A synthetic genetic edge detection program.

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

Selenium-dependent metabolic reprogramming during inflammation and resolution.

Trace element selenium (Se) is incorporated as the 21st amino acid, selenocysteine, into selenoproteins through tRNA[Ser]Sec. Selenoproteins act as gatekeepers of redox homeostasis and modulate immune function to effect anti-inflammation and resolution. However, mechanistic underpinnings involving metabolic reprogramming during inflammation and resolution remain poorly understood. Bacterial endotoxin lipopolysaccharide (LPS) activation of murine bone marrow-derived macrophages cultured in the presence or absence of Se (as selenite) was used to examine temporal changes in the proteome and metabolome by multiplexed tandem mass tag-quantitative proteomics, metabolomics, and machine-learning approaches. Kinetic deltagram and clustering analysis indicated that addition of Se led to extensive reprogramming of cellular metabolism upon stimulation with LPS enhancing the pentose phosphate pathway, tricarboxylic acid cycle, and oxidative phosphorylation, to aid in the phenotypic transition toward alternatively activated macrophages, synonymous with resolution of inflammation. Remodeling of metabolic pathways and consequent metabolic adaptation toward proresolving phenotypes began with Se treatment at 0 h and became most prominent around 8 h after LPS stimulation that included succinate dehydrogenase complex, pyruvate kinase, and sedoheptulokinase. Se-dependent modulation of these pathways predisposed bone marrow-derived macrophages to preferentially increase oxidative phosphorylation to efficiently regulate inflammation and its timely resolution. The use of macrophages lacking selenoproteins indicated that all three metabolic nodes were sensitive to selenoproteome expression. Furthermore, inhibition of succinate dehydrogenase complex with dimethylmalonate affected the proresolving effects of Se by increasing the resolution interval in a murine peritonitis model. In summary, our studies provide novel insights into the role of cellular Se via metabolic reprograming to facilitate anti-inflammation and proresolution.

Precise quantification of translation inhibition by mRNA structures that overlap with the ribosomal footprint in N-terminal coding sequences.

A mRNA's translation rate is controlled by several sequence determinants, including the presence of RNA structures within the N-terminal regions of its coding sequences. However, the physical rules that govern when such mRNA structures will inhibit translation remain unclear. Here, we introduced systematically designed RNA hairpins into the N-terminal coding region of a reporter protein with steadily increasing distances from the start codon, followed by characterization of their mRNA and expression levels in Escherichia coli. We found that the mRNAs' translation rates were repressed, by up to 530-fold, when mRNA structures overlapped with the ribosome's footprint. In contrast, when the mRNA structure was located outside the ribosome's footprint, translation was repressed by <2-fold. By combining our measurements with biophysical modeling, we determined that the ribosomal footprint extends 13 nucleotides into the N-terminal coding region and, when a mRNA structure overlaps or partially overlaps with the ribosomal footprint, the free energy to unfold only the overlapping structure controlled the extent of translation repression. Overall, our results provide precise quantification of the rules governing translation initiation at N-terminal coding regions, improving the predictive design of post-transcriptional regulatory elements that regulate translation rate.

Automated physics-based design of synthetic riboswitches from diverse RNA aptamers.

Riboswitches are shape-changing regulatory RNAs that bind chemicals and regulate gene expression, directly coupling sensing to cellular actuation. However, it remains unclear how their sequence controls the physics of riboswitch switching and activation, particularly when changing the ligand-binding aptamer domain. We report the development of a statistical thermodynamic model that predicts the sequence-structure-function relationship for translation-regulating riboswitches that activate gene expression, characterized inside cells and within cell-free transcription-translation assays. Using the model, we carried out automated computational design of 62 synthetic riboswitches that used six different RNA aptamers to sense diverse chemicals (theophylline, tetramethylrosamine, fluoride, dopamine, thyroxine, 2,4-dinitrotoluene) and activated gene expression by up to 383-fold. The model explains how aptamer structure, ligand affinity, switching free energy and macromolecular crowding collectively control riboswitch activation. Our model-based approach for engineering riboswitches quantitatively confirms several physical mechanisms governing ligand-induced RNA shape-change and enables the development of cell-free and bacterial sensors for diverse applications.

