Biology subjects

Mikaberidze, A.

Publications and source records attributed to Mikaberidze, A..

12 recordsLinked to original sources

bioRxiv2024

A genome-wide association study identifies markers and candidate genes affecting tolerance to the wheat pathogen Zymoseptoria tritici

Plants defend themselves against pathogens using either resistance, measured as the hosts ability to limit pathogen multiplication, or tolerance, measured as the hosts ability to reduce the negative effects of infection. Tolerance is a promising trait for crop breeding, but its genetic basis has rarely been studied and remains poorly understood. Here, we reveal the genetic basis of leaf tolerance to the fungal pathogen Zymoseptoria tritici that causes the globally important septoria tritici blotch disease on wheat. Leaf tolerance to Z. tritici is a quantitative trait that was recently discovered in wheat by using automated image analyses that quantified the symptomatic leaf area and counted the number of pycnidia found on the same leaf. A genome-wide association study identified four chromosome intervals associated with tolerance and a separate chromosome interval associated with resistance. Within these intervals, we identified candidate genes, including wall-associated kinases similar to Stb6, the first cloned STB resistance gene. Our analysis revealed a strong negative genetic correlation between tolerance and resistance to STB, indicative of a trade-off. Such a trade-off between tolerance and resistance would hinder breeding simultaneously for both traits, but our findings suggest a way forward using marker-assisted breeding. We expect that the methods described here can be used to characterize tolerance to other fungal diseases that produce visible fruiting bodies, such as speckled leaf blotch on barley, potentially unveiling conserved tolerance mechanisms shared among plant species.

plant biology↗

bioRxiv2023

Economic analysis of disease and control of multi-field epidemics in agriculture

Epidemics of plant diseases are estimated to cause significant economic losses in crop production. Fungicide applications are widely used to control crop diseases but incur substantial indirect costs. One essential class of indirect costs arises due to the evolution of fungicide resistance. This indirect cost must be estimated reliably to design economic policy for more sustainable use of fungicides. Such estimation is difficult because the cost depends on economic parameters and the evo-epidemiological properties of crop pathogens. Even a conceptual framework for such estimation is missing. To address this problem, we combined a spatially implicit mathematical model of crop epidemics with an economic analysis at the landscape scale. We investigated how the net economic return from a landscape depends on the proportion of fungicide-treated fields. We discovered a pattern of accelerating (or decelerating) returns, contrary to expected diminishing returns. Next, we calculated the economic cost of the evolution of fungicide resistance as the difference between the optimal net return of the landscape in the absence and presence of resistance. We found that this cost depends strongly on the fungicide price, the degree of resistance, the pathogens basic reproduction number and the yield loss due to disease. Surprisingly, the cost declines with the fungicide price and exhibits a non-monotonic pattern as a function of the basic reproduction number. Hence, to calculate the cost, we must estimate these parameters robustly, incorporating variations in environmental conditions, crop varieties and the genetic composition of pathogen populations. Appropriate estimation of the cost of resistance evolution can inform economic policy and encourage more sustainable use of fungicides. Author SummaryFungicides protect crops from diseases and are essential for securing the global food supply, but they incur serious indirect costs to society and the environment. One such cost arises because of the evolution of fungicide resistance. Part of the pathogen population (fungicide-resistant) can gain protection from a fungicide via a genetic change. If fungicide applications continue, the fungicide-resistant subpopulation increases and dominates the population, leading to low efficacy. Resistance can lead to severe economic losses, but no conceptual framework exists for estimating them. We present a novel mathematical framework to estimate the economic costs of fungicide resistance at the landscape scale. We combined an epidemiological model describing disease spread with an economic cost-benefit analysis. Surprisingly, we found that the economic cost of resistance declines for more expensive fungicides. This cost also depends on the pathogens capacity to spread (invasiveness): the cost is highest for pathogens with intermediate invasiveness. Thus, the cost of resistance depends on economic parameters and the biological characteristics of plant diseases. Our findings can inform economic policies for sustainable fungicide use, such as taxes or subsidies. Our paper also contributes to the broader discourse on agricultural sustainability while ensuring global food security.

evolutionary biology↗

bioRxiv2022

How large and diverse are field populations of fungal plant pathogens? The case of Zymoseptoria tritici

Pathogen populations differ in the amount of genetic diversity they contain. Populations carrying higher genetic diversity are thought to have a greater evolutionary potential than populations carrying less diversity. We used published studies to estimate the range of values associated with two critical components of genetic diversity, the number of unique pathogen genotypes and the number of spores produced during an epidemic, for the septoria tritici blotch pathogen Zymoseptoria tritici. We found that wheat fields experiencing typical levels of infection are likely to carry between 3.2 and 14.9 million pathogen genotypes per hectare and produce at least 2.3 to 10.5 trillion pycnidiospores per hectare. Given the experimentally derived mutation rate of 3 x 10-10 substitutions per site per cell division, we estimate that between 28 and 130 million pathogen spores carrying adaptive mutations to counteract fungicides and resistant cultivars will be produced per hectare during a growing season. This suggests that most of the adaptive mutations that have been observed in Z. tritici populations can emerge through local selection from standing genetic variation that already exists within each field. The consequences of these findings for disease management strategies are discussed.

