TM4: a free, open-source system for microarray data management and analysis

A synthetic de-greening gene circuit provides a reporting system that is remotely detectable and has a re-set capacity

Plants have evolved elegant mechanisms to continuously sense and respond to their environment, suggesting that these properties can be adapted to make inexpensive and widely used biological monitors, or sentinels, for human threats. For a plant to be a sentinel, a reporting system is needed for large areas and widespread monitoring. The reporter or readout mechanism must be easily detectable, allow remote monitoring and provide a re-set capacity; all current gene reporting technologies fall short of these requirements. Chlorophyll is one of the best-recognized plant pigments with an already well-developed remote imaging technology. However, chlorophyll is very abundant, with levels regulated by both genetic and environmental factors. We designed a synthetic de-greening circuit that produced rapid chlorophyll loss on perception of a specific input. With induction of the de-greening circuit, changes were remotely detected within 2 h. Analyses of multiple de-greening circuits suggested that the de-greening circuit functioned, in part, via light-dependent damage to photosystem cores and the production of reactive oxygen species. Within 24-48 h of induction, an easily recognized white phenotype resulted. Microarray analysis showed that the synthetic de-greening initiated a process largely distinct from normal chlorophyll loss in senescence. Remarkably, synthetically de-greened white plants re-greened after removal of the inducer, providing the first easily re-settable reporter system for plants and the capacity to make re-settable biosensors. Our results showed that the de-greening circuit allowed chlorophyll to be employed as a simple but powerful reporter system useful for widespread areas.

Use of protein microarrays to define the humoral immune response in leprosy patients and identification of disease-state-specific antigenic profiles

Although the global prevalence of leprosy has decreased over the last few decades due to an effective multidrug regimen, large numbers of new cases are still being reported, raising questions as to the ability to identify patients likely to spread disease and the effects of chemotherapy on the overall incidence of leprosy. This can partially be attributed to the lack of diagnostic markers for different clinical states of the disease and the consequent implementation of differential, optimal drug therapeutic strategies. Accordingly, comparative bioinformatics and Mycobacterium leprae protein microarrays were applied to investigate whether leprosy patients with different clinical forms of the disease can be categorized based on differential humoral immune response patterns. Evaluation of sera from 20 clinically diagnosed leprosy patients using native protein and recombinant protein microarrays revealed unique disease-specific, humoral reactivity patterns. Statistical analysis of the serological patterns yielded distinct groups that correlated with phenolic glycolipid I reactivity and clinical diagnosis, thus demonstrating that leprosy patients, including those diagnosed with the paucibacillary, tuberculoid form of disease, can be classified based on humoral reactivity to a subset of M. leprae protein antigens produced in recombinant form.

Arabidopsis seed development and germination is associated with temporally distinct metabolic switches

While the metabolic networks in developing seeds during the period of reserve accumulation have been extensively characterized, much less is known about those present during seed desiccation and subsequent germination. Here we utilized metabolite profiling, in conjunction with selective mRNA and physiological profiling to characterize Arabidopsis (Arabidopsis thaliana) seeds throughout development and germination. Seed maturation was associated with a significant reduction of most sugars, organic acids, and amino acids, suggesting their efficient incorporation into storage reserves. The transition from reserve accumulation to seed desiccation was associated with a major metabolic switch, resulting in the accumulation of distinct sugars, organic acids, nitrogen-rich amino acids, and shikimate-derived metabolites. In contrast, seed vernalization was associated with a decrease in the content of several of the metabolic intermediates accumulated during seed desiccation, implying that these intermediates might support the metabolic reorganization needed for seed germination. Concomitantly, the levels of other metabolites significantly increased during vernalization and were boosted further during germination sensu stricto, implying their importance for germination and seedling establishment. The metabolic switches during seed maturation and germination were also associated with distinct patterns of expression of genes encoding metabolism-associated gene products, as determined by semiquantitative reverse transcription-polymerase chain reaction and analysis of publicly available microarray data. When taken together our results provide a comprehensive picture of the coordinated changes in primary metabolism that underlie seed development and germination in Arabidopsis. They furthermore imply that the metabolic preparation for germination and efficient seedling establishment initiates already during seed desiccation and continues by additional distinct metabolic switches during vernalization and early germination.

Profiling of apoptosis genes allows for clinical stratification of primary nodal diffuse large B-cell lymphomas

Intrinsic resistance of lymphoma cells to apoptosis is a probable mechanism causing chemotherapy resistance and eventual fatal outcome in patients with diffuse large B cell lymphomas (DLBCL). We investigated whether microarray expression profiling of apoptosis related genes predicts clinical outcome in 46 patients with primary nodal DLBCL. Unsupervised cluster analysis using genes involved in apoptosis (n = 246) resulted in three separate DLBCL groups partly overlapping with germinal centre B-lymphocytes versus activated B-cells like phenotype. One group with poor clinical outcome was characterised by high expression levels of pro-and anti-apoptotic genes involved in the intrinsic apoptosis pathway. A second group, also with poor clinical outcome, was characterised by high levels of apoptosis inducing cytotoxic effector genes, possibly reflecting a cellular cytotoxic immune response. The third group showing a favourable outcome was characterised by low expression levels of genes characteristic for both other groups. Our results suggest that chemotherapy refractory DLBCL are characterised either by an intense cellular cytotoxic immune response or by constitutive activation of the intrinsic mediated apoptosis pathway with concomitant downstream inhibition of this apoptosis pathway. Consequently, strategies neutralising the function of apoptosis-inhibiting proteins might be effective as alternative treatment modality in part of chemotherapy refractory DLBCL.

Evaluating different methods of microarray data normalization

Background

With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration.

Results

Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets.

Conclusion

In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.

Transcriptional profiles of the response to ketoconazole and amphotericin B in Trichophyton rubrum

Trichophyton rubrum is a pathogenic filamentous fungus of increasing medical concern. Two antifungal agents, ketoconazole (KTC) and amphotericin B (AMB), have specific activity against dermatophytes. To identify the mechanisms of action of KTC and AMB against T. rubrum, a cDNA microarray was constructed from the expressed sequence tags of the cDNA library from different developmental stages, and transcriptional profiles of the responses to KTC and AMB were determined. T. rubrum was exposed to subinhibitory concentrations of KTC and AMB for 12 h, and microarray analysis was used to examine gene transcription. KTC exposure induced transcription of genes involved in lipid, fatty acid, and sterol metabolism, including ERG11, ERG3, ERG25, ERG6, ERG26, ERG24, ERG4, CPO, INO1, DW700960, CPR, DW696584, DW406350, and ATG15. KTC also increased transcription of the multidrug resistance gene ABC1. AMB exposure increased transcription of genes involved in lipid, fatty acid, and sterol metabolism (DW696584, EB801458, IVD, DW694010, DW688343, DW684992), membrane transport (Git1, DW706156, DW684040, DMT, DW406136, CCH1, DW710650), and stress-related responses (HSP70, HSP104, GSS, AOX, EB801455, EB801702, TDH1, UBI4) but reduced transcription of genes involved in maintenance of cell wall integrity and signal transduction pathways (FKS1, SUN4, DW699324, GAS1, DW681613, SPS1, DW703091, STE7, DW703091, DW695308) and some ribosomal proteins. This is the first report of the use of microarray analysis to determine the effects of drug action in T. rubrum.

