Transcript analysis of 1003 novel yeast genes using high-throughput northern hybridizations

Abstract

upshift tend to employ favoured codons, whereas those overexpressed in starvation conditions do not. These results are interpreted in terms of a model in which competition between mRNA molecules for translational capacity selects for codons translated by abundant tRNAs. Keywords: gene expression/genome analysis/mRNA/ Saccharomyces cerevisiae/stress responses IntroductionThe availability of the complete genome sequence of the eukaryotic microorganism, Saccharomyces cerevisiae (Goffeau et al., 1996) has allowed researchers to monitor gene transcription on a global (or genome-wide) scale for the ®rst time. The resulting pro®les de®ne the complete set of mRNA molecules (the transcriptome; Velculescu et al., 1997) present in the yeast cell under a given set of physiological or developmental conditions (Oliver, 1997). Massively parallel analytical procedures are used in transcriptome analysis that involve the hybridization of labelled mRNA or cDNA molecules to arrays of`target' molecules representing all of the~6000 protein-encoding genes de®ned by the yeast genome (Mewes et al., 1997). These targets may be either oligonucleotides (Wodicka et al., 1997) or PCR products (Lashkari et al., 1997;Hauser et al., 1998) fabricated in either`micro' (on glass slides or chips; Lashkari et al., 1997;Wodicka et al., 1997) or`macro' (on nylon or polypropylene membranes; Hauser et al., 1998) formats. The mRNA or cDNA hybridization probes may be labelled either radioactively (usually with 33 P; Hauser et al., 1998) or¯uorescently (usually with Cy5 or Cy3; Winzeler et al., 1999). Whatever the experimental protocol employed, all transcriptome analyses using hybridization arrays have in common that they produce massive amounts of data that have to be`mined', using computational techniques, in order to extract meaningful biological information. A number of algorithms have been developed Brown et al., 2000;Kell and King, 2000) to permit the comparison of the transcription patterns of all 6000 protein-encoding genes in different physiological conditions or throughout a time course of development (Cho et al., 1998;Chu, 1998;Spellman et al., 1998) or physiological adaptation . While these algorithms are effective in clustering together genes that show similar patterns of regulation, it is clear that the composition of any particular cluster is enormously sensitive to the thresholds set either for transcript detection or for a signi®cant level of regulation, and thus to the way in which the data have been normalized or otherwise processed.Because of these concerns about data processing, it is important that we make use of existing biological knowledge in mining hybridization array data. This may be done in two ways, either empirically (e.g. by adjusting threshold levels until genes already known to be co-regulated are clustered together) or, more formally, by using supervised methods of machine learning (Brown et al., 2000;Kell and King, 2000). Whatever approach is used, there is the problem that the prior knowledge has been gained using a di...

Gis1 is required for transcriptional reprogramming of carbon metabolism and the stress response during transition into stationary phase in yeast

“…Total RNA was isolated from cultures as described previously (Wu et al, 2004). Individual transcripts were assayed by Northern analysis according to Brown et al (2001) or RT-PCR (using a kit from ABgene Inc.). Microarray experiments were carried out by using yeast genome S98 oligonucleotide arrays (Affymetrix, Inc.) according to the manufacturer's instructions.…”

Global Role of the Protein Kinase Gcn2 in the Human Pathogen Candida albicans

“…Southern (35), Northern (9), and Western (12) blotting were performed as described previously. The rabbit anti-phospho-eIF2(pSer51) antibody was from Sigma (St Louis, MO).…”

Metabolic Consequences of p300 Gene Deletion in Human Colon Cancer Cells

“…Indeed, it has been shown that the transcripts most responsive to a gene deletion are generally those that show the highest overall variability, and hence would be very poor biomarkers (44); the same is likely to be true of metabolites. If simple experimental modulations, such as changing medium composition, can be used to prioritize metabolites (or mRNAs or proteins) for further study by selecting those that are affected under the greatest range of physiologic conditions, this approach could be of great general significance (45).…”