Given a systematic approach for associating genetic interactions with physical interpretations, it is of interest to ask which type of interpretation is most common. Focusing on large-scale SGA measurements, roughly three-and-a-half times as many genetic interactions are associated with between- as opposed to within-pathway models (1,377 versus 394 SGA interactions). These figures can be viewed as an a priori expectation that a newly determined SGA interaction will fall between versus within pathways, suggesting that SGA interactions typically span between multiple physical network regions instead of occurring within a single complex or pathway. One reason for the preference towards between-pathway models may be that SGA interactions are mainly targeted to nonessential genes (due to their use of complete gene deletions as opposed to, e.g., point mutations made by classical techniques).
Using physical models, it is possible to characterize approximately 40% of the genetic interactions as occurring between or within pathways. Whether the remaining interactions belong to between-pathway models, within-pathway models or are best characterized as ‘indirect’ (Box 1
) cannot be reliably determined at this stage. For example, consider the case of two related pathways, each with only one protein required for pathway function. In this case, only the required proteins would be connected by a (single) genetic interaction across the pathways, making it difficult for the between-pathway model to achieve statistical significance.
Further examination of the between-pathway models reveals that many of the genetically linked pathways have clear interdependent functional relationships. For example, pathway M contains members of the prefoldin complex, which have synthetic-lethal interactions with members of pathways N and T forming parts of the dynactin complex and kinetochore, respectively (). The prefoldin complex promotes folding of α- and β-tubulin into functional microtubules31
. These are important for the function of dynactin, an adaptor complex involved in translocating the spindle and other molecular cargos along microtubules32
, as well as the kinetochore, which anchors chromosomes to spindle microtubules during metaphase33
. Apparently, deletion of proteins in the prefoldin complex reduces micro-tubule stability, leading to synthetic-lethal interactions with pathways that are directly dependent on microtubule function.
These pathways also predict a new function for the uncharacterized protein Yll049w (pathway N). This protein binds Jnm1, a dynactin protein which is required for spindle partitioning in anaphase32
. In addition, it has synthetic-lethal interactions with members of the prefoldin complex in a manner similar to dynactin genes. Together, these relationships suggest that Yll049w is associated with dynactin during spindle partitioning. However, because Jnm1 has 12 physical interactions overall, and Yll049w has a total of 14 interactions in the genetic network, this prediction would have been difficult to make without an integrated approach.
Pathways O, U and Y provide another example of synergistic pathways linked by genetic interactions (). Pathways U and Y mediate retrograde transport of proteins to the Golgi apparatus34,35
. Pathway O (Bre1, Lge1) is involved in histone ubiquitination and cell size control, where cell size is influenced by the histone ubiquitination activity by an unknown process36
. The abundant genetic interactions between pathways O and U indicate a possible role for retrograde transport in histone ubiquitination, or reciprocally, for histone ubiquitination in retrograde transport. Moreover, the uncharacterized protein Yel043w is physically associated with Bre1 and Lge1 and also has the same pattern of genetic interactions, suggesting that the three proteins may function together.
In summary, we have presented a methodology for integrating large-scale genetic and physical networks to capture the physical context behind observed genetic interactions. Approximately 40% of yeast synthetic-lethal genetic interactions can be incorporated into high-level physical pathway models and are approximately three and a half times as likely to span pairs of pathways than to occur within pathways. Further studies will be needed to address other types of genetic effects to extend this approach from yeast to the growing number of other organisms for which protein networks are now available. As systematic approaches generate ever larger databases of interactions across a variety of species, integrative modeling approaches such as the one proposed here will be indispensable for selecting and organizing the information into predictive models.