Modular coherence of protein dynamics in yeast cell polarity system

The Path towards Predicting Evolution as Illustrated in Yeast Cell Polarity

A bottom-up route towards predicting evolution relies on a deep understanding of the complex network that proteins form inside cells. In a rapidly expanding panorama of experimental possibilities, the most difficult question is how to conceptually approach the disentangling of such complex networks. These can exhibit varying degrees of hierarchy and modularity, which obfuscate certain protein functions that may prove pivotal for adaptation. Using the well-established polarity network in budding yeast as a case study, we first organize current literature to highlight protein entrenchments inside polarity. Following three examples, we see how alternating between experimental novelties and subsequent emerging design strategies can construct a layered understanding, potent enough to reveal evolutionary targets. We show that if you want to understand a cell’s evolutionary capacity, such as possible future evolutionary paths, seemingly unimportant proteins need to be mapped and studied. Finally, we generalize this research structure to be applicable to other systems of interest.

A nonparametric significance test for sampled networks

Motivation

Our work is motivated by an interest in constructing a protein–protein interaction network that captures key features associated with Parkinson’s disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation.

Results

We provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork. The proposed method uses a Monte Carlo approach. As different seed lists can give rise to the same subnetwork, we control for redundancy by constructing a minimal seed list as the starting point for the significance test. The null model is based on random seed lists of the same length as a minimum seed list that generates the subnetwork; in this random seed list the nodes have (approximately) the same degree distribution as the nodes in the minimum seed list. We use this null model to select subnetworks which deviate significantly from random on an appropriate set of statistics and might capture useful information for a real world protein–protein interaction network.

Availability and implementation

The software used in this paper are available for download at https://sites.google.com/site/elliottande/. The software is written in Python and uses the NetworkX library.

Supplementary information

Supplementary data are available at Bioinformatics online.

SPLIFF: A Single-Cell Method to Map Protein-Protein Interactions in Time and Space

Protein interactions occur at certain times and at specific cellular places. The past years have seen a massive accumulation of binary protein-protein interaction data. The rapid increase of this context-free information necessitates robust methods to monitor protein interactions with temporal and spatial resolution in single cells. We have developed a simple split-ubiquitin-based method (SPLIFF) that uses the ratio of two fluorescent reporters as a signal for protein-protein interactions. One protein of the pair of interest is attached to the linear fusion of mCherry, the C-terminal half of ubiquitin, and GFP (mCherry-Cub-GFP). The other potential binding partner is expressed as a C-terminal fusion to the N-terminal half of ubiquitin (Nub). Upon co-expression the interaction between the two proteins of interest induces the reassociation of Nub and Cub to the native-like ubiquitin. GFP is subsequently cleaved from the C-terminus of Cub and degraded whereas the red-fluorescent mCherry stays attached to the Cub-fusion protein. We first implemented this method in the model yeast Saccharomyces cerevisiae. One fusion protein is expressed in cells of the a-mating type and the complementary fusion protein in cells of the α-mating type. Upon mixing, both cell types fuse and the Nub- and Cub-fusion proteins are free to interact. The red and green fluorescence is monitored by two-channel fluorescence time-lapse microcopy. The moment of cell fusion defines the start of the analysis. The calculated ratio of green to red fluorescence allows mapping the spatiotemporal interaction profiles of the investigated proteins in single cells.

ModuleRole: a tool for modulization, role determination and visualization in protein-protein interaction networks

Rapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network and defines the roles for every node, and produces files for visualization in Cytoscape and Pajek. For given proteins, it analyzes the PPI network from BioGRID database, finds and visualizes the modules these proteins form, and then defines the role every node plays in this network, based on two topological parameters Participation Coefficient and Z-score. This is the first program which provides interactive and very friendly interface for biologists to find and visualize modules and roles of proteins in PPI network. It can be tested online at the website http://www.bioinfo.org/modulerole/index.php, which is free and open to all users and there is no login requirement, with demo data provided by "User Guide" in the menu Help. Non-server application of this program is considered for high-throughput data with more than 200 nodes or user's own interaction datasets. Users are able to bookmark the web link to the result page and access at a later time. As an interactive and highly customizable application, ModuleRole requires no expert knowledge in graph theory on the user side and can be used in both Linux and Windows system, thus a very useful tool for biologist to analyze and visualize PPI networks from databases such as BioGRID.

