Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Regulatory Mutations Affecting Sulfur Metabolism Induce Environmental Stress Response in Aspergillus nidulas

ABSTRACT: Mutations in the cysB, sconB and sconC genes affect sulfur metabolism in Aspergillus nidulans in different ways. Mutation of cysB blocks synthesis of cysteine by the main pathway leaving only the less effective alternative route. Mutations of sconB or sconC affect subunits of the SCF ubiquitin ligase complex, a negative controller of METR transcription factor, resulting in specific activation of a number of sulfur assimilation and metabolism genes, ultimately leading to elevated levels of cysteine and glutathione in scon mutants. We compared transcriptomes of these three mutants with that of a wild type strain finding that expression of a few hundred genes is altered at least twofold indicating a major reorganization of cellular metabolism. Despite the different effects of the cysB and scon mutations on sulfur metabolism they affect expression of overlapping sets of genes. We identified categories of the Functional Catalogue assigned to the differentially expressed genes and selected categories most enriched among the up- and down-regulated gene groups. Besides those involved in sulfur metabolism, many up-regulated genes are related to stress responses. The two-component signal transduction system is most enriched in genes up-regulated in the cysB, sconB and sconC mutants. This category contains histidine kinases that sense and transduce environmental signals to a cascade of protein kinases. Genes encoding heat shock proteins are also up-regulated, as are genes of the glutamate degradation pathway. The latter, also known as the GABA shunt, is induced by oxidative stress. A large group of up-regulated genes are involved in carbohydrate and energy metabolism, including genes coding for enzymes of trehalose and glycerol synthesis. Genes coding for enzymes of alcohol fermentation, which are induced in response to anaerobic stress, are also up-regulated in the mutants studied. The altered expression of the carbohydrate metabolism genes is accompanied by changes in sugar accumulation in mycelia and conidia of the mutants. Among the down-regulated genes the most numerous are those encoding membrane proteins and enzymes involved in secondary metabolism, including the penicillin biosynthesis cluster. Genes coding for lysozyme are down-regulated too. As secondary metabolites often inhibit growth of other organisms, lowered expression of genes responsible for their synthesis suggests a decreased response to biotic stress in the mutants studied. Comparison of transcriptomes of the filamentous fungus Aspergillus nidulans strains bearing mutations in the cysB, sconB or sconC genes, affecting sulfur metabolism in different ways, with that of the wild type (WT) strain. Three biological replicates of WT vs cysB and WT vs sconC comparisons were performed and four biological replicates of WT vs sconB comparison were done. Each biological replicate had two technical replicates with dye swapping. A total of twenty individual hybridizations were made.

ORGANISM(S): Aspergillus nidulans

SUBMITTER: Jerzy Brzywczy

PROVIDER: E-GEOD-54130 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Regulatory mutations affecting sulfur metabolism induce environmental stress response in Aspergillus nidulans.

Sieńko Marzena M Natorff Renata R Skoneczny Marek M Kruszewska Joanna J Paszewski Andrzej A Brzywczy Jerzy J

Fungal genetics and biology : FG & B 20140208

Mutations in the cysB, sconB and sconC genes affect sulfur metabolism in Aspergillus nidulans in different ways. The cysB mutation blocks synthesis of cysteine by the main pathway and leads to a shortage of this amino acid. The sconB and sconC mutations affect subunits of the SCF ubiquitin ligase complex, which inactivates the MetR transcription factor in the presence of an excess of cysteine. In effect, both cysB and scon mutations lead to permanent derepression of MetR-dependent genes. We comp ...[more]

