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Research and Scientists
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Model Systems: Yeast Cell Differentiation
Tim Galitski
is seeking to understand biological information transmission from signaling circuits to transcriptional regulation paths
that control filamentous-form cell differentiation.
Significance:
Our work on signaling network mapping and mechanisms has added a new regulatory dimension, protein-RNA interactions, to
the networks controlling yeast cell differentiation, a prototypical signaling system. These findings establish a
biological function for the emerging genome-scale network of protein-RNA interactions.
We have predicted the effects of genetic interaction on genomic expression and phenotype. Our work provides a
systematic strategy for network modeling that predicts the effects of genetic interactions on genomic expression
and cell differentiation in yeast. The merits of this study are the novel data analysis methods for decomposing
genetic complexity, the strategic integration of data from high-throughput sources, the predictive power of the
resulting network model, and the experimental verification of multiple explicit predictions.
Through microfluidics and imaging, we have completed designs for an integrated microfluidic platform for
high-throughput multiparameter dynamic microenvironment control and imaging. This platform will enable us
to experiment directly on the signaling dynamics and gene-expression output of the yeast filamentation
system at the level of individual cells.
Research and Results:
Continuing our previous work on the structure and function of the yeast-filamentation signaling network,
we have investigated a role for the interface of this network with protein-RNA interaction networks.
Signaling-protein mRNAs tend to have long untranslated regions (UTRs) containing binding sites for RNA-binding
proteins regulating gene expression. We have shown that a PUF-family RNA-binding protein, Mpt5, represses the
yeast MAP-kinase pathway controlling differentiation to the filamentous form. Mpt5 This RNA-binding protein
represses the protein levels of two pathway components, the Ste7 MAP-kinase kinase and the Tec1 transcriptional
activator, and negatively regulates the kinase activity of the Kss1 MAP kinase. Moreover, Mpt5 it specifically
inhibits the output of the pathway in the absence of stimuli, and thereby prevents inappropriate cell differentiation.
The results provide an example of what is likely to be a genome-scale level of regulation at the interface of
signaling networks and protein-RNA binding networks.
Prediction of genomic expression and phenotype for combinations of mutations
We integrated data from molecular and genetic interactions, complementary data types, to model the transcription
network controlling cell differentiation in yeast. This network included both known and novel regulatory
influences, and predicted genetic interactions. For corresponding combinations of mutations, the network model
predicted quantitative genomic expression profiles and precise phenotypic effects. Multiple predictions were
tested and verified.
Microfluidic approaches to kinetic phenotypes in signaling
We have focused on the development and application of single-cell microfluidics and imaging platform in collaboration with
Carl Hansen of the Proteomics Center at the
University of British Columbia to begin observing the dynamics of yeast filamentation signaling.
Plans:
We are applying genome-tiling microarrays to study the full transcriptome of yeast, and its regulation by the filamentation network.
Recent work from our group has demonstrated the biological importance of natural antisense control of gene expression in yeast, and
filamentation in particular. We will extend this study to the entire genome.
In our work on the complex genetics of interacting perturbations, we will: 1) extend our modeling capabilities to predict the effects
of gain-of-function perturbations; 2) conduct a further round of modeling and experimental testing using these extended models; 3)
magnify the throughput and quantitation of our phenotype assays with cell-filament imaging and computational analyses that classify
shapes by harmonic decomposition; 4) study the occurrence of network motifs in large-scale networks of mixed-mode genetic interactions.
We will integrate information we have collected into models of the filamentation network to study signaling dynamics and specificity.
We will develop dynamic and stochastic models of the interconnected mMAPK and fMAPK pathways. To test these models, we will use our
microfluidics and imaging capabilities to observe the dynamics of signaling specificity and crosstalk between the mating MAPK
and fMAPK pathways.
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Institute for Systems Biology
CSB Home
Center for Systems Biology at the Institute for Systems Biology
