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Molecular Methods in Plant Disease Diagnostics
Molecular diagnostic of plant diseases have helped by increasing the efficacy, accuracy or speed of diagnosis, while their common technological basis also reduces reliance on staff with very specialist skills. This book provides protocols for the majority of nucleic acid-based methods applied to plant pathogen detection and identification.
eBook
Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling
Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine – SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants’ defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.
Journal Article
Transcriptome analysis provides insights into the role of TLP16 in Musa acuminata Resistance to Fusarium oxysporum f. sp. cubense wilt
Background
Thaumatin-like proteins (TLPs) are crucial pathogenesis-related proteins that significantly contribute to plant defense rection.
Fusarium oxysporum
f. sp.
cubense
(
Foc
) causes Fusarium wilt of bananas, a serious threat to global production. However, the role of TLPs in disease resistance remains unclear.
Results
This study identified 49 TLP genes in banana, predominantly localized in the extracellular space, and distributed across 11 chromosomes. The ancestor–descendant relationship was explained, six genes remained remarkably conserved across species could represent the ancestral genes of the TLP gene family. Promoter regions, transcriptome and qRT-PCR analysis suggested that
MaTLP16
might be involved in disease resistance. Furthermore, transcriptional silencing of
MaTLP16
resulted in more severe leaf damage compared to the control, indicating that
MaTLP16
is an important
Foc
resistance-related gene.
Conclusion
This study conducted a comprehensive genome-wide identification and systematic analysis of the TLP gene family in bananas. Our findings establish a foundation for further functional studies of
MaTLP
genes and highlight
MaTLP16
as a strong candidate for use in breeding programs aimed at enhancing resistance to
Musa
diseases.
Journal Article
Comparative transcriptome revealed the molecular responses of Aconitum carmichaelii Debx. to downy mildew at different stages of disease development
Background
Aconitum carmichaelii
Debx. has been widely used as a traditional medicinal herb for a long history in China. It is highly susceptible to various dangerous diseases during the cultivation process. Downy mildew is the most serious leaf disease of
A. carmichaelii
, affecting plant growth and ultimately leading to a reduction in yield
.
To better understand the response mechanism of
A. carmichaelii
leaves subjected to downy mildew, the contents of endogenous plant hormones as well as transcriptome sequencing were analyzed at five different infected stages.
Results
The content of 3-indoleacetic acid, abscisic acid, salicylic acid and jasmonic acid has changed significantly in
A. carmichaelii
leaves with the development of downy mildew, and related synthetic genes such as 9-cis-epoxycarotenoid dioxygenase and phenylalanine ammonia lyase were also significant for disease responses. The transcriptomic data indicated that the differentially expressed genes were primarily associated with plant hormone signal transduction, plant-pathogen interaction, the mitogen-activated protein kinase signaling pathway in plants, and phenylpropanoid biosynthesis. Many of these genes also showed potential functions for resisting downy mildew. Through weighted gene co-expression network analysis, the hub genes and genes that have high connectivity to them were identified, which could participate in plant immune responses.
Conclusions
In this study, we elucidated the response and potential genes of
A. carmichaelii
to downy mildew, and observed the changes of endogenous hormones content at different infection stages, so as to contribute to the further screening and identification of genes involved in the defense of downy mildew.
Journal Article
Comparative proteomic and metabolomic analysis of resistant and susceptible Kentucky Bluegrass cultivars in response to infection by powdery mildew
Background
Poa pratensis
is a predominant cool-season turfgrass utilized in urban landscaping and ecological management. It is extensively employed in turf construction and in the regulation of ecological environments. However, it is susceptible to powdery mildew, a prevalent disease in humid regions. Currently, the primary control measure for powdery mildew involves the application of pesticides, a practice that is both costly and environmentally detrimental. Developing superior disease-resistant cultivars represents a more cost-effective and sustainable strategy for managing turfgrass diseases. Furthermore, an in-depth investigation into the response mechanisms of
P. pratensis
to powdery mildew infection could significantly advance research on the identification of disease resistance genes and the molecular breeding of resistant varieties.
