CRISPR-Cas tools for simultaneous transcription & translation control in bacteria.

Robust control over gene translation at arbitrary mRNA targets is an outstanding challenge in microbial synthetic biology. The development of tools that can regulate translation will greatly expand our ability to precisely control genes across the genome. In Escherichia coli, most genes are contained in multi-gene operons, which are subject to polar effects where targeting one gene for repression leads to silencing of other genes in the same operon. These effects pose a challenge for independently regulating individual genes in multi-gene operons. Here, we use CRISPR-dCas13 to address this challenge. We find dCas13-mediated repression exhibits up to 6-fold lower polar effects compared to dCas9. We then show that we can selectively activate single genes in a synthetic multi-gene operon by coupling dCas9 transcriptional activation of an operon with dCas13 translational repression of individual genes within the operon. We also show that dCas13 and dCas9 can be multiplexed for improved biosynthesis of a medically-relevant human milk oligosaccharide. Taken together, our findings suggest that combining transcriptional and translational control can access effects that are difficult to achieve with either mode independently. These combined tools for gene regulation will expand our abilities to precisely engineer bacteria for biotechnology and perform systematic genetic screens.

Engineering activatable promoters for scalable and multi-input CRISPRa/i circuits.

Dynamic, multi-input gene regulatory networks (GRNs) are ubiquitous in nature. Multilayer CRISPR-based genetic circuits hold great promise for building GRNs akin to those found in naturally occurring biological systems. We develop an approach for creating high-performing activatable promoters that can be assembled into deep, wide, and multi-input CRISPR-activation and -interference (CRISPRa/i) GRNs. By integrating sequence-based design and in vivo screening, we engineer activatable promoters that achieve up to 1,000-fold dynamic range in an Escherichia coli-based cell-free system. These components enable CRISPRa GRNs that are six layers deep and four branches wide. We show the generalizability of the promoter engineering workflow by improving the dynamic range of the light-dependent EL222 optogenetic system from 6-fold to 34-fold. Additionally, high dynamic range promoters enable CRISPRa systems mediated by small molecules and protein-protein interactions. We apply these tools to build input-responsive CRISPRa/i GRNs, including feedback loops, logic gates, multilayer cascades, and dynamic pulse modulators. Our work provides a generalizable approach for the design of high dynamic range activatable promoters and enables classes of gene regulatory functions in cell-free systems.

Portable bacterial CRISPR transcriptional activation enables metabolic engineering in Pseudomonas putida.

CRISPR-Cas transcriptional programming in bacteria is an emerging tool to regulate gene expression for metabolic pathway engineering. Here we implement CRISPR-Cas transcriptional activation (CRISPRa) in P. putida using a system previously developed in E. coli. We provide a methodology to transfer CRISPRa to a new host by first optimizing expression levels for the CRISPRa system components, and then applying rules for effective CRISPRa based on a systematic characterization of promoter features. Using this optimized system, we regulate biosynthesis in the biopterin and mevalonate pathways. We demonstrate that multiple genes can be activated simultaneously by targeting multiple promoters or by targeting a single promoter in a multi-gene operon. This work will enable new metabolic engineering strategies in P. putida and pave the way for CRISPR-Cas transcriptional programming in other bacterial species.

Complex dependence of CRISPR-Cas9 binding strength on guide RNA spacer lengths.

It is established that for CRISPR-Cas9 applications guide RNAs with 17-20 bp long spacer sequences are optimal for accurate target binding and cleavage. In this work we perform cell-free CRISPRa (CRISPR activation) and CRISPRi (CRISPR inhibition) experiments to demonstrate the existence of a complex dependence of CRISPR-Cas9 binding as a function of the spacer length and complementarity. Our results show that significantly truncated or mismatched spacer sequences can form stronger guide-target bonds than the conventional 17-20 bp long spacers. To explain this phenomenon, we take into consideration previous structural and single-molecule CRISPR-Cas9 experiments and develop a novel thermodynamic model of CRISPR-Cas9 target recognition.

Prospects for engineering dynamic CRISPR-Cas transcriptional circuits to improve bioproduction.

