A multiscale analysis of early flower development in Arabidopsis provides an integrated view of molecular regulation and growth control.

We have analyzed the link between the gene regulation and growth during the early stages of flower development in Arabidopsis. Starting from time-lapse images, we generated a 4D atlas of early flower development, including cell lineage, cellular growth rates, and the expression patterns of regulatory genes. This information was introduced in MorphoNet, a web-based platform. Using computational models, we found that the literature-based molecular network only explained a minority of the gene expression patterns. This was substantially improved by adding regulatory hypotheses for individual genes. Correlating growth with the combinatorial expression of multiple regulators led to a set of hypotheses for the action of individual genes in morphogenesis. This identified the central factor LEAFY as a potential regulator of heterogeneous growth, which was supported by quantifying growth patterns in a leafy mutant. By providing an integrated view, this atlas should represent a fundamental step toward mechanistic models of flower development

DNA:RNA Immunoprecipitation from S. pombe Cells for qPCR and Genome-Wide Sequencing

International audienceBy temporarily distorting the DNA double helix, the moving RNA polymerases can lead to the formation of non-B DNA structures. One of the most abundant and largest non-B DNA structures in the genome is the R-loop, a three-stranded structure forming when the nascent RNA hybridizes with its DNA template, thereby extruding the non-template DNA strand. Growing evidence suggests that at least a subset of R-loops could induce transcription stress and genome instability, although the direct, primary consequences of R-loop formation on the surrounding chromatin are still unclear.To understand the direct impact of R-loops on transcription and genome stability, accurate and quantitative mapping of R-loops is essential. R-loop mapping is commonly achieved using the antibody-based DNA:RNA Immunoprecipitation (DRIP) strategy. While it is reasonably straightforward to obtain robust DRIP enrichments from human cells, this has proved harder in yeast, where DRIP signals are often relatively weak, with a poor signal-to-noise ratio. Although it is unclear whether such weak signals stem from a technical or a biological reality, they make the accurate quantification of DRIP signals all the more important, especially when deep sequencing is used to monitor and quantify the distribution of R-loops genome-wide. Here we propose a DRIP protocol that has been optimized for the mapping and the quantification of R-loops in Schizosaccharomyces pombe but that can also be used in Saccharomyces cerevisiae. As a result, this protocol can be used to generate calibrated DRIP-seq data, where genomic DNA extracted from S. cerevisiae serves as spike-in reference

RNA polymerase backtracking results in the accumulation of fission yeast condensin at active genes

International audienceThe mechanisms leading to the accumulation of the SMC complexes condensins around specific transcription units remain unclear. Observations made in bacteria suggested that RNA polymerases (RNAPs) constitute an obstacle to SMC translocation, particularly when RNAP and SMC travel in opposite directions. Here we show in fission yeast that gene termini harbour intrinsic condensin-accumulating features whatever the orientation of transcription, which we attribute to the frequent backtracking of RNAP at gene ends. Consistent with this, to relocate backtracked RNAP2 from gene termini to gene bodies was sufficient to cancel the accumulation of condensin at gene ends and to redistribute it evenly within transcription units, indicating that RNAP backtracking may play a key role in positioning condensin. Formalization of this hypothesis in a mathematical model suggests that the inclusion of a sub-population of RNAP with longer dwell-times is essential to fully recapitulate the distribution profiles of condensin around active genes. Taken together, our data strengthen the idea that dense arrays of proteins tightly bound to DNA alter the distribution of condensin on chromosomes