A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae

Abstract

The relationship between the similarity of expression patterns for a pair of genes and interaction of the proteins they encode is demonstrated both for the simple genome of the bacteriophage T7 and the considerably more complex genome of the yeast Saccharomyces cerevisiae. Statistical analysis of large-scale gene expression and protein interaction data shows that protein pairs encoded by co-expressed genes interact with each other more frequently than with random proteins. Furthermore, the mean similarity of expression profiles is significantly higher for respective interacting protein pairs than for random ones. Such coupled analysis of gene expression and protein interaction data may allow evaluation of the results of large-scale gene expression and protein interaction screens as demonstrated for several publicly available datasets. The role of this link between expression and interaction in the evolution from monomeric to oligomeric protein structures is also discussed.

Coevolution of gene expression among interacting proteins

“…The expectation that expression levels should coevolve stems in part from the observation that the expression levels of genes encoding interacting proteins are strongly correlated over different experimental conditions in Saccharomyces cerevisiae (9)(10)(11). This observation is thought to reflect the requirement for interacting proteins to be present in the cell in similar amounts at the same time to properly form stoichiometric complexes and execute their function.…”

Bioinformatics Study of Functional Associations Observed in Multiple Sources of Human Genome Data

“…Consistent with this reasoning, it was shown that yeast genes with similar expression profiles are more likely to encode interacting proteins than randomly chosen genes [13]. A related study of the yeast genome showed that genes encoding interacting proteins exhibit higher than average co-expression [12]. This study also showed that the yeast protein-protein interaction (PPI) dataset contains a larger proportion of strongly co-expressed proteins, compared to their baseline proportion in the entire yeast proteome.…”

“…The integrative approach can also be used for predicting properties of one type of data based on other types of 'omic' (genomic, proteomic, etc) data [2,[5][6][7], for evaluating 'omic' datasets [8], and for functional prediction and inference [9][10][11]. Such a promise of the integrative approach is based on the general assumption that, within a given genome, there exist inter-relationships between heterogeneous types of genomic data [12]. Since even seemingly different data types describe various functional aspects of the same genome (e.g., the human genome), it seems reasonable to anticipate the existence of non-random associations among them.…”

Protein Interactome and Its Application to Protein Function Prediction

“…(Aloy & Russell, 2003;Ezkurdia et al, 2009;Hosur et al, 2011;Shoemaker et al, 2010;Singh et al, 2010;Zhang et al, 2010). A huge amount of genome-wide gene expression profiles are another useful data to predict PPIs and they are investigated to define gene co-expression patterns of any pairs and consider higher correlation degree as higher probability of PPIs (Grigoriev, 2001;Lukk et al, 2010;Stuart et al, 2003). As shown in the earlier section, there are many literature-curated PPI databases.…”