microbiology↗

bioRxiv2021

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.

plant biology↗

bioRxiv2020

A polyetic modelling framework for plant disease emergence

Plant disease emergences have dramatically increased recently as a result of global changes, especially with respect to trade, host genetic uniformity, and climate change. A better understanding of the conditions and processes determining epidemic outbreaks caused by the emergence of a new pathogen, or pathogen strain, is needed to develop strategies and inform decisions to manage emerging diseases. A polyetic process-based model is developed to analyse conditions of disease emergence. This model simulates polycyclic epidemics during successive growing seasons, the yield losses they cause, and the pathogen survival between growing seasons. This framework considers an immigrant strain coming into a system where a resident strain is already established. Outcomes are formulated in terms of probability of emergence, time to emergence, and yield loss, resulting from deterministic and stochastic simulations. An analytical solution to determine a threshold for emergence is also derived. Analyses focus on the effects of two fitness parameters on emergence: the relative rate of reproduction (speed of epidemics), and the relative rate of mortality (decay of population between seasons). Analyses revealed that stochasticity is a critical feature of disease emergence. The simulations suggests that: (1) emergence may require a series of independent immigration events before a successful invasion takes place; (2) an explosion in the population size of the new pathogen (or strain) may be preceded by many successive growing seasons of cryptic presence following an immigration event, and; (3) survival between growing seasons is as important as reproduction during the growing season in determining disease emergence.

ecology↗

bioRxiv2019

Spatially-explicit modeling improves empirical characterization of dispersal: theory and a case study

Dispersal is a key ecological process, but remains difficult to measure. By recording numbers of dispersed individuals at different distances from the source one can acquire a dispersal gradient. Although dispersal gradients contain information on dispersal, they are influenced by the spatial extent of the source. How can we separate the two contributions to extract knowledge on dispersal? One could use a small, point-like source for which a dispersal gradient represents a dispersal kernel, which quantifies the probability of an individual dispersal event from a source to a destination point. However, the validity of this approximation cannot be established before conducting measurements. We formulated a theory that incorporates the spatial extent of sources to estimate dispersal kernels from dispersal gradients. We re-analyzed published dispersal gradients for three major plant pathogens. We also demonstrated using simulations that this approach provides more accurate estimates of dispersal kernels across biologically plausible scenarios. We concluded that the three plant pathogens disperse over substantially shorter distances compared to conventional estimates. Using this method, a significant proportion of published dispersal gradients can be re-analyzed to improve our knowledge about spatial scales of dispersal. Thus, our results can boost progress in characterization of dispersal across taxa.

ecology↗

bioRxiv2019

In-field detection and quantification of Septoria tritici blotch in diverse wheat germplasm using spectral-temporal features

Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding.\n\nWe hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases whilst minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis.\n\nOur results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of >300 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.

plant biology↗

bioRxiv2019

Improved control of Septoria tritici blotch in durum wheat using cultivar mixtures

Mixtures of cultivars with contrasting levels of resistance can suppress infectious diseases in wheat, as demonstrated in numerous field experiments. Most studies focused on airborne pathogens in bread wheat, while splash-dispersed pathogens have received less attention, and no studies have been conducted in durum wheat. We conducted a two-year field experiment in Tunisia, to evaluate the performance of cultivar mixtures with varying proportions of resistance (0-100%) in controlling the polycyclic, splash-dispersed disease Septoria tritici blotch (STB) in durum wheat. To measure STB severity, we used a high-throughput method based on digital image analysis of 3074 infected leaves collected from 42 and 40 experimental plots during the first and second years, respectively. This allowed us to quantify pathogen reproduction on wheat leaves and to acquire a large dataset that exceeds previous studies with respect to accuracy and precision. Our analyses show that introducing only 25% of a disease-resistant cultivar into a pure stand of a susceptible cultivar provides a substantial reduction of almost 50% in disease severity compared to the susceptible pure stand. However, comprising the resistant component of two cultivars instead of one did not further improve disease control, contrary to predictions of epidemiological theory. Susceptible cultivars can be agronomically superior to resistant cultivars or be better accepted by growers for other reasons. Hence, if mixtures with only a moderate proportion of the resistant cultivar provide a similar degree of disease control as resistant pure stands, as our analysis indicates, such mixtures are more likely to be accepted by growers. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/664078v3_ufig1.gif" ALT="Figure 1"> View larger version (100K): org.highwire.dtl.DTLVardef@1d153c3org.highwire.dtl.DTLVardef@1d23ca7org.highwire.dtl.DTLVardef@18c3045org.highwire.dtl.DTLVardef@b504db_HPS_FORMAT_FIGEXP M_FIG C_FIG

plant biology↗

bioRxiv2018

Precision phenotyping reveals novel loci for quantitative resistance to septoria tritici blotch in European winter wheat

