Modeling predicts that CRISPR-based activators, unlike CRISPR-based repressors, scale well with increasing gRNA competition and dCas9 bottlenecking

Synthetic transcriptional networks built from CRISPR-based repressors (CRISPRi) rely on shared use of a core dCas9 protein. In E. coli, CRISPRi cannot sup- port more than about a dozen simultaneous gRNAs before the fold repression of any individual gRNA drops below 10x. We show with a simple model based on previous characterization of competition in CRISPRi that activation by CRISPR-based activators (CRISPRa) is much less sensitive to dCas9 bottle- necking than CRISPRi. We predict that E. coli should be able to support dozens to hundreds of CRISPRa gRNAs at > 10-fold activation

Modeling Dynamic Transcriptional Circuits with CRISPRi

Targeted transcriptional repression with catalytically inactive Cas9 (CRISPRi) can be used to build gene regulatory nets similar in principle to those made with traditional transcription factors, and promises to do so with better orthogonality, programmability, and extensibility. We use a simple dynamical model of CRISPRi to understand its behavior and requirements, and to show that CRISPRi can recapitulate several classic gene regulatory circuits, including the repressilator, a toggle switch, and an incoherent feed-forward loop pulse generator. Our model also predicts that these circuits are highly sensitive to promoter leak, but that promoter leak can be offset with active degradation of dCas. We provide specifications for required fold-repression and dCas degradation rates for several dynamic circuits. Our modeling reveals key engineering requirements and considerations for the construction of dynamic CRISPRi circuits, and provides a roadmap for building those circuits

Transcriptomic Characterization of an Infection of Mycobacterium smegmatis by the Cluster A4 Mycobacteriophage Kampy

The mycobacteriophages, phages that infect the genus Mycobacterium, display profound genetic diversity and widespread geographical distribution, and possess significant medical and ecological importance. However, most of the majority of functions of mycobacteriophage proteins and the identity of most genetic regulatory elements remain unknown. We characterized the gene expression profile of Kampy, a cluster A4 mycobacteriophage, during infection of its host, Mycobacterium smegmatis, using RNA-Seq and mass spectrometry. We show that mycobacteriophage Kampy transcription occurs in roughly two phases, an early phase consisting of genes for metabolism, DNA synthesis, and gene regulation, and a late phase consisting of structural genes and lysis genes. Additionally, we identify the earliest genes transcribed during infection, along with several other possible regulatory units not obvious from inspection of Kampy\u27s genomic structure. The transcriptional profile of Kampy appears similar to that of mycobacteriophage TM4 but unlike that of mycobacteriophage Giles, a result which further expands our understanding of the diversity of mycobacteriophage gene expression programs during infection

Modeling predicts that CRISPR-based activators, unlike CRISPR-based repressors, scale well with increasing gRNA competition and dCas9 bottlenecking

Synthetic transcriptional networks built from CRISPR-based repressors (CRISPRi) rely on shared use of a core dCas9 protein. In E. coli, CRISPRi cannot sup- port more than about a dozen simultaneous gRNAs before the fold repression of any individual gRNA drops below 10x. We show with a simple model based on previous characterization of competition in CRISPRi that activation by CRISPR-based activators (CRISPRa) is much less sensitive to dCas9 bottle- necking than CRISPRi. We predict that E. coli should be able to support dozens to hundreds of CRISPRa gRNAs at > 10-fold activation

A Regression-Based Differential Expression Detection Algorithm for Microarray Studies with Ultra-Low Sample Size

Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality

Analysis of Circuits for Dosage Control in Microbial Populations

Designing genetic circuits to control the behaviors of microbial populations is an ongoing challenge in synthetic biology. Here we analyze circuits which implement dosage control by controlling levels of a global signal in a microbial population in face of varying cell density, growth rate, and environmental dilution. We utilize the Lux quorum sensing system to implement dosage control circuits, and we analyze the dynamics of circuits using both simplified analytical analysis and in silico simulations. We demonstrate that strong negative feedback through inhibiting LuxI synthase expression along with AiiA degradase activity results in circuits with fast response times and robustness to cell density and dilution rate. We find that degradase activity yields robustness to variations in population density for large population sizes, while negative feedback to synthase production decreases sensitivity to dilution rates

Modeling Dynamic Transcriptional Circuits with CRISPRi

Targeted transcriptional repression with catalytically inactive Cas9 (CRISPRi) can be used to build gene regulatory nets similar in principle to those made with traditional transcription factors, and promises to do so with better orthogonality, programmability, and extensibility. We use a simple dynamical model of CRISPRi to understand its behavior and requirements, and to show that CRISPRi can recapitulate several classic gene regulatory circuits, including the repressilator, a toggle switch, and an incoherent feed-forward loop pulse generator. Our model also predicts that these circuits are highly sensitive to promoter leak, but that promoter leak can be offset with active degradation of dCas. We provide specifications for required fold-repression and dCas degradation rates for several dynamic circuits. Our modeling reveals key engineering requirements and considerations for the construction of dynamic CRISPRi circuits, and provides a roadmap for building those circuits

Methylmercury exposure during early Xenopus laevis development affects cell proliferation and death but not neural progenitor specification

Methylmercury (MeHg) is a widespread environmental toxin that preferentially and adversely affects developing organisms. To investigate the impact of MeHg toxicity on the formation of the vertebrate nervous system at physiologically relevant concentrations, we designed a graded phenotype scale for evaluating Xenopus laevis embryos exposed to MeHg in solution. Embryos displayed a range of abnormalities in response to MeHg, particularly in brain development, which is influenced by both MeHg concentration and the number of embryos per ml of exposure solution. A TC50 of similar to 50 mu g/l and LC50 of similar to 100 mu g/l were found when maintaining embryos at a density of one per ml, and both increased with increasing embryo density. In situ hybridization and microarray analysis showed no significant change in expression of early neural patterning genes including sox2, en2, or delta; however a noticeable decrease was observed in the terminal neural differentiation genes GAD and xGAT, but not xVGlut. PCNA, a marker for proliferating cells, was negatively correlated with MeHg dose, with a significant reduction in cell number in the forebrain and spinal cord of exposed embryos by tadpole stages. Conversely, the number of apoptotic cells in neural regions detected by a TUNEL (terminal deoxynucleotidyl transferase dUTP nick end labeling) assay was significantly increased. These results provide evidence that disruption of embryonic neural development by MeHg may not be directly due to a loss of neural progenitor specification and gene transcription, but to a more general decrease in cell proliferation and increase in cell death throughout the developing nervous system. (C) 2014 The Authors. Published by Elsevier Inc