Expression of a Structural Protein of the Mycovirus FgV-ch9 Negatively Affects the Transcript Level of a Novel Symptom Alleviation Factor and Causes Virus Infection-Like Symptoms in Fusarium graminearum.

Infections of fungi by mycoviruses are often symptomless but sometimes also fatal, as they perturb sporulation, growth, and, if applicable, virulence of the fungal host. Hypovirulence-inducing mycoviruses, therefore, represent a powerful means to defeat fungal epidemics on crop plants. Infection with Fusarium graminearum virus China 9 (FgV-ch9), a double-stranded RNA (dsRNA) chrysovirus-like mycovirus, debilitates Fusarium graminearum, the causal agent of fusarium head blight. In search for potential symptom alleviation or aggravation factors in F. graminearum, we consecutively infected a custom-made F. graminearum mutant collection with FgV-ch9 and found a mutant with constantly elevated expression of a gene coding for a putative mRNA-binding protein that did not show any disease symptoms despite harboring large amounts of virus. Deletion of this gene, named virus response 1 (vr1), resulted in phenotypes identical to those observed in the virus-infected wild type with respect to growth, reproduction, and virulence. Similarly, the viral structural protein coded on segment 3 (P3) caused virus infection-like symptoms when expressed in the wild type but not in the vr1 overexpression mutant. Gene expression analysis revealed a drastic downregulation of vr1 in the presence of virus and in mutants expressing P3. We conclude that symptom development and severity correlate with gene expression levels of vr1 This was confirmed by comparative transcriptome analysis, showing a large transcriptional overlap between the virus-infected wild type, the vr1 deletion mutant, and the P3-expressing mutant. Hence, vr1 represents a fundamental host factor for the expression of virus-related symptoms and helps us understand the underlying mechanism of hypovirulence.IMPORTANCE Virus infections of phytopathogenic fungi occasionally impair growth, reproduction, and virulence, a phenomenon referred to as hypovirulence. Hypovirulence-inducing mycoviruses, therefore, represent a powerful means to defeat fungal epidemics on crop plants. However, the poor understanding of the molecular basis of hypovirulence induction limits their application. Using the devastating fungal pathogen on cereal crops, Fusarium graminearum, we identified an mRNA binding protein (named virus response 1, vr1) which is involved in symptom expression. Downregulation of vr1 in the virus-infected fungus and vr1 deletion evoke virus infection-like symptoms, while constitutive expression overrules the cytopathic effects of the virus infection. Intriguingly, the presence of a specific viral structural protein is sufficient to trigger the fungal response, i.e., vr1 downregulation, and symptom development similar to virus infection. The advancements in understanding fungal infection and response may aid biological pest control approaches using mycoviruses or viral proteins to prevent future Fusarium epidemics.

Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration.

Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and develop effective therapies. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device together with a deep learning tool that we developed for the enhanced-throughput analysis of AxD on microscopic images. The trained convolutional neural network (CNN) sensitively and specifically segmented the features of AxD including axons, axonal swellings, and axonal fragments. Its performance exceeded that of the human evaluators. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected a time-dependent degeneration of axons leading to a decrease in axon area, while axonal swelling and fragment areas increased. Axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network (RNN), we identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration). These findings indicate a morphological heterogeneity of AxD in hemorrhagic stroke. Our EntireAxon platform enables the systematic analysis of axons and AxD in time-lapse microscopy and unravels a so-far unknown intricacy in which AxD can occur in a disease context.