Transcriptomic analysis of melon/squash graft junction reveals molecular mechanisms potentially underlying the graft union development

Oriental melon (Cucumis melo var. makuwa Makino) has become a widely planted horticultural crop in China especially in recent years and has been subjected to the grafting technique for the improvement of cultivation and stress resistance. Although grafting has a long history in horticulture, there is little known about the molecular mechanisms of the graft healing process in oriental melon. This study aims to reveal the molecular changes involved in the graft healing process. In the present work, anatomical observations indicated that the 2, 6, and 9 DAG were three critical stages for the graft healing and therefore, were selected for the subsequent high-throughput RNA-seq analysis. A total of 1,950 and 1,313 DEGs were identified by comparing IL vs. CA and CA vs. VB libraries, respectively. More DEGs in the melon scion exhibited abundant transcriptional changes compared to the squash rootstock, providing increased metabolic activity and thus more material basis for the graft healing formation in the scion. Several DEGs were enriched in the plant hormone signal transduction pathway, phenylpropanoid biosynthesis, and carbon metabolism. In addition, the results showed that concentrations of IAA, GA3, and ZR were induced in the graft junctions. In conclusion, our study determined that genes involved in the hormone-signaling pathway and lignin biosynthesis played the essential roles during graft healing. These findings expand our current understandings of the molecular basis of the graft junction formation and facilitate the improvement and success of melon grafting in future production.

Development of Strategies to Minimize the Risk of Listeria monocytogenes Contamination in Radish, Oriental Melon, and Carrots

Contamination by Listeria monocytogenes in packaged produce is a major concern. The purpose of this study was to find natural and affordable sanitizers to reduce L. monocytogenes contamination in agricultural products. Organic acids, ultraviolet-C (UV-C), and ethanol were analyzed either alone or in combination to assess their ability to reduce L. monocytogenes population in radish, oriental melon, and carrot samples. In radish samples, 3% malic acid combined with UV-C at a dosage of 144 mj/cm2 significantly reduced (>4 log CFU/g) the population of L. monocytogenes (1.44 ± 0.5) compared to the control sample (5.14 ± 0.09). In the case of the melon samples, exposure to UV-C at a dosage of 144 mj/cm2 combined with 3% lactic acid (2.73 ± 0.75) or 50% ethanol (2.30 ± 0.01) was effective against L. monocytogenes compared to the control sample (5.10 ± 0.19). In carrot samples, 3% lactic acid combined with 144 mj/cm2 dosage UV-C reduced L. monocytogenes population (4.48 ± 0.25) more than in the control sample (5.85 ± 0.08). These results reveal that sanitizers that are effective for one crop are less effective for another crop indicating that effective prevention methods should be customized for each crop to prevent pathogen cross contamination during postharvest washing.

First report of Cucurbit chlorotic yellows virus infecting Cucumis melo (muskmelon and oriental melon) in Korea