Results
In this study, we first assessed the disease incidence across various disease-resistant
P. pratensis
cultivars and subsequently examined alterations in their in vivo redox states. We employed isobaric tags for relative and absolute quantification (iTRAQ) proteomics alongside non-targeted metabolomics to elucidate the response mechanisms of
P. pratensis
to powdery mildew invasion. A comprehensive analysis of the shared KEGG pathways among differentially abundant proteins (DAPs) and differentially enriched metabolites (DEMs) led to the identification of four common KEGG pathways. Notably, the phenylpropanoid biosynthesis pathway, enriched in both examined
P. pratensis
cultivars, was selected for further investigation. This analysis indicated that lignin biosynthesis plays a crucial role in the response of
P. pratensis
to powdery mildew infection.
Conclusions
The findings of this study enhance our understanding of the mechanisms underlying powdery mildew resistance in
P. pratensis
and serve as a valuable reference for the selection of powdery mildew-resistant cultivars, as well as for the identification and application of associated disease resistance genes.
Clinical trial number
Not applicable.
Journal Article
Genome-wide association study identifies novel loci and candidate genes for rust resistance in wheat (Triticum aestivum L.)
Background
Wheat rusts are important biotic stresses, development of rust resistant cultivars through molecular approaches is both economical and sustainable. Extensive phenotyping of large mapping populations under diverse production conditions and high-density genotyping would be the ideal strategy to identify major genomic regions for rust resistance in wheat. The genome-wide association study (GWAS) population of 280 genotypes was genotyped using a 35 K Axiom single nucleotide polymorphism (SNP) array and phenotyped at eight, 10, and, 10 environments, respectively for stem/black rust (SR), stripe/yellow rust (YR), and leaf/brown rust (LR).
Results
Forty-one Bonferroni corrected marker-trait associations (MTAs) were identified, including 17 for SR and 24 for YR. Ten stable MTAs and their best combinations were also identified. For YR,
AX-94990952
on 1A +
AX-95203560
on 4A +
AX-94723806
on 3D +
AX-95172478
on 1A showed the best combination with an average co-efficient of infection (ACI) score of 1.36. Similarly, for SR,
AX-94883961
on 7B +
AX-94843704
on 1B and
AX-94883961
on 7B +
AX-94580041
on 3D +
AX-94843704
on 1B showed the best combination with an ACI score of around 9.0. The genotype PBW827 have the best MTA combinations for both YR and SR resistance. In silico study identifies key prospective candidate genes that are located within MTA regions. Further, the expression analysis revealed that 18 transcripts were upregulated to the tune of more than 1.5 folds including 19.36 folds (TraesCS3D02G519600) and 7.23 folds (TraesCS2D02G038900) under stress conditions compared to the control conditions. Furthermore, highly expressed genes in silico under stress conditions were analyzed to find out the potential links to the rust phenotype, and all four genes were found to be associated with the rust phenotype.
Conclusion
The identified novel MTAs, particularly stable and highly expressed MTAs are valuable for further validation and subsequent application in wheat rust resistance breeding. The genotypes with favorable MTA combinations can be used as prospective donors to develop elite cultivars with YR and SR resistance.
Journal Article
Chat Demeter: a multi-agent system for plant disease diagnosis integrating CNN-transformer models
Plant diseases remain a significant challenge in global agricultural production. Achieving efficient and accurate disease detection is essential for reducing crop losses, controlling agricultural costs, and improving yields. As agriculture rapidly advances toward digitalization and intelligent transformation, the application of artificial intelligence technologies has become a key pathway to enhancing industrial competitiveness. In this study, Chat Demeter, a multi-agent system for plant disease diagnosis based on deep learning. The system captures real-time leaf images through camera devices. It employs a CNN-Transformer model to perform instance segmentation and object detection, thereby enabling automatic identification of diseased leaves and classification of disease types. To enhance interactivity and practical value, the system incorporates a natural language interface, allowing users to upload images and receive automated diagnostic results and treatment suggestions. Experimental results demonstrate that the system achieves an accuracy of 99.50% and an AUC o f 99.91% on the validation dataset, highlighting its superior performance. Overall, Chat Demeter provides an effective tool for crop health monitoring and disease intervention, while offering a feasible pathway and developmental direction for integrating and optimizing future agricultural multi-agent systems.
