Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis.

A spectral image analysis has the potential to replace traditional approaches for assessing plant responses to different types of stresses, including herbicides, through non-destructive and high-throughput screening (HTS). Therefore, this study was conducted to develop a rapid bioassay method using a multi-well plate and spectral image analysis for the diagnosis of herbicide activity and modes of action. Crabgrass (Digitaria ciliaris), as a model weed, was cultivated in multi-well plates and subsequently treated with six herbicides (paraquat, tiafenacil, penoxsulam, isoxaflutole, glufosinate, and glyphosate) with different modes of action when the crabgrass reached the 1-leaf stage, using only a quarter of the recommended dose. To detect the plant's response to herbicides, plant spectral images were acquired after herbicide treatment using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors and analyzed for diagnosing herbicide efficacy and modes of action. A principal component analysis (PCA), using all spectral data, successfully distinguished herbicides and clustered depending on their modes of action. The performed experiments showed that the multi-well plate assay combined with a spectral image analysis can be successfully applied for herbicide bioassays. In addition, the use of spectral image sensors, especially CF images, would facilitate HTS by enabling the rapid observation of herbicide responses at as early as 3 h after herbicide treatment.

Deep learning models for predicting RNA degradation via dual crowdsourcing.

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ('Stanford OpenVaccine') on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102-130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.

Computationally-guided design and selection of high performing ribosomal active site mutants.

Understanding how modifications to the ribosome affect function has implications for studying ribosome biogenesis, building minimal cells, and repurposing ribosomes for synthetic biology. However, efforts to design sequence-modified ribosomes have been limited because point mutations in the ribosomal RNA (rRNA), especially in the catalytic active site (peptidyl transferase center; PTC), are often functionally detrimental. Moreover, methods for directed evolution of rRNA are constrained by practical considerations (e.g. library size). Here, to address these limitations, we developed a computational rRNA design approach for screening guided libraries of mutant ribosomes. Our method includes in silico library design and selection using a Rosetta stepwise Monte Carlo method (SWM), library construction and in vitro testing of combined ribosomal assembly and translation activity, and functional characterization in vivo. As a model, we apply our method to making modified ribosomes with mutant PTCs. We engineer ribosomes with as many as 30 mutations in their PTCs, highlighting previously unidentified epistatic interactions, and show that SWM helps identify sequences with beneficial phenotypes as compared to random library sequences. We further demonstrate that some variants improve cell growth in vivo, relative to wild type ribosomes. We anticipate that SWM design and selection may serve as a powerful tool for rRNA engineering.

Ribosome-mediated biosynthesis of pyridazinone oligomers in vitro.

The ribosome is a macromolecular machine that catalyzes the sequence-defined polymerization of L-α-amino acids into polypeptides. The catalysis of peptide bond formation between amino acid substrates is based on entropy trapping, wherein the adjacency of transfer RNA (tRNA)-coupled acyl bonds in the P-site and the α-amino groups in the A-site aligns the substrates for coupling. The plasticity of this catalytic mechanism has been observed in both remnants of the evolution of the genetic code and modern efforts to reprogram the genetic code (e.g., ribosomal incorporation of non-canonical amino acids, ribosomal ester formation). However, the limits of ribosome-mediated polymerization are underexplored. Here, rather than peptide bonds, we demonstrate ribosome-mediated polymerization of pyridazinone bonds via a cyclocondensation reaction between activated γ-keto and α-hydrazino ester monomers. In addition, we demonstrate the ribosome-catalyzed synthesis of peptide-hybrid oligomers composed of multiple sequence-defined alternating pyridazinone linkages. Our results highlight the plasticity of the ribosome's ancient bond-formation mechanism, expand the range of non-canonical polymeric backbones that can be synthesized by the ribosome, and open the door to new applications in synthetic biology.

Agronomic evaluation of shade tolerance of 16 spring Camelina sativa (L.) Crantz genotypes under different artificial shade levels using a modified membership function.

