Aeromonas hydrophila LC-MSMS - This project was designed to observe changes in proteins expression and toxicity proteins expression of Aeromonas hydrophila under normal and iron restriction conditions.
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive
ORGANISM(S): Aeromonas Hydrophila Nj-35
TISSUE(S): Cell Culture
SUBMITTER: 
Bingwen Xi
LAB HEAD: Bingwen Xi
PROVIDER: PXD007641 | Pride | 2018-02-16
REPOSITORIES: Pride
Similar Datasets
Project description:The mRNA microarray analysises was performed to explore the expression profiles of miRNAs using the same liver tissues of NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: miRCURY™ LNA expression array information. Data Analysis for miRNAs: 1. Low intensity filtering and data normalization: After low intensity miRNAs filtering, raw signal intensities are normalized in Median method. (miRNAs that intensities>=30 in all samples are chosen for calculating normalization factor) 2. Quality assessment of miRNA data after filtering: Contains box plot, Correlation Matrix and scatter plot for miRNAs after normalization. 3. Differentially expressed miRNAs screening: Contains significant differentially expressed miRNAs that pass Volcano Plot filtering. (Fold Change>=1.5, P-value<=0.05) 4. Heat map and hierarchical clustering: Hierarchical clustering on the significant differentially expressed miRNAs that passed Volcano Plots filtering. Sample RNA Quality Control: Sample quality control data file from Nanodrop 1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction for microarray, experiment, and data analysis. FAQ: Frequently asked question Additional miRNA Array Analysis (charge an extra fee): Prediction Analysis for Microarrays (PAM analysis) miRNA Target Gene Prediction and Functional Analysis Additional files provided: Graphs (*.jpg) Raw Intensity File(*.xls, raw miRNAs signal intensity) Layout File (*.gal, the files contain information on the positioning of the capture probes on the array and microRNA annotations for your species of interest) Raw data files produced by GenePix Pro 6.0
2023-06-01 | E-MTAB-9169 | biostudies-arrayexpress
Project description:LncRNA expression profiling for liver tissues of mice fed for NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
2022-11-24 | E-MTAB-9167 | biostudies-arrayexpress
Project description:The whole human genome microarray was used for analysis of the differentially expressed genes in SMMC-7721 cells when exposed to 1 μM IMB5043 treatment for 24 h.
2018-01-22 | E-MTAB-6359 | biostudies-arrayexpress
Project description:In the alpha subclass of proteobacteria iron homeostasis is controlled by diverse iron responsive regulators. Caulobacter crescentus, an important freshwater α-proteobacterium, uses the ferric uptake repressor (Fur) for such purpose. However, the impact of the iron availability on the C. crescentus transcriptome and an overall perspective of the regulatory networks involved remain unknown. In this work we report the identification of iron-responsive and Fur-regulated genes in C. crescentus using microarray-based global transcriptional analyses. We identify 46 genes that were strongly upregulated both by mutation of fur and by iron limitation condition. Among them, there are genes involved in iron uptake (four TonB dependent receptor gene clusters, feoAB), riboflavin biosynthesis and some genes encoding hypothetical proteins. Most of these genes are associated with Fur binding sites, implicating them as direct targets of Fur-mediated repression. These data were validated by β-galactosidase and EMSA assays for two operons encoding putative transporters. The role of Fur as a positive regulator is also evident, given that 50 genes were downregulated both by mutation of fur and under low-iron condition. As expected, this group includes many genes involved in energy metabolism, mostly iron-using enzymes. Surprisingly, are also included in this group many genes encoding TonB dependent receptors and the genes fixK, fixT and ftrB encoding an oxygen signaling network required for growth during hypoxia. Bioinformatics analyses performed in the promoters of these genes suggest that positive regulation by Fur is mainly indirect. In addition to the Fur modulon, iron limitation altered expression of more 103 genes, including upregulation of genes involved in Fe-S cluster assembly, oxidative stress and heat shock response, as well as downregulation of genes implicated in amino acid metabolism, chemotaxis and motility. Altogether, our results showed that adaptation of C. crescentus to iron limitation involves increasing the transcription of iron-acquisition systems and decreasing the production of iron-using proteins as a general strategy Two experimental procedures, each of them performed in two replicates. A total of four independent biological samples were used
2013-08-15 | E-GEOD-45653 | biostudies-arrayexpress
Project description:Investigation of whole genome changes in six serotypes of Actinobacillus pleuropneumoniae in control cultures compared to bacteria grown in media containg the iron chelator 2,2'-dipyridyl. A 48 chip study using total RNA recovered from four different control cultures and four different iron depleted cultures of Actinobacillus pleuropneumoniea serotype 1 (4074), serotype 2 (4226), serotype 3 (1421), serotype 5b(L209, serotype 6 (7712640) and serotype 7 (WF87).
