Comparative interaction networks: bridging genotype to phenotype

Abstract

Over the past decade, biomedical research has witnessed an exponential increase in the throughput of the characterization of biological systems. Here we review the recent progress in large-scale methods to determine protein-protein, genetic and chemical-genetic interaction networks. We discuss some of the limitations and advantages of the different methods and give examples of how these networks are being used to study the evolutionary process. Comparative studies have revealed that different types of protein-protein interactions diverge at different rates with high conservation of co-complex membership but rapid divergence of more promiscuous interactions like those that mediate post-translational modifications. These evolutionary trends have consistent genetic consequences with highly conserved epistatic interactions within complex subunits but faster divergence of epistatic interactions across complexes or pathways. Finally, we discuss how these evolutionary observations are being used to interpret cross-species chemical-genetic studies and how they might shape therapeutic strategies. Together, these interaction networks offer us an unprecedented level of detail into how genotypes are translated to phenotypes, and we envision that they will be increasingly useful in the interpretation of genetic and phenotypic variation occurring within populations as well as the rational design of combinatorial therapeutics.

Fig. 7.1. Timeline of Bioinformatics, Genomic and Proteomic developments

We selected and illustrated here several important landmarks in the development of genomics, proteomics, and bioinformatics, over the past 40 years. Examples of these include: the atlas of protein sequences, published as a book, by the bioinformatics pioneer Margaret Dayhoff; the first protein sequence analysis algorithms like the Needleman–Wunsch algorithm and analysis of gap-penalty costs by Haber and Koshland [87]; the creation of a protein structure repository (P.D.B-Protein Data Bank) that in 1974 contained atomic coordinates for 12 proteins; the first full genome sequences (1982–phage lambda, 1995–E. coli and 1996–budding yeast) along with the creation of the GenBank database that started with 606 sequences; the several technological developments in mass spectrometry (MS) like the first use of MS for peptide sequencing (1966), of electrospray ionization for biomolecules (1984), and novel ion traps like the orbitrap (1999) with increased mass accuracy and resolution, culminating in the first large-scale quantification of protein abundances of an eukaryotic cell (2008)

Fig. 7.2. Timeline for the first large-scale protein–protein, epistatic, and chemical–genetic interaction networks in different species

We selected from the literature the first articles describing large-scale protein–protein, epistatic, and chemical–genetic interaction networks for several model organisms (E. coli, S. cerevisiae, S. pombe, the fly D. melanogaster, the worm C. elegans, and human). We illustrate here the timeline in which these studies were conducted with additional information provided in Table 7.1. Y2H, PPIs derived from yeast-two-hybrid; AP-MS, PPIs derived from affinity tag-purification followed by mass spectrometry

Fig. 7.3. Evolution of co-complex interactions in the group II chaperonins

Computational studies have shown that protein complexes usually evolve by duplication and divergence of their subunits. The group II chaperonin complexes provide a good illustration of this general trend. The archeal group II chaperonin complexes (termed thermosomes) usually contain 1–3 homologous chaperonins and it represented here by the thermosome of Thermococcus strain KS-1 (PDB:1Q2V). The eukaryotic complexes (called TriC or CCT) are composed of eight chaperonin paralogs, represented here by the S. cerevisiae CCT complex (PDB:3P9E). All of the subunits are structurally similar, exemplified here by the S. cerevisiae CCT1 subunit structure

Fig. 7.4. Evolution of epistatic interactions within and between modules

We compared the genetic interactions within and between modules for the SWR-C, HIR-C, and SET3-C complexes in S. cerevisiae and S. pombe. These illustrate the general trend that genetic interactions within complexes tend to be conserved across species while the genetic interactions between complexes diverge at a higher rate. In this example the positive genetic interactions measured within the SWR-C complex subunits is highly conserved between S. cerevisiae and S. pombe, while the negative genetic interactions between SWR-C and HIR-C and between SET3-C and HIR-C observed in S. pombe are not conserved in S. cerevisiae