An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization

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.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Native and non-native host assessment towards metabolic pathway reconstructions of plant natural products

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Plant metabolic networks are highly complex.

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Engineering the phytochemical pathways fully in heterologous hosts is challenging.

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Single plant cells with amplified multiple fission enable homogeneity.

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Homogeneity and high cell division rate can facilitate stable product scale-up.

Plant-based biopreparations are reasonably priced and are devoid of viral, prion and endotoxin contaminants. However, synthesizing these natural plant products by chemical methods is quite expensive. The structural complexity of plant-derived natural products poses a challenge for chemical synthesis at a commercial scale. Failure of commercial-scale synthesis is the chief reason why metabolic reconstructions in heterologous hosts are inevitable. This review discusses plant metabolite pathway reconstructions experimented in various heterologous hosts, and the inherent challenges involved. Plants as native hosts possess enhanced post-translational modification ability, along with rigorous gene edits, unlike microbes. To achieve a high yield of metabolites in plants, increased cell division rate is one of the requisites. This improved cell division rate will promote cellular homogeneity. Incorporation and maintenance of plant cell synchrony, in turn, can program stable product scale-up.

Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage

To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.

Towards a fully automated algorithm driven platform for biosystems design

Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.

Existing efforts have been focused on one of the elements in the automation of the design, build, test, and learn (DBTL) cycle for biosystems design. Here, the authors integrate a robotic system with machine learning algorithms to fully automate the DBTL cycle and apply it in optimizing the lycopene biosynthetic pathway.

Optimization of Artificial Curcumin Biosynthesis in E. coli by Randomized 5'-UTR Sequences To Control the Multienzyme Pathway

One of the optimization strategies of an artificial biosynthetic metabolic flux with a multienzyme pathway is when the enzyme concentrations are present at the appropriate ratios rather than at their maximum expression. Thus, many recent research efforts have focused on the development of tools that fine-tune the enzyme expression, and these research efforts have facilitated the search for the optimum balance between pathway expression and cell viability. However, the rational approach has some limitations in finding the most optimized expression ratio in in vivo systems. In our study, we focused on fine-tuning the expression level of a six-enzyme reaction for the artificial biosynthesis of curcumin by screening a library of 5'-untranslational region (UTR) sequence mutants made by a multiplex automatic genome engineering (MAGE) tool. From the screening results, a variant (6M08rv) showed about a 38.2-fold improvement in the production of curcumin compared to the parent strain, in which the calculated expression levels of 4-coumarate:CoA ligase (4CL) and phenyldiketide-CoA synthase (DCS), two of the six enzymes, were much lower than those of the parent strain.