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

Phenotypic impact of regulatory noise in cellular stress-response pathways

Recent studies indicate that intrinsic promoter-mediated gene expression noise can confer a selective advantage under acute environmental stress by providing beneficial phenotypic diversity within cell populations. To investigate how extrinsic gene expression noise impacts the fitness of cell populations under stress, we engineered two nearly isogenic budding yeast strains; one carrying a two-step regulatory cascade that allows for precise control of the noise transmitted from a transcriptional regulator to a downstream stress-inducing gene, and one carrying a network with low constant upstream noise. The fitness and gene expression of these strains were compared under acute and prolonged stress exposure. Using a phenomenological modeling approach, we predicted that increased noise should confer a fitness advantage under high stress conditions, but reciprocally reduce the resistance of the population to low stress. The model also predicted that extrinsic noise might serve as a basis for phenotypic plasticity whereby gene expression distributions are modulated in response to prolonged stress. Experimentally, we confirmed the predicted differential fitness advantage of extrinsic noise under acute stress, as well as the predicted modulation of gene expression under prolonged stress. However, contrary to model predictions, strains with low and high extrinsic noise showed very similar adaptive responses to prolonged stress. This suggests that while phenotypic heterogeneity generated by noise in regulatory signals can confer increased robustness to acute stress, it is not a requirement for the observed long-term phenotypic plasticity

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

Bivalent non-human gal-α1-3-gal glycan epitopes in the Fc region of a monoclonal antibody model can be recognized by anti-Gal-α1-3-Gal IgE antibodies

ABSTRACTMonoclonal antibody (mAb) production using non-human cells can introduce non-human glycan epitopes including terminal galactosyl-α1–3-galactose (α1–3-Gal) moieties. Cetuximab is a commercial mAb associated with causing anaphylaxis in some patients due to the binding of endogenous anti-α1–3-Gal IgE to the Fab (containing bi-α1–3-galactosylated glycans) but not to the Fc region (containing mono-α1–3-galactosylated glycans). Despite being low in abundance in typical commercial mAbs, the inherent sensitivity of cell culture conditions on glycosylation profiles, and the development of novel glycoengineering strategies, novel antibody-based modalities, and biosimilars by various manufacturers with varying procedures, necessitates a better understanding of the structural requirements for anti-α1–3-Gal IgE binding to the Fc region. Herein, we synthesized mAb glycoforms with varying degrees and regioisomers of α1–3-galactosylation and tested their binding to two commercial anti-α1–3-Gal human IgE antibodies derived from a human patient with allergies to red meat (comprising α1–3-Gal epitopes), as well as to the FcγRIIIA receptor. Our results demonstrate that unexpectedly, anti-α1–3-Gal human IgE antibodies can bind to Fc glycans, with bi-α1–3-galactosylation being the most important factor, highlighting that their presence in the Fc region may be considered as a potential critical quality attribute, particularly when using novel platforms in mAb-based biotherapeutics

Phenotypic impact of regulatory noise in cellular stress-response pathways

Recent studies indicate that intrinsic promoter-mediated gene expression noise can confer a selective advantage under acute environmental stress by providing beneficial phenotypic diversity within cell populations. To investigate how extrinsic gene expression noise impacts the fitness of cell populations under stress, we engineered two nearly isogenic budding yeast strains; one carrying a two-step regulatory cascade that allows for precise control of the noise transmitted from a transcriptional regulator to a downstream stress-inducing gene, and one carrying a network with low constant upstream noise. The fitness and gene expression of these strains were compared under acute and prolonged stress exposure. Using a phenomenological modeling approach, we predicted that increased noise should confer a fitness advantage under high stress conditions, but reciprocally reduce the resistance of the population to low stress. The model also predicted that extrinsic noise might serve as a basis for phenotypic plasticity whereby gene expression distributions are modulated in response to prolonged stress. Experimentally, we confirmed the predicted differential fitness advantage of extrinsic noise under acute stress, as well as the predicted modulation of gene expression under prolonged stress. However, contrary to model predictions, strains with low and high extrinsic noise showed very similar adaptive responses to prolonged stress. This suggests that while phenotypic heterogeneity generated by noise in regulatory signals can confer increased robustness to acute stress, it is not a requirement for the observed long-term phenotypic plasticity

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