Dynamic control of gene expression is emerging as an important strategy for controlling flux in metabolic pathways and improving bioproduction of valuable compounds. Integrating dynamic genetic control tools with CRISPR-Cas transcriptional regulation could significantly improve our ability to fine-tune the expression of multiple endogenous and heterologous genes according to the state of the cell. In this mini-review, we combine an analysis of recent literature with examples from our own work to discuss the prospects and challenges of developing dynamically regulated CRISPR-Cas transcriptional control systems for applications in synthetic biology and metabolic engineering.

Label-free selection of RNA aptamers for metabolic engineering.

RNA aptamers can be assembled into genetic regulatory devices that sense and respond to levels of specific cellular metabolites and thus serve an integral part of designing dynamic control into engineered metabolic pathways. Here, we describe a practical method for generating specific and high affinity aptamers to enable the wider use of in vitro selection and a broader application of aptamers for metabolic engineering. Conventional selection methods involving either radioactive labeling of RNA or the use of label-free methods such as SPR to track aptamer enrichment require resources that are not widely accessible to research groups. We present a label-free selection method that uses small volume spectrophotometers to track RNA enrichment paired with previously characterized affinity chromatography methods. Borrowing techniques used in solid phase peptide synthesis, we present an approach for immobilizing a wide range of metabolites to an amino PEGA matrix. As an illustration, we detail laboratory techniques employed to generate aptamers that bind p-aminophenylalanine, a metabolic precursor for bio-based production of plastics and the pristinamycin family of antibiotics. We focused on the development of methods for ligand immobilization, selection via affinity chromatography, and nucleic acid quantification that can be performed with common laboratory equipment.

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.

Systems-Level Modeling for CRISPR-Based Metabolic Engineering.

The CRISPR-Cas system has enabled the development of sophisticated, multigene metabolic engineering programs through the use of guide RNA-directed activation or repression of target genes. To optimize biosynthetic pathways in microbial systems, we need improved models to inform design and implementation of transcriptional programs. Recent progress has resulted in new modeling approaches for identifying gene targets and predicting the efficacy of guide RNA targeting. Genome-scale and flux balance models have successfully been applied to identify targets for improving biosynthetic production yields using combinatorial CRISPR-interference (CRISPRi) programs. The advent of new approaches for tunable and dynamic CRISPR activation (CRISPRa) promises to further advance these engineering capabilities. Once appropriate targets are identified, guide RNA prediction models can lead to increased efficacy in gene targeting. Developing improved models and incorporating approaches from machine learning may be able to overcome current limitations and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic engineering.

CRISPR Tools for Engineering Prokaryotic Systems: Recent Advances and New Applications.

In the past decades, the broad selection of CRISPR-Cas systems has revolutionized biotechnology by enabling multimodal genetic manipulation in diverse organisms. Rooted in a molecular engineering perspective, we recapitulate the different CRISPR components and how they can be designed for specific genetic engineering applications. We first introduce the repertoire of Cas proteins and tethered effectors used to program new biological functions through gene editing and gene regulation. We review current guide RNA (gRNA) design strategies and computational tools and how CRISPR-based genetic circuits can be constructed through regulated gRNA expression. Then, we present recent advances in CRISPR-based biosensing, bioproduction, and biotherapeutics across in vitro and in vivo prokaryotic systems. Finally, we discuss forthcoming applications in prokaryotic CRISPR technology that will transform synthetic biology principles in the near future.

Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling.

Engineering metabolism to efficiently produce chemicals from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important tools for programming multi-gene expression, but poor predictability of guide RNA folding can disrupt expression control. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a computational kinetic parameter describing scRNA folding rate into the active structure (rS = 0.8). This parameter also enables forward design of scRNAs, allowing us to design a system of three synthetic CRISPRa promoters that can orthogonally activate (>35-fold) expression of chosen outputs. Through combinatorial activation tuning, we profile a three-dimensional design space expressing two different biosynthetic pathways, demonstrating variable production of pteridine and human milk oligosaccharide products. This RNA design approach aids combinatorial optimization of metabolic pathways and may accelerate routine design of effective multi-gene regulation programs in bacterial hosts.

Standards, dissemination, and best practices in systems biology.

