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

ABSTRACT: 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.

ORGANISM(S): Tetrahymena thermophila

PROVIDER: GSE26385 | GEO | 2011/06/13

SECONDARY ACCESSION(S): PRJNA136827

REPOSITORIES: GEO

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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.

2011-06-13 | GSE26384 | GEO

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. For starvation, CU427 cells were starved at 2x10^5 cells/ml in 10 mM Tris (pH 7.5) for 0 and 9 hours (referred to as S-0 and S-9, respectively).

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

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

2011-06-13 | E-GEOD-26384 | 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.

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Genome-Wide gene expression of Tetrahymena thermophila conjugation

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

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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

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

Large-Scale Mapping and Validation of E. coli Transcriptional Regulation from a Compendium of Expression Profiles.

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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.

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