Identification of Viruses and Viroids Infecting Tomato and Pepper Plants in Vietnam by Metatranscriptomics

Diagnosis and Characterization of Plant Viruses Using HTS to Support Virus Management and Tomato Breeding

Viral diseases pose a significant threat to tomato crops (Solanum lycopersicum L.), one of the world's most economically important vegetable crops. The limited genetic diversity of cultivated tomatoes contributes to their high susceptibility to viral infections. To address this challenge, tomato breeding programs must harness the genetic resources found in native populations and wild relatives. Breeding efforts may aim to develop broad-spectrum resistance against the virome. To identify the viruses naturally infecting 19 advanced lines, derived from native tomatoes, high-throughput sequencing (HTS) of small RNAs and confirmation with PCR and RT-PCR were used. Single and mixed infections with tomato mosaic virus (ToMV), tomato golden mosaic virus (ToGMoV), and pepper huasteco yellow vein virus (PHYVV) were detected. The complete consensus genomes of three variants of Mexican ToMV isolates were reconstructed, potentially forming a new ToMV clade with a distinct 3' UTR. The absence of reported mutations associated with resistance-breaking to ToMV suggests that the Tm-1, Tm-2, and Tm-22 genes could theoretically be used to confer resistance. However, the high mutation rates and a 63 nucleotide insertion in the 3' UTR, as well as amino acid mutations in the ORFs encoding 126 KDa, 183 KDa, and MP of Mexican ToMV isolates, suggest that it is necessary to evaluate the capacity of these variants to overcome Tm-1, Tm-2, and Tm-22 resistance genes. This evaluation, along with the characterization of advanced lines using molecular markers linked to these resistant genes, will be addressed in future studies as part of the breeding strategy. This study emphasizes the importance of using HTS for accurate identification and characterization of plant viruses that naturally infect tomato germplasm based on the consensus genome sequences. This study provides crucial insights to select appropriate disease management strategies and resistance genes and guide breeding efforts toward the development of virus-resistant tomato varieties.

One-Step Reverse-Transcription Recombinase-Aided Amplification CRISPR/Cas12a-Based Lateral Flow Assay for Fast Field Screening and Accurate Differentiation of Four Major Tobamoviruses Infecting Tomato and Pepper

Several tobamoviruses cause substantial economic losses to tomato and pepper crops globally, especially the pepper mild mosaic virus (PMMoV), tomato brown rugose fruit virus (ToBRFV), tomato mosaic virus (ToMV), and tomato mottle mosaic virus (ToMMV). A fast and accurate detection method is essential for virus identification. An all-in-one reaction method combining a one-step reverse-transcription recombinase-aided amplification (RT-RAA) and CRISPR/Cas12a-based lateral flow assay in one mixture was developed to rapidly screen and accurately differentiate among these four tobamoviruses for field detection in tomato and pepper plants. With a generic RT-RAA primer set and a mix of four specific crRNAs, along with a portable metal incubator and the use of a crude extraction method, this method screened for PMMoV, ToBRFV, ToMV, and ToMMV concurrently in less than 1 h, enabling field workers to take action immediately. The accurate differentiation of these four viruses could be achieved by later adding a single specific crRNA.

Advances and Prospects of Virus-Resistant Breeding in Tomatoes

Plant viruses are the main pathogens which cause significant quality and yield losses in tomato crops. The important viruses that infect tomatoes worldwide belong to five genera: Begomovirus, Orthotospovirus, Tobamovirus, Potyvirus, and Crinivirus. Tomato resistance genes against viruses, including Ty gene resistance against begomoviruses, Sw gene resistance against orthotospoviruses, Tm gene resistance against tobamoviruses, and Pot 1 gene resistance against potyviruses, have been identified from wild germplasm and introduced into cultivated cultivars via hybrid breeding. However, these resistance genes mainly exhibit qualitative resistance mediated by single genes, which cannot protect against virus mutations, recombination, mixed-infection, or emerging viruses, thus posing a great challenge to tomato antiviral breeding. Based on the epidemic characteristics of tomato viruses, we propose that future studies on tomato virus resistance breeding should focus on rapidly, safely, and efficiently creating broad-spectrum germplasm materials resistant to multiple viruses. Accordingly, we summarized and analyzed the advantages and characteristics of the three tomato antiviral breeding strategies, including marker-assisted selection (MAS)-based hybrid breeding, RNA interference (RNAi)-based transgenic breeding, and CRISPR/Cas-based gene editing. Finally, we highlighted the challenges and provided suggestions for improving tomato antiviral breeding in the future using the three breeding strategies.

