Gremmeniella abietina DAOM 170408 v1.0
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Scleroderris canker of pine and spruce (Gremmeniella abietina)
Image Credit: USDA Forest Service - Forest Health Protection - St. Paul , USDA Forest Service, Bugwood.org.
Licensed under a Creative Commons Attribution 3.0 License.
Scleroderris canker of pine and spruce (Gremmeniella abietina)
Image Credit: Robert L. Anderson, USDA Forest Service, Bugwood.org.
Licensed under a Creative Commons Attribution 3.0 License.
Pycnidia of Gremmeniella abietina lined with conidia colored in orange on this confocal microscopic image.
Pycnidia of Gremmeniella abietina lined with conidia colored in orange on this confocal microscopic image.
Image credit: Martine Blais, Laurentian Forestry Centre, Natural Resources Canada

Scleroderris canker, caused by the Ascomycete Gremmeniella abietina, is a disease of conifers that can cause severe defoliation and lethal stem cankers. It comprises multiple races that differ in their pathogenicity and epidemiology. The European race is more aggressive and is widespread in Europe and North America and can cause severe outbreak and mortality on large trees while the North America race attacks young pines only in North America. The European race reproduces via asexual conidia produced in pycnidia while the North American race produces ascospores in apothecia. The European race was first introduced to North America in the 1950s and has since been regulated by quarantines in Canada and the USA. One of the challenges of preventing the spread of these pathogens is that they can have endophytic stages and therefore be transmitted on asymptomatic seedlings. Sequencing the genomes of these pathogens will greatly accelerate our understanding of their epidemiology and biology and will help develop tools to better detect, identify and monitor outbreaks. The genome sequencing of Gremmeniella abietina is part of a larger effort, the Pathobiome Database For Bioenergy Trees Project that aims to sequence the genomes of multiple pathogens that share the same host trees in order to identify common and unique genomic signatures. This data will be used to create a database that will help the development of tools for the detection, monitoring and surveillance of pathogens in these economically and ecologically important trees.