Discovery of Reactive Microbiota-Derived Metabolites that Inhibit Host Proteases.

The gut microbiota modulate host biology in numerous ways, but little is known about the molecular mediators of these interactions. Previously, we found a widely distributed family of nonribosomal peptide synthetase gene clusters in gut bacteria. Here, by expressing a subset of these clusters in Escherichia coli or Bacillus subtilis, we show that they encode pyrazinones and dihydropyrazinones. At least one of the 47 clusters is present in 88% of the National Institutes of Health Human Microbiome Project (NIH HMP) stool samples, and they are transcribed under conditions of host colonization. We present evidence that the active form of these molecules is the initially released peptide aldehyde, which bears potent protease inhibitory activity and selectively targets a subset of cathepsins in human cell proteomes. Our findings show that an approach combining bioinformatics, synthetic biology, and heterologous gene cluster expression can rapidly expand our knowledge of the metabolic potential of the microbiota while avoiding the challenges of cultivating fastidious commensals.

Sentinel cells programmed to respond to environmental DNA including human sequences.

Monitoring environmental DNA can track the presence of organisms, from viruses to animals, but requires continuous sampling of transient sequences from a complex milieu. Here we designed living sentinels using Bacillus subtilis to report the uptake of a DNA sequence after matching it to a preencoded target. Overexpression of ComK increased DNA uptake 3,000-fold, allowing for femtomolar detection in samples dominated by background DNA. This capability was demonstrated using human sequences containing single-nucleotide polymorphisms (SNPs) associated with facial features. Sequences were recorded with high efficiency and were protected from nucleases for weeks. The SNP could be determined by sequencing or in vivo using CRISPR interference to turn on reporter expression in response to a specific base. Multiple SNPs were recorded by one cell or through a consortium in which each member recorded a different sequence. Sentinel cells could surveil for specific sequences over long periods of time for applications spanning forensics, ecology and epidemiology.

Realizing the potential of synthetic biology.

Synthetic biology, despite still being in its infancy, is increasingly providing valuable information for applications in the clinic, the biotechnology industry and in basic molecular research. Both its unique potential and the challenges it presents have brought together the expertise of an eclectic group of scientists, from cell biologists to engineers. In this Viewpoint article, five experts discuss their views on the future of synthetic biology, on its main achievements in basic and applied science, and on the bioethical issues that are associated with the design of new biological systems.

Analysis of Poly(ethylene terephthalate) degradation kinetics of evolved IsPETase variants using a surface crowding model.

Poly(ethylene terephthalate) (PET) is a major plastic polymer utilized in the single-use and textile industries. The discovery of PET-degrading enzymes (PETases) has led to an increased interest in the biological recycling of PET in addition to mechanical recycling. IsPETase from Ideonella sakaiensis is a candidate catalyst, but little is understood about its structure-function relationships with regards to PET degradation. To understand the effects of mutations on IsPETase productivity, we develop a directed evolution assay to identify mutations beneficial to PET film degradation at 30 °C. IsPETase also displays enzyme concentration-dependent inhibition effects, and surface crowding has been proposed as a causal phenomenon. Based on total internal reflectance fluorescence microscopy and adsorption experiments, IsPETase is likely experiencing crowded conditions on PET films. Molecular dynamics simulations of IsPETase variants reveal a decrease in active site flexibility in free enzymes and reduced probability of productive active site formation in substrate-bound enzymes under crowding. Hence, we develop a surface crowding model to analyze the biochemical effects of three hit mutations (T116P, S238N, S290P) that enhanced ambient temperature activity and/or thermostability. We find that T116P decreases susceptibility to crowding, resulting in higher PET degradation product accumulation despite no change in intrinsic catalytic rate. In conclusion, we show that a macromolecular crowding-based biochemical model can be used to analyze the effects of mutations on properties of PETases and that crowding behavior is a major property to be targeted for enzyme engineering for improved PET degradation.

Engineering orthogonal signalling pathways reveals the sparse occupancy of sequence space.

