Noise in gene expression is generated at multiple levels, such as transcription and translation, chromatin remodeling and pathway-specific regulation. Studies of individual promoters have suggested different dominating noise sources, raising the question of whether a general trend exists across a large number of genes and conditions. We examined the variation in the expression levels of 43 Saccharomyces cerevisiae proteins, in cells grown under 11 experimental conditions. For all classes of genes and under all conditions, the expression variance was approximately proportional to the mean; the same scaling was observed at steady state and during the transient responses to the perturbations. Theoretical analysis suggests that this scaling behavior reflects variability in mRNA copy number, resulting from random 'birth and death' of mRNA molecules or from promoter fluctuations. Deviation of coexpressed genes from this general trend, including high noise in stress-related genes and low noise in proteasomal genes, may indicate fluctuations in pathway-specific regulators or a differential activation pattern of the underlying gene promoters.
Adeno-associated viral vectors are highly safe and efficient gene delivery vehicles. However, numerous challenges in vector design remain, including neutralizing antibody responses, tissue transport and infection of resistant cell types. Changes must be made to the viral capsid to overcome these problems; however, very often insufficient information is available for rational design of improvements. We therefore applied a directed evolution approach involving the generation of large mutant capsid libraries and selection of adeno-associated virus (AAV) 2 variants with enhanced properties. High-throughput selection processes were designed to isolate mutants within the library with altered affinities for heparin or the ability to evade antibody neutralization and deliver genes more efficiently than wild-type capsid in the presence of anti-AAV serum. This approach, which can be extended to additional gene delivery challenges and serotypes, directs viral evolution to generate 'designer' gene delivery vectors with specified, enhanced properties.
Within a population of genetically identical cells there can be significant variation, or noise, in gene expression. Yet even with this inherent variability, cells function reliably. This review focuses on our understanding of noise at the level of both single genes and genetic regulatory networks, emphasizing comparisons between theoretical models and experimental results whenever possible. To highlight the importance of noise, we particularly emphasize examples in which a stochastic description of gene expression leads to a qualitatively different outcome than a deterministic one.
Transcriptional positive-feedback loops are widely associated with bistability, characterized by two stable expression states that allow cells to respond to analog signals in a digital manner. Using a synthetic system in budding yeast, we show that positive feedback involving a promoter with multiple transcription factor (TF) binding sites can induce a steady-state bimodal response without cooperative binding of the TF. Deterministic models of this system do not predict bistability. Rather, the bimodal response requires a short-lived TF and stochastic fluctuations in the TF's expression. Multiple binding sites provide these fluctuations. Because many promoters possess multiple binding sites and many TFs are unstable, positive-feedback loops in gene regulatory networks may exhibit bimodal responses, but not necessarily because of deterministic bistability, as is commonly thought.
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