Evolution of developmental gene regulatory networks in echinoids

Developmental gene regulatory networks (dGRNs) are assemblages of regulatory genes that direct embryonic development of animal body plans and their morpho-logical structures. dGRNs exhibit recursively-wired circuitry that is encoded in the genome and executed during development. Alteration to the regulatory architecture of dGRNs causes variation in developmental programs both during the development of an individual organism and during the evolution of an individual lineage. The ex-planatory power of these networks is best exemplified by the global dGRN directing early development of the euechinoid sea urchin Strongylocentrotus purpuratus. This network consists of numerous regulatory genes engaging in hundreds of genomic regulatory transactions that collectively direct the delineation of early embryonic domains and the specification of cell lineages. Research on closely-related euechi-noid sea urchins, e.g. Lytechinus variegatus and Paracentrotus lividus, has revealed marked conservation of dGRN architecture in echinoid development, suggesting little appreciable alteration has occurred since their divergence in evolution at least 90 million years ago (mya).

We sought to test whether this observation extends to all sea urchins (echinoids) and undertook a systematic analysis of over 50 regulatory genes in the cidaroid sea urchin Eucidaris tribuloides, surveing their regulatory activity and function in a sea urchin that diverged from euechinoid sea urchins at least 268 mya. Our results revealed extensive alterations have occurred to all levels of echinoid dGRN archi-tecture since the cidaroid-euechinoid divergence. Alterations to mesodermal sub-circuits were particularly striking, including functional di˙erences in specification of non-skeletogenic mesenchyme (NSM), skeletogenic mesenchyme (SM), and en-domesodermal segregation. Specification of endomesodermal embryonic domains revealed that, while their underlying network circuitry had clearly diverged, regu-latory states established in pregastrular embryos of these two groups are strikingly similar. Analyses of E. tribuloides specification leading to the estab-lishment of dorsal-ventral (aboral-oral) larval polarity indicated that regulation of regulatory genes expressed in mesodermal embryonic domains had incurred significantly more alterations than those expressed in endodermal and ectodermal domains. Taken together, this study highlights the ability of dGRN architecture to buffer extensive alterations in the evolution and early development of echinoids and adds further support to the notion that alterations can occur at all levels of dGRN architecture and all stages of embryonic development.

Functional, clustered, feedforward, and mesoscale brain networks

The brain is a network spanning multiple scales from subcellular to macroscopic. In this thesis I present four projects studying brain networks at different levels of abstraction. The first involves determining a functional connectivity network based on neural spike trains and using a graph theoretical method to cluster groups of neurons into putative cell assemblies. In the second project I model neural networks at a microscopic level. Using diferent clustered wiring schemes, I show that almost identical spatiotemporal activity patterns can be observed, demonstrating that there is a broad neuro-architectural basis to attain structured spatiotemporal dynamics. Remarkably, irrespective of the precise topological mechanism, this behavior can be predicted by examining the spectral properties of the synaptic weight matrix. The third project introduces, via two circuit architectures, a new paradigm for feedforward processing in which inhibitory neurons have the complex and pivotal role in governing information flow in cortical network models. Finally, I analyze axonal projections in sleep deprived mice using data collected as part of the Allen Institute's Mesoscopic Connectivity Atlas. After normalizing for experimental variability, the results indicate there is no single explanatory difference in the mesoscale network between control and sleep deprived mice. Using machine learning techniques, however, animal classification could be done at levels significantly above chance. This reveals that intricate changes in connectivity do occur due to chronic sleep deprivation.