Gene duplication is a common and powerful mechanism by which cells create new signalling pathways1,2, but recently duplicated proteins typically must become insulated from each other and from other paralogues to prevent unwanted crosstalk3. A similar challenge arises when new sensors or synthetic signalling pathways are engineered within cells or transferred between genomes. How easily new pathways can be introduced into cells depends on the density and distribution of paralogous pathways in the sequence space that is defined by their specificity-determining residues4,5. Here we directly investigate how crowded this sequence space is, by generating novel two-component signalling proteins in Escherichia coli using cell sorting coupled to deep sequencing to analyse large libraries designed on the basis of coevolutionary patterns. We produce 58 insulated pathways comprising functional kinase-substrate pairs that have different specificities than their parent proteins, and demonstrate that several of these new pairs are orthogonal to all 27 paralogous pathways in E. coli. Additionally, from the kinase-substrate pairs generated, we identify sets consisting of six pairs that are mutually orthogonal to each other, which considerably increases the two-component signalling capacity of E. coli. These results indicate that sequence space is not densely occupied. The relative sparsity of paralogues in sequence space suggests that new insulated pathways can arise easily during evolution, or be designed de novo. We demonstrate the latter by engineering a signalling pathway in E. coli that responds to a plant cytokinin, without crosstalk to extant pathways. Our work also demonstrates how coevolution-guided mutagenesis and the mapping of sequence space can be used to design large sets of orthogonal protein-protein interactions.

Engineered plant control of associative nitrogen fixation.

Engineering N2-fixing symbioses between cereals and diazotrophic bacteria represents a promising strategy to sustainably deliver biologically fixed nitrogen (N) in agriculture. We previously developed novel transkingdom signaling between plants and bacteria, through plant production of the bacterial signal rhizopine, allowing control of bacterial gene expression in association with the plant. Here, we have developed both a homozygous rhizopine producing (RhiP) barley line and a hybrid rhizopine uptake system that conveys upon our model bacterium Azorhizobium caulinodans ORS571 (Ac) 103-fold improved sensitivity for rhizopine perception. Using this improved genetic circuitry, we established tight rhizopine-dependent transcriptional control of the nitrogenase master regulator nifA and the N metabolism σ-factor rpoN, which drove nitrogenase expression and activity in vitro and in situ by bacteria colonizing RhiP barley roots. Although in situ nitrogenase activity was suboptimally effective relative to the wild-type strain, activation was specific to RhiP barley and was not observed on the roots of wild-type plants. This work represents a key milestone toward the development of a synthetic plant-controlled symbiosis in which the bacteria fix N2 only when in contact with the desired host plant and are prevented from interaction with nontarget plant species.

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.

Prediction of whole-cell transcriptional response with machine learning.

MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS: The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION: The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterizing chemical signaling between engineered "microbial sentinels" in porous microplates.

Living materials combine a material scaffold, that is often porous, with engineered cells that perform sensing, computing, and biosynthetic tasks. Designing such systems is difficult because little is known regarding signaling transport parameters in the material. Here, the development of a porous microplate is presented. Hydrogel barriers between wells have a porosity of 60% and a tortuosity factor of 1.6, allowing molecular diffusion between wells. The permeability of dyes, antibiotics, inducers, and quorum signals between wells were characterized. A "sentinel" strain was constructed by introducing orthogonal sensors into the genome of Escherichia coli MG1655 for IPTG, anhydrotetracycline, L-arabinose, and four quorum signals. The strain's response to inducer diffusion through the wells was quantified up to 14 mm, and quorum and antibacterial signaling were measured over 16 h. Signaling distance is dictated by hydrogel adsorption, quantified using a linear finite element model that yields adsorption coefficients from 0 to 0.1 mol m-3 . Parameters derived herein will aid the design of living materials for pathogen remediation, computation, and self-organizing biofilms.

Competitive dCas9 binding as a mechanism for transcriptional control.

Catalytically dead Cas9 (dCas9) is a programmable transcription factor that can be targeted to promoters through the design of small guide RNAs (sgRNAs), where it can function as an activator or repressor. Natural promoters use overlapping binding sites as a mechanism for signal integration, where the binding of one can block, displace, or augment the activity of the other. Here, we implemented this strategy in Escherichia coli using pairs of sgRNAs designed to repress and then derepress transcription through competitive binding. When designed to target a promoter, this led to 27-fold repression and complete derepression. This system was also capable of ratiometric input comparison over two orders of magnitude. Additionally, we used this mechanism for promoter sequence-independent control by adopting it for elongation control, achieving 8-fold repression and 4-fold derepression. This work demonstrates a new genetic control mechanism that could be used to build analog circuit or implement cis-regulatory logic on CRISPRi-targeted native genes.

