Identification of plant transcriptional activation domains.

Gene expression in Arabidopsis is regulated by more than 1,900 transcription factors (TFs), which have been identified genome-wide by the presence of well-conserved DNA-binding domains. Activator TFs contain activation domains (ADs) that recruit coactivator complexes; however, for nearly all Arabidopsis TFs, we lack knowledge about the presence, location and transcriptional strength of their ADs1. To address this gap, here we use a yeast library approach to experimentally identify Arabidopsis ADs on a proteome-wide scale, and find that more than half of the Arabidopsis TFs contain an AD. We annotate 1,553 ADs, the vast majority of which are, to our knowledge, previously unknown. Using the dataset generated, we develop a neural network to accurately predict ADs and to identify sequence features that are necessary to recruit coactivator complexes. We uncover six distinct combinations of sequence features that result in activation activity, providing a framework to interrogate the subfunctionalization of ADs. Furthermore, we identify ADs in the ancient AUXIN RESPONSE FACTOR family of TFs, revealing that AD positioning is conserved in distinct clades. Our findings provide a deep resource for understanding transcriptional activation, a framework for examining function in intrinsically disordered regions and a predictive model of ADs.

Pleiotropy, a feature or a bug? Toward co-ordinating plant growth, development, and environmental responses through engineering plant hormone signaling.

The advent of gene editing technologies such as CRISPR has simplified co-ordinating trait development. However, identifying candidate genes remains a challenge due to complex gene networks and pathways. These networks exhibit pleiotropy, complicating the determination of specific gene and pathway functions. In this review, we explore how systems biology and single-cell sequencing technologies can aid in identifying candidate genes for co-ordinating specifics of plant growth and development within specific temporal and tissue contexts. Exploring sequence-function space of these candidate genes and pathway modules with synthetic biology allows us to test hypotheses and define genotype-phenotype relationships through reductionist approaches. Collectively, these techniques hold the potential to advance breeding and genetic engineering strategies while also addressing genetic diversity issues critical for adaptation and trait development.

TidyTron: Reducing lab waste using validated wash-and-reuse protocols for common plasticware in Opentrons OT-2 lab robots.

Every year biotechnology labs generate a combined total of ∼5.5 million tons of plastic waste. As the global bioeconomy expands, biofoundries will inevitably increase plastic consumption in-step with synthetic biology scaling. Decontamination and reuse of single-use plastics could increase sustainability and reduce recurring costs of biological research. However, throughput and variable cleaning quality make manual decontamination impractical in most instances. Automating single-use plastic cleaning with liquid handling robots makes decontamination more practical by offering higher throughput and consistent cleaning quality. However, open-source, validated protocols using low-cost lab robotics for effective decontamination of plasticware-facilitating safe reuse-have not yet been developed. Here we introduce and validate TidyTron: a library of protocols for cleaning micropipette tips and microtiter plates that are contaminated with DNA, E. coli, and S. cerevisiae. We tested a variety of cleaning solutions, contact times, and agitation methods with the aim of minimizing time and cost, while maximizing cleaning stringency and sustainability. We tested and validated these cleaning procedures by comparing fresh (first-time usage) versus cleaned tips and plates for contamination with cells, DNA, or cleaning solutions. We assessed contamination by measuring colony forming units by plating, PCR efficiency and DNA concentration by qPCR, and event counts and debris by flow cytometry. Open source cleaning protocols are available at https://github.com/PlantSynBioLab/TidyTron and hosted on a graphical user interface at https://jbryantvt.github.io/TidyTron/.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering.

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Identification of potential Auxin Response Candidate genes for soybean rapid canopy coverage through comparative evolution and expression analysis.

Glycine max, soybean, is an abundantly cultivated crop worldwide. Efforts have been made over the past decades to improve soybean production in traditional and organic agriculture, driven by growing demand for soybean-based products. Rapid canopy cover development (RCC) increases soybean yields and suppresses early-season weeds. Genome-wide association studies have found natural variants associated with RCC, however causal mechanisms are unclear. Auxin modulates plant growth and development and has been implicated in RCC traits. Therefore, modulation of auxin regulatory genes may enhance RCC. Here, we focus on the use of genomic tools and existing datasets to identify auxin signaling pathway RCC candidate genes, using a comparative phylogenetics and expression analysis approach. We identified genes encoding 14 TIR1/AFB auxin receptors, 61 Aux/IAA auxin co-receptors and transcriptional co-repressors, and 55 ARF auxin response factors in the soybean genome. We used Bayesian phylogenetic inference to identify soybean orthologs of Arabidopsis thaliana genes, and defined an ortholog naming system for these genes. To further define potential auxin signaling candidate genes for RCC, we examined tissue-level expression of these genes in existing datasets and identified highly expressed auxin signaling genes in apical tissues early in development. We identified at least 4 TIR1/AFB, 8 Aux/IAA, and 8 ARF genes with highly specific expression in one or more RCC-associated tissues. We hypothesize that modulating the function of these genes through gene editing or traditional breeding will have the highest likelihood of affecting RCC while minimizing pleiotropic effects.

