The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis.

The 5' UTRs of mRNAs are critical for translation regulation, but their in vivo regulatory features are poorly characterized. Here, we report the regulatory landscape of 5' UTRs during early zebrafish embryogenesis using a massively parallel reporter assay of 18,154 sequences coupled to polysome profiling. We found that the 5' UTR is sufficient to confer temporal dynamics to translation initiation, and identified 86 motifs enriched in 5' UTRs with distinct ribosome recruitment capabilities. A quantitative deep learning model, DaniO5P, revealed a combined role for 5' UTR length, translation initiation site context, upstream AUGs and sequence motifs on in vivo ribosome recruitment. DaniO5P predicts the activities of 5' UTR isoforms and indicates that modulating 5' UTR length and motif grammar contributes to translation initiation dynamics. This study provides a first quantitative model of 5' UTR-based translation regulation in early vertebrate development and lays the foundation for identifying the underlying molecular effectors.

Machine Learning for Designing Next-Generation mRNA Therapeutics.

Over just the last 2 years, mRNA therapeutics and vaccines have undergone a rapid transition from an intriguing concept to real-world impact. However, whereas some aspects of mRNA therapeutics, such as the use of chemical modifications to increase stability and reduce immunogenicity, have been extensively optimized for over two decades, other aspects, particularly the selection and design of the noncoding leader and trailer sequences which control translation efficiency and stability, have received comparably less attention. In practice, such 5' and 3' untranslated regions (UTRs) are often borrowed from highly expressed human genes with few or no modifications, as in the case for the Pfizer/BioNTech Covid vaccine. Focusing on the 5'UTR, we here argue that model-driven design is a promising alternative that provides unprecedented control over 5'UTR function. We review recent work that combines synthetic biology with machine learning to build quantitative models that relate ribosome loading, and thus translation efficiency, to the 5'UTR sequence. We first introduce an experimental approach that uses polysome profiling and high-throughput sequencing to quantify ribosome loading for hundreds of thousands of 5'UTRs in parallel. We apply this approach to measure ribosome loading in synthetic RNA libraries with a random sequence inserted into the 5'UTR. We then review Optimus 5-Prime, a convolutional neural network model trained on the experimental data. We highlight that very accurate models of biological regulation can be learned from synthetic data sets with degenerate 5'UTRs. We validate model predictions not only on held-out data sets from our random library but also on a large library of over 30 000 human 5'UTR fragments and using translation reporter data collected independently by other groups. Both the experiment and model are compatible with commonly used chemically modified nucleosides, in particular, pseudouridine (Ψ) and 1-methyl-pseudouridine (m1Ψ). We find that, in general, 5'UTRs have very similar impacts when combined with different protein-coding sequences and even in the context of different chemical modifications. We demonstrate that Optimus 5-Prime can be combined with design algorithms to generate de novo sequences with precisely defined translation efficiencies. We emphasize recent developments in design algorithms that rely on activation maximization and generative modeling to improve both the fitness and diversity of designed sequences. Compared with prior approaches such as genetic algorithms, we show that these approaches are not only faster but also less likely to get stuck in local sequence optima. Finally, we discuss how the approach reviewed here can be generalized to other gene regions and applications.

DIY optogenetics: Building, programming, and using the Light Plate Apparatus.

Optogenetic systems enable unmatched precision for controlling molecular biological processes but require the use of specialized electrical and optical hardware. In an effort to make working with optogenetic systems more accessible, we recently described an open-source hardware platform called the Light Plate Apparatus (LPA). The LPA is a device capable of delivering two independent light signals from standard LEDs to wells of a 24-well culture plate. Basic to advanced level light programs can be created for the LPA in Iris, an easy to use, open-source web application. In this chapter, we describe each step required to build, program, and use the LPA.

Optogenetic control of Bacillus subtilis gene expression.

The Gram-positive bacterium Bacillus subtilis exhibits complex spatial and temporal gene expression signals. Although optogenetic tools are ideal for studying such processes, none has been engineered for this organism. Here, we port a cyanobacterial light sensor pathway comprising the green/red photoreversible two-component system CcaSR, two metabolic enzymes for production of the chromophore phycocyanobilin (PCB), and an output promoter to control transcription of a gene of interest into B. subtilis. Following an initial non-functional design, we optimize expression of pathway genes, enhance PCB production via a translational fusion of the biosynthetic enzymes, engineer a strong chimeric output promoter, and increase dynamic range with a miniaturized photosensor kinase. Our final design exhibits over 70-fold activation and rapid response dynamics, making it well-suited to studying a wide range of gene regulatory processes. In addition, the synthetic biology methods we develop to port this pathway should make B. subtilis easier to engineer in the future.

An Engineered B. subtilis Inducible Promoter System with over 10 000-Fold Dynamic Range.

