Meeting Measurement Precision Requirements for Effective Engineering of Genetic Regulatory Networks.

Reliable, predictable engineering of cellular behavior is one of the key goals of synthetic biology. As the field matures, biological engineers will become increasingly reliant on computer models that allow for the rapid exploration of design space prior to the more costly construction and characterization of candidate designs. The efficacy of such models, however, depends on the accuracy of their predictions, the precision of the measurements used to parametrize the models, and the tolerance of biological devices for imperfections in modeling and measurement. To better understand this relationship, we have derived an Engineering Error Inequality that provides a quantitative mathematical bound on the relationship between predictability of results, model accuracy, measurement precision, and device characteristics. We apply this relation to estimate measurement precision requirements for engineering genetic regulatory networks given current model and device characteristics, recommending a target standard deviation of 1.5-fold. We then compare these requirements with the results of an interlaboratory study to validate that these requirements can be met via flow cytometry with matched instrument channels and an independent calibrant. On the basis of these results, we recommend a set of best practices for quality control of flow cytometry data and discuss how these might be extended to other measurement modalities and applied to support further development of genetic regulatory network engineering.

Probing remote residues important for catalysis in Escherichia coli ornithine transcarbamoylase.

Understanding how enzymes achieve their tremendous catalytic power is a major question in biochemistry. Greater understanding is also needed for enzyme engineering applications. In many cases, enzyme efficiency and specificity depend on residues not in direct contact with the substrate, termed remote residues. This work focuses on Escherichia coli ornithine transcarbamoylase (OTC), which plays a central role in amino acid metabolism. OTC has been reported to undergo an induced-fit conformational change upon binding its first substrate, carbamoyl phosphate (CP), and several residues important for activity have been identified. Using computational methods based on the computed chemical properties from theoretical titration curves, sequence-based scores derived from evolutionary history, and protein surface topology, residues important for catalytic activity were predicted. The roles of these residues in OTC activity were tested by constructing mutations at predicted positions, followed by steady-state kinetics assays and substrate binding studies with the variants. First-layer mutations R57A and D231A, second-layer mutation H272L, and third-layer mutation E299Q, result in 57- to 450-fold reductions in kcat/KM with respect to CP and 44- to 580-fold reductions with respect to ornithine. Second-layer mutations D140N and Y160S also reduce activity with respect to ornithine. Most variants had decreased stability relative to wild-type OTC, with variants H272L, H272N, and E299Q having the greatest decreases. Variants H272L, E299Q, and R57A also show compromised CP binding. In addition to direct effects on catalytic activity, effects on overall protein stability and substrate binding were observed that reveal the intricacies of how these residues contribute to catalysis.

The response of Escherichia coli to the alkylating agents chloroacetaldehyde and styrene oxide.

DNA damage is ubiquitous and can arise from endogenous or exogenous sources. DNA-damaging alkylating agents are present in environmental toxicants as well as in cancer chemotherapy drugs and are a constant threat, which can lead to mutations or cell death. All organisms have multiple DNA repair and DNA damage tolerance pathways to resist the potentially negative effects of exposure to alkylating agents. In bacteria, many of the genes in these pathways are regulated as part of the SOS reponse or the adaptive response. In this work, we probed the cellular responses to the alkylating agents chloroacetaldehyde (CAA), which is a metabolite of 1,2-dichloroethane used to produce polyvinyl chloride, and styrene oxide (SO), a major metabolite of styrene used in the production of polystyrene and other polymers. Vinyl chloride and styrene are produced on an industrial scale of billions of kilograms annually and thus have a high potential for environmental exposure. To identify stress response genes in E. coli that are responsible for tolerance to the reactive metabolites CAA and SO, we used libraries of transcriptional reporters and gene deletion strains. In response to both alkylating agents, genes associated with several different stress pathways were upregulated, including protein, membrane, and oxidative stress, as well as DNA damage. E. coli strains lacking genes involved in base excision repair and nucleotide excision repair were sensitive to SO, whereas strains lacking recA and the SOS gene ybfE were sensitive to both alkylating agents tested. This work indicates the varied systems involved in cellular responses to alkylating agents, and highlights the specific DNA repair genes involved in the responses.

