Slow release of a synthetic auxin induces formation of adventitious roots in recalcitrant woody plants.

Clonal propagation of plants by induction of adventitious roots (ARs) from stem cuttings is a requisite step in breeding programs. A major barrier exists for propagating valuable plants that naturally have low capacity to form ARs. Due to the central role of auxin in organogenesis, indole-3-butyric acid is often used as part of commercial rooting mixtures, yet many recalcitrant plants do not form ARs in response to this treatment. Here we describe the synthesis and screening of a focused library of synthetic auxin conjugates in Eucalyptus grandis cuttings and identify 4-chlorophenoxyacetic acid-L-tryptophan-OMe as a competent enhancer of adventitious rooting in a number of recalcitrant woody plants, including apple and argan. Comprehensive metabolic and functional analyses reveal that this activity is engendered by prolonged auxin signaling due to initial fast uptake and slow release and clearance of the free auxin 4-chlorophenoxyacetic acid. This work highlights the utility of a slow-release strategy for bioactive compounds for more effective plant growth regulation.

Open-source workflow design and management software to interrogate duckweed growth conditions and stress responses.

Duckweeds, a family of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed cultivation, for the purposes of scientific research, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to develop optimized cultivation conditions and coordinate maintenance tasks for duckweed stocks. Additionally, computational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to develop optimized cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.

Modular, robust, and extendible multicellular circuit design in yeast.

Division of labor between cells is ubiquitous in biology but the use of multicellular consortia for engineering applications is only beginning to be explored. A significant advantage of multicellular circuits is their potential to be modular with respect to composition but this claim has not yet been extensively tested using experiments and quantitative modeling. Here, we construct a library of 24 yeast strains capable of sending, receiving or responding to three molecular signals, characterize them experimentally and build quantitative models of their input-output relationships. We then compose these strains into two- and three-strain cascades as well as a four-strain bistable switch and show that experimentally measured consortia dynamics can be predicted from the models of the constituent parts. To further explore the achievable range of behaviors, we perform a fully automated computational search over all two-, three-, and four-strain consortia to identify combinations that realize target behaviors including logic gates, band-pass filters, and time pulses. Strain combinations that are predicted to map onto a target behavior are further computationally optimized and then experimentally tested. Experiments closely track computational predictions. The high reliability of these model descriptions further strengthens the feasibility and highlights the potential for distributed computing in synthetic biology.

Large-scale design and refinement of stable proteins using sequence-only models.

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we use a high-throughput, low-fidelity assay to experimentally evaluate the stability of approximately 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We build a neural network model that predicts protein stability given only sequences of amino acids, and compare its performance to the assayed values. We also report another network model that is able to generate the amino acid sequences of novel stable proteins given requested secondary sequences. Finally, we show that the predictive model-despite weaknesses including a noisy data set-can be used to substantially increase the stability of both expert-designed and model-generated proteins.

Implementation of an interactive mobile application to pilot a rapid assay to detect HIV drug resistance mutations in Kenya.

Usability is an overlooked aspect of implementing lab-based assays, particularly novel assays in low-resource-settings. Esoteric instructions can lead to irreproducible test results and patient harm. To address these issues, we developed a software application based on "Aquarium", a laboratory-operating system run on a computer tablet that provides step-by-step digital interactive instructions, protocol management, and sample tracking. Aquarium was paired with a near point-of-care HIV drug resistance test, "OLA-Simple", that detects mutations associated with virologic failure. In this observational study we evaluated the performance of Aquarium in guiding untrained users through the multi-step laboratory protocol with little supervision. To evaluate the training by Aquarium software we conducted a feasibility study in a laboratory at Coptic Hope Center in Nairobi, Kenya. Twelve volunteers who were unfamiliar with the kit performed the test on blinded samples (2 blood specimens; 5 codons/sample). Steps guided by Aquarium included: CD4+ T-Cell separation, PCR, ligation, detection, and interpretation of test results. Participants filled out a short survey regarding their demographics and experience with the software and kit. None of the laboratory technicians had prior experience performing CD4+ separation and 7/12 had no experience performing laboratory-based molecular assays. 12/12 isolated CD4+ T cells from whole blood with yields comparable to isolations performed by trained personnel. The OLA-Simple workflow was completed by all, with genotyping results interpreted correctly by unaided-eye in 108/120 (90%) and by software in 116/120 (97%) of codons analyzed. In the surveys, participants favorably assessed the use of software guidance. The Aquarium digital instructions enabled first-time users in Kenya to complete the OLA-simple kit workflow with minimal training. Aquarium could increase the accessibility of laboratory assays in low-resource-settings and potentially standardize implementation of clinical laboratory tests.

