Walnut Witches'-Broom Disease Threatens Butternut Restoration Efforts in Indiana.

Due to the devastating effects of butternut canker disease, efforts to protect the endangered butternut (Juglans cinerea) tree through resistance breeding have been a primary focus of forest restoration efforts. Walnut witches'-broom (WWB) disease poses a serious threat to these restoration efforts. This study sought to confirm the presence of the WWB disease phytoplasma, 'Candidatus Phytoplasma pruni', in butternut research plantings in Indiana using molecular methods and Sanger sequencing and to identify butternut families affected by the disease. We also sought to better understand the incidence of the WWB phytoplasma in asymptomatic trees and asymptomatic branches of symptomatic trees to better direct management decisions. Sanger sequencing confirmed the presence of the WWB phytoplasma in the butternut restoration plantings, the first confirmation in Indiana based on sequencing to our knowledge, in both symptomatic and some asymptomatic trees. In addition, the WWB phytoplasma was detected in asymptomatic branches of symptomatic trees, indicating that phytoplasma infection is not necessarily localized to symptomatic tissues in a tree. Trees with positive molecular confirmation of the WWB phytoplasma consisted of 23 different butternut families and one family of Japanese walnut (J. ailantifolia), which is considered to be one of the most susceptible species to WWB disease. Based on these findings, future studies should prioritize identifying the hybridity and pedigrees of families and their susceptibility to WWB disease to aid in butternut restoration efforts.

Phytobiome Metabarcoding: A Tool to Help Identify Prokaryotic and Eukaryotic Causal Agents of Undiagnosed Tree Diseases.

Recent advancements in high-throughput sequencing have provided scientists with vastly enhanced tools to diagnose unknown tree diseases. One of these techniques is referred to as metabarcoding, which uses phylogenetically informative reference genes to taxonomically classify short DNA sequences amplified from environmental samples. Using metabarcoding, we are able to compare the microbiota of symptomatic and asymptomatic (including presumably naïve) samples and identify microbe(s) that are only present in symptomatic samples and could therefore be responsible for the undiagnosed disease. Metabarcoding involves two main steps: library preparation and bioinformatic processing. For library preparation, the appropriate reference gene for the organism of interest (i.e., bacteria, phytoplasma, fungi, or other eukaryotes, such as nematodes) is amplified from the DNA extracted from the environmental samples using PCR and prepared for sequencing. The bioinformatic processing includes four major steps: (1) quality check and cleanup on raw reads; (2) classification of the sequences into taxonomically informative groups (ASVs or OTUs); (3) taxonomy assignments based on the reference database; and (4) differential abundance and diversity analyses to identify microbes that are significantly associated with just symptomatic samples and that point toward the putative causal agent of the disease.