Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Genome-Wide Microarray Analysis of Gene Expression During Starvation in Tetrahymena thermophila

ABSTRACT: Two genome-wide microarrays containing the predicted coding sequences (putative genes) for the ciliated protozoan Tetrahymena thermophila were used to study gene expression in starved cells (starvation 0 hour and starvation 24 hour). Combining these two microarrays with 50 microarrays described in Miao et al. (2009) and 15 other microarrays, we constructed the Tetrahymena gene network (TGN) using three methods: the Pearson correlation coefficient, the Spearman correlation coefficient and the context likelihood of relatedness (CLR) algorithm. The accuracy and coverage of the three networks were evaluated using four conserved protein complexes in yeast, and the CLR network was found to be the best network, with a Z-score threshold 3.49. Then the TGN was partitioned, and 55 modules were found. In addition, analysis for the arbitrarily determined 1200 hubs showed that these hubs could be sorted into six groups according to expression profiles. We also investigated human disease orthologs in Tetrahymena that are missing in yeast and found evidence indicating that some of these were involved in the same process in Tetrahymena as in human.

ORGANISM(S): Tetrahymena thermophila

PROVIDER: GSE26384 | GEO | 2011/06/13

SECONDARY ACCESSION(S): PRJNA136825

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Genome-Wide Microarray Analysis of Gene Expression During Starvation in Tetrahymena thermophila

Project description:Four genome wide microarrays containing the predicted coding sequences (putative genes) for the ciliated protozoan Tetrahymena thermophila used to study gene expression in starved cells (Starvation 0 hour and Starvation 9 hour, each two replicates). Combined these four microarrays with 50 microarrays described in Miao et al (2009, PMID: 19204800; GSE11300) and other 13 microarrays, we constructed the Tetrahymena gene network (TGN) using three methods: the Pearson correlation coefficient, the Spearman correlation coefficient and the context likelihood of relatedness (CLR) algorithm. The accuracy and coverage of the three networks were evaluated using four conserved protein complexes in yeast, and the CLR network was found to be the best network, with a Z-score threshold 3.49. Then the TGN was partitioned, and 55 modules were found. In addition, analysis for the arbitrarily determined 1200 hubs showed that these hubs could be sorted into six groups according to expression profiles. We also investigated human disease orthologs in Tetrahymena that are missing in yeast and found evidence indicating that some of these were involved in the same process in Tetrahymena as in human.

2011-06-13 | GSE26385 | GEO

Genome-Wide Microarray Analysis of Gene Expression During Starvation in Tetrahymena thermophila

Project description:Two genome-wide microarrays containing the predicted coding sequences (putative genes) for the ciliated protozoan Tetrahymena thermophila were used to study gene expression in starved cells (starvation 0 hour and starvation 24 hour). Combining these two microarrays with 50 microarrays described in Miao et al. (2009) and 15 other microarrays, we constructed the Tetrahymena gene network (TGN) using three methods: the Pearson correlation coefficient, the Spearman correlation coefficient and the context likelihood of relatedness (CLR) algorithm. The accuracy and coverage of the three networks were evaluated using four conserved protein complexes in yeast, and the CLR network was found to be the best network, with a Z-score threshold 3.49. Then the TGN was partitioned, and 55 modules were found. In addition, analysis for the arbitrarily determined 1200 hubs showed that these hubs could be sorted into six groups according to expression profiles. We also investigated human disease orthologs in Tetrahymena that are missing in yeast and found evidence indicating that some of these were involved in the same process in Tetrahymena as in human. For starvation, CU428 cells were starved at 2x10^5 cells/ml in 10 mM Tris (pH 7.5) for 0 and 24 hours (referred to as S-0 and S-24, respectively).

2011-06-13 | E-GEOD-26384 | biostudies-arrayexpress

Genome-Wide Microarray Analysis of Gene Expression During Starvation in Tetrahymena thermophila

2011-06-13 | E-GEOD-26385 | biostudies-arrayexpress

Genome-Wide gene expression of Tetrahymena thermophila conjugation

Project description:Eleven genome wide microarrays containing the predicted coding sequences (putative genes) for the ciliated protozoan Tetrahymena thermophila used to study gene expression in conjugation cells (C-0, C-15m, C-2, C-4, C-6, C-8, C-10, C-12, C-14, C-16, C-18). Combined these eleven microarrays with 50 microarrays described in Miao et al (2009) and other 6 microarrays, we constructed the Tetrahymena gene network (TGN) using three methods: the Pearson correlation coefficient, the Spearman correlation coefficient and the context likelihood of relatedness (CLR) algorithm. The accuracy and coverage of the three networks were evaluated using four conserved protein complexes in yeast, and the CLR network was found to be the best network, with a Z-score threshold 3.49. Then the TGN was partitioned, and 55 modules were found. In addition, analysis for the arbitrarily determined 1200 hubs showed that these hubs could be sorted into six groups according to expression profiles. We also investigated human disease orthologs in Tetrahymena that are missing in yeast and found evidence indicating that some of these were involved in the same process in Tetrahymena as in human.