Journal Article
AhMTP9-Mediated manganese tolerance mechanisms in peanut: integrated physiological and molecular responses of roots and leaves to Mn stress
Background
Peanut exhibits sensitivity to manganese (Mn) toxicity in acidic soil, but the molecular mechanisms remain unclear. This study used physiological, transcriptomic, and metabolomic analyses to investigate peanut root and leaf responses to Mn toxicity.
Results
Mn toxicity reduced plant biomass, plant height, leaf chlorophyll and caused leaf yellowing. In roots, the contents of Proline (Pro), Ascorbate Peroxidase (APX) and soluble sugar increased by 41.78%, 148.98% and 107.52%, respectively, whereas malondialdehyde (MDA) decreased by 28.92%. In leaves, Pro, APX and soluble sugar increased by 876.49%, 138.98% and 455.20%, respectively, whereas MDA decreased by 39.54%. High Mn stress (300 µM vs. 10 µM) elevated the levels of IAA, T-Zeatin, ABA, SA, JA, and GA3 in leaves (by 33.01%, 49.76%, 58.56%, 435.56%, 2368.97%, and 1218.10%) and SA, JA, and GA3 in roots (by 478.36%, 413.53%, and 730.56%), while decreasing T-Zeatin in roots by 27.37%. Additionally, Mn stress disrupted ion absorption and transport. Transcriptomic analysis identified 4107 differentially expressed genes (DEGs) in roots and 657 in leaves, including those involved in antioxidant enzymes, ion transport and hormone pathways. Metabolomic analysis revealed 399 differentially expressed metabolites (DEMs) in leaves and 362 in roots. Key enriched pathways included phenylpropanoid biosynthesis in roots and tryptophan metabolism and plant hormone signal transduction in leaves. Functional analysis of
AhMTP9
, a key gene in the plant hormone signal transduction pathway, showed its localization to the cell membrane. Heterologous expression of
AhMTP9
in
Arabidopsis thaliana
increased plant height, rosette leaf diameter, soluble sugar and reduced MDA content under high Mn stress, paralleling trends observed in peanut.
Conclusions
These findings advance understanding of Mn toxicity effects on peanut development and offer a foundation for improving Mn tolerance in peanuts through genetic approaches.
Journal Article
Writers, readers, and erasers of N6-Methyladenosine (m6A) methylomes in oilseed rape: identification, molecular evolution, and expression profiling
Background
m6A RNA modifications are the most prevalent internal modifications in eukaryotic mRNAs and are crucial for plant growth and development, as well as for responses to biotic or abiotic stresses. The modification is catalyzed by writers, removed by erasers, and decoded by various m6A-binding proteins, which are readers.
Brassica napus
is a major oilseed crop. The dynamic regulation of m6A modifications by writers, erasers, and readers offers potential targets for improving the quality of this crop.
Results
In this study, we identified 92 m6A-regulatory genes in
B. napus
, including 13 writers, 29 erasers, and 50 readers. A phylogenetic analysis revealed that they could be further divided into four, three, and two clades, respectively. The distribution of protein motifs and gene structures among members of the same clade exhibited notable similarity. During the course of evolution, whole genome duplication (WGD) and segmental duplication were the primary drivers of the expansion of m6A-related gene families. The genes were subjected to rigorous purification selection. Additionally, several sites under positive selection were identified in the proteins. RNA-seq and quantitative real-time PCR (qRT-PCR) expression analyses revealed that the identified
Bnam6As
exhibit tissue-specific expression patterns, as well as their expression patterns in response to various abiotic and biotic stresses. The 2000 bp sequence upstream of
Bnam6As
contained a number of
cis
-acting elements that regulate plant growth and environmental response. Furthermore, the protein interaction network revealed their interactions with a number of proteins of significant functional importance.
Conclusion
The identification of m6A modifiers in oilseed rape and their molecular evolution and expression profiling have revealed potential functions and molecular mechanisms of m6A, thus establishing a foundation for further functional validation and molecular breeding.
Journal Article