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1
Functionalization of a Protosynaptic
Gene Expression Network
CECILIA CONACO,*† DANIELLE S. BASSETT,‡§ HONGJUN
ZHOU,*† MARY LUZ ARCILA,*† SANDIE M. DEGNAN,
BERNARD M. DEGNAN, AND KENNETH S. KOSIK*†#
Assembly of a functioning neuronal synapse requires the precisely coor-
dinated synthesis of many proteins. To understand the evolution of this
complex cellular machine, we tracked the developmental expression
patterns of a core set of conserved synaptic genes across a representative
sampling of the animal kingdom. Coregulation, as measured by correla-
tion of gene expression over development, showed a marked increase
as functional nervous systems emerged. In the earliest branching ani-
mal phyla (Porifera), in which a nearly complete set of synaptic genes
exists in the absence of morphological synapses, these “protosynaptic”
genes displayed a lack of global coregulation although small modules
of coexpressed genes are readily detectable by using network analysis
techniques. These findings suggest that functional synapses evolved by
exapting preexisting cellular machines, likely through some modifica-
tion of regulatory circuitry. Evolutionarily ancient modules continue to
operate seamlessly within the synapses of modern animals. This work
shows that the application of network techniques to emerging genomic
and expression data can provide insights into the evolution of complex
cellular machines such as the synapse.
*Neuroscience Research Institute, †Department of Molecular, Cellular and Developmental
Biology, ‡Department of Physics, and §Sage Center for the Study of the Mind, University
of California, Santa Barbara, CA 93106; and School of Biological Sciences, University of
Queensland, Brisbane, Queensland 4072, Australia. #To whom correspondence should be
addressed. E-mail: kosik@lifesci.ucsb.edu.
3
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4 / Cecilia Conaco et al.
I
n the tree of life, sponges (Porifera), generally recognized as the old-
est surviving metazoan phyletic lineage (Fig. 1.1B), occupy a highly
informative position for understanding the evolution of features that
uniquely characterize animals (Srivastava et al., 2010). The synapse, a cel-
lular machine formed through the dynamic assembly of multiple proteins
that together perform a specific biological function, is one such metazoan
specialization. The synaptic machinery delivers a chemical signal via
vesicle fusion at the presynaptic neuronal membrane to postsynaptic
receptors, which convert that signal back to an electrical impulse in the
postsynaptic neuronal cell. Surprisingly, the genome of the Poriferan
demosponge, Amphimedon queenslandica, contains an almost complete set
of genes homologous to those found in mammalian synapses (Fig. 1.1A),
although the organism does not assemble any structure morphologi-
cally resembling a synapse (Sakarya et al., 2007; Srivastava et al., 2010).
Although limited gene innovation and the invention of new protein inter-
action sites can partially explain how preexisting genes came together to
form the synaptic complex (Sakarya et al., 2010), the multiple evolutionary
steps involved in building a cellular machine through the assembly of an
interaction network that can operate as a unit with a discrete biological
function remains unknown.
Changes in conserved transcriptional programs arising from modi-
fication of instructions encoded in the genome have contributed to our
understanding of animal evolution (Barabási and Oltvai, 2004; Oldham
et al., 2006, 2008; Brawand et al., 2011). Specific patterns of expression can
define discrete tissues, cell types, and even functional protein complexes.
Genes with similar expression patterns often have similar function (Eisen
et al., 1998). Furthermore, when comparing orthologues across divergent
species, highly conserved coexpression is a strong predictor of shared
function in similar pathways (Quackenbush, 2003; Stuart et al., 2003;
van Noort et al., 2003). These results suggest that functionally related
genes might be under similar expression constraints (Carlson et al., 2006).
Thus, changes in coexpression relationships for any group of genes may
contain information on the assembly and evolution of cellular machines.
