The phylogeographic history of tomato mosaic virus in Eurasia

Cassava brown streak virus evolves with a nucleotide-substitution rate that is typical for the family Potyviridae

The ipomoviruses (family Potyviridae) that cause cassava brown streak disease (cassava brown streak virus [CBSV] and Uganda cassava brown streak virus [UCBSV]) are damaging plant pathogens that affect the sustainability of cassava production in East and Central Africa. However, little is known about the rate at which the viruses evolve and when they emerged in Africa - which inform how easily these viruses can host shift and resist RNAi approaches for control. We present here the rates of evolution determined from the coat protein gene (CP) of CBSV (Temporal signal in a UCBSV dataset was not sufficient for comparable analysis). Our BEAST analysis estimated the CBSV CP evolves at a mean rate of 1.43 × 10-3 nucleotide substitutions per site per year, with the most recent common ancestor of sampled CBSV isolates existing in 1944 (95% HPD, between years 1922 - 1963). We compared the published measured and estimated rates of evolution of CPs from ten families of plant viruses and showed that CBSV is an average-evolving potyvirid, but that members of Potyviridae evolve more quickly than members of Virgaviridae and the single representatives of Betaflexiviridae, Bunyaviridae, Caulimoviridae and Closteroviridae.

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.

Bayesian phylodynamic analysis reveals the evolutionary history and the dispersal patterns of citrus tristeza virus in China based on the p25 gene

BACKGROUND

Citrus tristeza virus (CTV) is one of the most serious threats to the citrus industry, and is present in both wild and cultivated citrus. The origin and dispersal patterns of CTV is still poorly understood in China.

METHODS

In this study, 524 CTV suspected citrus samples from China were collected, including 354 cultivated citrus samples and 174 wild citrus samples. Finally, 126 CTV coat protein sequences were obtained with time-stamped from 10 citrus origins in China. Bayesian phylodynamic inference were performed for CTV origin and dispersal patterns study in China.

RESULT

We found that CTV was mainly distributed in southern and coastal areas of China. The substitution rate of CTV was 4.70 × 10- 4 subs/site/year (95% credibility interval: 1.10 × 10- 4 subs/site/year ~ 9.10 × 10- 4 subs/site/year), with a slight increasing trend in CTV populations between 1990 and 2006. The CTV isolates in China shared a most common recent ancestor around 1875 (95% credibility interval: 1676.57 ~ 1961.02). The CTV in China was originated from wild citrus in Hunan and Jiangxi, and then spread from the wild citrus to cultivated citrus in the growing regions of Sichuan, Chongqing, Hubei, Fujian, Zhejiang, Guangxi and Guangdong provinces.

CONCLUSIONS

This study has proved that CTV in China was originated from wild citrus in Hunan and Jiangxi. The spatial-temporal distribution and dispersal patterns has uncovered the population and pandemic history of CTV, providing hints toward a better understanding of the spread and origin of CTV in China.

The Effect of Benzo(1,2,3)-thiadiazole-7-carbothioic Acid S-Methyl Ester (BTH) and Its Cholinium Ionic Liquid Derivative on the Resistance Induction and Antioxidant Properties of Tomato (Solanum lycopersicum L.)

Tomatoes are one of the most important vegetables thanks to their taste attributes and nutritional value. Their cultivation is threatened by various pathogens including viruses. The application of resistance inducers (RI), such as benzo(1,2,3)-thiadiazole-7-carbothioic acid S-methyl ester (BTH) may be used to enhance plant performance against viruses. Here we aimed to compare the impact of BTH and its choline derivative (Chol-BTH) on resistance induction and antioxidant properties of healthy plants and tomato mosaic virus (ToMV)-infected ones. The response of tomato plants to treatment with BTH or Chol-BTH was manifested by increased expression of not only pathogenesis-related (PR) genes but also WRKY and Jasmonate Zim-domain protein (JAZ) genes and increased jasmonic acid (JA) levels. The effect of BTH as a resistance inducer was observed early after application, while with Chol-BTH the plant defense system reacted more strongly after 8 days. The antioxidant properties of RI-treated tomatoes are related to both glutathione content and peroxidase activity. In the case of BTH, an increase in these activities occurred early after application, while in the case of Chol-BTH, the glutathione level was particularly high in the plant early after treatment, and high peroxidase activity was observed 8 days post-treatment. Overall, the collected results indicate that Chol-BTH, due to its physicochemical parameters (e.g., good solubility) and biological activity (increased expression of lignification-related genes, supported by increases in peroxidase activity and total phenolic compounds levels), can also be a very useful agent inducing tomato resistance against viral pathogens.

