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

Exceptional response and multisystem autoimmune-like toxicities associated with the same T cell clone in a patient with uveal melanoma treated with immune checkpoint inhibitors

Abstract Balancing the potential for durable remissions with autoimmune-like toxicities is a key clinical challenge in the use of immune checkpoint inhibitors (ICI). Certain toxicities are associated with an increased response rate; however, the molecular underpinnings of this association are poorly understood. Here, we report a patient with wide spread uveal melanoma who had an exceptional response to treatment with ipilimumab and nivolumab, but suffered severe immune-related sequelae, including central serous retinopathy with retinal detachment, tinnitus, and vitiligo resembling Vogt-Koyanagi-Harada disease, and refractory enteritis. TCR-sequencing of the primary tumor, a hepatic metastasis, duodenal biopsy and peripheral blood mononuclear cells, identified the identical T cell clone in all four tissues. This case provides preliminary evidence for cross-reactivity as a mechanism for the association between effect and toxicity of ICIs

Chromosomal Position Effects Are Linked to Sir2-Mediated Variation in Transcriptional Burst Size

Gene expression noise varies with genomic position and is a driving force in the evolution of chromosome organization. Nevertheless, position effects remain poorly characterized. Here, we present a systematic analysis of chromosomal position effects by characterizing single-cell gene expression from euchromatic positions spanning the length of a eukaryotic chromosome. We demonstrate that position affects gene expression by modulating the size of transcriptional bursts, rather than their frequency, and that the histone deacetylase Sir2 plays a role in this process across the chromosome