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Mycorrhizal colonization of roots is traditionally evaluated by empirical methods, such as root microscopy. We compared this method with data from using a real time PCR technique, and determined the correlation between methods, indicating particularities of a promising system for a quick and accurate molecular diagnostic of arbuscular mycorrhization., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Crown and root rot is the most important and destructive strawberry diseases in Korea as it causes substantial economic loss. In August 2020, a severe outbreak of crown and root rot on strawberries (Fragaria × ananassa Duch.) was observed in the greenhouse at Sangju, South Korea. Infected plantlets displayed browning rot within the crown and root, stunted growth, and poor rooting., Methods and Results: Thirty fungal isolates were obtained from the affected plantlet. Isolates were identified based on morphological characteristics and pathogenicity test as well as sequence data obtained from internal transcribed spacer, large subunit ribosomal ribonucleic acid, translation elongation factor, and RNA polymerase II-second largest subunit. Results showed that the crown and root rot of strawberry in Korea was caused by three distinct fungal species: Fusarium oxysporum f. sp. fragariae, F. solani, and Plectosphaerella cucumerina. To the best of our knowledge, F. solani, and P. cucumerina are reported for the first time as the causal agents of the crown and root rot of strawberry in South Korea. Pathogenicity tests confirmed that these isolates are pathogenic to strawberry., Conclusions: Understanding the composition and biology of the pathogen population will be helpful to provide effective control strategies for the disease., (© 2021. The Author(s), under exclusive licence to Springer Nature B.V.)

The inclusion of phylogenetic metrics in community ecology has provided insights into important ecological processes, particularly when combined with high-throughput sequencing methods; however, these approaches have not been widely used in studies of fungal communities relative to other microbial groups. Two obstacles have been considered: (1) the internal transcribed spacer (ITS) region has limited utility for constructing phylogenies and (2) most PCR primers that target the large subunit (LSU) ribosomal unit generate amplicons that exceed current limits of high-throughput sequencing platforms. We designed and tested a PCR primer (LR22R) to target approximately 300-400 bp region of the D2 hypervariable region of the fungal LSU for use with the Illumina MiSeq platform. Both in silico and empirical analyses showed that the LR22R-LR3 pair captured a broad range of fungal taxonomic groups with a small fraction of non-fungal groups. Phylogenetic placement of publically available LSU D2 sequences showed broad agreement with taxonomic classification. Comparisons of the LSU D2 and the ITS2 ribosomal regions from environmental samples and known communities showed similar discriminatory abilities of the two primer sets. Together, these findings show that the LR22R-LR3 primer pair has utility for phylogenetic analyses of fungal communities using high-throughput sequencing methods., (Published by Oxford University Press on behalf of FEMS 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Although conventional identification of pathogenic fungi is based on the combination of tests evaluating their morphological and biochemical characteristics, they can fail to identify the less common species or the differentiation of closely related species. In addition these tests are time consuming, labour-intensive and require experienced personnel. We evaluated the feasibility and sufficiency of DNA extraction by Whatman FTA filter matrix technology and DNA sequencing of D1-D2 region of the large ribosomal subunit gene for identification of clinical isolates of 21 yeast and 160 moulds in our clinical mycology laboratory. While the yeast isolates were identified at species level with 100% homology, 102 (63.75%) clinically important mould isolates were identified at species level, 56 (35%) isolates at genus level against fungal sequences existing in DNA databases and two (1.25%) isolates could not be identified. Consequently, Whatman FTA filter matrix technology was a useful method for extraction of fungal DNA; extremely rapid, practical and successful. Sequence analysis strategy of D1-D2 region of the large ribosomal subunit gene was found considerably sufficient in identification to genus level for the most clinical fungi. However, the identification to species level and especially discrimination of closely related species may require additional analysis., (© 2015 Blackwell Verlag GmbH.)

Nuclear large subunit ribosomal DNA is widely used in fungal phylogenetics and to an increasing extent also amplicon-based environmental sequencing. The relatively short reads produced by next-generation sequencing, however, makes primer choice and sequence error important variables for obtaining accurate taxonomic classifications. In this simulation study we tested the performance of three classification methods: 1) a similarity-based method (BLAST + Metagenomic Analyzer, MEGAN); 2) a composition-based method (Ribosomal Database Project naïve bayesian classifier, NBC); and, 3) a phylogeny-based method (Statistical Assignment Package, SAP). We also tested the effects of sequence length, primer choice, and sequence error on classification accuracy and perceived community composition. Using a leave-one-out cross validation approach, results for classifications to the genus rank were as follows: BLAST + MEGAN had the lowest error rate and was particularly robust to sequence error; SAP accuracy was highest when long LSU query sequences were classified; and, NBC runs significantly faster than the other tested methods. All methods performed poorly with the shortest 50-100 bp sequences. Increasing simulated sequence error reduced classification accuracy. Community shifts were detected due to sequence error and primer selection even though there was no change in the underlying community composition. Short read datasets from individual primers, as well as pooled datasets, appear to only approximate the true community composition. We hope this work informs investigators of some of the factors that affect the quality and interpretation of their environmental gene surveys.