RESUMO
Siderophores are microbially-produced ferric iron chelators. They are essential for microbial survival, but their presence and function for cave microorganisms have not been extensively studied. Cave environments are nutrient-limited and previous evidence suggests siderophore usage in carbonate caves. We hypothesize that siderophores are likely used as a mechanism in caves to obtain critical nutrients such as iron. Cave bacteria were collected from Long-term parent cultures (LT PC) or Short-term parent cultures (ST PC) inoculated with ferromanganese deposits (FMD) and carbonate secondary minerals from Lechuguilla and Spider caves in Carlsbad Caverns National Park (CCNP), NM. LT PC were incubated for 10-11 years to identify potential chemolithoheterotrophic cultures able to survive in nutrient-limited conditions. ST PC were incubated for 1-3 days to identify a broader diversity of cave isolates. A total of 170 LT and ST cultures,18 pure and 152 mixed, were collected and used to classify siderophore production and type and to identify siderophore producers. Siderophore production was slow to develop (>10 days) in LT cultures with a greater number of weak siderophore producers in comparison to the ST cultures that produced siderophores in <10 days, with a majority of strong siderophore producers. Overall, 64% of the total cultures were siderophore producers, which the majority preferred hydroxamate siderophores. Siderophore producers were classified into Proteobacteria (Alpha-, Beta-, or Gamma-), Actinobacteria, Bacteroidetes, and Firmicutes phyla using 16S rRNA gene sequencing. Our study supports our hypothesis that cave bacteria have the capability to produce siderophores in the subsurface to obtain critical ferric iron.
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BACKGROUND: Quiescent cells have a low level of gene activity compared to growing cells. Using a yeast model for cellular quiescence, we defined the genome-wide profiles of three species of histone methylation associated with active transcription between growing and quiescent cells, and correlated these profiles with the presence of RNA polymerase II and transcripts. RESULTS: Quiescent cells retained histone methylations normally associated with transcriptionally active chromatin and had many transcripts in common with growing cells. Quiescent cells also contained significant levels of RNA polymerase II, but only low levels of the canonical initiating and elongating forms of the polymerase. The RNA polymerase II associated with genes in quiescent cells displayed a distinct occupancy profile compared to its pattern of occupancy across genes in actively growing cells. Although transcription is generally repressed in quiescent cells, analysis of individual genes identified a period of active transcription during the development of quiescence. CONCLUSIONS: The data suggest that the transcript profile and histone methylation marks in quiescent cells were established both in growing cells and during the development of quiescence and then retained in these cells. Together, this might ensure that quiescent cells can rapidly adapt to a changing environment to resume growth.
Assuntos
Regulação Fúngica da Expressão Gênica , Histonas/metabolismo , Fase de Repouso do Ciclo Celular/genética , Transcriptoma , Leveduras/genética , Estudo de Associação Genômica Ampla , Genômica/métodos , Metilação , Mutação , Ligação Proteica , RNA Polimerase II/metabolismo , Leveduras/metabolismoRESUMO
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 GFP-tagged 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 a manner analogous to that of 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 EC(50)'s 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.
Assuntos
Inibidores de Proteínas Quinases/análise , Proteínas de Saccharomyces cerevisiae/antagonistas & inibidores , Saccharomyces cerevisiae/efeitos dos fármacos , Fatores de Transcrição/antagonistas & inibidores , Citometria de Fluxo , Proteínas de Fluorescência Verde , Humanos , Transdução de Sinais/efeitos dos fármacosRESUMO
MOTIVATION: Condition-specific networks capture system-wide behavior under varying conditions such as environmental stresses, cell types or tissues. These networks frequently comprise parts that are unique to each condition, and parts that are shared among related conditions. Existing approaches for learning condition-specific networks typically identify either only differences or only similarities across conditions. Most of these approaches first learn networks per condition independently, and then identify similarities and differences in a post-learning step. Such approaches do not exploit the shared information across conditions during network learning. RESULTS: We describe an approach for learning condition-specific networks that identifies the shared and unique subgraphs during network learning simultaneously, rather than as a post-processing step. Our approach learns networks across condition sets, shares data from different conditions and produces high-quality networks that capture biologically meaningful information. On simulated data, our approach outperformed an existing approach that learns networks independently for each condition, especially for small training datasets. On microarray data of hundreds of deletion mutants in two, yeast stationary-phase cell populations, the inferred network structure identified several common and population-specific effects of these deletion mutants and several high-confidence cases of double-deletion pairs, which can be experimentally tested. Our results are consistent with and extend the existing knowledge base of differentiated cell populations in yeast stationary phase. AVAILABILITY AND IMPLEMENTATION: C++ code can be accessed from http://www.broadinstitute.org/~sroy/condspec/ .