Genetic Variability and Molecular Evolution of Tomato Mosaic Virus Populations in Three Northern China Provinces

RNA viruses tend to mutate during transmission and host infection, which is critical to viral adaptation and evolution. Tomato mosaic virus (ToMV) is a member of the genus Tobamovirus (family Virgaviridae) and an economically important virus with detrimental effects on tomatoes worldwide. Although the ToMV gene sequences have been completed in China, their genetic diversity and population structure remain unclear. We collected 425 tomato samples from tomato-growing areas in three northern Chinese provinces 2016. Reverse transcription PCR results showed that the average incidence of the virus in the field samples was 67.15%, and ToMV was detected in all test areas. The analysis of ToMV single nucleotide polymorphisms in China showed that ToMV was evolutionarily conserved, and the variation in the whole genome was uneven. Pairwise identity analysis showed significant variability in genome sequences among ToMV strains with genomic nucleotide identities of 73.2-99.6%. The ToMV population in the northern Chinese provinces had purification and selection functions, which were beneficial in the evolution of the ToMV population. Although there has been some distribution of ToMV strains in China, the virus was generally stabilized as a uniform strain under the pressure of purification selection. Our findings show how to monitor the prevalent strains of ToMV and their virulence in China and provide useful information for its prevention and control.

Evolutionary history and global spatiotemporal pattern of alfalfa mosaic virus

Alfalfa mosaic virus (AMV) is an important plant virus causing considerable economic loss to alfalfa production. Knowledge of the evolutionary and demographic history of the pathogen is limited but essential to the development of effective and sustainable pathogen management schemes. In this study, we performed worldwide phylodynamic analyses of AMV based on 154 nucleotide sequences of the coat protein gene, sampled from 1985 to 2020, to understand the epidemiology of this pathogen. Bayesian phylogenetic reconstruction estimates that the crown group of AMV dates back to 1840 (95% credibility interval, 1687-1955). We revealed that AMV continuously evolves at a rate of 4.14 × 10^-4 substitutions/site/year (95% credibility interval, 1.04 × 10^-4 - 6.68 × 10^-4). Our phylogeographic analyses identified multiple migration links between Europe and other regions, implying that Europe played a key role in spreading the virus worldwide. Further analyses showed that the clustering pattern of AMV isolates is significantly correlated to geographic regions, indicating that geography-driven adaptation may be a factor that affects the evolution of AMV. Our findings may be potentially used in the development of effective control strategies for AMV.

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.

Analysis of the genome of grapevine red blotch virus and related grabloviruses indicates diversification prior to the arrival of Vitis vinifera in North America

In this study 163 complete whole-genome sequences of the emerging pathogen grapevine red blotch virus (GRBV; genus Grablovirus, family Geminiviridae) were used to reconstruct phylogenies using Bayesian analyses on time-tipped (heterochronous) data. Using different combinations of priors, Bayes factors identified heterochronous datasets (3×200 million chains) generated from strict clock and exponential tree priors as being the most robust. Substitution rates of 3.2×10^-5 subsitutions per site per year (95% HPD 4.3-2.1×10^-5) across the whole of the GRBV genome were estimated, suggesting ancestral GRBV diverged from ancestral wild Vitis latent virus 1 around 9 000 years ago, well before the first documented arrival of Vitis vinifera in North America. Whole-genome analysis of GRBV isolates in a single infected field-grown grapevine across 12 years identified 12 single nucleotide polymorphisms none of which were fixed substitutions: an observation not discordant with the in silico estimate. The substitution rate estimated here is lower than those estimated for other geminiviruses and is the first for a woody-host-infecting geminivirus.

Analysis of the global origin, evolution and transmission dynamics of the emerging novel variant IBDV (A2dB1b): The accumulation of critical aa-residue mutations and commercial trade contributes to the emergence and transmission of novel variants

The Chinese IBDV novel variant (nvIBDV), belonging to the genotype A2dB1b, an emerging pathotype that can cause subclinical disease with severe, prolonged immunosuppression, poses a new threat to the poultry industry. The process of the global origin, evolution and transmission dynamics of nvIBDV, however, is poorly understood. In this study, phylogenetic trees, site substitutions of amino acid (aa) and highly accurate protein structure modelling, selection pressure, evolutionary and transmission dynamics of nvIBDV were analysed. Interestingly, nvIBDV was classified into the same genogroup with the early US antigenic variants (avIBDV) but in a new lineage with a markedly different and specific pattern of 17 aa-residual substitutions: 13 in VP2 (77D, 213N, 221K, 222T, 249K, 252I, 253Q, 254N, 284A, 286I, 299S, 318D and 323E) and four in VP1 (141I, 163V, 240E and 508K). Importantly, the aa-residues 299S and 163V may play a key role in cell binding and polymerase activity, respectively. The effective population size of the circulating avIBDV experienced two growth phases, respectively, in the years 1999-2007 (in North America) and 2015-2021 (in Asia), which is consistent with the observed trend of the epidemic outbreaks. The most recent common ancestor (tMRCA) of avIBDV most first originated in the USA and was dated around the 1970s. After its emergence, the ancestor virus of this group probably spread to China around the 1990s and the variants experienced a long-term latent circulation with the accumulation of several critical aa-residue mutations in VP2 until re-emerging in 2016. At present, central China has become the epicentre of nvIBDV spread to other parts of China and Asian countries. Importantly, a strong correlation seems to exist between the transmission patterns of virus and the flow of commercial trade of live poultry and products. These findings provide important insights into the origin, evolution and transmission of the nvIBDV and will assist in the development of programs for control strategies for these emerging viruses.

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.