A Biophysical Model of CRISPR/Cas9 Activity for Rational Design of Genome Editing and Gene Regulation.

The ability to precisely modify genomes and regulate specific genes will greatly accelerate several medical and engineering applications. The CRISPR/Cas9 (Type II) system binds and cuts DNA using guide RNAs, though the variables that control its on-target and off-target activity remain poorly characterized. Here, we develop and parameterize a system-wide biophysical model of Cas9-based genome editing and gene regulation to predict how changing guide RNA sequences, DNA superhelical densities, Cas9 and crRNA expression levels, organisms and growth conditions, and experimental conditions collectively control the dynamics of dCas9-based binding and Cas9-based cleavage at all DNA sites with both canonical and non-canonical PAMs. We combine statistical thermodynamics and kinetics to model Cas9:crRNA complex formation, diffusion, site selection, reversible R-loop formation, and cleavage, using large amounts of structural, biochemical, expression, and next-generation sequencing data to determine kinetic parameters and develop free energy models. Our results identify DNA supercoiling as a novel mechanism controlling Cas9 binding. Using the model, we predict Cas9 off-target binding frequencies across the lambdaphage and human genomes, and explain why Cas9's off-target activity can be so high. With this improved understanding, we propose several rules for designing experiments for minimizing off-target activity. We also discuss the implications for engineering dCas9-based genetic circuits.

A predictive biophysical model of translational coupling to coordinate and control protein expression in bacterial operons.

Natural and engineered genetic systems require the coordinated expression of proteins. In bacteria, translational coupling provides a genetically encoded mechanism to control expression level ratios within multi-cistronic operons. We have developed a sequence-to-function biophysical model of translational coupling to predict expression level ratios in natural operons and to design synthetic operons with desired expression level ratios. To quantitatively measure ribosome re-initiation rates, we designed and characterized 22 bi-cistronic operon variants with systematically modified intergenic distances and upstream translation rates. We then derived a thermodynamic free energy model to calculate de novo initiation rates as a result of ribosome-assisted unfolding of intergenic RNA structures. The complete biophysical model has only five free parameters, but was able to accurately predict downstream translation rates for 120 synthetic bi-cistronic and tri-cistronic operons with rationally designed intergenic regions and systematically increased upstream translation rates. The biophysical model also accurately predicted the translation rates of the nine protein atp operon, compared to ribosome profiling measurements. Altogether, the biophysical model quantitatively predicts how translational coupling controls protein expression levels in synthetic and natural bacterial operons, providing a deeper understanding of an important post-transcriptional regulatory mechanism and offering the ability to rationally engineer operons with desired behaviors.

Translation Initiation is Controlled by RNA Folding Kinetics via a Ribosome Drafting Mechanism.

RNA folding plays an important role in controlling protein synthesis as well as other cellular processes. Existing models have focused on how RNA folding energetics control translation initiation rate under equilibrium conditions but have largely ignored the effects of nonequilibrium RNA folding. We introduce a new mechanism, called "ribosome drafting", that explains how a mRNA's folding kinetics and the ribosome's binding rate collectively control its translation initiation rate. During cycles of translation, ribosome drafting emerges whenever successive ribosomes bind to a mRNA faster than the mRNA can refold, maintaining it in a nonequilibrium state with an acceleration of protein synthesis. Using computational design, time-correlated single photon counting, and expression measurements, we demonstrate that slow-folding and fast-folding RNA structures with equivalent folding energetics can vary protein synthesis rates by 1000-fold. We determine the necessary conditions for ribosome drafting by characterizing mRNAs with rationally designed ribosome binding rates, folding kinetics, and folding energetics, confirming the predictions of a nonequilibrium Markov model of translation. Our results have widespread implications, illustrating how competitive folding and assembly kinetics can shape the gene expression machinery's sequence-structure-function relationship inside cells.

Reversing methanogenesis to capture methane for liquid biofuel precursors.

