Cyclin E2, the cycle continues

The eukaryotic cell cycle is regulated by a family of serine/threonine protein kinases known as cyclin-dependent kinases (CDKs). The activation of a CDK is dependent on its association with a cyclin regulatory subunit. The formation of distinct cyclin-CDK complexes controls the progression through the first gap phase (G(1)) and initiation of DNA synthesis (S phase). These complexes are in turn regulated by protein phosphorylation and cyclin-dependent kinase inhibitors (CKIs). Cyclin E2 has emerged as the second member of the E-type cyclin family. Cyclin E2-associated kinase activity is regulated in a cell cycle dependent manner with peak activity at the G(1) to S transition. Ectopic expression of cyclin E2 in human cells accelerates G(1), suggesting that cyclin E2 is rate limiting for G(1) progression. Although the pattern and level of cyclin E2 expression in some primary tumor and normal tissue RNAs are distinct from cyclin E1, both E-type cyclins appear to have inherent functional redundancies. This functional redundancy has facilitated the rapid characterization of cyclin E2 and uncovered unique features associated with each E-type cyclin.     

Distinct roles for cyclins E and A during DNA replication complex assembly and activation

Initiation of DNA replication is regulated by cyclin-dependent protein kinase 2 (Cdk2) in association with two different regulatory subunits, cyclin A and cyclin E (reviewed in ref. 1). But why two different cyclins are required and why their order of activation is tightly regulated are unknown. Using a cell-free system for initiation of DNA replication that is based on G1 nuclei, G1 cytosol and recombinant proteins, we find that cyclins E and A have specialized roles during the transition from G0 to S phase. Cyclin E stimulates replication complex assembly by cooperating with Cdc6, to make G1 nuclei competent to replicate in vitro. Cyclin A has two separable functions: it activates DNA synthesis by replication complexes that are already assembled, and it inhibits the assembly of new complexes. Thus, cyclin E opens a 'window of opportunity' for replication complex assembly that is closed by cyclin A. The dual functions of cyclin A ensure that the assembly phase (G1) ends before DNA synthesis (S) begins, thereby preventing re-initiation until the next cell cycle.     

A method for analyzing the ubiquitination and degradation of aurora-A

The cell cycle machinery consists of regulatory proteins that control the progression through the cell cycle ensuring that DNA replication alternates with DNA segregation in mitosis to maintain cell integrity. Some of these key regulators have to be degraded at each cell cycle to prevent cellular dysfunction. Mitotic exit requires the inactivation of cyclin dependent kinase1 (cdk1) and it is the degradation of the cyclin subunit that inactivates the kinase. Cyclin degradation has been well characterized and it was shown that it is ubiquitin proteasome pathway that leads to the elimination of cyclins. By now, many other regulatory proteins were shown to be degraded by the same pathway, among them members of the aurora kinase family, degraded many other regulatory proteins. Aurora kinases are involved in mitotic spindle formation as well as in cytokinesis. The abundance and activity of the kinase is precisely regulated during the cell cycle. To understand how proteolysis regulates transitions through the cell cycle we describe two assays for ubiquitination and degradation of xenopus aurora kinase A using extracts from xenopus eggs or somatic cell lines. Published: November 11, 2002     

Developmental downregulation of Xenopus cyclin E is phosphorylation and nuclear import dependent and is mediated by ubiquitination

Cyclins are regulatory subunits that bind to and activate catalytic Cdks. Cyclin E associates with Cdk2 to mediate the G1/S transition of the cell cycle. Cyclin E is overexpressed in breast, lung, skin, gastrointestinal, cervical, and ovarian cancers. Its overexpression correlates with poor patient prognosis and is involved in the etiology of breast cancer. We have been studying how cyclin E is normally downregulated during development in order to determine if disruption of similar mechanisms could either contribute to its overexpression in cancer, or be exploited to decrease its expression. In Xenopus laevis embryos, cyclin E protein level is high and constant until its abrupt destabilization by an undefined mechanism after the 12th cell cycle, which corresponds to the midblastula transition (MBT) and remodeling of the embryonic to the adult cell cycle. Since degradation of mammalian cyclin E is regulated by the ubiquitin proteasome system and is phosphorylation dependent, we examined the role of phosphorylation in Xenopus cyclin E turnover. We show that similarly to human cyclin E, phosphorylation of serine 398 and threonine 394 plays a role in cyclin E turnover at the MBT. Immunofluorescence analysis shows that cyclin E relocalizes from the cytoplasm to the nucleus preceding its degradation. When nuclear import is inhibited, cyclin E stability is markedly increased after the MBT. To investigate whether degradation of Xenopus cyclin E is mediated by the proteasomal pathway, we used proteasome inhibitors and observed a progressive accumulation of cyclin E in the cytoplasm after the MBT. Ubiquitination of cyclin E precedes its proteasomal degradation at the MBT. These results show that cyclin E destruction at the MBT requires both phosphorylation and nuclear import, as well as proteasomal activity.     