2011-06-13 | GSE26650 | GEO

Genome-Wide gene expression of Tetrahymena thermophila conjugation

2011-06-13 | E-GEOD-26650 | biostudies-arrayexpress

Five genome-wide microarrays were used to indentify gene expression in exposure to Tributyltin, Dichlorodiphenyltrichloroethane and 2, 3, 7, 8-Tetrachlorodibenzo-p-dioxin.

Project description:Five genome-wide microarrays were used to indentify gene expression in exposure to Tributyltin, Dichlorodiphenyltrichloroethane and 2, 3, 7, 8-Tetrachlorodibenzo-p-dioxin. The three environmental chemicals are persistent in environment and cause continuous toxical effects on human and aquatic life. Tetrahymena thermophila has the potential as a toxical model. The genome-wide microarray in Tetrahymena thermophila exposed to these toxicants was analyzed. First, genes differentially expressed under the treatment of each toxicant were identified and then their functions were categorized using GO enrichment analysis. The results may suggest that Tetrahymena thermophila is able to reflect different mechanism of different toxic-type toxicants which are similar to those in multicellular organisms. Regarding the complexity of TCDD effects, context likelihood of relatedness method (CLR) was applied to construct a TCDD-relevant network. General functions of the network are related to the epigenetic mechanism of TCDD. Based on the network analysis, a model of TCDD effect on T. thermophila was infered. Thus, Tetrahymena has the potential to be a good unicellular eukaryotic model for toxic mechanism research at genome level.

2011-06-30 | GSE26515 | GEO

Five genome-wide microarrays were used to indentify gene expression in exposure to Tributyltin, Dichlorodiphenyltrichloroethane and 2, 3, 7, 8-Tetrachlorodibenzo-p-dioxin.

2011-06-30 | E-GEOD-26515 | biostudies-arrayexpress

RNA-seq analysis of Tetrahymena thermophila

Project description:We present a comprehensive transcriptome of ciliate T. thermophila using the Illumina RNA-seq platform. The data was generated from the six mRNA samples of growth, starvation and conjugation of Tetrahymena. Despite an AT rich genome, there are about 124.6 million reads mapped to T. thermophila genome. Using these mapped reads, we have significantly improved the previous genome annotation and investigated the gene expression. Besides, our result also provided a comprehensive understanding of the alternative splicing in T. thermophila, and suggested the existence of the regulated unproductive splicing and translation (RUST) in the single-celled eukaryote. RNA-seq for six samples of Tetrahymena growth, starvation and conjugation.

2012-03-15 | E-GEOD-27971 | biostudies-arrayexpress

Transcription profiling of E. coli under 121 conditions to generate a compendium of gene expression

Project description:Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data. Experiment Overall Design: To explore pathways of particular importance to antibiotic resistance, we assayed 121 conditions using 266 microarrays, including more than 50 genetic perturbations (overexpression or knockout) during norfloxacin-induced DNA damage response, overexpression of the ccdB toxin, and growth to stationary phase on low and high glucose

2008-06-15 | E-GEOD-6836 | biostudies-arrayexpress

QUANTITATIVE PROTEOME PROFILING OF NEUROSPORA CRASSA REVEALS A CELLOBIOSE-DEPENDENT NETWORK COORDINATING LIGNOCELLULOLYTIC RESPONSES

Project description:Cellulose, particularly the major cellulolytic product cellobiose, can induce the production of enzymes associated with deconstruction of lignocellulose in filamentous fungi. However, the detailed mechanisms underlying this biotechnologically important process remain to be disclosed. Here, the proteome response to cellobiose, crystalline cellulose (Avicel), and carbon starvation of a Neurospora crassa triple β-glucosidase mutant were compared using tandem mass tag (TMT)-based proteome quantification. Improved quantification accuracy was achieved with synchronous precursor selection (SPS)-based MS3 technology compared to MS2 using a high resolution tribrid mass spectrometer. Exposure to carbon starvation, cellobiose or Avicel induced the production of cellulase and lytic polysaccharide monooxygenase enzymes in N. crassa, as well as a cellobionic acid transporter, indicating their functional roles in the early adaptation to plant cell wall. In particular, cellobiose specifically induced the production of proteins in the functional categories of protein processing and export as well as cell wall organization. The data presented here integrates the signaling pathway associated with cellobiose transporters CDT-1 and/or CDT-2 with the direct targets of the transcription factors CLR-1, CLR-2, and XLR-1, the unfolded protein response (UPR) mediated by Ire-1/Hac-1, as well as calcium homeostasis and cell wall organization. The cellobiose-dependent response network will be useful for rational strain improvement to facilitate the production of lignocellulases in filamentous fungi and plant biomass-based products.

2020-05-15 | PXD011598 | Pride

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