Assuntos
Inteligência Artificial , Biologia Computacional/métodos , Saccharomyces cerevisiae/fisiologia , Simulação por Computador , Saccharomyces cerevisiae/citologia , Saccharomyces cerevisiae/genéticaRESUMO
Engineered enzyme conjugate of the small laccase enzyme from Streptomyces coelicolor and zinc finger DNA binding domain from Zif268 is demonstrated to bind double stranded DNA in a site specific manner while retaining enzymatic activity.
Assuntos
DNA/metabolismo , Lacase/genética , Lacase/metabolismo , Engenharia de Proteínas/métodos , Streptomyces coelicolor/enzimologia , Sequência de Bases , DNA/genética , Lacase/química , Modelos Moleculares , Estrutura Terciária de Proteína , Especificidade por Substrato , Dedos de ZincoRESUMO
As yeast cultures enter stationary phase in rich, glucose-based medium, differentiation of two major subpopulations of cells, termed quiescent and nonquiescent, is observed. Differences in mRNA abundance between exponentially growing and stationary-phase cultures and quiescent and nonquiescent cells are known, but little was known about protein abundance in these cells. To measure protein abundance in exponential and stationary-phase cultures, the yeast GFP-fusion library (4159 strains) was examined during exponential and stationary phases, using high-throughput flow cytometry (HyperCyt). Approximately 5% of proteins in the library showed twofold or greater changes in median fluorescence intensity (abundance) between the two conditions. We examined 38 strains exhibiting two distinct fluorescence-intensity peaks in stationary phase and determined that the two fluorescence peaks distinguished quiescent and nonquiescent cells, the two major subpopulations of cells in stationary-phase cultures. GFP-fusion proteins in this group were more abundant in quiescent cells, and half were involved in mitochondrial function, consistent with the sixfold increase in respiration observed in quiescent cells and the relative absence of Cit1p:GFP in nonquiescent cells. Finally, examination of quiescent cell-specific GFP-fusion proteins revealed symmetry in protein accumulation in dividing quiescent and nonquiescent cells after glucose exhaustion, leading to a new model for the differentiation of these cells.
Assuntos
Proteômica , Saccharomyces cerevisiae/fisiologia , Ciclo Celular/fisiologia , Citometria de Fluxo , Regulação Fúngica da Expressão Gênica , Proteínas de Fluorescência Verde/genética , Proteínas de Fluorescência Verde/metabolismo , Consumo de Oxigênio , Proteínas Recombinantes de Fusão/genética , Proteínas Recombinantes de Fusão/metabolismo , Saccharomyces cerevisiae/citologia , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/metabolismoRESUMO
BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.
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Aminoácidos/química , Mapeamento de Interação de Proteínas , Algoritmos , Animais , Caenorhabditis elegans , Biologia Computacional/métodos , Dimerização , Drosophila , Evolução Molecular , Proteínas Fúngicas/química , Fases de Leitura Aberta , Probabilidade , Proteínas/química , Proteoma , Proteômica/métodosRESUMO
Ssd1 is an RNA-binding protein that affects literally hundreds of different processes and is polymorphic in both wild and lab yeast strains. We have used transcript microarrays to compare mRNA levels in an isogenic pair of mutant (ssd1-d) and wild-type (SSD1-V) cells across the cell cycle. We find that 15% of transcripts are differentially expressed, but there is no correlation with those mRNAs bound by Ssd1. About 20% of cell cycle regulated transcripts are affected, and most show sharper amplitudes of oscillation in SSD1-V cells. Many transcripts whose gene products influence longevity are also affected, the largest class of which is involved in translation. Ribosomal protein mRNAs are globally down-regulated by SSD1-V. SSD1-V has been shown to increase replicative life span currency and we show that SSD1-V also dramatically increases chronological life span (CLS). Using a new assay of CLS in pure populations of quiescent prototrophs, we find that the CLS for SSD1-V cells is twice that of ssd1-d cells.
