The FABI auditorium was packed full for Alicia Fick’s Prestige Seminar “Elucidation of avocado immune response activation: Insights into NLR regulation and NLR-effector interactions” on 28 February which marked the successful completion of all requirements for her PhD degree. Alicia’s PhD was completed as part of the Avocado Research Programme having joined the group in 2020 to start her MSc degree under the supervision of Prof. Noëlani van den Berg, who was also her primary supervisor for her PhD along with Dr Velushka Swart and Prof. Aureliano Bombarely from the Instituto de Biología Molecular y Celular de Plantas (IBMCP) in Valencia, Spain. The examiners for Alicia’s were Prof. Chae Eunyoung (University of Oxford, UK) and Prof. Ksenia Krasileva (University of California, Berkeley, USA) while Prof. Fourie Joubert was the internal examiner.

Noëlani congratulated Alicia on completing her PhD and said that she was a phenomenal student and that it was an honour to have supervised her for what was “an excellent piece of work”. She said that Alicia had a natural knack for data and computers and would be an excellent bioinformatician.

Alicia’s PhD project sought to understand the mechanisms behind Avocado’s immune response activation in response to pathogen infection by Phytophthora cinnamomi, a hemibiotrophic oomycete. This pathogen causes root necrosis of infected trees, and ultimately leads to plant death, and thus severe economic losses. The ability of plants to successfully activate immune responses and control pathogen infection is mainly governed by the ability of recognising infection, either through the recognition of pathogen-associated molecular patterns, or pathogen effector proteins. The aim of this study was to investigate the transcription factors (TFs) which may contribute to the regulation of Nucleotide-binding Leucine-rich Repeat (NLR) expression during P. cinnamomi infection of a partially-resistant (Dusa®), and -susceptible (R0.12) avocado rootstock, and to develop a method for predicting NLR-effector interactions. A bioinformatics approach was used to create and train an Ensemble machine learning model to classify novel NLR-effector interactions as being true, with high accuracy. This model was published as a downloadable application, which could be used to investigate NLR-effector interactions for any pathosystem type. This research shows the strength of using machine learning models in studying plant-pathogen interactions.