Phylogenetic Triage and Risk Assessment: How to Predict Emerging Phytoplasma Diseases

Katrin Janik; Bernd Panassiti; Christine Kerschbamer; Johannes Burmeister; Valeria Trivellone

doi:10.3390/biology12050732

Phylogenetic Triage and Risk Assessment: How to Predict Emerging Phytoplasma Diseases

Biology (Basel). 2023 May 17;12(5):732. doi: 10.3390/biology12050732.

Authors

Katrin Janik¹, Bernd Panassiti², Christine Kerschbamer¹, Johannes Burmeister³, Valeria Trivellone⁴

Affiliations

¹ Laimburg Research Centre, Functional Genomics, Laimburg 6-Pfatten (Vadena), 39040 Auer, South Tyrol, Italy.
² Independent Researcher, D-81543 Munich, Germany.
³ Institute for Organic Farming, Soil and Resource Management, Bavarian State Research Center for Agriculture, 85354 Freising, Germany.
⁴ Illinois Natural History Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.

Abstract

Phytoplasma diseases pose a substantial threat to diverse crops of agricultural importance. Management measures are usually implemented only after the disease has already occurred. Early detection of such phytopathogens, prior to disease outbreak, has rarely been attempted, but would be highly beneficial for phytosanitary risk assessment, disease prevention and mitigation. In this study, we present the implementation of a recently proposed proactive disease management protocol (DAMA: Document, Assess, Monitor, Act) for a group of vector-borne phytopathogens. We used insect samples collected during a recent biomonitoring program in southern Germany to screen for the presence of phytoplasmas. Insects were collected with malaise traps in different agricultural settings. DNA was extracted from these mass trap samples and subjected to PCR-based phytoplasma detection and mitochondrial cytochrome c oxidase subunit I (COI) metabarcoding. Phytoplasma DNA was detected in two out of the 152 insect samples analyzed. Phytoplasma identification was performed using iPhyClassifier based on 16S rRNA gene sequence and the detected phytoplasmas were assigned to 'Candidatus Phytoplasma asteris'-related strains. Insect species in the sample were identified by DNA metabarcoding. By using established databases, checklists, and archives, we documented historical associations and records of phytoplasmas and its hosts in the study region. For the assessment in the DAMA protocol, phylogenetic triage was performed in order to determine the risk for tri-trophic interactions (plant-insect-phytoplasma) and associated disease outbreaks in the study region. A phylogenetic heat map constitutes the basis for risk assessment and was used here to identify a minimum number of seven leafhopper species suggested to be monitored by stakeholders in this region. A proactive stance in monitoring changing patterns of association between hosts and pathogens can be a cornerstone in capabilities to prevent future phytoplasma disease outbreaks. To the best of our knowledge, this is the first time that the DAMA protocol has been applied in the field of phytopathology and vector-borne plant diseases.

Keywords: Bavaria; DAMA protocol; archives; aster yellow; bio-inventories; geographic distribution; metabarcoding; phytopathogens; risk evaluation; risk heat map.

Grants and funding

This research was partially supported by the Bavarian State Ministry of Food, Agriculture and Forestry (StMELF). The work was partially supported by the Autonomous Province of Bozen/Bolzano, Italy and the South Tyrolean Apple Consortium (APPLiv, awarded to Katrin Janik). The authors thank the Department of Innovation, Research, University and Museums of the Autonomous Province of Bozen/Bolzano for covering the Open Access publication costs.