BACKGROUND: Energy from remote methane reserves is transformative; however, unintended release of this potent greenhouse gas makes it imperative to convert methane efficiently into more readily transported biofuels. No pure microbial culture that grows on methane anaerobically has been isolated, despite that methane capture through anaerobic processes is more efficient than aerobic ones. RESULTS: Here we engineered the archaeal methanogen Methanosarcina acetivorans to grow anaerobically on methane as a pure culture and to convert methane into the biofuel precursor acetate. To capture methane, we cloned the enzyme methyl-coenzyme M reductase (Mcr) from an unculturable organism, anaerobic methanotrophic archaeal population 1 (ANME-1) from a Black Sea mat, into M. acetivorans to effectively run methanogenesis in reverse. Starting with low-density inocula, M. acetivorans cells producing ANME-1 Mcr consumed up to 9 ± 1 % of methane (corresponding to 109 ± 12 µmol of methane) after 6 weeks of anaerobic growth on methane and utilized 10 mM FeCl3 as an electron acceptor. Accordingly, increases in cell density and total protein were observed as cells grew on methane in a biofilm on solid FeCl3. When incubated on methane for 5 days, high-densities of ANME-1 Mcr-producing M. acetivorans cells consumed 15 ± 2 % methane (corresponding to 143 ± 16 µmol of methane), and produced 10.3 ± 0.8 mM acetate (corresponding to 52 ± 4 µmol of acetate). We further confirmed the growth on methane and acetate production using (13)C isotopic labeling of methane and bicarbonate coupled with nuclear magnetic resonance and gas chromatography/mass spectroscopy, as well as RNA sequencing. CONCLUSIONS: We anticipate that our metabolically-engineered strain will provide insights into how methane is cycled in the environment by Archaea as well as will possibly be utilized to convert remote sources of methane into more easily transported biofuels via acetate.

Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites.

The ribosome's interactions with mRNA govern its translation rate and the effects of post-transcriptional regulation. Long, structured 5' untranslated regions (5' UTRs) are commonly found in bacterial mRNAs, though the physical mechanisms that determine how the ribosome binds these upstream regions remain poorly defined. Here, we systematically investigate the ribosome's interactions with structured standby sites, upstream of Shine-Dalgarno sequences, and show that these interactions can modulate translation initiation rates by over 100-fold. We find that an mRNA's translation initiation rate is controlled by the amount of single-stranded surface area, the partial unfolding of RNA structures to minimize the ribosome's binding free energy penalty, the absence of cooperative binding and the potential for ribosomal sliding. We develop a biophysical model employing thermodynamic first principles and a four-parameter free energy model to accurately predict the ribosome's translation initiation rates for 136 synthetic 5' UTRs with large structures, diverse shapes and multiple standby site modules. The model predicts and experiments confirm that the ribosome can readily bind distant standby site modules that support high translation rates, providing a physical mechanism for observed context effects and long-range post-transcriptional regulation.

Rational design of a synthetic Entner-Doudoroff pathway for improved and controllable NADPH regeneration.

NADPH is an essential cofactor for the biosynthesis of several high-value chemicals, including isoprenoids, fatty acid-based fuels, and biopolymers. Tunable control over all potentially rate-limiting steps, including the NADPH regeneration rate, is crucial to maximizing production titers. We have rationally engineered a synthetic version of the Entner-Doudoroff pathway from Zymomonas mobilis that increased the NADPH regeneration rate in Escherichia coli MG1655 by 25-fold. To do this, we combined systematic design rules, biophysical models, and computational optimization to design synthetic bacterial operons expressing the 5-enzyme pathway, while eliminating undesired genetic elements for maximum expression control. NADPH regeneration rates from genome-integrated pathways were estimated using a NADPH-binding fluorescent reporter and by the productivity of a NADPH-dependent terpenoid biosynthesis pathway. We designed and constructed improved pathway variants by employing the RBS Library Calculator to efficiently search the 5-dimensional enzyme expression space and by performing 40 cycles of MAGE for site-directed genome mutagenesis. 624 pathway variants were screened using a NADPH-dependent blue fluorescent protein, and 22 were further characterized to determine the relationship between enzyme expression levels and NADPH regeneration rates. The best variant exhibited 25-fold higher normalized mBFP levels when compared to wild-type strain. Combining the synthetic Entner-Doudoroff pathway with an optimized terpenoid pathway further increased the terpenoid titer by 97%.