Assuntos
Regulação Fúngica da Expressão Gênica , Longevidade/genética , Isoformas de Proteínas , Proteínas de Saccharomyces cerevisiae , Saccharomyces cerevisiae , Ciclo Celular/fisiologia , Análise por Conglomerados , Perfilação da Expressão Gênica , Análise de Sequência com Séries de Oligonucleotídeos , Polimorfismo Genético , Isoformas de Proteínas/genética , Isoformas de Proteínas/metabolismo , Proteínas Ribossômicas/genética , Proteínas Ribossômicas/metabolismo , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/fisiologia , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/metabolismoRESUMO
Analysis of condition-specific behavior under stressful environmental conditions can provide insight into mechanisms causing different healthy and diseased cellular states. Functional networks (edges representing statistical dependencies) inferred from condition-specific expression data can provide fine-grained, network level information about conserved and specific behavior across different conditions. In this paper, we examine novel microarray compendia measuring gene expression from two unique stationary phase yeast cell populations, quiescent and non-quiescent. We make the following contributions: (a) develop a new algorithm to infer functional networks modeled as undirected probabilistic graphical models, Markov random fields, (b) infer functional networks for quiescent, non-quiescent cells and exponential cells, and (c) compare the inferred networks to identify processes common and different across these cells. We found that both non-quiescent and exponential cells have more gene ontology enrichment than quiescent cells. The exponential cells share more processes with non-quiescent than with quiescent, highlighting the novel and relatively under-studied characteristics of quiescent cells. Analysis of inferred subgraphs identified processes enriched in both quiescent and non-quiescent cells as well processes specific to each cell type. Finally, SNF1, which is crucial for quiescence, occurs exclusively among quiescent network hubs, while non-quiescent network hubs are enriched in human disease causing homologs.
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Modelos Biológicos , Saccharomyces cerevisiae/citologia , Saccharomyces cerevisiae/genética , Algoritmos , Biometria , Ciclo Celular , Bases de Dados Genéticas , Doença/genética , Expressão Gênica , Redes Reguladoras de Genes , Genes Fúngicos , Humanos , Cadeias de Markov , Modelos Genéticos , Análise de Sequência com Séries de Oligonucleotídeos/estatística & dados numéricosRESUMO
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the statistical dependency structure than directed graphical models. Unfortunately, structure learning of undirected graphs using likelihood-based scores remains difficult because of the intractability of computing the partition function. We describe a new Markov random field structure learning algorithm, motivated by canonical parameterization of Abbeel et al. We provide computational improvements on their parameterization by learning per-variable canonical factors, which makes our algorithm suitable for domains with hundreds of nodes. We compare our algorithm against several algorithms for learning undirected and directed models on simulated and real datasets from biology. Our algorithm frequently outperforms existing algorithms, producing higher-quality structures, suggesting that enforcing consistency during structure learning is beneficial for learning undirected graphs.
RESUMO
UNLABELLED: We have developed a new software system, REgulatory Network generator with COmbinatorial control (RENCO), for automatic generation of differential equations describing pre-transcriptional combinatorics in artificial regulatory networks. RENCO has the following benefits: (a) it explicitly models protein-protein interactions among transcription factors, (b) it captures combinatorial control of transcription factors on target genes and (c) it produces output in Systems Biology Markup Language (SBML) format, which allows these equations to be directly imported into existing simulators. Explicit modeling of the protein interactions allows RENCO to incorporate greater mechanistic detail of the transcription machinery compared to existing models and can provide a better assessment of algorithms for regulatory network inference. AVAILABILITY: RENCO is a C++ command line program, available at http://sourceforge.net/projects/renco/
Assuntos
Regulação da Expressão Gênica/fisiologia , Modelos Biológicos , Mapeamento de Interação de Proteínas/métodos , Transdução de Sinais/fisiologia , Fatores de Transcrição/metabolismo , Ativação Transcricional/fisiologia , Técnicas de Química Combinatória/métodos , Simulação por ComputadorRESUMO
Cells in glucose-limited Saccharomyces cerevisiae cultures differentiate into quiescent (Q) and nonquiescent (NQ) fractions before entering stationary phase. To understand this differentiation, Q and NQ cells from 101 deletion-mutant strains were tested for viability and reproductive capacity. Eleven mutants that affected one or both phenotypes in Q or NQ fractions were identified. NQ fractions exhibit a high level of petite colonies, and nine mutants affecting this phenotype were identified. Microarray analysis revealed >1300 mRNAs distinguished Q from NQ fractions. Q cell-specific mRNAs encode proteins involved in membrane maintenance, oxidative stress response, and signal transduction. NQ-cell mRNAs, consistent with apoptosis in these cells, encode proteins involved in Ty-element transposition and DNA recombination. More than 2000 protease-released mRNAs were identified only in Q cells, consistent with these cells being physiologically poised to respond to environmental changes. Our results indicate that Q and NQ cells differentiate significantly, with Q cells providing genomic stability and NQ cells providing nutrients to Q cells and a regular source of genetic diversity through mutation and transposition. These studies are relevant to chronological aging, cell cycle, and genome evolution, and they provide insight into complex responses that even simple organisms have to starvation.
