Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Quantitative detection of microbial genes by using DNA microarrays.

ABSTRACT: To quantify target genes in biological samples using DNA microarrays, we employed reference DNA to normalize variations in spot size and hybridization. This method was tested using nitrate reductase (nirS), naphthalene dioxygenase (nahA), and Escherichia coli O157 O-antigen biosynthesis genes as model genes and lambda DNA as the reference DNA. We observed a good linearity between the log signal ratio and log DNA concentration ratio at DNA concentrations above the method's detection limit, which was approximately 10 pg. This approach for designing quantitative microarrays and the inferred equation from this study provide a simple and convenient way to estimate the target gene concentration from the hybridization signal ratio.

SUBMITTER: Cho JC

PROVIDER: S-EPMC123775 | biostudies-literature | 2002 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Quantitative detection of microbial genes by using DNA microarrays.

Cho Jae-Chang JC Tiedje James M JM

Applied and environmental microbiology 20020301 3

To quantify target genes in biological samples using DNA microarrays, we employed reference DNA to normalize variations in spot size and hybridization. This method was tested using nitrate reductase (nirS), naphthalene dioxygenase (nahA), and Escherichia coli O157 O-antigen biosynthesis genes as model genes and lambda DNA as the reference DNA. We observed a good linearity between the log signal ratio and log DNA concentration ratio at DNA concentrations above the method's detection limit, which ...[more]

PMID: 11872496

Similar Datasets

Monitoring gene expression in mixed microbial communities by using DNA microarrays.

Project description:A DNA microarray to monitor the expression of bacterial metabolic genes within mixed microbial communities was designed and tested. Total RNA was extracted from pure and mixed cultures containing the 2,4-dichlorophenoxyacetic acid (2,4-D)-degrading bacterium Ralstonia eutropha JMP134, and the inducing agent 2,4-D. Induction of the 2,4-D catabolic genes present in this organism was readily detected 4, 7, and 24 h after the addition of 2,4-D. This strain was diluted into a constructed mixed microbial community derived from a laboratory scale sequencing batch reactor. Induction of two of five 2,4-D catabolic genes (tfdA and tfdC) from populations of JMP134 as low as 10(5) cells/ml was clearly detected against a background of 10(8) cells/ml. Induction of two others (tfdB and tfdE) was detected from populations of 10(6) cells/ml in the same background; however, the last gene, tfdF, showed no significant induction due to high variability. In another experiment, the induction of resin acid degradative genes was statistically detectable in sludge-fed pulp mill effluent exposed to dehydroabietic acid in batch experiments. We conclude that microarrays will be useful tools for the detection of bacterial gene expression in wastewaters and other complex systems.

| S-EPMC143597 | biostudies-literature

Identification of embryonic pancreatic genes using Xenopus DNA microarrays.

Project description:The pancreas is both an exocrine and endocrine endodermal organ involved in digestion and glucose homeostasis. During embryogenesis, the anlagen of the pancreas arise from dorsal and ventral evaginations of the foregut that later fuse to form a single organ. To better understand the molecular genetics of early pancreas development, we sought to isolate markers that are uniquely expressed in this tissue. Microarray analysis was performed comparing dissected pancreatic buds, liver buds, and the stomach region of tadpole stage Xenopus embryos. A total of 912 genes were found to be differentially expressed between these organs during early stages of organogenesis. K-means clustering analysis predicted 120 of these genes to be specifically enriched in the pancreas. Of these, we report on the novel expression patterns of 24 genes. Our analyses implicate the involvement of previously unsuspected signaling pathways during early pancreas development. Developmental Dynamics 238:1455-1466, 2009. (c) 2009 Wiley-Liss, Inc.