To understand the evolutionary transition leading to the emergence of
a functional synapse, we used network analysis to identify unique pat-
terns of synaptic gene coexpression in representative species from diverse
phylogenetic positions. We show that “protosynaptic” genes have an
inherent modular structure and that the coregulatory links between these
modules characterize species with functional synapses. In contrast, ancient
e
ukaryotic cellular machines, such as the proteasome and nuclear pore,
already operate in early metazoans, and their associated genes display
highly correlated expression patterns over development. These findings
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Functionalization of a Protosynaptic Gene Expression Network / 5
A PRE-SYNAPTIC
VGLUT
SYN SV2
SVOP
Docking
Synaptic
RIMBP
RAB3 vesicle
BASSOON
SYNGR
RIMS
Synaptic
vesicle vATPase
PICCOLO
SYT
SYP VAMP
Priming
PFN
ERC
CTNNA
UNC13
Synaptic
vesicle
STXBP1
VELI/LIN7 C
CTNNB
c h a a 2+
PPF1A
nne
MINT/LIN10 l
SNAP25
SNAP25
CPLX CASK/LIN2
STX
ch
NAPA
STX
Ca nn
PTPRF
a
2+
Cadherin
NSF
PTPRF
Neurexin
el
Ephrin
Cadherin
receptor
Dopamine
AMPA
receptor
receptor
Serotonin
receptor
receptor
Ephrin
receptor
AMPA
Neuroligin
NMDA
tor
Shaker K+
PMCA
recErbB
channel
Stargazin
ep
R
lu
G
CTNND
m
PICK
GRIP -B r
CTNNB
BApto
Erbin
GRASP
PS
CTNNA
AK
GA c e
LG
D
re
AP
CRIPT
95
/D
79
SynGAP
95
/D
AR
LG
D
SP
Kalirin
Citron
PS
lu R
mG
NOS
GKAP
Scribble Homer
Homer
SHANK
Densin-180 Cortactin
M
AG
I
POST-SYNAPTIC
B on
ge
s)
ell
ate
s
s r
(spndica ians
ag
ofl is de
s od ste tes
i ae an oll
a
fer sla ar ra ato s rop oga bra
ng revisi ho revic Pori ueen Cnid illepo Nemlegan Arthmelan rte
Fu ce C .b q m e . Ve rerio icalis
S. M A. A. C. D D. op tr
X.
Bilateria
Eumetazoa
Metazoa
Holozoa
Eukaryote
FIGURE 1.1 Origins of synaptic genes. (A) Homologues of genes in the human
synaptic complex were identified in the genomes of selected organisms represent-
ing key phylogenetic steps in animal evolution. Colors indicate the inferred ances-
tor of origin for each gene, as indicated in B. (B) Evolutionary relationships among
animal phyla. The names of representative species are shown. [Note: Figure can
be viewed in color in the PDF version of this volume on the National Academies
Press website, www.nap.edu.]
suggest that reorganization of gene expression, most likely through the
modification of transcriptional regulation, was a key factor in the evolu-
tion of cellular machines such as the synapse.
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6 / Cecilia Conaco et al.
RESULTS
To study functionalization of the synaptic gene network [Fig. 1.2A;
Conaco et al. (2012, Fig. S1A)], we obtained the expression profiles of
sponge synaptic gene homologues by sequencing the A. queenslandica
transcriptome at four developmental stages from larva to adult. For com-
parison, expression data were also obtained for the same set of synaptic
genes from five representative animals with varying complexities in tissue
organization (Fig. 1.1B). Animal species included in this study were the
cnidarian coral, Acropora millepora; invertebrate bilaterians, Caenorhabditis
elegans (nematode) and Drosophila melanogaster (arthropod); and verte-
A B
C E 0.50 Synapse
NPC
Epithelia
Proteasome
0.40
Frequency
0.30
D 0.20
0.10
0.00
0 5 10 15 20 25 30 35 40 45 50
Degree
FIGURE 1.2 Structure of protein interactions within the (A) synaptic, (B) epithe-
lial, (C) NPC, and (D) 26S proteasome networks. Each node represents a gene.
Node size represents the number of interactions formed by a protein, and edge
length is proportional to the strength of evidence for a functional link between
two proteins. Network structures are based on the human interactome annotated
in STRING (Snel et al., 2000) and visualized by using Cytoscape (Shannon et al.,
2003). (E) Degree distribution patterns of gene networks based on the human
interactome. The frequency of nodes that exhibit the indicated number of connec-
tions (degree) is shown.
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Functionalization of a Protosynaptic Gene Expression Network / 7
brates, Danio rerio (zebrafish) and Xenopus tropicalis (frog) (Hillier et al.,
2009; Domazet-Lošo and Tautz, 2010; Graveley et al., 2011; Meyer et al.,
2011; Yanai et al., 2011). The correlation matrix for synaptic gene homo-
logues from each species was constructed by computing the Pearson
correlation coefficient between all pairs of gene expression profiles across
development (Fig. 1.3A). The correlation matrix represents a network in
which the genes are nodes and the correlations between gene expression
patterns are edges. We averaged all elements of the correlation matrix to
obtain a measure of connectivity or coregulation, R (Fig. 1.3B, D, F, and H).
By using a community detection algorithm (Girvan and Newman, 2002;
Blondel et al., 2008; Porter et al., 2009), the modularity, Q, of each network
was computed by determining the optimal partition of the network into
communities whose nodes were more connected to other nodes inside of
their own community than expected in a random null model (Fig. 1.3C,
E, G, and I). The modularity, Q, can be interpreted as a measure of the
cohesiveness of coregulation: higher Q values indicate more segregation
between coregulated groups. To determine the statistical significance of
our results, we computed the same properties (R and Q) for various ran-
dom control models.
