Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR29061200_1.fastq.gz | Fastqsanger.gz | |||
| SRR29061200_2.fastq.gz | Fastqsanger.gz | |||
| SRR29061201_1.fastq.gz | Fastqsanger.gz | |||
| SRR29061201_2.fastq.gz | Fastqsanger.gz | |||
| SRR29061202_1.fastq.gz | Fastqsanger.gz |
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Similar Datasets
Project description:Melioidosis, caused by Gram negative bacteria Burkholderia pseudomallei, is a major type of community-acquired septicemia in Southeast Asia and Northern Australia with high mortality and morbidity rate. More accurate and rapid diagnosis is needed for improving the management of septicemic melioidosis. We previously identified 37-gene candidate signature to distinguish septicemic melioidosis from sepsis due to other pathogens. The aims of this current study were to independently validate our previous biomarker and consolidate gene selection from each of our microarray data set for establishing a targeted assay for the differential diagnosis of melioidosis. Blood samples were collected from patients who presented with severe inflammatory response syndromes from 3 provincial hospitals in Northeast of Thailand during September 2009 and November 2011. Only culture-confirmed sepsis were included in the study (n=166). We generated a new microarray dataset comprising of 29 patients with septicemic melioidosis and 54 patients with sepsis due to other pathogens. Validation of the 37-gene signature using this new dataset demonstrated the prediction accuracy of approximately 80% for detecting type of sepsis. In order to develop a nanoliter-scale high throughput PCR technology, we further identified additional gene signature from this new microarray dataset and by revisiting our published data. Altogether 85 genes including 6 housekeeping genes were selected. Using multi-steps iteration approach we could reduce the number of biomarkers to 12 genes while the performance is comparable to that of the full panel. The high performance (accuracy >70%) of this 12-gene signature could be validated in a second independent set of samples. The 12-gene panel identified by our study provides high performance for the differential diagnosis of septicemic melioidosis. This finding will be useful for improving the management of septicemic melioidosis in term of diagnosis, treatment and follow up.
2015-06-04 | GSE69528 | GEO
Project description:Melioidosis is a neglected tropical disease caused by the Gram-negative bacterium Burkholderia pseudomallei. It is widespread in Southeast Asia and under-reported across tropical regions worldwide. Patients present with a range of clinical syndromes including sepsis, pneumonia and focal abscesses, with a mortality rate of 40% in hospitalized patients in Thailand. Up to two-thirds of patients with melioidosis have diabetes mellitus. In this experiment we sought to characterize pathways activated by whole killed B. pseudomallei bacteria and by three vaccine candidate proteins from B. pseudomallei, BPSL2520 (uncharacterized protein), BPSS1525 (BopE) and BPSL2096 (AhpC) in patients with diabetes and acute melioidosis.
2017-09-22 | E-MTAB-6093 | biostudies-arrayexpress
Project description:Melioidosis, caused by Gram negative bacteria Burkholderia pseudomallei, is a major type of community-acquired septicemia in Southeast Asia and Northern Australia with high mortality and morbidity rate. More accurate and rapid diagnosis is needed for improving the management of septicemic melioidosis. We previously identified 37-gene candidate signature to distinguish septicemic melioidosis from sepsis due to other pathogens. The aims of this current study were to independently validate our previous biomarker and consolidate gene selection from each of our microarray data set for establishing a targeted assay for the differential diagnosis of melioidosis. Blood samples were collected from patients who presented with severe inflammatory response syndromes from 3 provincial hospitals in Northeast of Thailand during September 2009 and November 2011. Only culture-confirmed sepsis were included in the study (n=166). We generated a new microarray dataset comprising of 29 patients with septicemic melioidosis and 54 patients with sepsis due to other pathogens. Validation of the 37-gene signature using this new dataset demonstrated the prediction accuracy of approximately 80% for detecting type of sepsis. In order to develop a nanoliter-scale high throughput PCR technology, we further identified additional gene signature from this new microarray dataset and by revisiting our published data. Altogether 85 genes including 6 housekeeping genes were selected. Using multi-steps iteration approach we could reduce the number of biomarkers to 12 genes while the performance is comparable to that of the full panel. The high performance (accuracy >70%) of this 12-gene signature could be validated in a second independent set of samples. The 12-gene panel identified by our study provides high performance for the differential diagnosis of septicemic melioidosis. This finding will be useful for improving the management of septicemic melioidosis in term of diagnosis, treatment and follow up. Total RNA from whole blood obtained from patients with sepsis caused by B.pseudomallei (n=29) or other pathogens (n=54) and uninfected controls (28 healthy and 27 subjects with type 2 diabetes mellitus) were collected. In order to validate the published signature, microarray data were generated from these samples. This dataset was also used for an independent selection of signature for septicemic melioidosis. The same RNA samples were used for validation by a high throughput real-time PCR technique, Fluidigm.
