The serial and generation intervals from SARS-CoV-2 transmission dynamics and their potential application in the epidemiology of two citrus diseases

<p>Since the start of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the coronavirus disease 2019 (COVID-19) pandemic, the concepts of serial and generation intervals have been used as key epidemiological measures to understand the transmission dynamics of the disease. We carefully examined and repurposed these concepts to the understanding of the transmission chain and dynamics of two major citrus diseases: tristeza virus (caused by Citrus tristeza virus, CTV) and Huanglongbing (caused by <em>Candidatus</em> Liberibacter asiaticus). Following the fundamental definition of the concepts, the review delineates the transmission chain in the SARS-CoV-2 and that of CTV and CLas, pointing out their major similarities and differences. Then, it discusses estimation of the serial and generation intervals and their distributions for both plant diseases. Identification of infector-infectee tree pairs in a transmission chain within orchards is proposed through use of disease incidence data from intensive mapping, spatial pattern analysis, conditional probability, and simulation approaches. Like in SARS-CoV-2 dynamics, pre-symptomatic transmission in these two plant pathosystems is of epidemiological significance. Hence, estimation of the serial and generation interval can lay the foundations to understanding of early disease transmission dynamics, thus the implementation of vector control measures or eradication of infected trees. We hope this review motivates discussions on estimation and usage of these concepts to enhance understanding of the epidemiology of both of the herein examined citrus diseases.</p>

Untangling the Pea Root Rot Complex Reveals Microbial Markers for Plant Health

Plant health is recognised as a key element to ensure global food security. While plant breeding has substantially improved crop resistance against individual pathogens, it showed limited success for diseases caused by the interaction of multiple pathogens such as root rot in pea (Pisum sativum L.). To untangle the causal agents of the pea root rot complex and determine the role of the plant genotype in shaping its own detrimental or beneficial microbiome, fungal and oomycete root rot pathogens, as well as previously identified beneficials, i.e., arbuscular mycorrhizal fungi (AMF) and Clonostachys rosea, were qPCR quantified in diseased roots of eight differently resistant pea genotypes grown in four agricultural soils under controlled conditions. We found that soil and pea genotype significantly determined the microbial compositions in diseased pea roots. Despite significant genotype x soil interactions and distinct soil-dependent pathogen complexes, our data revealed key microbial taxa that were associated with plant fitness. Our study indicates the potential of fungal and oomycete markers for plant health and serves as a precedent for other complex plant pathosystems. Such microbial markers can be used to complement plant phenotype- and genotype-based selection strategies to improve disease resistance in one of the world’s most important pulse crops of the world.

Robust Cre recombinase activity in the biotrophic smut fungus Ustilago maydis enables efficient conditional null mutants in planta

Site-specific recombinases have been used in higher eukaryotes, especially in animals, for a broad range of applications, including chromosomal translocations, large deletions, site-specific integration, and tissue-specific as well as conditional knock-outs. The application of site-specific recombination has also been demonstrated in simple eukaryotes like fungi and protozoa. However, its use in fungal research, especially in phytopathogenic fungi, has often been limited to “recycle” the marker genes used in transformation experiments. We show that Cre recombinase can be used for conditional gene deletions in the phytopathogenic fungus Ustilago maydis. Conditional gene knock-outs can be generated via the transcriptional control of the recombinase by U. maydis promoters specifically activated during the biotrophic phase of fungal growth, enabling gene deletions at defined developmental stages inside the plant tissue. Also, we show that a tamoxifen-activated Cre-recombinase allows the tight control necessary for the induced deletion of essential genes by the addition of tamoxifen. These tools will be helpful to address the function of genes under both axenic and in planta conditions for the U. maydis-maize pathosystem and should pave the way for similar approaches in other plant pathosystems.