Assuntos
Diferenciação Celular , Saccharomyces cerevisiae/citologia , Técnicas de Cultura de Células , Diferenciação Celular/efeitos dos fármacos , Regulação Fúngica da Expressão Gênica/efeitos dos fármacos , Genes Fúngicos , Viabilidade Microbiana/efeitos dos fármacos , Mutação/genética , Análise de Sequência com Séries de Oligonucleotídeos , Peptídeo Hidrolases/farmacologia , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Reprodução/efeitos dos fármacos , Saccharomyces cerevisiae/efeitos dos fármacos , Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/genética , Proteínas de Saccharomyces cerevisiae/metabolismo , Transdução de Sinais/genética , Frações Subcelulares/efeitos dos fármacosRESUMO
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.
Assuntos
Biologia Computacional/métodos , Regulação da Expressão Gênica , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Algoritmos , Ciclo Celular , Interpretação Estatística de Dados , Proteínas Fúngicas/química , Perfilação da Expressão Gênica , Modelos Biológicos , Análise Multivariada , Análise de Componente Principal , Saccharomyces cerevisiae/metabolismo , Fatores de TempoRESUMO
Quiescence is the most common and, arguably, most poorly understood cell cycle state. This is in part because pure populations of quiescent cells are typically difficult to isolate. We report the isolation and characterization of quiescent and nonquiescent cells from stationary-phase (SP) yeast cultures by density-gradient centrifugation. Quiescent cells are dense, unbudded daughter cells formed after glucose exhaustion. They synchronously reenter the mitotic cell cycle, suggesting that they are in a G(0) state. Nonquiescent cells are less dense, heterogeneous, and composed of replicatively older, asynchronous cells that rapidly lose the ability to reproduce. Microscopic and flow cytometric analysis revealed that nonquiescent cells accumulate more reactive oxygen species than quiescent cells, and over 21 d, about half exhibit signs of apoptosis and necrosis. The ability to isolate both quiescent and nonquiescent yeast cells from SP cultures provides a novel, tractable experimental system for studies of quiescence, chronological and replicative aging, apoptosis, and the cell cycle.
Assuntos
Saccharomyces cerevisiae/citologia , Saccharomyces cerevisiae/isolamento & purificação , Apoptose/fisiologia , Ciclo Celular/fisiologia , Separação Celular/métodos , Células Cultivadas , Centrifugação com Gradiente de Concentração/métodos , Citometria de Fluxo , Glucose/química , Microscopia/métodos , Mitose , Espécies Reativas de Oxigênio/metabolismo , Fase de Repouso do Ciclo Celular/fisiologia , Sensibilidade e EspecificidadeRESUMO
Amphiphilic phospholipids were used to direct the formation of biocompatible, uniform silica nanostructures in the presence of Saccharomyces cerevisiae and bacterial cell lines. The cell surfaces organize multilayered phospholipid vesicles that interface coherently with the silica host and help relieve drying stresses that develop with conventional templates. These host structures maintain cell accessibility, addressability, and viability in the absence of buffer or an external fluidic architecture. The cell surfaces are accessible and can be used to localize added proteins, plasmids, and nanocrystals. Prolonged cell viability combined with reporter protein expression enabled stand-alone cell-based sensing.