The synaptic gene expression profiles were more highly correlated
in eumetazoan species than in the sponge (Fig. 1.3B). This is apparent in
the cnidarian coral, A. millepora, which possesses nerve cells organized
into a simple diffuse net. The bilaterian synaptic gene networks showed
even greater coregulation compared with sponge or coral. Synaptic genes
showed significantly increased correlation compared with permuted and
random controls in all species [Fig. 1.3B; Conaco et al. (2012, Table S1)]. To
verify the observed differences in expression coregulation, we performed
pairwise comparisons of subsets of synaptic genes common between
species. Comparison of genes found in sponge and the other five species
showed that the increased correlation in eumetazoans was significant
[P < 1 × 10−5, two-tailed t test; Conaco et al. (2012, Table S2)]. Pairwise
comparison of average coregulation for genes common between coral
and each of the other species further revealed significantly greater cor-
relation in bilaterian organisms (P < 1 × 10−10, two-tailed t test). These
pairwise correlation values were significantly greater than coregulation
within three separate random control models (P < 0.05, two-tailed t test;
Materials and Methods). However, Q values for most of the synaptic gene
networks did not show the consistent decrease relative to controls that
would be expected in a set of genes that were coherently coregulated.
This suggests that the synaptic gene network is composed of subsets of
genes with distinguishable differences in their developmental expression
patterns, similar to what we would expect from a random collection of
genes taken from the transcriptome. These distinct modules may be per-
OCR for page 8
8 / Cecilia Conaco et al.
A SPONGE CORAL
A. queenslandica A. millepora
WORM
C. elegans D. melanogaster
FLY FISH
D. rerio
FROG
X. tropicalis
(Aqu) (Ami) (Cel) (Dme) (Dre) (Xtr)
SYNAPSE
EPITHELIA
NPC
PROTEASOME
−1.0 −0.5 0.0 0.5 1.0
Pearson correlation
B 0.5
C 0.5
Average correlation
0.4 0.4
*
SYNAPSE
0.3 * 0.3
Q value
*
0.2 * 0.2
0.1 * 0.1
0.0 0.0
*
Aqu Ami Cel Dme Dre Xtr Aqu Ami Cel Dme Dre Xtr
(87) (87) (107) (112) (84) (107) (87) (87) (107) (112) (84) (107)
D E
0.5 0.5
*
Average correlation
0.4 0.4
EPITHELIA
0.3 0.3
Q value
0.2
* 0.2
0.1
* 0.1
*
0.0
Aqu Ami Cel Dme Dre Xtr
0.0 *
Aqu Ami Cel Dme Dre Xtr
(70) (74) (68) (75) (76) (99) (70) (74) (68) (75) (76) (99)
F G
1.0 0.5
*
Average correlation
0.8 0.4
0.6 * * 0.3
Q value
NPC
0.4 * * 0.2
* *
0.2 0.1 *
0.0 0.0
*
Aqu Ami Cel Dme Dre Xtr Aqu Ami Cel Dme Dre Xtr
(23) (14) (18) (17) (23) (26) (23) (14) (18) (17) (23) (26)
H 1.0
I 0.5
*
Average correlation
0.8 0.4
*
PROTEASOME
0.6 0.3
Q value
* *
0.4 * 0.2
*
0.2 0.1 * * *
0.0 0.0
*
Aqu Ami Cel Dme Dre Xtr Aqu Ami Cel Dme Dre Xtr
(39) (38) (39) (38) (31) (46) (39) (38) (39) (38) (31) (46)
network time-permuted random gene set random number matrix
OCR for page 9
Functionalization of a Protosynaptic Gene Expression Network / 9
forming disparate activities that are necessary for the overall function of
the synaptic machinery [Fig. 1.3C; Conaco et al. (2012, Table S1)].
The detection of coregulated gene communities is a data-driven pro-
cess that is not biased by any prior knowledge of function. We sought to
determine whether functionally defined subsets of synaptic proteins cor-
responded to the gene communities found in the coregulation modules.
Nodes in the synaptic protein interaction network of each species were
colored according to the coregulation module from which they were
derived (Fig. 1.4A). Module composition (i.e., node colors) of the three
largest functional complexes were tabulated (Fig. 1.4B). Those genes that
comprise the postsynaptic density tended to fall within a single module
for most eumetazoans. This same tendency was also true for the synaptic
vesicle genes in most bilaterians. In contrast, sponge synaptic genes in
these functional complexes showed a more heterogeneous expression pat-
tern that appeared to follow a different regulatory logic than that of func-
tional synaptic networks, as reflected by the greater diversity in module
composition within each biological complex. One striking exception is the
vacuolar ATPase complex (vATPase), which is tightly coregulated even in
sponge, suggesting a gain of functionality long before animal divergence
(Finnigan et al., 2012). It should be noted, however, that, although we did
not see similar module enrichment patterns for these functional complexes
in the frog, we did observe a strong correlation of synaptic gene expression
in this species (Fig. 1.3A).