Measuring splash-dispersal of a major wheat pathogen in the field

Capacity for dispersal is a fundamental fitness component of plant pathogens. Empirical characterization of plant pathogen dispersal is of prime importance for understanding how plant pathogen populations change in time and space. We measured dispersal of Zymoseptoria tritici in natural environment. Primary disease gradients were produced by rain-splash driven dispersal and subsequent transmission via asexual pycnidiospores from infected source. To achieve this, we inoculated field plots of wheat (Triticum aestivum) with two distinct Z. tritici strains and a 50/50 mixture of the two strains. We measured effective dispersal of the Z. tritici population based on pycnidia counts using automated image analysis. The data were analyzed using a spatially-explicit mathematical model that takes into account the spatial extent of the source. We employed robust bootstrapping methods for statistical testing and adopted a two-dimensional hypotheses test based on the kernel density estimation of the bootstrap distribution of parameter values. Genotyping of re-isolated pathogen strains with strain-specific PCR-reaction further confirmed the conclusions drawn from the phenotypic data. The methodology presented here can be applied to other plant pathosystems.We achieved the first estimates of the dispersal kernel of the pathogen in field conditions. The characteristic spatial scale of dispersal is tens of centimeters – consistent with previous studies in controlled conditions. Our estimation of the dispersal kernel can be used to parameterize epidemiological models that describe spatial-temporal disease dynamics within individual wheat fields. The results have the potential to inform spatially targeted control of crop diseases in the context of precision agriculture.

A novel in silico method for the molecular mimicry detection finds a formin with the potential to manipulate maize cell cytoskeleton

Molecular mimicry is one of the evolutionary strategies that parasites use to manipulate the host metabolism and perform an effective infection. This phenomenon has been observed in several animal and plant pathosystems. Despite the relevance of this mechanism in pathogenesis, little is known about it in fungus-plant interactions. For that reason, we performed an in silico method to select plausible mimicry candidates for the Ustilago maydis-maize interaction. Our methodology uses a tripartite sequence comparison between the parasite, the host and non-parasitic organisms’ genomes. Furthermore, we use RNA-seq information to identify gene co-expression, and we determine subcellular localization to detect potential cases of co-localization in the imitator-imitated pairs. With these approximations, we found a putative extracellular formin in U. maydis with the potential to rearrange the host cell cytoskeleton. In parallel, we detect at least two maize genes involved in the cytoskeleton rearrangement differentially expressed under U. maydis infection; thus, this find increases the expectation for the potential mimicry role of the fungal protein. The use of several sources of data led us to develop a strict and replicable in silico methodology to detect molecular mimicry in pathosystems with enough information available. Furthermore, this is the first time that a genome-wide search has been performed to detect molecular mimicry in a U. maydis-maize system. Additionally, to allow the reproducibility of this experiment and the use of this pipeline, we create a Web server called Molecular mimicry finder, available in https://bioquimio.udla.edu.ec/molecular-mimicry/

Hyperlocal Variation in Soil Iron and the Rhizosphere Bacterial Community Determines Dollar Spot Development in Amenity Turfgrass

