Whole-genome single nucleotide polymorphism analysis for typing the pandemic pathogen Fusarium graminearum sensu stricto

Measurement of the effectiveness of Clonostachys rosea in reducing Fusarium biomass on wheat straw

The survival and growth of plant pathogens on crop residues are key factors facilitating the dynamics of crop diseases. Spores (e.g., perithecia, and chlamydospores) and mycelium of pathogenic fungi overwinter on harvest residues, such as straw, and serve as initial inoculum infecting crops in the next growing season. Therefore, targeting overwintering fungi is essential to attaining effective disease control. Beneficial microorganisms offer advantages in controlling pathogens through their ability to colonize and exploit different environmental niches. In this study, we applied qPCR assays to explore the biocontrol performance of locally isolated strains of Clonostachys against various Fusarium pathogens. We proved that prior colonization of wheat straw by Fusarium spp. can be effectively reduced by Clonostachys rosea. We demonstrated that the efficiency of C. rosea to reduce Fusarium inoculum appears to remain at a similar level for most studied strains regardless of the target pathogen and the level of colonization of substrates by pathogens. Efficient performance of local C. rosea strains identifies possible targets for future strategies to control Fusarium diseases in cereals. Our study also highlights the challenge in sequence-based determination of C. rosea, which is crucial for the efficient selection of beneficial strains for biocontrol purposes.

Mycologists and Virologists Align: Proposing Botrytis cinerea for Global Mycovirus Studies

Mycoviruses are highly genetically diverse and can significantly change their fungal host's phenotype, yet they are generally under-described in genotypic and biological studies. We propose Botrytis cinerea as a model mycovirus system in which to develop a deeper understanding of mycovirus epidemiology including diversity, impact, and the associated cellular biology of the host and virus interaction. Over 100 mycoviruses have been described in this fungal host. B. cinerea is an ideal model fungus for mycovirology as it has highly tractable characteristics-it is easy to culture, has a worldwide distribution, infects a wide range of host plants, can be transformed and gene-edited, and has an existing depth of biological resources including annotated genomes, transcriptomes, and isolates with gene knockouts. Focusing on a model system for mycoviruses will enable the research community to address deep research questions that cannot be answered in a non-systematic manner. Since B. cinerea is a major plant pathogen, new insights may have immediate utility as well as creating new knowledge that complements and extends the knowledge of mycovirus interactions in other fungi, alone or with their respective plant hosts. In this review, we set out some of the critical steps required to develop B. cinerea as a model mycovirus system and how this may be used in the future.

New insights into mycotoxin risk management through fungal population genetics and genomics

Mycotoxin contamination of food and feed is a major global concern. Chronic or acute dietary exposure to contaminated food and feed can negatively affect both human and animal health. Contamination occurs through plant infection by toxigenic fungi, primarily Aspergillus and Fusarium spp., either before or after harvest. Despite the application of various management strategies, controlling these pathogens remains a major challenge primarily because of their ability to adapt to environmental changes and selection pressures. Understanding the genetic structure of plant pathogen populations is pivotal for gaining new insights into their biology and epidemiology, as well as for understanding the mechanisms behind their adaptability. Such deeper understanding is crucial for developing effective and preemptive management strategies tailored to the evolving nature of pathogenic populations. This review focuses on the population-level variations within the two most economically significant toxigenic fungal genera according to space, host, and pathogenicity. Outcomes in terms of migration patterns, gene flow within populations, mating abilities, and the potential for host jumps are examined. We also discuss effective yet often underutilized applications of population genetics and genomics to address practical challenges in the epidemiology and disease control of toxigenic fungi.

Genetic and Pathogenic Variability among Isolates of Sporisorium reilianum Causing Sorghum Head Smut

Sporisorium reilianum, the causal agent of sorghum (Sorghum bicolor (L.) Moench) head smut, is present in most sorghum-producing regions. This seed replacement fungal disease can reduce yield by up to 80% in severely infected fields. Management of this disease can be challenging due to the appearance of different pathotypes within the pathogenic population. In this research, the genetic variability and pathogenicity of isolates collected from five Texas Counties was conducted. Due to the lack of available space, 21 out of 32 sequenced isolates were selected and evaluated for virulence patterns on the six sorghum differentials, Tx7078, BTx635, SC170-6-17 (TAM2571), SA281 (Early Hegari), Tx414, and BTx643. The results reveal the occurrence of a new pathotype, 1A, and four previously documented US pathotypes when the 21 isolates were evaluated for virulence patterns on the differentials. The most prevalent was pathotype 5, which was recovered from Brazos, Hidalgo, Nueces, and Willacy Counties, Texas. This pathotype was followed by 1A and 6 in frequency of recovery. Pathotype 4 was identified only from isolates collected from Hidalgo County, while pathotype 1 was from Burleson County, Texas. It appeared that the previous US head smut pathotypes (2 and 3) are no longer common, and the new pathotypes, 1A, 5, and 6, are now predominant. The phylogenetic tree constructed from the single-nucleotide polymorphism (SNP) data through the neighbor-joining method showed high genetic diversity among the tested isolates. Some of the diverse clades among the tested isolates were independent of their sampled locations. Notably, HS37, HS49, and HS65 formed a clade and were classified as 1A in the virulence study, while HS 61 and HS 66, which were collected from Nueces County, were grouped and identified as pathotype 5.

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.

Next-Generation Sequencing Applications for the Study of Fungal Pathogens

Next-generation sequencing (NGS) has become a widely used technology in biological research. NGS applications for clinical pathogen detection have become vital technologies. It is increasingly common to perform fast, accurate, and specific detection of clinical specimens using NGS. Pathogenic fungi with high virulence and drug resistance cause life-threatening clinical infections. NGS has had a significant biotechnological impact on detecting bacteria and viruses but is not equally applicable to fungi. There is a particularly urgent clinical need to use NGS to help identify fungi causing infections and prevent negative impacts. This review summarizes current research on NGS applications for fungi and offers a visual method of fungal detection. With the development of NGS and solutions for overcoming sequencing limitations, we suggest clinicians test specimens as soon as possible when encountering infections of unknown cause, suspected infections in vital organs, or rapidly progressive disease.