Development of CRISPR as an Antiviral Strategy to Combat SARS-CoV-2 and Influenza.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the SARS-CoV-2 virus, has highlighted the need for antiviral approaches that can target emerging viruses with no effective vaccines or pharmaceuticals. Here, we demonstrate a CRISPR-Cas13-based strategy, PAC-MAN (prophylactic antiviral CRISPR in human cells), for viral inhibition that can effectively degrade RNA from SARS-CoV-2 sequences and live influenza A virus (IAV) in human lung epithelial cells. We designed and screened CRISPR RNAs (crRNAs) targeting conserved viral regions and identified functional crRNAs targeting SARS-CoV-2. This approach effectively reduced H1N1 IAV load in respiratory epithelial cells. Our bioinformatic analysis showed that a group of only six crRNAs can target more than 90% of all coronaviruses. With the development of a safe and effective system for respiratory tract delivery, PAC-MAN has the potential to become an important pan-coronavirus inhibition strategy.

Definitive demonstration by synthesis of genome annotation completeness.

We develop a method for completing the genetics of natural living systems by which the absence of expected future discoveries can be established. We demonstrate the method using bacteriophage øX174, the first DNA genome to be sequenced. Like many well-studied natural organisms, closely related genome sequences are available-23 Bullavirinae genomes related to øX174. Using bioinformatic tools, we first identified 315 potential open reading frames (ORFs) within the genome, including the 11 established essential genes and 82 highly conserved ORFs that have no known gene products or assigned functions. Using genome-scale design and synthesis, we made a mutant genome in which all 11 essential genes are simultaneously disrupted, leaving intact only the 82 conserved but cryptic ORFs. The resulting genome is not viable. Cell-free gene expression followed by mass spectrometry revealed only a single peptide expressed from both the cryptic ORF and wild-type genomes, suggesting a potential new gene. A second synthetic genome in which 71 conserved cryptic ORFs were simultaneously disrupted is viable but with ∼50% reduced fitness relative to the wild type. However, rather than finding any new genes, repeated evolutionary adaptation revealed a single point mutation that modulates expression of gene H, a known essential gene, and fully suppresses the fitness defect. Taken together, we conclude that the annotation of currently functional ORFs for the øX174 genome is formally complete. More broadly, we show that sequencing and bioinformatics followed by synthesis-enabled reverse genomics, proteomics, and evolutionary adaptation can definitely establish the sufficiency and completeness of natural genome annotations.

Switches, switches, every where, in any drop we drink.

In this issue, Broussard et al. (2013) report genetic switches that regulate cell fate selection; a recombinase attachment site is embedded within a repressor coding sequence, such that integration truncates a proteolysis domain, stabilizing the repressor and setting the switch.

Fail-safe genetic codes designed to intrinsically contain engineered organisms.

One challenge in engineering organisms is taking responsibility for their behavior over many generations. Spontaneous mutations arising before or during use can impact heterologous genetic functions, disrupt system integration, or change organism phenotype. Here, we propose restructuring the genetic code itself such that point mutations in protein-coding sequences are selected against. Synthetic genetic systems so-encoded should fail more safely in response to most spontaneous mutations. We designed fail-safe codes and simulated their expected effects on the evolution of so-encoded proteins. We predict fail-safe codes supporting expression of 20 or 15 amino acids could slow protein evolution to ∼30% or 0% the rate of standard-encoded proteins, respectively. We also designed quadruplet-codon codes that should ensure all single point mutations in protein-coding sequences are selected against while maintaining expression of 20 or more amino acids. We demonstrate experimentally that a reduced set of 21 tRNAs is capable of expressing a protein encoded by only 20 sense codons, whereas a standard 64-codon encoding is not expressed. Our work suggests that biological systems using rationally depleted but otherwise natural translation systems should evolve more slowly and that such hypoevolvable organisms may be less likely to invade new niches or outcompete native populations.

Measurements of translation initiation from all 64 codons in E. coli.

