SUPERVISOR: Juliane DOHM

PROJECT ASSIGNED TO: Daniela WÖBER

The sugar beet weevil (Asproparthenis punctiventris) is a major pest in sugar beet cultivation, as it feeds on the leaves of young sugar beet plants during its maturation. The combined effects of climate change, the ban of plant protection products, and the absence of suitable alternatives for effective pest control have led to more frequent pest outbreaks resulting in reduced sugar beet cultivation area and yield loss. In this dissertation project high-throughput sequencing data (RNA, DNA) are generated and bioinformatics approaches (transcriptomics, genomics, metagenomics, machine learning) are applied to develop and support sugar beet plant protection measures against the sugar beet weevil. The main objectives are:

  1. The development of a reference genome and gene set of the sugar beet weevil: Comprehensive molecular resources are generated for the identification of regulatory key genes and potential population-specific genetic differences. Additionally, data from related or co-occurring species will be used for comparative analyses. This objective also aims for the identification of genes that could potentially serve as targets for RNA interference (RNAi), a natural biological process in eukaryotes that is lately used as novel molecular genetic technique for plant protection.

  2. The analysis of the gut microbiome of the sugar beet weevil: To identify microbial differences in the gut microbiome of weevils, both infected and uninfected by the entomopathogenic fungus Metarhizium brunneum are compared. The analysis of associated bacteria or fungi may identify mycosis-related microbes as potential improvement for the development of biopesticides against the sugar beet weevil.

  3. The elaboration of ML-based models for predicting pest infestation level: Multi-modal data will be analysed to predict to predict the annual occurrence of the sugar beet weevil, including its prevalence in previous years, climate, soil humidity and (soil) biodiversity. 

Figure 1: Overview of the PhD project. TRANSCRIPTOMICS & GENOMICS: Identification of putative RNAi-related genes and comparison of genetic resources with NON-TARGET ORGANISMS. SUGAR BEET WEEVIL: Identification of bio-indicators related to M. brunneum via a gut MICROBIOMICS analysis. PREDICTIVE MODELLING: Prediction of the annual occurrence of the sugar beet weevil through the optimization of multi-modal prediction models.

This dissertation is part of a collaborative research project of the AIT Austrian Institute of Technology – where I am co-supervised by Dr. Eva Maria-Molin – AGRANA Research and Innovation Center (ARIC), Österreichische Agentur für Gesundheit und Ernährungssicherheit GmbH (AGES), and Fraunhofer Institut für Molekularbiologie und angewandte Ökologie.