarized in Table 2 and related to gene
duplication history in Figure 5. Most of the inferred positive selection took place within respiratory chain genes

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Figure 4 Similarity of gene duplication histories in the GSIS system. The cluster diagram is constructed from Euclidian distances between
vectors of gene duplication history as described in the Materials and Methods. Gene names are coloured according to their phylogenetic
classification (red = Metazoan, orange = Universal, brown = Eukaryotic, green = Vertebrate). Coloured dots represent pathway membership as
shown in the key. Red stars indicate genes inferred to have undergone at least one episode of positive selection, according to the Selectome
database (see Table 2 for more detail).

and none within the glycolysis, TCA cycle and glutathione pathways. One respiratory chain gene, Cox4a,
showed five inferred episodes of positive selection and
also underwent two inferred gene duplication events
which coincided approximately with one of these periods
of positive selection, being placed at the root of the Euteleostomi c. 420 MYA. A second respiratory chain Cox
gene, Cox7R, also showed coincident duplication and
positive selection, in this case at the root of the Catarrhini 31 MYA (Table 2). To test the relationship between
purifying selection and gene duplication we carried out
rank correlation analysis of divergence rate versus number of duplications. This showed a significant positive
correlation (r = 0.339; P = 0.004; 2-tailed) indicating that
in this system as elsewhere genes that are more conserved tend to have undergone more duplication [23].

Disease Association

24 of the genes analysed here are associated with Mendelian diseases in the OMIM database (Table 3). We
found no relationship between pathway membership,
phylogenetic category and disease involvement in the
ANOVA analysis. We did however identify a deficit of
genes that had undergone positive selection in the disease class (P = 0.01, Chi squared, 1 DOF).

Discussion
This study reveals a highly significant relationship
between pathway membership and human-mouse divergence in the GSIS system. The analysis indicated two
cross-pathway clusters of divergence - a higher conservation cluster spanning glycolysis, the TCA cycle plus
metabolite transport and the components of the NADH

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Table 2 Positive selection within GSIS
Protein

Sub-Pathway

Inferred node (approximate date)

MT-ND4

R

Percomorpha (190 MYA)

MT-ND6

R

Percomorpha

MT-ND8

R

UQCRC2

R

Catarrhini (31MYA)
Eutheria (102 MYA)
Tetrapoda (359 MYA)
Theria (166 MYA)
Tetrapoda
Cox8A

R

Clupeocephala (320 MYA)
Eutheria

Cox4A

R

Theria
Murinae (37 MYA)
Clupeocephala*
Euteleostomi (420 MYA)†

Cox7R

R

Catarrhini†

CIC

M

Eutheria

ETF-QO

N

Percomorpha

* Two episodes inferred; † Coincides with a duplication

Figure 5 Matrix of inferred gene duplications in the 69 GSIS
genes. Events are taken from the Ensembl gene tree displays and
binned according to the taxonomic and date information given
there. Bins with one or more inferred duplication are shaded, and
numbers of inferred duplications at a given taxonomic level are
given. These values were included in the calculation of the cluster
diagram in Figure 4.

shuttle and pyruvate cycle, and a lower conservation
cluster comprising respiratory chain proteins and components of the glutathione pathway. All the genes considered, with the exceptions of the mitochondrially
encoded Cox1 and Cox2 in Complex IV of the respiratory chain, are encoded by the nuclear genome, so a
genome effect did not play a role in this result. It should
be noted that, as we have considered protein similarity
in these analyses, they cannot be used to directly infer
differences of evolutionary rate at the DNA level as they