Contrasting evolution of expression differences in the testis between species and subspecies of the house mouse

Regulatory changes in genes involved in reproduction are thought to be prime targets for divergence during speciation, since they are expected to play an important role in sexual selection and sexual conflict. We used microarray analysis of RNA from different wild populations of house mouse subspecies (including Mus m. musculus, Mus m. domesticus, and Mus m. castaneus) and from the sister species Mus spretus to test this assumption. A comparison of expression divergence in brain, liver/kidney, and testis shows a major difference in the evolutionary dynamics of testis-related genes. While the comparison between species confirms an excess in divergence in testis genes, we find that all comparisons between subspecies yield only a very small number of genes with significantly different expression levels in the testis. These results suggest that the early phase of the speciation process may not be driven by regulatory changes in genes that are potential targets of sexual selection, and that the divergence in these genes is only established during a later phase of the speciation process.

Gene signature of breast cancer cell lines treated with lycopene

Among the micronutrients studied in relation between nutrition and cancer, lycopene appears to be a breast cancer preventive phytochemical candidate found in raw tomatoes and tomato-derived products. In order to investigate the responsiveness of breast cancer genes to lycopene and to better understand the molecular mechanisms underlying the effects of lycopene, we used an oligonucleotide microarray approach. Human breast cancer cell lines (MCF-7 and MDA-MB-231) and a fibrocystic breast cell line (MCF-10a) were either exposed or not exposed to 10 microM lycopene for 48 h. Microarrays comprising 202 genes were used to identify genes responsive to lycopene supplementation. Hierarchical clustering revealed a cell line-specific lycopene modulation of breast cells. Based on the observed results, lycopene seems to exert regulation on apoptosis, cell cycle and DNA repair mechanisms according to estrogen and retinoic acid receptor cell status.

Helicobacter pylori HP1512 is a nickel-responsive NikR-regulated outer membrane protein

Helicobacter pylori is dependent upon the production of the highly abundant and active metalloenzyme urease for colonization of the human stomach. Thus, H. pylori has an absolute requirement for the transition metal nickel, a required cofactor for urease. To investigate the contribution of genes that are factors in this process, microarray analysis comparing the transcriptome of wild-type H. pylori 26695 cultured in brucella broth containing fetal calf serum (BBF) alone or supplemented with 100 microM NiCl(2) suggested that HP1512 is repressed in the presence of 100 microM supplemental nickel. When measured by comparative real-time quantitative PCR (qPCR), HP1512 transcription was reduced 43-fold relative to the value for the wild type when cultured in BBF supplemented with 10 microM NiCl(2). When grown in unsupplemented BBF, urease activity of an HP1512::cat mutant was significantly reduced compared to the wild type, 4.9 +/- 0.5 micromol/min/mg of protein (n = 7) and 17.1 +/- 4.9 micromol/min/mg of protein (n = 13), respectively (P < 0.0001). In silico analysis of the HP1511-HP1512 (HP1511-1512) intergenic region identified a putative NikR operator upstream of HP1512. Gel shift analysis with purified recombinant NikR verified nickel-dependent binding of H. pylori NikR to the HP1511-1512 intergenic region. Furthermore, comparative real-time qPCR of four nickel-related genes suggests that mutation of HP1512 results in reduced intracellular nickel concentration relative to wild-type H. pylori 26695. Taken together, these data suggest that HP1512 encodes a NikR-nickel-regulated outer membrane protein.

Molecular karyotyping and aneuploidy detection in Arabidopsis thaliana using quantitative fluorescent polymerase chain reaction

Certain cellular processes are sensitive to changes in gene dosage. Aneuploidy is deleterious because of an imbalance of gene dosage on a chromosomal scale. Identification, classification and characterization of aneuploidy are therefore important for molecular, population and medical genetics and for a deeper understanding of the mechanisms underlying dosage sensitivity. Notwithstanding recent progress in genomic technologies, limited means are available for detecting and classifying changes in chromosome dose. The development of an inexpensive and scalable karyotyping method would allow rapid detection and characterization of both simple and complex aneuploid types. In addition to the problem of karyotyping, genomic and molecular genetic studies of aneuploids and polyploids are complicated by multiple heterozygous combinations possible at loci present in more than two copies. Quantitative scoring of allele genotypes would enable large-scale population genetic experiments in polyploids, and permit genetic analyses on bulked populations in diploid species. Here, we demonstrate that quantitative fluorescent-polymerase chain reaction (QF-PCR) can be used to simultaneously genotype and karyotype aneuploid and polyploid Arabidopsis thaliana. Comparison of QF-PCR with flow cytometric determination of nuclear DNA content indicated near perfect agreement between the methods, but complete karyotype resolution was only possible using QF-PCR. A complex karyotype, determined by QF-PCR, was validated by comparative genomic hybridization to microarrays. Finally, we screened the progeny of tetraploid individuals and found that more than 25% were aneuploid and that our artificially induced tetraploid strain produced fewer aneuploid individuals than a tetraploid strain isolated from nature.

The kinetics of transcriptomic changes induced by cigarette smoke in rat lungs reveals a specific program of defense, inflammation, and circadian clock gene expression

Gene expression profiling in animal models exposed to cigarette mainstream smoke (CS) shapes up as a promising tool for investigating the molecular mechanisms involved in the onset and development of CS-related disease and may aid in the identification of disease candidate genes. Here we report on differential gene expression in lungs of rats exposed for 2, 7, and 13 weeks to 300 and 600 microg total particulate matter/l CS with sacrifice 2, 6, or 20 h after the last exposure. Regarding antioxidant and xenobiotic-metabolizing (phase I/II) enzymes, a stereotypic, mostly transient, expression pattern of differentially expressed genes was observed after each exposure period. The expression patterns were generally dose dependent for antioxidant and phase II genes and not dose dependent for phase I genes at the CS concentrations tested. However, with increasing length of exposure, there was a distinct, mostly sustained and dose-sensitive, expression of genes implicated in innate and adaptive immune responses, clearly pointing to an emerging inflammatory response. Notably, this inflammatory response included the expression of lung disease-related genes not yet linked to CS exposure, such as galectin-3, arginase 1, and chitinase, as well as genes encoding proteolytic enzymes. Finally, our experiments also revealed a CS exposure-dependent shift in the cyclical expression of genes involved in controlling the circadian rhythm. Altogether, these results provide further insight into the molecular mechanisms of CS-dependent disease onset and development and thus may also be useful for defining CS-specific molecular biomarkers of disease.

A tumor progression model for hepatocellular carcinoma: bioinformatic analysis of genomic data

BACKGROUND & AIMS

It is widely recognized that genomic abnormalities underpin the development of human cancers. Aberrant patterns of chromosomal changes may represent useful information that can be used in classifying the complex traits of liver cancer cases for the genetic events involved in tumor carcinogenesis, tumor progression, and prognosis.