2015-06-04 | E-GEOD-69528 | biostudies-arrayexpress
Project description:Melioidosis is a severe infectious disease caused by Burkholderia pseudomallei, a gram-negative bacillus classified by the NIAID as a category B priority agent. Septicemia is the most common presentation of the disease with 40% mortality rate even with appropriate treatments. Faster diagnostic procedures are required to improve therapeutic response and survival rates. We have used microarray technology to generate genome-wide transcriptional profiles (>48,000 transcripts) of whole blood obtained from patients with septicemic melioidosis (n=32), patients with sepsis caused by other pathogens (n=31), and uninfected controls (n=29). Unsupervised analyses demonstrated the existence of a whole blood transcriptional signature distinguishing patients with sepsis from control subjects. The majority of changes observed were common to both septicemic melioidosis and sepsis caused by other infections, including genes related to inflammation, interferon-related genes, neutrophils, cytotoxic cells, and T cells. Finally, class prediction analysis identified a 37 transcript candidate diagnostic signature that distinguished melioidosis from sepsis caused by other organisms with 100% and 78% accuracy in training and independent test sets, respectively. This finding was confirmed by the independent validation set, which showed 80% prediction accuracy. This signature was highly enriched in genes coding for products involved in the MHC Class II antigen processing and presentation pathway. Transcriptional patterns of whole blood RNA distinguish patients with septicemic melioidosis from patients with sepsis caused by other pathogens. Once confirmed in a large scale trial this diagnostic signature might constitute the basis of a differential diagnostic assay.
2009-07-01 | GSE13015 | GEO
Project description:To gain insight into the role of glibenclamide treatment of diabetic patients in melioidosis (Burkholderia pseudomaleii infection)
2010-12-31 | E-TABM-852 | biostudies-arrayexpress
Project description:Natural isolates of Burkholderia pseudomallei (Bp), the causative agent of melioidosis, are known to exhibit diverse phenotypic traits, suggesting significant intraspecies genetic heterogeneity. Using whole-genome Bp microarrays, we experimentally mapped patterns of large-scale genomic variation in 93 South East Asian clinical, environmental, and animal Bp isolates. 14% of the reference Bp K96243 genome was variably present across the strain panel, more than double previous estimates, and both hypothetical proteins and paralogous gene pairs (PGPs) were significantly over-represented in the set of strain-variable genes. Examining patterns of PGP retention and loss, we successfully sub-categorized the PGPs into non-redundant, functionally biased, and completely redundant classes. We then identified 20 novel regions (“islands”) variably present between strains previously missed by computational analysis. Three of these novel islands contained lipopolysaccharide (LPS) biosynthesis genes, and strains lacking one such LPS island demonstrated reduced virulence in mouse infection assays. Clinical isolates associated with human melioidosis were strongly associated with the presence of specific genomic islands, but a common set of virulence-related genes was present in all strains. Our results suggest that most Bp strains possess a core virulence machinery capable of causing disease, but accessory functions provided by mobile elements may predispose distinct host species and ecological niches to specific individual strains. This hierarchical model of Bp virulence reconciles previous conflicting studies comparing Bp environmental and clinical isolates, and suggests novel molecular strategies for disease surveillance and outbreak detection efforts in melioidosis. Keywords: aCGH of 93 Bp strains
2008-12-26 | GSE9491 | GEO
Project description:There is a challenge of thyroid cancer versus benign follicular denoma differential diagnostics using fine-needle biopsy. RNA expression profiles can be used to identify biomarkers suitable for molecular diagnostics. Here we present raw RNA sequencing data obtained during Oncobox platform observational study for 96 human pathological thyroid biosamples. Data are compatible with Oncobox Atlas of Normal Tissue Expression (ANTE) database including profiles for healthy thyroid tissues. . The data provided are helpful to those implicated in thyroid cancer research and diagnostics, and in comparative analyses of human cancers.
2019-12-01 | GSE138042 | GEO
Project description:Gene expression profiles of human cell (THP-1) lines exposed to a novel Daboiatoxin (DbTx) isolated from Daboia russelli russelli, and specific cytokines and inflammatory pathways involved in acute infection caused by Burkholderia pseudomallei. Keywords: Melioidosis, Burkholderia pseudomallei, Daboiatoxin, Cytokines, Inflammation.
2010-05-31 | GSE7577 | GEO
Project description:Opportunistic pathogen that causes melioidosis
| PRJNA19491 | ENA
Project description:Opportunistic pathogen that causes melioidosis
| PRJNA16182 | ENA