FIGURE 1.3 Correlation and modularity analysis for gene networks in six organ-
isms. (A) The strength of genetic coregulation for any two genes in a network was
estimated by computing the Pearson correlation coefficient of their expression
across developmental stages. Heat maps represent N×N correlation matrices for
genes in each network in each species (red, positive correlation; blue, negative cor-
relation). (B, D, F, and H) Average correlation, R, was computed from the matrices
in A. (C, E, G, and I) The presence of distinct coregulated modules was estimated
by the Q value (Blondel et al., 2008). The computations for each true network (red
circles) were also performed on control datasets: time-permuted (1,000 randomly
scrambled versions of the correlation matrix, orange diamonds), random gene set
(100 gene sets of size N randomly sampled from the entire transcriptome, blue tri-
angles), and random number matrix (100 matrices generated with the same gene
number and developmental stages as the true network, green squares). The num-
ber of genes included in the analysis for each network in each species is shown in
parentheses. Error bars represent SD of R and Q; some SDs are smaller than the
marker size. Asterisks indicate a significant difference from the random gene set
control (P < 0.05, two-tailed t test). [Note: Figure can be viewed in color in the PDF
version of this volume on the National Academies Press website, www.nap.edu.]
OCR for page 10
10
A B
SPONGE CORAL WORM Post-synaptic density
SPONGE 38 25 25 12
vacuolar
ATPase CORAL 50 38 66
WORM 63 26 11
*
Synaptic FLY 45 55
*
vesicle *
FISH 50 36 14
Post-synaptic FROG 33 45 22
*
density
Synaptic vesicle
SPONGE 41 35 6 18
CORAL 29 29 42
WORM 54 36 55
FLY 39 61
*
FLY FISH FROG *
FISH 38 50 12
FROG 26 35 35 4
*
vacuolar ATPase
SPONGE 60 20 20
CORAL 20 40 40
*
WORM 18 82
FLY 9 9 82
*
FISH 14 72 14
*
FROG 45 45 10
*
0 25 50 75 100
Module size: blue > red > yellow > green Percent of genes from each module
FIGURE 1.4 Functionally defined protein complexes correspond to detected coregulation modules. (A) Genes in the synaptic
network of each species were assigned to coregulation modules by modularity optimization. Genes were colored according to the
module from which they are derived (module size: blue > red > yellow > green). Genes in gray are not represented in the organism
or have no available expression data. Dashed circles represent the approximate boundaries of the postsynaptic density, synaptic
vesicle, and vATPases. (B) Percent of genes in each functional complex that belong to coregulation modules detected by modular-
ity optimization. Colors correspond to the gene modules in A. Asterisks indicate complexes for which ≥50% of genes belong to the
same coregulation module. Only genes with available expression data in each species were included in the analysis. [Note: Figure
can be viewed in color in the PDF version of this volume on the National Academies Press website, www.nap.edu.]
OCR for page 11
Functionalization of a Protosynaptic Gene Expression Network / 11
Like the synaptic network, the epithelial network also lacks a morpho-
logical correlate in the sponge. In epithelial cells, the adherens junction
links to apical-basal polarity genes and Wnt/planar polarity genes [Fig.
1.2B; Conaco et al. (2012, Fig. S1B)]. Although A. queenslandica expresses
many orthologues of epithelial genes, the sponge exhibits only rudimen-
tary features of a functional epithelium (Adams et al., 2010; Fahey and
Degnan, 2010). As in the synaptic gene set analysis, we extracted the
expression patterns of epithelial genes from six species and calculated the
average correlation, R, and modularity, Q, of the coregulation network
(Fig. 1.3D and E). The epithelial network in all species that were tested
showed significantly greater R when compared pairwise vs sponge [P < 1
× 10−8, two-tailed t test; Conaco et al. (2012, Table S2)]. As in the synaptic
network, the modularity of epithelial networks was not consistently lower
compared with random controls for most of the species tested.
Neurons and epithelial cells and their defining cellular machines
appear in eumetazoans after sponges diverged from other animals. We
asked whether genes drawn from more ancient machines present in all
eukaryotes might show a different pattern of expression characteristic
of machines that were functionalized before the origin of animals. We
performed a similar modularity optimization on transcriptome data for
homologues of genes in the nuclear pore complex (NPC) and the 26S pro-
teasome [Fig. 1.2C and D; Conaco et al. (2012, Fig. S1C and D)]. These net-
works are highly interconnected and exhibit a negatively skewed degree
distribution, which differs from the relatively large hubs and positively
skewed degree distribution observed in mammalian synaptic and epithe-
lial networks (Fig. 1.2E).
