Dissecting the Regulatory Circuitry of a Eukaryotic Genome

Holstege, Frank C.P.; Jennings, Ezra G.; Wyrick, John J.; Lee, Tong Ihn; Hengartner, Christoph J.; Green, Michael R.; Golub, Todd R.; Lander, Eric S.; Young, Richard A.

doi:10.1016/s0092-8674(00)81641-4

Cited by 1,689 publications

(1,833 citation statements)

References 65 publications

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“…The high transcription of enzymes involved in highly active pathways during fermentation (and possibly not during aerobic respiration) correlates particularly well with the high CAI value of these enzymes. This observation is supported also by the available transcriptomic data on seripauperine proteins (Holstege et al 1998;James et al 2003;Viswanathan et al 1994) and on Hem13 proteins (Amillet et al 1995).…”

Section: Introductionsupporting

confidence: 67%

“…Transcriptomic data for S. cerevisiae are taken from the study reported by Holstege et al (1998) and based on Affymetrix GeneChip high-density oligonucleotide array (HDA) technology (downloaded from http://www.wi.mit.edu/young/expression.html in 1999, and Table 1, Mycobacterium tuberculosis H37Rv (mtbrv), and Helicobacter pylori (hpy). For each organism, the RPI value associated with a pathway, if it exists, is indicated by the corresponding ranking color.…”

Section: Transcriptomic Data and Expression Levelsmentioning

confidence: 99%

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Insights on the Evolution of Metabolic Networks of Unicellular Translationally Biased Organisms from Transcriptomic Data and Sequence Analysis

Carbone

Madden

2005

J Mol Evol

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Abstract. Codon bias is related to metabolic functions in translationally biased organisms, and two facts are argued about. First, genes with high codon bias describe in meaningful ways the metabolic characteristics of the organism; important metabolic pathways corresponding to crucial characteristics of the lifestyle of an organism, such as photosynthesis, nitrification, anaerobic versus aerobic respiration, sulfate reduction, methanogenesis, and others, happen to involve especially biased genes. Second, gene transcriptional levels of sets of experiments representing a significant variation of biological conditions strikingly confirm, in the case of Saccharomyces cerevisiae, that metabolic preferences are detectable by purely statistical analysis: the high metabolic activity of yeast during fermentation is encoded in the high bias of enzymes involved in the associated pathways, suggesting that this genome was affected by a strong evolutionary pressure that favored a predominantly fermentative metabolism of yeast in the wild. The ensemble of metabolic pathways involving enzymes with high codon bias is rather well defined and remains consistent across many species, even those that have not been considered as translationally biased, such as Helicobacter pylori, for instance, reveal some weak form of translational bias for this genome. We provide numerical evidence, supported by experimental data, of these facts and conclude that the metabolic networks of translationally biased genomes, observable today as projections of eons of evolutionary pressure, can be analyzed numerically and predictions of the role of specific pathways during evolution can be derived. The new concepts of Comparative Pathway Index, used to compare organisms with respect to their metabolic networks, and Evolutionary Pathway Index, used to detect evolutionarily meaningful bias in the genetic code from transcriptional data, are introduced.

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Section: Introductionsupporting

confidence: 67%

Section: Transcriptomic Data and Expression Levelsmentioning

confidence: 99%

Insights on the Evolution of Metabolic Networks of Unicellular Translationally Biased Organisms from Transcriptomic Data and Sequence Analysis

Carbone

Madden

2005

J Mol Evol

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“…The distributions of mRNA concentration in the three strains were compared by means of the matched pair t-test, the non-parametric sign test and the non-parametric Wilcoxon signed-rank test, which always rendered p values < 0.0001. A strong correlation between the mRNA levels in the three strains was observed, and also with the mRNA levels from Holstege et al (1998), which are widely considered to be accurate absolute gene expression-level data (Table 2).…”

Section: Genechip Expression Analysismentioning

confidence: 67%

Relationship between G+C content, ORF‐length and mRNA concentration in Saccharomyces cerevisiae

Marı́n

Gallardo

Kato

et al. 2003

Yeast

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RNA biogenesis is a tightly-regulated process. The levels and timing of expression of a gene depends on its particular function. However, gene expression levels may also depend on structural features. Here we describe the analysis of gene expression of 4977 ORFs using DNA microarrays covering the whole genome of three different S. cerevisiae strains, wild-type and tho2 and thp1 mutants with a general effect on mRNA biogenesis. We show that transcripts from G+C-rich ORFs accumulate at higher concentrations than those from G+C-poor ones, in different ORF-length categories in all strains tested. In addition, we found a negative correlation between ORF length and G+C content. Our results indicate that length and G+C content of a gene have a clear effect on its levels of expression. We discuss the biological relevance of these results, as well as different ways that these structural features could modulate mRNA biogenesis.

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“…Unexpectedly, initial studies revealed that the mutation or depletion of seven TAF II s individually resulted in gene-specific defects, and not global reductions in transcription Moqtaderi et al, 1996;Walker et al, 1996). In contrast, genetic analysis of other TFIID subunits that have sequence similarities to the core histones has shown that they are required for the transcription of a larger number of genes (Apone et al, 1998;Holstege et al, 1998;Michel et al, 1998;Moqtaderi et al, 1998;Natajaran et al, 1998;Sanders et al, 1999;Reese et al, 2000). The interpretation of these results is complicated by the presence of certain TAF II s in the SAGA histone acetyltransferase complex (Grant et al, 1998), and their function in multiple complexes may partially explain the expanded requirement for these TAF II s in transcription (Lee et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Genetic analysis of TAF68/61 reveals links to cell cycle regulators

Reese

Green

2001

Yeast

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In yeast, inactivation of certain TBP-associated factors (TAF II s) results in arrest at specific stages of the cell cycle. In some cases, cell cycle arrest is not observed because overlapping defects in other cellular processes precludes the manifestation of an arrest phenotype. In the latter situation, genetic analysis has the potential to reveal the involvement of TAF II s in cell cycle regulation. In this report, a temperature-sensitive mutant of TAF68/61 was used to screen for high-copy dosage suppressors of its growth defect. Ten genes were isolated: TAF suppressor genes, TSGs 1-10. Remarkably, most TSGs have either a genetic or a direct link to control of the G 2 /M transition. Moreover, eight of the 10 TSGs can suppress a CDC28 mutant specifically defective for mitosis (cdc28-1N) but not an allele defective for passage through start. The identification of these genes as suppressors of cdc28-1N has identified four unreported suppressors of this allele. Moreover, synthetic lethality is observed between taf68-9 and cdc28-1N. The isolation of multiple genes involved in the control of a specific phase of the cell cycle argue that the arrest phenotypes of certain TAF II mutants reflect their role in specifically regulating cell cycle functions.

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Dissecting the Regulatory Circuitry of a Eukaryotic Genome

Cited by 1,689 publications

References 65 publications

Insights on the Evolution of Metabolic Networks of Unicellular Translationally Biased Organisms from Transcriptomic Data and Sequence Analysis

Insights on the Evolution of Metabolic Networks of Unicellular Translationally Biased Organisms from Transcriptomic Data and Sequence Analysis

Relationship between G+C content, ORF‐length and mRNA concentration in Saccharomyces cerevisiae

Genetic analysis of TAF68/61 reveals links to cell cycle regulators

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