Lars Juhl JensenEMBL Heidelberg

Dynamic complex formationduring the yeast cell cycle

© Chen et al., Mol. Biol. Cell, 2004

A qualitative model of the yeast cell cycle

• Should be accurate even at the level individual interactions

• Provides a global overview of temporal complex formation

Cell cycle microarray

data

Physical PPI interactions with

confidence scores

Expand the set of proteins to include non-periodic proteins that are strongly connected to

periodic proteins

Raw DataNode selection

List of periodically expressed proteins

with peak time

Interactions

Require compatible compartments and high confidence

Extract cell cycle network

Extracting a cell cycle interaction network

Cho & Spellman et al.

Yeast culture Microarrays Gene expression Expression profile

600 periodically expressed genes (with associated peak times) that encode “dynamic

proteins”

The parts listNew analysis

Getting the parts list

Topology based quality scores

• We developed scoring schemes for identifying sticky interactions

• Yeast two-hybrid data: S1 = -log((N1+1)·(N2+1))

• Scoring scheme for complex pull-down data: S2 = log[N12·N/((N1+1)·(N2+1))]

• Score calibration against KEGG A different raw quality score is

used for each evidence type Quality differences exist among

data sets of the same type

Filtering by subcellular localization

• Proteins cannot interact if they are not in the same place Large-scale subcellular localization screens have been made in yeast A matrix can be constructed that described the compartments between

which interactions should be allowed Two proteins cannot interact if no combination of observed subcellular

compartments allow for interaction

Benchmark of published interaction sets against the MIPS curated yeast complexes

• Data sets were filtered to remove the most obvious biases by removing ribosomal proteins and interactions obtained from MIPS

• High specificity is often obtained at the price of low coverage

The temporal interaction network

Interacting proteins are expressed close in time

Two thirds of the dynamic proteins lack interactions but likely participate in transient interactions

Static proteins comprise a third of the interactions at all times of the cell cycle

Their time of action can be predicted from interactions with dynamic proteins

Static proteins play a major role

Cdc28p and its interaction partners

Just-in-time synthesis vs. just-in-time assembly

Most dynamic proteins are expressed just before they are needed to carry out their function

Most complexes also contain static proteins

Just-in-time assembly of complexes appear to be the general principle

The time of assembly is controlled synthesizing the last subunits just-in-time

Assembly of the pre-replication complex

Network as a discovery tools

The network enables us to place 30+ uncharacterized proteins in a temporal interaction context

Quite detailed hypotheses can be made concerning the their function

The network also contains entire novel modules and complexes

Nucleosome / bud formation module

Rediscovering the “party” hubs and “date” hubs

“Date” hubs: the hub protein interacts with different proteins at different times.

“Party” hubs: the hub protein and its interactors are expressed close in time.

Transcription is linked to phosphorylation

A genome-wide screen identified 332 Cdc28p targets, which include 6% of all yeast proteins 8% of the static proteins 27% of the dynamic ones

A similar correlation was observed with predicted PEST regions

This suggests a hitherto undescribed link between transcriptional and post-translational control

Conclusions

• It is possible to construct highly reliable models from microarray data and high-throughput interaction screen

• Temporal interaction networks can provide an overview of how and when protein complexes are assembled

• Different mechanism for regulating protein activity appear to be tightly linked to each other

Acknowledgments

• The yeast cell cycle interaction network Ulrik de Lichtenberg Søren Brunak Peer Bork

• Re-analysis of cell cycle microarray expression data Thomas Skøt Jensen Anders Fausbøll

• Also thanks to Sean Hooper Christian von Mering

Thank you!