The nuclear envelope is a defining feature of eukaryotic cells (Wente and
Rout, 2010). Transport of molecules between the nucleus and cytoplasm is
mediated by the NPC, which is made up of approximately 30 nucleoporin
genes. Coregulation analysis of nucleoporin homologues represented in the
transcriptome set revealed higher average correlation and generally lower
modularity compared with the synaptic or epithelial networks in the same
species (Fig. 1.3F and G). Most of the NPC networks showed consistently
greater R and lower Q compared with permuted or random size-matched
data, suggesting that the components of the NPC act as a single functional
unit (Conaco et al., 2012, Table S1). In contrast, greater modularity of the
synaptic and epithelial polarity networks suggests a requirement for some
modularity in the operation of these machines, perhaps as a result of the
presence of ancient submachines, such as the vATPase community.
The 26S proteasome is a well-conserved protein degradation machine
composed of products from more than 31 genes (Voges et al., 1999). Coreg-
ulation analysis of homologues of proteasomal genes revealed that, like
the NPC, the proteasome has higher average correlation and lower modu-
OCR for page 12
12 / Cecilia Conaco et al.
larity compared with the synaptic or epithelial networks within each
species (Fig. 1.3H and I). All eumetazoans showed significantly higher
correlation when compared pairwise vs. sponge (P < 1 × 10−52, two-tailed
t test; Conaco et al., 2012, Table S2). Coregulation and modularity of
proteasomal genes differed significantly from permuted or random data,
except in the sponge (Conaco et al., 2012, Table S1). Nevertheless, in all
species tested, including the sponge, the proteasome gene set emerged
as a distinct community when analyzed together with NPC genes (Fig.
1.5) and is therefore likely to represent a functionally significant module.
NPC
Proteasome
Modules
Genes
A B NPC Proteasome
1.0
1.0
Expression
SPONGE
0.5 0.5
0.0 0.0
Ave. part. sim. = 0.048 ± 0.011 1 2 3 4 1 2 3 4
(p < 0.05) Stage Stage
1.0 1.0
Expression
CORAL
0.5 0.5
0.0 0.0
Ave. part. sim. = 0.230 ± 0.022 1 2 3 4 5 6 1 2 3 4 5 6
(p < 0.05) Stage Stage
1.0 1.0
Expression
0.8
WORM
0.8
0.6
0.6
0.4
Ave. part. sim. = 0.153 ± 0.023 1 2 3 4 1 2 3 4
(p < 0.001) Stage Stage
1.0
1.0
Expression
0.8
0.8
0.6
FLY
0.6
0.4 0.4
0.2 0.2
Ave. part. sim. = 0.716 ± 0.024 1 5 10 15 1 5 10 15
(p < 0.001) Stage Stage
1.0 1.0
Expression
FISH
0.5 0.5
0.0 0.0
Ave. part. sim. = 0.220 ± 0.000 1 35 70 1 35 70
(p < 0.001) Stage Stage
1.0 1.0
Expression
0.9 0.8
FROG
0.6
0.8
0.4
0.7
0.2
Ave. part. sim. = 0.153 ± 0.028 1 5 10 14 1 5 10 14
(p < 0.001) Stage Stage
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Functionalization of a Protosynaptic Gene Expression Network / 13
In a unicellular eukaryote, like the yeast, Saccharomyces cerevisiae, the
NPC and proteasome gene networks exhibit high correlation and low
modularity that are quite similar to the average values observed for the
metazoans (Conaco et al., 2012, Table S1). These findings further support
the hypothesis that gene networks that establish their modern function
long before the origin of metazoa exhibit significantly higher correlation
and lower modularity, consistent with a greater and more homogeneous
connectivity between genes.
These results show that data-driven detection of transcriptional
expression patterns can reliably reveal a reorganization of gene networks
in association with the emergence of their modern collective function
from the unknown functions of these same gene sets in the common ani-
mal ancestor. This reorganization appears as increased connectivity and
a change in the network structure with functional complexes clustering
into coregulated modules. In contrast, more ancient machines, such as the
proteasome and the NPC, show a cohesiveness of expression as far back
as the eukaryotic ancestor.
DISCUSSION
Synaptic proteins must be available in concentrations that drive self-
assembly by mass action according to the affinities among their various
interaction domains. Among the core features of synapses are scaffolding
proteins that position receptors and ion channels in register with synaptic
vesicles across the synaptic cleft and link the pre- and postsynaptic ele-
ments to intracellular signaling cascades. Coordinated expression of these
proteins, as well as the affinity of the interactions, are among the drivers
FIGURE 1.5 Modularity optimization detects biologically relevant gene commu-
nities. (A) Heat maps represent the N×N Pearson correlation matrices for union
networks of NPC and proteasome genes (red, positive correlation; blue, negative
correlation). Average partition similarities (ave. part. sim.) computed from per-
mutation testing with 1,000 iterations showed that, compared with the randomly
scrambled gene set, genes in the union network clustered into communities that
more closely recapitulated the true partition between networks (P < 0.05). Color
bars to the right of the heat maps indicate the boundaries of detected coregulation
modules (Modules) and the relative location of NPC (orange) and proteasome
(blue) genes within the detected communities (Genes). (B) Box plots show the
developmental expression patterns of genes within the NPC (Left) and prote some
a
network (Right) for each of the six representative species. [Note: Figure can be
viewed in color in the PDF version of this volume on the National Academies
Press website, www.nap.edu.]
