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Abstract
To capture the global gene network regulating the differentiation of immature T cells in an unbiased manner, large-scale forward genetic screens in zebrafish were conducted and combined with genetic interaction analysis. After ENU mutagenesis, genetic lesions associated with failure of T cell development were identified by meiotic recombination mapping, positional cloning, and whole genome sequencing. Recessive genetic variants in 33 genes were identified and confirmed as causative by additional experiments. The mutations affected T cell development but did not perturb the development of an unrelated cell type, growth hormone-expressing somatotrophs, providing an important measure of cell-type specificity of the genetic variants. The structure of the genetic network encompassing the identified components was established by a subsequent genetic interaction analysis, which identified many instances of positive (alleviating) and negative (synthetic) genetic interactions. Several examples of synthetic lethality were subsequently phenocopied using combinations of small molecule inhibitors. These drugs not only interfered with normal T cell development, but also elicited remission in a model of T cell acute lymphoblastic leukaemia. Our findings illustrate how genetic interaction data obtained in the context of entire organisms can be exploited for targeted interference with specific cell types and their malignant derivatives.
O’Meara et al. utilize a panel of zebrafish mutants to perform a whole organism genetic interaction screen, examining the network regulating T cell differentiation. The authors use a T cell acute lymphoblastic leukemia (T-ALL) model to integrate the effects of small molecule inhibitors of the T cell differentiation pathway and establish a combination therapy for T-ALL in juvenile zebrafish.
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1 Max Planck Institute of Immunobiology and Epigenetics, Department of Developmental Immunology, Freiburg, Germany (GRID:grid.429509.3) (ISNI:0000 0004 0491 4256)
2 Max Planck Institute of Immunobiology and Epigenetics, Department of Developmental Immunology, Freiburg, Germany (GRID:grid.429509.3) (ISNI:0000 0004 0491 4256); National Institutes of Health (NIH), Laboratory of Neurogenetics, National Institute of Alcohol Abuse and Alcoholism (NIAAA), Bethesda, USA (GRID:grid.94365.3d) (ISNI:0000 0001 2297 5165)
3 Max Planck Institute of Immunobiology and Epigenetics, Department of Developmental Immunology, Freiburg, Germany (GRID:grid.429509.3) (ISNI:0000 0004 0491 4256); University of Cologne, Institute of Zoology, Developmental Biology Unit, Cologne, Germany (GRID:grid.6190.e) (ISNI:0000 0000 8580 3777)
4 Max Planck Institute of Immunobiology and Epigenetics, Department of Developmental Immunology, Freiburg, Germany (GRID:grid.429509.3) (ISNI:0000 0004 0491 4256); Utsunomiya University, Center for Bioscience Research and Education, Utsunomiya, Japan (GRID:grid.267687.a) (ISNI:0000 0001 0722 4435)
5 Max Planck Institute of Immunobiology and Epigenetics, Department of Developmental Immunology, Freiburg, Germany (GRID:grid.429509.3) (ISNI:0000 0004 0491 4256); Fundacion Oncoloop & Center for Nuclear Medicine, Santiago, Chile (GRID:grid.429509.3)
6 Max Planck Institute of Immunobiology and Epigenetics, Bioinformatics Unit, Freiburg, Germany (GRID:grid.429509.3) (ISNI:0000 0004 0491 4256)
7 Max Planck Institute of Immunobiology and Epigenetics, Bioinformatics Unit, Freiburg, Germany (GRID:grid.429509.3) (ISNI:0000 0004 0491 4256); University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Esch-sur-Alzette, Luxembourg (GRID:grid.16008.3f) (ISNI:0000 0001 2295 9843)
8 Max Planck Institute of Immunobiology and Epigenetics, Department of Developmental Immunology, Freiburg, Germany (GRID:grid.429509.3) (ISNI:0000 0004 0491 4256); University of Freiburg, Faculty of Medicine, Freiburg, Germany (GRID:grid.5963.9) (ISNI:0000 0004 0491 7203)