Biology subjects

Wright, R. C.

Publications and source records attributed to Wright, R. C..

7 recordsLinked to original sources

bioRxiv2025

Glow with the Flow: Reproducible Analysis of Transiently Transformed Protoplasts Using Dual Fluorescent Reporters in R

Transient transformation assays using protoplasts have become a widely employed technique in plant research. Positive fluorescent selection was subsequently developed to assess the effect of transient effector gene expression in only successfully transfected cells using flow cytometry. This process, though effective, often requires considerable manual effort and subjective judgment to quantify reporter gene expression in the intended cell populations. To address this, we introduce a new, open-source workflow based on the R programming language. This method enhances the reproducibility and scalability of such experiments, which enable rapid study of gene regulation and signal transduction in plants. This workflow is available at https://github.com/PlantSynBioLab/positive-fluorescence-selection.

plant biology↗

bioRxiv2023

Biofoundry-assisted Golden Gate cloning with AssemblyTron

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 AssemblyTrons 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.

synthetic biology↗

bioRxiv2023

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 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/.

molecular biology↗

bioRxiv2023

Genetically encoded, noise-tolerant, auxin biosensors in yeast facilitate metabolic engineering and directed evolution

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. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/533585v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@82743dorg.highwire.dtl.DTLVardef@9d5528org.highwire.dtl.DTLVardef@141cbfaorg.highwire.dtl.DTLVardef@b690e2_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2022

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 approach to simultaneously learn how 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 Design-Build-Test-Learn cycle. However, there are many fewer options for automating the Build step. Build typically involves DNA assembly, which remains manual, low throughput, and unreliable in most cases, limiting 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 PCRs 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 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 in vivo assembly 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.

synthetic biology↗

bioRxiv2018

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 this data remains 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/).

bioinformatics↗

bioRxiv2017

Synthetic analysis of natural variants yields insights into the evolution and function of auxin signaling F-box proteins in Arabidopsis thaliana

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. Towards 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.

plant biology↗