Our understanding of translation underpins our capacity to engineer living systems. The canonical start codon (AUG) and a few near-cognates (GUG, UUG) are considered as the 'start codons' for translation initiation in Escherichia coli. Translation is typically not thought to initiate from the 61 remaining codons. Here, we quantified translation initiation of green fluorescent protein and nanoluciferase in E. coli from all 64 triplet codons and across a range of DNA copy number. We detected initiation of protein synthesis above measurement background for 47 codons. Translation from non-canonical start codons ranged from 0.007 to 3% relative to translation from AUG. Translation from 17 non-AUG codons exceeded the highest reported rates of non-cognate codon recognition. Translation initiation from non-canonical start codons may contribute to the synthesis of peptides in both natural and synthetic biological systems.

Precise and reliable gene expression via standard transcription and translation initiation elements.

An inability to reliably predict quantitative behaviors for novel combinations of genetic elements limits the rational engineering of biological systems. We developed an expression cassette architecture for genetic elements controlling transcription and translation initiation in Escherichia coli: transcription elements encode a common mRNA start, and translation elements use an overlapping genetic motif found in many natural systems. We engineered libraries of constitutive and repressor-regulated promoters along with translation initiation elements following these definitions. We measured activity distributions for each library and selected elements that collectively resulted in expression across a 1,000-fold observed dynamic range. We studied all combinations of curated elements, demonstrating that arbitrary genes are reliably expressed to within twofold relative target expression windows with ∼93% reliability. We expect the genetic element definitions validated here can be collectively expanded to create collections of public-domain standard biological parts that support reliable forward engineering of gene expression at genome scales.

Quantitative estimation of activity and quality for collections of functional genetic elements.

The practice of engineering biology now depends on the ad hoc reuse of genetic elements whose precise activities vary across changing contexts. Methods are lacking for researchers to affordably coordinate the quantification and analysis of part performance across varied environments, as needed to identify, evaluate and improve problematic part types. We developed an easy-to-use analysis of variance (ANOVA) framework for quantifying the performance of genetic elements. For proof of concept, we assembled and analyzed combinations of prokaryotic transcription and translation initiation elements in Escherichia coli. We determined how estimation of part activity relates to the number of unique element combinations tested, and we show how to estimate expected ensemble-wide part activity from just one or two measurements. We propose a new statistic, biomolecular part 'quality', for tracking quantitative variation in part performance across changing contexts.

Composability of regulatory sequences controlling transcription and translation in Escherichia coli.

The inability to predict heterologous gene expression levels precisely hinders our ability to engineer biological systems. Using well-characterized regulatory elements offers a potential solution only if such elements behave predictably when combined. We synthesized 12,563 combinations of common promoters and ribosome binding sites and simultaneously measured DNA, RNA, and protein levels from the entire library. Using a simple model, we found that RNA and protein expression were within twofold of expected levels 80% and 64% of the time, respectively. The large dataset allowed quantitation of global effects, such as translation rate on mRNA stability and mRNA secondary structure on translation rate. However, the worst 5% of constructs deviated from prediction by 13-fold on average, which could hinder large-scale genetic engineering projects. The ease and scale this of approach indicates that rather than relying on prediction or standardization, we can screen synthetic libraries for desired behavior.

Measurement and modeling of intrinsic transcription terminators.

The reliable forward engineering of genetic systems remains limited by the ad hoc reuse of many types of basic genetic elements. Although a few intrinsic prokaryotic transcription terminators are used routinely, termination efficiencies have not been studied systematically. Here, we developed and validated a genetic architecture that enables reliable measurement of termination efficiencies. We then assembled a collection of 61 natural and synthetic terminators that collectively encode termination efficiencies across an ∼800-fold dynamic range within Escherichia coli. We simulated co-transcriptional RNA folding dynamics to identify competing secondary structures that might interfere with terminator folding kinetics or impact termination activity. We found that structures extending beyond the core terminator stem are likely to increase terminator activity. By excluding terminators encoding such context-confounding elements, we were able to develop a linear sequence-function model that can be used to estimate termination efficiencies (r = 0.9, n = 31) better than models trained on all terminators (r = 0.67, n = 54). The resulting systematically measured collection of terminators should improve the engineering of synthetic genetic systems and also advance quantitative modeling of transcription termination.

Rewritable digital data storage in live cells via engineered control of recombination directionality.