The reproducibility of scientific research is crucial to the success of the scientific method. Here, we review the current best practices when publishing mechanistic models in systems biology. We recommend, where possible, to use software engineering strategies such as testing, verification, validation, documentation, versioning, iterative development, and continuous integration. In addition, adhering to the Findable, Accessible, Interoperable, and Reusable modeling principles allows other scientists to collaborate and build off of each other's work. Existing standards such as Systems Biology Markup Language, CellML, or Simulation Experiment Description Markup Language can greatly improve the likelihood that a published model is reproducible, especially if such models are deposited in well-established model repositories. Where models are published in executable programming languages, the source code and their data should be published as open-source in public code repositories together with any documentation and testing code. For complex models, we recommend container-based solutions where any software dependencies and the run-time context can be easily replicated.

Gene expression dynamics in input-responsive engineered living materials programmed for bioproduction.

Engineered living materials (ELMs) fabricated by encapsulating microbes in hydrogels have great potential as bioreactors for sustained bioproduction. While long-term metabolic activity has been demonstrated in these systems, the capacity and dynamics of gene expression over time is not well understood. Thus, we investigate the long-term gene expression dynamics in microbial ELMs constructed using different microbes and hydrogel matrices. Through direct gene expression measurements of engineered E. coli in F127-bisurethane methacrylate (F127-BUM) hydrogels, we show that inducible, input-responsive genetic programs in ELMs can be activated multiple times and maintained for multiple weeks. Interestingly, the encapsulated bacteria sustain inducible gene expression almost 10 times longer than free-floating, planktonic cells. These ELMs exhibit dynamic responsiveness to repeated induction cycles, with up to 97% of the initial gene expression capacity retained following a subsequent induction event. We demonstrate multi-week bioproduction cycling by implementing inducible CRISPR transcriptional activation (CRISPRa) programs that regulate the expression of enzymes in a pteridine biosynthesis pathway. ELMs fabricated from engineered S. cerevisiae in bovine serum albumin (BSA) - polyethylene glycol diacrylate (PEGDA) hydrogels were programmed to express two different proteins, each under the control of a different chemical inducer. We observed scheduled bioproduction switching between betaxanthin pigment molecules and proteinase A in S. cerevisiae ELMs over the course of 27 days under continuous cultivation. Overall, these results suggest that the capacity for long-term genetic expression may be a general property of microbial ELMs. This work establishes approaches for implementing dynamic, input-responsive genetic programs to tailor ELM functions for a wide range of advanced applications.

Perspectives for self-driving labs in synthetic biology.

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

Selecting RNA aptamers for synthetic biology: investigating magnesium dependence and predicting binding affinity.

The ability to generate RNA aptamers for synthetic biology using in vitro selection depends on the informational complexity (IC) needed to specify functional structures that bind target ligands with desired affinities in physiological concentrations of magnesium. We investigate how selection for high-affinity aptamers is constrained by chemical properties of the ligand and the need to bind in low magnesium. We select two sets of RNA aptamers that bind planar ligands with dissociation constants (K(d)s) ranging from 65 nM to 100 microM in physiological buffer conditions. Aptamers selected to bind the non-proteinogenic amino acid, p-amino phenylalanine (pAF), are larger and more informationally complex (i.e., rarer in a pool of random sequences) than aptamers selected to bind a larger fluorescent dye, tetramethylrhodamine (TMR). Interestingly, tighter binding aptamers show less dependence on magnesium than weaker-binding aptamers. Thus, selection for high-affinity binding may automatically lead to structures that are functional in physiological conditions (1-2.5 mM Mg(2+)). We hypothesize that selection for high-affinity binding in physiological conditions is primarily constrained by ligand characteristics such as molecular weight (MW) and the number of rotatable bonds. We suggest that it may be possible to estimate aptamer-ligand affinities and predict whether a particular aptamer-based design goal is achievable before performing the selection.

Multi-layer CRISPRa/i circuits for dynamic genetic programs in cell-free and bacterial systems.

CRISPR-Cas transcriptional circuits hold great promise as platforms for engineering metabolic networks and information processing circuits. Historically, prokaryotic CRISPR control systems have been limited to CRISPRi. Creating approaches to integrate CRISPRa for transcriptional activation with existing CRISPRi-based systems would greatly expand CRISPR circuit design space. Here, we develop design principles for engineering prokaryotic CRISPRa/i genetic circuits with network topologies specified by guide RNAs. We demonstrate that multi-layer CRISPRa/i cascades and feedforward loops can operate through the regulated expression of guide RNAs in cell-free expression systems and E. coli. We show that CRISPRa/i circuits can program complex functions by designing type 1 incoherent feedforward loops acting as fold-change detectors and tunable pulse-generators. By investigating how component characteristics relate to network properties such as depth, width, and speed, this work establishes a framework for building scalable CRISPRa/i circuits as regulatory programs in cell-free expression systems and bacterial hosts. A record of this paper's transparent peer review process is included in the supplemental information.

