Long-read sequencing reveals the evolutionary drivers of intra-host diversity across natural RNA mycovirus infections

Predicting pathogen evolution and immune evasion in the age of artificial intelligence

The genomic diversification of viral pathogens during viral epidemics and pandemics represents a major adaptive route for infectious agents to circumvent therapeutic and public health initiatives. Historically, strategies to address viral evolution have relied on responding to emerging variants after their detection, leading to delays in effective public health responses. Because of this, a long-standing yet challenging objective has been to forecast viral evolution by predicting potentially harmful viral mutations prior to their emergence. The promises of artificial intelligence (AI) coupled with the exponential growth of viral data collection infrastructures spurred by the COVID-19 pandemic, have resulted in a research ecosystem highly conducive to this objective. Due to the COVID-19 pandemic accelerating the development of pandemic mitigation and preparedness strategies, many of the methods discussed here were designed in the context of SARS-CoV-2 evolution. However, most of these pipelines were intentionally designed to be adaptable across RNA viruses, with several strategies already applied to multiple viral species. In this review, we explore recent breakthroughs that have facilitated the forecasting of viral evolution in the context of an ongoing pandemic, with particular emphasis on deep learning architectures, including the promising potential of language models (LM). The approaches discussed here employ strategies that leverage genomic, epidemiologic, immunologic and biological information.

Deciphering mixed infections by plant RNA virus and reconstructing complete genomes simultaneously present within-host

Local co-circulation of multiple phylogenetic lineages is particularly likely for rapidly evolving pathogens in the current context of globalisation. When different phylogenetic lineages co-occur in the same fields, they may be simultaneously present in the same host plant (i.e. mixed infection), with potentially important consequences for disease outcome. This is the case in Burkina Faso for the rice yellow mottle virus (RYMV), which is endemic to Africa and a major constraint on rice production. We aimed to decipher the distinct RYMV isolates that simultaneously infect a single rice plant and to sequence their genomes. To this end, we tested different sequencing strategies, and we finally combined direct cDNA ONT (Oxford Nanopore Technology) sequencing with the bioinformatics tool RVhaplo. This method was validated by the successful reconstruction of two viral genomes that were less than a hundred nucleotides apart (out of a genome of 4450nt length, i.e. 2-3%), and present in artificial mixes at a ratio of up to a 99/1. We then used this method to subsequently analyze mixed infections from field samples, revealing up to three RYMV isolates within one single rice plant sample from Burkina Faso. In most cases, the complete genome sequences were obtained, which is particularly important for a better estimation of viral diversity and the detection of recombination events. The method described thus allows to identify various haplotypes of RYMV simultaneously infecting a single rice plant, obtaining their full-length sequences, as well as a rough estimate of relative frequencies within the sample. It is efficient, cost-effective, as well as portable, so that it could further be implemented where RYMV is endemic. Prospects include unravelling mixed infections with other RNA viruses that threaten crop production worldwide.

Recent advances and challenges in plant viral diagnostics

Accurate and timely diagnosis of plant viral infections plays a key role in effective disease control and maintaining agricultural productivity. Recent advances in the diagnosis of plant viruses have significantly expanded our ability to detect and monitor viral pathogens in agricultural crops. This review discusses the latest advances in diagnostic technologies, including both traditional methods and the latest innovations. Conventional methods such as enzyme-linked immunosorbent assay and DNA amplification-based assays remain widely used due to their reliability and accuracy. However, diagnostics such as next-generation sequencing and CRISPR-based detection offer faster, more sensitive and specific virus detection. The review highlights the main advantages and limitations of detection systems used in plant viral diagnostics including conventional methods, biosensor technologies and advanced sequence-based techniques. In addition, it also discusses the effectiveness of commercially available diagnostic tools and challenges facing modern diagnostic techniques as well as future directions for improving informed disease management strategies. Understanding the main features of available diagnostic methodologies would enable stakeholders to choose optimal management strategies against viral threats and ensure global food security.

Discovery of novel RNA viruses through analysis of fungi-associated next-generation sequencing data

BACKGROUND

Like all other species, fungi are susceptible to infection by viruses. The diversity of fungal viruses has been rapidly expanding in recent years due to the availability of advanced sequencing technologies. However, compared to other virome studies, the research on fungi-associated viruses remains limited.

RESULTS

In this study, we downloaded and analyzed over 200 public datasets from approximately 40 different Bioprojects to explore potential fungal-associated viral dark matter. A total of 12 novel viral sequences were identified, all of which are RNA viruses, with lengths ranging from 1,769 to 9,516 nucleotides. The amino acid sequence identity of all these viruses with any known virus is below 70%. Through phylogenetic analysis, these RNA viruses were classified into different orders or families, such as Mitoviridae, Benyviridae, Botourmiaviridae, Deltaflexiviridae, Mymonaviridae, Bunyavirales, and Partitiviridae. It is possible that these sequences represent new taxa at the level of family, genus, or species. Furthermore, a co-evolution analysis indicated that the evolutionary history of these viruses within their groups is largely driven by cross-species transmission events.

CONCLUSIONS

These findings are of significant importance for understanding the diversity, evolution, and relationships between genome structure and function of fungal viruses. However, further investigation is needed to study their interactions.

