Evolution of Opposing Regulatory Interactions Underlies the Emergence of Eukaryotic Cell Cycle Checkpoints

In eukaryotes the entry into mitosis is initiated by activation of cyclin-dependent kinases (CDKs), which in turn activate a large number of protein kinases to induce all mitotic processes. The general view is that kinases are active in mitosis and phosphatases turn them off in interphase. Kinases activate each other by cross- and self-phosphorylation, while phosphatases remove these phosphate groups to inactivate kinases. Crucial exceptions to this general rule are the interphase kinase Wee1 and the mitotic phosphatase Cdc25. Together they directly control CDK in an opposite way of the general rule of mitotic phosphorylation and interphase dephosphorylation. Here we investigate why this opposite system emerged and got fixed in almost all eukaryotes. Our results show that this reversed action of a kinase-phosphatase pair, Wee1 and Cdc25, on CDK is particularly suited to establish a stable G2 phase and to add checkpoints to the cell cycle. We show that all these regulators appeared together in LECA (Last Eukaryote Common Ancestor) and co-evolved in eukaryotes, suggesting that this twist in kinase-phosphatase regulation was a crucial step happening at the emergence of eukaryotes

Babelomics 5.0: functional interpretation for new generations of genomic data

This article has been accepted for publication in Nucleic Acids Research Published by Oxford University Press.Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.Spanish Ministry of Economy and Competitiveness [BIO2011-27069], Conselleria d'Educacio of the Valencian Community [PROMETEOII/2014/025]; EU FP7-PEOPLE Project MLPM [316861]; Fundació la Marató TV3 [151/C/2013]. Funding for open access charge: Spanish Ministry of Economy and Competitiveness [BIO2011-27069]

Network emulation.

<p>Condensed and extended wiring diagrams of AM (above) and MI (below) and their deterministic behaviour in time-course diagrams. A morphism <i>m</i>:(S,R)→(S’,R’) between two reaction networks (S,R) and (S’,R’) is a mapping of species S (e.g., y₀, y₁, y_2, z₀, z₁, z₂ of MI) to species S’ (e.g., x₀, x₁, x₂ of AM, by corresponding colours) and of reactions R to reactions R’. Structural properties: A morphism that preserves the reactants and products of each reaction under the mapping is called a homomorphism. One that preserves stoichiometry under the mapping (by appropriately summing multiplicities and rates) is called a stoichiomorphism. These properties can be calculated directly on the network representation. Dynamical properties: A morphism <i>m</i> is an emulation if it preserves all trajectories of species concentrations over time under the mapping (e.g., the trajectories on the right are preserved). That is, <i>m</i> is an emulation if for any choice of initial conditions I’ for S’ there exist initial conditions I for S such that the trajectory of each species s in S overlaps exactly the trajectory of <i>m</i>(s) in S’. Theorem [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.ref044" target="_blank">44</a>]: A morphism that is a homomorphism and a stoichiomorphism is also an emulation.</p

AM and network notation.

<p>(A) We recast the AM algorithm of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.g001" target="_blank">Fig 1</a> as a wiring diagram. Left: the four reaction arrows correspond to the four state changes in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.g001" target="_blank">Fig 1</a> (the hollow circle stands for a catalytic reaction). The system presents two active states (X and Y) that are antagonist: X activates itself and inhibits Y, and Y activates itself and inhibits X. Background arrows indicate the generated feedbacks loops: red arrows represent the pure positive feedback loops and green ones represent the antagonistic double-negative, thus also positive, feedback loop. Right: a condensed representation of the same network according to the abbreviation explained in (B). The condensed graph shows that x activates itself (solid line indicates X’s actions) and inhibits itself (dashed line indicates Y’s actions). (B) Notation for condensed influence networks. A node X (right diagram) represents three species (x₀, x₁, x₂) and four reactions (left diagram). A node X influences other nodes when it is in either of its extremal states: 0 state (Out0—solid outgoing edge) or 2 state (Out2—dashed outgoing edge). A node can be activated (ball-end edge) or inhibited (bar-end edge) by other nodes, the reactions of which drive the node between its various states. Note how the collapsed notation in (B) collapses the network (A, left) into the network (A, right). In the sequel, we mostly draw collapsed networks, which can be systematically expanded.</p

Similarity in switching dynamics between approximate majority (AM) and two cell cycle model systems.

