Proteolysis targeting chimeras are bifunctional small molecules capable of recruiting a target protein of interest to an E3 ubiquitin ligase that facilitates target ubiquitination followed by proteasome-mediated degradation. The first molecules acting on this novel therapeutic paradigm have just entered clinical testing. Here, by using Bromodomain Containing 4 (BRD4) degraders engaging cereblon and Von Hippel-Lindau E3 ligases, we investigated key determinants of resistance to this new mode of action. A loss-of-function screen for genes required for BRD4 degradation revealed strong dependence on the E2 and E3 ubiquitin ligases as well as for members of the COP9 signalosome complex for both cereblon-and Von Hippel− Lindau-engaging BRD4 degraders. Cancer cell lines raised to resist BRD4 degraders manifested a degrader-specific mechanism of resistance, resulting from the loss of components of the ubiquitin proteasome system. In addition, degrader profiling in a cancer cell line panel revealed a differential pattern of activity of Von Hippel−Lindau-and cereblonbased degraders, highlighting the need for the identification of degradation-predictive biomarkers enabling effective patient stratification.

The ade4 package provides tools for multivariate analyses. Whereas new statistical methods have been added regularly in the package since its first release in 2002, the graphical functions, that are used to display the main outputs of an analysis, have not benefited from such enhancements. In this context, the adegraphics package, available on CRAN since 2015, is a complete reimplementation of the ade4 graphical functionalities but with large improvements. The package uses the S4 object system (each graph is an object) and is based on the graphical framework provided by lattice and grid. We give a brief description of the package and illustrate some important functionalities to build elegant graphs.

Synthetic biology has boomed since the early 2000s when it started being shown that it was possible to efficiently synthetize compounds of interest in a much more rapid and effective way by using other organisms than those naturally producing them. However, to thus engineer a single organism, often a microbe, to optimise one or a collection of metabolic tasks may lead to difficulties when attempting to obtain a production system that is efficient, or to avoid toxic effects for the recruited microorganism. The idea of using instead a microbial consortium has thus started being developed in the last decade. This was motivated by the fact that such consortia may perform more complicated functions than could single populations and be more robust to environmental fluctuations. Success is however not always guaranteed. In particular, establishing which consortium is best for the production of a given compound or set thereof remains a great challenge. This is the problem we address in this paper. We thus introduce an initial model and a method that enable to propose a consortium to synthetically produce compounds that are either exogenous to it, or are endogenous but where interaction among the species in the consortium could improve the production line.