METHODS

Genome-wide chromosomal aberrations of 158 hepatitis B virus-associated hepatocellular carcinoma (HCC) were studied by comparative genomic hybridization (CGH). By application of a self-organizing tree algorithm, statistically significant CGH events were used to construct an evolutionary tree that could infer patient subgroups with different degrees of tumor progression. The key CGH events in the subgroups were identified. The clinical significance of the groupings and the key CGH events were examined.

RESULTS

Based on the patterns of significant chromosomal aberrations derived, 3 HCC subgroups organized in an evolutionary tree were identified. The groupings possessed information reflecting the degrees of tumor progression, including numbers of chromosomal aberrations, tumor stages, tumor sizes, and disease outcome. Gains of 1q21-23 and 8q22-24 were identified as genomic events associated with the early development of HCC. Gain of 3q22-24, however, was identified as 1 of the late genomic events found to be associated with tumor recurrence and poor overall patient survival.

CONCLUSIONS

A tumor progression model for HCC was constructed and revealed chromosomal imbalances that were significantly associated with clinical pathologic characteristics of the disease. This model explains a significant part of the variations in clinical outcome among HCC patients.

Parallel protein and transcript profiles of FSHD patient muscles correlate to the D4Z4 arrangement and reveal a common impairment of slow to fast fibre differentiation and a general deregulation of MyoD-dependent genes

Here, we present the first study of a human neuromuscular disorder at transcriptional and proteomic level. Autosomal dominant facio-scapulo-humeral muscular dystrophy (FSHD) is caused by a deletion of an integral number of 3.3-kb KpnI repeats inside the telomeric region D4Z4 at the 4q35 locus. We combined a muscle-specific cDNA microarray platform with a proteomic investigation to analyse muscle biopsies of patients carrying a variable number of KpnI repeats. Unsupervised cluster analysis divides patients into three classes, according to their KpnI repeat number. Expression data reveal a transition from fast-glycolytic to slow-oxidative phenotype in FSHD muscle, which is accompanied by a deficit of proteins involved in response to oxidative stress. Besides, FSHD individuals show a disruption in the MyoD-dependent gene network suggesting a coregulation at transcriptional level during myogenesis. We also discuss the hypothesis that D4Z4 contraction may affect in trans the expression of a set of genes involved in myogenesis, as well as in the regeneration pathway of satellite cells in adult tissue. Muscular wasting could result from the inability of satellite cells to successfully differentiate into mature fibres and from the accumulation of structural damages caused by a reactive oxygen species (ROS) imbalance induced by an increased oxidative metabolism in fibres.

Zyxin is upregulated in the nucleus by thymosin β4 in SiHa cells

Thymosin beta4 is a 43-amino acid actin-binding protein that promotes cell migration and is important in angiogenesis, wound healing, and tumor metastasis. We searched for genes upregulated by thymosin beta4 and identified zyxin as increased in SiHa cells in the presence of exogenously added thymosin beta4 and when thymosin beta4 is overexpressed using adenoviral vectors. Both zyxin and thymosin beta4 show increased localization in the nucleus. We conclude that thymosin beta4 may exert some of its migration promoting activity via increased zyxin expression.

A scalable method for integration and functional analysis of multiple microarray datasets

MOTIVATION

The diverse microarray datasets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions.

RESULTS

To aid in the analysis of such large compendia of microarray experiments, we present Microarray Experiment Functional Integration Technology (MEFIT), a scalable Bayesian framework for predicting functional relationships from integrated microarray datasets. Furthermore, MEFIT predicts these functional relationships within the context of specific biological processes. All results are provided in the context of one or more specific biological functions, which can be provided by a biologist or drawn automatically from catalogs such as the Gene Ontology (GO). Using MEFIT, we integrated 40 Saccharomyces cerevisiae microarray datasets spanning 712 unique conditions. In tests based on 110 biological functions drawn from the GO biological process ontology, MEFIT provided a 5% or greater performance increase for 54 functions, with a 5% or more decrease in performance in only two functions.

Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana

We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.

MicroArray Facility: a laboratory information management system with extended support for Nylon based technologies

Background

High throughput gene expression profiling (GEP) is becoming a routine technique in life science laboratories. With experimental designs that repeatedly span thousands of genes and hundreds of samples, relying on a dedicated database infrastructure is no longer an option.

GEP technology is a fast moving target, with new approaches constantly broadening the field diversity. This technology heterogeneity, compounded by the informatics complexity of GEP databases, means that software developments have so far focused on mainstream techniques, leaving less typical yet established techniques such as Nylon microarrays at best partially supported.

Results

MAF (MicroArray Facility) is the laboratory database system we have developed for managing the design, production and hybridization of spotted microarrays. Although it can support the widely used glass microarrays and oligo-chips, MAF was designed with the specific idiosyncrasies of Nylon based microarrays in mind. Notably single channel radioactive probes, microarray stripping and reuse, vector control hybridizations and spike-in controls are all natively supported by the software suite. MicroArray Facility is MIAME supportive and dynamically provides feedback on missing annotations to help users estimate effective MIAME compliance. Genomic data such as clone identifiers and gene symbols are also directly annotated by MAF software using standard public resources. The MAGE-ML data format is implemented for full data export. Journalized database operations (audit tracking), data anonymization, material traceability and user/project level confidentiality policies are also managed by MAF.

Conclusion

MicroArray Facility is a complete data management system for microarray producers and end-users. Particular care has been devoted to adequately model Nylon based microarrays. The MAF system, developed and implemented in both private and academic environments, has proved a robust solution for shared facilities and industry service providers alike.

Hepatic gene downregulation following acute and subchronic exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin

Chronic exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) has been shown to lead to the development of hepatotoxicity and carcinogenicity in the liver of female rats. In this study, we investigated hepatic gene downregulation in response to acute and subchronic TCDD exposure. We identified 61 probes which exhibited a downregulation of twofold or greater following subchronic (13 weeks) exposure to TCDD. Comparative analysis of the hepatic expression of these 61 probes was conducted with rats subchronically exposed to PeCDF, PCB126, PCB153, and a mixture of PCB126 and PCB153. PCB153 produced little or no alteration in these probes, while the binary mixture mimicked most closely the downregulation observed with TCDD. To discern if the repression of genes within this probe set occur as a primary response to TCDD exposure, we analyzed the early responsiveness of 11 genes at 6, 24, and 72 h following a single exposure to TCDD. We observed early repression of the 11 genes within this early time course, indicating that the repression of this subset of genes occurs as a primary response to TCDD exposure and not as a secondary response to 13 weeks of subchronic treatment. In addition, the gender, species, and AhR dependence of these responses were also investigated. Gender- and species-dependent repression was observed within this subset of genes. Furthermore, utilizing AhR knockout mice, we were able to determine the AhR-dependent downregulation of seven of 11 genes. Together these results assist efforts to understand the multitude of effects imposed by TCDD and AhR ligands on gene expression.