Cyclin F is degraded during G2-M by mechanisms fundamentally different from other cyclins

Cyclin F, a cyclin that can form SCF complexes and bind to cyclin B, oscillates in the cell cycle with a pattern similar to cyclin A and cyclin B. Ectopic expression of cyclin F arrests the cell cycle in G(2)/M. How the level of cyclin F is regulated during the cell cycle is completely obscure. Here we show that, similar to cyclin A, cyclin F is degraded when the spindle assembly checkpoint is activated and accumulates when the DNA damage checkpoint is activated. Cyclin F is a very unstable protein throughout much of the cell cycle. Unlike other cyclins, degradation of cyclin F is independent of ubiquitination and proteasome-mediated pathways. Interestingly, proteolysis of cyclin F is likely to involve metalloproteases. Rapid destruction of cyclin F does not require the N-terminal F-box motif but requires the COOH-terminal PEST sequences. The PEST region alone is sufficient to interfere with the degradation of cyclin F and confer instability when fused to cyclin A. These data show that although cyclin F is degraded at similar time as the mitotic cyclins, the underlying mechanisms are entirely distinct.     

The N-terminal Regulatory Domain of Cyclin A Contains Redundant Ubiquitination Targeting Sequences and Acceptor Sites

Cyclin A is destroyed during mitosis by the ubiquitin-proteasome system. Like cyclin B, a destruction box (D-box) motif is required for the destruction of cyclin A. However, Cyclin A degradation is more complicated than cyclin B because cyclin A’s D-box motif is more extensive and proteolysis involves complex signaling in some organisms. In this study, we found that in addition to the D-box, the region between residues 123-157 also contributed to the ubiquitination and degradation of human cyclin A. Indeed, removal of the bulk of the N-terminal regulatory domain was needed to completely stabilize cyclin A and eliminate ubiquitination. A putative second RxxL motif around residue 138 played only a minor role in cyclin A degradation. To distinguish between sequences recognized by the ubiquitination machinery and the ubiquitin acceptor sites per se, we utilized a novel approach involving in vitro cleavage of cyclin A after ubiquitination. We found that several lysine residues proximal to the D-box (Lys37, Lys54, and Lys68) were ubiquitin acceptor sites. Cyclin A lacking the three lysine residues was degraded slower than the wild-type protein. Although these lysines were normally used, ubiquitination could shift to other cryptic sites when the preferred sites were unavailable, suggesting the exact positions of the ubiquitin chains also contributed to degradation. Together, these data revealed that ubiquitination does not occur randomly on cyclin A and open up questions on the precise function of the D-box.     

TPR proteins required for anaphase progression mediate ubiquitination of mitotic B-type cyclins in yeast.

The abundance of B-type cyclin-CDK complexes is determined by regulated synthesis and degradation of cyclin subunits. Cyclin proteolysis is required for the final exit from mitosis and for the initiation of a new cell cycle. In extracts from frog or clam eggs, degradation is accompanied by ubiquitination of cyclin. Three genes, CDC16, CDC23, and CSE1 have recently been shown to be required specifically for cyclin B proteolysis in yeast. To test whether these genes are required for cyclin ubiquitination, we prepared extracts from G1-arrested yeast cells capable of conjugating ubiquitin to the B-type cyclin Clb2. The ubiquitination activity was cell cycle regulated, required Clb2's destruction box, and was low if not absent in cdc16, cdc23, cdc27, and cse1 mutants. Furthermore all these mutants were also defective in ubiquitination of another mitotic B-type cyclin, Clb3. The Cdc16, Cdc23, and Cdc27 proteins all contain several copies of the tetratricopeptide repeat and are subunits of a complex that is required for the onset of anaphase. The finding that gene products that are required for ubiquitination of Clb2 and Clb3 are also required for cyclin proteolysis in vivo provides the best evidence so far that cyclin B is degraded via the ubiquitin pathway in living cells. Xenopus homologues of Cdc16 and Cdc27 have meanwhile been shown to be associated with a 20S particle that appears to function as a cell cycle-regulated ubiquitin-protein ligase.     

Modeling stochasticity and variability in gene regulatory networks

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.     

The transition from differential equations to Boolean networks: a case study in simplifying a regulatory network model

Methods for modeling cellular regulatory networks as diverse as differential equations and Boolean networks co-exist, however, without much closer correspondence to each other. With the example system of the fission yeast cell cycle control network, we here discuss these two approaches with respect to each other. We find that a Boolean network model can be formulated as a specific coarse-grained limit of the more detailed differential equations model for this system. This demonstrates the mathematical foundation on which Boolean networks can be applied to biological regulatory networks in a controlled way.     

