Crystal Structure of the Yeast Inner Kinetochore Subunit cep3p

In budding yeast, the four-protein CBF3 complex (Skp1p-Ctf13p-Cep3p-Ndc10p) initiates kinetochore assembly by binding to the CDEIII locus of centromeric DNA. A Cep3p dimer recruits a Ctf13p-Skp1p heterodimer and contacts two sites on CDEIII. We report here the crystal structure, determined at 2.8 Å resolution by multiple isomorphous replacement with anomalous scattering, of a truncated Cep3p [Cep3p (47−608)], comprising all but an N-terminal, Zn2Cys6-cluster, DNAbinding module. Cep3p has a well-ordered structure throughout essentially all of its polypeptide chain, unlike most yeast transcription factors, including those with Zn2Cys6 clusters like Gal4p. This difference may reflect an underlying functional distinction: while any particular transcription factor must adapt to a variety of upstream activating sites, Cep3p scaffolds kinetochore assembly on centromeres uniformly configured on all 16 yeast chromosomes. We have, using the structure of Cep3p (47−603) and the known structures of Zn2Cys6 cluster domains, modeled the interaction of Cep3p with CDEIII.Chemistry and Chemical Biolog

Roles for the Conserved Spc105p/Kre28p Complex in Kinetochore-Microtubule Binding and the Spindle Assembly Checkpoint

Background: Kinetochores attach sister chromatids to microtubules of the mitotic spindle and orchestrate chromosome disjunction at anaphase. Although S. cerevisiae has the simplest known kinetochores, they nonetheless contain ∼70 subunits that assemble on centromeric DNA in a hierarchical manner. Developing an accurate picture of the DNA-binding, linker and microtubule-binding layers of kinetochores, including the functions of individual proteins in these layers, is a key challenge in the field of yeast chromosome segregation. Moreover, comparison of orthologous proteins in yeast and humans promises to extend insight obtained from the study of simple fungal kinetochores to complex animal cell kinetochores. Principal Findings: We show that S. cerevisiae Spc105p forms a heterotrimeric complex with Kre28p, the likely orthologue of the metazoan kinetochore protein Zwint-1. Through systematic analysis of interdependencies among kinetochore complexes, focused on Spc105p/Kre28p, we develop a comprehensive picture of the assembly hierarchy of budding yeast kinetochores. We find Spc105p/Kre28p to comprise the third linker complex that, along with the Ndc80 and MIND linker complexes, is responsible for bridging between centromeric heterochromatin and kinetochore MAPs and motors. Like the Ndc80 complex, Spc105p/Kre28p is also essential for kinetochore binding by components of the spindle assembly checkpoint. Moreover, these functions are conserved in human cells. Conclusions/Significance: Spc105p/Kre28p is the last of the core linker complexes to be analyzed in yeast and we show it to be required for kinetochore binding by a discrete subset of kMAPs (Bim1p, Bik1p, Slk19p) and motors (Cin8p, Kar3p), all of which are nonessential. Strikingly, dissociation of these proteins from kinetochores prevents bipolar attachment, even though the Ndc80 and DASH complexes, the two best-studied kMAPs, are still present. The failure of Spc105 deficient kinetochores to bind correctly to spindle microtubules and to recruit checkpoint proteins in yeast and human cells explains the observed severity of missegregation phenotypes

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

Programming biological models in Python using PySB

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis

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