The use of synthetic biological systems in research, healthcare, and manufacturing often requires autonomous history-dependent behavior and therefore some form of engineered biological memory. For example, the study or reprogramming of aging, cancer, or development would benefit from genetically encoded counters capable of recording up to several hundred cell division or differentiation events. Although genetic material itself provides a natural data storage medium, tools that allow researchers to reliably and reversibly write information to DNA in vivo are lacking. Here, we demonstrate a rewriteable recombinase addressable data (RAD) module that reliably stores digital information within a chromosome. RAD modules use serine integrase and excisionase functions adapted from bacteriophage to invert and restore specific DNA sequences. Our core RAD memory element is capable of passive information storage in the absence of heterologous gene expression for over 100 cell divisions and can be switched repeatedly without performance degradation, as is required to support combinatorial data storage. We also demonstrate how programmed stochasticity in RAD system performance arising from bidirectional recombination can be achieved and tuned by varying the synthesis and degradation rates of recombinase proteins. The serine recombinase functions used here do not require cell-specific cofactors and should be useful in extending computing and control methods to the study and engineering of many biological systems.

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range.

Although the proteins comprising many signaling systems are known, less is known about their numbers per cell. Existing measurements often vary by more than 10-fold. Here, we devised improved quantification methods to measure protein abundances in the Saccharomyces cerevisiae pheromone response pathway, an archetypical signaling system. These methods limited variation between independent measurements of protein abundance to a factor of two. We used these measurements together with quantitative models to identify and investigate behaviors of the pheromone response system sensitive to precise abundances. The difference between the maximum and basal signaling output (dynamic range) of the pheromone response MAPK cascade was strongly sensitive to the abundance of Ste5, the MAPK scaffold protein, and absolute system output depended on the amount of Fus3, the MAPK. Additional analysis and experiment suggest that scaffold abundance sets a tradeoff between maximum system output and system dynamic range, a prediction supported by recent experiments.

Engineering tRNA abundances for synthetic cellular systems.

Routinizing the engineering of synthetic cells requires specifying beforehand how many of each molecule are needed. Physics-based tools for estimating desired molecular abundances in whole-cell synthetic biology are missing. Here, we use a colloidal dynamics simulator to make predictions for how tRNA abundances impact protein synthesis rates. We use rational design and direct RNA synthesis to make 21 synthetic tRNA surrogates from scratch. We use evolutionary algorithms within a computer aided design framework to engineer translation systems predicted to work faster or slower depending on tRNA abundance differences. We build and test the so-specified synthetic systems and find qualitative agreement between expected and observed systems. First principles modeling combined with bottom-up experiments can help molecular-to-cellular scale synthetic biology realize design-build-work frameworks that transcend tinker-and-test.

Colloidal Physics Modeling Reveals How Per-Ribosome Productivity Increases with Growth Rate in Escherichia coli.

Faster-growing cells must synthesize proteins more quickly. Increased ribosome abundance only partly accounts for increases in total protein synthesis rates. The productivity of individual ribosomes must increase too, almost doubling by an unknown mechanism. Prior models point to diffusive transport as a limiting factor but raise a paradox: faster-growing cells are more crowded, yet crowding slows diffusion. We suspected that physical crowding, transport, and stoichiometry, considered together, might reveal a more nuanced explanation. To investigate, we built a first-principles physics-based model of Escherichia coli cytoplasm in which Brownian motion and diffusion arise directly from physical interactions between individual molecules of finite size, density, and physiological abundance. Using our microscopically detailed model, we predicted that physical transport of individual ternary complexes accounts for ~80% of translation elongation latency. We also found that volumetric crowding increases during faster growth even as cytoplasmic mass density remains relatively constant. Despite slowed diffusion, we predicted that improved proximity between ternary complexes and ribosomes wins out, illustrating a simple physics-based mechanism for how individual elongating ribosomes become more productive. We speculate that crowding imposes a physical limit on growth rate and undergirds cellular behavior more broadly. Unfitted colloidal-scale modeling offers systems biology a complementary "physics engine" for exploring how cellular-scale behaviors arise from physical transport and reactions among individual molecules. IMPORTANCE Ribosomes are the factories in cells that synthesize proteins. When cells grow faster, there are not enough ribosomes to keep up with the demand for faster protein synthesis without individual ribosomes becoming more productive. Yet, faster-growing cells are more crowded, seemingly making it harder for each ribosome to do its work. Our computational model of the physics of translation elongation reveals the underlying mechanism for how individual ribosomes become more productive: proximity and stoichiometry of translation molecules overcome crowding. Our model also suggests a universal physical limitation of cell growth rates.

