Extent of in vivo binding by an upstream activation factor and the role of multiple binding sites in synergistic transcriptional activation

Uncovering Biological Network Function via Graphlet Degree Signatures

Motivation

Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker's yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI) networks. Since proteins interact to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines.

Results

We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method summarizes a protein's local topology in a PPI network into the vector of graphlet degrees called the signature of the protein and computes the signature similarities between all protein pairs. We group topologically similar proteins under this measure in a PPI network and show that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research such as disease protein prediction.

Availability

Data is available upon request.

Compensatory energetic relationships between upstream activators and the RNA polymerase II general transcription machinery

Activation of RNA polymerase II transcription in vivo and in vitro is synergistic with respect to increasing numbers of activator binding sites or increasing concentrations of activator. The Epstein-Barr virus ZEBRA protein manifests both forms of synergy during activation of genes involved in the viral lytic cycle. The synergy has an underlying mechanistic basis that we and others have proposed is founded largely on the energetic contributions of (i) upstream ZEBRA binding to its sites, (ii) the general pol II machinery binding to the core promoter, and (iii) interactions between ZEBRA and the general machinery. We hypothesize that these interactions form a network for which a minimum stability must be attained to activate transcription. One prediction of this model is that the energetic contributions should be reciprocal, such that a strong core promoter linked to a weak upstream promoter would be functionally analogous to a weak core linked to a strong upstream promoter. We tested this view by measuring the transcriptional response after systematically altering the upstream and core promoters. Our data provide strong qualitative support for this hypothesis and provide a theoretical basis for analyzing Epstein-Barr virus gene regulation.