also include the influence of selection on protein
sequences.
The results are consistent with those of Vinogradov
[13], who showed that the strongest predictor of the
human-mouse divergence of a given protein was the evolutionary rate of coexpressed genes or members of the
same pathway. In the context of the metabolic processes
studied here, the two coevolving sets of proteins (or coalitions [13]) we have observed correspond to the core
biochemistry of the system and the membrane-based
ATP synthesis machinery, respectively. Previous work
comparing 12 Drosophila genomes has also suggested
that core metabolic enzymes tend to evolve slowly [24].
The similar rates of evolution in the glutathione pathway
and the ATP synthesis machinery may reflect coevolution
of these pathways. This would be explicable as the role of
the glutathione pathway is to detoxify peroxide radicals
produced by oxidative phosphorylation.
We also observed a second, weaker coevolutionary
pattern than that of gene divergence within pathways.
This was of evolutionary duplication within at least
some of the pathways. Gene duplication is a classic
mechanism of gene diversification which can be followed by functional diversification processes such as
subfunctionalisation, neofunctionalisation and microfunctionalisation [25-27]. Thus, common patterns of
preserved duplication during evolution may indicate
responses to common evolutionary pressures. The cluster diagram (Figure 4) shows five clusters of size n > 3
with identical duplication histories. The largest of these,
with 15 members (labelled A in Figure 4), predominantly contains respiratory chain proteins from Complexes I and IV and includes Cox1 and Cox2. The other

Ainali et al. BMC Evolutionary Biology 2011, 11:142
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Table 3 GSIS genes with disease associations
Gene

OMIM
Code

Disease

GCK

125851

MODY, type II,

125853
602485

Insulin resistance, susceptibility to,
Hyperinsulinemic hypoglycemia, familial, 3

606176

Diabetes, permanent neonatal

FBa

229600

Fructose intolerance

FBc

611881

Glycogen storage disease XII

PGP2

261670

Glycogen storage disease X

PK

102900

Adenosine triphosphate, elevated, of erythrocytes

266200

Pyruvate kinase deficiency

308930
312170

Leigh syndrome, X-linked
Pyruvate dehydrogenase deficiency

PDCa
PDCb

312170

Pyruvate dehydrogenase deficiency

OGDC

203740

Alpha-ketoglutarate dehydrogenase deficiency

SCSa

245400

Lactic acidosis, fatal infantile

SDHa

115310

Paraganglioma, familial chromaffin, 4

171300

Pheochromocytoma, modifier of

606864

Paraganglioma and gastric stromal sarcoma

612359
252011

Cowden-like syndrome
Mitochondrial respiratory chain complex II
deficiency
Leigh syndrome, due to Cox deficiency

SDHb

256000
FM

150800

Multiple cutaneous and uterine leiomyomata

605839

Leiomyomatosis and renal cell cancer

606812

Fumarase deficiency

MTND1

252010

Mitochondrial complex I deficiency

MTND2

256000

Leigh syndrome, due to Cox deficiency

UQCRQ

124000

Mitochondrial complex III deficiency

Cox6B1

220110

Mitochondrial complex IV deficiency

Cox2

220110

Mitochondrial complex IV deficiency

Cox1
GUT2P

220110
125853

Mitochondrial complex IV deficiency
Insulin resistance, susceptibility to

IDHcc

137800

Glioblastoma, susceptibility to

PC

266150

Pyruvate carboxylase deficiency

ETF-QO

231680

Glutaricaciduria, type IIC

SOD2c

105400

Amyotrophic lateral sclerosis, susceptibility to

SOD2
m

612634

Microvascular complications of diabetes,
susceptibility to

two large groups B and C (with seven and six members
each, respectively) show less homogeneous compositions. Group A contains genes which show no identifiable gene duplications as far back as the root of their
respective gene family tree. This coincides with the
observation that oxidative phosphorylation genes tend
to show an under-representation of gene duplication
[12]. The second largest group, with 7 members,
(labelled B in Figure 4) corresponds to genes which
underwent a duplication at or around the origin of the
Euteleostomi but have not undergone a duplication

