Mechanisms underlying off-target V(D)J recombination
Supervisors: Dr Hashem Koohy, Prof Alain Townsend
In this project, we aim to shed light into genetics and epigenetics mechanisms underpinning the deleterious chromosomal rearrangements known as off-target V(D)J recombination. V(D)J recombination is a key determinant of antigen receptor diversity, ensuring effective immune response. It takes places in developing B and T lymphocytes, where individual Variable (V), Diversity (D) and Joining (J) segments are chosen at (seemingly) random and assembled to form functional immunoglobulin (Ig) and T-Cell receptors. The recombination is initiated upon binding of Recombining Activating Gene (RAG) to Recombination Signal Sequence (RSS) which are adjacent to each of V, D and J gene segments. These RSSs are known as canonical RSSs 1. However, in our genome we carry tens of thousands of non-canonical RSSs which are known as Cryptic-RSS. Functions and purposes of Cryptic-RSSs are unclear. A number of studies have linked RAG-mediated chromosomal translocation induced by Cryptic-RSSs to very life-threatening diseases such as leukaemia2, whereas a number of very recent studies have illustrated that under severe circumstances, Cryptic-RSSs are used as a novel mechanism of antibody diversification3.
We have recently established that sequence context and chromatin context are two key components of efficient VDJ recombination in B lymphocyres4. Meaning that the specificity of preferred RAG binding to canonical RSSs has an important ‘enabler’ role in initiating V(D)J recombination, whereas the efficient recombination level is driven by the local chromatin features at the RSS regions.
In this project, we aim to gain insight into the genetic and epigenetic involvements to off-target RAG-mediated chromosomal rearrangements. We want to know why majority of cryptic-RSSs are suppressed, some contribute to leukaemia whereas some help immune repertoire diversification.
This project is a modelling-intense project in which we aim to train cutting-edge machine learning techniques such as neural networks using both canonical- and cryptic- RSSs DNA sequences as well as local epigenetic enrichments from a number of transcription factors and histone marks.
- Computational Biology
- Machine Learning (Development and applications in life sciences)
- Statistical inference
- Molecular and cell biology of B and T lymphocytes
- Biology of innate and adaptive immune systems in vertebrate
Computational biology and immunology, Genetics, Epigenetics and Immunology, Vaccine Development.
- Schatz, D. G. & Ji, Y. Recombination centres and the orchestration of V(D)J recombination. Nat Rev Immunol 11, 251–263 (2011).
- Papaemmanuil, E. et al. RAG-mediated recombination is the predominant driver of oncogenic rearrangement in ETV6-RUNX1 acute lymphoblastic leukemia. Nat Genet 46, 116–125 (2014).
- Tan, J. et al. A LAIR1 insertion generates broadly reactive antibodies against malaria variant antigens. Nature 529, 105–109 (2016).
- Bolland, D. J. et al. Two mutually exclusive local chromatin states drive efficient V(D)J recombination. Cell Rep 15, 2475–2487 (2016).