Stochastic Turing patterns in a synthetic bacterial population.

The origin of biological morphology and form is one of the deepest problems in science, underlying our understanding of development and the functioning of living systems. In 1952, Alan Turing showed that chemical morphogenesis could arise from a linear instability of a spatially uniform state, giving rise to periodic pattern formation in reaction-diffusion systems but only those with a rapidly diffusing inhibitor and a slowly diffusing activator. These conditions are disappointingly hard to achieve in nature, and the role of Turing instabilities in biological pattern formation has been called into question. Recently, the theory was extended to include noisy activator-inhibitor birth and death processes. Surprisingly, this stochastic Turing theory predicts the existence of patterns over a wide range of parameters, in particular with no severe requirement on the ratio of activator-inhibitor diffusion coefficients. To explore whether this mechanism is viable in practice, we have genetically engineered a synthetic bacterial population in which the signaling molecules form a stochastic activator-inhibitor system. The synthetic pattern-forming gene circuit destabilizes an initially homogenous lawn of genetically engineered bacteria, producing disordered patterns with tunable features on a spatial scale much larger than that of a single cell. Spatial correlations of the experimental patterns agree quantitatively with the signature predicted by theory. These results show that Turing-type pattern-forming mechanisms, if driven by stochasticity, can potentially underlie a broad range of biological patterns. These findings provide the groundwork for a unified picture of biological morphogenesis, arising from a combination of stochastic gene expression and dynamical instabilities.

Prediction of distal residue participation in enzyme catalysis.

A scoring method for the prediction of catalytically important residues in enzyme structures is presented and used to examine the participation of distal residues in enzyme catalysis. Scores are based on the Partial Order Optimum Likelihood (POOL) machine learning method, using computed electrostatic properties, surface geometric features, and information obtained from the phylogenetic tree as input features. Predictions of distal residue participation in catalysis are compared with experimental kinetics data from the literature on variants of the featured enzymes; some additional kinetics measurements are reported for variants of Pseudomonas putida nitrile hydratase (ppNH) and for Escherichia coli alkaline phosphatase (AP). The multilayer active sites of P. putida nitrile hydratase and of human phosphoglucose isomerase are predicted by the POOL log ZP scores, as is the single-layer active site of P. putida ketosteroid isomerase. The log ZP score cutoff utilized here results in over-prediction of distal residue involvement in E. coli alkaline phosphatase. While fewer experimental data points are available for P. putida mandelate racemase and for human carbonic anhydrase II, the POOL log ZP scores properly predict the previously reported participation of distal residues.

Multiple strategies for translesion synthesis in bacteria.

Damage to DNA is common and can arise from numerous environmental and endogenous sources. In response to ubiquitous DNA damage, Y-family DNA polymerases are induced by the SOS response and are capable of bypassing DNA lesions. In Escherichia coli, these Y-family polymerases are DinB and UmuC, whose activities are modulated by their interaction with the polymerase manager protein UmuD. Many, but not all, bacteria utilize DinB and UmuC homologs. Recently, a C-family polymerase named ImuC, which is similar in primary structure to the replicative DNA polymerase DnaE, was found to be able to copy damaged DNA and either carry out or suppress mutagenesis. ImuC is often found with proteins ImuA and ImuB, the latter of which is similar to Y­family polymerases, but seems to lack the catalytic residues necessary for polymerase activity. This imuAimuBimuC mutagenesis cassette represents a widespread alternative strategy for translesion synthesis and mutagenesis in bacteria. Bacterial Y­family and ImuC DNA polymerases contribute to replication past DNA damage and the acquisition of antibiotic resistance.