Generation of a binary transgenic zebrafish model to study myeloid gene regulation in response to pre-neoplastic melanocytes

A complex network of inflammation succeeds somatic cell transformation and malignant disease. Immune cells and their associated molecules are responsible for detecting and eliminating cancer cells as they establish themselves as the precursors of a tumour. By the time a patient has a detectable solid tumour, cancer cells have escaped the initial immune response mechanisms. To date, no model exists to allow us to study the underlying mechanisms that govern the initial phase of the immune response as cells are transformed to become the precursors of cancer. Here we describe the development of an innovative double binary animal model designed in zebrafish for exploring regulatory programming of the myeloid cells as they respond to oncogenic transformed melanocytes. This modular system harnesses the power of zebrafish genetics. For studies of melanocyte transformation we generated a hormone-inducible binary system allowing for temporal control of different Ras-oncogene (NRasK61Q, HRasG12V, KRasG12V) expression in melanocytes allowing us to truly study melanoma initiation. This binary model was then coupled to a model for regulatory profiling of the active transcriptome of macrophages and neutrophils which is based on the in vivo biotinylation of nuclei and their subsequent isolation by streptavidin affinity purification. For the first time regulatory profiling of neutrophils as they respond to the earliest precursors of melanoma, revealed a number of factors upregulated in neutrophils that may promote progression to melanoma including fgf1, fgf6, cathepsin H, cathepsin L, galectin 1 and galectin 3. Overall design: We report the design of a double binary approach in zebrafish to study the neutrophil response to transformed melanocytes. By coupling a novel inducible model for melanocyte transformation to a model for the in vivo biotinylation of neutrophil nuclei we can isolate the neutrophil nuclei directly from the in vivo context allowing for RNA-seq analysis of the active transcriptome.

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Kenyon A, Gavriouchkina D, Zorman J, Chong-Morrison V, Napolitani G, Cerundolo V, Sauka-Spengler T