Aquarium: open-source laboratory software for design, execution and data management.

Automation has been shown to improve the replicability and scalability of biomedical and bioindustrial research. Although the work performed in many labs is repetitive and can be standardized, few academic labs can afford the time and money required to automate their workflows with robotics. We propose that human-in-the-loop automation can fill this critical gap. To this end, we present Aquarium, an open-source, web-based software application that integrates experimental design, inventory management, protocol execution and data capture. We provide a high-level view of how researchers can install Aquarium and use it in their own labs. We discuss the impacts of the Aquarium on working practices, use in biofoundries and opportunities it affords for collaboration and education in life science laboratory research and manufacture.

Perturbing the energy landscape for improved packing during computational protein design.

The FastDesign protocol in the molecular modeling program Rosetta iterates between sequence optimization and structure refinement to stabilize de novo designed protein structures and complexes. FastDesign has been used previously to design novel protein folds and assemblies with important applications in research and medicine. To promote sampling of alternative conformations and sequences, FastDesign includes stages where the energy landscape is smoothened by reducing repulsive forces. Here, we discover that this process disfavors larger amino acids in the protein core because the protein compresses in the early stages of refinement. By testing alternative ramping strategies for the repulsive weight, we arrive at a scheme that produces lower energy designs with more native-like sequence composition in the protein core. We further validate the protocol by designing and experimentally characterizing over 4000 proteins and show that the new protocol produces higher stability proteins.

Automated design of thousands of nonrepetitive parts for engineering stable genetic systems.

Engineered genetic systems are prone to failure when their genetic parts contain repetitive sequences. Designing many nonrepetitive genetic parts with desired functionalities remains a difficult challenge with high computational complexity. To overcome this challenge, we developed the Nonrepetitive Parts Calculator to rapidly generate thousands of highly nonrepetitive genetic parts from specified design constraints, including promoters, ribosome-binding sites and terminators. As a demonstration, we designed and experimentally characterized 4,350 nonrepetitive bacterial promoters with transcription rates that varied across a 820,000-fold range, and 1,722 highly nonrepetitive yeast promoters with transcription rates that varied across a 25,000-fold range. We applied machine learning to explain how specific interactions controlled the promoters' transcription rates. We also show that using nonrepetitive genetic parts substantially reduces homologous recombination, resulting in greater genetic stability.

OLA-Simple: A software-guided HIV-1 drug resistance test for low-resource laboratories.