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria.

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.

Autoclaving is at least as effective as gamma irradiation for biotic clearing and intentional microbial recolonization of soil.

Sterilization is commonly used to remove or reduce the biotic constraints of a soil to allow recolonization by soil-dwelling organisms, with autoclaving and gamma irradiation being the most frequently used approaches. Many studies have characterized sterilization impacts on soil physicochemical properties, with gamma irradiation often described as the preferred approach, despite the lower cost and higher scalability of autoclaving. However, few studies have compared how sterilization techniques impact soil recolonization by microorganisms. Here, we compared how two sterilization approaches (autoclaving; gamma irradiation) and soil washing impacted microbial recolonization of soil from a diverse soil inoculum. Sterilization method had little impact on microbial alpha diversity across recolonized soils. For sterile soil regrowth microcosms, species richness and diversity were significantly reduced by autoclaving relative to gamma irradiation, particularly for fungi. There was no impact of sterilization method on bacterial composition in recolonized soils and minimal impact on fungal composition (P = 0.05). Washing soils had a greater impact on microbial composition than sterilization method, and sterile soil regrowth had negligible impacts on microbial recolonization. These data suggest that sterilization method has no clear impact on microbial recolonization, at least across the soils tested, indicating that soil autoclaving is an appropriate and economical approach for biotically clearing soils.IMPORTANCESterilized soils represent soil-like environments that act as a medium to study microbial colonization dynamics in more "natural" settings relative to artificial culturing environments. Soil sterilization is often carried out by gamma irradiation or autoclaving, which both alter soil properties, but gamma irradiation is thought to be the gentler technique. Gamma irradiation can be cost prohibitive and does not scale well for larger experiments. We sought to examine how soil sterilization technique can impact microbial colonization, and additionally looked at the impact of soil washing which is believed to remove soil toxins that inhibit soil recolonization. We found that both gamma-irradiated and autoclaved soils showed similar colonization patterns when reintroducing microorganisms. Soil washing, relative to sterilization technique, had a greater impact on which microorganisms were able to recolonize the soil. When allowing sterilized soils to regrow (i.e., persisting microorganisms), gamma irradiation performed worse, suggesting that gamma irradiation does not biotically clear soils as well as autoclaving. These data suggest that both sterilization techniques are comparable, and that autoclaving may be more effective at biotically clearing soil.

Genetic circuitry boosts cell longevity.

Reprogramming cellular dynamics is used to study and delay the onset of aging in yeast.

Automated design of protein-binding riboswitches for sensing human biomarkers in a cell-free expression system.

Cell-free genetically encoded biosensors have been developed to detect small molecules and nucleic acids, but they have yet to be reliably engineered to detect proteins. Here we develop an automated platform to convert protein-binding RNA aptamers into riboswitch sensors that operate within low-cost cell-free assays. We demonstrate the platform by engineering 35 protein-sensing riboswitches for human monomeric C-reactive protein, human interleukin-32γ, and phage MS2 coat protein. The riboswitch sensors regulate output expression levels by up to 16-fold with input protein concentrations within the human serum range. We identify two distinct mechanisms governing riboswitch-mediated regulation of translation rates and leverage computational analysis to refine the protein-binding aptamer regions, improving design accuracy. Overall, we expand the cell-free sensor toolbox and demonstrate how computational design is used to develop protein-sensing riboswitches with future applications as low-cost medical diagnostics.

Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria.

Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combine massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ70 promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.

Tuning Cell-Free Composition Controls the Time Delay, Dynamics, and Productivity of TX-TL Expression.