Multicolor plate reader fluorescence calibration.

Plate readers are commonly used to measure cell growth and fluorescence, yet the utility and reproducibility of plate reader data is limited by the fact that it is typically reported in arbitrary or relative units. We have previously established a robust serial dilution protocol for calibration of plate reader measurements of absorbance to estimated bacterial cell count and for green fluorescence from proteins expressed in bacterial cells to molecules of equivalent fluorescein. We now extend these protocols to calibration of red fluorescence to the sulforhodamine-101 fluorescent dye and blue fluorescence to Cascade Blue. Evaluating calibration efficacy via an interlaboratory study, we find that these calibrants do indeed provide comparable precision to the prior calibrants and that they enable effective cross-laboratory comparison of measurements of red and blue fluorescence from proteins expressed in bacterial cells.

Direct and indirect impacts of synthetic biology on biodiversity conservation.

The world's biodiversity is in crisis. Synthetic biology has the potential to transform biodiversity conservation, both directly and indirectly, in ways that are negative and positive. However, applying these biotechnology tools to environmental questions is fraught with uncertainty and could harm cultures, rights, livelihoods, and nature. Decisions about whether or not to use synthetic biology for conservation should be understood alongside the reality of ongoing biodiversity loss. In 2022, the 196 Parties to the United Nations Convention on Biological Diversity are negotiating the post-2020 Global Biodiversity Framework that will guide action by governments and other stakeholders for the next decade to conserve the worlds' biodiversity. To date, synthetic biologists, conservationists, and policy makers have operated in isolation. At this critical time, this review brings these diverse perspectives together and emerges out of the need for a balanced and inclusive examination of the potential application of these technologies to biodiversity conservation.

Foundations for engineering biology.

Engineered biological systems have been used to manipulate information, construct materials, process chemicals, produce energy, provide food, and help maintain or enhance human health and our environment. Unfortunately, our ability to quickly and reliably engineer biological systems that behave as expected remains quite limited. Foundational technologies that make routine the engineering of biology are needed. Vibrant, open research communities and strategic leadership are necessary to ensure that the development and application of biological technologies remains overwhelmingly constructive.

Regulated cell-to-cell variation in a cell-fate decision system.

Here we studied the quantitative behaviour and cell-to-cell variability of a prototypical eukaryotic cell-fate decision system, the mating pheromone response pathway in yeast. We dissected and measured sources of variation in system output, analysing thousands of individual, genetically identical cells. Only a small proportion of total cell-to-cell variation is caused by random fluctuations in gene transcription and translation during the response ('expression noise'). Instead, variation is dominated by differences in the capacity of individual cells to transmit signals through the pathway ('pathway capacity') and to express proteins from genes ('expression capacity'). Cells with high expression capacity express proteins at a higher rate and increase in volume more rapidly. Our results identify two mechanisms that regulate cell-to-cell variation in pathway capacity. First, the MAP kinase Fus3 suppresses variation at high pheromone levels, while the MAP kinase Kss1 enhances variation at low pheromone levels. Second, pathway capacity and expression capacity are negatively correlated, suggesting a compensatory mechanism that allows cells to respond more precisely to pheromone in the presence of a large variation in expression capacity.

Determination of cell fate selection during phage lambda infection.

Bacteriophage lambda infection of Escherichia coli can result in distinct cell fate outcomes. For example, some cells lyse whereas others survive as lysogens. A quantitative biophysical model of lambda infection supports the hypothesis that spontaneous differences in the timing of individual molecular events during lambda infection leads to variation in the selection of cell fates. Building from this analysis, the lambda lysis-lysogeny decision now serves as a paradigm for how intrinsic molecular noise can influence cellular behavior, drive developmental processes, and produce population heterogeneity. Here, we report experimental evidence that warrants reconsidering this framework. By using cell fractioning, plating, and single-cell fluorescent microscopy, we find that physical differences among cells present before infection bias lambda developmental outcomes. Specifically, variation in cell volume at the time of infection can be used to help predict cell fate: a approximately 2-fold increase in cell volume results in a 4- to 5-fold decrease in the probability of lysogeny. Other cell fate decisions now thought to be stochastic might also be determined by pre-existing variation.