An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

The Role of relA and spoT in Yersinia pestis KIM5+ Pathogenicity

The ppGpp molecule is part of a highly conserved regulatory system for mediating the growth response to various environmental conditions. This mechanism may represent a common strategy whereby pathogens such as Yersinia pestis, the causative agent of plague, regulate the virulence gene programs required for invasion, survival and persistence within host cells to match the capacity for growth. The products of the relA and spoT genes carry out ppGpp synthesis. To investigate the role of ppGpp on growth, protein synthesis, gene expression and virulence, we constructed a ΔrelA ΔspoT Y. pestis mutant. The mutant was no longer able to synthesize ppGpp in response to amino acid or carbon starvation, as expected. We also found that it exhibited several novel phenotypes, including a reduced growth rate and autoaggregation at 26°C. In addition, there was a reduction in the level of secretion of key virulence proteins and the mutant was>1,000-fold less virulent than its wild-type parent strain. Mice vaccinated subcutaneously (s.c.) with 2.5×104 CFU of the ΔrelA ΔspoT mutant developed high anti-Y. pestis serum IgG titers, were completely protected against s.c. challenge with 1.5×105 CFU of virulent Y. pestis and partially protected (60% survival) against pulmonary challenge with 2.0×104 CFU of virulent Y. pestis. Our results indicate that ppGpp represents an important virulence determinant in Y. pestis and the ΔrelA ΔspoT mutant strain is a promising vaccine candidate to provide protection against plague