A proposal for using the ensemble approach to understand genetic regulatory networks

Understanding the genetic regulatory network comprising genes, RNA, proteins and the network connections and dynamical control rules among them, is a major task of contemporary systems biology. I focus here on the use of the ensemble approach to find one or more well-defined ensembles of model networks whose statistical features match those of real cells and organisms. Such ensembles should help explain and predict features of real cells and organisms. More precisely, an ensemble of model networks is defined by constraints on the "wiring diagram" of regulatory interactions, and the "rules" governing the dynamical behavior of regulated components of the network. The ensemble consists of all networks consistent with those constraints. Here I discuss ensembles of random Boolean networks, scale free Boolean networks, "medusa" Boolean networks, continuous variable networks, and others. For each ensemble, M statistical features, such as the size distribution of avalanches in gene activity changes unleashed by transiently altering the activity of a single gene, the distribution in distances between gene activities on different cell types, and others, are measured. This creates an M-dimensional space, where each ensemble corresponds to a cluster of points or distributions. Using current and future experimental techniques, such as gene arrays, these M properties are to be measured for real cells and organisms, again yielding a cluster of points or distributions in the M-dimensional space. The procedure then finds ensembles close to those of real cells and organisms, and hill climbs to attempt to match the observed M features. Thus obtains one or more ensembles that should predict and explain many features of the regulatory networks in cells and organisms.     

Symbolic reachable set computation of piecewise affine hybrid automata and its application to biological modelling: Delta-Notch protein signalling

Hybrid automata are an eminently suitable modelling framework for biological protein regulatory networks, as the protein concentration dynamics inside each biological cell are modelled using linear differential equations; inputs activate or deactivate these continuous dynamics through discrete switches, which themselves are controlled by protein concentrations reaching given thresholds. This paper proposes an iterative refinement algorithm for computing discrete abstractions of a class of hybrid automata with piecewise affine continuous dynamics and forced discrete transitions, defined completely in terms of symbolic variables and parameters. Furthermore, these discrete abstractions are utilised to compute symbolic parametric backward reachable sets from the equilibria of the hybrid automata, that are guaranteed to be exact or conservative under-approximations. The algorithm is then implemented using MATLAB and QEPCAD, to compute reachable sets for the biologically observed equilibria of the multiple cell Delta-Notch protein signalling automaton with symbolic parameters. The results are analysed to show that novel, non-intuitive, and biologically interesting properties can be deduced from the reachability computation, thus demonstrating the utility of the algorithm.     

Human cyclin F.

Cyclins are important regulators of cell cycle transitions through their ability to bind and activate cyclin-dependent protein kinases. In mammals several classes of cyclins exist which are thought to co-ordinate the timing of different events necessary for cell cycle progression. Here we describe the identification of a novel human cyclin, cyclin F, isolated as a suppressor of the G1/S deficiency of a Saccharomyces cerevisiae cdc4 mutant. Cyclin F is the largest cyclin, with a molecular weight of 87 kDa, and migrates as a 100-110 kDa protein. It contains an extensive PEST-rich C-terminus and a cyclin box region that is most closely related to cyclins A and B. Cyclin F mRNA is ubiquitiously expressed in human tissues. It fluctuates dramatically through the cell cycle, peaking in G2 like cyclin A and decreasing prior to decline of cyclin B mRNA. Cyclin F protein accumulates in interphase and is destroyed at mitosis at a time distinct from cyclin B. Cyclin F shows regulated subcellular localization, being localized in the nucleus in most cells, with a significant percentage of cells displaying only perinuclear staining. Overexpression of cyclin F, or a mutant lacking the PEST region, in human cells resulted in a significant increase in the G2 population, implicating cyclin F in the regulation of cell cycle transitions. The ubiquitous expression and phylogentic conservation of cyclin F suggests that it is likely to coordinate essential cell cycle events distinct from those regulated by other cyclins.     

Integrative analysis of cell cycle control in budding yeast

The adaptive responses of a living cell to internal and external signals are controlled by networks of proteins whose interactions are so complex that the functional integration of the network cannot be comprehended by intuitive reasoning alone. Mathematical modeling, based on biochemical rate equations, provides a rigorous and reliable tool for unraveling the complexities of molecular regulatory networks. The budding yeast cell cycle is a challenging test case for this approach, because the control system is known in exquisite detail and its function is constrained by the phenotypic properties of >100 genetically engineered strains. We show that a mathematical model built on a consensus picture of this control system is largely successful in explaining the phenotypes of mutants described so far. A few inconsistencies between the model and experiments indicate aspects of the mechanism that require revision. In addition, the model allows one to frame and critique hypotheses about how the division cycle is regulated in wild-type and mutant cells, to predict the phenotypes of new mutant combinations, and to estimate the effective values of biochemical rate constants that are difficult to measure directly in vivo.