Genome-enhanced detection and identification of fungal pathogens responsible for pine and poplar rust diseases.

PLoS One

Forest Sciences Centre, Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada.

Published: November 2019

Biosurveillance is a proactive approach that may help to limit the spread of invasive fungal pathogens of trees, such as rust fungi which have caused some of the world's most damaging diseases of pines and poplars. Most of these fungi have a complex life cycle, with up to five spore stages, which is completed on two different hosts. They have a biotrophic lifestyle and may be propagated by asymptomatic plant material, complicating their detection and identification. A bioinformatics approach, based on whole genome comparison, was used to identify genome regions that are unique to the white pine blister rust fungus, Cronartium ribicola, the poplar leaf rust fungi Melampsora medusae and Melampsora larici-populina or to members of either the Cronartium and Melampsora genera. Species- and genus-specific real-time PCR assays, targeting these unique regions, were designed with the aim of detecting each of these five taxonomic groups. In total, twelve assays were developed and tested over a wide range of samples, including different spore types, different infected plant parts on the pycnio-aecial or uredinio-telial host, and captured insect vectors. One hundred percent detection accuracy was achieved for the three targeted species and two genera with either a single assay or a combination of two assays. This proof of concept experiment on pine and poplar leaf rust fungi demonstrates that the genome-enhanced detection and identification approach can be translated into effective real-time PCR assays to monitor tree fungal pathogens.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364900PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210952PLOS

Publication Analysis

Top Keywords

detection identification
12
fungal pathogens
12
rust fungi
12
genome-enhanced detection
8
pine poplar
8
poplar leaf
8
leaf rust
8
real-time pcr
8
pcr assays
8
rust
5

Similar Publications

Summary: Gene and genome duplications are major evolutionary forces that shape the diversity and complexity of life. However, different duplication modes have distinct impacts on gene function, expression, and regulation. Existing tools for identifying and classifying duplicated genes are either outdated or not user-friendly.

View Article and Find Full Text PDF

Background/aims: Certain sociodemographic groups are routinely underrepresented in clinical trials, limiting generalisability. Here, we describe the extent to which enriched enrolment approaches yielded a diverse trial population enriched for older age in a randomised controlled trial of a blood-based multi-cancer early detection test (NCT05611632).

Methods: Participants aged 50-77 years were recruited from eight Cancer Alliance regions in England.

View Article and Find Full Text PDF

Neurological manifestations associated with human parvovirus B19 (B19V) infections are rare and varied. Acute encephalitis and encephalopathy are the most common, accounting for 38.8% of all neurological manifestations associated with human B19V.

View Article and Find Full Text PDF

The present study aims to better understand the nature of currently circulating GPV strains and their pathological impact on the immune system during natural outbreaks among different duck breeds in Egypt. For this purpose, 99 ducks (25 flocks) of different breeds, aged 14-75 days, were clinically examined, and 75 tissue pools from the thymus, bursa of Fabricius, and spleen were submitted for virus detection and identification. Clinical and postmortem findings were suggestive of GPV infection.

View Article and Find Full Text PDF

Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for the residual strength of CFRP laminates based on machine learning. A diverse dataset is acquired and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!