| S-EPMC4203858 | biostudies-literature

VIPR: A probabilistic algorithm for analysis of microbial detection microarrays

Project description:The development of rapid and sensitive assays capable of detecting a wide range of infectious agents is critical for the effective diagnosis of diseases that have multiple etiologies. In recent years, many microarray-based diagnostics have been developed to identify viruses present in clinical specimens in a highly parallel fashion. Unfortunately, the rate of development of algorithms to interpret data generated from such platforms has not been commensurate. In particular, none of the existing interpretive algorithms is capable of utilizing empirical training data in a Bayesian framework. We have developed an interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), to capitalize on our ability to generate positive control data for analysis of microbial diagnostic arrays. To illustrate this approach, we have focused on the analysis of viruses that cause hemorrhagic fever (HF). To assess the efficacy of VIPR, we hybridized 33 viruses to 100 microarrays and applied our algorithm to this dataset. A microarray composed of nearly 15,000 oligonucleotides was designed using a custom viral taxonomy-based strategy. The performance of VIPR was assessed by performing a leave-one-out cross validation. VIPR was able to identity the infecting virus with an accuracy of 94%. VIPR outperformed previously described algorithms for the set of HF viruses tested. Bayesian interpretative algorithms such as VIPR should be considered for diagnostic microarray applications.

2011-04-19 | GSE21407 | GEO

Screening insertion libraries for mutations in many genes simultaneously using DNA microarrays.

Project description:We describe a method to screen pools of DNA from multiple transposon lines for insertions in many genes simultaneously. We use thermal asymmetric interlaced-PCR, a hemispecific PCR amplification protocol that combines nested, insertion-specific primers with degenerate primers, to amplify DNA flanking the transposons. In reconstruction experiments with previously characterized Arabidopsis lines carrying insertions of the maize Dissociation (Ds) transposon, we show that fluorescently labeled, transposon-flanking fragments overlapping ORFs hybridize to cognate expressed sequence tags (ESTs) on a DNA microarray. We further show that insertions can be detected in DNA pools from as many as 100 plants representing different transposon lines and that all of the tested, transposon-disrupted genes whose flanking fragments can be amplified individually also can be detected when amplified from the pool. The ability of a transposon-flanking fragment to hybridize declines rapidly with decreasing homology to the spotted DNA fragment, so that only ESTs with >90% homology to the transposon-disrupted gene exhibit significant cross-hybridization. Because thermal asymmetric interlaced-PCR fragments tend to be short, use of the present method favors recovery of insertions in and near genes. We apply the technique to screening pools of new Ds lines using cDNA microarrays containing ESTs for approximately 1,000 stress-induced and -repressed Arabidopsis genes.

| S-EPMC34684 | biostudies-literature

Identification of neural genes using Xenopus DNA microarrays

Project description:In order to isolate novel genes regulating neural induction, we utilized a DNA microarray approach. As neural induction is thought to occur via the inhibition of BMP signaling, BMP signaling was inhibited in ectodermal cells by overexpression of a dominant-negative receptor. RNAs were isolated from control animal cap explants and from dominant-negative BMP receptor expressing animal caps and subjected to a microarray experiment using newly generated high-density Xenopus DNA microarray chips. Keywords = neural induction Keywords = BMP Keywords = nervous system Keywords = Xenopus Keywords = microarray Keywords: parallel sample

2005-03-05 | GSE2266 | GEO

VIPR: A probabilistic algorithm for analysis of microbial detection microarrays.

Project description:All infectious disease oriented clinical diagnostic assays in use today focus on detecting the presence of a single, well defined target agent or a set of agents. In recent years, microarray-based diagnostics have been developed that greatly facilitate the highly parallel detection of multiple microbes that may be present in a given clinical specimen. While several algorithms have been described for interpretation of diagnostic microarrays, none of the existing approaches is capable of incorporating training data generated from positive control samples to improve performance.To specifically address this issue we have developed a novel interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm), which uses Bayesian inference to capitalize on empirical training data to optimize detection sensitivity. To illustrate this approach, we have focused on the detection of viruses that cause hemorrhagic fever (HF) using a custom HF-virus microarray. VIPR was used to analyze 110 empirical microarray hybridizations generated from 33 distinct virus species. An accuracy of 94% was achieved as measured by leave-one-out cross validation.VIPR outperformed previously described algorithms for this dataset. The VIPR algorithm has potential to be broadly applicable to clinical diagnostic settings, wherein positive controls are typically readily available for generation of training data.

| S-EPMC2921407 | biostudies-literature

Identifying antimicrobial resistance genes with DNA microarrays.

