In the Light of Evolution Volume VI: Brain and Behavior (2013) / Chapter Skim
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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
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
Pages 3-20
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From page 3... ...
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.
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, 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 modification 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)
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suggest that reorganization of gene expression, most likely through the modification of transcriptional regulation, was a key factor in the evolution of cellular machines such as the synapse.
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queenslandica transcriptome at four developmental stages from larva to adult. For comparison, expression data were also obtained for the same set of synaptic genes from five representative animals with varying complexities in tissue organization (Fig.
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. The correlation matrix for synaptic gene homologues from each species was constructed by computing the Pearson correlation coefficient between all pairs of gene expression profiles across development (Fig.
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(46) network time-permuted random gene set random number matrix
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. 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.
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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.
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. 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.
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. Nevertheless, in all species tested, including the sponge, the proteasome gene set emerged as a distinct community when analyzed together with NPC genes (Fig.
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From page 13... ...
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 animal ancestor. This reorganization appears as increased connectivity and a change in the network structure with functional complexes clustering into coregulated modules.
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From page 14... ...
. 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
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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 communities.
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Expression data for gene homologues were extracted from transcriptomes obtained by RNA sequencing of four developmental stages in sponge, A queenslandica (Conaco et al., 2012, SI Materials and Methods)
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Particular emphasis was placed on the positive correlations in the coregulation 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)
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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.
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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.
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