Gram-negative bacteria
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ABSTRACT:
PROVIDER: PRJNA388450 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. Keywords: disease state analysis, gram-positive sepsis, gram-negative sepsis
2007-12-01 | GSE6535 | GEO
Project description:Signal intensities of BC-GP array for 105 speimens The Verigene Gram-Positive Blood Culture (BC-GP) nucleic acid assay is an automated microarray-based test, which can detect 12 Gram-positive bacterial genes and 3 resistance determinants using blood culture broths. We investigated signal intensities of microarray spots, and reclassified undetermined results where the automated system failed and various errors were called in blood culture specimens and spiked samples. Signal intensity analysis of BC-GP assay. SAMPLE_077 [Blood culture, Escherichia coli, Gram-negative, BacTALERT, Supernatant, Peripheral blood] had no signal data in the array raw data. Thus, SAMPLE_077 is not represented in this Series.
2014-05-30 | E-GEOD-57245 | biostudies-arrayexpress
Project description:soil metagenome breed:Gram-negative bacteria Raw sequence reads
| PRJNA545773 | ENA
Project description:Purpose: To ensure that ABX464 acted specifically on HIV splicing and did not significantly or globally affect the splicing events of human genes, we used an assembly approach of HIV (YU2 strain) putative transcripts and human long non-coding sequences from paired-reads (2x75bp) captured on a NimbleGen SeqCap® EZ Developer Library (Roche/NimbleGen). Methods: Cells were infected with 80 ng of p24/106 cells of the YU-2 strain for 4 to 6 hours and then rinsed with PBS before medium renewal, followed by high-throughput RNAseq from custom SeqCap EZ capture libraries. Each raw dataset of the samples contained between 5 and 30 million paired-end reads (75 bp), with an average of approximately 12 million raw reads per sample. Results: The raw reads were then cleaned and assembled per library to generate contigs, giving an average of 930 contigs per sample for further analyses. Conclusions: Our results show that high-throughput analyses coupled with bioinformatics-specific tools offers a comprehensive and more accurate view of mRNA splicing within a cell.
2018-12-31 | GSE120109 | GEO
Project description:Staph aureus and gram negative isolates from blood culture Raw sequence reads
| PRJNA393003 | ENA
Project description:Total RNA extracted from prostate cancer LNCaP cells transfected with siRNA against CTCF(siCTCF), or negative control siRNA (si-)were processed, and sequenced by two different companies using Illumina Hi-seq 2000 platform to generate RNA sequencing with two output sequences: paired-end 50bp and 101bp in read length. Nearly 100 million and 50 million raw reads were yielded from each sample respectively. We used FastQC to confirm the quality of raw fastq sequencing data, and SOAPfuse software to detect fusion transcripts.
2015-02-09 | GSE63487 | GEO
Project description:We profiled the expression of circulating microRNAs (miRNAs) in mice exposed to gram-positive and gram-negative bacteria using Illumina small RNA deep sequencing. Recombinant-specific gram-negative pathogen Escherichia coli (Xen14) and gram-positive pathogen Staphylococcus aureus (Xen29) were used to induce bacterial infection in mice at a concentration of 1 × 108 bacteria/100 μL of phosphate buffered saline (PBS). Small RNA libraries generated from the serum of mice after exposure to PBS, Xen14, Xen29, and Xen14+Xen29 via the routes of subcutaneous injection (I), cut wound (C), or under grafted skin (S) were analyzed using an Illumina HiSeq2000 Sequencer. Following exposure to gram-negative bacteria alone, no differentially expressed miRNA was found in the injection, cut, or skin graft models. Exposure to mixed bacteria induced a similar expression pattern of the circulating miRNAs to that induced by gram-positive bacterial infection. Upon gram-positive bacterial infection, 9 miRNAs (mir-193b-3p, mir-133a-1-3p, mir-133a-2-3p, mir-133a-1-5p, mir-133b-3p, mir-434-3p, mir-127-3p, mir-676-3p, mir-215-5p) showed upregulation greater than 4-fold with a p-value < 0.01. Among them, mir-193b-3p, mir-133a-1-3p, and mir-133a-2-3p presented the most common miRNA targets expressed in the mice exposed to gram-positive bacterial infection.
2014-08-19 | GSE60492 | GEO
Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. The study included seventy-two critically ill patients admitted to the intensive care unit (ICU) of Nepean Hospital, Sydney, Australia. Of these, fifty-five patients were diagnosed to have sepsis, as confirmed by microbiological culture. The remaining seventeen patients did not have sepsis and were therefore used as controls. The study was approved by the hospital ethics committee and informed consent was obtained from all patients or their relatives. Patient Samples. Whole blood was taken from each patient on admission to ICU. Neutrophils were separated from whole blood using density-gradient separation with Ficoll-PaqueP P(Amersham). Subsequent neutrophil RNA extraction was performed using guanidinium thiocyanate (Ambion). Microarray Experiment. The neutrophil RNA was converted to cDNA, fluorescently labeled and hybridized to its complimentary sequences on the microarray (Invitrogen). The fluorescent signals on each micrroarray were captured using the GenePix 4000B laser scanner (Axon Instruments). Expression level of each gene was represented by the intensity of its fluorescent signal. Data Extraction. All signal intensity values were processed using background-subtraction method. Prior to analysis, all values were log-transformed and normalized by fitting a print-tip group Lowess curve. Normalization minimizes bias due to dye chemistry, signal intensity or location of a gene on the array. It ensures the detection of genes that are truly differentially expressed, instead of those caused by experimental artifacts or variation in the hybridization process. After normalization, genes that had more than 50% of data missing were removed. We then selected genes that had at least 80% of the data showing two-fold changes from the geneâs median values. After filtering, 1617 genes were available for further analysis.
2010-05-25 | E-GEOD-6535 | biostudies-arrayexpress
Project description:Gram negative bacteria Genome sequencing
| PRJNA594823 | ENA
Project description:Gram negative bacilli Genome sequencing
| PRJNA353060 | ENA