Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis

<p>Abstract</p> <p>Background</p> <p>High-throughput genomics has enabled the global mapping of genetic interactions based on the phenotypic impact of combinatorial genetic perturbations. An important next step is to understand how these networks are dynamically remodelled in response to environmental stimuli. Here, we report on the development and testing of a method to identify such interactions. The method was developed from first principles by treating the impact on cellular growth of environmental perturbations equivalently to that of gene deletions. This allowed us to establish a novel neutrality function marking the absence of epistasis in terms of sensitivity phenotypes rather than fitness. We tested the method by identifying fitness- and sensitivity-based interactions involved in the response to drug-induced DNA-damage of budding yeast <it>Saccharomyces cerevisiae </it>using two mutant libraries - one containing transcription factor deletions, and the other containing deletions of DNA repair genes.</p> <p>Results</p> <p>Within the library of transcription factor deletion mutants, we observe significant differences in the sets of genetic interactions identified by the fitness- and sensitivity-based approaches. Notably, among the most likely interactions, only ~50% were identified by both methods. While interactions identified solely by the sensitivity-based approach are modulated in response to drug-induced DNA damage, those identified solely by the fitness-based method remained invariant to the treatment. Comparison of the identified interactions to transcriptional profiles and protein-DNA interaction data indicate that the sensitivity-based method improves the identification of interactions involved in the DNA damage response. Additionally, for the library containing DNA repair mutants, we observe that the sensitivity-based method improves the grouping of functionally related genes, as well as the identification of protein complexes, involved in DNA repair.</p> <p>Conclusion</p> <p>Our results show that the identification of response-modulated genetic interactions can be improved by incorporating the effect of a changing environment directly into the neutrality function marking the absence of epistasis. We expect that this extension of conventional epistatic analysis will facilitate the development of dynamic models of gene networks from quantitative measurements of genetic interactions. While the method was developed for growth phenotype, it should apply equally well for other phenotypes, including the expression of fluorescent reporters.</p

Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ∼80% of the known relationships without any false positives

Transcriptional Dynamics of the Eukaryotic Cell

Gene regulatory networks are dynamic and continuously remodelled in response to internal and external stimuli. To understand how these networks alter cellular phenotype in response towards specific challenges, my first project sought to develop a methodology to explore how the strength of genetic interactions changes according to environmental context. Defined as sensitivity-based epistasis, the results obtained using this methodology were compared to those generated under the conventional fitness-based approach. By integrating this information with gene expression profiles and physical interaction datasets, we demonstrate that sensitivity-based epistasis specifically highlights genetic interactions with a dynamic component. Having investigated how an external stimulus regulates network dynamics, we next sought to understand of how genome positioning impacts transcription kinetics. This feat was accomplished by cloning two gene-reporter constructs, representing contrasting promoter architectures, across 128 loci along chromosome III in S.Cerevisiae. By comparing expression and noise measurements for promoters with “covered” and “open” chromatin structures against a stochastic model for eukaryotic gene expression, we demonstrate that while promoter structure regulates burst frequency (the rate of promoter activation), positional effects in turn appear to primarily modulate burst size (the number of mRNA produced per gene activation event). By integrating these datasets with information describing global chromatin structure, we suggest that the acetylation state of chromatin regulates burst size across the genome. Interestingly, this hypothesis is further supported by nicotinamide-mediated inhibition of Sir2 which would appear to modulate burst size globally across the genome

Development of a Mathematical Model to Understand, Design & Improve Oncolytic Virus Therapies

Oncolytic viruses (OVs) are emerging as a potent therapeutic platform for the treatment of malignant disease. The tumor cells inability to induce antiviral defences in response to a small cytokine known as interferon (IFN) is a common defect exploited by OVs. Heterogeneity in IFN signalling across tumors is therefore a pillar element of resistance to these therapies. I have generated a mathematical model and simulation platform to study the impact of IFN on OV dynamics in normal and cancerous tissues. In the first part of my thesis, I used this model to identify novel OV engineering strategies which could be implemented to overcome IFN based resistance in tumor tissues. From these simulations, it appears that a positive feedback loop, established by virus-mediated expression of an interferon-binding decoy receptor, could increase tumor cytotoxicity without compromising normal cells. The predictions set forth by this model have been validated both qualitatively and quantitatively in in-vitro and in-vivo models using two independent OV strains. This model has subsequently been used to investigate OV attenuation mechanisms, the impact of tumor cell heterogeneity, as well as drug-OV interactions. Following these results, it became apparent that selectivity should equally be observed when overwhelming the cell with a non replicating virus. While normal tissues will clear this pseudo-infection rapidly, owing to their high baseline in antiviral products at the onset of infection, tumor cells with defective anti-viral pathways should not have readily available biomachinery required to degrade this pro-apoptotic signal. Recapitulated by the mathematical model, non-replicating virus-derived particles generated by means of UV irradiation selectively kill tumor cells in cultured cell lines and patient samples, leading to long term cures in murine models. Taken together, this thesis uses a novel mathematical model and simulation platform to understand, design & improve oncolytic virus-based therapeutics

Transcriptional Dynamics of the Eukaryotic Cell

Gene regulatory networks are dynamic and continuously remodelled in response to internal and external stimuli. To understand how these networks alter cellular phenotype in response towards specific challenges, my first project sought to develop a methodology to explore how the strength of genetic interactions changes according to environmental context. Defined as sensitivity-based epistasis, the results obtained using this methodology were compared to those generated under the conventional fitness-based approach. By integrating this information with gene expression profiles and physical interaction datasets, we demonstrate that sensitivity-based epistasis specifically highlights genetic interactions with a dynamic component. Having investigated how an external stimulus regulates network dynamics, we next sought to understand of how genome positioning impacts transcription kinetics. This feat was accomplished by cloning two gene-reporter constructs, representing contrasting promoter architectures, across 128 loci along chromosome III in S.Cerevisiae. By comparing expression and noise measurements for promoters with “covered” and “open” chromatin structures against a stochastic model for eukaryotic gene expression, we demonstrate that while promoter structure regulates burst frequency (the rate of promoter activation), positional effects in turn appear to primarily modulate burst size (the number of mRNA produced per gene activation event). By integrating these datasets with information describing global chromatin structure, we suggest that the acetylation state of chromatin regulates burst size across the genome. Interestingly, this hypothesis is further supported by nicotinamide-mediated inhibition of Sir2 which would appear to modulate burst size globally across the genome