Mouse retinal development: a dark horse model for systems biology research.

Bioinformatics and Biology Insights Review Open Access Full open access to this and thousands of other papers at http://www.la-press.com. Mouse Retinal Development: a Dark Horse Model for Systems Biology Research Xia Zhang1,3, Jeanne M. Serb1,3 and M. Heather West Greenlee2,3,4 Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, USA. 2Department of Biomedical Sciences, Iowa State University, Ames, Iowa, USA. 3Interdepartmental Genetics Program, Iowa State University, Ames, Iowa, USA. 4Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA. Corresponding author email: 1 Abstract: The developing retina is an excellent model to study cellular fate determination and differentiation in the context of a complex tissue. Over the last decade, many basic principles and key genes that underlie these processes have been experimentally identified. In this review, we construct network models to summarize known gene interactions that underlie determination and fundamentally affect differentiation of each retinal cell type. These networks can act as a scaffold to assemble subsequent discoveries. In addition, these summary networks provide a rational segue to systems biology approaches necessary to understand the many events leading to appropriate cellular determination and differentiation in the developing retina and other complex tissues. Keywords: retina, cell fate determination, network, systems biology Bioinformatics and Biology Insights 2011:5 99–113 doi: 10.4137/BBI.S6930 This article is available from http://www.la-press.com. © the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited. Bioinformatics and Biology Insights 2011:5 99 Zhang et al Introduction Multicellular organisms are made of tissues with multiple specialized cell types. Understanding the determination and differentiation of heterogeneous cell types within the context of complex tissues is fundamental to many areas of biology. This knowledge will have widespread application in treatment of developmental disorders and disease states such as cancer and will be critical for successful bioengineering and transplantation of tissue types to replace damaged or degenerate structures. The determination and differentiation of a given cell within a tissue is the culmination of the expression of many gene products and their subsequent intra- and intercellular signaling events. To address the challenge of understanding cell fate determination and differentiation we must adopt a broad systems biology approach to adequately take into account the activities of large numbers of genes and signaling pathways. One emerging systems-based strategy to analyze and integrate large datasets is to generate network models, in which genes or proteins are represented by nodes and their relationships by edges in the graph (network). However, most large expression datasets are too sparse to infer high statistical confidence gene relationships which are based on the estimate of a covariance matrix.1 In addition, the networks generated de novo are often large, and do not facilitate prioritization of candidate genes and gene relationships for hypothesis based validation. To address this problem, we have previously described a heuristic approach that uses a seed network to summarize prior knowledge of a small part of the gene network involved in cellular development.2,3 The seed network can then be used to query large datasets in order to identify additional molecules with putative relationships to seed genes. These candidate molecules can then be used to expand the network and are the basis for generating testable hypotheses to validate their functional role. Cell fate determination and differentiation in the vertebrate retina provides many opportunities to generate and utilize systems-based tools and approaches to understand development of cells within complex tissues. First, development of the retina is well-characterized4–6 and the sequence of cell genesis and differentiation is well-documented and largely conserved among vertebrates.7–11 Thus, activity of gene networks that underlie the fate determination 100 and differentiation in a particular retinal cell type will take place in known cells with known birthdates and known locations within the tissue. Second, the retina is highly accessible and is very amenable to in vivo hypothesis testing,12 thus the role of hypothesized gene candidates and network interactions in cell fate determination and differentiation can be readily assessed. Third, we can build on the foundational system-based approaches developed through the study of single cell organisms like yeast,13 diffuse systems like the immune system,14 or cultured tissue systems,15 and extend these methods to examine the development of more complex tissues that comprise living organisms. Here we review what is presently known about the genetic networks that underlie cell fate determination and differentiation in the developing retina and present the seed networks that we have constructed based on our examination of published literature. The developing retina is an extensively reviewed16–20 system regarding cell fate determination during retinogenesis, but a summary of literature-curated gene networks underlying differentiation of each retinal cell type has not been previously presented. In order to demonstrate its potential as a model to study determination and differentiation of multiple cell types within the context of a complex tissue, we have assembled seed networks to summarize what is known about the genes and their relationships that underlie cell fate determination and largely influence the differentiation of each of the basic retinal cell types. Finally, we demonstrate that the experimentally-based summary network for photoreceptors can be extracted from an independent gene expression data set. Retinal Cell Types The mature mouse retina is composed of seven basic cell types, six neuronal and one glial (Fig. 1). While this review focuses on only the differentiation of the basic cell types, many retinal cells can be further subdivided morphologically, biochemically and functionally.21–31 Photoreceptors (rods and cones) reside in the outer nuclear layer (ONL) and are responsible for phototransduction and necessary for vision.32 Photoreceptors synapse with bipolar cells, neurons that reside in the inner nuclear layer (INL). Bipolar cells relay visual stimulus to retinal ganglion cells in the ganglion cell layer either directly Bioinformatics and Biology Insights 2011:5 Systems biology to study retinal development Rod Cone Horizontal Amacrine Bipolar Müller Ganglion Figure 1. The retinal cell types in the adult mouse retina. The adult mouse retina is comprised of three cellular layers separated by two synaptic layers (...truncated)