Assuntos
Bacillus subtilis/fisiologia , Células Imobilizadas , Escherichia coli/fisiologia , Nanoestruturas , Fosfolipídeos , Saccharomyces cerevisiae/fisiologia , Dióxido de Silício , Soluções Tampão , Membrana Celular , Recuperação de Fluorescência Após Fotodegradação , Proteínas de Fluorescência Verde/biossíntese , Concentração de Íons de Hidrogênio , Bicamadas Lipídicas , Micelas , Microscopia Eletrônica , Proteínas Recombinantes/biossíntese , Espalhamento de Radiação , Raios XRESUMO
BACKGROUND: As carbon sources are exhausted, Saccharomyces cerevisiae cells exhibit reduced metabolic activity and cultures enter the stationary phase. We asked whether cells in stationary phase cultures respond to additional stress at the level of transcript abundance. RESULTS: Microarrays were used to quantify changes in transcript abundance in cells from stationary phase cultures in response to stress. More than 800 mRNAs increased in abundance by one minute after oxidative stress. A significant number of these mRNAs encode proteins involved in stress responses. We tested whether mRNA increases were due to new transcription, rapid poly-adenylation of message (which would not be detected by microarrays), or potential release of mature mRNA present in the cell but resistant to extraction during RNA isolation. Examination of the response to oxidative stress in an RNA polymerase II mutant, rpb1-1, suggested that new transcription was not required. Quantitative RT-PCR analysis of a subset of these transcripts further suggested that the transcripts present in isolated total RNA from stationary phase cultures were polyadenylated. In contrast, over 2,000 transcripts increased after protease treatment of cell-free lysates from stationary phase but not exponentially growing cultures. Different subsets of transcripts were released by oxidative stress and temperature upshift, suggesting that mRNA release is stress-specific. CONCLUSIONS: Cells in stationary phase cultures contain a large number of extraction-resistant mRNAs in a protease-labile, rapidly releasable form. The transcript release appears to be stress-specific. We hypothesize that these transcripts are associated with P-bodies.
Assuntos
Estresse Oxidativo/genética , RNA Fúngico/genética , RNA Mensageiro/genética , Saccharomyces cerevisiae/crescimento & desenvolvimento , Saccharomyces cerevisiae/genética , Transcrição Gênica , Sistema Livre de Células , Endopeptidase K/farmacologia , Regulação Fúngica da Expressão Gênica , Mutação , Análise de Sequência com Séries de Oligonucleotídeos , RNA Polimerase II/genética , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Saccharomyces cerevisiae/efeitos dos fármacosRESUMO
Microarrays measure gene expression typically from a mixture of cell populations during different stages of a biological process. However, the specific effects of the distinct or pure populations on measured gene expression are difficult or impossible to determine. The ability to deconvolve measured gene expression into the contributions from pure populations is critical to maximizing the potential of microarray analysis for investigating complex biological processes. In this paper, we describe a novel approach called the multinomial hidden Markov model (MHMM) that produces: (i) a maximum a posteriori estimate of the fraction represented by each pure population and (ii) gene expression values for each pure population. Our method uses an unsupervised, probabilistic approach for handling missing data points and clusters genes based on expression in pure populations. MHMM, used with several yeast datasets, identified statistically significant temporal dynamics. This method, unlike the linear decomposition models used previously for deconvolution, can extract information from different types of data, does not require a priori identification of pure gene expression, exploits the temporal nature of time series data, and is less affected by missing data.
Assuntos
Células/metabolismo , Perfilação da Expressão Gênica , Cadeias de Markov , Modelos Biológicos , Algoritmos , Ciclo Celular , Células/citologia , Mutação , Análise de Sequência com Séries de OligonucleotídeosRESUMO
Here we describe an automated, pressure-driven, sampling device for harvesting 10 to 30 ml samples, in replicate, with intervals as short as 10 s. Correlation between biological replicate time courses measured by microarrays was extremely high. The sampler enables sampling at intervals within the range of many important biological processes.
Assuntos
Técnicas Microbiológicas/instrumentação , Leveduras , Automação , Meios de Cultura , Desenho de Equipamento , Análise de Sequência com Séries de Oligonucleotídeos , RNA Fúngico/análise , RNA Fúngico/isolamento & purificação , Reprodutibilidade dos Testes , Leveduras/genética , Leveduras/crescimento & desenvolvimento , Leveduras/isolamento & purificação , Leveduras/metabolismoRESUMO
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.