BACKGROUND: HIV drug resistance (HIVDR) testing can assist clinicians in selecting treatments. However, high complexity and cost of genotyping assays limit routine testing in settings where HIVDR prevalence has reached high levels. METHODS: The oligonucleotide ligation assay (OLA)-Simple kit was developed for detection of HIVDR against first-line non-nucleoside/nucleoside reverse transcriptase inhibitors and validated on 672 codons (168 specimens) from subtypes A, B, C, D, and AE. The kit uses dry reagents to facilitate assay setup, lateral flow devices for visual HIVDR detections, and in-house software with an interface for guiding users and analyzing results. FINDINGS: HIVDR analysis of specimens by OLA-Simple compared to Sanger sequencing revealed 99.6 ±â€¯0.3% specificity and 98.2 ±â€¯0.9% sensitivity, and compared to high-sensitivity assays, 99.6 ±â€¯0.6% specificity and 86.2 ±â€¯2.5% sensitivity, with 2.6 ±â€¯0.9% indeterminate results. OLA-Simple was performed more rapidly compared to Sanger sequencing (<4 h vs. 35-72 h). Forty-one untrained volunteers blindly tested two specimens each with 96.8 ±â€¯0.8% accuracy. INTERPRETATION: OLA-Simple compares favorably with HIVDR genotyping by Sanger and sensitive comparators. Instructional software enabled inexperienced, first-time users to perform the assay with high accuracy. The reduced complexity, cost, and training requirements of OLA-Simple could improve access to HIVDR testing in low-resource settings and potentially allow same-day selection of appropriate antiretroviral therapy. FUND: USA National Institutes of Health R01; the Clinical and Retrovirology Research Core and the Molecular Profiling and Computational Biology Core of the UW CFAR; Seattle Children's Research Institute; UW Holloman Innovation Challenge Award; Pilcher Faculty Fellowship.

Synthetic Bistability and Differentiation in Yeast.

Engineered systems that control cellular differentiation and pattern formation are essential for applications like tissue engineering, biomaterial fabrication, and synthetic ecosystems. Synthetic circuits that can take on multiple states have been made to engineer multicellular systems. However, how to use these states to drive interesting cellular behavior remains challenging. Here, we present a cellular differentiation program involving a novel synthetic bistable switch coupled to an antibiotic resistance gene that affects growth in yeast ( S. cerevisiae). The switch is composed of a positive feedback loop involving a novel transcription factor and can be switched ON and OFF via two different transient inducer inputs. By further coupling the bistable switch with an antibiotic resistance gene, we obtained a growth differentiation circuit, where yeast cells can be switched to stable HIGH or LOW growth rate states via transient inducer inputs. This work demonstrates a rationally designed and experimentally validated cellular differentiation behavior in yeast.

Synthetic hormone-responsive transcription factors can monitor and re-program plant development.

Developmental programs sculpt plant morphology to meet environmental challenges, and these same programs have been manipulated to increase agricultural productivity (Doebley et al., 1997; Khush, 2001). Hormones coordinate these programs, creating chemical circuitry (Vanstraelen and Benková, 2012) that has been represented in mathematical models (Refahi et al., 2016; Prusinkiewicz et al., 2009); however, model-guided engineering of plant morphology has been limited by a lack of tools (Parry et al., 2009; Voytas and Gao, 2014). Here, we introduce a novel set of synthetic and modular hormone activated Cas9-based repressors (HACRs) in Arabidopsis thaliana that respond to three hormones: auxin, gibberellins and jasmonates. We demonstrate that HACRs are sensitive to both exogenous hormone treatments and local differences in endogenous hormone levels associated with development. We further show that this capability can be leveraged to reprogram development in an agriculturally relevant manner by changing how the hormonal circuitry regulates target genes. By deploying a HACR to re-parameterize the auxin-induced expression of the auxin transporter PIN-FORMED1 (PIN1), we decreased shoot branching and phyllotactic noise, as predicted by existing models (Refahi et al., 2016; Prusinkiewicz et al., 2009).

Synthetic genetic circuits in crop plants.

The love affair between crop breeding and genetics began over a century ago and has continued unabated, from mass selection programs to targeted genome modifications. Synthetic genetic circuits, a recent development, are combinations of regulatory and coding DNA introduced into a crop plant to achieve a desired function. Genetic circuits could accelerate crop improvement, allowing complex traits to be rationally designed and requisite DNA parts delivered directly into a genome of interest. However, there is not yet a standardized pipeline from exploratory laboratory testing to crop trials, and bringing transgenic products to market remains a considerable barrier. We highlight successes so far and future developments necessary to make genetic circuits a viable crop improvement technology over this century.