Page 9 of 13

since. Finally, the 6-member group (labelled C in Figure
4) represents another group showing little duplication,
this time since around the origin of the Bilateria (c. 580
MYA). 40% of the GSIS proteins therefore show one of
three common patterns of evolutionary duplication history involving either no detectable duplication or a single duplication deep in the evolutionary past. Beyond
the presence of numerous respiratory chain proteins in
the large cluster, visual inspection does not reveal significant clustering of pathway members or phylogenetic
class members in this cluster diagram. Statistical analysis
supported this conclusion but did however show significant anticlustering of glycolysis proteins, i.e. glycolysis
proteins show a more diverse pattern of clustering history than average and this effect can be observed even if
glyceraldehyde-3-phosphate dehydrogenase (GAPD) is
excluded.
GAPD showed very high frequencies of gene duplication throughout its recent evolutionary history, leading
to it being an outlier in Figure 4. Indeed, GAPD has
been identified with an exceedingly high number of processed pseudogenes in vertebrates and possibly it is this
which is reflected in its inferred duplication history [28];
certainly Ensembl identified 75 duplicates of GAPD in
rat and 16 in mouse, consistent with the over 300 pseudogenes identified in these species [28]. The reasons for
this high level of retrotransposition are not known, but
may reflect the wider non-glycolytic role of GAPD or
some as yet unidentified sequence feature of the GAPD
cDNA sequence [28]. It has been suggested that nonpseudogene paralogs have been retained because of a
role for GAPD in the bundling of microtubules and
other cellular functions as well as functional diversification into testis- and muscle-specific forms [11].
Gene duplication is usually considered to be followed
by a period of relaxed selection followed by the continuation of a regime of purifying selection [29]. The co-occurrence of gene duplication with episodes of positive
selection would therefore be of interest in interpreting
the functional evolution of a gene. We observed two such
apparent co-occurrences: in Cox4A and Cox7R - both
components of Complex IV of the respiratory chain.
Cox4A is a transmembrane protein which binds ATP
and mediates respiratory control by inhibiting cytochrome c oxidase activity at high intramitochondrial
ATP concentrations [30]. Cox4A showed both duplication and positive selection at the base of the Euteleostomi
c. 420 MYA but also additional positive selection, in the
absence of gene duplication, on three separate occasions:
in the fish lineage at the base of the Clupeocephala c. 320
MYA and in the land vertebrates at the origin of Theria
(c. 166 MYA) and murinae (c. 37 MYA). This rich history
of gene duplication suggests an important adaptive role
for this gene. Cox7R, about which less is known but is

Ainali et al. BMC Evolutionary Biology 2011, 11:142
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also likely to be a regulatory subunit [30], underwent
three duplications including one near to the origin of the
Catarrhini (c. 31 MYA) which was also accompanied by
an episode of positive selection.
These observations present us with two opposing views
of the evolution of GSIS proteins: high conservation of
core biochemical pathways with higher divergence of the
respiratory chain, but also restricted duplication of
respiratory chain genes compared to core biochemistry
proteins, especially glycolytic enzymes which show the
highest conservation. A correlation analysis showed a significant negative correlation between evolutionary divergence and numbers of gene duplications, although this
only accounted for 11% of variance.
It has been suggested that “older” genes tend to evolve
more slowly than “younger” ones [31] and there is evidence that newly-born duplicates evolve more rapidly
than unduplicated genes [29,32]. Vinogradov [13] also
identified gene age as an important correlate of divergence rate. In this analysis, we use phylogenetic category
as a measure of gene age. Taken as a whole, the GSIS
network is enriched in Universal genes that is, genes with
detectable homologues in bacteria, compared to the
whole metabolic complement of the human genome.
This is consistent with the ancient nature of the biochemical pathways making up the system and with the
slow evolution of the genes they contain. The glycolysis
pathway, which is most conserved at the sequence level
in human-mouse comparisons is, along with the glutathione pathway, also the richest in Universal genes. The
respiratory chain, which is the most rapidly evolving set,
has a much higher content of “younger” genes. Therefore,
a close relationship appears to exist between gene age
and evolutionary rate, with older genes evolving more
slowly, as suggested elsewhere [31]. However, as ancient
but rapidly evolving genes may be difficult or impossible
to detect by BLAST-based methods [33], the phylogenetic categorization of genes used here may be strongly
influenced by the rates of evolution of individual genes,
which would explain the association of Universal genes
with low human-mouse divergence. An additional possible confounding effect is inhomogeneity in evolutionary
rate within proteins. Proteins which contain highly conserved cores interspersed with or flanked by highly variable sequences might show lower average conservation
than proteins accepting substitutions evenly throughout
their length but remain detectable by BLAST because of
their conserved core. This should not affect alignmentbased measures of sequence similarity where the distribution of conserved and non-conserved sites is immaterial
but could affect these BLAST-based methods and should
be investigated further.
Unlike Vinogradov [13], we did not find any significant
relationship in ANOVA between phylogenetic category

