Poster Presentation AUS-oMicS 2025

Integrative Multi-omics Analysis of Rett Syndrome: Deciphering Disease Mechanisms and Putative Novel Biomarkers (118528)

Ignatius Pang 1 , Ashley Hertzog 2 3 4 , Mark Graham 5 , Nader Aryamanesh 6 7 , Brian Gloss 8 , Caitlin A. Finney 3 9 , Artur Svetcov 10 , Lisa Riley 4 11 , Carolyn Ellaway 3 12 , Adviye Ayper Tolun 2 3 , Gladys Ho 3 13 , Wendy Gold 3 4 11
  1. Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW, Australia
  2. NSW Biochemical Genetics Service, The Children’s Hospital at Westmead, Westmead, NSW, Australia
  3. The Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
  4. Molecular Neurobiology Research Laboratory, Kids Research, Children's Hospital at Westmead and Children's Medical Research Institute, Westmead, NSW, Australia
  5. Biomedical Proteomics Facility, Children's Medical Research Institute, Westmead, NSW, Australia
  6. Bioinformatics Core Facility, Children's Medical Research Institute, Westmead, NSW, Australia
  7. Embryology Research Unit, Children's Medical Research Institute, Westmead, NSW, Australia
  8. Westmead Research Hub, Westmead Institute for Medical Research, Westmead, NSW, Australia
  9. Neuroinflammation Research Group, Centre for Immunology and Allergy Research, Westmead Institute for Medical Research, Westmead, NSW, Australia
  10. Sydney Children’s Hospital Network, Sydney, NSW
  11. Kids Neuroscience Centre, Kids Research, Children's Hospital at Westmead, Westmead, NSW, Australia
  12. Genetic Metabolic Disorders Service, Sydney Children’s Hospitals Network, Sydney, NSW, Australia
  13. Department of Molecular Genetics, Sydney Genome Diagnostics, The Children's Hospital at Westmead, Westmead, NSW, Australia

Integrative multi-omics analysis provides a more holistic approach for studying the mechanism of diseases and identifying putative biomarkers that are not easily achieved by single -omics analysis. Rett syndrome (RTT) is a neurodevelopmental disorder caused by congenital MECP2 gene mutations and primarily affect females. This study performed transcriptomics, proteomics, phosphoproteomics and metabolomics analyses of blood samples from patients with classical RTT (n=9) and age- and sex-matched controls (n=8). Each -omics dataset was analysed individually and then the multi-omics datasets were analysed collectively using both supervised (MixOmics) and unsupervised [Multi‐Omics Factor Analysis (MOFA2)] machine learning algorithms. To minimise batch effects from each data set, the remove unwanted variation (RUVIII-C) tool was used. Differential abundance and pathway enrichment analysis of the transcriptomic and proteomic data sets showed dysregulation of mRNA processing, mitochondrial function, and ribosome. Changes in amino acid and lipid pathways were observed in the metabolomics datasets. Analysis of phosphoproteomics data highlighted putative dysregulation of upstream kinases involved in regulating cell growth, differentiation and inflammatory response. MixOmics and MOFA2 analyses support the results identified above and highlight a small subset of transcripts, proteins, metabolites that were differentially co-expressed in disease versus controls and could be putative biomarkers. To our knowledge, our study is the first to integrate multi-omics data from the blood of patients with RTT, providing mechanistic insights, discovering potential new biomarkers, and identifying possible new therapeutic targets. This methodology may also be useful in other rare genetic diseases.