Relating whole-genome expression data with protein-protein interactions.

By R. Jansen, D. Greenbaum, Mark Gerstein. Genome Research, 2002

683 citations recorded by the source corpus.

We investigate the relationship of protein-protein interactions with mRNA expression levels, by integrating a variety of data sources for yeast. We focus on known protein complexes that have clearly defined interactions between their subunits. We find that subunits of the same protein complex show significant coexpression, both in terms of similarities of absolute mRNA levels and expression profiles, e.g., we can often see subunits of a complex having correlated patterns of expression over a time course. We classify the yeast protein complexes as either permanent or transient, with permanent ones being maintained through most cellular conditions. We find that, generally, permanent complexes, such as the ribosome and proteasome, have a particularly strong relationship with expression, while transient ones do not. However, we note that several transient complexes, such as the RNA polymerase II holoenzyme and the replication complex, can be subdivided into smaller permanent ones, which do have a strong relationship to gene expression. We also investigated the interactions in aggregated, genome-wide data sets, such as the comprehensive yeast two-hybrid experiments, and found them to have only a weak relationship with gene expression, similar to that of transient complexes. (Further details on genecensus.org/expression/interactions and bioinfo.mbb.yale.edu/expression/interactions.)

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1897a39f137039868110c5abd9331b240866e7e7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Functional topology in a network of protein interactions (2004, background): “It has been observed that PPI data uncovers both stable and transient complexes (Jansen et al., 2002).”
Gaining confidence in high-throughput protein interaction networks (2004, background): “Interacting proteins whose transcripts are coexpressed are more likely to be credibl”
FlyMine: an integrated database for Drosophila and Anopheles genomics (2007, background): “Studies with Saccharomyces cerevisiae, for example, have indicated that combining protein-protein interaction and gene expression data to identify potential interacting proteins that are also co-expressed is a powerful way to cross-validate noisy protein interaction data [3-6].”
Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae*S (2007, background): “Finally it is known that genes encoding physically interacting proteins are more likely to have similar expression profiles (10, 23, 27, 28), and so we examined the distribution of Pearson correlation coefficients between expression patterns of interacting pairs over a set of 1000 previously publish”
A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference (2010, background): “, 2004), they are highly correlated with the expression profile (Jansen et al., 2002).”
Comparing protein abundance and mRNA expression levels on a genomic scale (2003, neutral): “Correlations have been found between the mRNA expression levels of different protein subunits within protein complexes [49].”
A Protein Complex Network of Drosophila melanogaster (2011, methodology): “Following Jansen et al. (2002), we calculated the normalized differences between absolute mRNA expression levels from the modEncode RNA-Seq data (Cherbas et al., 2011) and confirmed this trend in flies (Figure 3D).”
A gene expression map of Arabidopsis thaliana development (2005, background): “, genes encoding components of conserved protein complexes such as the ribosome are coexpressed in other organism”
Graph theory and networks in Biology. (2006, background): “On the other hand, the node-degrees in networks with broad-tailed distributions 1In fact, the form P (k) ∼ (k + k0)−γe−k/kc with offset k0 and an exponential cutoff kc is more usually fitted to real network data. vary substantially from their mean value, and 〈k〉 cannot be thought of as a typical…”
Interactome: gateway into systems biology. (2005, background): “…types of data (133), either data from alternative PPI approaches (101,134,135), or data from altogether different functional genomics approaches (17,120,129,136–138), or both (79,96,102), with interactome maps is the most effective way to assign function to uncharacterized proteins that are…”
Discovering molecular pathways from protein interaction and gene expression data (2003, background): “…properties is supported by the work of Ge et al. (2001), that showed that genes with similar expression proﬁles are more likely to encode interacting proteins, and by the study of Jansen et al. (2002), which showed that the genes of experimentally derived protein complexes are often co-expressed.”
What is Bioinformatics? A Proposed Definition and Overview of the Field (2001, background): “However, a recent study showed that this relationship is not so simple [ 103 ].”
The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics (2007, background): “Besides directionality, another important but often over-looked aspect of interaction networks is that there are two major classes of interactions: permanent and transient [25,26].”
Network-based prediction of protein function (2007, methodology): “Other works used the reverse approach by analyzing the expression coherence of known pathways or complexes (Zien et al , 2000; Jansen et al , 2002; Tornow and Mewes, 2003; Simonis et al , 2004).”
Identification of functional modules using network topology and high-throughput data (2007, methodology): “An efficient computation of this step is done as suggested in [15].”