Presentation 1: B⁰AT1/SLC6A19: A new potential therapeutic target for the prevention of obesity-related type 2 diabetes

Presenter: Dr Muhammad Shamoon received his B.Sc and M.Sc (Honours) oversees, and has recently completed his PhD at The Australian National University. While working in the diabetes and endocrinology research laboratory of Professor Christopher Nolan (School of Medicine and Psychology, John Curtin School of Medical Research), his research focused on interventions aimed at limiting insulin hypersecretion to prevent and treat obesity-related T2D. Muhammad has extensively presented findings of his PhD at scientific meetings both nationally and internationally. More recently, in the Nolan laboratory, as a postdoctoral fellow, he is investigating the effects of post-weaning timing spectrum of dietary fat exposure on the body composition, hyperinsulinaemia, diabetes, and end-organ failure. Prior to starting his PhD, at the Jiangnan University (China), he researched how gut microbiota interacts with the immune system to protect from, or cause, diseases such as inflammatory bowel diseases; and probiotic bacteria may prove beneficial for the prevention of obesity and type 2 diabetes (T2D), as well as, how targeting of immune system and immuno-nutritional therapies could optimise for the improved pancreatic inflammation (e.g., acute pancreatitis)

Abstract: The rising prevalence of type 2 diabetes (T2D) is a major health concern globally. Additionally, heterogeneity in the pathophysiological basis of adult onset T2D is also increasingly being recognised, such that new approaches to prevent and treat individuals with T2D need to tailored accordingly. We and others have proposed that hyperinsulinaemia, as a consequence of hyper-responsiveness of islet beta-cells to a chronic fuel surfeit, is upstream to insulin resistance in the severe insulin resistance subtype of T2D (SIR-T2D), and that targeting insulin hypersecretion as the primary pathogenic defect should be beneficial.

The main aim of my recent research was to determine, in the western diet fed NOD^k mouse model of SIR-T2D, if genetic deficiency of the brush-border intestinal and renal tubular epithelium broad neutral amino acid transporter AT1 (B⁰AT1) (Slc6a19 gene) could prevent hyperinsulinaemia and T2D. My research showed that genetic B⁰AT1 deficiency slowed the progression of hyperinsulinaemia and completely prevented diabetes in the western diet fed male NOD^k mouse. The beneficial effect is likely to be multifactorial, including via increases in glucagon-like peptide 1 (GLP-1), growth differentiation factor 15 (GDF15), fibroblast growth factor 21 (FGF21) and adiponectin, as well as by limiting hyperinsulinaemia to protect pancreatic islet insulin producing cells from overwork.

In conclusion, my research supports future research to develop pharmacotherapeutic agents that target the B⁰AT1 amino acid transporter for the prevention and treatment of the severe insulin resistance subtype of type 2 diabetes.

Presentation 2: Improving methods of diagnosis and prognostication in Parkinson’s disease through electroencephalography analysis and machine learning

Presenter: Robin Vlieger received his BA (Hons) and MSc in Cognitive Neuroscience from Maastricht University, the Netherlands, and his PhD from the Australian National University. During his PhD studies he was part of the Big Data team of the 'Our Health in Our Hands' grand challenge. Currently, he is a research fellow with the Neuroinformatics group headed by Prof. Hanna Suominen. Robin’s research interests include the application of machine learning to neurodegenerative conditions diagnosis and prognostication.

Abstract: Parkinson’s disease (PD) is a neurodegenerative disorder for which no definitive diagnostic test exists. Diagnosis is based on detection of motor features, exclusion of other diseases, and response to dopaminergic medication. Potential biomarkers of PD are widely researched as they would be of major benefit to clinicians treating people with PD (PwPD): they might enable earlier diagnosis, personalisation of treatment, and more accurate prognosis.

One potential biomarker is electroencephalography (EEG). Recently, machine learning (ML) methods have been applied to EEG data to develop diagnostic and prognostic models of PD. The published studies use many different methods of preprocessing, ranging from using raw data to advanced data cleaning, featurisation, and classification. This hinders comparing results across studies and, in addition, no one has yet investigated how different methods of preprocessing might influence results.

In this seminar, I will discuss my studies into effects of pre-processing, featurization, and classification methods on performance metrics in classification tasks of people with neurodegenerative diseases and controls, with the aim to provide insight into the effects of different methods and give recommendations for future studies. Additionally, I will discuss the effects of characteristics like sex, age, and disease severity score on classification metrics.