How chemical pollutants can become more harmful when combined

So-called endocrine disruptors — environmental pollutants that can cause reproductive, metabolic, or neurological disorders — target the pregnane X receptor (PXR), a nuclear receptor unique for its sensitivity to a wide range of compounds. Recent studies have demonstrated that PXR, in its heterodimeric form with the retinoid X receptor (RXR), can mediate both harmful effects in response to xenobiotics, although the mechanisms by which pollutants activate PXR are still being investigated. Vanessa Delfosse, Tiphaine Huet, Deborah Harrus, Meritxell Granell, Maxime Bourguet, et al. utilize cell-based, biophysical, structural, and in vivo approaches to identify novel mixtures of chemicals, whose constituents have low individual affinity for PXR but combine synergistically to robustly activate the receptor. As an extension of their previous work, which showed that binary cocktails of xenobiotics synergistically activate RXR–PXR, the authors describe previously unreported binding mechanisms, substantiating the versatility of PXR's ligand binding domain and the receptor's role in sensing a diverse array of chemicals. Furthermore, the study shows that environmental compounds targeting RXR can boost the activity of two synergizing PXR ligands. The findings suggest that certain chemicals such as endocrine disruptors or pharmaceuticals, at levels deemed individually safe, can potentially interact synergistically and disrupt critical cell signaling.

Inserm press release:
https://presse.inserm.fr/en/leffet-cocktail-des-perturbateurs-endocriniens-mieux-compris/41920/

Link to the article: https://www.pnas.org/content/118/1/e2020551118

Quantifying ribosome traffic from profiling data

A non-equilibrium analysis model was developed for inferring the relative rates of translation initiation and elongation rates from ribosome profiling data.

One of the greatest challenges in modelling mRNA translation is to identify coding sequence features determining protein synthesis rates. Recent technical advances such as ribosome profiling allow probing translation at codon resolution, and make quantitative studies of transcript efficiency more accessible. We have developed a method called NEAR that integrates experimental ribosome profiling data and a well known non-equilibrium model of ribosome traffic. Our approach provides biological insights of traffic control and infers the kinetics of ribosomes on each transcript. When examining ribosome profiling in yeast, we observe that translation initiation and elongation are close to their optima and traffic is minimized at the beginning of the transcript to favour ribosome recruitment. Our work provides new measures of translation initiation and elongation efficiencies, emphasizing the importance of rating these two stages of translation separately.

Reference: Juraj Szavits-Nossan & Luca Ciandrini. Inferring efficiency of translation initiation and elongation from ribosome profiling. Nucleic Acids Res, 2020

Programming history-dependent logic in cellular populations

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities.

In this paper, researchers from the synthetic biology group at the CBS implemented robust, and scalable history-dependent programs by distributing the computational labor across a cellular population. The group also developed automated workflows that researchers can use to streamline program design and optimization.

These history-dependent cellular programs will support many applications using cellular populations for material engineering, biomanufacturing, and healthcare.

These results have been published in Nature Communications. Together with a 'Behind the paper' blog post shared in Nature Bioengineering Community.

link to the paper:

https://www.nature.com/articles/s41467-020-18455-z

link to the blog post:

https://bioengineeringcommunity.nature.com/posts/history-dependent-logic-in-cellular-populations

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