A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays

<p>Abstract</p> <p>Background</p> <p>Microarray pre-processing usually consists of normalization and summarization. Normalization aims to remove non-biological variations across different arrays. The normalization algorithms generally require the specification of reference and target arrays. The issue of reference selection has not been fully addressed. Summarization aims to estimate the transcript abundance from normalized intensities. In this paper, we consider normalization and summarization jointly by a new strategy of reference selection.</p> <p>Results</p> <p>We propose a Probe-Treatment-Reference (PTR) model to streamline normalization and summarization by allowing multiple references. We estimate parameters in the model by the Least Absolute Deviations (LAD) approach and implement the computation by median polishing. We show that the LAD estimator is robust in the sense that it has bounded influence in the three-factor PTR model. This model fitting, implicitly, defines an "optimal reference" for each probe-set. We evaluate the effectiveness of the PTR method by two Affymetrix spike-in data sets. Our method reduces the variations of non-differentially expressed genes and thereby increases the detection power of differentially expressed genes.</p> <p>Conclusion</p> <p>Our results indicate that the reference effect is important and should be considered in microarray pre-processing. The proposed PTR method is a general framework to deal with the issue of reference selection and can readily be applied to existing normalization algorithms such as the invariant-set, sub-array and quantile method.</p

Inference of transcription modification in long-live yeast strains from their expression profiles

<p>Abstract</p> <p>Background</p> <p>Three kinases: Sch9, PKA and TOR, are suggested to be involved in both the replicative and chronological ageing in yeast. They function in pathways whose down-regulation leads to life span extension. Several stress response proteins, including two transcription factors Msn2 and Msn4, mediate the longevity extension phenotype associated with decreased activity of either Sch9, PKA, or TOR. However, the mechanisms of longevity, especially the underlying transcription program have not been fully understood.</p> <p>Results</p> <p>We measured the gene expression profiles in wild type yeast and three long-lived mutants: <it>sch9</it>Δ, <it>ras2</it>Δ, and <it>tor1</it>Δ. To elucidate the transcription program that may account for the longevity extension, we identified the transcription factors that are systematically and significantly associated with the expression differentiation in these mutants with respect to wild type by integrating microarray expression data with motif and ChIP-chip data, respectively. Our analysis suggests that three stress response transcription factors, Msn2, Msn4 and Gis1, are activated in all the three mutants. We also identify some other transcription factors such as Fhl1 and Hsf1, which may also be involved in the transcriptional modification in the long-lived mutants.</p> <p>Conclusion</p> <p>Combining microarray expression data with other data sources such as motif and ChIP-chip data provides biological insights into the transcription modification that leads to life span extension. In the chronologically long-lived mutant: <it>sch9</it>Δ, <it>ras2</it>Δ, and <it>tor1</it>Δ, several common stress response transcription factors are activated compared with the wild type according to our systematic transcription inference.</p

Life Span Extension by Calorie Restriction Depends on Rim15 and Transcription Factors Downstream of Ras/PKA, Tor, and Sch9

Calorie restriction (CR), the only non-genetic intervention known to slow aging and extend life span in organisms ranging from yeast to mice, has been linked to the down-regulation of Tor, Akt, and Ras signaling. In this study, we demonstrate that the serine/threonine kinase Rim15 is required for yeast chronological life span extension caused by deficiencies in Ras2, Tor1, and Sch9, and by calorie restriction. Deletion of stress resistance transcription factors Gis1 and Msn2/4, which are positively regulated by Rim15, also caused a major although not complete reversion of the effect of calorie restriction on life span. The deletion of both RAS2 and the Akt and S6 kinase homolog SCH9 in combination with calorie restriction caused a remarkable 10-fold life span extension, which, surprisingly, was only partially reversed by the lack of Rim15. These results indicate that the Ras/cAMP/PKA/Rim15/Msn2/4 and the Tor/Sch9/Rim15/Gis1 pathways are major mediators of the calorie restriction-dependent stress resistance and life span extension, although additional mediators are involved. Notably, the anti-aging effect caused by the inactivation of both pathways is much more potent than that caused by CR

Comparative analyses of time-course gene expression profiles of the long-lived sch9Δ mutant