AVAILABILITY

ModuleRole is implemented in Java and C, and is freely available at http://www.bioinfo.org/modulerole/index.php. Supplementary information (user guide, demo data) is also available at this website. API for ModuleRole used for this program can be obtained upon request.

Spatially distinct functions of Clb2 in the DNA damage response

In budding yeast four mitotic cyclins (Clb1-4) cooperate in a partially redundant manner to bring about M-phase specific events, including the apical isotropic switch that ends polarized bud growth initiated at bud emergence. How exactly this morphogenetic transition is regulated by mitotic CDKs remains poorly understood. We have taken advantage of the isotropic bud growth that prevails in cells responding to DNA damage to unravel the contribution of mitotic cyclins in this cellular context. We find that clb2∆, in contrast to the other mitotic cyclin mutants, inappropriately respond to the presence of DNA damage. This aberrant response is characterized by a Cdc42- and Bni1-dependent but Cln-independent resumption of polarized bud growth after a brief period of actin depolarization. Biochemical and genetic evidence is presented that formally excludes the possibility of indirect effects due for instance to unrestrained APC activity, untimely mitotic exit or Swe1-mediated CDK inhibition. Importantly, our data demonstrate that in order to maintain the characteristic dumbbell arrest phenotype upon checkpoint activation Clb2 needs to be efficiently exported into the cytoplasm. We propose that the inhibition of mitotic cyclin destruction by the DNA damage checkpoint pathway leads to a buildup of Clb2 in the cytoplasm where this cyclin can stabilize the apical isotropic switch throughout a G 2/M checkpoint arrest. Our study also unveils an essential role of nuclear Clb2 in both survival and adaptation to the DNA damage checkpoint, illustrating a spatially distinct dual function of this mitotic cyclin in the response to DNA damage.

A fluorescent reporter for mapping cellular protein-protein interactions in time and space

A method based on a combination of the Split-Ubiquitin system with two spectrally different fluorescent proteins (SPLIFF) is shown to enable measurement of protein interactions in vivo with high spatial and temporal resolution in yeast.

SPLIFF visualizes protein interactions with high spatial and temporal resolution.

Spc72p and Kar9p interact with the MAP Stu2p at opposite poles of microtubules.

Histone chaperone Nap1p and Kcc4 kinase interact preferentially at the bud site.

F-BAR protein Hof1p associates with the polarisome during cell fusion and cytokinesis.

We introduce a fluorescent reporter for monitoring protein–protein interactions in living cells. The method is based on the Split-Ubiquitin method and uses the ratio of two auto-fluorescent reporter proteins as signal for interaction (SPLIFF). The mating of two haploid yeast cells initiates the analysis and the interactions are followed online by two-channel time-lapse microscopy of the diploid cells during their first cell cycle. Using this approach we could with high spatio-temporal resolution visualize the differences between the interactions of the microtubule binding protein Stu2p with two of its binding partners, monitor the transient association of a Ran-GTPase with its receptors at the nuclear pore, and distinguish between protein interactions at the polar cortical domain at different phases of polar growth. These examples further demonstrate that protein–protein interactions identified from large-scale screens can be effectively followed up by high-resolution single-cell analysis.

Cell polarity: quantitative modeling as a tool in cell biology

Among a number of innovative approaches that have modernized cell biology, modeling has a prominent yet unusual place. One popular view is that we progress linearly, from conceptual to ever more detailed models. We review recent discoveries of cell polarity mechanisms, in which modeling played an important role, to demonstrate that the experiment-theory feedback loop requires diverse models characterized by varying levels of biological detail and mathematical complexity. We argue that a quantitative model is a tool that has to fit an experimental study, and the model's value should be judged not by how complex and detailed it is, but by what could be learned from it.