Applications of DNA and protein microarrays in comparative physiology

DNA microarrays have revolutionized gene expression studies and made large-scale parallel measurement of whole genome expression a feasible technique in model species where genomes are well characterized. Such studies are perfectly suited to unraveling the complex regulation and/or interaction of both genes and proteins likely involved in most physiological processes. Gene expression profiles are currently being used to identify genes underlying a range of physiological responses. Characterization of these genes will help to elucidate the pathways and processes regulating physiological processes. Expanding the use of DNA microarrays to non-model species that have been critical in elucidating certain physiological pathways will be valuable in determining the genes associated with these processes. Approaches that do not require complete genome information have recently been applied to "non-model" organisms. As whole genomes are sequenced for non-model organisms, the application of DNA microarrays to comparative physiology will expand even further. The recent development of protein microarrays will be critical in understanding the regulation of physiological processes not accounted for at the genomic level. Together, DNA and protein microarrays provide the most thorough and efficient method of understanding the molecular basis of physiological processes to date. In turn, classical physiological approaches will be vital in characterizing and verifying the function of the novel genes identified by microarray experiments. Ultimately, DNA and protein microarray expression profiles may be used to predict physiological responses.     

Microarray challenges in ecology

Microarrays are used to measure simultaneously the amount of mRNAs transcribed from many genes. They were originally designed for gene expression profiling in relatively simple biological systems, such as cell lines and model systems under constant laboratory conditions. This poses a challenge to ecologists who increasingly want to use microarrays to unravel the genetic mechanisms underlying complex interactions among organisms and between organisms and their environment. Here, we discuss typical experimental and statistical problems that arise when analyzing genome-wide expression profiles in an ecological context. We show that experimental design and environmental confounders greatly influence the identification of candidate genes in ecological microarray studies, and that following several simple recommendations could facilitate the analysis of microarray data in ecological settings.     

Comparison of gene expression measurements from cDNA and 60-mer oligonucleotide microarrays

As the data generated by microarray technology continue to amass, it is necessary to compare and combine gene expression data from different platforms. To evaluate the performance of cDNA and long oligonucleotide (60-mer) arrays, we generated gene expression profiles for two cancer cell lines and compared the data between the two platforms. All 6182 unique genes represented on both platforms were included in the analysis. A limited correlation (r = 0.4708) was obtained and the difference in measurement of low-expression genes was considered to contribute to the limited correlation. Further restriction of the data set to differentially expressed genes detected in cDNA microarrays (1205 genes) and oligonucleotide arrays (1325 genes) showed modest correlations of 0.7076 and 0.6441 between the two platforms. Quantitative real-time PCR measurements of a set of 10 genes showed better correlation with oligonucleotide arrays. Our results demonstrate that there is substantial variation in the data generated from cDNA and 60-mer oligonucleotide arrays. Although general agreement was observed in measurements of differentially expressed genes, we suggest that data from different platforms could not be directly amassed.     

Genome-wide expression analysis of plant cell cycle modulated genes

Genome-wide expression analysis is rapidly becoming an essential tool for identifying and analysing genes involved in, or controlling, various biological processes ranging from development to responses to environmental cues. The control of cell division involves the temporal expression of different sets of genes, allowing the dividing cell to progress through the different phases of the cell cycle. A landmark study using DNA microarrays to follow the patterns of gene expression in synchronously dividing yeast cells has allowed the identification of several hundreds of genes that are involved in the cell cycle. Although DNA microarrays provide a convenient tool for genome-wide expression analysis, their use is limited to organisms for which the complete genome sequence or a large cDNA collection is available. For other organisms, including most plant species, DNA fragment analysis based methods, such as cDNA-AFLP, provide a more appropriate tool for genome-wide expression analysis. Furthermore, cDNA-AFLP exhibits properties that complement DNA microarrays and, hence, constitutes a useful tool for gene discovery.     