PMID: 24513272

Similar Datasets

Project description:Significant insight about biological networks arises from the study of network motifs âoverly abundant network subgraphs, but such wiring patterns do not specify when and how potential routes within a cellular network are used. To address this limitation, we introduce activity motifs, which capture patterns in the dynamic use of a network. Using this framework to analyze transcription in Saccharomyces cerevisiae metabolism, we find that cells use different timing activity motifs to optimize transcription timing in response to changing conditions: forward activation to produce metabolic compounds efficiently, backward shutoff to rapidly stop production of a detrimental product and synchronized activation for co-production of metabolites required for the same reaction Measuring protein abundance over a time course reveals that mRNA timing motifs also occur at the protein level. Timing motifs significantly overlap with binding activity motifs, where genes in a linear chain have ordered binding affinity to a transcription factor, suggesting a mechanism for ordered transcription. Finely timed transcriptional regulation is therefore abundant in yeast metabolism, optimizing the organism's adaptation to new environmental conditions. We generated a set of 13 time courses by measuring gene expression after a metabolic change. Yeast strain KCN118 (MATalpha ade2) was grown at 28 Â°C in 400 ml of synthetic complete media with 2% dextrose (SCD) to an OD600 of 0.6. Synthetic complete was prepared using the standard recipe, except 75 uM inositol was included. At OD600 of 0.6, 100 ml of cells were collected by centrifugation and frozen as a reference sample, and the remaining cells were rapidly collected by filtration, washed with distilled water and resuspended in 300 ml of one of the following media: SCE (SC + 2% ethanol), SCG (SC + 2% galactose), SM1 (SCD lacking amino acids A, R, N, C, Q, G, K, P, S, F and T), SM2 (SCD lacking amino acids L, I, V, W, H and M), S0 (SCD lacking all amino acids), S0G (no amino acids, 2% galactose) or S0E (no amino acids, 2% ethanol). The data appears in Figures 2 and 4 of the manuscript, as it relates to the global analysis of all the arrays used in the dataset. All time courses consist of the following time points (in min): 15, 30, 60, 120, 240, and were hybridized against the t = 0 time point of cells grown in SCD. Each time course was performed as one single biological replicate and one technological replicate, except where noted below. Specifically, the 13 time courses break down into the following groups: Media key: SCD (synthetic complete, not including inositol) SCE (SC + 2% ethanol) SCG (SC + 2% galactose) SM1 (SCD lacking amino acids A, R, N, C, Q, G, K, P, S, F and T) SM2 (SCD lacking amino acids L, I, V, W, H and M) S0 (SCD lacking all amino acids) S0G (no amino acids, 2% galactose) S0E (no amino acids, 2% ethanol). ino = inositol aa = all amino acids supplemented The following group descriptions are the media as described above, followed by the description as indicated in the long title of the individual arrays: 1. S0 (5 arrays) = SD 2. SCD (6 arrays, t = 240 min has 2 tech replicates) = SD+aa 3. SM2 (5 arrays) = SD+aa:ARNCQGKPSDEFTY+ino 4. SM1 + ino (5 arrays) = SD+aa:LIVWHM+ino 5. S0 + ino (6 arrays, t = 240 min has 2 tech replicates) = SD+ino 6. S0E (5 arrays) = SEtOH 7. S0E + aa (7 arrays, t = 240 min and 60 min each have 2 tech replicates) = SEtOH+aa 8. S0E + aa + ino (5 arrays) = SEtOH+aa+ino 9. S0E + ino (8 arrays, t = 240 min, 15 min and 30 min each have 2 tech replicates) = SEtOH+ino 10. S0G (5 arrays) = Sgal 11. S0G + aa (5 arrays) = Sgal+aa 12. S0G + aa + ino (5 arrays) = Sgal+aa+ino 13. S0G + ino (6 arrays, t = 60 min has 2 tech replicates) = Sgal+ino

Project description:We used cDNA microarray technology to compare the genome-wide expression profiles of a wild type strain (BY4700) (E02, E04, E06) or the isogenic strain deleted of GLN3 and GAT1 genes (E01, E03, E05) grown in YNB medium with glutamine as nitrogen source (M.Gln) against the wild type strain grown in M.Gln after addition of rapamacyn (20 min) (E01, E02), or M.proline (M.Pro) (E03, E04) or after a two hours shift from M.Gln to M.Pro (E05, E06), all growth conditions known to modify the expression of genes involved in nitrogen utilization. These microarrays allowed to identify the set of genes that were up or down-regulated in response to the quality of the nitrogen source. To evaluate whether the majority of genes responding to the nitrogen source were dependent on Gln3 and Gat1, we compared the expression profiles of the wild type strain and of the isogenic strain deleted of GLN3 and GAT1 genes (03167b: ura3, gln3â, gat1â), when both strains were grown on M.Gln + rapamycin (E07), or M.Pro (E08) or after a shift from M.Gln to M.Pro (E09). We also used an independent means of identifying Gln3-Gat1 regulated genes by comparing the expression profiles in wild type and ure2â (4709âURE2) strains on M.Gln medium (E10). The hybridization signal was measured using a GSM418 laser scanner. Image analysis for each array was processed using the GenePix Pro 4.0 (Axon Instruments, Inc.) software package, which measures fluorescence intensity pairs for each gene. Following image acquisition, a visual inspection of the individual spots on each microarray (size, signal-to-noise ratio, background level, and spot uniformity) completed the flagging (present/not present, good/bad) of the data. To maximize sensitivity two scans were made, one at high laser power and high PMT (Photo Multiplier Tube) gain to detect the faintest spots, and a second one using low laser power and a PMT gain avoiding saturation. The values for spots presenting â¥ 5% saturation in the first scan were calculated based on an extrapolation after linear regression analysis of the intensities from both scans. These data were then imported in the GeneSpring 7.1 (Silicon Genetics) software package, applying a per spot per chip intensity-dependent (Lowess) normalization for further analysis.