Bacillus subtilis is the leading model Gram-positive bacterium, and a widely used chassis for industrial protein production. However, B. subtilis research is limited by a lack of inducible promoter systems with low leakiness and high dynamic range. Here, we engineer an inducible promoter system based on the T7 RNA Polymerase (T7 RNAP), the lactose repressor LacI, and the chimeric promoter PT7lac, integrated as a single copy in the B. subtilis genome. In the absence of IPTG, LacI strongly represses T7 RNAP and PT7lac and minimizes leakiness. Addition of IPTG derepresses PT7lac and simultaneously induces expression of T7RNAP, which results in very high output expression. Using green fluorescent and ß-galactosidase reporter proteins, we estimate that this LacI-T7 system can regulate expression with a dynamic range of over 10 000, by far the largest reported for an inducible B. subtilis promoter system. Furthermore, LacI-T7 responds to similar IPTG concentrations and with similar kinetics as the widely used Phy-spank IPTG-inducible system, which we show has a dynamic range of at most 300 in a similar genetic context. Due to its superior performance, our LacI-T7 system should have broad applications in fundamental B. subtilis biology studies and biotechnology.

An open-hardware platform for optogenetics and photobiology.

In optogenetics, researchers use light and genetically encoded photoreceptors to control biological processes with unmatched precision. However, outside of neuroscience, the impact of optogenetics has been limited by a lack of user-friendly, flexible, accessible hardware. Here, we engineer the Light Plate Apparatus (LPA), a device that can deliver two independent 310 to 1550 nm light signals to each well of a 24-well plate with intensity control over three orders of magnitude and millisecond resolution. Signals are programmed using an intuitive web tool named Iris. All components can be purchased for under $400 and the device can be assembled and calibrated by a non-expert in one day. We use the LPA to precisely control gene expression from blue, green, and red light responsive optogenetic tools in bacteria, yeast, and mammalian cells and simplify the entrainment of cyanobacterial circadian rhythm. The LPA dramatically reduces the entry barrier to optogenetics and photobiology experiments.

FlowCal: A User-Friendly, Open Source Software Tool for Automatically Converting Flow Cytometry Data from Arbitrary to Calibrated Units.

Flow cytometry is widely used to measure gene expression and other molecular biological processes with single cell resolution via fluorescent probes. Flow cytometers output data in arbitrary units (a.u.) that vary with the probe, instrument, and settings. Arbitrary units can be converted to the calibrated unit molecules of equivalent fluorophore (MEF) using commercially available calibration particles. However, there is no convenient, nonproprietary tool available to perform this calibration. Consequently, most researchers report data in a.u., limiting interpretation. Here, we report a software tool named FlowCal to overcome current limitations. FlowCal can be run using an intuitive Microsoft Excel interface, or customizable Python scripts. The software accepts Flow Cytometry Standard (FCS) files as inputs and is compatible with different calibration particles, fluorescent probes, and cell types. Additionally, FlowCal automatically gates data, calculates common statistics, and produces publication quality plots. We validate FlowCal by calibrating a.u. measurements of E. coli expressing superfolder GFP (sfGFP) collected at 10 different detector sensitivity (gain) settings to a single MEF value. Additionally, we reduce day-to-day variability in replicate E. coli sfGFP expression measurements due to instrument drift by 33%, and calibrate S. cerevisiae Venus expression data to MEF units. Finally, we demonstrate a simple method for using FlowCal to calibrate fluorescence units across different cytometers. FlowCal should ease the quantitative analysis of flow cytometry data within and across laboratories and facilitate the adoption of standard fluorescence units in synthetic biology and beyond.

Design principles for robust oscillatory behavior.

Oscillatory responses are ubiquitous in regulatory networks of living organisms, a fact that has led to extensive efforts to study and replicate the circuits involved. However, to date, design principles that underlie the robustness of natural oscillators are not completely known. Here we study a three-component enzymatic network model in order to determine the topological requirements for robust oscillation. First, by simulating every possible topological arrangement and varying their parameter values, we demonstrate that robust oscillators can be obtained by augmenting the number of both negative feedback loops and positive autoregulations while maintaining an appropriate balance of positive and negative interactions. We then identify network motifs, whose presence in more complex topologies is a necessary condition for obtaining oscillatory responses. Finally, we pinpoint a series of simple architectural patterns that progressively render more robust oscillators. Together, these findings can help in the design of more reliable synthetic biomolecular networks and may also have implications in the understanding of other oscillatory systems.

How to train your microbe: methods for dynamically characterizing gene networks.

Gene networks regulate biological processes dynamically. However, researchers have largely relied upon static perturbations, such as growth media variations and gene knockouts, to elucidate gene network structure and function. Thus, much of the regulation on the path from DNA to phenotype remains poorly understood. Recent studies have utilized improved genetic tools, hardware, and computational control strategies to generate precise temporal perturbations outside and inside of live cells. These experiments have, in turn, provided new insights into the organizing principles of biology. Here, we introduce the major classes of dynamical perturbations that can be used to study gene networks, and discuss technologies available for creating them in a wide range of microbial pathways.