Plant peroxisomes respire in the light: some gaps of the photorespiratory C2 cycle have become filled--others remain

The most prominent role of peroxisomes in photosynthetic plant tissues is their participation in photorespiration, a process also known as the oxidative C2 cycle or the oxidative photosynthetic carbon cycle. Photorespiration is an essential process in land plants, as evident from the conditionally lethal phenotype of mutants deficient in enzymes or transport proteins involved in this pathway. The oxidative C2 cycle is a salvage pathway for phosphoglycolate, the product of the oxygenase activity of ribulose 1,5-bisphosphate carboxylase/oxygenase (RubisCO), to the Calvin cycle intermediate phosphoglycerate. The pathway is highly compartmentalized and involves reactions in chloroplasts, peroxisomes, and mitochondria. The H2O2-producing enzyme glycolate oxidase, catalase, and several aminotransferases of the photorespiratory cycle are located in peroxisomes, with catalase representing the major constituent of the peroxisomal matrix in photosynthetic tissues. Although photorespiration is of major importance for photosynthesis, the identification of the enzymes involved in this process has only recently been completed. Only little is known about the metabolite transporters for the exchange of photorespiratory intermediates between peroxisomes and the other organelles involved, and about the regulation of the photorespiratory pathway. This review highlights recent developments in understanding photorespiration and identifies remaining gaps in our knowledge of this important metabolic pathway.

Quantitative proteome analysis of breast cancer cell lines using 18O-labeling and an accurate mass and time tag strategy

Proteome comparison of cell lines derived from cancer and normal breast epithelium provide opportunities to identify differentially expressed proteins and pathways associated with specific phenotypes. We employed 16O/18O peptide labeling, FT-ICR MS, and an accurate mass and time (AMT) tag strategy to simultaneously compare the relative abundance of hundreds of proteins in non-cancer and cancer cell lines derived from breast tissue. A cell line reference panel allowed relative protein abundance comparisons among multiple cell lines and across multiple experiments. A peptide database generated from multidimensional LC separations and MS/MS analysis was used for subsequent AMT tag-based peptide identifications. This peptide database represented a total of 2299 proteins, including 514 that were quantified in five cell lines using the AMT tag and 16O/18O strategies. Eighty-six proteins showed at least a threefold protein abundance change between cancer and non-cancer cell lines. Hierarchical clustering of protein abundance ratios revealed that several groups of proteins were differentially expressed between the cancer cell lines.

Evaluation of two methods for generating cRNA for microarray experiments from nanogram amounts of total RNA

Several methods have been developed for amplification of RNA, making it possible to use cDNA microarrays for analysis of samples limited in amount of total RNA. The most widely used amplification protocol, the Eberwine method, amplifies RNA in a linear manner through in vitro transcription (IVT). However, when starting material is limited to nanogram amounts of total RNA, several rounds of amplification are necessary, making this method both expensive and labor-intensive. Amplification by PCR is robust and is able to amplify extremely limiting material. However, it is possible that the nonlinear nature of PCR could result in reduced reproducibility of the amplification compared with IVT. We have evaluated two methods that use a combination of PCR and IVT for amplification of nanogram amounts of total RNA. We have compared microarray results obtained by these methods with results obtained by two established methods: indirect labeling of 20 microg total RNA and Eberwine amplification of 1 microg total RNA. Starting from as little as 5 ng of total RNA, both methods yielded results in concordance with the Eberwine method.

Whole genome expression profiling of glucose-dependent insulinotropic peptide (GIP)- and adrenocorticotropin-dependent adrenal hyperplasias reveals novel targets for the study of GIP-dependent Cushing's syndrome

CONTEXT

The mechanisms responsible for the ectopic adrenal expression of glucose-dependent insulinotropic peptide (GIP) receptor (GIPR) in GIP-dependent Cushing's syndrome (CS) are unknown. Chronic adrenal stimulation by ACTH in Cushing's disease or GIP in GIP-dependent ACTH-independent macronodular adrenal hyperplasia both lead to the induction of genes implicated in adrenal proliferation and steroidogenesis.

OBJECTIVE

The objective of the study was to identify genes differentially expressed specifically in GIP-dependent CS that could be implicated in the ectopic expression of GIPR.

METHODS

We used the Affymetrix U133 plus 2.0 microarray oligochips to compare the whole genome expression profile of adrenal tissues from five cases of GIP-dependent bilateral ACTH-independent macronodular adrenal hyperplasia with CS, one case of GIP-dependent unilateral adenoma with CS, five cases of ACTH-dependent hyperplasias, and a pool of adrenals from 62 normal individuals.

RESULTS

After data normalization and statistical filtering, 723 genes with differential expression were identified, including 461 genes or sequences with a known functional implication, classified in eight dominant functional classes. Specific findings include repression of perilipin, the overexpression of 13 G protein-coupled receptors, and the potential involvement of Rho-GTPases. We also isolated 94 probe sets potentially linked to the formation of GIP-dependent nodules adjacent to the diffuse hyperplasia. These included probe sets related to the linker histone H1 and repression of RXRa and CCND2. The expression profiles for eight genes were confirmed by real-time RT-PCR.

CONCLUSION

This study identified an extensive series of potentially novel target candidate genes that could be implicated in the molecular mechanisms of ectopic expression of the GIPR as well as in the multistep progression of GIP-dependent CS.

Transcriptional profiling of the Bacillus anthracis life cycle in vitro and an implied model for regulation of spore formation

The life cycle of Bacillus anthracis includes both vegetative and endospore morphologies which alternate based on nutrient availability, and there is considerable evidence indicating that the ability of this organism to cause anthrax depends on its ability to progress through this life cycle in a regulated manner. Here we report the use of a custom B. anthracis GeneChip in defining the gene expression patterns that occur throughout the entire life cycle in vitro. Nearly 5,000 genes were expressed in five distinct waves of transcription as the bacteria progressed from germination through sporulation, and we identified a specific set of functions represented within each wave. We also used these data to define the temporal expression of the spore proteome, and in doing so we have demonstrated that much of the spore's protein content is not synthesized de novo during sporulation but rather is packaged from preexisting stocks. We explored several potential mechanisms by which the cell could control which proteins are packaged into the developing spore, and our analyses were most consistent with a model in which B. anthracis regulates the composition of the spore proteome based on protein stability. This study is by far the most comprehensive survey yet of the B. anthracis life cycle and serves as a useful resource in defining the growth-phase-dependent expression patterns of each gene. Additionally, the data and accompanying bioinformatics analyses suggest a model for sporulation that has broad implications for B. anthracis biology and offer new possibilities for microbial forensics and detection.

The effects of elevated CO2 concentration on soybean gene expression. An analysis of growing and mature leaves

Improvements in carbon assimilation and water-use efficiency lead to increases in maximum leaf area index at elevated carbon dioxide concentration ([CO(2)]); however, the molecular drivers for this increase are unknown. We investigated the molecular basis for changes in leaf development at elevated [CO(2)] using soybeans (Glycine max) grown under fully open air conditions at the Soybean Free Air CO(2) Enrichment (SoyFACE) facility. The transcriptome responses of rapidly growing and fully expanded leaves to elevated [CO(2)] were investigated using cDNA microarrays. We identified 1,146 transcripts that showed a significant change in expression in growing versus fully expanded leaves. Transcripts for ribosomal proteins, cell cycle, and cell wall loosening, necessary for cytoplasmic growth and cell proliferation, were highly expressed in growing leaves. We further identified 139 transcripts with a significant [CO(2)] by development interaction. Clustering of these transcripts showed that transcripts involved in cell growth and cell proliferation were more highly expressed in growing leaves that developed at elevated [CO(2)] compared to growing leaves that developed at ambient [CO(2)]. The 327 [CO(2)]-responsive genes largely suggest that elevated [CO(2)] stimulates the respiratory breakdown of carbohydrates, which provides increased energy and biochemical precursors for leaf expansion and growth at elevated [CO(2)]. While increased photosynthesis and carbohydrate production at elevated [CO(2)] are well documented, this research demonstrates that at the transcript and metabolite level, respiratory breakdown of starch is also increased at elevated [CO(2)].

Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes

Background

A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species.

Results

In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency.

Conclusion

Functional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set.

Cross-platform array comparative genomic hybridization meta-analysis separates hematopoietic and mesenchymal from epithelial tumors

A series of studies have been published that evaluate the chromosomal copy number changes of different tumor classes using array comparative genomic hybridization (array CGH); however, the chromosomal aberrations that distinguish the different tumor classes have not been fully characterized. Therefore, we performed a meta-analysis of different array CGH data sets in an attempt to classify samples tested across different platforms. As opposed to RNA expression, a common reference is used in dual channel CGH arrays: normal human DNA, theoretically facilitating cross-platform analysis. To this aim, cell line and primary cancer data sets from three different dual channel array CGH platforms obtained by four different institutes were integrated. The cell line data were used to develop preprocessing methods, which performed noise reduction and transformed samples into a common format. The transformed array CGH profiles allowed perfect clustering by cell line, but importantly not by platform or institute. The same preprocessing procedures used for the cell line data were applied to data from 373 primary tumors profiled by array CGH, including controls. Results indicated that there is no apparent feature related to the institute or platform and that array CGH allows for unambiguous cross-platform meta-analysis. Major clusters with common tissue origin were identified. Interestingly, tumors of hematopoietic and mesenchymal origins cluster separately from tumors of epithelial origin. Therefore, it can be concluded that chromosomal aberrations of tumors from hematopoietic and mesenchymal origin versus tumors of epithelial origin are distinct, and these differences can be picked up by meta-analysis of array CGH data. This suggests the possibility of prospectively using combined analysis of diverse copy number data sets for cancer subtype classification.

Transcriptional profile of Haemophilus influenzae: effects of iron and heme

Haemophilus influenzae requires either heme or a porphyrin and iron source for growth. Microarray studies of H. influenzae strain Rd KW20 identified 162 iron/heme-regulated genes, representing approximately 10% of the genome, with > or =1.5-fold changes in transcription in response to iron/heme availability in vitro. Eighty genes were preferentially expressed under iron/heme restriction; 82 genes were preferentially expressed under iron/heme-replete conditions.

Global transcriptome analysis of Tropheryma whipplei in response to temperature stresses

Tropheryma whipplei, the agent responsible for Whipple disease, is a poorly known pathogen suspected to have an environmental origin. The availability of the sequence of the 0.92-Mb genome of this organism made a global gene expression analysis in response to thermal stresses feasible, which resulted in unique transcription profiles. A few genes were differentially transcribed after 15 min of exposure at 43 degrees C. The effects observed included up-regulation of the dnaK regulon, which is composed of six genes and is likely to be under control of two HspR-associated inverted repeats (HAIR motifs) found in the 5' region. Putative virulence factors, like the RibC and IspDF proteins, were also overexpressed. While it was not affected much by heat shock, the T. whipplei transcriptome was strongly modified following cold shock at 4 degrees C. For the 149 genes that were differentially transcribed, eight regulons were identified, and one of them was composed of five genes exhibiting similarity with genes encoding ABC transporters. Up-regulation of these genes suggested that there was an increase in nutrient uptake when the bacterium was exposed to cold stress. As observed for other bacterial species, the major classes of differentially transcribed genes encode membrane proteins and enzymes involved in fatty acid biosynthesis, indicating that membrane modifications are critical. Paradoxically, the heat shock proteins GroEL2 and ClpP1 were up-regulated. Altogether, the data show that despite the lack of classical regulation pathways, T. whipplei exhibits an adaptive response to thermal stresses which is consistent with its specific environmental origin and could allow survival under cold conditions.

MASQOT-GUI: spot quality assessment for the two-channel microarray platform

MASQOT-GUI provides an open-source, platform-independent software pipeline for two-channel microarray spot quality control. This includes gridding, segmentation, quantification, quality assessment and data visualization. It hosts a set of independent applications, with interactions between the tools as well as import and export support for external software. The implementation of automated multivariate quality control assessment, which is a unique feature of MASQOT-GUI, is based on the previously documented and evaluated MASQOT methodology. Further abilities of the application are outlined and illustrated.

AVAILABILITY

MASQOT-GUI is Java-based and licensed under the GNU LGPL. Source code and installation files are available for download at http://masqot-gui.sourceforge.net/

Selective modulation of protein kinase A I and II reveals distinct roles in thyroid cell gene expression and growth

A global gene expression profiling of TSH stimulation on differentiated (FRTL5) and partially dedifferentiated [FRT/TSHR (TSH receptor)] rat thyroid cells was performed. A total of 123 TSH-regulated genes (95 newly described) were identified in FRTL5, whereas no significant transcriptional modifications were seen in FRT/TSHR cells. Because regulatory subunit IIbeta (RIIbeta) of protein kinase A (PKA), a key element downstream of cAMP, was expressed in FRTL5 but not in cAMP-refractory FRT/TSHR cells, we hypothesized that this gene may play an important role in TSH signaling. We therefore performed a series of experiments to investigate the involvement of RIIbeta and the different PKA isoforms. A positive effect of PKA II- but not of PKA I-selective activation on gene transcription and proliferation in FRTL5 cells, as well as an impairment of TSH nuclear effects after RIIbeta silencing were observed, suggesting that PKA II plays an essential role in TSH signaling. This view was supported by the restoration of TSH nuclear effects after reexpression of RIIbeta in FRT/TSHR cells. Because PKA I stimulation could increase iodide uptake in FRTL5 cells without affecting gene transcription, PKA I may mediate TSH actions at posttranscriptional levels. Analyses on three human cancer cell lines confirmed the possible loss of RIIbeta expression and antiproliferative activity of PKA I-selective cAMP analogs ( approximately 60% at 200 microm in BRAF-mutated cells). The inhibitory effect of PKA I apparently required constitutive MAPK activation and was associated with an inhibition of ERK phosphorylation. These findings may open new therapeutic perspectives in patients with thyroid cancer.