Camelina [Camelina sativa (L.) Crantz] is currently gaining considerable attention as a potential oilseed feedstock for biofuel, oil and feed source, and bioproducts. Studies have shown the potential of using camelina in an intercropping system. However, there are no camelina genotypes evaluated or bred for shade tolerance. The objective of this study was to evaluate and determine the shade tolerance of sixteen spring camelina genotypes (growth stage: BBCH 103; the plants with 4-5 leaves) for intercropping systems. In this study, we simulated three different shade levels, including low (LST), medium (MST), and high shade treatments (HST; 15, 25, and 50% reduction of natural light intensity, respectively), and evaluated the photosynthetic and physiological parameters, seed production, and seed quality. The mean chlorophyll pigments, including the total chlorophyll and chlorophyll a and b across the 16 genotypes increased as shade level increased, while the chlorophyll fluorescence parameter Fv/Fm, chlorophyll a/b, leaf area, the number of silicles and branches plant-1 decreased as shade level increased. The first day of anthesis and days of flowering duration of camelina treated with shade were significantly delayed and shortened, respectively, as shade increased. The shortened lifecycle and altered flowering phenology decreased camelina seed yield. Additionally, the shade under MST and HST reduced the seed oil content and unsaturated fatty acids, but not saturated fatty acids. The dendrograms constructed using the comprehensive tolerance membership values revealed that CamK9, CamC4, and 'SO-40' were the relatively shade-tolerant genotypes among the 16 camelina genotypes. These camelina genotypes can grow under the shade level up to a 25% reduction in natural light intensity producing a similar seed yield and seed oil quality, indicating the potential to intercrop with maize or other small grain crops. The present study provided the baseline information on the response of camelina genotypes to different shade levels, which would help in selecting or breeding shade-tolerant genotypes.

Three-dimensional structure-guided evolution of a ribosome with tethered subunits.

RNA-based macromolecular machines, such as the ribosome, have functional parts reliant on structural interactions spanning sequence-distant regions. These features limit evolutionary exploration of mutant libraries and confound three-dimensional structure-guided design. To address these challenges, we describe Evolink (evolution and linkage), a method that enables high-throughput evolution of sequence-distant regions in large macromolecular machines, and library design guided by computational RNA modeling to enable exploration of structurally stable designs. Using Evolink, we evolved a tethered ribosome with a 58% increased activity in orthogonal protein translation and a 97% improvement in doubling times in SQ171 cells compared to a previously developed tethered ribosome, and reveal new permissible sequences in a pair of ribosomal helices with previously explored biological function. The Evolink approach may enable enhanced engineering of macromolecular machines for new and improved functions for synthetic biology.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics.

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured "superfolder" mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Deep learning models for predicting RNA degradation via dual crowdsourcing.

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.

Theoretical basis for stabilizing messenger RNA through secondary structure design.

RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Here, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall hydrolysis rate. To characterize the stabilization achievable through structure design, we compare AUP optimization by conventional mRNA design methods to results from more computationally sophisticated algorithms and crowdsourcing through the OpenVaccine challenge on the Eterna platform. We find that rational design on Eterna and the more sophisticated algorithms lead to constructs with low AUP, which we term 'superfolder' mRNAs. These designs exhibit a wide diversity of sequence and structure features that may be desirable for translation, biophysical size, and immunogenicity. Furthermore, their folding is robust to temperature, computer modeling method, choice of flanking untranslated regions, and changes in target protein sequence, as illustrated by rapid redesign of superfolder mRNAs for B.1.351, P.1 and B.1.1.7 variants of the prefusion-stabilized SARS-CoV-2 spike protein. Increases in in vitro mRNA half-life by at least two-fold appear immediately achievable.

Modeling the Effects of Elevated Temperature and Weed Interference on Rice Grain Yield.

A 3-year phytotron study was conducted in Suwon (37.27°N, 126.99°E), Korea, to evaluate and model the effects of elevated temperature on rice-weed competition. The dry weight and the number of panicles in rice were the most susceptible components to weed interference during the early growth of rice, regardless of weed species, while other yield components, including the number of grains, % ripened grain, and 1000-grain weight, were more susceptible to elevated temperature. A rectangular hyperbolic model well demonstrated that rice grain yield was affected by weed interference under elevated temperature, showing that the competitiveness of late watergrass (Echinochloa oryzicola) and water chestnut (Eleocharis kuroguwai) increased under elevated temperature conditions. Quadratic and linear models well described the effects of elevated temperature on the weed-free rice grain yield and weed competitiveness values of the rectangular hyperbolic model for the two weed species, respectively. Thus, a combined rectangular hyperbolic model incorporated with the quadratic and linear models well demonstrated the effects of elevated temperature and weed interference on rice grain yield across years. Using the combined model and estimated parameters, the rice grain yields were estimated to be 58.9, 48.5, 41.3, and 35.9% of the yields under weed-free conditions for 80 plants m-2 of late watergrass and 86.8, 64.3, 51.1, and 42.3% of the yields under weed-free conditions for 80 plants m-2 of water chestnut at 1,300, 1,500, 1,700, and 1,900°C·days of accumulated growing degree days (GDD; from transplanting to flowering, 89 days), respectively. The combined model developed in this study can provide an empirical description of both the elevated temperature and weed interference effects on rice yield and can be used for predicting rice grain yields due to weed interference under future elevated temperature conditions.