Project description:This data series contains spotted oligo microarray data from 10 different experiments using Agilent Rat v2 microarrays. This data is being made public in support of Fillon S et al. Journal of Immunology, (2006). Proinflammatory bacterial components are at least partially responsible for causing the clinical features of sepsis, a syndrome that causes >100,000 deaths each year in the US (1). In the case of Gram positive infection, a key bacterial element recognized by the innate immune system is the cell wall, a complex network of peptidoglycan covalently linked to teichoic acids, proteins and lipoproteins. The current model of innate immune recognition of Gram positive bacteria suggests bacterial cell wall interacts with host recognition proteins, such as toll-like receptors (TLR) and Nod proteins. We describe an additional recognition system mediated by the platelet activating factor receptor (PAFr) and directed to the pathogen associated molecular pattern (PAMP) phosphorylcholine that results in uptake of bacterial components into host cells. Intravascular choline-containing cell walls bound to endothelial cells and caused rapid lethality in wild type, Tlr2-/- and Nod2-/- mice, but not in Pafr-/- mice. Cell wall exited the vasculature into the heart and brain, accumulating within endothelial cells, cardiomyocytes and neurons in a PAFr-dependent way. Physiological consequences of the cell wall/PAFr interaction were cell specific, being noninflammatory in endothelial cells and neurons, but causing rapid loss of cardiomyocyte contractility that contributed to death. Thus, PAFr shepherds phosphorylcholine-containing bacterial components such as cell wall into host cells from where the response ranges from quiescence to severe pathophysiology. Keywords: Competitive hybridizations The ten experiments in this series comprise of four distinct experiments, two of which were performed as biological triplicates and two as biological duplicates. The table below describes the overall design in detail: File Name Experiment 16011868017643v41_GEO_format.txt RBCEC Replicate 1 16011868017644v41_GEO_format.txt RBCEC Replicate 2 251186821865v41_GEO_format.txt Neuron Replicate 1 16011868021377v41_GEO_format.txt Neuron Replicate 2 251186821690v41_GEO_format.txt CW/Lyt44 Replicate 1 251186821691v41_GEO_format.txt CW/Lyt44 Replicate 2 251186821692v41_GEO_format.txt CW/Lyt44 Replicate 1 251186821693v41_GEO_format.txt CW+TNF/Lyt44+TNF Replicate 1 251186821694v41_GEO_format.txt CW+TNF/Lyt44+TNF Replicate 2 251186829677v41_GEO_format.txt CW+TNF/Lyt44+TNF Replicate 3

Project description:Cows were selected from two groups of 12 cows enrolled in a large experiment to assess the effects of ad libitum or restricted intake of moderate-energy diets during the entire dry period on pre-partum metabolism and post-partum metabolism and performance. A corn silage-based diet (26% of diet dry matter) providing 1.59 Mcal/kg during the far-off dry period (first 5 wk of an 8-wk dry period) or providing 1.61 Mcal/kg during the close-up dry period (last 3 wk of the dry period) was fed for ad libitum or restricted intake. Four multiparous Holstein cows were randomly selected from the ad libitum and restricted intake groups. A 7,872-element cDNA microarray spotted in duplicate on amino silane-coated glass slides (Everts et al. 2005, Vet. Immunol. Immunopathol. 105:235-245) was used for transcript profiling. Annotation was based on similarity searches (January, 2005) using sequential BLASTN and TBLASTX against human and mouse UniGene databases and the human genome. The 7,872-element array represents more than 6,300 unique genes. Gene Ontology (GO) terms were parsed from LocusLink to functionally annotate cDNA sequences. RNA (20 ug) from liver tissue and a reference standard derived from a mixture of tissues not including liver were used to make aminoallyl-labeled cDNA followed by incorporation of Cy3-ester and Cy5-ester (Amersham, Piscataway, NJ). Each experimental sample was co-hybridized with the reference standard, which allowed the treatment of fluorescence ratios as measurements of relative expression across all samples and time points. Probes were hybridized to the array for 2 days at 42 C. All samples were hybridized to duplicate slides for a total of 4 spots per cDNA element. A total of 106 microarrays were run to complete the experiment. Slides were scanned for both dye channels with a Scanarray 4000 (GSI-Lumonics, Billerica, MA) dual-laser confocal scanner and images were processed using GenePix 4.0 (Axon Instruments, Inc., CA).

Dataset Information

Regulatory Mutations Affecting Sulfur Metabolism Induce Environmental Stress Response in Aspergillus nidulas

Publications

Regulatory mutations affecting sulfur metabolism induce environmental stress response in Aspergillus nidulans.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets