Looking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests

In-depth population genetic study of Vitis vinifera ssp. sylvestris from the Black Sea region and its virome

The spread of cultivated grapevine from primary centers of origin is inevitably accompanied by the range expansion of its pathogens, including viruses. A limited number of wild Vitis vinifera L. ssp. sylvestris (Gmelin) Hegi populations have survived in the centers of grapevine domestication and can be used for comprehensive studies. We analyzed 50 grapevines collected in protected areas of the Black Sea region, which belong to the Caucasian domestication center. Based on genotyping of grapevines using simple sequence repeats as DNA markers, we determined the phylogenetic placement of V. vinifera ssp. sylvestris from the Black Sea region compared to cultivated and wild grapevines of the world. Using high-throughput sequencing of total RNA, we obtained the viromes of these grapevines. Ten viruses and one viroid were identified. The most common viruses detected were Vitis cryptic virus, grapevine rupestris stem pitting-associated virus, grapevine Pinot gris virus, and grapevine virus T. Among the economically significant viruses, we identified grapevine leafroll-associated virus 1 and grapevine virus A. A total of 91 complete or nearly complete virus genomes and one viroid genome were assembled, and phylogenetic analysis was performed. Two novel (+) ssRNA viruses were discovered, tentatively named Abrau grapevine-associated virus in the order Hepelivirales and Taurida grapevine-associated virus in the order Picornavirales. It is important to comprehensively consider the phylogeography of both viruses and their plant hosts. This is the first study that simultaneously addresses the population genetics of V. vinifera ssp. sylvestris from the Caucasian domestication center and its viruses.

A ribodepletion and tagging protocol to multiplex samples for RNA-seq based virus detection: application to the cassava virome

BACKGROUND

Cassava (Manihot esculenta, Crantz), is a staple food and the main source of calories for many populations in Africa, but the plant is beset by several damaging viruses. So far, eight families of virus infecting cassava have been identified; the Geminiviridae (ssDNA viruses responsible for cassava mosaic disease, CMD) and Potyviridae (ssRNA + viruses responsible for cassava brown streak disease, CBSD) families being the most damaging to cassava in Africa. In several cassava-growing regions, the co-existence of species and strains from these two families results in a complex epidemiological situation making it difficult to correctly identify the viruses in circulation and delaying the implementation of disease management schemes. Nevertheless, the development of next generation sequencing (NGS) methods has revolutionized plant virus detection and identification. One NGS method that has been successfully used in virus detection and identification is ribodepleted RNA sequencing. Unfortunately, the relatively high cost makes it difficult to upscale this method to large epidemiological surveys and limits its adoption as a diagnostic tool.

RESULTS

Here, we develop a high-throughput sequencing protocol, named Ribo-M-Seq, that combines plant rRNA ribodepletion, cDNA synthesis, tagging with a 96 multiplexing scheme and Illumina sequencing. We evaluated the protocol on a series of cassava samples with a known assemblage of viruses. After confirming that the protocol was suitable for ribodepletion, we demonstrated it was possible to detect RNA and DNA viruses via identification of near full-size genomes. Additional phylogenetic analyses confirmed the presence of begomoviruses and ipomoviruses responsible for CMD and CBSD, respectively. We also detected a recently described ampelovirus (Manihot esculenta-associated virus) that was not detected in previous analyses.

CONCLUSIONS

The use of the Ribo-M-Seq protocol will pave the way for large-scale sample analyses of collections with potentially complex viromes, such as those collected in the West African cassava integrated pest management program.

Correction: Haegeman et al. Looking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests. Plants 2023, 12, 2139

In the original publication [...].

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data

BACKGROUND

Detection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen.

RESULTS

We have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license.

CONCLUSIONS

PhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe .

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model's resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.