Join our online ASP Seminar Series Friday 18 September @1pm AEST, featuring Lena Chng, PhD Student, Walter and Eliza Hall Institute presenting “Identification and Characterisation of Cryptosporidium Effector Proteins in Host-Pathogen Interaction” and Frank Weate, PhD student, University of New South Wales presenting “Deep Learning in Malaria, from Parasite Detection to Interaction Discovery” with chair Ben Liffner, Adelaide University.
Please register online using this link for your unique passcode to join the seminar. After registering, you will receive a confirmation email containing information about joining the meeting.
Lena was born and raised in Singapore, completed her BSc at University of Queensland before moving on to a Genomics lab in Singapore conducting surveillance of zoonotic pathogens – where she worked with bats and small rodents. Lena was interested in doing a PhD and returning to Australia, hence found her way to Chris Tonkin’s lab which focuses on host-pathogen interaction in apicomplexans.
Talk Title: Identification and Characterisation of Cryptosporidium Effector Proteins in Host-Pathogen Interaction
Ch’ng, L.1,2*, Seizova, S.1,2, Marasigan, J. 1,2, Wiradiputri, K.1,2, Abdalla, W.1,2, Alammar A. 1,2, Uboldi, A. 1,2, Hesping, E. 1,2, Boddey, J. 1,2, Tonkin, C. 1,2
1 Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; 2 Department of Medical Biology, The University of Melbourne, Melbourne, VIC, Australia; *Presenting Author: chng.l@wehi.edu.au
The apicomplexan parasite Cryptosporidium causes the severe diarrheal disease, cryptosporidiosis, a leading cause of childhood mortality globally. Chronic diarrhoea leads to malnutrition and wasting in children under five, affecting physical and cognitive development1. The lack of diagnostic tools, effective treatment and vaccines for cryptosporidiosis exacerbates the global impact of this neglected pathogen on vulnerable populations. Cryptosporidium infects host enterocytes and like its cousins, Plasmodium and Toxoplasma, secretes proteins into its host cell to establish infection2. Remodelling of the host actin cytoskeleton is a key process that occurs during Cryptosporidum infection. Host filamentus actin accumulates at the host-pathogen interface, leading to the formation of an “actin-pedestal” which is vital for infection3. Another hallmark of Cyrptosporidium infection in the enterocytes include actin-dependent elongated microvilli, a morphological feature shared between enteric pathogens4.
Recently, a highly disordered exported effector, MVP1, was identified to facilitate microvilli elongation through the activation of actin polymerisation pathways5. Despite new findings, mechanistic involvement of secreted effectors in parasite invasion remains poorly understood.
In this study, we used TurboID and APEX- expressing host cell lines to identify effector proteins of Cryptosporidium. Using newly developed CRISPR-Cas12 technology and mouse models, we have generated transgenic HA-tagged parasites and show that two of these effectors are exported into the host upon invasion, localising with host actin. Using Yeast Two-hybrid screening, we identify binding partners for one of the effectors, providing evidence as to how they manipulate the host actin network. This study greatly expands our molecular understanding of host manipulation in Cryptosporidium infection, and highlights a new therapeutic pathway for the development of an efficacious anti-cryptosporidial agent.
Frank is a PhD student in the Baum Lab at UNSW whose research applies deep learning and data science to malaria research. His work incorporates computer vision, through the parasite detection tool PlasmoCount, and structural bioinformatics, with a focus on proteome-wide prediction of protein-protein interactions (PPIs).
Talk title: Deep Learning in Malaria, from Parasite Detection to Interaction Discovery
Deep learning methods have revolutionised a wide variety of approaches to medical research. These in silico approaches complement traditional experimental strategies by computationally accelerating the time-consuming and labour-intensive processes. Here, we present two distinct but conceptually related deep learning platforms designed to streamline such processes in malaria research.
We first present PlasmoCount 2.0, a comprehensively redesigned platform that enables robust analysis across parasite species, imaging magnifications and sample preparations. Furthermore, by training on diverse multi-species datasets, we have improved classification performance across included species and enhanced generalisation to previously unseen Plasmodium species. The platform is deployed as an offline smartphone-compatible application that performs on-device inference without network connectivity. Together, these advances have the potential to transform automated malaria smear analysis from a specialised deep learning application into a practical, standardised platform for routine laboratory research and establish a foundation adaptable for future clinical and field deployment, including remote areas with low connectivity.
Protein–protein interactions (PPIs) form the foundation of biological processes but are extremely time-consuming and resource-intensive to discover and characterise experimentally. To address this challenge, we present a deep learning pipeline that screens the Plasmodium falciparum proteome for predicted PPIs on the parasite surface. The platform utilises deep learning surface prediction methods, co-expression data and multimeric protein structural prediction. We use interface scoring to rank and prioritise candidate interactions for experimental validation, benchmarking against known protein interactions available in the literature.
Our ASP Online Seminar Series image is created by Thorey Jonsdottir.
