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Biology subjects

Nissen, L.

Publications and source records attributed to Nissen, L..

2 recordsLinked to original sources

Synthetic gene circuits that selectively target RAS-driven cancers

Therapies targeting mutated RAS, the most frequently mutated oncogene in human cancers, could benefit millions of patients. Recently approved RAS inhibitors represent a breakthrough, but are limited to a specific KRASG12C mutation and prone to resistance. Synthetic gene circuits offer a promising alternative by sensing and integrating cancer-specific biomolecular inputs, including mutated RAS, to selectively express therapeutic proteins in cancer cells. A key challenge for these circuits is achieving high cancer selectivity to prevent toxicity in healthy cells. To address this challenge, we present a novel approach combining multiple RAS sensors into RAS-targeting gene circuits, which allowed us to express an output protein in cells with mutated RAS with unprecedented selectivity. We implemented a modular design strategy and modelled the impact of individual circuit components on output expression. This enabled cell-line specific adaptation of the circuits to optimize selectivity and fine-tune expression. We further demonstrate the targeting capabilities of the circuits by employing them in different RAS-driven cancer cells and provide evidence for their therapeutic potential by linking them to the expression of a clinically relevant output protein, which induced robust killing of cancer cells with mutated RAS. This work highlights the potential of synthetic gene circuits as a novel therapeutic strategy for RAS-driven cancers, advancing the application of synthetic biology in oncology.

synthetic biology↗

ePlatypus: an ecosystem for computational analysis of immunogenomics data

The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is a web-based platform and provides programming tutorials and an integrative database that elucidates selection patterns of adaptive immunity. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science. Accessibilityhttps://alexyermanos.github.io/Platypus/index.html

immunology↗