Development of Nanobody-Displayed Whole-Cell Biosensors for the Colorimetric Detection of SARS-CoV-2.

The accurate and effective detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential to preventing the spread of infectious diseases and ensuring human health. Herein, a nanobody-displayed whole-cell biosensor was developed for colorimetric detection of SARS-CoV-2 spike proteins. Serving as bioreceptors, yeast surfaces were genetically engineered to display SARS-CoV-2 binding of llama-derived single-domain antibodies (nanobodies) with high capture efficiency, facilitating the concentration and purification of SARS-CoV-2. Gold nanoparticles (AuNPs) employed as signal transductions were functionalized with horseradish peroxidase (HRP) and anti-SARS monoclonal antibodies to enhance the detection sensitivity. In the presence of SARS-CoV-2 spike proteins, the sandwiched binding will be formed by linking engineered yeast, SARS-CoV-2 spike proteins, and reporter AuNPs. The colorimetric signal was generated by the enzymatic reaction of HRP and its corresponding colorimetric substrate/chromogen system. At the optimal conditions, the developed whole-cell biosensor enables the sensitive detection of SARS-CoV-2 spike proteins in a linear range from 0.01 to 1 µg/mL with a limit of detection (LOD) of 0.037 µg/mL (about 4 × 108 virion particles/mL). Furthermore, the whole-cell biosensor was demonstrated to detect the spike protein of different SARS-CoV-2 variants in human serum, providing new possibilities for the detection of future SARS-CoV-2 variants.

Nitric oxide-mediated S-nitrosylation of IAA17 protein in intrinsically disordered region represses auxin signaling.

The phytohormone auxin plays crucial roles in nearly every aspect of plant growth and development. Auxin signaling is activated through the phytohormone-induced proteasomal degradation of the Auxin/INDOLE-3-ACETIC ACID (Aux/IAA) family of transcriptional repressors. Notably, many auxin-modulated physiological processes are also regulated by nitric oxide (NO) that executes its biological effects predominantly through protein S-nitrosylation at specific cysteine residues. However, little is known about the molecular mechanisms in regulating the interactive NO and auxin networks. Here, we show that NO represses auxin signaling by inhibiting IAA17 protein degradation. NO induces the S-nitrosylation of Cys-70 located in the intrinsically disordered region of IAA17, which inhibits the TIR1-IAA17 interaction and consequently the proteasomal degradation of IAA17. The accumulation of a higher level of IAA17 attenuates auxin response. Moreover, an IAA17C70W nitrosomimetic mutation renders the accumulation of a higher level of the mutated protein, thereby causing partial resistance to auxin and defective lateral root development. Taken together, these results suggest that S-nitrosylation of IAA17 at Cys-70 inhibits its interaction with TIR1, thereby negatively regulating auxin signaling. This study provides unique molecular insights into the redox-based auxin signaling in regulating plant growth and development.

Engineered Yeast Displaying Specific Norovirus-Binding Nanobodies for the Concentration and Detection of Human Norovirus in Food Matrix.

Human noroviruses pose grave threats to public health and economy. In this study, we genetically engineered yeast (Saccharomyces cerevisiae EBY100) to display specific norovirus-binding nanobodies (Nano-26 and Nano-85) on cell surface to facilitate the concentration of noroviruses for improved detection. Binding of norovirus virus-like particles (VLPs) to these nanobody-displaying yeasts was confirmed and characterized using confocal microscopy and flow cytometry. The ability of our engineered yeasts to capture norovirus VLPs can reach up to 91.3%. Furthermore, this approach was applied to concentrate and detect norovirus VLPs in a real food matrix. A wide linear detection range (1-104 pg/g) was observed, and the detection limit on spiked spinach was calculated as low as 0.071 pg/g. Overall, our engineered yeasts could be a promising approach to concentrate and purify noroviruses in food samples for easy detection, which allows us to prevent the spread of food-borne virus in the food supply chain.

AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots.

