Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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CP5: Immunology 1 - 15 min talks Location: Lecture Theatre 2 Session Chair: Danielle Stanisic, Institute for Biomedicine and Glycomics, Griffith University Session Chair: Hannah Siddle, The University of Queensland | |
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Systems Immunology and Multi-omics approaches to understanding host immune heterogeneity in Plasmodium infection 1: Centre for Superbug Solutions, Institute for Moleculer Biosciences,The University of Queensland, Brisbane, Queensland, Australia; 2: Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, Queensland, Australia; 3: Centre for Population and Disease Genomics, Institute for Moleculer Biosciences, The University of Queensland, Brisbane, Queensland, Australia; 4: Genomics and Machine Learning Lab, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. Malaria remains a major global health burden, with an estimated 282 million cases and 610,000 deaths across 80 countries in 2024. However, the mechanisms underlying protective immunity and inter-individual immune heterogeneity are still poorly understood, contributing to variation in susceptibility, disease severity, and vaccine efficacy. This study applies a systems immunology and multi-omics approach to investigate the molecular basis of immune heterogeneity during Controlled Human Malaria Infection (CHMI). We aim to define an integrated framework of genes, proteins, and immune cell states that underpin Plasmodium parasite control and immune heterogeneity. Using bulk RNA sequencing and single-cell data from peripheral blood mononuclear cells (PBMCs), we identified highly expressed and strongly correlated transcript pairs across mRNA, miRNA, and long non-coding RNAs (lncRNAs). These were incorporated into co-expression and regulatory networks to map transcriptional interactions. Correlation analyses revealed putative regulatory relationships, with lncRNA–mRNA pairs showing predominantly positive associations, suggesting coordinated expression across patient samples and over time. Building on these findings, we are applying a probabilistic unsupervised machine learning framework to link transcriptional programs with proteomic signatures of immune activation. This integrative approach provides new insights into immune mechanisms driving parasite control while advancing multi-omics capabilities to the field of parasitology. | |