Gene expression profiling of clinical stages II and III breast cancer

Clinical stage (CS) is an established indicator of breast cancer outcome. In the present study, a cDNA microarray platform containing 692 genes was used to identify molecular differences between CSII and CSIII disease. Tumor samples were collected from patients with CSII or CSIII breast cancer, and normal breast tissue was collected from women without invasive cancer. Seventy-eight genes were deregulated in CSIII tumors and 22 in CSII tumors when compared to normal tissue, and 20 of them were differentially expressed in both CSII and CSIII tumors. In addition, 58 genes were specifically altered in CSIII and expression of 6 of them was tested by real time RT-PCR in another cohort of patients with CSII or CSIII breast cancer and in women without cancer. Among these genes, MAX, KRT15 and S100A14, but not APOBEC3G or KRT19, were differentially expressed on both CSIII and CSII tumors as compared to normal tissue. Increased HMOX1 levels were detected only in CSIII tumors and may represent a molecular marker of this stage. A clear difference in gene expression pattern occurs at the normal-to-cancer transition; however, most of the differentially expressed genes are deregulated in tumors of both CS (II and III) compared to normal breast tissue.

From 'omes to biology

Technologies that have emerged from the genome project have dramatically increased our ability to generate data on the way in which organisms respond to their environments, how they execute their programmes of development and growth, and how these are altered in the development of disease states. However, our ability to analyse these large datasets has not kept pace with our ability to generate them and consequently new strategies must be developed to address the issues associated with their analysis. One approach that we have employed quite successfully is to look at data from microarrays (or proteomics or metabolomics experiments) not as independent datasets, but rather as elements of a much larger body of biological information across various scales that must be integrated with, and interpreted within, the context of such ancillary data. Here we outline the general approach and provide three examples from published studies of the way in which we have applied this strategy.

Microarray-MD: a system for exploratory analysis of microarray gene expression data

In this paper, we present Microarray Medical Data explorer (Microarray-MD), a novel software system that is able to assist in the exploratory analysis of gene expression microarray data. It implements a combination scheme of multiple Support Vector Machines, which integrates a variety of gene selection criteria and allows for the discrimination of multiple diseases or subtypes of a disease. The system can be trained and automatically tune its parameters with the provision of pathologically characterized gene expression data to its input. Given a set of new, uncharacterized, patient's data as input, it outputs a decision on the type or the subtype of a disease. A graphical user interface provides easy access to the system operations and direct adjustment of its parameters. It has been tested on various publicly available datasets. The overall accuracy it achieves was estimated to exceed 90%.

Selection of suitable reference genes for accurate normalization of gene expression profile studies in non-small cell lung cancer

Background

In real-time RT quantitative PCR (qPCR) the accuracy of normalized data is highly dependent on the reliability of the reference genes (RGs). Failure to use an appropriate control gene for normalization of qPCR data may result in biased gene expression profiles, as well as low precision, so that only gross changes in expression level are declared statistically significant or patterns of expression are erroneously characterized. Therefore, it is essential to determine whether potential RGs are appropriate for specific experimental purposes. Aim of this study was to identify and validate RGs for use in the differentiation of normal and tumor lung expression profiles.

Methods

A meta-analysis of lung cancer transcription profiles generated with the GeneChip technology was used to identify five putative RGs. Their consistency and that of seven commonly used RGs was tested by using Taqman probes on 18 paired normal-tumor lung snap-frozen specimens obtained from non-small-cell lung cancer (NSCLC) patients during primary curative resection.

Results

The 12 RGs displayed showed a wide range of Ct values: except for rRNA18S (mean 9.8), the mean values of all the commercial RGs and ESD ranged from 19 to 26, whereas those of the microarray-selected RGs (BTF-3, YAP1, HIST1H2BC, RPL30) exceeded 26. RG expression stability within sample populations and under the experimental conditions (tumour versus normal lung specimens) was evaluated by: (1) descriptive statistic; (2) equivalence test; (3) GeNorm applet. All these approaches indicated that the most stable RGs were POLR2A, rRNA18S, YAP1 and ESD.

Conclusion

These data suggest that POLR2A, rRNA18S, YAP1 and ESD are the most suitable RGs for gene expression profile studies in NSCLC. Furthermore, they highlight the limitations of commercial RGs and indicate that meta-data analysis of genome-wide transcription profiling studies may identify new RGs.

An open-access long oligonucleotide microarray resource for analysis of the human and mouse transcriptomes

Two collections of oligonucleotides have been designed for preparing pangenomic human and mouse microarrays. A total of 148 993 and 121 703 oligonucleotides were designed against human and mouse transcripts. Quality scores were created in order to select 25 342 human and 24 109 mouse oligonucleotides. They correspond to: (i) a BLAST-specificity score; (ii) the number of expressed sequence tags matching each probe; (iii) the distance to the 3′ end of the target mRNA. Scores were also used to compare in silico the two microarrays with commercial microarrays. The sets described here, called RNG/MRC collections, appear at least as specific and sensitive as those from the commercial platforms. The RNG/MRC collections have now been used by an Anglo-French consortium to distribute more than 3500 microarrays to the academic community. Ad hoc identification of tissue-specific transcripts and a ∼80% correlation with hybridizations performed on Affymetrix GeneChip™ suggest that the RNG/MRC microarrays perform well. This work provides a comprehensive open resource for investigators working on human and mouse transcriptomes, as well as a generic method to generate new microarray collections in other organisms. All information related to these probes, as well as additional information about commercial microarrays have been stored in a freely-accessible database called MEDIANTE.

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables - and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

Gene expression differences associated with human papillomavirus status in head and neck squamous cell carcinoma

Human papillomavirus (HPV) is associated with a subset of head and neck squamous cell carcinoma (HNSCC). Between 15% and 35% of HNSCCs harbor HPV DNA. Demographic and exposure differences between HPV-positive (HPV+) and negative (HPV-) HNSCCs suggest that HPV+ tumors may constitute a subclass with different biology, whereas clinical differences have also been observed. Gene expression profiles of HPV+ and HPV- tumors were compared with further exploration of the biological effect of HPV in HNSCC. Thirty-six HNSCC tumors were analyzed using Affymetrix Human 133U Plus 2.0 GeneChip and for HPV by PCR and real-time PCR. Eight of 36 (22%) tumors were positive for HPV subtype 16. Statistical analysis using Significance Analysis of Microarrays based on HPV status as a supervising variable resulted in a list of 91 genes that were differentially expressed with statistical significance. Results for a subset of these genes were verified by real-time PCR. Genes highly expressed in HPV+ samples included cell cycle regulators (p16(INK4A), p18, and CDC7) and transcription factors (TAF7L, RFC4, RPA2, and TFDP2). The microarray data were also investigated by mapping genes by chromosomal location (DIGMAP). A large number of genes on chromosome 3q24-qter had high levels of expression in HPV+ tumors. Further investigation of differentially expressed genes may reveal the unique pathways in HPV+ tumors that may explain the different natural history and biological properties of these tumors. These properties may be exploited as a target of novel therapeutic agents in HNSCC treatment.