As one of the newest fields of engineering, synthetic biology relies upon a trial-and-error Design-Build-Test-Learn (DBTL) approach to simultaneously learn how a function is encoded in biology and attempt to engineer it. Many software and hardware platforms have been developed to automate, optimize and algorithmically perform each step of the DBTL cycle. However, there are many fewer options for automating the build step. Build typically involves deoxyribonucleic acid (DNA) assembly, which remains manual, low throughput and unreliable in most cases and limits our ability to advance the science and engineering of biology. Here, we present AssemblyTron, an open-source Python package to integrate j5 DNA assembly design software outputs with build implementation in Opentrons liquid handling robotics with minimal human intervention. We demonstrate the versatility of AssemblyTron through several scarless, multipart DNA assemblies, beginning from fragment amplification. We show that AssemblyTron can perform polymerase chain reactions across a range of fragment lengths and annealing temperatures by using an optimal annealing temperature gradient calculation algorithm. We then demonstrate that AssemblyTron can perform Golden Gate and homology-dependent in vivo assemblies (IVAs) with comparable fidelity to manual assemblies by simultaneously building four four-fragment assemblies of chromoprotein reporter expression plasmids. Finally, we used AssemblyTron to perform site-directed mutagenesis reactions via homology-dependent IVA also achieving comparable fidelity to manual assemblies as assessed by sequencing. AssemblyTron can reduce the time, training, costs and wastes associated with synthetic biology, which, along with open-source and affordable automation, will further foster the accessibility of synthetic biology and accelerate biological research and engineering.

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.

The Systems and Synthetic Biology of Auxin.

Auxin biology as a field has been at the forefront of advances in delineating the structures, dynamics, and control of plant growth networks. Advances have been enabled by combining the complementary fields of top-down, holistic systems biology and bottom-up, build-to-understand synthetic biology. Continued collaboration between these approaches will facilitate our understanding of and ability to engineer auxin's control of plant growth, development, and physiology. There is a need for the application of similar complementary approaches to improving equity and justice through analysis and redesign of the human systems in which this research is undertaken.

Temporal Control of Morphogenic Factor Expression Determines Efficacy in Enhancing Regeneration.

BACKGROUND: Regeneration of fertile plants from tissue culture is a critical bottleneck in the application of new plant breeding technologies. Ectopic overexpression of morphogenic factors is a promising workaround for this hurdle. METHODS: Conditional overexpression of WUS and ARF5Δ was used to study the effect of timing the overexpression of these morphogenic factors during shoot regeneration from root explants in Arabidopsis. In addition, their effect on auxin-signaling activation was examined by visualization and cytometric quantification of the DR5:GFP auxin-signaling reporter in roots and protoplasts, respectively. RESULTS: The induced expression of both WUS and ARF5Δ led to an activation of auxin signaling in roots. Activation of auxin signaling by WUS and ARF5Δ was further quantified by transient transformation of protoplasts. Ectopic overexpression of both WUS and ARF5Δ enhanced regeneration efficiency, but only during the shoot-induction stage of regeneration and not during the callus-induction stage. CONCLUSIONS: The overexpression of WUS and ARF5Δ both lead to activation of auxin signaling. Expression during the shoot-induction stage is critical for the enhancement of shoot regeneration by WUS and ARF5Δ.

Vision, challenges and opportunities for a Plant Cell Atlas.

With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.

Accelerating structure-function mapping using the ViVa webtool to mine natural variation.

Thousands of sequenced genomes are now publicly available capturing a significant amount of natural variation within plant species; yet, much of these data remain inaccessible to researchers without significant bioinformatics experience. Here, we present a webtool called ViVa (Visualizing Variation) which aims to empower any researcher to take advantage of the amazing genetic resource collected in the Arabidopsis thaliana 1001 Genomes Project (http://1001genomes.org). ViVa facilitates data mining on the gene, gene family, or gene network level. To test the utility and accessibility of ViVa, we assembled a team with a range of expertise within biology and bioinformatics to analyze the natural variation within the well-studied nuclear auxin signaling pathway. Our analysis has provided further confirmation of existing knowledge and has also helped generate new hypotheses regarding this well-studied pathway. These results highlight how natural variation could be used to generate and test hypotheses about less-studied gene families and networks, especially when paired with biochemical and genetic characterization. ViVa is also readily extensible to databases of interspecific genetic variation in plants as well as other organisms, such as the 3,000 Rice Genomes Project ( http://snp-seek.irri.org/) and human genetic variation ( https://www.ncbi.nlm.nih.gov/clinvar/).

Insights into the Evolution and Function of Auxin Signaling F-Box Proteins in Arabidopsis thaliana Through Synthetic Analysis of Natural Variants.