Page 10 of 13

and either human-mouse divergence (P = 0.46) or number of gene duplications (P = 0.83) after accounting for
pathway effects. This could reflect the smaller number of
categories used in this analysis compared to the 12 used
by Vinogradov [13] or the smaller number of pathways
studied. Also, Vinogradov’s categorization is based on
Clusters of Orthologous Groups (COGs; [34]), which,
although they are BLAST-based, make use of protein
rather than DNA BLASTs. We conclude that we cannot
exclude influences of gene age on evolutionary rate
although it does not provide a significant signal in this
analysis.
It has been suggested that disease genes tend to be
“ancient” [35]. Of the 24 disease genes in the GSIS system 20 are Universal, but the proportion of Universal
genes is not disproportionately high compared to the
entire set of GSIS proteins (P = 0.68, Chi-square). Similarly, although disease genes in the GSIS system are on
average more conserved than other genes (95.1% humanmouse similarity vs 93.1%), this difference is not statistically significant (P = 0.07; t-test). Thus, while we cannot
exclude the hypothesis that disease genes tend to be old,
we cannot find strong support for it in our data set. One
property that was observed in this set of disease genes
was a deficit of genes with a history of positive selection.
This may be consistent with the suggestion that disease
genes tend to be conserved as, by implication, they
should also not have undergone accelerated evolution.
Although the main feature of Figure 3 is the pathwaydependent divergence of the GSIS proteins, it also identifies four clear outliers from three of the pathways: NDK
and PDCc from the TCA cycle, IDHcp from the pyruvate
cycle and SOD2e from the glutathione pathway. Three
and perhaps all of these lie at the points of interaction
between subpathways of the GSIS network (see Additional File 1 and Figure 2). NDK links to the output of
the GSIS system, ATP production. It is activated by
increasing levels of ADP, which in turn up-regulates the
TCA cycle. PDCc is found at a clear intersection between
glycolysis and the TCA cycle and is involved in the production of Acetyl-CoA, regulated by NAD + and CoA.
The IDHcp intersection is less clear, but is one step in
linking the TCA cycle to glycolysis, and appears to be
involved in regulating the pyruvate-malate equilibrium in
the cytosol. SOD2e is at a key step in the detoxification
of reactive oxygen species generated by the respiratory
chain and creates the intersection with the glutathione
pathway by converting O2- to H2O2.
Taken together, the picture we obtain from these analyses is that the GSIS system consists of two evolutionary
coalitions, each evolving in a concerted manner. The
soluble enzymes carrying out core metabolic processes,
particularly glycolysis and the TCA cycle, evolve slowly
at the sequence level but undergo a relatively high rate of

Ainali et al. BMC Evolutionary Biology 2011, 11:142
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gene duplication compared to the respiratory chain and
glutathione pathway proteins, which evolve more rapidly
but undergo relatively little gene duplication. The glycolysis pathway, although highly conserved at the sequence
level, displays a wider range of duplication histories than
other pathways. Steinke et al [11] analysed the patterns
of gene duplication within the glycolysis pathway in some
detail and concluded that some of the pattern of duplication seen in this pathway could be explained by diversification of tissues and the evolution of tissue-specific
patterns of expression following on from two suggested
gene duplication events (three in fish). However tissuespecific expression could not explain the complex pattern
of gene duplication and retention/loss in its entirety. The
high frequency of fixed duplications in the glycolysis
pathway may reflect the fact that it is the only cytoplasmic pathway in the system. It may therefore need to
adapt to a wider range of changes in other, interlinked
pathways than, for example, the TCA cycle, which may
be relatively protected from such pressures. In particular,
glycolysis acts as the source of ATP in anaerobic respiration and so may adapt to the external availability of oxygen. It also appears to be used to drive DNA replication
and cell division as it is less damaging to DNA as it does
not produce reactive oxygen species [36].
Although our sample of positive selection may be
biased, most positive selection episodes appear to have
taken place in respiratory chain genes. The reason for the
more rapid evolution of the respiratory chain genes is
unknown, but it is worth noting that most of the proteins
here are nuclearly encoded proteins involved in the regulation of the function of the respiratory chain [30]. It may
therefore be that their rapid evolution reflects the
requirement for fast adaptation of regulation of the
respiratory chain in response to organismal pressures or
to variations in the availability of oxygen. Some proteins
in the network show particularly high rates of evolution
and these tend to lie at the interfaces between recognizable pathways. These proteins may act as “adapters”,
facilitating the interactions between different pathways
which may themselves evolve as units without coevolution between pathways. Adapters may then need to
evolve rapidly to accommodate the divergence between
the linked pathways.
Most of the functions of the component pathways of
the GSIS network seem likely to have been conserved
during evolution. Despite this the system has accommodated changes through sequence evolution and gene
duplication. While it will require more extensive functional analysis to characterise the pressures driving the
evolution of this system, it appears that these pressures
have been accommodated in different ways by different
parts of the system, as detailed above.