Project description:We developed and tested a glass-based microarray suitable for detecting multiple tetracycline (tet) resistance genes. Microarray probes for 17 tet genes, the beta-lactamase bla(TEM-1) gene, and a 16S ribosomal DNA gene (Escherichia coli) were generated from known controls by PCR. The resulting products (ca. 550 bp) were applied as spots onto epoxy-silane-derivatized, Teflon-masked slides by using a robotic spotter. DNA was extracted from test strains, biotinylated, hybridized overnight to individual microarrays at 65 degrees C, and detected with Tyramide Signal Amplification, Alexa Fluor 546, and a microarray scanner. Using a detection threshold of 3x the standard deviation, we correctly identified tet genes carried by 39 test strains. Nine additional strains were not known to harbor any genes represented on the microarray, and these strains were negative for all 17 tet probes as expected. We verified that R741a, which was originally thought to carry a novel tet gene, tet(I), actually harbored a tet(G) gene. Microarray technology has the potential for screening a large number of different antibiotic resistance genes by the relatively low-cost methods outlined in this paper.

| S-EPMC201141 | biostudies-literature

VIPR: A probabilistic algorithm for analysis of microbial detection microarrays

2011-04-19 | E-GEOD-21407 | biostudies-arrayexpress

Identification of Embryonic Pancreatic Genes using Xenopus DNA Microarrays

Project description:The pancreas is an exocrine and endocrine endodermal organ involved in digestion and glucose homeostasis. During embryogenesis, the anlagen of the pancreas arise from dorsal and ventral evaginations of the foregut that later fuse to form a single organ. In order to better understand the molecular genetics of early pancreas development, we sought to isolate markers that are uniquely expressed in this tissue. Microarray analysis was performed comparing dissected pancreatic buds, liver buds, and the stomach region of tadpole stage Xenopus embryos. A total of 912 genes were found to be differentially expressed between these organs during early stages of organogenesis. K-means clustering analysis predicted 120 of these genes to be specifically enriched in the pancreas. Of these, we report on the novel expression patterns of 24 genes. Among them, STXBP6 and KIRREL2 were expressed by the early tailbud stage, suggesting the possible involvement of novel signaling pathways in early pancreas development. Keywords: organ type comparison

2009-02-03 | GSE10124 | GEO

Optimized detection of sequence variation in heterozygous genomes using DNA microarrays with isothermal-melting probes.

Project description:The use of DNA microarrays to identify nucleotide variation is almost 20 years old. A variety of improvements in probe design and experimental conditions have brought this technology to the point that single-nucleotide differences can be efficiently detected in unmixed samples, although developing reliable methods for detection of mixed sequences (e.g., heterozygotes) remains challenging. Surprisingly, a comprehensive study of the probe design parameters and experimental conditions that optimize discrimination of single-nucleotide polymorphisms (SNPs) has yet to be reported, so the limits of this technology remain uncertain. By targeting 24,549 SNPs that differ between two Saccharomyces cerevisiae strains, we studied the effect of SNPs on hybridization efficiency to DNA microarray probes of different lengths under different hybridization conditions. We found that the critical parameter for optimization of sequence discrimination is the relationship between probe melting temperature (T(m)) and the temperature at which the hybridization reaction is performed. This relationship can be exploited through the design of microarrays containing probes of equal T(m) by varying the length of probes. We demonstrate using such a microarray that we detect >90% homozygous SNPs and >80% heterozygous SNPs using the SNPScanner algorithm. The optimized design and experimental parameters determined in this study should guide DNA microarray designs for applications that require sequence discrimination such as mutation detection, genotyping of unmixed and mixed samples, and allele-specific gene expression. Moreover, designing microarray probes with optimized sensitivity to mismatches should increase the accuracy of standard microarray applications such as copy-number variation detection and gene expression analysis.

| S-EPMC2824413 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data