Poster Presentation 46th Lorne Genome Conference 2025

Characterising the diversity of T cell receptor repertoires across Indonesian populations (#243)

Pongsakorn Sukonthamarn 1 2 , Muhamad Fachrul 1 , Pradiptajati Kusuma 3 , Monika Meili Novita 3 , Isabella Apriyana 3 , Murray P. Cox 4 , Safarina G Malik 3 , Herawati Sudoyo 3 , Nicholas Banovich 5 , Irene Gallego Romero 1 6 7
  1. Human Genomics and Evolution Group, St. Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia
  2. School of BioScience, University of Melbourne, Parkville, Victoria, Australia
  3. Mochtar Riady Institute for Nanotechnology, Tangerang, Jakarta, Indonesia
  4. Massey University, Palmerston North, New Zealand
  5. Translational Genomics Institute, Phoenix, Arizona, USA
  6. Center for Genomics, Evolution and Medicine, University of Tartu, Tartu, Estonia
  7. School of Medicine, University of Melbourne, Parkville, Victoria, Australia

Long-term exposure to infectious disease is thought to be one of the strongest selective pressures experienced by human populations throughout their evolutionary history, and has the potential to shape genetic diversity at immune loci. Using single-cell V(D)J sequencing we have examined the diversity of T cell receptor (TCR) sequences in approximately 200 donors from Indonesia, a tropical country with a high infectious disease burden. By comparing samples collected across two sites in the islands of Bali and New Guinea, we explore how environmental factors interact with genetic influences and shape the adaptive immune system in healthy individuals. We find that different populations within each island exhibit varying V(D)J gene usage, which may be influenced by environmental factors. Furthermore, Balinese donors exhibit TCR sequences that more closely match those in public V(D)J diversity databases, in contrast to the Papuans, which may be due to a limited representation of TCR diversity being captured by public databases, or other factors affecting TCR variation within donors. We integrate V(D)J data with gene expression data from the same cells to refine our immune cell types classification and investigate differences in V(D)J usage during T cell development. Finally, we find that sample processing time has a significant impact on data quality, which may reflect differential mortality of specific T cell subtypes, and introduces significant challenges in the interpretation of data generated in the field.