Phylogenetic and structural analysis of centromeric DNA and kinetochore proteins

BACKGROUND: Kinetochores are large multi-protein structures that assemble on centromeric DNA (CEN DNA) and mediate the binding of chromosomes to microtubules. Comprising 125 base-pairs of CEN DNA and 70 or more protein components, Saccharomyces cerevisiae kinetochores are among the best understood. In contrast, most fungal, plant and animal cells assemble kinetochores on CENs that are longer and more complex, raising the question of whether kinetochore architecture has been conserved through evolution, despite considerable divergence in CEN sequence. RESULTS: Using computational approaches, ranging from sequence similarity searches to hidden Markov model-based modeling, we show that organisms with CENs resembling those in S. cerevisiae (point CENs) are very closely related and that all contain a set of 11 kinetochore proteins not found in organisms with complex CENs. Conversely, organisms with complex CENs (regional CENs) contain proteins seemingly absent from point-CEN organisms. However, at least three quarters of known kinetochore proteins are present in all fungi regardless of CEN organization. At least six of these proteins have previously unidentified human orthologs. When fungi and metazoa are compared, almost all have kinetochores constructed around Spc105 and three conserved multi-protein linker complexes (MIND, COMA, and the NDC80 complex). CONCLUSION: Our data suggest that critical structural features of kinetochores have been well conserved from yeast to man. Surprisingly, phylogenetic analysis reveals that human kinetochore proteins are as similar in sequence to their yeast counterparts as to presumptive Drosophila melanogaster or Caenorhabditis elegans orthologs. This finding is consistent with evidence that kinetochore proteins have evolved very rapidly relative to components of other complex cellular structures

Decision-Tree Based Model Analysis for Efficient Identification of Parameter Relations Leading to Different Signaling States

In systems biology, a mathematical description of signal transduction processes is used to gain a more detailed mechanistic understanding of cellular signaling networks. Such models typically depend on a number of parameters that have different influence on the model behavior. Local sensitivity analysis is able to identify parameters that have the largest effect on signaling strength. Bifurcation analysis shows on which parameters a qualitative model response depends. Most methods for model analysis are intrinsically univariate. They typically cannot consider combinations of parameters since the search space for such analysis would be too large. This limitation is important since activation of a signaling pathway often relies on multiple rather than on single factors. Here, we present a novel method for model analysis that overcomes this limitation. As input to a model defined by a system of ordinary differential equations, we consider parameters for initial chemical species concentrations. The model is used to simulate the system response, which is then classified into pre-defined classes (e.g., active or not active). This is combined with a scan of the parameter space. Parameter sets leading to a certain system response are subjected to a decision tree algorithm, which learns conditions that lead to this response. We compare our method to two alternative multivariate approaches to model analysis: analytical solution for steady states combined with a parameter scan, and direct Lyapunov exponent (DLE) analysis. We use three previously published models including a model for EGF receptor internalization and two apoptosis models to demonstrate the power of our approach. Our method reproduces critical parameter relations previously obtained by both steady-state and DLE analysis while being more generally applicable and substantially less computationally expensive. The method can be used as a general tool to predict multivariate control strategies for pathway activation and to suggest strategies for drug intervention

Spindle checkpoint proteins and chromosome–microtubule attachment in budding yeast

Accurate chromosome segregation depends on precise regulation of mitosis by the spindle checkpoint. This checkpoint monitors the status of kinetochore–microtubule attachment and delays the metaphase to anaphase transition until all kinetochores have formed stable bipolar connections to the mitotic spindle. Components of the spindle checkpoint include the mitotic arrest defective (MAD) genes MAD1–3, and the budding uninhibited by benzimidazole (BUB) genes BUB1 and BUB3. In animal cells, all known spindle checkpoint proteins are recruited to kinetochores during normal mitoses. In contrast, we show that whereas Saccharomyces cerevisiae Bub1p and Bub3p are bound to kinetochores early in mitosis as part of the normal cell cycle, Mad1p and Mad2p are kinetochore bound only in the presence of spindle damage or kinetochore lesions that interfere with chromosome–microtubule attachment. Moreover, although Mad1p and Mad2p perform essential mitotic functions during every division cycle in mammalian cells, they are required in budding yeast only when mitosis goes awry. We propose that differences in the behavior of spindle checkpoint proteins in animal cells and budding yeast result primarily from evolutionary divergence in spindle assembly pathways

Logic-Based Models for the Analysis of Cell Signaling Networks

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks.National Institutes of Health (U.S.) (Grant P50- GM68762)National Institutes of Health (U.S.) (Grant U54-CA112967)United States. Dept. of Defense (Institute for Collaborative Biotechnologies

Rapid Phospho-Turnover by Receptor Tyrosine Kinases Impacts Downstream Signaling and Drug Binding

Epidermal growth factor receptors (ErbB1–4) are oncogenic receptor tyrosine kinases (RTKs) that regulate diverse cellular processes. In this study, we combine measurement and mathematical modeling to quantify phospho-turnover at ErbB receptors in human cells and to determine the consequences for signaling and drug binding. We find that phosphotyrosine residues on ErbB1 have half-lives of a few seconds and therefore turn over 100–1000 times in the course of a typical immediate-early response to ligand. Rapid phospho-turnover is also observed for EGF-activated ErbB2 and ErbB3, unrelated RTKs, and multiple intracellular adaptor proteins and signaling kinases. Thus, the complexes formed on the cytoplasmic tail of active receptors and the downstream signaling kinases they control are highly dynamic and antagonized by potent phosphatases. We develop a kinetic scheme for binding of anti-ErbB1 drugs to receptors and show that rapid phospho-turnover significantly impacts their mechanisms of action.National Institutes of Health (U.S.) (Grant GM68762)National Institutes of Health (U.S.) (Grant CA112967

Fundamental trade-offs between information flow in single cells and cellular populations

Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3–4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases

Lyapunov exponents and phase diagrams reveal multi-factorial control over TRAIL-induced apoptosis

Kinetic modeling, phase diagrams analysis, and quantitative single-cell experiments are combined to investigate how multiple factors, including the XIAP:caspase-3 ratio and ligand concentration, regulate receptor-mediated apoptosis