ABSTRACT Dollar spot, caused by the fungal pathogen Clarireedia spp., is an economically important foliar disease of amenity turfgrass in temperate climates worldwide. This disease often occurs in a highly variable manner, even on a local scale with relatively uniform environmental conditions. The objective of this study was to investigate mechanisms behind this local variation, focusing on contributions of the soil and rhizosphere microbiome. Turfgrass, rhizosphere, and bulk soil samples were collected from within a 256-m2 area of healthy turfgrass, transported to a controlled environment chamber, and inoculated with Clarireedia jacksonii. Bacterial communities were profiled by targeting the 16S rRNA gene, and 16 different soil chemical properties were assessed. Despite their initial uniform appearance, the samples differentiated into highly susceptible and moderately susceptible groups following inoculation in the controlled environment chamber. The highly susceptible samples harbored a unique rhizosphere microbiome with suggestively lower relative abundance of putative antibiotic-producing bacterial taxa and higher predicted abundance of genes associated with xenobiotic biodegradation pathways. In addition, stepwise regression revealed that bulk soil iron content was the only significant soil characteristic that positively regressed with decreased dollar spot susceptibility during the peak disease development stage. These findings suggest that localized variation in soil iron induces the plant to select for a particular rhizosphere microbiome that alters the disease outcome. More broadly, further research in this area may indicate how plot-scale variability in soil properties can drive variable plant disease development through alterations in the rhizosphere microbiome. IMPORTANCE Dollar spot is the most economically important disease of amenity turfgrass, and more fungicides are applied targeting dollar spot than any other turfgrass disease. Dollar spot symptoms are small (3 to 5 cm), circular patches that develop in a highly variable manner within plot scale even under seemingly uniform conditions. The mechanism behind this variable development is unknown. This study observed that differences in dollar spot development over a 256-m2 area were associated with differences in bulk soil iron concentration and correlated with a particular rhizosphere microbiome. These findings provide interesting avenues for future research to further characterize the mechanisms behind the highly variable development of dollar spot, which may inform innovative control strategies. Additionally, these results suggest that small changes in soil properties can alter plant activity and hence the plant-associated microbial community, which has important implications for a broad array of agricultural and horticultural plant pathosystems.

The PhoPQ Two-Component System Is the Major Regulator of Cell Surface Properties, Stress Responses and Plant-Derived Substrate Utilisation During Development of Pectobacterium versatile-Host Plant Pathosystems

Pectobacterium versatile (formerly P. carotovorum) is a recently defined species of soft rot enterobacteria capable of infecting many plant hosts and damaging different tissues. Complex transcriptional regulation of virulence properties can be expected for such a versatile pathogen. However, the relevant information is available only for related species and is rather limited. The PhoPQ two-component system, originally described in pectobacteria as PehRS, was previously shown to regulate a single gene, pehA. Using an insertional phoP mutant of Pectobacterium versatile (earlier—P. carotovorum), we demonstrate that PhoP regulates at least 115 genes with a majority of them specific for pectobacteria. The functions performed by PhoP-controlled genes include degradation, transport and metabolism of plant-derived carbon sources (polygalacturonate, arabinose-containing polysaccharides and citrate), modification of bacterial cell envelope and stress resistance. We also demonstrated PhoP involvement in establishing the order of plant cell wall decomposition and utilisation of the corresponding breakdown products. Based on experimental data and in silico analysis, we defined a PhoP binding site motif and provided proof for its universality in enteric bacteria. Scanning P. versatile genome for the locations of this motif suggested a much larger PhoP regulon enriched with the genes important for a plant pathogen, which makes PhoP a global virulence regulator. Potential PhoP targets include many regulatory genes and PhoP control over one of them, expI, was confirmed experimentally, highlighting the link between the PhoPQ two-component and quorum sensing systems. High concentrations of calcium and magnesium ions were found to abolish the PhoPQ-dependent transcription activation but did not relieve repression. Reduced PhoP expression and minimisation of PhoP dependence of regulon members’ expression in P. versatile cells isolated from potato tuber tissues suggest that PhoPQ system is a key switch of expression levels of multiple virulence-related genes fine-tuned to control the development of P. versatile-host plant pathosystem.

Indicator Species Analysis: A Useful Tool for Plant Disease Studies

Indicator species analysis (ISA) uses indices of an organism’s relative abundance and occurrence to estimate the strength of its associations with a priori groups of interest and a simple randomization test to evaluate the probability of association. Because ISA values tend to be greatest when a species is both relatively more abundant than other species in a particular group and it occurs more frequently in that same group (the expectations of a causal agent in diseased plants), ISA should be useful for identifying and narrowing the list of potential causal agents from a pool of pathogens in both emerging plant diseases and when the causal agent is unclear. Recent ISA plant disease applications suggests it may either directly identify a single causal agent from a pool of potential pathogens or narrow the pool of pathogens as candidates for pathogenicity tests in the process of fulfilling Koch’s postulates. In this letter, we explain the underpinnings of ISA, summarize the known applications to plant pathosystems, offer caveats about the analysis, and suggest scenarios where ISA may be broadly applicable for plant disease studies.

