Coordinated response of the Desulfovibrio desulfuricans 27774 transcriptome to nitrate, nitrite and nitric oxide

International audienceThe sulfate reducing bacterium Desulfovibrio desulfuricans inhabits both the human gut and external environments. It can reduce nitrate and nitrite as alternative electron acceptors to sulfate to support growth. Like other sulphate reducing bacteria, it can also protect itself against nitrosative stress caused by NO generated when nitrite accumulates. By combining in vitro experiments with bioinformatic and RNA-seq data, metabolic responses to nitrate or NO and how nitrate and nitrite reduction are coordinated with the response to nitrosative stress were revealed. Although nitrate and nitrite reduction are tightly regulated in response to substrate availability, the global responses to nitrate or NO were largely regulated independently. Multiple NADH dehydrogenases, transcription factors of unknown function and genes for iron uptake were differentially expressed in response to electron acceptor availability or nitrosative stress. Amongst many fascinating problems for future research, the data revealed a YtfE orthologue, Ddes_1165, that is implicated in the repair of nitrosative damage. The combined data suggest that three transcription factors coordinate this regulation in which NrfS-NrfR coordinates nitrate and nitrite reduction to minimize toxicity due to nitrite accumulation, HcpR1 serves a global role in regulating the response to nitrate, and HcpR2 regulates the response to nitrosative stress

Erratum: JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. In the 2018 release of JASPAR, the CORE collection has been expanded with 322 new PFMs (60 for vertebrates and 262 for plants) and 33 PFMs were updated (24 for vertebrates, 8 for plants and 1 for insects). These new profiles represent a 30% expansion compared to the 2016 release. In addition, we have introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3 for insects). This release incorporates clusters of similar PFMs in each taxon and each TF class per taxon. The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome. The predictions are made available to the scientific community through a UCSC Genome Browser track data hub. Finally, this update comes with a new web framework with an interactive and responsive user-interface, along with new features. All the underlying data can be retrieved programmatically using a RESTful API and through the JASPAR 2018 R/Bioconductor package

Epigenetic reprogramming at estrogen-receptor binding sites alters 3D chromatin landscape in endocrine-resistant breast cancer

Endocrine therapy resistance frequently develops in estrogen receptor positive (ER+) breast cancer, but the underlying molecular mechanisms are largely unknown. Here, we show that 3-dimensional (3D) chromatin interactions both within and between topologically associating domains (TADs) frequently change in ER+ endocrine-resistant breast cancer cells and that the differential interactions are enriched for resistance-associated genetic variants at CTCF-bound anchors. Ectopic chromatin interactions are preferentially enriched at active enhancers and promoters and ER binding sites, and are associated with altered expression of ER-regulated genes, consistent with dynamic remodelling of ER pathways accompanying the development of endocrine resistance. We observe that loss of 3D chromatin interactions often occurs coincidently with hypermethylation and loss of ER binding. Alterations in active A and inactive B chromosomal compartments are also associated with decreased ER binding and atypical interactions and gene expression. Together, our results suggest that 3D epigenome remodelling is a key mechanism underlying endocrine resistance in ER+ breast cancer

Mineralization of composted 15N-labelled farmyard manure during soil incubation

Fresh farmyard manure (C-to-N ratio = 20.3) was composted in the presence of 15N-labelled (NH4)2SO4using a bench-scale reactor under temperature conditions which reproduce the evolution of compost in a pile. C and N mineralization of composted farmyard manure (C-to-N ratio = 12.5) was monitored in two silty soils during an aerobic incubation at 28°C and 100% of WHC for 76 weeks. C mineralization of compost was assumed to come from a labile and a recalcitrant fraction which decomposed according to 1 and 0 order kinetic reactions, respectively [Cm = Ciabile.(l-e-kiabile.t) + kresistam.t]. The size of the labile fraction (Clabile) represented 31.3 and 19.9% of compost-C for the two soils, and its mineralization rate constant (kiabile) ranged from 0.03 to 0.09 day"1. N-mineralization was assumed to come from only one labile fraction [Nm = Niabile.(l-e-kiabile.t)]. This fraction ranged from 34 to 25% of compost-N for the two soils; its mineralization rate constant did not vary with soils (0.006 day-1). Composted farmyard manure produced under these laboratory conditions may not have been completely mature when added to soil and totally representative of comparable material composted in a pile. Nevertheless, it was considered as fairly mineralizable and its mineralization was greatly influenced by soil type. © 1994 A B Academic Publishers.SCOPUS: ar.jSCOPUS: ar.jinfo:eu-repo/semantics/publishe

ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments

With this latest release of ReMap (http://remap.cisreg.eu), we present a unique collection of regulatory regions in human, as a result of a large-scale integrative analysis of ChIP-seq experiments for hundreds of transcriptional regulators (TRs) such as transcription factors, transcriptional co-activators and chromatin regulators. In 2015, we introduced the ReMap database to capture the genome regulatory space by integrating public ChIP-seq datasets, covering 237 TRs across 13 million (M) peaks. In this release, we have extended this catalog to constitute a unique collection of regulatory regions. Specifically, we have collected, analyzed and retained after quality control a total of 2829 ChIP-seq datasets available from public sources, covering a total of 485 TRs with a catalog of 80M peaks. Additionally, the updated database includes new search features for TR names as well as aliases, including cell line names and the ability to navigate the data directly within genome browsers via public track hubs. Finally, full access to this catalog is available online together with a TR binding enrichment analysis tool. ReMap 2018 provides a significant update of the ReMap database, providing an in depth view of the complexity of the regulatory landscape in human

Nitrogen cycles in soils coming from conventional and organic farms studied with microlysimeters and 15N labelling techniques

info:eu-repo/semantics/publishe

A map of direct TF–DNA interactions in the human genome

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most popular assay to identify genomic regions, called ChIP-seq peaks, that are bound in vivo by transcription factors (TFs). These regions are derived from direct TF–DNA interactions, indirect binding of the TF to the DNA (through a co-binding partner), nonspecific binding to the DNA, and noise/bias/artifacts. Delineating the bona fide direct TF–DNA interactions within the ChIP-seq peaks remains challenging. We developed a dedicated software, ChIP-eat, that combines computational TF binding models and ChIP-seq peaks to automatically predict direct TF–DNA interactions. Our work culminated with predicted interactions covering >2% of the human genome, obtained by uniformly processing 1983 ChIP-seq peak data sets from the ReMap database for 232 unique TFs. The predictions were a posteriori assessed using protein binding microarray and ChIP-exo data, and were predominantly found in high quality ChIP-seq peaks. The set of predicted direct TF–DNA interactions suggested that high-occupancy target regions are likely not derived from direct binding of the TFs to the DNA. Our predictions derived co-binding TFs supported by protein-protein interaction data and defined cis-regulatory modules enriched for disease- and trait-associated SNPs. We provide this collection of direct TF–DNA interactions and cis-regulatory modules through the UniBind web-interface (http://unibind.uio.no)