Resilient living materials built by printing bacterial spores.

Materials can be made multifunctional by embedding them with living cells that perform sensing, synthesis, energy production, and physical movement. A challenge is that the conditions needed for living cells are not conducive to materials processing and require continuous water and nutrients. Here, we present a three dimensional (3D) printer that can mix material and cell streams to build 3D objects. Bacillus subtilis spores were printed within the material and germinated on its exterior surface, including spontaneously in new cracks. The material was resilient to extreme stresses, including desiccation, solvents, osmolarity, pH, ultraviolet light, and γ-radiation. Genetic engineering enabled the bacteria to respond to stimuli or produce chemicals on demand. As a demonstration, we printed custom-shaped hydrogels containing bacteria that can sense or kill Staphylococcus aureus, a causative agent of infections. This work demonstrates materials endued with living functions that can be used in applications that require storage or exposure to environmental stresses.

Biosynthesis of the nitrogenase active-site cofactor precursor NifB-co in Saccharomyces cerevisiae.

The radical S-adenosylmethionine (SAM) enzyme NifB occupies a central and essential position in nitrogenase biogenesis. NifB catalyzes the formation of an [8Fe-9S-C] cluster, called NifB-co, which constitutes the core of the active-site cofactors for all 3 nitrogenase types. Here, we produce functional NifB in aerobically cultured Saccharomyces cerevisiae Combinatorial pathway design was employed to construct 62 strains in which transcription units driving different expression levels of mitochondria-targeted nif genes (nifUSXB and fdxN) were integrated into the chromosome. Two combinatorial libraries totaling 0.7 Mb were constructed: An expression library of 6 partial clusters, including nifUSX and fdxN, and a library consisting of 28 different nifB genes mined from the Structure-Function Linkage Database and expressed at different levels according to a factorial design. We show that coexpression in yeast of the nitrogenase maturation proteins NifU, NifS, and FdxN from Azotobacter vinelandii with NifB from the archaea Methanocaldococcus infernus or Methanothermobacter thermautotrophicus yields NifB proteins equipped with [Fe-S] clusters that, as purified, support in vitro formation of NifB-co. Proof of in vivo NifB-co formation was additionally obtained. NifX as purified from aerobically cultured S. cerevisiae coexpressing M. thermautotrophicus NifB with A. vinelandii NifU, NifS, and FdxN, and engineered yeast SAM synthase supported FeMo-co synthesis, indicative of NifX carrying in vivo-formed NifB-co. This study defines the minimal genetic determinants for the formation of the key precursor in the nitrogenase cofactor biosynthetic pathway in a eukaryotic organism.

Rapid and simultaneous screening of pathway designs and chassis organisms, applied to engineered living materials.

Achieving a high product titer through pathway optimization often requires screening many combinations of enzymes and genetic parts. Typically, a library is screened in a single chassis that is a model or production organism. Here, we present a technique where the library is first introduced into B. subtilis XPORT, which has the ability to transfer the DNA to many Gram-positive species using an inducible integrated conjugated element (ICE). This approach is demonstrated using a two-gene pathway that converts tyrosine to melanin, a pigment biopolymer that can serve as a protective coating. A library of 18 pathway variants is conjugated by XPORT into 18 species, including those isolated from soil and industrial contaminants. The resulting 324 strains are screened and the highest titer is 1.2 g/L in B. amyloliquefaciens BT16. The strains were evaluated as co-cultures in an industrial process to make mycelia-grown bulk materials, where the bacteria need to be productive in a stressful, spatially non-uniform and dynamic environment. B. subtilis BGSC 3A35 is found to perform well under these conditions and make melanin in the material, which can be seen visually. This approach enables the simultaneous screening of genetic designs and chassis during the build step of metabolic engineering.

Precision design of stable genetic circuits carried in highly-insulated E. coli genomic landing pads.

Genetic circuits have many applications, from guiding living therapeutics to ordering process in a bioreactor, but to be useful they have to be genetically stable and not hinder the host. Encoding circuits in the genome reduces burden, but this decreases performance and can interfere with native transcription. We have designed genomic landing pads in Escherichia coli at high-expression sites, flanked by ultrastrong double terminators. DNA payloads >8 kb are targeted to the landing pads using phage integrases. One landing pad is dedicated to carrying a sensor array, and two are used to carry genetic circuits. NOT/NOR gates based on repressors are optimized for the genome and characterized in the landing pads. These data are used, in conjunction with design automation software (Cello 2.0), to design circuits that perform quantitatively as predicted. These circuits require fourfold less RNA polymerase than when carried on a plasmid and are stable for weeks in a recA+ strain without selection. This approach enables the design of synthetic regulatory networks to guide cells in environments or for applications where plasmid use is infeasible.