Hyperlocal Variation in Soil Iron and Rhizosphere Microbiome Determines Disease Development in Amenity Turfgrass

ABSTRACTDollar spot, caused by the fungal pathogen Clarireedia spp., is an economically important disease of amenity turfgrass in temperate climates worldwide. This disease often occurs in a highly variable manner, even on a local scale with relatively uniform environmental conditions. The objective of this study was to investigate mechanisms behind this local variation, focusing on contributions of the soil and rhizosphere microbiome. Turfgrass, rhizosphere, and bulk soil samples were taken from within a 256 m2 area of healthy turfgrass, transported to a controlled environment chamber, and inoculated with C. jacksonii. Bacterial communities were profiled targeting the 16s rRNA gene, and 16 different soil chemical properties were assessed. Despite their initial uniform appearance, the samples differentiated into highly susceptible and moderately susceptible groups following inoculation in the controlled environment chamber. The highly susceptible samples harbored a unique rhizosphere microbiome with lower relative abundance of antibiotic-producing bacterial taxa and higher predicted abundance of genes associated with xenobiotic biodegradation pathways. In addition, stepwise regression revealed that bulk soil iron content was the only significant soil characteristic that positively regressed with decreased dollar spot susceptibility during the peak disease development stage. These findings suggest that localized variation in soil iron induces the plant to select for a particular rhizosphere microbiome that alters the disease outcome. More broadly, further research in this area may indicate how plot-scale variability in soil properties can drive variable plant disease development through alterations in the rhizosphere microbiome.IMPORTANCEDollar spot is the most economically important disease of amenity turfgrass, and more fungicides are applied targeting dollar spot than any other turfgrass disease. Dollar spot symptoms are small (3-5 cm), circular patches that develop in a highly variable manner within plot-scale even under seemingly uniform conditions. The mechanism behind this variable development is unknown. This study observed that differences in dollar spot development over a 256 m2 area were associated with differences in bulk soil iron concentration and correlated with a particular rhizosphere microbiome. These findings provide important clues for understanding the mechanisms behind the highly variable development of dollar spot, which may offer important clues for innovative control strategies. Additionally, these results also suggest that small changes in soil properties can alter plant activity and hence the plant-associated microbial community which has important implications for a broad array of important agricultural and horticultural plant pathosystems.

Phenotyping Austrian Pine for Resistance Using Fourier-Transform Infrared Spectroscopy

Austrian pine (Pinus nigra) is a valuable component of the urban landscape in the Midwestern USA. In this area, it is impacted by the fungal pathogen Diplodia sapinea, which causes a tip blight and canker on infected trees. While the disease can be managed through the application of fungicides and/or by preventing environmental conditions that are favorable for the pathogen, these practices only temporarily alleviate the problem. A more sustainable solution is to use resistant trees. The objective of this study was to evaluate whether Fourier-transform infrared (FT-IR) spectroscopy combined with chemometric analysis can distinguish between trees that vary in susceptibility to D. sapinea. Trees were phenotyped for resistance to D. sapinea by artificially inoculating shoots and measuring ensuing lesions seven days following inoculation. Then, three different chemometric approaches, including a type of machine learning called support vector machine (SVM), were used to evaluate whether or not trees that varied in susceptibility could be distinguished. Trees that varied in susceptibility could be discriminated based on FT-IR spectra collected prior to pathogen infection using the three chemometric approaches: soft independent modeling of class analogy, partial least squares regression, and SVM. While further validation of the predictive models is needed, the results suggest that the approach may be useful as a tool for screening and breeding Austrian pine for resistance to D. sapinea. Furthermore, this approach may have wide applicability in other tree/plant pathosystems of concern and economic value to the nursery and ornamental industries.