The composition of cell-free expression systems (TX-TL) is adjusted by adding macromolecular crowding agents and salts. However, the effects of these cosolutes on the dynamics of individual gene expression processes have not been quantified. Here, we carry out kinetic mRNA and protein level measurements on libraries of genetic constructs using the common cosolutes PEG-8000, Ficoll-400, and magnesium glutamate. By combining these measurements with biophysical modeling, we show that cosolutes have differing effects on transcription initiation, translation initiation, and translation elongation rates with trade-offs between time delays, expression tunability, and maximum expression productivity. We also confirm that biophysical models can predict translation initiation rates in TX-TL using Escherichia coli lysate. We discuss how cosolute composition can be tuned to maximize performance across different cell-free applications, including biosensing, diagnostics, and biomanufacturing.

Systematic Quantification of Sequence and Structural Determinants Controlling mRNA stability in Bacterial Operons.

mRNA degradation is a central process that affects all gene expression levels, and yet, the determinants that control mRNA decay rates remain poorly characterized. Here, we applied a synthetic biology, learn-by-design approach to elucidate the sequence and structural determinants that control mRNA stability in bacterial operons. We designed, constructed, and characterized 82 operons in Escherichia coli, systematically varying RNase binding site characteristics, translation initiation rates, and transcriptional terminator efficiencies in the 5' untranslated region (UTR), intergenic, and 3' UTR regions, followed by measuring their mRNA levels using reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays during exponential growth. We show that introducing long single-stranded RNA into 5' UTRs reduced mRNA levels by up to 9.4-fold and that lowering translation rates reduced mRNA levels by up to 11.8-fold. We also found that RNase binding sites in intergenic regions had much lower effects on mRNA levels. Surprisingly, changing the transcriptional termination efficiency or introducing long single-stranded RNA into 3' UTRs had no effect on upstream mRNA levels. From these measurements, we developed and validated biophysical models of ribosome protection and RNase activity with excellent quantitative agreement. We also formulated design rules to rationally control a mRNA's stability, facilitating the automated design of engineered genetic systems with desired functionalities.

Purification of Cas9-RNA complexes by ultrafiltration.

The discovery of CRISPR-Cas9 has revolutionized molecular biology, greatly accelerating the introduction of genetic modifications into organisms and facilitating the development of novel therapeutics and diagnostics. For many applications, guide RNA and Cas9 protein are expressed, combined, and purified to produce a ribonucleic enzyme complex that is then added into a diagnostic device or delivered into cells. The objective of this work was to develop an ultrafiltration process for the selective purification of Cas9 ribonucleoprotein by removal of excess guide RNA. A His-tagged Streptococcus pyogenes Cas9 protein was produced in Escherichia coli, purified by metal affinity chromatography, and complexed with a 40 kDa (124 nucleotide) single guide RNA. Ultrafiltration experiments were first performed on solutions containing either guide RNA or Cas9 protein to identify the effect of filtration conditions and membrane pore size on the selectivity. Shear-induced aggregation of the Cas9 led to significant fouling under some conditions. A diafiltration process was then developed using a Biomax® 300 kDa polyethersulfone membrane to selectively remove excess guide RNA from a solution containing Cas9-bound guide RNA and free guide RNA. These results demonstrate the potential of using ultrafiltration for the removal of excess RNA during the production of functional ribonucleoprotein complexes.

An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models.

Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions remains a significant challenge. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce model capacity, a new information theoretic metric for correct cross-data-set comparisons. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We then applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning.

The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We developed a machine learning model, called the Synthesis Success Calculator, to predict whether a long DNA fragment can be readily synthesized with a short turnaround time. The model also identifies the sequence determinants associated with the synthesis outcome. We trained a random forest classifier using biophysical features and a compiled data set of 1076 DNA fragment sequences to achieve high predictive performance (F1 score of 0.928 on 251 unseen sequences). Feature importance analysis revealed that repetitive DNA sequences were the most important contributor to synthesis failures. We then applied the Synthesis Success Calculator across large sequence data sets and found that 84.9% of the Escherichia coli MG1655 genome, but only 34.4% of sampled plasmids in NCBI, could be readily synthesized. Overall, the Synthesis Success Calculator can be applied on its own to prevent synthesis failures or embedded within optimization algorithms to design large genetic systems that can be rapidly synthesized and assembled.