Phytopythium litorale: A Novel Killer Pathogen of Plane (Platanus orientalis) Causing Canker Stain and Root and Collar Rot.

Decline symptoms associated with lethal stem and branch canker stain along with root and collar rots were observed on 5- to 7-year-old roadside oriental plane trees (Platanus orientalis) in Diyarbakir, Turkey. Above-ground symptoms included leaf necrosis, leaf curling, extensive bluish or blackish staining of shoots, branches, stem bark, and wood surfaces, as well as stem cankers and exfoliation of branch bark scales. A general decline of the trees was distinctly visible from a distance. A Phytophthora/Pythium-like oomycete species with globose to ovoid, often papillate and internally proliferating sporangia was consistently isolated from the fine and coarse roots and stained branch parts and shoots. The pathogen was identified as Phytopythium litorale based on several morphological features. Partial DNA sequences of three loci, including nuclear rDNA internal transcribed spacer (ITS) and the large ribosomal subunit (LSU), and mitochondrial cytochrome c oxidase subunit II (coxII) confirmed the morphological identification. All P. litorale isolates were homothallic, developing gametangia, ornamented oogonia with elongate to lobate antheridia. Pathogenicity of P. litorale was tested by inoculation on excised shoots and by root inoculation on seedlings. P. litorale produced large lesions and blights on shoots in just 5 days and killed 100% of the seedlings in a month. This paper presents the first confirmed report of P. litorale as an important pathogen on a plant species causing branch and stem cankers, and root and collar rot, in and on P. orientalis, resulting in a rapid decline of trees and suggesting a threat to plane.

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 [...].

Detection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: large-scale performance testing of sequence analysis strategies.

Recent developments in high-throughput sequencing (HTS) technologies and bioinformatics have drastically changed research in virology, especially for virus discovery. Indeed, proper monitoring of the viral population requires information on the different isolates circulating in the studied area. For this purpose, HTS has greatly facilitated the sequencing of new genomes of detected viruses and their comparison. However, bioinformatics analyses allowing reconstruction of genome sequences and detection of single nucleotide polymorphisms (SNPs) can potentially create bias and has not been widely addressed so far. Therefore, more knowledge is required on the limitations of predicting SNPs based on HTS-generated sequence samples. To address this issue, we compared the ability of 14 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 21 variants of pepino mosaic virus (PepMV) in three samples through large-scale performance testing (PT) using three artificially designed datasets. To evaluate the impact of bioinformatics analyses, they were divided into three key steps: reads pre-processing, virus-isolate identification, and variant calling. Each step was evaluated independently through an original, PT design including discussion and validation between participants at each step. Overall, this work underlines key parameters influencing SNPs detection and proposes recommendations for reliable variant calling for plant viruses. The identification of the closest reference, mapping parameters and manual validation of the detection were recognized as the most impactful analysis steps for the success of the SNPs detections. Strategies to improve the prediction of SNPs are also discussed.

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

High-throughput sequencing (HTS), more specifically RNA sequencing of plant tissues, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases. In this way, they are neglecting sequences without homologies to viruses, which usually represent the majority of sequencing reads. We hypothesized that traces of other pathogens might be detected in this unused sequence data. In the present study, our goal was to investigate whether total RNA-seq data, as generated for plant virus detection, is also suitable for the detection of other plant pathogens and pests. As proof of concept, we first analyzed RNA-seq datasets of plant materials with confirmed infections by cellular pathogens in order to check whether these non-viral pathogens could be easily detected in the data. Next, we set up a community effort to re-analyze existing Illumina RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total, 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests. The organisms most frequently detected in this way were fungi (15/37 datasets), followed by insects (13/37) and mites (9/37). The presence of some of the detected pathogens was confirmed by independent (q)PCRs analyses. After communicating the results, 6 out of the 15 participants indicated that they were unaware of the possible presence of these pathogens in their sample(s). All participants indicated that they would broaden the scope of their bioinformatic analyses in future studies and thus check for the presence of non-viral pathogens. In conclusion, we show that it is possible to detect non-viral pathogens or pests from total RNA-seq datasets, in this case primarily fungi, insects, and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (mycology, entomology, bacteriology) as well.