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14 / Cecilia Conaco et al.
of synapse assembly. Positive selection at specific sites in PDZ scaffolds
appear to have roles in determining the binding partners of these highly
connected proteins (Sakarya et al., 2010), an observation consistent with
network growth by link dynamics, that is, link detachment and attachment
(Berg et al., 2004). Just as mutations in coding sequences can change link
dynamics and enable new protein–protein interactions, mutations in cis
regulatory sequences can lead to the evolution of new transcriptional link-
ages and coexpression of gene batteries that were not previously associ-
ated. In fact, the sponge already possesses homologues of genes that func-
tion in bilaterian neurogenesis, although it is yet to be determined if these
factors were responsible for a biological unit originating in the sponge
ancestor that was selected for an unknown function and later exapted to
assemble the synapse (Richards et al., 2008). These conserved bilaterian
developmental and neurogenic genes are associated with spatial pattern-
ing of the cnidarian nerve net (Marlow et al., 2009; Layden et al., 2012).
Further modification of gene regulatory mechanisms in vertebrates placed
many synaptic genes under the control of the transcriptional repressor,
REST, thus ensuring exclusive and coordinated expression in neurons
(Schoenherr and Anderson, 1995; Bruce et al., 2004; Otto et al., 2007).
The hierarchical structure of gene regulatory signaling networks that
control the body plan are thought to evolve by changes in cis regula-
tory regions resulting in changes in timing, level, and location of gene
expression (Peter and Davidson, 2011). In contrast, the network edges of
cellular machines represent physical interactions rather than a cascade
of signaling events (Spirin and Mirny, 2003). Nevertheless, the resolution
of a signaling or interaction network depends on the extent of coregulatory
data available to inform the graph edges. Our analysis required that we
compare the coregulation and modularity of the same set of genes; how-
ever, inclusion of genes linked to the synaptic network that are not shared
between the comparison groups would likely improve the coregulation
signal as gene innovation and duplication can affect network structure
through dynamic interactome rewiring (Arabidopsis Interactome Map-
ping Consortium, 2011). Although these limitations increase the likelihood
of detecting biologically spurious correlations and may contribute to the
apparent modularity observed in some random gene sets, the ability of
the community detection algorithm to partition genes into their respective
cellular machines indicates a functional correlate of the structural com-
munities derived simply from transcriptional coregulation (Fig. 1.5). The
generation of more transcriptomes at finer temporal and spatial resolution
and the sequencing of genomes from other basal metazoans, as well as
improved homologue detection, may strengthen or weaken an alternative
explanation that the gene expression patterns in A. queenslandica represent
a loss of more ancient gene regulatory patterns.
OCR for page 15
Functionalization of a Protosynaptic Gene Expression Network / 15
Evolutionary growth of gene interaction networks is a key facet of
organismal complexity. Several publications have claimed that gene expres-
sion networks are scale-free (Barabási and Oltvai, 2004), and although no
rigorous proof of the claim exists, many gene expression networks do
display a tail in their degree distributions, indicating the presence of large
hubs. Interestingly, one particular model of scale-free network growth sug-
gests that (i) networks expand continuously by the addition of new nodes,
and (ii) new nodes attach preferentially to sites that are already well con-
nected (Udny Yule, 1925). Gene number has not increased by much over
the course of metazoan evolution. Thus, the expansion of gene interaction
networks, which is required to functionalize metazoan cellular machines,
places an exceptionally high premium on enhancing coregulatory patterns
between existing genes.
CONCLUSIONS
By using genomewide transcriptome data, we tracked the expression
of a common set of synaptic genes in a representative sampling of the ani-
mal kingdom. In bilaterians, the expression of synaptic genes is strikingly
well coordinated, with smaller coregulation modules detectable within the
expression matrix. A particularly prominent module is the vATPase com-
plex found within the presynaptic gene set. Interestingly, synaptic genes
in the earliest branching metazoan phyla (Porifera) exhibit a lack of global
coregulation compared with eumetazoans with functional nervous systems.
Protosynaptic gene expression modules from the sponge, A. queenslandica,
which lacks synapses and a nervous system, but possesses a nearly complete
complement of synaptic genes, are organized into independent communi-
ties. These findings suggest that functional synapses evolved through the
exaptation of preexisting genes and smaller cellular machines, presumably
by modification of regulatory circuitries resulting in coordinated neuronal
expression. This work demonstrates that the modularity approach based
on network theory provides a very simple and data-driven method for
the identification of gene communities, linking this study to a larger array
of network diagnostics that could be used in subsequent investigations of
the topological organization of gene coexpression networks across species.
