Differentiation of Function among the RsbR Paralogs in the General Stress Response of Bacillus subtilis with Regard to Light Perception

The general stress response of Bacillus subtilis can be activated by a wide range of signals, including low intensities of visible light. It is regulated by a dedicated σ factor via a complex signal transduction pathway that makes use of stressosomes: hetero-oligomeric complexes that include one or more of the RsbR proteins (RsbRA, RsbRB, RsbRC, and RsbRD). The response to blue light is mediated by the photoreceptor YtvA. We show here which of the four RsbR proteins are necessary for the activation of the σ(B) response by blue light. Experiments performed with single-, double-, and triple-deletion strains in the rsbR genes show that RsbRB and RsbRA function antagonistically, with the former being a negative regulator and the latter a positive regulator of the YtvA-dependent light activation of the stress response. A strain with RsbRB as the only RsbR protein is unable to respond to light-activation of σ(B). Furthermore, RsbRC and RsbRD can replace RsbRA's function only in the absence of RsbRB. This differentiation of function is confined to light stress, since strains with RsbRA or RsbRB as the only RsbR protein behave similarly in our experimental conditions in response to physicochemical stresses. Interestingly, RsbRB's absence is sufficient to result in light activation of the general stress response at wild-type expression levels of ytvA, while it was previously reported that YtvA could only activate σ(B) when overproduced, or when cells are supplemented with an additional environmental stress

MOESM1 of Genetic engineering of Synechocystis PCC6803 for the photoautotrophic production of the sweetener erythritol

Additional file 1. Figure S1. Erythrose reductase activity of Synechocystis soluble lysates; Figure S2. CBB-stained SDS-PAGE analysis of the soluble lysates of mutants SEP021 to SEP025; Table S1. Primers used in this study; Table S2. E. coli strains and plasmids used in this study; Table S3. Erythrose reductases and their published catalytic characteristics; Table S4. Erythrose-4-phosphatases and their published catalytic characteristics

Integration of VDR genome wide binding and GWAS genetic variation data reveals co-occurrence of VDR and NF-κB binding that is linked to immune phenotypes

Background The nuclear hormone receptor superfamily acts as a genomic sensor of diverse signals. Their actions are often intertwined with other transcription factors. Nuclear hormone receptors are targets for many therapeutic drugs, and include the vitamin D receptor (VDR). VDR signaling is pleotropic, being implicated in calcaemic function, antibacterial actions, growth control, immunomodulation and anti-cancer actions. Specifically, we hypothesized that the biologically significant relationships between the VDR transcriptome and phenotype-associated biology could be discovered by integrating the known VDR transcription factor binding sites and all published trait- and disease-associated SNPs. By integrating VDR genome-wide binding data (ChIP-seq) with the National Human Genome Research Institute (NHGRI) GWAS catalog of SNPs we would see where and which target gene interactions and pathways are impacted by inherited genetic variation in VDR binding sites, indicating which of VDR’s multiple functions are most biologically significant. Results To examine how genetic variation impacts VDR function we overlapped 23,409 VDR genomic binding peaks from six VDR ChIP-seq datasets with 191,482 SNPs, derived from GWAS-significant SNPs (Lead SNPs) and their correlated variants (r 2 > 0.8) from HapMap3 and the 1000 genomes project. In total, 574 SNPs (71 Lead and 503 SNPs in linkage disequilibrium with Lead SNPs) were present at VDR binding loci and associated with 211 phenotypes. For each phenotype a hypergeometric test was used to determine if SNPs were enriched at VDR binding sites. Bonferroni correction for multiple testing across the 211 phenotypes yielded 42 SNPs that were either disease- or phenotype-associated with seven predominately immune related including self-reported allergy; esophageal cancer was the only cancer phenotype. Motif analyses revealed that only two of these 42 SNPs reside within a canonical VDR binding site (DR3 motif), and that 1/3 of the 42 SNPs significantly impacted binding and gene regulation by other transcription factors, including NF-κB. This suggests a plausible link for the potential cross-talk between VDR and NF-κB. Conclusions These analyses showed that VDR peaks are enriched for SNPs associated with immune phenotypes suggesting that VDR immunomodulatory functions are amongst its most important actions. The enrichment of genetic variation in non-DR3 motifs suggests a significant role for the VDR to bind in multimeric complexes containing other transcription factors that are the primary DNA binding component. Our work provides a framework for the combination of ChIP-seq and GWAS findings to provide insight into the underlying phenotype-associated biology of a given transcription factor

Additional file 1: Table S1. of Integration of VDR genome wide binding and GWAS genetic variation data reveals co-occurrence of VDR and NF-κB binding that is linked to immune phenotypes

Showing number of VDR peaks and number of Lead and LD SNPs overlapping with VDR peaks in different cell line models. Number of peaks for individual VDR ChIP-seq data and the number of Lead SNPs along with associated LD SNPs which fall under VDR peaks for each data set is shown here. Table S2. Study design, analytical methods and results. Summary table describing different stages of the analyses including the principal methods (computational approaches, databases and webtools) and key finding from each stage. Table S3. List of 574 SNPs (GWAS + LD SNPs) under VDR peaks. Table S4. List of 42 SNPs showing significant enrichment for associated traits. Table S5. RegulomeDB scores for 42 significant trait associated SNPs. Table S6. Summary of HaploReg results for 42 trait associated SNPs. Table S7. HaploReg summary table of enrichment analysis of enhancers and DNase sensitive regions associated with 42 trait associated SNPs. (XLSX 94 kb