Due to its low cost of manufacturing, poly(ethylene terephthalate) (PET, a polyester plastic) has been the most widely used plastic material for food packaging. However, PET is nonbiodegradable. It can take years to degrade when it is discarded into the environment. In recent years, plastic pollution has received much attention and has become a major environmental issue. In this study, we engineered yeast surfaces to display two PET-degrading enzymes (PETase and MHETase) to degrade PET plastics. The enzymes displayed on the yeast surface were characterized by using confocal microscopy and flow cytometry. The reaction conditions to degrade PET plastics using the engineered yeasts were optimal at pH 9 and 30 °C. In addition, the engineered yeasts showed great stability and reusability to degrade PET films. Furthermore, we demonstrated that the engineered yeasts as whole-cell catalysts can be used to degrade drinking water bottles into value-added products. This study provides a novel whole-cell biocatalyst using engineered yeasts to degrade plastic waste, offering a new strategy to solve plastic pollution and recycling challenges.

Reporter-gene activation studies using transient transformation of protoplasts are a powerful tool for the investigation of transcriptional regulation in plants. Here, we perform a comparative analysis of reporter-gene activation sensitivity using an integrated versus a co-transfected reporter-gene construct in Arabidopsis seedling mesophyll protoplasts. The DR5 synthetic auxin-responsive promoter was used to assay the response to auxin treatment and over-expression of activator Auxin Response Factors. We show that sensitivity, as measured by the fold-change in fluorescent-protein reporter-gene expression, is significantly increased by using a co-transfected reporter-gene construct.

Golden Gate assembly is a requisite method in synthetic biology that facilitates critical conventions such as genetic part abstraction and rapid prototyping. However, compared to robotic implementation, manual Golden Gate implementation is cumbersome, error-prone, and inconsistent for complex assembly designs. AssemblyTron is an open-source python package that provides an affordable automation solution using open-source Opentrons OT-2 lab robots. Automating Golden Gate assembly with AssemblyTron can reduce failure-rate, resource consumption, and training requirements for building complex DNA constructs, as well as indexed and combinatorial libraries. Here, we dissect a panel of upgrades to AssemblyTron’s Golden Gate assembly capabilities, which include Golden Gate assembly into modular cloning part vectors, error-prone PCR combinatorial mutant library assembly, and modular cloning indexed plasmid library assembly. These upgrades enable a broad pool of users with varying levels of experience to readily implement advanced Golden Gate applications using low-cost, open-source lab robotics.

The model plant Arabidopsis thaliana has been a cornerstone of research in plant biology, contributing transformative insights into fundamental biological processes across eukaryotes. In this vignette, we explore the role of Arabidopsis in elucidating immune mechanisms, where plant studies have informed mammalian immunity and translational regulation. We discuss how Arabidopsis-driven advancements in pangenomics and repeat expansions have reshaped our understanding of genomic variability and its implications for diseases like Friedreich's ataxia. Breakthroughs in synthetic biology and bioproduction underscore the role of Arabidopsis as a testbed for engineering specialized metabolites and advancing biotechnological applications. Finally, we examine how the development of tools like auxin-inducible degradation has extended beyond plant research, providing critical methodologies to study protein function and develop novel therapeutics.

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 ADs(1). 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.

Auxins are crucial signaling molecules that regulate the growth, metabolism, and behavior of various organisms, most notably plants but also bacteria, fungi, and animals. Many microbes synthesize and perceive auxins, primarily indole-3-acetic acid (IAA, referred to as auxin herein), the most prevalent natural auxin, which influences their ability to colonize plants and animals. Understanding auxin biosynthesis and signaling in fungi may allow us to better control interkingdom relationships and microbiomes from agricultural soils to the human gut. Despite this importance, a biological tool for measuring auxin with high spatial and temporal resolution has not been engineered in fungi. In this study, we present a suite of genetically encoded, ratiometric, protein-based auxin biosensors designed for the model yeast Saccharomyces cerevisiae. Inspired by auxin signaling in plants, the ratiometric nature of these biosensors enhances the precision of auxin concentration measurements by minimizing clonal and growth phase variation. We used these biosensors to measure auxin production across diverse growth conditions and phases in yeast cultures and calibrated their responses to physiologically relevant levels of auxin. Future work will aim to improve the fold change and reversibility of these biosensors. These genetically encoded auxin biosensors are valuable tools for investigating auxin biosynthesis and signaling in S. cerevisiae and potentially other yeast and fungi and will also advance quantitative functional studies of the plant auxin perception machinery, from which they are built.

