Frazer Institute

Frazer Institute Summer Research projects - 2027

  1. Modelling hepatic ILC1 development and function in vitro
  2. What do clinicians actually want from digital tools? Understanding the case for a modular clinical platform
  3. The Mitochondria Strike Back: How a Novel Cancer Drug Rewires Neuroblastoma Energy Production
  4. Stem cell therapy effects on mitochondrial dysfunction in the fetal growth restricted piglet brain
  1. Effectiveness of Intradermal Hepatitis B Vaccine Boosters in Vaccine Non-Responders
  2. Arming natural killer cells to attack hard to cure solid cancers
  3. Understanding transcriptional regulation of antiviral CD8+ T cells

7. Understanding transcriptional regulation of antiviral CD8+ T cells

Hours of engagement and delivery mode

The project will be conducted on-site at the Frazer Institute over six weeks.  Hours of engagement must be approx. 36 hrs per week and must fall within the official program dates (11 January to 19 February 2027). 

Project description

CD8+ T cells are critical arm of adaptive immune response that protect the host against viral infections and tumours. Following virus infection, T cells can differentiate into distinct functional states. Chronic persistent viral infections can promote a functionally exhausted CD8+ T cell state that is characterised by progressive loss of effector functions and a distinct underlying gene-regulatory programme.

Chronic Latent viral infection on the other hand, produce a diametrically different response known as memory inflation, where CD8+ effector T cells continuously accumulate and remain functional over prolonged periods. These CD8+ T cells are transcriptionally distinct from the exhausted CD8+ T cell population observed during chronic persistent infection. However, it is not known what are the specific regulator that governs the differentiation towards inflationary memory state vs exhausted states.

Expected outcomes and deliverables

Student will work alongside researchers to analyse existing annotated CD8+ T cell single-cell dataset. They will use python and/or R-based computational workflow to explore the dataset and identify genes and transcription factors that differ between sample conditions.

Depending on progress and experience, the project may extend to downstream bioinformatic analysis by introducing gene-regulatory-network analysis to explore relationship between transcription factors, regulatory elements and their target genes. This approach will allow us to integrate single cell data to conduct in-silico prediction to predict how alteration of genes and transcription factor may alter T cell states and differentiation.

Throughout the six-week project, you will maintain accurate code base and records, discuss results with the research team and contribute to decisions about subsequent experiments. At the end of the placement, you will summarise and present your findings to the laboratory.

Suitable for

This project suits 4th-year Honours or Master of Bioinformatics students with a computational background. Basic coding proficiency in R or Python is required, but prior experience in single-cell genomics is not essential.

Primary supervisor

Dr Zeeshan Chaudry

Em mz.chaudry@uq.edu.au

Instructions to applicants

Students may contact the supervisor before applying