Page 11 of 13

Conclusions
The GSIS system consists of two evolutionary coalitions,
each evolving in a concerted manner. The soluble
enzymes carrying out core metabolic processes evolve
slowly at the sequence level but undergo a relatively
high rate of gene duplication compared to the respiratory chain and glutathione pathway proteins, which
evolve more rapidly but undergo relatively little gene
duplication. A small number of rapidly evolving proteins
may represent evolutionary adapters, which mediate the
different evolutionary trajectories of the different subpathways.
The comparative analysis of the GSIS network provides key insights into the evolution of the involved proteins that might be used in future research within a
functional context, by simulating the function of the
entire network in health and disease [7]. Elements of the
system undergoing positive selection, rapid evolution
more broadly, or gene duplication should be investigated
for functional differences between humans and model
organisms.
Methods
Data Collection

The analysis is based on the components of a mathematical model of the core biochemistry of mouse glucose-stimulated insulin secretion published previously [7],
consisting of 77 enzymes. Of the 55 protein identifiers
(IDs) listed in that model, 50 were included and their
human orthologues retrieved. The five remaining mouse
proteins not considered here are: pyruvate carrier
(Q4A5HQ1), isocitrate dehydrogenase NAD+ beta
(O43837), phosphate carrier (Q00325), aspartate/glutamate carrier (Q59GB0) and ATP/ADP carrier (P12236).
This set was then augmented by an additional 19 human
proteins, NADH:Ubiquinone oxidoreductase (9 components), ubiquinol:cytochrome c oxidoreductase (4 components), manganese-dependent superoxide dismutase 2
(3 components), nicotinamide nucleotide transhydrogenase (Q13423), glutathione peroxidase (P07203) and ATP/
ADP Carrier 2 (P05141). The resulting 69 mouse-human
orthologue pairs corresponding to components of the system were unambiguously identified using the Ensembl
database (V52) [37]. As many of these are members of
gene families, they were curated manually to ensure that
the correct gene was being analyzed. Orthologous pairs of
sequences were extracted from Swiss-Prot [38] based on
unique identifiers (IDs) obtained from Ensembl. The set of
69 proteins analysed is summarised in Table 1. Sequence
pairs from human and mouse were aligned using EMBLEBI Align using the Needleman-Wunsch global alignment
algorithm [39] and the BLOSUM62 transition matrix [40].

Ainali et al. BMC Evolutionary Biology 2011, 11:142
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Pairwise sequence similarity values were then determined
and analysed.

Page 12 of 13

Additional material
Additional file 1: Overview of the GSIS network. Reactions are
represented as arrows (which may be uni- or bidirectional) and labelled
in green v1 to v53 according to [7] which also provides a key to
abbreviations. Metabolites are labelled in black. Enzymes catalysing
reactions are labelled in brown.