Physiological variations in hypovirus-infected wild and model long-term laboratory strains of Cryphonectria parasitica

Introduction

Forest ecosystems are highly threatened by the simultaneous effects of climate change and invasive pathogens. Chestnut blight, caused by the invasive phytopathogenic fungus Cryphonectria parasitica, has caused severe damage to European chestnut groves and catastrophic dieback of American chestnut in North America. Within Europe, the impacts of the fungus are widely mitigated through biological control that utilizes the RNA mycovirus: Cryphonectria hypovirus 1 (CHV1). Viral infections, similarly to abiotic factors, can cause oxidative stress in their hosts leading to physiological attrition through stimulating ROS (reactive oxygen species) and NOx production.

Methods

To fully understand the interactions leading to the biocontrol of chestnut blight, it is vital to determine oxidative stress damage arising during CHV1 infection, especially considering that other abiotic factors, like long-term cultivation of model fungal strains, can also impact oxidative stress. Our study compared CHV1-infected C. parasitica isolates from two Croatian wild populations with CHV1-infected model strains (EP713, Euro7 and CR23) that have experienced long-term laboratory cultivation.

Results and Discussion

We determined the level of oxidative stress in the samples by measuring stress enzymes' activity and oxidative stress biomarkers. Furthermore, for the wild populations, we studied the activity of fungal laccases, expression of the laccase gene lac1, and a possible effect of CHV1 intra-host diversity on the observed biochemical responses. Relative to the wild isolates, the long-term model strains had lower enzymatic activities of superoxide dismutase (SOD) and glutathione S-transferase (GST), and higher content of malondialdehyde (MDA) and total non-protein thiols. This indicated generally higher oxidative stress, likely arising from their decades-long history of subculturing and freeze-thaw cycles. When comparing the two wild populations, differences between them in stress resilience and levels of oxidative stress were also observed, as evident from the different MDA content. The intra-host genetic diversity of the CHV1 had no discernible effect on the stress levels of the virus-infected fungal cultures. Our research indicated that an important determinant modulating both lac1 expression and laccase enzyme activity is intrinsic to the fungus itself, possibly related to the vc type of the fungus, i.e., vegetative incompatibility genotype.

How do emerging long-read sequencing technologies function in transforming the plant pathology research landscape?

Long-read sequencing technologies are revolutionizing the sequencing and analysis of plant and pathogen genomes and transcriptomes, as well as contributing to emerging areas of interest in plant-pathogen interactions, disease management techniques, and the introduction of new plant varieties or cultivars. Long-read sequencing (LRS) technologies are progressively being implemented to study plants and pathogens of agricultural importance, which have substantial economic effects. The variability and complexity of the genome and transcriptome affect plant growth, development and pathogen responses. Overcoming the limitations of second-generation sequencing, LRS technology has significantly increased the length of a single contiguous read from a few hundred to millions of base pairs. Because of the longer read lengths, new analysis methods and tools have been developed for plant and pathogen genomics and transcriptomics. LRS technologies enable faster, more efficient, and high-throughput ultralong reads, allowing direct sequencing of genomes that would be impossible or difficult to investigate using short-read sequencing approaches. These benefits include genome assembly in repetitive areas, creating more comprehensive and exact genome determinations, assembling full-length transcripts, and detecting DNA and RNA alterations. Furthermore, these technologies allow for the identification of transcriptome diversity, significant structural variation analysis, and direct epigenetic mark detection in plant and pathogen genomic regions. LRS in plant pathology is found efficient for identifying and characterization of effectors in plants as well as known and unknown plant pathogens. In this review, we investigate how these technologies are transforming the landscape of determination and characterization of plant and pathogen genomes and transcriptomes efficiently and accurately. Moreover, we highlight potential areas of interest offered by LRS technologies for future study into plant-pathogen interactions, disease control strategies, and the development of new plant varieties or cultivars.

Temporal changes in pathogen diversity in a perennial plant-pathogen-hyperparasite system

Hyperparasites can affect the evolution of pathosystems by influencing the stability of both pathogen and host populations. However, how pathogens of perennial hosts evolve in the presence of a hyperparasite has rarely been studied. Here, we investigated temporal changes in genetic diversity of the invasive chestnut blight pathogen Cryphonectria parasitica in the presence of its parasitic mycovirus Cryphonectria hypovirus 1 (CHV1). The virus reduces fungal virulence and represents an effective natural biocontrol agent against chestnut blight in Europe. We analysed genome-wide diversity and CHV1 prevalence in C. parasitica populations in southern Switzerland that were sampled twice at an interval of about 30 years. Overall, we found that both pathogen population structure and CHV1 prevalence were retained over time. The results suggest that recent bottlenecks have influenced the structure of C. parasitica populations in southern Switzerland. Strong balancing selection signals were found at a single vegetative incompatibility (vic) locus, consistent with negative frequency-dependent selection imposed by the vegetative incompatibility system. High levels of mating among related individuals (i.e., inbreeding) and genetic drift are probably at the origin of imbalanced allele ratios at vic loci and subsequently low vc type diversity. Virus infection rates were stable at ~30% over the study period and we found no significant impact of the virus on fungal population diversity. Consequently, the efficacy of CHV1-mediated biocontrol was probably retained.