<p>Top: AM, represented as an influence network in the notation of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.g002" target="_blank">Fig 2</a>. CC: The classical cell cycle module of the G2/M transition regulation, represented as a molecular level wiring diagram in the notation of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.g002" target="_blank">Fig 2</a>, with a and i serving as proxies for a constant phosphatase counteracting the effects of Cdk. GW: The cell cycle model extended with the regulation of phosphatases (PP1 and PP2A) through the Greatwall kinase pathway [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.ref045" target="_blank">45</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.ref046" target="_blank">46</a>], represented as molecular wiring diagram. Bottom: Examples of the deterministic behaviour of each network initiated from an undecided state in which all species are present in similar amounts. The AM and the GW systems show equal dynamics. Only the various forms of Cdk of the CC system resemble the behaviour of AM and GW, but because of the external influence of a and i, the other species do not overlap with these while they properly align with them in GW.</p

Biological networks with switching dynamics.

<p>(A) The epigenetic switch model proposed by Dodd et al. (2007) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.ref061" target="_blank">61</a>]. (B) The polarity regulatory model of Motegi et al. (2013) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.ref063" target="_blank">63</a>]. (C) The septation initiation network asymmetry establishment model of Bajpai et al. (2013) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.ref064" target="_blank">64</a>]. The middle panels show the respective models with the condensed network notation, in which each node (molecule) represents three forms: inactive, non-decided, and active (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.g002" target="_blank">Fig 2</a>); the right panels show the behaviour of the models when initiated from equal initial conditions and simulated with equal parameter values (all rates = 1). On panels B and C, only three traces are visible, as they totally overlap with the other three traces.</p

Cell cycle oscillations controlled by the GW switch.

<p>The extended GW model of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.g003" target="_blank">Fig 3B</a> is embedded into the negative feedback loop in which Cdk activates Cdc20 and by this induces its own removal. (A) Model with autocatalytic activation of PP1/PP2A. (B) Model with PP1/PP2A activated by Cdc20. (C, D) Simulations of the models on A and B, respectively. On the left panels, the GW model is drawn in black, the negative feedback loop components in light grey (following [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.ref026" target="_blank">26</a>]), and the new proposed interaction in purple.</p

Population protocols.

<p>A communication protocol is, in general, a set of rules about how a set of agents can interact. In population protocols, in particular, these rules very closely mirror assumptions about chemical solutions. Namely, agents interact only in pairs (like in a molecular collision), the next pair to interact is chosen randomly (like in a well-mixed solution), each agent can have only a finite number of states (like phosphorylation states), and each interaction can result in a change of state in either or both agents. It is therefore easy to draw a parallel between agent states and chemical species (interacting molecules) and between binary interactions and bimolecular reactions (unimolecular reactions can be handled as a special case). In that way, population algorithms can be translated into chemical reaction networks and vice versa [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.ref023" target="_blank">23</a>]. Population protocols are also used at a different abstraction level, modelling interactions between species of a biological population [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005100#pcbi.1005100.ref024" target="_blank">24</a>]. The set of four state changes in this figure implements the Approximate Majority population protocol. Each square is an agent in one of its three possible states (colours). Pairs of agents have the potential to interact according to the left-hand-side patterns of the state changes and produce new states according to the corresponding right-hand-side patterns. The interactions that actually happen are determined randomly, but starting from the configuration on the top left they may result in four steps in the new configuration on the bottom left, where further interactions become possible.</p