Expanding the Scope of Bacterial CRISPR Activation with PAM-Flexible dCas9 Variants.

CRISPR-Cas transcriptional tools have been widely applied for programmable regulation of complex biological networks. In comparison to eukaryotic systems, bacterial CRISPR activation (CRISPRa) has stringent target site requirements for effective gene activation. While genes may not always have an NGG protospacer adjacent motif (PAM) at the appropriate position, PAM-flexible dCas9 variants can expand the range of targetable sites. Here we systematically evaluate a panel of PAM-flexible dCas9 variants for their ability to activate bacterial genes. We observe that dxCas9-NG provides a high dynamic range of gene activation for sites with NGN PAMs while dSpRY permits modest activity across almost any PAM. Similar trends were observed for heterologous and endogenous promoters. For all variants tested, improved PAM-flexibility comes with the trade-off that CRISPRi-mediated gene repression becomes less effective. Weaker CRISPR interference (CRISPRi) gene repression can be partially rescued by expressing multiple sgRNAs to target many sites in the gene of interest. Our work provides a framework to choose the most effective dCas9 variant for a given set of gene targets, which will further expand the utility of CRISPRa/i gene regulation in bacterial systems.

Membrane Augmented Cell-Free Systems: A New Frontier in Biotechnology.

Membrane proteins are present in a wide array of cellular processes from primary and secondary metabolite synthesis to electron transport and single carbon metabolism. A key barrier to applying membrane proteins industrially is their difficult functional production. Beyond expression, folding, and membrane insertion, membrane protein activity is influenced by the physicochemical properties of the associated membrane, making it difficult to achieve optimal membrane protein performance outside the endogenous host. In this review, we highlight recent work on production of membrane proteins in membrane augmented cell-free systems (CFSs) and applications thereof. CFSs lack membranes and can thus be augmented with user-specified, tunable, mimetic membranes to generate customized environments for production of functional membrane proteins of interest. Membrane augmented CFSs would enable the synthesis of more complex plant secondary metabolites, the growth and division of synthetic cells for drug delivery and cell therapeutic applications, as well as enable green energy applications including methane capture and artificial photosynthesis.

Challenges and opportunities with CRISPR activation in bacteria for data-driven metabolic engineering.

Creating CRISPR gene activation (CRISPRa) technologies in industrially promising bacteria could be transformative for accelerating data-driven metabolic engineering and strain design. CRISPRa has been widely used in eukaryotes, but applications in bacterial systems have remained limited. Recent work shows that multiple features of bacterial promoters impose stringent requirements on CRISPRa-mediated gene activation. However, by systematically defining rules for effective bacterial CRISPRa sites and developing new approaches for encoding complex functions in engineered guide RNAs, there are now clear routes to generalize synthetic gene regulation in bacteria. When combined with multi-omics data collection and machine learning, the full development of bacterial CRISPRa will dramatically improve the ability to rapidly engineer bacteria for bioproduction through accelerated design-build-test-learn cycles.

Effective CRISPRa-mediated control of gene expression in bacteria must overcome strict target site requirements.

In bacterial systems, CRISPR-Cas transcriptional activation (CRISPRa) has the potential to dramatically expand our ability to regulate gene expression, but we lack predictive rules for designing effective gRNA target sites. Here, we identify multiple features of bacterial promoters that impose stringent requirements on CRISPRa target sites. Notably, we observe narrow, 2-4 base windows of effective sites with a periodicity corresponding to one helical turn of DNA, spanning ~40 bases and centered ~80 bases upstream of the TSS. However, we also identify two features suggesting the potential for broad scope: CRISPRa is effective at a broad range of σ70-family promoters, and an expanded PAM dCas9 allows the activation of promoters that cannot be activated by S. pyogenes dCas9. These results provide a roadmap for future engineering efforts to further expand and generalize the scope of bacterial CRISPRa.