Seasonal activities of the phyllosphere microbiome of perennial crops

Understanding the interactions between plants and microorganisms can inform microbiome management to enhance crop productivity and resilience to stress. Here, we apply a genome-centric approach to identify ecologically important leaf microbiome members on replicated plots of field-grown switchgrass and miscanthus, and to quantify their activities over two growing seasons for switchgrass. We use metagenome and metatranscriptome sequencing and curate 40 medium- and high-quality metagenome-assembled-genomes (MAGs). We find that classes represented by these MAGs (Actinomycetia, Alpha- and Gamma- Proteobacteria, and Bacteroidota) are active in the late season, and upregulate transcripts for short-chain dehydrogenase, molybdopterin oxidoreductase, and polyketide cyclase. Stress-associated pathways are expressed for most MAGs, suggesting engagement with the host environment. We also detect seasonally activated biosynthetic pathways for terpenes and various non-ribosomal peptide pathways that are poorly annotated. Our findings support that leaf-associated bacterial populations are seasonally dynamic and responsive to host cues.

Novel RNA Viruses Discovered in Weeds in Rice Fields

Weeds often grow alongside crop plants. In addition to competing with crops for nutrients, water and space, weeds host insect vectors or act as reservoirs for viral diversity. However, little is known about viruses infecting rice weeds. In this work, we used metatranscriptomic deep sequencing to identify RNA viruses from 29 weed samples representing 23 weed species. A total of 224 RNA viruses were identified: 39 newly identified viruses are sufficiently divergent to comprise new families and genera. The newly identified RNA viruses clustered within 18 viral families. Of the identified viruses, 196 are positive-sense single-stranded RNA viruses, 24 are negative-sense single-stranded RNA viruses and 4 are double-stranded RNA viruses. We found that some novel RNA viruses clustered within the families or genera of several plant virus species and have the potential to infect plants. Collectively, these results expand our understanding of viral diversity in rice weeds. Our work will contribute to developing effective strategies with which to manage the spread and epidemiology of plant viruses.

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming, challenging and error-prone. To reduce the risk of disease severity, machine vision technology (i.e., artificial intelligence) can play a significant role. In the alternative method, the severity of the disease can be diminished through computer technologies and the cooperation of humans. These methods can also eliminate the disadvantages of manual observation. In this work, we proposed a solution to detect tomato plant disease using a deep leaning-based system utilizing the plant leaves image data. We utilized an architecture for deep learning based on a recently developed convolutional neural network that is trained over 18,161 segmented and non-segmented tomato leaf images-using a supervised learning approach to detect and recognize various tomato diseases using the Inception Net model in the research work. For the detection and segmentation of disease-affected regions, two state-of-the-art semantic segmentation models, i.e., U-Net and Modified U-Net, are utilized in this work. The plant leaf pixels are binary and classified by the model as Region of Interest (ROI) and background. There is also an examination of the presentation of binary arrangement (healthy and diseased leaves), six-level classification (healthy and other ailing leaf groups), and ten-level classification (healthy and other types of ailing leaves) models. The Modified U-net segmentation model outperforms the simple U-net segmentation model by 98.66 percent, 98.5 IoU score, and 98.73 percent on the dice. InceptionNet1 achieves 99.95% accuracy for binary classification problems and 99.12% for classifying six segmented class images; InceptionNet outperformed the Modified U-net model to achieve higher accuracy. The experimental results of our proposed method for classifying plant diseases demonstrate that it outperforms the methods currently available in the literature.

Viromes of 15 Pepper (Capsicum annuum L.) Cultivars

Pepper (Capsicum annuum L.) plants produce berry fruits that are used as spices. Here, we examined the viromes of 15 pepper cultivars through RNA sequencing. We obtained 1,325 virus-associated contigs derived from 8 virus species. Bean broad wilt virus 2 (BBWV2) and cucumber mosaic virus (CMV) were identified as the major viruses infecting pepper plants, followed by potato virus Y, bell pepper endornavirus, and hot pepper endornavirus. The proportion of viral reads in each transcriptome ranged from 0.04% to 24.5%. BBWV2 was the dominant virus in seven cultivars, whereas CMV was dominant in five cultivars. All the bell pepper cultivars showed severe viral disease symptoms, whereas the commercially developed hot pepper cultivars were asymptomatic or had mild symptoms. In addition, 111 complete viral segments were obtained from 7 viruses. Based on the obtained viral genomes, the genetic relationship between the identified viruses and quasispecies of BBWV2 and CMV in each pepper plant was determined. Newly designed primers for nine viruses confirmed the results of RNA sequencing. Taken together, this study, for the first time, provides a comprehensive overview of viromes in 15 major pepper cultivars through RNA sequencing.