Molecular characterization of early-stage bladder carcinomas by expression profiles, FGFR3 mutation status, and loss of 9q

We used gene expression profiling, mutation analyses of FGFR3 and TP53, and LOH analyses of chromosome 9 and the TP53 region on chromosome arm 17p, to molecularly characterize 75 Ta and T1 bladder carcinomas. We identified four major cellular processes related to cell cycle, protein synthesis, immune response, and extra cellular components that contribute to the expressional heterogeneity of early-stage urothelial cell carcinoma (UCC). Activating FGFR3 mutations were found at the highest frequency in G1 tumors (80%), and showed a strong correlation with FGFR3 expression. In contrast, G3 tumors displayed mutations in less than 10% of the cases and a low level of FGFR3 expression. Even though LOH on chromosome 9 was not associated with any specific expression pattern, our data indicate that loss of chromosome 9 is associated with tumor development rather than initiation. The combined analyses suggest the existence of two types of UCC tumors, one which is characterized by FGFR3 mutation or expression, high expression of protein synthesis genes, and low expression of cell cycle genes. Furthermore, the presented data underscore FGFR3 receptor involvement in urothelial cell transformation as the presence of FGFR3 mutations has a major impact on the global gene expression profile of bladder carcinomas.

Combined application of behavior genetics and microarray analysis to identify regional expression themes and gene-behavior associations

In this report we link candidate genes to complex behavioral phenotypes by using a behavior genetics approach. Gene expression signatures were generated for the prefrontal cortex, ventral striatum, temporal lobe, periaqueductal gray, and cerebellum in eight inbred strains from priority group A of the Mouse Phenome Project. Bioinformatic analysis of regionally enriched genes that were conserved across all strains revealed both functional and structural specialization of particular brain regions. For example, genes encoding proteins with demonstrated anti-apoptotic function were over-represented in the cerebellum, whereas genes coding for proteins associated with learning and memory were enriched in the ventral striatum, as defined by the Expression Analysis Systematic Explorer (EASE) application. Association of regional gene expression with behavioral phenotypes was exploited to identify candidate behavioral genes. Phenotypes that were investigated included anxiety, drug-naive and ethanol-induced distance traveled across a grid floor, and seizure susceptibility. Several genes within the glutamatergic signaling pathway (i.e., NMDA/glutamate receptor subunit 2C, calmodulin, solute carrier family 1 member 2, and glutamine synthetase) were identified in a phenotype-dependent and region-specific manner. In addition to supporting evidence in the literature, many of the genes that were identified could be mapped in silico to surrogate behavior-related quantitative trait loci. The approaches and data set described herein serve as a valuable resource to investigate the genetic underpinning of complex behaviors.

SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology

Background

The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.

Results

SBEAMS-Microarray, a module of the open-source Systems Biology Experiment Analysis Management System (SBEAMS), enables MIAME-compliant storage, management, analysis, and integration of high-throughput genomic expression data. It is interoperable with the Cytoscape network integration, visualization, analysis, and modeling software platform.

Conclusion

SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research.

Microarray analysis of transcript accumulation during perithecium development in the filamentous fungus Gibberella zeae (anamorph Fusarium graminearum)

Gibberella zeae (anamorph Fusarium graminearum) is the causal agent of Fusarium head blight (FHB) of wheat and barley in the United States. Ascospores forcibly discharged from mature fruiting bodies, the perithecia, serve as the primary inoculum for FHB epidemics. To identify genes important for perithecium development and function, a cDNA microarray that covered 11% of the G. zeae genome was constructed. The microarray was used to measure changes in transcription levels of genes expressed during three successive stages of perithecium development. When compared with vegetative mycelia, 651 (31%) cDNA clones showed changes in transcript levels in at least one of the three developmental stages. During perithecium development, 263 (13%) cDNA clones showed temporal changes in transcript profiles. Transcripts that showed the greatest changes in levels in maturing perithecia belonged to genes in the FunCat main functional categories of cell rescue, metabolism, cell type differentiation, energy, and cellular transport. For genes related to metabolism and cell type differentiation, transcripts showed the highest levels in immature perithecia, whereas for cellular transport-related genes, transcripts showed the highest levels in mature perithecia. This study represents the first large-scale investigation of both spatial and temporal changes in transcript levels during perithecium development. It provides clear evidence that the sexual development in fungi is a complex, multigenic process and identifies genes involved in sexual development of this agriculturally important fungus.

Involvement of human micro-RNA in growth and response to chemotherapy in human cholangiocarcinoma cell lines

BACKGROUND & AIMS

Micro-RNA (miRNA) are endogenous regulatory RNA molecules that modulate gene expression. Alterations in miRNA expression can contribute to tumor growth by modulating the functional expression of critical genes involved in tumor cell proliferation or survival. Our aims were to identify specific miRNA involved in the regulation of cholangiocarcinoma growth and response to chemotherapy.

METHODS

miRNA expression in malignant and nonmalignant human cholangiocytes was assessed using a microarray. Expression of selected miRNA and their precursors was evaluated by Northern blots and real-time polymerase chain reaction, respectively. The effect of selected miRNA on cell growth and response to chemotherapy was assessed using miRNA-specific antisense oligonucleotides to decrease miRNA expression or with precursor miRNA to increase cellular expression.

RESULTS

miRNA expression was markedly different in malignant cholangiocytes, with decreased expression of many miRNA compared with nonmalignant cells. A cluster of miRNA, including miR-320, miR-200b, miR-21, miR-23a, miR-141, miR-27a, and miR-34a, were expressed in all cell lines. MiR-21, miR-141, and miR-200b were highly over-expressed in malignant cholangiocytes. Inhibition of miR-21 and miR-200b increased sensitivity to gemcitabine, whereas inhibition of miR-141 decreased cell growth. Treatment of tumor cell xenografts with systemic gemcitabine altered the expression of a significant number of miRNA. miR-21 modulates gemcitabine-induced apoptosis by phosphatase and tensin homolog deleted on chromosome 10 (PTEN)-dependent activation of PI 3-kinase signaling. Potential target genes that were modulated by selected miRNA were identified.

CONCLUSIONS

Alterations in miRNA expression contribute to tumor growth and response to chemotherapy. Aberrantly expressed miRNA or their targets will provide mechanistic insight and therapeutic targets for cholangiocarcinoma.

Developmental Aberrations of Liver Gene Expression in Bovine Fetuses Derived from Somatic Cell Nuclear Transplantation

Cloning by somatic cell nuclear transfer (NT) has been accomplished. However, the process itself is inefficient since most clones die before birth and survivors often display various anomalies. In an effort to determine global expression profiles of developmentally regulated liver genes in NT bovine fetuses, we employed a custom-made bovine liver complementary DNA (cDNA) microarray. The NT fetuses in early pregnancy were derived from cumulus cells as the nuclear donor cells. Normal fetuses were derived from in vitro fertilization (IVF) and artificial insemination (AI). Gene expression levels in NT, IVF, and AI fetal livers were obtained by comparing individual fetal liver samples with that of adult liver of nonpregnant cycling cows. Statistical analyses of the expression data showed widespread dysregulation of developmentally important genes in the three NT fetuses examined. It was found that the number of dysregulated genes was within a range of 3.5-7.7% of the tested genes in the NT fetal livers. The analyses revealed that one NT fetus was markedly different in liver gene expression profile from the other two NT fetal livers in which the expression profiles were highly correlated. Thus, our findings demonstrate that widespread dysregulation of liver genes occurs in the developing liver of NT bovine fetuses. It is possible that inappropriate genomic reprogramming after NT is a key factor associated with abnormal gene expressions in the livers of NT fetuses, whereas distinct expression patterns between the fellow cloned fetuses likely have resulted from variable epigenetic status of the donor nuclei.