In an attempt to elucidate the underlying longevity-promoting mechanisms of mutants lacking SCH9, which live three times as long as wild type chronologically, we measured their time-course gene expression profiles. We interpreted their expression time differences by statistical inferences based on prior biological knowledge, and identified the following significant changes: (i) between 12 and 24 h, stress response genes were up-regulated by larger fold changes and ribosomal RNA (rRNA) processing genes were down-regulated more dramatically; (ii) mitochondrial ribosomal protein genes were not up-regulated between 12 and 60 h as wild type were; (iii) electron transport, oxidative phosphorylation and TCA genes were down-regulated early; (iv) the up-regulation of TCA and electron transport was accompanied by deep down-regulation of rRNA processing over time; and (v) rRNA processing genes were more volatile over time, and three associated cis-regulatory elements [rRNA processing element (rRPE), polymerase A and C (PAC) and glucose response element (GRE)] were identified. Deletion of AZF1, which encodes the transcriptional factor that binds to the GRE element, reversed the lifespan extension of sch9Δ. The significant alterations in these time-dependent expression profiles imply that the lack of SCH9 turns on the longevity programme that extends the lifespan through changes in metabolic pathways and protection mechanisms, particularly, the regulation of aerobic respiration and rRNA processing

Tor1/Sch9-Regulated Carbon Source Substitution Is as Effective as Calorie Restriction in Life Span Extension

The effect of calorie restriction (CR) on life span extension, demonstrated in organisms ranging from yeast to mice, may involve the down-regulation of pathways, including Tor, Akt, and Ras. Here, we present data suggesting that yeast Tor1 and Sch9 (a homolog of the mammalian kinases Akt and S6K) is a central component of a network that controls a common set of genes implicated in a metabolic switch from the TCA cycle and respiration to glycolysis and glycerol biosynthesis. During chronological survival, mutants lacking SCH9 depleted extracellular ethanol and reduced stored lipids, but synthesized and released glycerol. Deletion of the glycerol biosynthesis genes GPD1, GPD2, or RHR2, among the most up-regulated in long-lived sch9Δ, tor1Δ, and ras2Δ mutants, was sufficient to reverse chronological life span extension in sch9Δ mutants, suggesting that glycerol production, in addition to the regulation of stress resistance systems, optimizes life span extension. Glycerol, unlike glucose or ethanol, did not adversely affect the life span extension induced by calorie restriction or starvation, suggesting that carbon source substitution may represent an alternative to calorie restriction as a strategy to delay aging

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays-5

Ference" across all the probe-sets. It is computed from the residual assessment after the PTR method with the invariant-set normalization on the data set "Expt-3-4".<p><b>Copyright information:</b></p><p>Taken from "A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays"</p><p>http://www.biomedcentral.com/1471-2105/9/194</p><p>BMC Bioinformatics 2008;9():194-194.</p><p>Published online 14 Apr 2008</p><p>PMCID:PMC2375129.</p><p></p

A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays-2

Ent of non-spike-in genes. The PTR method gives the smallest variation for all three normalization algorithms.<p><b>Copyright information:</b></p><p>Taken from "A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays"</p><p>http://www.biomedcentral.com/1471-2105/9/194</p><p>BMC Bioinformatics 2008;9():194-194.</p><p>Published online 14 Apr 2008</p><p>PMCID:PMC2375129.</p><p></p

A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays-4

E invariant-set normalization. The first reference array, , has been perturbed by adding noise. It shows a quite different distribution than others.<p><b>Copyright information:</b></p><p>Taken from "A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays"</p><p>http://www.biomedcentral.com/1471-2105/9/194</p><p>BMC Bioinformatics 2008;9():194-194.</p><p>Published online 14 Apr 2008</p><p>PMCID:PMC2375129.</p><p></p

A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays-0

Ing of summarization. Here, we only illustrate the cross strategy for the reference and target selection.<p><b>Copyright information:</b></p><p>Taken from "A probe-treatment-reference (PTR) model for the analysis of oligonucleotide expression microarrays"</p><p>http://www.biomedcentral.com/1471-2105/9/194</p><p>BMC Bioinformatics 2008;9():194-194.</p><p>Published online 14 Apr 2008</p><p>PMCID:PMC2375129.</p><p></p