MATERIALS AND METHODS
Expression Data
Genes in the synaptic, epithelial, NPC, and 26S proteasome networks
were compiled from the literature (Voges et al., 1999; Fahey and Degnan,
2010; Srivastava et al., 2010; Wente and Rout, 2010). Protein interaction
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16 / Cecilia Conaco et al.
networks were based on the human interactome annotated in STRING
(Snel et al., 2000) and visualized by using Cytoscape (Shannon et al., 2003).
Homologues for genes in these networks were determined by recipro-
cal best-hit BLAST alignments of human gene sequences to the genome
of each species of interest. Expression data for gene homologues were
extracted from transcriptomes obtained by RNA sequencing of four devel-
opmental stages in sponge, A. queenslandica (Conaco et al., 2012, SI Materi-
als and Methods); six experimental treatments of coral larvae, A. millepora
(Meyer et al., 2011); four developmental stages in worm, C. elegans (Hillier
et al., 2009); and 15 developmental stages in fly, D. melanogaster (Graveley
et al., 2011). Microarray data for 70 developmental stages in zebrafish, D.
rerio (Domazet-Lošo and Tautz, 2010), and 14 developmental stages in frog,
X. tropicalis (Yanai et al., 2011), were also included. Microarray expression
data for the yeast, S. cerevisiae, were obtained from cultures grown to
stationary phase (Gasch et al., 2000). To compare expression patterns in
transcriptomes obtained by using different methods, the expression for
every gene within each dataset was normalized to its maximum value
across development (Conaco et al., 2012, Dataset S1).
Coregulation and Modularity Analysis
For each organism, the strength of genetic coregulation of any two
genes throughout development was estimated by computing the Pearson
correlation coefficient of expression for those two genes over develop-
ment. By estimating the coregulation strength for all possible pairs of
genes, we constructed organism-specific N×N coregulation networks in
which genes were represented by nodes and connections between genes
were weighted by the correlation between their expression levels over
development. These coregulation networks were characterized by two
diagnostic variables: the average correlation, R, and the modularity, Q, as
defined in the following paragraphs.
The first diagnostic, the average correlation R, provides a measure
of within-network connectivity which can be interpreted as a measure
of coregulation. Significant differences in network coregulation between
species were identified using pairwise two-tailed t tests of the correlation
matrix elements. For these tests, correlation matrices were computed
only for the sets of genes that were common between the two species
being compared. These union gene sets for pairwise comparisons were
constructed without duplicates by using only genes with the best BLAST
score to the human protein sequence.
The second diagnostic, the modularity Q, provides a measure of com-
munity structure in the coregulation matrix. Importantly, the correlation
matrix we used to examine the amount of coregulation (R) can equiva-
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Functionalization of a Protosynaptic Gene Expression Network / 17
lently be viewed as a complex network in which gene–gene edges are
signed (i.e., positive or negative correlations) and weighted (correlations
range from −1 to 1). In each organism’s coregulation network, we tested
for the presence of uniquely coregulated groups of genes by using the
community detection approach (Porter et al., 2009) of optimizing modu-
larity (Girvan and Newman, 2002) by using the Louvain method (Blondel
et al., 2008) [note that a second heuristic, spectral optimization (Newman,
2006), gave nearly identical results: r = 0.9960, P < 0.01; Conaco et al. (2012,
Table S3)]. We define the correlation matrix A and then define wij+ to be an
N×N matrix containing the positive elements of Aij and wij− to be an N×N
matrix containing only the negative elements of Aij. The quality function
to be maximized is then given by the following equation:
⎡ ⎛ w+ w+ w− w− ⎞ ⎤
Q± =
1
∑∑
2w+ + 2w− i j ⎢ ⎝ 2w 2w ⎠ ⎥
(
⎢ Aij − ⎜ γ + i +j − γ − i −j ⎟ ⎥ δ g i , g j ) (1)
⎣ ⎦
where gi is the community to which node i is assigned, gj is the community
to which node j is assigned, γ + and γ − are resolution parameters, and the
following equation applies (Traag and Bruggeman, 2009):
wi+ = ∑ j wij , wi− = ∑ j wij . (2)
+ −
As evident from Eq. (1), two free parameters in the optimization of modu-
larity for such a signed, weighted network exist (Gómez et al., 2009):
the resolution parameters γ + and γ − (Fortunato and Barthélemy, 2007).
For simplicity in the present analysis, we chose the traditional value of
γ + of 1.0 and set γ − as 0.1 to dampen the effect of negative correlations.
