Jim A. Rogers

In vitro analysis of a mammalian retinal progenitor that gives rise to neurons and glia

In vivo lineage studies have shown that retinal cells arise from multipotential progenitors whose fates are regulated by cell-cell interactions. To understand the mechanism underlying their maintenance and differentiation, we have analyzed the differentiation potential of progenitors derived from embryonic rat retina in vitro. These progenitors proliferate and remain undifferentiated in vitro in the presence of epidermal growth factor (EGF) and display properties similar to stem cells. In addition to expressing nestin, the neuroectodermal stem cell marker, retinal progenitors are multipotential. Upon withdrawal of EGF and addition of serum, the progenitors downregulate the expression of nestin and express cell-type specific markers corresponding to neurons and glia. In addition to expressing cell-type specific markers, retinal progenitors and their progeny could be distinguished on the basis of their distinct voltage gated current profile. A proportion of progenitors is lineage restricted and the fate of these cells can be influenced by the microenvironment, suggesting that stage-specific interactions mediated by the local environment influence the progression of progenitors towards acquisition of differentiated phenotypes.

Scalar equations for synchronous Boolean networks with biological applications

One way of coping with the complexity of biological systems is to use the simplest possible models which are able to reproduce at least some nontrivial features of reality. Although two value Boolean models have a long history in technology, it is perhaps a little bit surprising that they can also represent important features of living organizms. In this paper, the scalar equation approach to Boolean network models is further developed and then applied to two interesting biological models. In particular, a linear reduced scalar equation is derived from a more rudimentary nonlinear scalar equation. This simpler, but higher order, two term equation gives immediate information about both cycle and transient structure of the network.

Emergent decision-making in biological signal transduction networks

The complexity of biochemical intracellular signal transduction networks has led to speculation that the high degree of interconnectivity that exists in these networks transforms them into an information processing network. To test this hypothesis directly, a large scale model was created with the logical mechanism of each node described completely to allow simulation and dynamical analysis. Exposing the network to tens of thousands of random combinations of inputs and analyzing the combined dynamics of multiple outputs revealed a robust system capable of clustering widely varying input combinations into equivalence classes of biologically relevant cellular responses. This capability was nontrivial in that the network performed sharp, nonfuzzy classifications even in the face of added noise, a hallmark of real-world decision-making.

Autophosphorylation of the Fes tyrosine kinase. Evidence for an intermolecular mechanism involving two kinase domain tyrosine residues.

The human c-fes proto-oncogene encodes a cytoplasmic tyrosine kinase (Fes) that is associated with multiple hematopoietic cytokine receptors. Fes tyrosine autophosphorylation sites may regulate kinase activity and recruit downstream signaling proteins with SH2 domains. To localize the Fes autophosphorylation sites, full-length Fes and deletion mutants lacking either the unique N-terminal or SH2 domain were autophosphorylated in vitro and analyzed by CNBr cleavage. Identical phosphopeptides of 10 and 4 kDa were produced with all three proteins, localizing the tyrosine autophosphorylation sites to the C-terminal kinase domain. Substitution of kinase domain tyrosine residues 713 or 811 with phenylalanine resulted in a loss of the 10- and 4-kDa phosphopeptides, respectively, identifying these tyrosines as in vitro autophosphorylation sites. CNBr cleavage analysis of Fes isolated from 32PO4-labeled 293T cells showed that Tyr-713 and Tyr-811 are also autophosphorylated in vivo. Mutagenesis of Tyr-713 reduced both autophosphorylation of Tyr-811 and transphosphorylation of Bcr, a recently identified Fes substrate, supporting a major regulatory role for Tyr-713. Wild-type Fes transphosphorylated a kinase-inactive Fes mutant on Tyr-713 and Tyr-811, suggesting that Fes autophosphorylation occurs via an intermolecular mechanism analogous to receptor tyrosine kinases.

The Cell Collective: toward an open and collaborative approach to systems biology.

BackgroundDespite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety.ResultsThe Cell Collective is a platform that allows the world-wide scientific community to create these models collectively. Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research. In addition, this platform allows scientists to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test what-if scenarios in real time.ConclusionsThe Cell Collective is a web-based platform that enables laboratory scientists from across the globe to collaboratively build large-scale models of various biological processes, and simulate/analyze them in real time. In this manuscript, we show examples of its application to a large-scale model of signal transduction.

The role of neuroD as a differentiation factor in the mammalian retina

NeuroD, a vertebrate homolog of Drosophila atonal gene, plays an important role in the differentiation of neuronal precursors (Lee et al., 1995). We have investigated whether NeuroD subserves a similar function in mammalian retinal neurogenesis. Expression of NeuroD is detected in successive stages of retinal neurogenesis and is associated with a differentiating population of retinal cells. The association of NeuroD predominantly with postmitotic precursors in early as well as late neurogenesis suggests that NeuroD expression plays an important role in the terminal differentiation of retinal neurons. This notion is supported by observations that overexpression of NeuroD during late neurogenesis promotes premature differentiation of late-born neurons, rod photoreceptors, and bipolar cells, and that NeuroD can interact specifically with the E-box element in the proximal promoter of the phenotype-specific gene, opsin.

ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists.

BackgroundNew mathematical models of complex biological structures and computer simulation software allow modelers to simulate and analyze biochemical systems in silico and form mathematical predictions. Due to this potential predictive ability, the use of these models and software has the possibility to compliment laboratory investigations and help refine, or even develop, new hypotheses. However, the existing mathematical modeling techniques and simulation tools are often difficult to use by laboratory biologists without training in high-level mathematics, limiting their use to trained modelers.ResultsWe have developed a Boolean network-based simulation and analysis software tool, ChemChains, which combines the advantages of the parameter-free nature of logical models while providing the ability for users to interact with their models in a continuous manner, similar to the way laboratory biologists interact with laboratory data. ChemChains allows users to simulate models in an automatic fashion under tens of thousands of different external environments, as well as perform various mutational studies.ConclusionChemChains combines the advantages of logical and continuous modeling and provides a way for laboratory biologists to perform in silico experiments on mathematical models easily, a necessary component of laboratory research in the systems biology era.

An inhibitor of chondroitin sulfate proteoglycan synthesis promotes central nervous system remyelination

Remyelination is the generation of new myelin sheaths after injury facilitated by processes of differentiating oligodendrocyte precursor cells (OPCs). Although this repair phenomenon occurs in lesions of multiple sclerosis patients, many lesions fail to completely remyelinate. A number of factors have been identified that contribute to remyelination failure, including the upregulated chondroitin sulfate proteoglycans (CSPGs) that comprise part of the astrogliotic scar. We show that in vitro, OPCs have dramatically reduced process outgrowth in the presence of CSPGs, and a medication library that includes a number of recently reported OPC differentiation drugs failed to rescue this inhibitory phenotype on CSPGs. We introduce a novel CSPG synthesis inhibitor to reduce CSPG content and find rescued process outgrowth from OPCs in vitro and accelerated remyelination following focal demyelination in mice. Preventing CSPG deposition into the lesion microenvironment may be a useful strategy to promote repair in multiple sclerosis and other neurological disorders.

Regulation of c-Fes Tyrosine Kinase and Biological Activities by N-Terminal Coiled-Coil Oligomerization Domains

ABSTRACT The cytoplasmic protein-tyrosine kinase Fes has been implicated in cytokine signal transduction, hematopoiesis, and embryonic development. Previous work from our laboratory has shown that active Fes exists as a large oligomeric complex in vitro. However, when Fes is expressed in mammalian cells, its kinase activity is tightly repressed. The Fes unique N-terminal sequence has two regions with strong homology to coiled-coil-forming domains often found in oligomeric proteins. Here we show that disruption or deletion of the first coiled-coil domain upregulates Fes tyrosine kinase and transforming activities in Rat-2 fibroblasts and enhances Fes differentiation-inducing activity in myeloid leukemia cells. Conversely, expression of a Fes truncation mutant consisting only of the unique N-terminal domain interfered with Rat-2 fibroblast transformation by an activated Fes mutant, suggesting that oligomerization is essential for Fes activation in vivo. Coexpression with the Fes N-terminal region did not affect the transforming activity of v-Src in Rat-2 cells, arguing against a nonspecific suppressive effect. Taken together, these findings suggest a model in which Fes activation may involve coiled-coil-mediated interconversion of monomeric and oligomeric forms of the kinase. Mutation of the first coiled-coil domain may activate Fes by disturbing intramolecular coiled-coil interaction, allowing for oligomerization via the second coiled-coil domain. Deletion of the second coiled-coil domain blocks fibroblast transformation by an activated form of c-Fes, consistent with this model. These results provide the first evidence for regulation of a nonreceptor protein-tyrosine kinase by coiled-coil domains.

Boolean modeling of biochemical networks

The use of modeling to observe and analyze the mechanisms of complex biochemical network function is be- coming an important methodological tool in the systems biology era. Number of different approaches to model these net- works have been utilized-- they range from analysis of static connection graphs to dynamical models based on kinetic in- teraction data. Dynamical models have a distinct appeal in that they make it possible to observe these networks in action, but they also pose a distinct challenge in that they require detailed information describing how the individual components of these networks interact in living cells. Because this level of detail is generally not known, dynamic modeling requires simplifying assumptions in order to make it practical. In this review Boolean modeling will be discussed, a modeling method that depends on the simplifying assumption that all elements of a network exist only in one of two states.