In December 2018, virus-like symptoms (yellowing, vein clearing) were observed on 2% of muskmelon (Cucumis melo L.) plants in plastic houses on a farm in Gyeongsang province, Korea Total RNA from two symptomatic and two asymptomatic plants was extracted using RNeasy Plant Mini Kit (Qiagen, Germany) for high throughput sequencing (HTS). After pre-processing and Ribo-Zero rRNA removal, a cDNA library was prepared (Illumina TruSeq Stranded Total RNA kit) and sequenced (Illumina NovaSeq 6000 system: Macrogen Inc. Korea). De novo assembly of 88,222,684 HTS reads with Trinity software (r20140717) yielded 146,269 contigs of 201-28,442 bp, which were screened against the NCBI viral genome database by BLASTn. Contigs from cucumber mosaic virus (CMV), melon necrotic spot virus (MNSV), tobacco mosaic virus (TMV) and watermelon mosaic virus (WMV) were identified, all previously reported in Korea. Two contigs (8,539 and 8,040 bp) with 99.9% sequence identity to distinct cucurbit chlorotic yellows virus (CCYV) isolates (JN641883, RNA1, Taiwan; MH819191, RNA2, China) were also identified. The ten sequences most closely related to each RNA of the Korean isolate (≥99% coverage, ≥99.6% nt identity) were from Japan, China, Taiwan, or Israel. CCYV presence was confirmed by reverse transcription-PCR (RT-PCR) using newly designed specific primers, RdRp-F/RdRp-R (5’-ACCGAACACTTGGCTATCCAA-3’/5’-CTTAATGCCGCGTATGAACTCA-3’) span style="font-family:'Times New Roman'; letter-spacing:-0.5pt">and HSP-F/HSP-R (5’-TGAACGACACTGAGTTCATTCCTA-3’/5’-CGCCAAGATCGTACATGAGGAA-3’), against RNA dependent RNA polymerase (RdRp; RNA1) and the heat shock protein 70 homolog (HSP70h; RNA2). Symptomatic samples yielded products of expected sizes (RdRp,450 bp; HSP70h, 510 bp) while asymptomatic samples did not. The amplicons were cloned, and two clones of each were sequenced (BIONEER, Korea; GenBank acc. nos. LC592226 and LC592227) showing 100% and 99.2% nt identity with RdRp and HSP70h genes of Chinese CCYV isolate SD (MH819190 and MH819191, respectively) and other Asian isolates. Primers specific for CMV, WMV, beet pseudo-yellows virus (BPYV) (Okuda et al., 2007), TMV (Kim et al., 2018), MNSV (F/R, 5ʹ-ATCTCGCATTTGGCATTACTC-3ʹ/5ʹ-ATTTGTAGAGATGCCAACGTA-3ʹ), cucurbit yellow stunting disorder virus (CYSDV; Zeng et al., 2011) and cucurbit aphid-borne yellows virus (CABYV; F/R, 5ʹ-CGGTCTATTGTCTGCAGTACCA-3ʹ/5ʹ- GTAGAGGATCTTGAATTGGTCCTCA-3ʹ) were also used. None of these viruses were detected in the symptomatic samples, but both asymptomatic plants were positive for CMV and WMV, and one also for MNSV. In June and September 2020, muskmelon and oriental melon (Cucumis melo L. var. makuwa) plants with yellowing disease (incidence 80-90%) and whiteflies were observed in all investigated plastic houses of one muskmelon and one oriental melon farm in Gyeonggi and Jeolla provinces. Symptomatic samples (14 muskmelon; 6 oriental melon) were collected and RT-PCR tested as above; 19/20 samples were positive for CCYV, but none for the other viruses. The oriental melon sequence (LC592895, LC592230) showed 99.7% and 100% nt identity with the RdRp and HSP70h genes of Chinese isolate SD, respectively. CCYV was first reported in Japan (Okuda et al., 2010), Taiwan, and China (Huang et al., 2010; Gu et al., 2011); to our knowledge, this is the first report of CCYV infecting muskmelon and oriental melon in Korea. Whitefly-transmitted CCYV could present a serious threat of yield losses to cucurbit crops in Korea, requiring control of vector populations to prevent spread of CCYV.

Neural Network Based Prediction of Soluble Solids Concentrationin Oriental Melon Using VIS/NIR Spectroscopy

Highlights Non-destructive soluble solids content prediction model for oriental melon was developed based on NIR spectrum data. Not only the classical ML or Neural-Network methods, but also the mixture of both techniques have also been tried. Comparing the various pre-processing methods, the MSC-PLS-ANN model showed the best results. MSC-PLS-ANN model demonstrated 6% of improvement in RMSE score over the PLSR model, which is commonly used in commercial products Abstract. Models for predicting the soluble solids concentration (SSC) of oriental melons were developed and evaluated by applying near infrared spectroscopy and an artificial neural network technique. For the evaluation, a total of 300 oriental melons, both ripe and unripe, were mixed together and sampled. To develop an SSC prediction model, the actual SSC values of specimens having the same spectra as those of the visible/near infrared wavelength bands were measured. The measured spectra were preprocessed using eight methods [Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Robust Normal Variate, Savitzky-Golay 1st and 2nd; Min-Max Normalization; Robust Normalization; Standardization], and the SSC prediction model was developed by applying three techniques (Partial Least Squared Regression [PLSR], Artificial Neural Network [ANN], and Convolutional Neural Network [CNN]). Among them, the PLSR technique also applied a Variable Importance in Projection (VIP) method for wavelength selection. Among the PLSR-based SSC prediction models, the SNV-preprocessed PLSR model showed the best SSC prediction performance (RMSEtest, 0.67; R2test, 0.81). Among the ANN-based models, the MSC-preprocessed PLS-ANN model showed the best SSC prediction performance (RMSEtest: 0.63, R2test: 0.83). Among the CNN-based models, the DeepSpectra model was applied, but showed the lowest prediction performance (RMSEtest: 0.79, R2test: 0.74). In conclusion, among the three SSC prediction algorithms tested in this study, the PLS-ANN-based prediction model showed the best SSC prediction performance, which was found to be higher than that of the PLSR-based SSC prediction model applied to the sugar sorters currently used in agricultural products at processing centers. Keywords: Artificial Neural Network, Convolution Neural Network, Korean melon, VIP-PLSR, VIS/NIR spectroscopy.