Phylogenetic Assignment

Phylogenetic profiles [14] of human protein sequences
involved in GSIS were estimated using BLAST searches
[41] against 243 fully sequenced genomes from the
COGENT database [42,43], including 197 Bacteria, 22
Archaea and 24 Eukaryota species, using an E-value cutoff of 10 (12,083 hits, of which 11,925 hits, or 98.7% of
the total, had an E-value < 1). The classification process
was non-hierarchical: proteins with hits to more than five
prokaryote species are classified as universal [U]; the
remaining proteins with at least a single hit to nonmetazoan eukaryotes are classified as eukaryote-specific
[E]; proteins with at least a single hit to non-mammalian
metazoa are classified as metazoan-specific [M]; and,
finally, proteins recognizing only other vertebrate proteins are classified as vertebrate-specific [V]. All proteins
were classified into one of the above taxonomy classes
using the last update of this analysis in COGENT. Profiles were also used to identify proteins with common
ancestors in other genomes. These protein clusters were
further analysed with reference to the Pfam database [44].
Duplication History and Divergence Time Estimates

Gene trees obtained from Ensembl were screened
visually for duplication events and used to construct
duplication vectors for each gene. These vectors consisted of the number of duplications falling at nodes
labelled with particular times and taxon names in
Ensembl (e.g. Clupeocephala, 320 MYA). Vectors were
only extended to the point at which a duplication created a new gene family, to avoid confusion with selective
forces acting on proteins with different functions, and
non-vertebrate duplications were excluded. Vectors were
subjected to hierarchical clustering using the R dist and
hclust functions and Euclidian distances [45]. Euclidian
distances between subpathway members and between
proteins classified into particular phylogenetic categories
were compared with distances for all other pairwise
comparisons to identify groups differing significantly
from average in their degree of clustering, using the
Mann-Whitney test.
Selection Forces and Disease Association

Lineages showing evidence of positive selection were
identified from the Selectome database [46] and confirmed using the Codeml program in the Paml (Phylogenetic Analysis by Maximum Likelihood) package [47].
Disease associations of GSIS genes were determined by
reference to the OMIM (Online Mendelian Inheritance
in Man) database [48]. Statistical analysis was carried
out using the R environment [45].

Acknowledgements
The authors thank EU-funded Enfin Network of Excellence (contract no
LSHG-CT-2005-518254) and the UK Medical Research Council for financial
support.
Author details
Centre for Bioinformatics, Department of Informatics, School of Natural and
Mathematical Sciences, King’s College London, Strand, London WC2R 2LS,
UK. 2Bioinformatics Group, MRC Harwell, Harwell Science and Innovation
Campus, Oxfordshire OX11 0RD, UK. 3The Blavatnik School of Computer
Sciences & Sackler School of Medicine, Tel Aviv University, Ramat Aviv 69978,
Israel. 4Department of Biochemistry, University of Oxford, UK, South Parks
Road, Oxford, OX1 3QU, UK. 5Institute of Cancer Research, Chester Beattie
Laboratories, London SW3 6JB, UK. 6Computational Genomics Unit, Institute
of Agrobiotechnology, Centre for Research & Technology Hellas (CERTH), GR57001 Thessalonica, Greece.
1