O_LIAccurate, high-throughput phenotyping for quantitative traits is the limiting factor for progress in plant breeding. We developed automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance.\nC_LIO_LI335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform a GWAS.\nC_LIO_LIWe identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four resistance traits. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance.\nC_LIO_LIOur experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes.\nC_LI

plant biology↗

bioRxiv2018

Measuring tolerance to a major fungal pathogen in elite wheat cultivars

O_LITolerance and resistance represent two strategies that hosts evolved to protect themselves from pathogens. Tolerance alleviates the reduction in host fitness due to infection without reducing a pathogens growth, while resistance reduces pathogen growth. We investigated tolerance of wheat to the major fungal pathogen Zymoseptoria tritici in 335 elite wheat cultivars. C_LIO_LIWe used a novel digital phenotyping approach that included 11,152 infected leaves and counted 2,069,048 pathogen fruiting bodies. C_LIO_LIWe discovered a new component of tolerance that is based on the relationship between the green area remaining on a leaf and the number of pathogen fruiting bodies. We found a negative correlation between tolerance and resistance among intolerant cultivars, presenting the first compelling evidence for a tradeoff between tolerance and resistance to plant pathogens. Surprisingly, the tradeoff arises due to limits in the host resources available to the pathogen and not due to metabolic constraints, contrary to what ecological theory suggests. C_LIO_LIThe mechanism underlying this tradeoff may be relevant for many plant diseases in which the amount of host resources available to the pathogen can limit the pathogen population. Our analysis indicates that European wheat breeders may have selected for tolerance instead of resistance to an important pathogen. C_LI

ecology↗

bioRxiv2018

Measurement of infection efficiency of a major wheat pathogen using time-resolved imaging of disease progress

Infection efficiency is a key epidemiological parameter that determines the proportion of pathogen spores able to infect and cause lesions once they have landed on a susceptible plant tissue. In this study, we present an improved method to measure infection efficiency of Zymoseptoria tritici using a replicated greenhouse experiment. Z. tritici is a fungal pathogen that infects wheat leaves and causes Septoria tritici blotch (STB), a major disease of wheat worldwide.\n\nWe devised an original experimental setup, where we (i) attached living wheat leaves to metal plates allowing for time-resolved imaging of disease progress in planta. Since lesions were continuously appearing, expanding and merging during the period of up to three weeks, daily measurements were necessary for accurate counting of lesions. We also (ii) used reference membranes to characterize the density and the spatial distribution of inoculated spores on leaf surfaces. In this way, we captured the relationship between the number of lesions and the number of viable spores deposited on the leaves and estimated the infection efficiency of about 4 % from the slope of this relationship.\n\nOur study provides a proof of principle for an accurate and reliable measurement of infection efficiency of Z. tritici. The method opens opportunities for determining the genetic basis of the component of quantitative resistance that suppresses infection efficiency. This knowledge would improve breeding for quantitative resistance against STB, a control measure considered more durable than deployment of major resistance genes.

epidemiology↗

bioRxiv2017

Ranking quantitative resistance to Septoria tritici blotch in elite wheat cultivars using automated image analysis

Quantitative resistance is likely to be more durable than major gene resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier studies hypothesized that resistance affecting the degree of host damage, as measured by the percentage of leaf area covered by STB lesions, is distinct from resistance that affects pathogen reproduction, as measured by the density of pycnidia produced within lesions. We tested this hypothesis using a collection of 335 elite European winter wheat cultivars that was naturally infected by a diverse population of Zymoseptoria tritici in a replicated field experiment. We used automated image analysis (AIA) of 21420 scanned wheat leaves to obtain quantitative measures of conditional STB intensity that were precise, objective, and reproducible. These measures allowed us to explicitly separate resistance affecting host damage from resistance affecting pathogen reproduction, enabling us to confirm that these resistance traits are largely independent. The cultivar rankings based on host damage were different from the rankings based on pathogen reproduction, indicating that the two forms of resistance should be considered separately in breeding programs aiming to increase STB resistance. We hypothesize that these different forms of resistance are under separate genetic control, enabling them to be recombined to form new cultivars that are highly resistant to STB. We found a significant correlation between rankings based on automated image analysis and rankings based on traditional visual scoring, suggesting that image analysis can complement conventional measurements of STB resistance, based largely on host damage, while enabling a much more precise measure of pathogen reproduction. We showed that measures of pathogen reproduction early in the growing season were the best predictors of host damage late in the growing season, illustrating the importance of breeding for resistance that reduces pathogen reproduction in order to minimize yield losses caused by STB. These data can already be used by breeding programs to choose wheat cultivars that are broadly resistant to naturally diverse Z. tritici populations according to the different classes of resistance.

plant biology↗