Gene expression profiling identifies potential relevant genes in alveolar rhabdomyosarcoma pathogenesis and discriminates PAX3-FKHR positive and negative tumors

We analyzed the expression signatures of 14 tumor biopsies from children affected by alveolar rhabdomyosarcoma (ARMS) to identify genes correlating to biological features of this tumor. Seven of these patients were positive for the PAX3-FKHR fusion gene and 7 were negative. We used a cDNA platform containing a large majority of probes derived from muscle tissues. The comparison of transcription profiles of tumor samples with fetal skeletal muscle identified 171 differentially expressed genes common to all ARMS patients. The functional classification analysis of altered genes led to the identification of a group of transcripts (LGALS1, BIN1) that may be relevant for the tumorigenic processes. The muscle-specific microarray platform was able to distinguish PAX3-FKHR positive and negative ARMS through the expression pattern of a limited number of genes (RAC1, CFL1, CCND1, IGFBP2) that might be biologically relevant for the different clinical behavior and aggressiveness of the 2 ARMS subtypes. Expression levels for selected candidate genes were validated by quantitative real-time reverse-transcription PCR.

Effect of fescue toxicosis on hepatic gene expression in mice1

Fescue toxicosis affects wild and domestic animals grazing fescue pasture infected with the endophytic fungus Neotyphodium coenophialum. Signs of fescue toxicosis include increased core body temperature and respiration rate and decreased milk yield and reproductive performance. Laboratory mice also exhibit symptoms of fescue toxicosis, as indicated by reduced growth rate and reproductive performance. Mice were used to study the effects of fescue toxicosis on hepatic gene expression. Twenty-seven mice were randomly allocated to a diet containing either 50% endophyte-infected (E+; 6 ppm ergovaline) or endophyte-free (E-) fescue seed for 2 wk under thermoneutral conditions. Liver genes differentially expressed due to fescue toxicosis were identified using DNA microarray. A 2-stage ANOVA of microarray data identified 36 differentially expressed genes between mice fed E+ and E- diets. Another analysis method, significance analysis of microarray, identified 9 genes as differentially expressed between treatment groups, and some genes overlapped with genes identified by ANOVA. Hierarchical clustering of 36 genes identified by ANOVA clearly separated the mice by diet, with 100% confidence as computed by bootstrap analysis. Expression of 11 genes was verified using quantitative real-time PCR. The E+ diet resulted in downregulation of genes involved in the sex-steroid metabolism pathway and genes involved in cholesterol and lipid metabolism. Genes coding for ribosomes and protein synthesis were upregulated by the E+ diet. Genes identified in the present analysis indicate some of the mechanisms by which fescue toxicosis occurs in animals.

Bioinformatics and cancer: an essential alliance

Modern research in cancer has been revolutionized by the introduction of new high-throughput methodologies such as DNA microarrays. Keeping the pace with these technologies, the bioinformatics offer new solutions for data analysis and, what is more important, it permits to formulate a new class of hypothesis inspired in systems biology, more oriented to blocks of functionally-related genes. Although software implementations for this new methodologies is new there are some options already available. Bioinformatic solutions for other high-throughput techniques such as array-CGH of large-scale genotyping is also revised.

Multistrain genetic comparisons reveal CCR5 as a receptor involved in airway hyperresponsiveness

Asthma is a ubiquitous disease with a broad range of clinical phenotypes. To better understand the complex genetic and environmental interactions underlying asthma, we compared the gene-gene interactions of four genetically distinct mouse strains that demonstrate biologically distinct responses to allergen. Using DNA microarrays and knock-out mouse studies, we showed that CCR5 plays a definitive role in the development of ovalbumin-induced allergic airway inflammatory disease. In addition, gene expression profiling data have revealed other potential novel targets for therapeutics-based research and has enhanced the understanding of the molecular mechanisms underlying the etiology of "asthma."

Prostaglandin J2 alters pro-survival and pro-death gene expression patterns and 26 S proteasome assembly in human neuroblastoma cells

Many neurodegenerative disorders are characterized by two pathological hallmarks: progressive loss of neurons and occurrence of inclusion bodies containing ubiquitinated proteins. Inflammation may be critical to neurodegeneration associated with ubiquitin-protein aggregates. We previously showed that prostaglandin J2 (PGJ2), one of the endogenous products of inflammation, induces neuronal death and the accumulation of ubiquitinated proteins into distinct aggregates. We now report that temporal microarray analysis of human neuroblastoma SK-N-SH revealed that PGJ2 triggered a "repair" response including increased expression of heat shock, protein folding, stress response, detoxification and cysteine metabolism genes. PGJ2 also decreased expression of cell growth/maintenance genes and increased expression of apoptotic genes. Over time pro-death responses prevailed over pro-survival responses, leading to cellular demise. Furthermore, PGJ2 increased the expression of proteasome and other ubiquitin-proteasome pathway genes. This increase failed to overcome PGJ2 inhibition of 26 S proteasome activity. Ubiquitinated proteins are degraded by the 26 S proteasome, shown here to be the most active proteasomal form in SK-N-SH cells. We demonstrate that PGJ2 impairs 26 S proteasome assembly, which is an ATP-dependent process. PGJ2 perturbs mitochondrial function, which could be critical to the observed 26 S proteasome disassembly, suggesting a cross-talk between mitochondrial and proteasomal impairment. In conclusion neurotoxic products of inflammation, such as PGJ2, may play a role in neurodegenerative disorders associated with the aggregation of ubiquitinated proteins by impairing 26 S proteasome activity and inducing a chain of events that culminates in neuronal cell death. Temporal characterization of these events is relevant to understanding the underlying mechanisms and to identifying potential early biomarkers.

Gene expression profiling of lymphoblastoid cell lines from monozygotic twins discordant in severity of autism reveals differential regulation of neurologically relevant genes

Background

The autism spectrum encompasses a set of complex multigenic developmental disorders that severely impact the development of language, non-verbal communication, and social skills, and are associated with odd, stereotyped, repetitive behavior and restricted interests. To date, diagnosis of these neurologically based disorders relies predominantly upon behavioral observations often prompted by delayed speech or aberrant behavior, and there are no known genes that can serve as definitive biomarkers for the disorders.

Results

Here we demonstrate, for the first time, that lymphoblastoid cell lines from monozygotic twins discordant with respect to severity of autism and/or language impairment exhibit differential gene expression patterns on DNA microarrays. Furthermore, we show that genes important to the development, structure, and/or function of the nervous system are among the most differentially expressed genes, and that many of these genes map closely in silico to chromosomal regions containing previously reported autism candidate genes or quantitative trait loci.

Conclusion

Our results provide evidence that novel candidate genes for autism may be differentially expressed in lymphoid cell lines from individuals with autism spectrum disorders. This finding further suggests the possibility of developing a molecular screen for autism based on expressed biomarkers in peripheral blood lymphocytes, an easily accessible tissue. In addition, gene networks are identified that may play a role in the pathophysiology of autism.