The interaction landscape between transcription factors and the nucleosome, bioRxiv, 2017-12-29

Nucleosomes cover most of the genome and are thought to be displaced by transcription factors (TFs) in regions that direct gene expression. However, the modes of interaction between TFs and nucleosomal DNA remain largely unknown. Here, we use nucleosome consecutive affinity-purification systematic evolution of ligands by exponential enrichment (NCAP-SELEX) to systematically explore interactions between the nucleosome and 220 TFs representing diverse structural families. Consistently with earlier observations, we find that the vast majority of TFs have less access to nucleosomal DNA than to free DNA. The motifs recovered from TFs bound to nucleosomal and free DNA are generally similar; however, steric hindrance and scaffolding by the nucleosome result in specific positioning and orientation of the motifs. Many TFs preferentially bind close to the end of nucleosomal DNA, or to periodic positions at its solvent-exposed side. TFs often also bind nucleosomal DNA in a particular orientation, because the nucleosome breaks the local rotational symmetry of DNA. Some TFs also specifically interact with DNA located at the dyad position where only one DNA gyre is wound, whereas other TFs prefer sites spanning two DNA gyres and bind specifically to each of them. Our work reveals striking differences in TF binding to free and nucleosomal DNA, and uncovers a rich interaction landscape between the TFs and the nucleosome.

Reproducible Bioinformatics Project A community for reproducible bioinformatics analysis pipelines, bioRxiv, 2017-12-27

AbstractBackgroundReproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described. However, today for reproducing a complex bioinformatics analysis, the raw data and a list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools andor of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues.ResultsTo address this challenge, we established the Reproducible Bioinformatics Project (RBP), which is a non-profit and open-source project, whose aim is to provide a schema and an infrastructure, based on docker images and R package, to provide reproducible results in Bioinformatics. One or more Docker images are then defined for a workflow (typically one for each task), while the workflow implementation is handled via R-functions embedded in a package available at github repository. Thus, a bioinformatician participating to the project has firstly to integrate herhis workflow modules into Docker image(s) exploiting an Ubuntu docker image developed ad hoc by RPB to make easier this task. Secondly, the workflow implementation must be realized in R according to an R-skeleton function made available by RPB to guarantee homogeneity and reusability among different RPB functions. Moreover shehe has to provide the R vignette explaining the package functionality together with an example dataset which can be used to improve the user confidence in the workflow utilization.ConclusionsReproducible Bioinformatics Project provides a general schema and an infrastructure to distribute robust and reproducible workflows. Thus, it guarantees to final users the ability to repeat consistently any analysis independently by the used UNIX-like architecture.

An ancient integration in a plant NLR is maintained as a trans-species polymorphism, bioRxiv, 2017-12-26

Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing, bioRxiv, 2017-12-26

Transcriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), but the majority of genes have similar detection across cells and nuclei. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

Rethinking dopamine as generalized prediction error, bioRxiv, 2017-12-26

AbstractMidbrain dopamine neurons are commonly thought to report a reward prediction error, as hypothesized by reinforcement learning theory. While this theory has been highly successful, several lines of evidence suggest that dopamine activity also encodes sensory prediction errors unrelated to reward. Here we develop a new theory of dopamine function that embraces a broader conceptualization of prediction errors. By signaling errors in both sensory and reward predictions, dopamine supports a form of reinforcement learning that lies between model-based and model-free algorithms. This account remains consistent with current canon regarding the correspondence between dopamine transients and reward prediction errors, while also accounting for new data suggesting a role for these signals in phenomena such as sensory preconditioning and identity unblocking, which ostensibly draw upon knowledge beyond reward predictions.

The null additivity of multi-drug combinations, bioRxiv, 2017-12-26

AbstractFrom natural ecology 1–4 to clinical therapy 5–8, cells are often exposed to mixtures of multiple drugs. Two competing null models are used to predict the combined effect of drugs response additivity (Bliss) and dosage additivity (Loewe) 9–11. Here, noting that these models diverge with increased number of drugs, we contrast their predictions with measurements of Escherichia coli growth under combinations of up to 10 different antibiotics. As the number of drugs increases, Bliss maintains accuracy while Loewe systematically loses its predictive power. The total dosage required for growth inhibition, which Loewe predicts should be fixed, steadily increases with the number of drugs, following a square root scaling. This scaling is explained by an approximation to Bliss where, inspired by RA Fisher’s classical geometric model 12, dosages of independent drugs adds up as orthogonal vectors rather than linearly. This dose-orthogonality approximation provides results similar to Bliss, yet uses the dosage language as Loewe and is hence easier to implement and intuit. The rejection of dosage additivity in favor of effect additivity and dosage orthogonality provides a framework for understanding how multiple drugs and stressors add up in nature and the clinic.