Multi-layer CRISPRa/i circuits for dynamic genetic programs in cell-free and bacterial systems.

CRISPR-Cas transcriptional circuits hold great promise as platforms for engineering metabolic networks and information processing circuits. Historically, prokaryotic CRISPR control systems have been limited to CRISPRi. Creating approaches to integrate CRISPRa for transcriptional activation with existing CRISPRi-based systems would greatly expand CRISPR circuit design space. Here, we develop design principles for engineering prokaryotic CRISPRa/i genetic circuits with network topologies specified by guide RNAs. We demonstrate that multi-layer CRISPRa/i cascades and feedforward loops can operate through the regulated expression of guide RNAs in cell-free expression systems and E. coli. We show that CRISPRa/i circuits can program complex functions by designing type 1 incoherent feedforward loops acting as fold-change detectors and tunable pulse-generators. By investigating how component characteristics relate to network properties such as depth, width, and speed, this work establishes a framework for building scalable CRISPRa/i circuits as regulatory programs in cell-free expression systems and bacterial hosts. A record of this paper's transparent peer review process is included in the supplemental information.

Effective CRISPRa-mediated control of gene expression in bacteria must overcome strict target site requirements.

In bacterial systems, CRISPR-Cas transcriptional activation (CRISPRa) has the potential to dramatically expand our ability to regulate gene expression, but we lack predictive rules for designing effective gRNA target sites. Here, we identify multiple features of bacterial promoters that impose stringent requirements on CRISPRa target sites. Notably, we observe narrow, 2-4 base windows of effective sites with a periodicity corresponding to one helical turn of DNA, spanning ~40 bases and centered ~80 bases upstream of the TSS. However, we also identify two features suggesting the potential for broad scope: CRISPRa is effective at a broad range of σ70-family promoters, and an expanded PAM dCas9 allows the activation of promoters that cannot be activated by S. pyogenes dCas9. These results provide a roadmap for future engineering efforts to further expand and generalize the scope of bacterial CRISPRa.

Signal-to-Noise Ratio in PVT Performance as a Cognitive Measure of the Effect of Sleep Deprivation on the Fidelity of Information Processing.

Study Objectives: There is a long-standing debate about the best way to characterize performance deficits on the psychomotor vigilance test (PVT), a widely used assay of cognitive impairment in human sleep deprivation studies. Here, we address this issue through the theoretical framework of the diffusion model and propose to express PVT performance in terms of signal-to-noise ratio (SNR). Methods: From the equations of the diffusion model for one-choice, reaction-time tasks, we derived an expression for a novel SNR metric for PVT performance. We also showed that LSNR-a commonly used log-transformation of SNR-can be reasonably well approximated by a linear function of the mean response speed, LSNRapx. We computed SNR, LSNR, LSNRapx, and number of lapses for 1284 PVT sessions collected from 99 healthy young adults who participated in laboratory studies with 38 hr of total sleep deprivation. Results: All four PVT metrics captured the effects of time awake and time of day on cognitive performance during sleep deprivation. The LSNR had the best psychometric properties, including high sensitivity, high stability, high degree of normality, absence of floor and ceiling effects, and no bias in the meaning of change scores related to absolute baseline performance. Conclusions: The theoretical motivation of SNR and LSNR permits quantitative interpretation of PVT performance as an assay of the fidelity of information processing in cognition. Furthermore, with a conceptual and statistical meaning grounded in information theory and generalizable across scientific fields, LSNR in particular is a useful tool for systems-integrated fatigue risk management.