The evolution of complex body plans in land plants has been paralleled by gene duplication and divergence within nuclear auxin-signaling networks. A deep mechanistic understanding of auxin signaling proteins therefore may allow rational engineering of novel plant architectures. Toward that end, we analyzed natural variation in the auxin receptor F-box family of wild accessions of the reference plant Arabidopsis thaliana and used this information to populate a structure/function map. We employed a synthetic assay to identify natural hypermorphic F-box variants and then assayed auxin-associated phenotypes in accessions expressing these variants. To more directly measure the impact of the strongest variant in our synthetic assay on auxin sensitivity, we generated transgenic plants expressing this allele. Together, our findings link evolved sequence variation to altered molecular performance and auxin sensitivity. This approach demonstrates the potential for combining synthetic biology approaches with quantitative phenotypes to harness the wealth of available sequence information and guide future engineering efforts of diverse signaling pathways.

Characterizing Auxin Response Circuits in Saccharomyces cerevisiae by Flow Cytometry.

Recapitulation of the nuclear auxin response pathway in Saccharomyces cerevisiae (yeast) provides a means to functionally assay the contribution of individual signaling components to response dynamics. Here, we describe a time course assay for characterizing auxin response circuits using flow cytometry. This method allows for quantitative measurements of the dynamic response of up to 12 circuits (strains) at once. We also describe a steady-state assay and how to utilize an R package we developed to facilitate data analysis.

New tangles in the auxin signaling web.

Plants use auxin to relay critical information that shapes their growth and development. Auxin perception and transcriptional activation are mediated by the degradation of Aux/IAA repressor proteins. Degradation of Aux/IAAs relieves repression on Auxin Response Factors (ARFs), which bind DNA sequences called Auxin Response Elements (AuxREs). In most higher plant genomes, multiple paralogs exist for each part of the auxin nuclear signaling pathway. This potential combinatorial diversity in signaling pathways likely contributes to the myriad of context-specific responses to auxin. Recent structures of several domains from ARF proteins have exposed new modes of ARF dimerization, new models for ARF-AuxRE specificity, and the strong likelihood of larger order complexes formed by ARF and Aux/IAA homo- and heteromultimerization. Preliminary experiments support a role for these novel interactions in planta, further increasing the potential architectural complexity of this seemingly simple pathway.

Advancements in the development of HIF-1α-activated protein switches for use in enzyme prodrug therapy.

While gene-directed enzyme prodrug therapy has shown potential as a cancer therapeutic in animal and clinical trials, concerns over the efficacy, selectivity, and safety of gene delivery vehicles have restricted its advance. In an attempt to relieve some of the demands on targeted gene delivery vehicles and achieve the full potential of enzyme prodrug therapy, cancer-targeted activity can be engineered into the enzyme itself. We previously engineered a switchable prodrug-activating enzyme that selectively kills human cancer cells accumulating the cancer marker hypoxia-inducible factor-1α (HIF-1α). This HIF-1α-activated protein switch (Haps59) is designed to increase its ability to convert the prodrug 5-fluorocytosine into the chemotherapeutic 5-fluorouracil in a HIF-1α-dependent manner. However, in cancer cell lines expressing Haps59 the 5FC sensitivity difference between the presence and absence of HIF-1α was not as large as desired. In this work, we aimed to improve the cancer specificity of this switch via a directed evolution approach utilizing random mutagenesis, linker mutagenesis, and random insertion and circular permutation. We identified improved HIF-1α-activated protein switches that confer E. coli with modest increases in HIF-1α-dependent 5FC toxicity. Additionally, the current bottleneck in the development of improved HIF-1α-activated protein switches is screening switch candidates in mammalian cells. To accommodate higher throughput and reduce experimental variability, we explored the use of Flp recombinase-mediated isogenic integration in 293 cells. These experiments raised the possibility that Haps59 can be activated by other interactors of the CH1 domain, and experiments in E. coli indicated that CITED2 can also activate Haps59. Although many CH1 binding partners are also oncogenes, CH1's promiscuous binding and subsequent off-target activation of Haps59 needs to be examined under normal physiological conditions to identify off-target activators. With aberrant activating molecules identified, further directed evolution can be performed to improve the cancer specificity of HIF-1α-activated protein switches.

Protein switch engineering by domain insertion.

The switch-like regulation of protein activity by molecular signals is abundant in native proteins. The ability to engineer proteins with novel regulation has applications in biosensors, selective protein therapeutics, and basic research. One approach to building proteins with novel switch properties is creating combinatorial libraries of gene fusions between genes encoding proteins that have the prerequisite input and output functions of the desired switch. These libraries are then subjected to selections and/or screens to identify those rare gene fusions that encode functional switches. Combinatorial libraries in which an insert gene is inserted randomly into an acceptor gene have been useful for creating switches, particularly when combined with circular permutation of the insert gene. Methods for creating random domain insertion libraries are described. Three methods for creating a diverse set of insertion sites in the acceptor gene are presented and compared: DNase I digestion, S1 nuclease digestion, and multiplex inverse PCR. A PCR-based method for creating a library of circular permutations of the insert gene is also presented.