Search

Targeted mutagenesis mediated by nucleotide base deaminase–T7 RNA polymerase fusions has recently emerged as a novel and broadly useful strategy to power genetic diversification in the context of in vivo directed evolution campaigns. Here, we expand the utility of this approach by introducing a highly active adenosine deaminase–T7 RNA polymerase fusion protein (eMutaT7A→G), resulting in higher mutation frequencies to enable more rapid directed evolution. We also assess the benefits and potential downsides of using this more active mutator. We go on to show in Escherichia coli that adenosine deaminase-bearing mutators (MutaT7A→G or eMutaT7A→G) can be employed in tandem with a cytidine deaminase-bearing mutator (MutaT7C→T) to introduce all possible transition mutations simultaneously. We illustrate the efficacy of this in vivo mutagenesis approach by exploring mutational routes to antibacterial drug resistance. This work sets the stage for general application of optimized MutaT7 tools able to induce all types of transition mutations during in vivo directed evolution campaigns across diverse organisms.

Motivation: Gene expression profiling using RNA-seq is a powerful technique for screening RNA species’ landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed. Results: In this study, we use RNA-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T-cell activation (Th0). To quantify Th17-specific gene expression dynamics, we present a novel statistical methodology, DyNB, for analyzing time-course RNA-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment-specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an MCMC sampling method to make inference of differential expression dynamics between conditions. DyNB identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use qRT-PCR to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint-wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected. Availability: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/, Academy of Finland (Centre of Excellence in Moleculary Systems Immunology and Physiology Research (2012-2017) Grant 135320), Seventh Framework Programme (European Commission) (Grant EC-FP7-SYBILLA-201106), EU ERASysBio ERA-NET, Sigrid Juslius Foundation, FICS Graduate School