High-throughput characterization of protein-protein interactions by reprogramming yeast mating.

High-throughput methods for screening protein-protein interactions enable the rapid characterization of engineered binding proteins and interaction networks. While existing approaches are powerful, none allow quantitative library-on-library characterization of protein interactions in a modifiable extracellular environment. Here, we show that sexual agglutination of Saccharomyces cerevisiae can be reprogrammed to link interaction strength with mating efficiency using synthetic agglutination (SynAg). Validation of SynAg with 89 previously characterized interactions shows a log-linear relationship between mating efficiency and protein binding strength for interactions with Kds ranging from below 500 pM to above 300 µM. Using induced chromosomal translocation to pair barcodes representing binding proteins, thousands of distinct interactions can be screened in a single pot. We demonstrate the ability to characterize protein interaction networks in a modifiable environment by introducing a soluble peptide that selectively disrupts a subset of interactions in a representative network by up to 800-fold. SynAg enables the high-throughput, quantitative characterization of protein-protein interaction networks in a fully defined extracellular environment at a library-on-library scale.

Digital logic circuits in yeast with CRISPR-dCas9 NOR gates.

Natural genetic circuits enable cells to make sophisticated digital decisions. Building equally complex synthetic circuits in eukaryotes remains difficult, however, because commonly used components leak transcriptionally, do not arbitrarily interconnect or do not have digital responses. Here, we designed dCas9-Mxi1-based NOR gates in Saccharomyces cerevisiae that allow arbitrary connectivity and large genetic circuits. Because we used the chromatin remodeller Mxi1, our gates showed minimal leak and digital responses. We built a combinatorial library of NOR gates that directly convert guide RNA (gRNA) inputs into gRNA outputs, enabling the gates to be 'wired' together. We constructed logic circuits with up to seven gRNAs, including repression cascades with up to seven layers. Modelling predicted the NOR gates have effectively zero transcriptional leak explaining the limited signal degradation in the circuits. Our approach enabled the largest, eukaryotic gene circuits to date and will form the basis for large, synthetic, cellular decision-making systems.

Cell-Cell Communication in Yeast Using Auxin Biosynthesis and Auxin Responsive CRISPR Transcription Factors.

An engineering framework for synthetic multicellular systems requires a programmable means of cell-cell communication. Such a communication system would enable complex behaviors, such as pattern formation, division of labor in synthetic microbial communities, and improved modularity in synthetic circuits. However, it remains challenging to build synthetic cellular communication systems in eukaryotes due to a lack of molecular modules that are orthogonal to the host machinery, easy to reconfigure, and scalable. Here, we present a novel cell-to-cell communication system in Saccharomyces cerevisiae (yeast) based on CRISPR transcription factors and the plant hormone auxin that exhibits several of these features. Specifically, we engineered a sender strain of yeast that converts indole-3-acetamide (IAM) into auxin via the enzyme iaaH from Agrobacterium tumefaciens. To sense auxin and regulate transcription in a receiver strain, we engineered a reconfigurable library of auxin-degradable CRISPR transcription factors (ADCTFs). Auxin-induced degradation is achieved through fusion of an auxin-sensitive degron (from IAA corepressors) to the CRISPR TF and coexpression with an auxin F-box protein. Mirroring the tunability of auxin perception in plants, our family of ADCTFs exhibits a broad range of auxin sensitivities. We characterized the kinetics and steady-state behavior of the sender and receiver independently as well as in cocultures where both cell types were exposed to IAM. In the presence of IAM, auxin is produced by the sender cell and triggers deactivation of reporter expression in the receiver cell. The result is an orthogonal, rewireable, tunable, and, arguably, scalable cell-cell communication system for yeast and other eukaryotic cells.

