Yevtushenko, Dmytro

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https://hdl.handle.net/10133/6823

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Browsing Yevtushenko, Dmytro by Author "Chatterton, Syama"

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    Combining air sampling and DNA metabarcoding to monitor plant pathogens
    (APS Publications, 2023) Reich, Jonathan; Chen, Wen; Radford, Devon; Turkington, Kelly; Yevtushenko, Dmytro P.; Hamelin, Richard; Chatterton, Syama
    Monitoring the air for airborne plant pathogens is an increasingly common method for the management of economically important plant diseases. In Alberta, Canada, several commodity clusters, including dry bean, canola, potato, and wheat, currently support air monitoring research programs for airborne pathogens of interest. In this study, we assessed the feasibility of monitoring for these, and more, plant fungal pathogens simultaneously using two different sampler types (cyclone versus rotation impaction) and by metabarcoding the ITS1 region using the Illumina sequencing platform. We collected air samples from four geographically distant sites across Alberta and monitored four crop types in southern Alberta. Overall, we found weak, but statistically significant, effects of geographic location and crop type on the aeromycobiota community composition. A few common taxa, such as Ramularia, Alternaria, and Epicoccum, constituted the vast majority of reads across all samples. Nevertheless, in each sample, we identified many plant pathogens of interest and organisms that previous research has found antagonistic to those pathogens, highlighting the utility of these approaches in understanding the pathobiome. In assessing the real-world implications of read counts, we discovered that they were only weakly correlated with spore counts quantified by qPCR. The two types of samplers collected different community profiles, reinforcing the importance of carefully considering which sampler type to use in monitoring programs. Taken together, our results show promise for the future of monitoring the air pathobiome, although much more work is required to understand the relationship of airborne communities to their in-field impact on disease development.
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    Predicting airborne ascospores of Sclerotinia sclerotiorum through machine learning and statistical methods
    (Wiley, 2024) Reich, Jonathan; McLaren, Debra; Kim, Yong Min; Wally, Owen; Yevtushenko, Dmytro P.; Hamelin, Richard; Chatterton, Syama
    A main biological constraint of dry bean (Phaseolus vulgaris) production in Canada is white mould, caused by the fungal pathogen Sclerotinia sclerotiorum. The primary infectious propagules of S. sclerotiorum are airborne ascospores and monitoring the air for inoculum levels could help predict the severity of white mould in bean fields. Daily air samples were collected in commercial dry bean fields in Alberta, Manitoba and Ontario and ascospores were quantified using quantitative PCR. Daily weather data was obtained from in-field weather stations. The number of ascospores on a given day was modelled using 63 different environmental variables and several modelling methods, both regression and classification approaches, were implemented with machine learning (ML) (random forests, logistic regression and support vector machines) and statistical (generalized linear models) approaches. Across all years and provinces, ascospores were most highly correlated with ascospore release from the previous day (r ranged from 0.15 to 0.6). This variable was also the only variable included in all models and had the greatest weight in all models. Models without this variable had much poorer performance than those with it. Correlations of ascospores with other environmental variables varied by province and sometimes by year. A comparison of ML and statistical models revealed that they both performed similarly, but that the statistical models were easier to interpret. However, the precise relationship between airborne ascospore levels and in-field disease severity remains unclear, and spore sampling methods will require further development before they can be deployed as a disease management tool.