Authors’ contributions
CA and JMH designed the research and outlined the manuscript. CA, MS, SF,
OE and LH performed the computational analysis. CA, MS, LH and JMH
interpreted the data. ST and CAO provided feedback on the results and
contributed in drafting the manuscript. CA and JMH wrote the paper. All
authors read and approved the final manuscript.
Received: 16 December 2010 Accepted: 25 May 2011
Published: 25 May 2011
References
1. Rashidi A, Kirkwood TBL, Shanley DP: Metabolic evolution suggests an
explanation for the weakness of antioxidant defences in beta-cells. Mech
Ageing Dev 2009, 130:216-221.
2. Garofalo RS: Genetic analysis of insulin signaling in Drosophila. Trends
Endocrinol Metabolism 2002, 13:156-162.
3. Rulifson EJ, Kim SK, Nusse R: Ablation of insulin-producing neurons in
flies: growth and diabetic phenotypes. Science 2002, 296:1118-1120.
4. Wu Q, Brown MR: Signaling and function of insulin-like peptides in
insects. Annu Rev Entomol 2006, 51:1-24.
5. Pieler T, Chen Y: Forgotten and novel aspects in pancreas development.
Biol Cell 2006, 98:79-88.
6. Ashcroft F, Rorsman P: Type 2 diabetes mellitus: not quite exciting
enough? Hum Mol Genet 2004, 13:R21-R31.
7. Jiang N, Cox RD, Hancock JM: A kinetic core model of the glucosestimulated insulin secretion network of pancreatic beta cells. Mamm
Genome 2007, 18:508-520.
8. Cunchillos C, Lecointre G: Integrating the universal metabolism into a
phylogenetic analysis. Mol Biol Evol 2005, 22:1-11.
9. Cunchillos C, Lecointre G: Ordering events of biochemical evolution.
Biochimie 2007, 89:555-573.
10. Huynen MA, Dandekar T, Bork P: Variation and evolution of the citric-acid
cycle: a genomic perspective. Trends Microbiol 1999, 7:281-291.
11. Steinke D, Hoegg S, Brinkmann H, Meyer A: Three rounds (1R/2R/3R) of
genome duplications and the evolution of the glycolytic pathway in
vertebrates. BMC Biology 2006, 4:16.
12. De Grassi A, Caggese C, D’Elia D, Lanave C, Pesole G, Saccone C: Evolution of
nuclearly encoded mitochondrial genes in Metazoa. Gene 2005, 354:181-188.
13. Vinogradov AE: Systemic factors dominate mammal protein evolution.
Proc R Soc B 2010, 277:1403-1408.
14. Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO:
Assigning protein functions by comparative genome analysis: protein
phylogenetic profiles. Proc Natl Acad Sci USA 1999, 96:4285-4288.

Ainali et al. BMC Evolutionary Biology 2011, 11:142
http://www.biomedcentral.com/1471-2148/11/142

