Optimization of Protoplast Isolation and Transformation for a Pilot Study of Genome Editing in Peanut by Targeting the Allergen Gene Ara h 2.

The cultivated peanut (Arachis hypogaea L.) is a legume consumed worldwide in the form of oil, nuts, peanut butter, and candy. Improving peanut production and nutrition will require new technologies to enable novel trait development. Clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR-Cas9) is a powerful and versatile genome-editing tool for introducing genetic changes for studying gene expression and improving crops, including peanuts. An efficient in vivo transient CRISPR-Cas9- editing system using protoplasts as a testbed could be a versatile platform to optimize this technology. In this study, multiplex CRISPR-Cas9 genome editing was performed in peanut protoplasts to disrupt a major allergen gene with the help of an endogenous tRNA-processing system. In this process, we successfully optimized protoplast isolation and transformation with green fluorescent protein (GFP) plasmid, designed two sgRNAs for an allergen gene, Ara h 2, and tested their efficiency by in vitro digestion with Cas9. Finally, through deep-sequencing analysis, several edits were identified in our target gene after PEG-mediated transformation in protoplasts with a Cas9 and sgRNA-containing vector. These findings demonstrated that a polyethylene glycol (PEG)-mediated protoplast transformation system can serve as a rapid and effective tool for transient expression assays and sgRNA validation in peanut.

Raman spectroscopy-based diagnostics of water deficit and salinity stresses in two accessions of peanut.

Water deficit and salinity are two major abiotic stresses that have tremendous effect on crop yield worldwide. Timely identification of these stresses can help limit associated yield loss. Confirmatory detection and identification of water deficit stress can also enable proper irrigation management. Traditionally, unmanned aerial vehicle (UAV)-based imaging and satellite-based imaging, together with visual field observation, are used for diagnostics of such stresses. However, these approaches can only detect salinity and water deficit stress at the symptomatic stage. Raman spectroscopy (RS) is a noninvasive and nondestructive technique that can identify and detect plant biotic and abiotic stress. In this study, we investigated accuracy of Raman-based diagnostics of water deficit and salinity stresses on two greenhouse-grown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76 days prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5 days, and salinity treatments received 1.0 L of 240-mM salt water per day for the duration of 5-day sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a hand-held Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1 day after treatment. Water deficit and salinity stressed plants could be differentiated from each other with 87% and 86% accuracy, respectively. In the tolerant accessions at the same timepoint, the identification accuracies were 66%, 65%, 67%, and 69% for control, combined stresses, water deficit, and salinity stresses, respectively. The high selectivity and specificity for presymptomatic identification of abiotic stresses in the susceptible line provide evidence for the potential of Raman-based surveillance in commercial-scale agriculture and digital farming.

A Proof-of-Principle Study of Non-invasive Identification of Peanut Genotypes and Nematode Resistance Using Raman Spectroscopy.

Identification of peanut cultivars for distinct phenotypic or genotypic traits whether using visual characterization or laboratory analysis requires substantial expertise, time, and resources. A less subjective and more precise method is needed for identification of peanut germplasm throughout the value chain. In this proof-of-principle study, the accuracy of Raman spectroscopy (RS), a non-invasive, non-destructive technique, in peanut phenotyping and identification is explored. We show that RS can be used for highly accurate peanut phenotyping via surface scans of peanut leaves and the resulting chemometric analysis: On average 94% accuracy in identification of peanut cultivars and breeding lines was achieved. Our results also suggest that RS can be used for highly accurate determination of nematode resistance and susceptibility of those breeding lines and cultivars. Specifically, nematode-resistant peanut cultivars can be identified with 92% accuracy, whereas susceptible breeding lines were identified with 81% accuracy. Finally, RS revealed substantial differences in biochemical composition between resistant and susceptible peanut cultivars. We found that resistant cultivars exhibit substantially higher carotenoid content compared to the susceptible breeding lines. The results of this study show that RS can be used for quick, accurate, and non-invasive identification of genotype, nematode resistance, and nutrient content. Armed with this knowledge, the peanut industry can utilize Raman spectroscopy for expedited breeding to increase yields, nutrition, and maintaining purity levels of cultivars following release.

Raman Spectroscopy Enables Non-Invasive Identification of Peanut Genotypes and Value-Added Traits.

Identification of specific genotypes can be accomplished by visual recognition of their distinct phenotypical appearance, as well as DNA analysis. Visual identification (ID) of species is subjective and usually requires substantial taxonomic expertise. Genotyping and sequencing are destructive, time- and labor-consuming. In this study, we investigate the potential use of Raman spectroscopy (RS) as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of peanut genotypes. We show that chemometric analysis of peanut leaflet spectra provides accurate identification of different varieties. This same analysis can be used for prediction of nematode resistance and oleic-linoleic oil (O/L) ratio. Raman-based analysis of seeds provides accurate genotype identification in 95% of samples. Additionally, we present data on the identification of carbohydrates, proteins, fiber and other nutrients obtained from spectroscopic signatures of peanut seeds. These results demonstrate that RS allows for fast, accurate and non-invasive screening and selection of plants which can be used for precision breeding.

A novel strategy for the identification of toxinlike structures in spider venom.

We compared two different approaches to sequence information analysis from the expressed sequence tag (EST) library constructed for the venom glands of the spider Agelena orientalis. Some results were more illustrative and reliable by the contig analysis technique, whereas our novel method, with specific structural markers introduced for protein structure detection, allowed us to overcome some limitations of the contig analysis. A novel technique was suggested for the identification in data banks of the spider's ion channel inhibitor toxins using primary structure features common to all spiders. Analysis of about 150 polypeptides made it possible to introduce 3 primary structure motifs for spider toxins: the Principal Structural Motif (PSM), which postulates the existence of 6 amino acid residues between the first and second cysteine residue and the Cys-Cys sequence at a distance of 5-10 amino acid residues from the second cysteine; the Extra Structural Motif (ESM), which postulates the existence of a pair of CXC fragments in the C-region; and the Processing Quadruplet Motif (PQM), which specifies the Arg residue at position -1 and Glu residues at positions -2, -3, and/or -4 in the precursor sequences just before the postprocessing site. In the processed data bank we found 48 toxinlike structures with ion channel inhibitor motifs. These include agelenin earlier isolated from Agelena opulenta and 25 more homologous sequences, 15 homologs of mu-agatoxin 2 from the spider Agelenopsis aperta, 3 structures with low homology to omega-agatoxin-IIIA, and 4 new structures. Also we showed that toxinlike structures exceed two thirds of the overall database sequences.