A simple method for normalization of DNA extraction to improve the quantitative detection of soil-borne plant pathogenic oomycetes by real-time PCR

Occurrence of Ascospores and White Mold Caused by Sclerotinia sclerotiorum in Dry Bean Fields in Alberta, Canada

White mold caused by the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary is one of the most important biological constraints to dry bean (Phaseolus vulgaris L.) production in Canada. Disease forecasting is one tool that could help growers manage the disease while reducing fungicide use. However, predicting white mold epidemics has remained difficult due to their sporadic occurrence. In this study, over the course of four growing seasons (2018 to 2021), we surveyed dry bean fields in Alberta and collected daily in-field weather data and daily in-field ascospore counts. White mold levels were variable and generally high in all years, confirming that the disease is ubiquitous and a constant threat to dry bean production. Ascospores were present throughout the growing season, and mean ascospore levels varied by field, month, and year. Models based on in-field weather and ascospore levels were not highly predictive of final disease incidence in a field, suggesting that environment and pathogen presence were not limiting factors to disease development. Rather, significant effects of market class on disease were found, with pinto beans, on average, having the highest disease incidence (33%) followed by great northern (15%), black (10%), red (6%), and yellow (5%). When incidence of these market classes was modeled separately, different environmental variables were important in each model; however, average wind speed was a significant variable in all models. Taken together, these findings suggest that white mold management in dry bean should focus on fungicide use, plant genetics, irrigation management, and other agronomic factors.

Improvement and application of qPCR assay revealed new insight on early warning of Phaeocystis globosa bloom

Phaeocystis globosa bloom develops from its early solitary cells, providing clues for early warning of its bloom and timely responding to possible consequences. However, the early prediction requires quantification of the solitary cells for a thorough understanding of bloom formation. Therefore, we developed an accurate, sensitive, and specific qPCR assay for this need. Results show that the accuracy of qPCR was significantly enhanced by ameliorating DNA barcode design, improving genomic DNA extraction, and introducing a strategy of internal amplification control (IAC). This approach reached a quantification limit of 1 cell/reaction, making low-abundance cells (10¹-10³ cells/L) detection possible, and we also observed a plunge in the abundance of the solitary cells before the bloom outbreak in two winters in 2019 and 2020 for the first time, which is quite unique from laboratory results showing an increase instead. The plunge in solitary-cell abundance might be associated with the attachment of solitary cells to solid matrices to form non-solitary attached aggregate, the precursor of colonies, which gains supports from other studies and needs more investigations in the future. Therefore, as the plunge in solitary-cell abundance is a sign of colony formation, it can be used as an early warning indicator to P. globosa bloom.

Genetic variation among einkorn genotypes based on gene targeted functional markers and its possible relationship with drought tolerance at seed germination stage

BACKGROUND

Einkorn wheat is one of the first primary genetic resources for discovery of new alleles related to biotic and abiotic stress tolerances for further genetic improvements while it became more popular especially for its native grain status as healthy food resources. Therefore, this study aims to determine germination parameters of 32 local einkorn (Triticum monococcum) genotypes collected from Kastamonu and its vicinity, Turkey under drought stress conditions, and to reveal the genetic relationship of those genotypes based on drought related gene targeted functional markers (GTFMs).

METHODS AND RESULTS

Germination test was conducted at 20 ± 0.5 °C in darkness with randomized complete block design with 4 replications. Seeds placed on double filter papers in a covered glass petri dishes (80 × 15 mm) were treated with polyethylene glycol (PEG₆₀₀₀) with a -0.23 MPa. The final germination percentages (FGPs), speed and span of germination parameters were determined. Drought stress severely worsened all germination parameters measured. The genotypes SG24 and SG12 were the most drought tolerant and sensitive genotypes based on 21.1% and 64.8% of reduction rates in FGPs, respectively. Twelve GTFMs produced a total of 32 bands and 26 of them were polymorphic. The mean polymorphism ratio of the markers and average band per marker were determined as 86.31% and 2.66, respectively. The highest polymorphism information content (PIC) was obtained from AIB1 gene marker (0.875).

CONCLUSIONS

This study revealed that there was a significant genetic variation for drought tolerance levels of local einkorn wheat genotypes at germination stage and drought related GTFMs can be used not only to reveal genetic variation but also to distinguish the drought tolerant genotypes.

LAMP Detection of Four Plant-Pathogenic Oomycetes and Its Application in Lettuce Fields

In Kagawa Prefecture, Japan, the pathogens Phytophthora pseudolactucae, Pythium irregulare, Pythium uncinulatum, and Pythium spinosum have caused huge losses in lettuce production. We used loop-mediated isothermal amplification (LAMP) to analyze soil and plants in lettuce fields for the presence of these four pathogens. To develop an effective on-site detection method, we contrasted the Plant-LAMP and Plant Culture-LAMP procedures for plant samples, and five soil DNA extraction methods for soil samples. Plant-LAMP and a Soil DNA Isolation kit were selected to analyze three fields for the pathogen species present, infected sites, and level of soil contamination. We found that the same wilting symptoms could be caused by Phytophthora or Pythium, or a mixture of species from both genera. Ph. pseudolactucae infects the pith of the lettuce in aboveground parts, whereas Pythium spp. mainly infect roots. Ph. pseudolactucae and Py. uncinulatum caused disease more frequently than the other two pathogens. Furthermore, not all of the pathogens existed in the soil near infected lettuce plants. Therefore, the LAMP method can be used to diagnose pathogenic oomycetes in the field, and will be useful in the development of control strategies in lettuce production.