IntroductionThroughout domestication, crop plants have gone through strong genetic bottlenecks, dramatically reducing the genetic diversity in today’s available germplasm. This has also reduced the diversity in traits necessary for breeders to develop improved varieties. Many strategies have been developed to improve both genetic and trait diversity in crops, from backcrossing with wild relatives, to chemical/radiation mutagenesis, to genetic engineering. However, even with recent advances in genetic engineering we still face the rate limiting step of identifying which genes and mutations we should target to generate diversity in specific traits.MethodsHere, we apply a comparative evolutionary approach, pairing phylogenetic and expression analyses to identify potential candidate genes for diversifying soybean (Glycine max) canopy cover development via the nuclear auxin signaling gene families, while minimizing pleiotropic effects in other tissues. In soybean, rapid canopy cover development is correlated with yield and also suppresses weeds in organic cultivation.Results and discussionWe identified genes most specifically expressed during early canopy development from the TIR1/AFB auxin receptor, Aux/IAA auxin co-receptor, and ARF auxin response factor gene families in soybean, using principal component analysis. We defined Arabidopsis thaliana and model legume species orthologs for each soybean gene in these families allowing us to speculate potential soybean phenotypes based on well-characterized mutants in these model species. In future work, we aim to connect genetic and functional diversity in these candidate genes with phenotypic diversity in planta allowing for improvements in soybean rapid canopy cover, yield, and weed suppression. Further development of this and similar algorithms for defining and quantifying tissue- and phenotype-specificity in gene expression may allow expansion of diversity in valuable phenotypes in important crops.

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.

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.

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.

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.

Auxins are important plant growth regulating compounds that are applied in vast quantities to crops across the globe to control weeds and improve crop quality and yield. Auxins are also produced by nearly every kingdom of life and control both organismal behavior as well as inter-kingdom interactions. Improving our understanding of auxin biosynthesis and signaling is critical to both improving crop plants and controlling symbiotic, commensal, and parasitic inter-kingdom relationships, many of which are critical to ecosystems from forests and oceans to the human microbiome. We present a suite of auxin biosensors that will advance our understanding of and ability to engineer auxin perception by plants and auxin production by fungi. We have developed genetically encoded, ratiometric auxin biosensors in the model yeast Saccharomyces cerevisiae, based on the mechanism plants use to perceive auxin. The ratiometric design of these biosensors improves measurements of auxin concentration by reducing clonal and growth phase variation. These biosensors are capable of measuring exogenous auxin in yeast cultures across five orders of magnitude, likely spanning the physiologically relevant range. We implement these biosensors to measure the production of auxin during different growth conditions and phases for S. cerevisiae. Finally, we demonstrate how these biosensors could be used to improve quantitative functional studies and directed evolution of plant auxin perception machinery. These genetically encoded auxin biosensors will enable future studies of auxin biosynthesis, transport, and signaling in a wide range of yeast species, as well as other fungi, and plants.

Humanity is faced with an enormous challenge in the coming decades. The world’s population is rapidly growing and we need to produce enough food, fuel, medicine and goods to support this growth in an environmentally sustainable and restorative way. Plants will inevitably provide many solutions to the problems we face, but we need to build environmentally sustainable, carbon-negative industries as soon as possible. Applying protein engineering to accelerate the development of improved crop varieties that can produce more while using less is a promising approach. Here we provide an introduction to the approaches, tools and philosophy of protein engineering, as well as several examples of problems in plant breeding and engineering that protein engineers are currently working to solve.

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.

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/.

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.

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 IAA17C70Wnitrosomimetic 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.