Tomato zonate spot virus induced hypersensitive resistance via an auxin-related pathway in pepper

Tomato zonate spotvirus (TZSV) often incurs significant losses in many food and ornamental crops in Yunnan province, China, and the surrounding areas. The pepper (Capsicum chinensePI152225)can develop hypersensitive resistance following infection with TZSV, through an as yet unknown mechanism. The transcriptome dataset showed a total of 45.81 GB of clean data were obtained from six libraries, and the average percentage of the reads mapped to the pepper genome was over 90.00 %. A total of 1403 differentially expressed genes (DEGs) were obtained after TZSV infection, including 825significantly up-regulated genes and 578 down-regulated genes. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that most up-regulated DEGs were involved in basal defenses. RT-qPCR, and virus induced gene silencing (VIGS) were used preliminarily to identifyBBC_22506 and BBC_18917, among total of 71 differentially expressed genes (DEGs), that play a key role in mediating the auxin-induced signaling pathway that might take part in hypersensitive response (HR) conferred resistance to viral infection in pepper (PI152225) byTZSV. This is the first study on the mechanism of auxin resistance, involved in defense responses of pepper against viral diseases, which lay the foundation for further study on the pathogenic mechanism of TZSV, as well as the mechanism of resistance to TZSV, in peppers.

Identification of Viruses Infecting Oats in Korea by Metatranscriptomics

Controlling infectious plant viruses presents a constant challenge in agriculture. As a source of valuable nutrients for human health, the cultivation of oats (Avena sativa L.) has recently been increased in Korea. To date, however, few studies have been undertaken to identify the viruses infecting oats in this country. In this study, we carried out RNA-sequencing followed by bioinformatics analyses to understand the virosphere in six different geographical locations in Korea where oats are cultivated. We identified three different virus species, namely, barley yellow dwarf virus (BYDV) (BYDV-PAV and BYDV-PAS), cereal yellow dwarf virus (CYDV) (CYDV-RPS and CYDV-RPV), and rice black-streaked dwarf virus (RBSDV). Based on the number of virus-associated reads and contigs, BYDV-PAV was a dominant virus infecting winter oats in Korea. Interestingly, RBSDV was identified in only a single region, and this is the first report of this virus infecting oats in Korea. Single nucleotide polymorphisms analyses indicated that most BYDV, CYDV, and RBSDV isolates show considerable genetic variations. Phylogenetic analyses indicated that BYDVs and CYDVs were largely grouped in isolates from Asia and USA, whereas RBSDV was genetically similar to isolates from China. Overall, the findings of this study provide a preliminary characterization of the types of plant viruses infecting oats in six geographical regions of Korea.

Comparative Study of Metagenomics and Metatranscriptomics to Reveal Microbiomes in Overwintering Pepper Fruits

Red pepper (Capsicum annuum, L.), is one of the most important spice plants in Korea. Overwintering pepper fruits are a reservoir of various microbial pepper diseases. Here, we conducted metagenomics (DNA sequencing) and metatranscriptomics (RNA sequencing) using samples collected from three different fields. We compared two different library types and three different analytical methods for the identification of microbiomes in overwintering pepper fruits. Our results demonstrated that DNA sequencing might be useful for the identification of bacteria and DNA viruses such as bacteriophages, while mRNA sequencing might be beneficial for the identification of fungi and RNA viruses. Among three analytical methods, KRAKEN2 with raw data reads (KRAKEN2_R) might be superior for the identification of microbial species to other analytical methods. However, some microbial species with a low number of reads were wrongly assigned at the species level by KRAKEN2_R. Moreover, we found that the databases for bacteria and viruses were better established as compared to the fungal database with limited genome data. In summary, we carefully suggest that different library types and analytical methods with proper databases should be applied for the purpose of microbiome study.

Global Advances in Tomato Virome Research: Current Status and the Impact of High-Throughput Sequencing

Viruses cause a big fraction of economically important diseases in major crops, including tomato. In the past decade (2011–2020), many emerging or re-emerging tomato-infecting viruses were reported worldwide. In this period, 45 novel viral species were identified in tomato, 14 of which were discovered using high-throughput sequencing (HTS). In this review, we first discuss the role of HTS in these discoveries and its general impact on tomato virome research. We observed that the rate of tomato virus discovery is accelerating in the past few years due to the use of HTS. However, the extent of the post-discovery characterization of viruses is lagging behind and is greater for economically devastating viruses, such as the recently emerged tomato brown rugose fruit virus. Moreover, many known viruses still cause significant economic damages to tomato production. The review of databases and literature revealed at least 312 virus, satellite virus, or viroid species (in 22 families and 39 genera) associated with tomato, which is likely the highest number recorded for any plant. Among those, here, we summarize the current knowledge on the biology, global distribution, and epidemiology of the most important species. Increasing knowledge on tomato virome and employment of HTS to also study viromes of surrounding wild plants and environmental samples are bringing new insights into the understanding of epidemiology and ecology of tomato-infecting viruses and can, in the future, facilitate virus disease forecasting and prevention of virus disease outbreaks in tomato.

Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature.

An Inside Look into Biological Miniatures: Molecular Mechanisms of Viroids

Viroids are tiny single-stranded circular RNA pathogens that infect plants. Viroids do not encode any proteins, yet cause an assortment of symptoms. The following review describes viroid classification, molecular biology and spread. The review also discusses viroid pathogenesis, host interactions and detection. The review concludes with a description of future prospects in viroid research.