Auxin-induced degradation dynamics set the pace for lateral root development.

Auxin elicits diverse cell behaviors through a simple nuclear signaling pathway initiated by degradation of Aux/IAA co-repressors. Our previous work revealed that members of the large Arabidopsis Aux/IAA family exhibit a range of degradation rates in synthetic contexts. However, it remained an unresolved issue whether differences in Aux/IAA turnover rates played a significant role in plant responses to auxin. Here, we use the well-established model of lateral root development to directly test the hypothesis that the rate of auxin-induced Aux/IAA turnover sets the pace for auxin-regulated developmental events. We did this by generating transgenic plants expressing degradation rate variants of IAA14, a crucial determinant of lateral root initiation. Progression through the well-established stages of lateral root development was strongly correlated with the engineered rates of IAA14 turnover, leading to the conclusion that Aux/IAAs are auxin-initiated timers that synchronize developmental transitions.

A low cost, customizable turbidostat for use in synthetic circuit characterization.

Engineered biological circuits are often disturbed by a variety of environmental factors. In batch culture, where the majority of synthetic circuit characterization occurs, environmental conditions vary as the culture matures. Turbidostats are powerful characterization tools that provide static culture environments; however, they are often expensive, especially when purchased in custom configurations, and are difficult to design and construct in a lab. Here, we present a low cost, open source multiplexed turbidostat that can be manufactured and used with minimal experience in electrical or software engineering. We demonstrate the utility of this system to profile synthetic circuit behavior in S. cerevisiae. We also demonstrate the flexibility of the design by showing that a fluorometer can be easily integrated.

Lightening the load in synthetic biology.

A new biological device known as a 'load driver' improves the performance of synthetic circuits by insulating genetic parts from each other.

Recapitulation of the forward nuclear auxin response pathway in yeast.

Auxin influences nearly every aspect of plant biology through a simple signaling pathway; however, it remains unclear how much of the diversity in auxin effects is explained by variation in the core signaling components and which properties of these components may contribute to diversification in response dynamics. Here, we recapitulated the entire Arabidopsis thaliana forward nuclear auxin signal transduction pathway in Saccharomyces cerevisiae to test whether signaling module composition enables tuning of the dynamic response. Sensitivity analysis guided by a small mathematical model revealed the centrality of auxin/indole-3-acetic acid (Aux/IAA) transcriptional corepressors in controlling response dynamics and highlighted the strong influence of natural variation in Aux/IAA degradation rates on circuit performance. When the basic auxin response circuit was expanded to include multiple Aux/IAAs, we found that dominance relationships between coexpressed Aux/IAAs were sufficient to generate distinct response modules similar to those seen during plant development. Our work provides a new method for dissecting auxin signaling and demonstrates the key role of Aux/IAAs in tuning auxin response dynamics.

Framework for engineering finite state machines in gene regulatory networks.

Finite state machines are fundamental computing devices at the core of many models of computation. In biology, finite state machines are commonly used as models of development in multicellular organisms. However, it remains unclear to what extent cells can remember state, how they can transition from one state to another reliably, and whether the existing parts available to the synthetic biologist are sufficient to implement specified finite state machines in living cells. Furthermore, how complex multicellular behaviors can be realized by multiple cells coordinating their states with signaling, growth, and division is not well understood. Here, we describe a method by which any finite state machine can be built using nothing more than a suitably engineered network of readily available repressing transcription factors. In particular, we show the mathematical equivalence of finite state machines with a Boolean model of gene regulatory networks. We describe how such networks can be realized with a small class of promoters and transcription factors. To demonstrate the effectiveness of our approach, we show that the behavior of the coarse grained ideal Boolean network model approximates a fine grained delay differential equation model of gene expression. Finally, we explore a framework for the design of more complex systems via an example, synthetic bacterial microcolony edge detection, that illustrates how finite state machines could be used together with cell signaling to construct novel multicellular behaviors.