Hyperspectral microarray scanning: impact on the accuracy and reliability of gene expression data

BACKGROUND: Commercial microarray scanners and software cannot distinguish between spectrally overlapping emission sources, and hence cannot accurately identify or correct for emissions not originating from the labeled cDNA. We employed our hyperspectral microarray scanner coupled with multivariate data analysis algorithms that independently identify and quantitate emissions from all sources to investigate three artifacts that reduce the accuracy and reliability of microarray data: skew toward the green channel, dye separation, and variable background emissions. RESULTS: Here we demonstrate that several common microarray artifacts resulted from the presence of emission sources other than the labeled cDNA that can dramatically alter the accuracy and reliability of the array data. The microarrays utilized in this study were representative of a wide cross-section of the microarrays currently employed in genomic research. These findings reinforce the need for careful attention to detail to recognize and subsequently eliminate or quantify the presence of extraneous emissions in microarray images. CONCLUSION: Hyperspectral scanning together with multivariate analysis offers a unique and detailed understanding of the sources of microarray emissions after hybridization. This opportunity to simultaneously identify and quantitate contaminant and background emissions in microarrays markedly improves the reliability and accuracy of the data and permits a level of quality control of microarray emissions previously unachievable. Using these tools, we can not only quantify the extent and contribution of extraneous emission sources to the signal, but also determine the consequences of failing to account for them and gain the insight necessary to adjust preparation protocols to prevent such problems from occurring

Localization of a 64-kDa phosphoprotein in the lumen between the outer and inner envelopes of pea chloroplasts

The identification and localization of a marker protein for the intermembrane space between the outer and inner chloroplast envelopes is described. This 64-kDa protein is very rapidly labeled by [γ-32P]ATP at very low (30 nM) ATP concentrations and the phosphoryl group exhibits a high turnover rate. It was possible to establish the presence of the 64-kDa protein in this plastid compartment by using different chloroplast envelope separation and isolation techniques. In addition comparison of labeling kinetics by intact and hypotonically lysed pea chloroplasts support the localization of the 64-kDa protein in the intermembrane space. The 64-kDa protein was present and could be labeled in mixed envelope membranes isolated from hypotonically lysed plastids. Mixed envelope membranes incorporated high amounts of 32P from [γ-32P]ATP into the 64-kDa protein, whereas separated outer and inner envelope membranes did not show significant phosphorylation of this protein. Water/Triton X-114 phase partitioning demonstrated that the 64-kDa protein is a hydrophilic polypeptide. These findings suggest that the 64-kDa protein is a soluble protein trapped in the space between the inner and outer envelope membranes. After sonication of mixed envelope membranes, the 64-kDa protein was no longer present in the membrane fraction, but could be found in the supernatant after a 110000 × g centrifugation

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins.AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains

Identification of a small molecule yeast TORC1 inhibitor with a flow cytometry-based multiplex screen

TOR (target of rapamycin) is a serine/threonine kinase, evolutionarily conserved from yeast to human, which functions as a fundamental controller of cell growth. The moderate clinical benefit of rapamycin in mTOR-based therapy of many cancers favors the development of new TOR inhibitors. Here we report a high throughput flow cytometry multiplexed screen using five GFPtagged yeast clones that represent the readouts of four branches of the TORC1 signaling pathway in budding yeast. Each GFP-tagged clone was differentially color-coded and the GFP signal of each clone was measured simultaneously by flow cytometry, which allows rapid prioritization of compounds that likely act through direct modulation of TORC1 or proximal signaling components. A total of 255 compounds were confirmed in dose-response analysis to alter GFP expression in one or more clones. To validate the concept of the high throughput screen, we have characterized CID 3528206, a small molecule most likely to act on TORC1 as it alters GFP expression in all five GFP clones in an analogous manner to rapamycin. We have shown that CID 3528206 inhibited yeast cell growth, and that CID 3528206 inhibited TORC1 activity both in vitro and in vivo with EC50s of 150 nM and 3.9 μM, respectively. The results of microarray analysis and yeast GFP collection screen further support the notion that CID 3528206 and rapamycin modulate similar cellular pathways. Together, these results indicate that the HTS has identified a potentially useful small molecule for further development of TOR inhibitors

Small but crucial : the novel small heat shock protein Hsp21 mediates stress adaptation and virulence in Candida albicans

Peer reviewedPublisher PD

Multivariate curve resolution of time course microarray data

BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements

Using steered molecular dynamics to predict and assess Hsp70 substrate-binding domain mutants that alter prion propagation.

Genetic screens using Saccharomyces cerevisiae have identified an array of cytosolic Hsp70 mutants that are impaired in the ability to propagate the yeast [PSI(+)] prion. The best characterized of these mutants is the Ssa1 L483W mutant (so-called SSA1-21), which is located in the substrate-binding domain of the protein. However, biochemical analysis of some of these Hsp70 mutants has so far failed to provide major insight into the specific functional changes in Hsp70 that cause prion impairment. In order to gain a better understanding of the mechanism of Hsp70 impairment of prions we have taken an in silico approach and focused on the Escherichia coli Hsp70 ortholog DnaK. Using steered molecular dynamics simulations (SMD) we demonstrate that DnaK variant L484W (analogous to SSA1-21) is predicted to bind substrate more avidly than wild-type DnaK due to an increase in numbers of hydrogen bonds and hydrophobic interactions between chaperone and peptide. Additionally the presence of the larger tryptophan side chain is predicted to cause a conformational change in the peptide-binding domain that physically impairs substrate dissociation. The DnaK L484W variant in combination with some SSA1-21 phenotypic second-site suppressor mutations exhibits chaperone-substrate interactions that are similar to wild-type protein and this provides a rationale for the phenotypic suppression that is observed. Our computational analysis fits well with previous yeast genetics studies regarding the functionality of the Ssa1-21 protein and provides further evidence suggesting that manipulation of the Hsp70 ATPase cycle to favor the ADP/substrate-bound form impairs prion propagation. Furthermore, we demonstrate how SMD can be used as a computational tool for predicting Hsp70 peptide-binding domain mutants that impair prion propagation

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

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