high-throughput-titration-of-luciferase-expressing-recombinant-viruses

High-throughput Titration of Luciferase-expressing Recombinant Viruses

Standard plaque assays to determine infectious viral titers can be time consuming, are not amenable to a high volume of samples, and cannot be done with ...

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 Saccharomyces cerevisiae using two mutant libraries - one containing transcription factor deletions, and the other containing deletions of DNA repair genes.

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

Gen Ausdruck L&auml;rm mit genomische Position variiert und ist eine treibende Kraft in der Entwicklung der Chromosom-Organisation. Trotzdem bleiben Positionseffekte schlecht charakterisiert. Hier pr&auml;sentieren wir eine systematische Analyse der chromosomalen Positionseffekte durch einzellige Genexpression von Histons Positionen &uuml;ber die L&auml;nge eines eukaryotischen Chromosoms charakterisieren. Wir zeigen, dass die Position Genexpression beeinflusst durch die Gr&ouml;&szlig;e des transkriptionelle platzt, anstatt ihre Frequenz Modulation und die Histon-Deacetylase Sir2 spielt eine Rolle in diesem Prozess auf dem Chromosom.

Chromosomale Positionseffekte sind verbunden mit sir2-vermittelte Variation in transkriptionelle Burst-Gr&ouml;&szlig;e.

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.

Chromosomal position effects are linked to sir2-mediated variation in transcriptional burst size.

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.

Quantitative epistasis analysis and pathway inference from genetic interaction data.

Oncolytic viruses are complex biological agents that interact at multiple levels with both tumour and normal tissues. Antiviral pathways induced by interferon are known to have a critical role in determining tumour cell sensitivity and normal cell resistance to infection with oncolytic viruses. Here we pursue a synthetic biology approach to identify methods that enhance antitumour activity of oncolytic viruses through suppression of interferon signalling. On the basis of the mathematical analysis of multiple strategies, we hypothesize that a positive feedback loop, established by virus-mediated expression of a soluble interferon-binding decoy receptor, increases tumour cytotoxicity without compromising normal cells. Oncolytic rhabdoviruses engineered to express a secreted interferon antagonist have improved oncolytic potential in cellular cancer models, and display improved therapeutic potential in tumour-bearing mice. Our results demonstrate the potential of this methodology in evaluating potential caveats of viral immune-evasion strategies and improving the design oncolytic viruses.

Article	Year
Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis. BMC genomics\| PubMed ID: 20831804	2010
Chromosomal position effects are linked to sir2-mediated variation in transcriptional burst size. Biophysical journal\| PubMed ID: 21575565	2011
Quantitative epistasis analysis and pathway inference from genetic interaction data. PLoS computational biology\| PubMed ID: 21589890	2011
Model-based rational design of an oncolytic virus with improved therapeutic potential. Nature communications\| PubMed ID: 23764612	2013

Cory Batenchuk

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