15. Janssen P, Enright AJ, Audit B, Cases I, Goldovsky L, Harte N, Kunin V,
Ouzounis CA: COmplete GENome Tracking (COGENT): a flexible data
environment for computational genomics. Bioinformatics 2003,
19:1451-1452.
16. Peregrin-Alvarez JM, Tsoka S, Ouzounis CA: The phylogenetic extent of
metabolic enzymes and pathways. Genome Res 2003, 13:422-427.
17. Freilich S, Goldovsky L, Ouzounis CA, Thornton JM: Metabolic innovations
towards the human lineage. BMC Evol Biol 2008, 8:247.
18. Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL,
Gunasekaran P, Ceric G, Forslund K, et al: The Pfam protein families
database. Nucleic Acids Res 2010, 38:D211-222.
19. Makalowski W, Zhang J, Boguski MS: Comparative analysis of 1196
orthologous mouse and human full-length mRNA and protein
sequences. Genome Res 1996, 6:846-857.
20. Mueller RL: Evolutionary rates, divergence dates, and the performance of
mitochondrial genes in Bayesian phylogenetic analysis. Syst Biol 2006,
55:289-300.
21. Shakhnovich BE, Koonin EV: Origins and impact of constraints in
evolution of gene families. Genome Res 2006, 16:1529-1536.
22. Kondrashov FA, Kondrashov AS: Role of selection in fixation of gene
duplications. J Theor Biol 2006, 239:141-151.
23. Jordan IK, Wolf YI, Koonin EV: Duplicated genes evolve slower than
singletons despite the initial rate increase. BMC Evol Biol 2004, 4:22.
24. Greenberg AJ, Stockwell SR, Clark AG: Evolutionary constraint and
adaptation in the metabolic network of Drosophila. Mol Biol Evol 2008,
25:2537-2546.
25. Hancock JM: Gene factories, microfunctionalization and the evolution of
gene families. Trends Genet 2005, 21:591-595.
26. Lynch M, Force A: The probability of duplicate gene preservation by
subfunctionalization. Genetics 2000, 154:459-473.
27. Lynch M, O’Hely M, Walsh B, Force A: The probability of preservation of a
newly arisen gene duplicate. Genetics 2001, 159:1789-1804.
28. Liu YJ, Zheng D, Balasubramanian S, Carriero N, Khurana E, Robilotto R,
Gerstein MB: Comprehensive analysis of the pseudogenes of glycolytic
enzymes in vertebrates: the anomalously high number of GAPDH
pseudogenes highlights a recent burst of retrotrans-positional activity.
BMC Genomics 2009, 10:480.
29. Kondrashov FA, Rogozin IB, Wolf YI, Koonin EV: Selection in the evolution
of gene duplications. Genome Biol 2002, 3:RESEARCH0008.
30. Kadenbach B, Huttemann M, Arnold S, Lee I, Bender E: Mitochondrial
energy metabolism is regulated via nuclear-coded subunits of
cytochrome c oxidase. Free Radic Biol Med 2000, 29:211-221.
31. Alba MM, Castresana J: Inverse relationship between evolutionary rate
and age of mammalian genes. Mol Biol Evol 2005, 22:598-606.
32. Lynch M, Conery JS: The evolutionary fate and consequences of
duplicate genes. Science 2000, 290:1151-1155.
33. Elhaik E, Sabath N, Graur D: The “inverse relationship between
evolutionary rate and age of mammalian genes” is an artifact of
increased genetic distance with rate of evolution and time of
divergence. Mol Biol Evol 2006, 23:1-3.
34. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV,
Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, et al: The COG
database: an updated version includes eukaryotes. BMC Bioinformatics
2003, 4:41.
35. Domazet-Loso T, Tautz D: An ancient evolutionary origin of genes
associated with human genetic diseases. Mol Biol Evol 2008, 25:2699-2707.
36. Chen Z, Odstrcil EA, Tu BP, McKnight SL: Restriction of DNA replication to
the reductive phase of the metabolic cycle protects genome integrity.
Science 2007, 316:1916-1919.
37. Flicek P, Aken BL, Ballester B, Beal K, Bragin E, Brent S, Chen Y, Clapham P,
Coates G, Fairley S, et al: Ensembl’s 10th year. Nucleic Acids Res 2010, 38:
D557-562.
38. Uniprot Consortium: The Universal Protein Resource (UniProt) in 2010.
Nucleic Acids Res 2010, 38:D142-148.
39. Needleman SB, Wunsch CD: A general method applicable to the search
for similarities in the amino acid sequence of two proteins. J Mol Biol
1970, 48:443-453.
40. Henikoff S, Henikoff JG: Performance evaluation of amino acid
substitution matrices. Proteins 1993, 17:49-61.
41. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment
search tool. J Mol Biol 1990, 215:403-410.

Page 13 of 13

42. Janssen P, Audit B, Cases I, Darzentas N, Goldovsky L, Kunin V, LopezBigas N, Peregrin-Alvarez JM, Pereira-Leal JB, Tsoka S, et al: Beyond 100
genomes. Genome Biol 2003, 4:402.
43. Goldovsky L, Janssen P, Ahren D, Audit B, Cases I, Darzentas N, Enright AJ,
Lopez-Bigas N, Peregrin-Alvarez JM, Smith M, et al: CoGenT++: an
extensive and extensible data environment for computational genomics.
Bioinformatics 2005, 21:3806-3810.
44. Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL,
Gunasekaran P, Ceric G, Forslund K, et al: The Pfam protein families
database. Nucleic Acids Res 2010, 38:D211-222.
45. R Development Core Team: R: A language and environment for statistical
computing. Vienna, Austria: R Foundation for Statistical Computing; 2005.
46. Proux E, Studer RA, Moretti S, Robinson-Rechavi M: Selectome: a database
of positive selection. Nucleic Acids Res 2009, 37:D404-407.
47. Yang Z: PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol
Evol 2007, 24:1586-1591.
48. Amberger J, Bocchini CA, Scott AF, Hamosh A: McKusick’s Online
Mendelian Inheritance in Man (OMIM). Nucleic Acids Res 2009, 37:
D793-796.
doi:10.1186/1471-2148-11-142
Cite this article as: Ainali et al.: Protein coalitions in a core mammalian
biochemical network linked by rapidly evolving proteins. BMC
Evolutionary Biology 2011 11:142.

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