Dynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator.

The circadian oscillator, an internal time-keeping device found in most organisms, enables timely regulation of daily biological activities by maintaining synchrony with the external environment. The mechanistic basis underlying the adjustment of circadian rhythms to changing external conditions, however, has yet to be clearly elucidated. We explored the mechanism of action of nicotinamide in Arabidopsis thaliana, a metabolite that lengthens the period of circadian rhythms, to understand the regulation of circadian period. To identify the key mechanisms involved in the circadian response to nicotinamide, we developed a systematic and practical modeling framework based on the identification and comparison of gene regulatory dynamics. Our mathematical predictions, confirmed by experimentation, identified key transcriptional regulatory mechanisms of circadian period and uncovered the role of blue light in the response of the circadian oscillator to nicotinamide. We suggest that our methodology could be adapted to predict mechanisms of drug action in complex biological systems.

Poly(alkyl glycidyl ether) hydrogels for harnessing the bioactivity of engineered microbes.

Herein, we describe a method to produce yeast-laden hydrogel inks for the direct-write 3D printing of cuboidal lattices for immobilized whole-cell catalysis. A poly(alkyl glycidyl ether)-based triblock copolymer was designed to have three important features for this application: (1) a temperature response, which allowed for facile processing of the material; (2) a shear response, which facilitated the extrusion of the material through a nozzle; and (3) UV light induced polymerization, which enabled the post-extrusion chemical crosslinking of network chains, and the fabrication of robust printed objects. These three key stimuli responses were confirmed via rheometrical characterization. A genetically-engineered yeast strain with an upregulated α-factor production pathway was incorporated into the hydrogel ink and 3D printed. The immobilized yeast cells exhibited adequate viability of 87.5% within the hydrogel. The production of the upregulated α-factor was detected using a detecting yeast strain and quantified at 268 nM (s = 34.6 nM) over 72 h. The reusability of these bioreactors was demonstrated via immersion of the yeast-laden hydrogel lattice in fresh SC media and confirmed by the detection of similar amounts of upregulated α-factor at 259 nM (s = 45.1 nM). These yeast-laden materials represent an attractive opportunity for whole-cell catalysis of other high-value products in a sustainable and continuous manner.

Slow release of a synthetic auxin induces formation of adventitious roots in recalcitrant woody plants.

Clonal propagation of plants by induction of adventitious roots (ARs) from stem cuttings is a requisite step in breeding programs. A major barrier exists for propagating valuable plants that naturally have low capacity to form ARs. Due to the central role of auxin in organogenesis, indole-3-butyric acid is often used as part of commercial rooting mixtures, yet many recalcitrant plants do not form ARs in response to this treatment. Here we describe the synthesis and screening of a focused library of synthetic auxin conjugates in Eucalyptus grandis cuttings and identify 4-chlorophenoxyacetic acid-L-tryptophan-OMe as a competent enhancer of adventitious rooting in a number of recalcitrant woody plants, including apple and argan. Comprehensive metabolic and functional analyses reveal that this activity is engendered by prolonged auxin signaling due to initial fast uptake and slow release and clearance of the free auxin 4-chlorophenoxyacetic acid. This work highlights the utility of a slow-release strategy for bioactive compounds for more effective plant growth regulation.

Modular, robust, and extendible multicellular circuit design in yeast.

Division of labor between cells is ubiquitous in biology but the use of multicellular consortia for engineering applications is only beginning to be explored. A significant advantage of multicellular circuits is their potential to be modular with respect to composition but this claim has not yet been extensively tested using experiments and quantitative modeling. Here, we construct a library of 24 yeast strains capable of sending, receiving or responding to three molecular signals, characterize them experimentally and build quantitative models of their input-output relationships. We then compose these strains into two- and three-strain cascades as well as a four-strain bistable switch and show that experimentally measured consortia dynamics can be predicted from the models of the constituent parts. To further explore the achievable range of behaviors, we perform a fully automated computational search over all two-, three-, and four-strain consortia to identify combinations that realize target behaviors including logic gates, band-pass filters, and time pulses. Strain combinations that are predicted to map onto a target behavior are further computationally optimized and then experimentally tested. Experiments closely track computational predictions. The high reliability of these model descriptions further strengthens the feasibility and highlights the potential for distributed computing in synthetic biology.

Microbial single-cell RNA sequencing by split-pool barcoding.

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

Identifying progressive gene network perturbation from single-cell RNA-seq data.

Identifying the gene regulatory networks that control development or disease is one of the most important problems in biology. Here, we introduce a computational approach, called PIPER (ProgressIve network PERturbation), to identify the perturbed genes that drive differences in the gene regulatory network across different points in a biological progression. PIPER employs algorithms tailor-made for single cell RNA sequencing (scRNA-seq) data to jointly identify gene networks for multiple progressive conditions. It then performs differential network analysis along the identified gene networks to identify master regulators. We demonstrate that PIPER outperforms state-of-the-art alternative methods on simulated data and is able to predict known key regulators of differentiation on real scRNA-Seq datasets.