2010-10-01 | E-GEOD-24470 | biostudies-arrayexpress
Project description:A. baumannii ATCC 17978 cells were incubated under iron replete (mueller-hinton) and iron limiting (MH + 200 µM 2,2'-dipyridyl) conditions, total RNA was extracted when cultures reached OD600=0.7. The probes on the microarray cover all predicted open reading frames (at least 4 per ORF) and additional replicates of housekeeping genes of the A. baumannii ATCC 17978 genome
2013-10-18 | E-GEOD-24921 | biostudies-arrayexpress
Project description:The miRNA microarray analysis was performed to explore the expression profiles of miRNAs using the same liver tissues of NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: miRCURY™ LNA expression array information. Data Analysis for miRNAs: 1. Low intensity filtering and data normalization: After low intensity miRNAs filtering, raw signal intensities are normalized in Median method. (miRNAs that intensities>=30 in all samples are chosen for calculating normalization factor) 2. Quality assessment of miRNA data after filtering: Contains box plot, Correlation Matrix and scatter plot for miRNAs after normalization. 3. Differentially expressed miRNAs screening: Contains significant differentially expressed miRNAs that pass Volcano Plot filtering. (Fold Change>=1.5, P-value<=0.05) 4. Heat map and hierarchical clustering: Hierarchical clustering on the significant differentially expressed miRNAs that passed Volcano Plots filtering. Sample RNA Quality Control: Sample quality control data file from Nanodrop 1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction for microarray, experiment, and data analysis. FAQ: Frequently asked question Additional miRNA Array Analysis (charge an extra fee): Prediction Analysis for Microarrays (PAM analysis) miRNA Target Gene Prediction and Functional Analysis Additional files provided: Graphs (*.jpg) Raw Intensity File(*.xls, raw miRNAs signal intensity) Layout File (*.gal, the files contain information on the positioning of the capture probes on the array and microRNA annotations for your species of interest) Raw data files produced by GenePix Pro 6.0
2022-11-24 | E-MTAB-9168 | biostudies-arrayexpress
Project description:Periodontitis can impair the osteogenic differentiation of human periodontal mesenchymal stem cells, but the underlying molecular mechanisms are still poorly understood. Long noncoding RNAs (lncRNAs) have been demonstrated to play significant roles under both physiologic and pathological conditions. We performed comprehensive lncRNAs profiling by lncRNA microarray to identify differentially expressed long noncoding RNA expression between Periodontal ligament stem cells from healthy Periodontal tissue and periodontal ligament stem cells from inflammatory periodontal tissue. Our analysis identified 233 lncRNAs and 423 mRNAs that were differently expressed (fold change >2.0, p-value < 0.05) between the two groups of cells. The GO analysis revealed that the significantly down-regulated biological processes included multicellular organismal process, developmental process and multicellular organismal development and the significantly up-regulated biological processes included cellular process, biological regulation and response to stimulus in periodontal ligament stem cells from inflammatory periodontal tissue. The Pathway analysis revealed that the differentially expressed mRNAs may involved in Focal adhesion, ECM-receptor interaction, Bacterial invasion of epithelial cells, Long-term depression, Circadian entrainment and HIF-1 signaling pathway. Two-condition experiment, periodontal ligament stem cells from healthy periodontal tissue (hPDLSCs) vs. periodontal ligament stem cells from inflammatory periodontal tissue (pPDLSCs), Biological replicates: 3 control replicates (hPDLSCs), 3 testing replicates (pPDLSCs).
2016-02-19 | E-GEOD-78074 | biostudies-arrayexpress
Project description:Alteration in metabolic repertoire is commonly associated with resistance phenotype. Although it’s a common phenotype, not much efforts have been undertaken to design effective strategies to target the metabolic drift in such cancerous cells and especially with drug resistant properties. In our study, we identified that drug resistant AML cell line HL-60/MX2 do not follow classical Warburg effect, instead these cells exhibit drastically low levels of aerobic glycolysis. Biochemical analysis confirms reduced glucose consumption and lactic acid production by resistant population with no differences in glutamine consumption. Raman spectroscopy revealed increased lipid and cytochrome content in resistant cells which were also visualized in the form of lipid droplets by Raman mapping, electron microscopy and lipid specific staining. Gene set enrichment analysis data from both the cell lines revealed significant enrichment of lipid metabolic pathways in HL-60/MX2 cells. Further drug resistant cells possess higher mitochondrial activity and increased OXPHOS suggested the role of fatty acid metabolism as energy source which was confirmed by increased rate of fatty acid oxidation. Pharmacological inhibition of fatty acid oxidation using Etomoxir affected the colony formation ability of resistant cells and inhibition of OXPHOS using Antimycin-A increased the sensitivity of resistant cells to chemotherapeutic drug, demonstrating requirement of fatty acid metabolism and increased dependency on OXPHOS by resistant leukemic cells for tumorigenicity.
2021-01-01 | E-MTAB-8981 | biostudies-arrayexpress
Project description:Antibodies and derivative drugs targeting immune checkpoints have been approved for the treatment of several malignancies, but there are fewer responses in patients with pancreatic cancer. Here, we designed a nanobody molecule with bi-targeting on PD-L1 and CXCR4, as both targets are overexpressed in many cancer cells and play important roles in tumorigenesis. The nanobody sequences targeting PD-L1 and CXCR4 were linked by the (G4S)3 flexible peptide to construct the anti-PD-L1/CXCR4 bispecific nanobody. The bispecific nanobody was expressed in E. coli cells and purified by affinity chromatography. The purified nanobody was biochemically characterized by mass spectrometry, Western blotting and flow cytometry to confirm the molecule and its association with both PD-L1 and CXCR4. The biological function of the nanobody and its anti-tumour effects were examined.
2023-03-11 | PXD037102 | Pride