Baseline Sensitivity of Echinochloa crus-gall and E. oryzicola to Florpyrauxifen-Benzyl, a New Synthetic Auxin Herbicide, in Korea.

Echinochloa species is one of the most problematic weed species due to its high competitiveness and increasing herbicide resistance. Florpyrauxifen-benzyl, a new auxin herbicide, was recently introduced for Echinochloa management; however, the potential risk for the development of herbicide resistance in Echinochloa species has not been well-investigated. Thus, this study was conducted to evaluate the baseline sensitivity of Echinochloa species to florpyrauxifen-benzyl to estimate the risk of future resistance development. A total of 70 and 71 accessions of Echinochloa crus-galli and Echinochloa oryzicola were collected from paddy fields in Korea, respectively. These two Echinochloa species were grown in plastic pots up to the 5-leaf stage, and treated with florpyrauxifen-benzyl at a range of doses from 2.2 g to 70.0 g a.i. ha-1. Nonlinear regression analyses revealed that GR50 values for E. oryzicola ranged from 4.54 g to 29.66 g a.i. ha-1, giving a baseline sensitivity index (BSI) of 6.53, while those for E. crus-galli ranged from 6.15 g to 16.06 g a.i. ha-1, giving a BSI of 2.61. Our findings suggest that E. oryzicola has a greater potential risk than E. crus-galli for the development of metabolism-based resistance to florpyrauxifen-benzyl.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics.

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop a new RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that "superfolder" mRNAs can be designed to improve both stability and expression that are further enhanced through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Theoretical basis for stabilizing messenger RNA through secondary structure design.

RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery, and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Here, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall hydrolysis rate. To characterize the stabilization achievable through structure design, we compare AUP optimization by conventional mRNA design methods to results from more computationally sophisticated algorithms and crowdsourcing through the OpenVaccine challenge on the Eterna platform. These computational tests were carried out on both model mRNAs and COVID-19 mRNA vaccine candidates. We find that rational design on Eterna and the more sophisticated algorithms lead to constructs with low AUP, which we term 'superfolder' mRNAs. These designs exhibit wide diversity of sequence and structure features that may be desirable for translation, biophysical size, and immunogenicity, and their folding is robust to temperature, choice of flanking untranslated regions, and changes in target protein sequence, as illustrated by rapid redesign of superfolder mRNAs for B.1.351, P.1, and B.1.1.7 variants of the prefusion-stabilized SARS-CoV-2 spike protein. Increases in in vitro mRNA half-life by at least two-fold appear immediately achievable.

Environmental risk assessment of glufosinate-resistant soybean by pollen-mediated gene flow under field conditions in the region of the genetic origin.

Pollen-mediated gene flow of genetically modified crops to their wild relatives can facilitate the spread of transgenes into the ecosystem and alter the fitness of the consequential progeny. A two-year field study was conducted to quantify the gene flow from glufosinate-ammonium resistant (GR) soybean (Glycinemax) to its wild relative, wild soybean (G. soja), and assess the potential weed risk of hybrids resulting from the gene flow during their entire life cycle under field conditions in Korea, where wild soybean is the natural inhabitant. Pollen-mediated gene flow from GR soybeans to wild soybeans ranged from 0.292% (mixed planting) to 0.027% at 8 m distance. The log-logistic model described the gene flow rate with increasing distance from GR soybean to wild soybean; the estimated effective isolation distance for 0.01% gene flow between GR and wild soybeans was 37.7 m. The F1 and F2 hybrids exhibited the intermediate characteristics of their parental soybeans in their vegetative and reproductive stages. Canopy height and stem length of hybrids were close to those of wild soybean, which shows an indeterminate growth; the numbers of flowers, pods, and seeds per hybrid plant were close to those of wild soybean and significantly higher than those of GR soybean. Seed longevity of F2 hybrid plants was also intermediate but significantly greater than that of GR soybean due to high seed dormancy. Our results suggest that transgenes of the GR soybean might disperse into wild populations and persist in the agroecosystem of the genetic origin regions due to the pollen-mediated gene flow and the relatively high fitness of the hybrid progeny.

Ribosome-mediated polymerization of long chain carbon and cyclic amino acids into peptides in vitro.

Ribosome-mediated polymerization of backbone-extended monomers into polypeptides is challenging due to their poor compatibility with the translation apparatus, which evolved to use α-L-amino acids. Moreover, mechanisms to acylate (or charge) these monomers to transfer RNAs (tRNAs) to make aminoacyl-tRNA substrates is a bottleneck. Here, we rationally design non-canonical amino acid analogs with extended carbon chains (γ-, δ-, ε-, and ζ-) or cyclic structures (cyclobutane, cyclopentane, and cyclohexane) to improve tRNA charging. We then demonstrate site-specific incorporation of these non-canonical, backbone-extended monomers at the N- and C- terminus of peptides using wild-type and engineered ribosomes. This work expands the scope of ribosome-mediated polymerization, setting the stage for new medicines and materials.

Emerging Plasma Technology That Alleviates Crop Stress During the Early Growth Stages of Plants: A Review.

Crops during their early growth stages are vulnerable to a wide range of environmental stressors; thus, earlier seed invigoration and seedling establishment are essential in crop production. As an alternative to synthetic chemical treatments, plasma technology could be one of the emerging technologies to enhance seed germination and seedling vigor by managing environmental stressors. Recent studies have shown its beneficial effects in various stress conditions, suggesting that plasma treatment can be used for early crop stress management. This paper reviewed the effects of different types of plasma treatments on plant responses in terms of the seed surface environment (seed scarification and pathogen inactivation) and physiological processes (an enhanced antioxidant system and activated defense response) during the early growth stages of plants. As a result, plasma treatment can enhance seed invigoration and seedling establishment by alleviating the adverse effects of environmental stressors such as drought, salinity, and pathogen infection. More information on plasma applications and their mechanisms against a broad range of stressors is required to establish a better plasma technology for early crop stress management.

Ribosomal incorporation of cyclic ß-amino acids into peptides using in vitro translation.

We demonstrate in vitro incorporation of cyclic ß-amino acids into peptides by the ribosome through genetic code reprogramming. Further, we show that incorporation efficiency can be increased through the addition of elongation factor P.

In vitro ribosome synthesis and evolution through ribosome display.

Directed evolution of the ribosome for expanded substrate incorporation and novel functions is challenging because the requirement of cell viability limits the mutations that can be made. Here we address this challenge by combining cell-free synthesis and assembly of translationally competent ribosomes with ribosome display to develop a fully in vitro methodology for ribosome synthesis and evolution (called RISE). We validate the RISE method by selecting active genotypes from a ~1.7 × 107 member library of ribosomal RNA (rRNA) variants, as well as identifying mutant ribosomes resistant to the antibiotic clindamycin from a library of ~4 × 103 rRNA variants. We further demonstrate the prevalence of positive epistasis in resistant genotypes, highlighting the importance of such interactions in selecting for new function. We anticipate that RISE will facilitate understanding of molecular translation and enable selection of ribosomes with altered properties.

Expanding the limits of the second genetic code with ribozymes.

The site-specific incorporation of noncanonical monomers into polypeptides through genetic code reprogramming permits synthesis of bio-based products that extend beyond natural limits. To better enable such efforts, flexizymes (transfer RNA (tRNA) synthetase-like ribozymes that recognize synthetic leaving groups) have been used to expand the scope of chemical substrates for ribosome-directed polymerization. The development of design rules for flexizyme-catalyzed acylation should allow scalable and rational expansion of genetic code reprogramming. Here we report the systematic synthesis of 37 substrates based on 4 chemically diverse scaffolds (phenylalanine, benzoic acid, heteroaromatic, and aliphatic monomers) with different electronic and steric factors. Of these substrates, 32 were acylated onto tRNA and incorporated into peptides by in vitro translation. Based on the design rules derived from this expanded alphabet, we successfully predicted the acylation of 6 additional monomers that could uniquely be incorporated into peptides and direct N-terminal incorporation of an aldehyde group for orthogonal bioconjugation reactions.