Particular emphasis was placed on the positive correlations in the coregu-
lation matrix for two reasons. First, we noted that most gene sets had
significantly more positive correlations, and in fact some gene sets had no
negative correlations at all (e.g., worm NPC). To ensure that our analysis
was consistent across both organisms and machines, we dulled the influ-
ence of negative correlations by setting γ − to be an order of magnitude
smaller than γ +. Secondly, we noted that the positive correlations showed
considerably more topological organization than the negative correlations
(Conaco et al., 2012, Fig. S2). Further details are provided in Conaco et al.
(2012, SI Materials and Methods).
We further examined the dependence of our results on the choice of
γ +. We varied γ + from 0 to 2 in intervals of 0.1. We find that, for values of
γ + higher than 1, the network disintegrates into a large number of com-
munities (Conaco et al., 2012, Fig. S3). Our results therefore focus on the
smallest yet still coherent modular structures present in these systems.
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18 / Cecilia Conaco et al.
Robustness and Statistical Validity
To examine the robustness and statistical validity of our findings, we
assessed the reliability of the group partitions and tested our results against
three separate postmodularity-optimization null models as described in
the following paragraphs.
The problem of optimizing the modularity quality function is nonde-
terministic polynomial-time–hard. It is therefore important to demonstrate
that the heuristics that we used produce robust results, that is, that the
partitions found by iterative optimizations are highly similar. For each
organism and each machine, we calculated the partition similarity (Danon
et al., 2005) (which is bounded in [0,1]) between 100 separate optimiza-
tions. We found that the average partition similarity was >0.8 for most
organisms and machines, with the mean over organisms and networks
being even higher (Conaco et al., 2012, Table S1).
In addition to quantifying the reliability of our findings, we exam-
ined the statistical validity of our results by comparing the diagnostic
variables (R′ and Q′) derived from the true network to those derived
from networks constructed from three separate random null models: true
random (random number matrix), time-permuted, and random gene set.
The true andom null-model network is constructed by generating uni-
r
formly distributed random numbers for the same number of genes and
developmental stages found in the true dataset (100 instantiations). A
coregulation matrix is then constructed and R′ and Q′ are calculated. The
time-permuted null-model network is constructed by randomly scram-
bling the order of expression for each gene within the network (1,000
instantiations), recomputing the coregulation matrix, and calculating
R′and Q′. The random gene set null-model network was constructed by
extracting the expression data for an identically sized randomly chosen
set of genes from the whole transcriptome (100 instantiations). Further
details are provided in Conaco et al. (2012, SI Materials and Methods). The
statistical significance of the true R and Q values was examined by using
a one-sample t test in comparison with the R′ and Q′ values, respectively,
for each random null model (Conaco et al., 2012, Table S1). We noted that
the level of background correlation and modularity observed within sets
of N genes randomly selected from each of the transcriptomes is variable
(Conaco et al., 2012, Fig. S4). One possible explanation for these differ-
ences is that the transcriptome datasets were obtained by using different
methods.
Biological Relevance of Detected Modules
We asked whether the modules detected from the coregulation matrix
could represent functional entities. We began by calculating the correla-
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Functionalization of a Protosynaptic Gene Expression Network / 19
tion matrix R2 between the combined gene set of the proteasome and
NPC for each species. We optimized the modularity quality function to
partition this combined matrix into groups in a data-driven manner. We
next asked whether this data-driven partition was statistically similar to
the true partition of the genes into the two groups of proteasome genes
and NPC genes. To answer this question, we computed the partition simi-
larity between the data-driven partition and the true partition and used
permutation testing to determine whether this similarity was statistically
significant. The permutation test was implemented by randomly reassign-
ing genes to the two groups of “proteasome” and “NPC,” recomputing
the correlation matrix R2′, partitioning the genes in the correlation matrix
into modules, and computing the similarity between this partition and the
true partition. This process was repeated 1,000 times to construct a dis-
tribution of similarity values expected under the null hypothesis that the
coregulation patterns between proteasome and NPC genes do not differ.
For each species, the P value to reject this null hypothesis was computed as
follows: the number of similarity values derived from the permuted data
that were greater than the real similarity value, divided by the number
of permutations.
ACKNOWLEDGMENTS
We thank Boris Shraiman, Mason Porter, Adel Dayarian, and Marija
Vucelja for invaluable suggestions and comments; Scott Grafton and Jean
Carlson for facilitating the collaboration; and Marc Kirschner and Leonid
Peshkin for sharing Xenopus tropicalis microarray data. This work was
supported by gifts from Harvey Karp and Gus Gurley (K.S.K.); David
and Lucile Packard Foundation (D.S.B.); Public Health Service Grant
NS44393 (to D.S.B.); Institute for Collaborative Biotechnologies Contract
W911NF-09-D-0001 from the U.S. Army Research Office (to D.S.B.); and
the Australian Research Council (S.M.D. and B.M.D.).
The data reported in this chapter have been deposited in the Gene
Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (Acces-
sion No. GSE29978).
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