Evaluation and Control of <i>Alternaria tenuissima</i> Causing Leaf Spots in Blue Honeysuckle in China

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Abstract

Blue honeysuckle is emerging as a popular edible fruit and is also used in folk medicine. However, from June to August 2021, a serious leaf-spot disease affected the yield and quality of blue honeysuckle in Harbin, Heilongjiang Province, China; the species and characteristics of the pathogens responsible for the disease were unknown. In this study, 30 fungal isolates were obtained from infected blue honeysuckle leaves and identified as Alternaria tenuissima based on morphological and molecular characteristics and phylogenetic analyses. To the best of our knowledge, this is one of the first studies to identify A. tenuissima as the causal agent of blue honeysuckle leaf spots in China. Pathogenicity tests of the isolates revealed that most isolates were moderately pathogenic. All blue honeysuckle cultivars tested were found to be susceptible to 30 A. tenuissima isolates. In addition, elder, Dahurian rose fruit, sea buckthorn, rowan, hawthorn, bird cherry, and sorb could be infected by A. tenuissima isolates, while European cranberry bush and Nanking cherry were not infected. A. tenuissima isolates were highly sensitive to prochloraz (EC ₅₀ ≤ 0.50 μg ml ^–1) with 86.21% efficacy at 400 μg ml ^–1 in the field trials. Therefore, crop rotation and application of chemical fungicides are considered to control the disease-causing leaf spots in blue honeysuckle. These results provide a basis for controlling A. tenuissima in blue honeysuckle in China.

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Fredrik Ronquist, Maxim Teslenko, Paul van der Mark … (2012)

Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.

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AMPLIFICATION AND DIRECT SEQUENCING OF FUNGAL RIBOSOMAL RNA GENES FOR PHYLOGENETICS

T.J White, T. Bruns, S. Lee … (1990)

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PhyloSuite: An integrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies

Dong Dong Zhang, Fangluan Gao, Ivan Jakovlić … (2019)

Multigene and genomic data sets have become commonplace in the field of phylogenetics, but many existing tools are not designed for such data sets, which often makes the analysis time-consuming and tedious. Here, we present PhyloSuite, a (cross-platform, open-source, stand-alone Python graphical user interface) user-friendly workflow desktop platform dedicated to streamlining molecular sequence data management and evolutionary phylogenetics studies. It uses a plugin-based system that integrates several phylogenetic and bioinformatic tools, thereby streamlining the entire procedure, from data acquisition to phylogenetic tree annotation (in combination with iTOL). It has the following features: (a) point-and-click and drag-and-drop graphical user interface; (b) a workplace to manage and organize molecular sequence data and results of analyses; (c) GenBank entry extraction and comparative statistics; and (d) a phylogenetic workflow with batch processing capability, comprising sequence alignment (mafft and macse), alignment optimization (trimAl, HmmCleaner and Gblocks), data set concatenation, best partitioning scheme and best evolutionary model selection (PartitionFinder and modelfinder), and phylogenetic inference (MrBayes and iq-tree). PhyloSuite is designed for both beginners and experienced researchers, allowing the former to quick-start their way into phylogenetic analysis, and the latter to conduct, store and manage their work in a streamlined way, and spend more time investigating scientific questions instead of wasting it on transferring files from one software program to another.

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Author and article information

Contributors

Jinxin Liu: (View ORCID Profile)

Yonggang Li: (View ORCID Profile)

Journal

Title: Plant Disease

Abbreviated Title: Plant Disease

Publisher: Scientific Societies

ISSN (Print): 0191-2917

ISSN (Electronic): 1943-7692

Publication date Created: May 01 2024

Publication date (Print): May 01 2024

Volume: 108

Issue: 5

Pages: 1270-1277

Affiliations

[1 ]College of Plant Protection, Northeast Agricultural University, Harbin 150030, China

[2 ]National-Local Joint Engineering Research Center for Development and Utilization of Small Fruits in Cole Regions, Harbin 150030, China

[3 ]Heilongjiang Agricultural Economy Vocational College, Mudanjiang 157041, China

Article

DOI: 10.1094/PDIS-07-23-1470-RE

SO-VID: 6c6eb3ad-dc82-4be0-a373-6e89d79b3bff

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