Programming Escherichia coli to function as a digital display.

Synthetic genetic circuits offer the potential to wield computational control over biology, but their complexity is limited by the accuracy of mathematical models. Here, we present advances that enable the complete encoding of an electronic chip in the DNA carried by Escherichia coli (E. coli). The chip is a binary-coded digit (BCD) to 7-segment decoder, associated with clocks and calculators, to turn on segments to visualize 0-9. Design automation is used to build seven strains, each of which contains a circuit with up to 12 repressors and two activators (totaling 63 regulators and 76,000 bp DNA). The inputs to each circuit represent the digit to be displayed (encoded in binary by four molecules), and output is the segment state, reported as fluorescence. Implementation requires an advanced gate model that captures dynamics, promoter interference, and a measure of total power usage (RNAP flux). This project is an exemplar of design automation pushing engineering beyond that achievable "by hand", essential for realizing the potential of biology.

Escherichia coli "Marionette" strains with 12 highly optimized small-molecule sensors.

Cellular processes are carried out by many genes, and their study and optimization requires multiple levers by which they can be independently controlled. The most common method is via a genetically encoded sensor that responds to a small molecule. However, these sensors are often suboptimal, exhibiting high background expression and low dynamic range. Further, using multiple sensors in one cell is limited by cross-talk and the taxing of cellular resources. Here, we have developed a directed evolution strategy to simultaneously select for lower background, high dynamic range, increased sensitivity, and low cross-talk. This is applied to generate a set of 12 high-performance sensors that exhibit >100-fold induction with low background and cross-reactivity. These are combined to build a single "sensor array" in the genomes of E. coli MG1655 (wild-type), DH10B (cloning), and BL21 (protein expression). These "Marionette" strains allow for the independent control of gene expression using 12 small-molecule inducers.

Engineered dCas9 with reduced toxicity in bacteria: implications for genetic circuit design.

Large synthetic genetic circuits require the simultaneous expression of many regulators. Deactivated Cas9 (dCas9) can serve as a repressor by having a small guide RNA (sgRNA) direct it to bind a promoter. The programmability and specificity of RNA:DNA basepairing simplifies the generation of many orthogonal sgRNAs that, in theory, could serve as a large set of regulators in a circuit. However, dCas9 is toxic in many bacteria, thus limiting how high it can be expressed, and low concentrations are quickly sequestered by multiple sgRNAs. Here, we construct a non-toxic version of dCas9 by eliminating PAM (protospacer adjacent motif) binding with a R1335K mutation (dCas9*) and recovering DNA binding by fusing it to the PhlF repressor (dCas9*_PhlF). Both the 30 bp PhlF operator and 20 bp sgRNA binding site are required to repress a promoter. The larger region required for recognition mitigates toxicity in Escherichia coli, allowing up to 9600 ± 800 molecules of dCas9*_PhlF per cell before growth or morphology are impacted, as compared to 530 ± 40 molecules of dCas9. Further, PhlF multimerization leads to an increase in average cooperativity from n = 0.9 (dCas9) to 1.6 (dCas9*_PhlF). A set of 30 orthogonal sgRNA-promoter pairs are characterized as NOT gates; however, the simultaneous use of multiple sgRNAs leads to a monotonic decline in repression and after 15 are co-expressed the dynamic range is <10-fold. This work introduces a non-toxic variant of dCas9, critical for its use in applications in metabolic engineering and synthetic biology, and exposes a limitation in the number of regulators that can be used in one cell when they rely on a shared resource.

Dynamic control of endogenous metabolism with combinatorial logic circuits.

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.

Engineering RGB color vision into Escherichia coli.

Optogenetic tools use colored light to rapidly control gene expression in space and time. We designed a genetically encoded system that gives Escherichia coli the ability to distinguish between red, green, and blue (RGB) light and respond by changing gene expression. We use this system to produce 'color photographs' on bacterial culture plates by controlling pigment production and to redirect metabolic flux by expressing CRISPRi guide RNAs.