一种新型细胞微阵列的制作和应用

为了高通量地检测大量培养细胞中基因原位表达, 发明了一种制作细胞微阵列的新方法,成功地制作含20种细胞系共100个供体细胞石蜡混合物点阵的细胞微阵列.免疫组化检测P53, P21, PTEN、P16基因在细胞微阵列中的蛋白质表达.原位杂交检测BRD7、NGX6 基因在细胞微阵列中mRNA原位表达.建立了P53、P21、PTEN、P16蛋白和BRD7、NGX6 mRNA 在不同培养细胞中的原位表达谱.细胞微阵列为基因功能研究提供一种新的高通量工具.细胞微阵列可广泛用于DNA、RNA和蛋白质水平上的基因原位表达研究.细胞微阵列还可用于筛选药物作用靶标的研究.     

Regulation of conidiation by light in Aspergillus nidulans

Light regulates several aspects of the biology of many organisms, including the balance between asexual and sexual development in some fungi. To understand how light regulates fungal development at the molecular level we have used Aspergillus nidulans as a model. We have performed a genome-wide expression analysis that has allowed us to identify >400 genes upregulated and >100 genes downregulated by light in developmentally competent mycelium. Among the upregulated genes were genes required for the regulation of asexual development, one of the major biological responses to light in A. nidulans, which is a pathway controlled by the master regulatory gene brlA. The expression of brlA, like conidiation, is induced by light. A detailed analysis of brlA light regulation revealed increased expression after short exposures with a maximum after 60 min of light followed by photoadaptation with longer light exposures. In addition to brlA, genes flbA-C and fluG are also light regulated, and flbA-C are required for the correct light-dependent regulation of the upstream regulator fluG. We have found that light induction of brlA required the photoreceptor complex composed of a phytochrome FphA, and the white-collar homologs LreA and LreB, and the fluffy genes flbA-C. We propose that the activation of regulatory genes by light is the key event in the activation of asexual development by light in A. nidulans.     

Bacterial artificial chromosome fingerprint arrays for the differentiation of transcriptomic differences in mycobacteria

Although microarray technology has become more widespread as a discovery tool for bacterial pathogenesis, it remains a method available only to laboratories with access to expensive equipment and costly analysis software. Mycobacterium tuberculosis, the causative agent for tuberculosis (TB), afflicts one-third of the global population, and kills between 2 and 3 million people per year. While the majority of cases of TB occur in developing areas of the world, facilities in these regions may not be able to support microarray analysis. Additionally, a major limitation of microarrays is that only genes on the array are being assayed. With acquired virulence and drug resistance in microbes, a method less dependent on a predetermined list of gene targets is advantageous. We present a method of expression analysis based on bacterial artificial chromosomes (BACs) that can be applied with standard laboratory equipment and free analysis software. This technique, bacterial artificial chromosome fingerprint arrays (BACFA), was developed and utilised to identify expression differences between intracellular strains of M. tuberculosis, one virulent (H37Rv) and one attenuated (H37Ra). Southern blots of restriction-enzyme digested BAC fragments were sequentially hybridised with strain-specific cDNA probes to generate expression profiles that were used to isolate expression differences in broth grown and intracellular bacteria. Repeat comparisons of intracellular profiles via BACFA identified genomic regions differentially expressed by the two strains. Quantitative real-time PCR was used to assess the genes located in these fragments in order to confirm or deny the differential regulation of genes. In total, we identified six genes that were differentially regulated between strains inside the host cell (pks2, aceE, Rv1571, and frdBCD). We report that BACFA is an effective technique in the expression analysis of bacteria and can be considered complementary to the high-throughput analysis offered by microarrays.     

Genopal?: A Novel Hollow Fibre Array for Focused Microarray Analysis

Expression profiling of target genes in patient blood is a powerful tool for RNA diagnosis. Here, we describe Genopal™, a novel platform ideal for efficient focused microarray analysis. Genopal™, which consists of gel-filled fibres, is advantageous for high-quality mass production via large-scale slicing of the Genopal™ block. We prepared two arrays, infectant and autoimmunity, that provided highly reliable data in terms of repetitive scanning of the same and/or distinct microarrays. Moreover, we demonstrated that Genopal™ had sensitivity sufficient to yield signals in short hybridization times (0.5 h). Application of the autoimmunity array to blood samples allowed us to identify an expression pattern specific to Takayasu arteritis based on the Spearman rank correlation by comparing the reference profile with those of several autoimmune diseases and healthy volunteers (HVs). The comparison of these data with those obtained by other methods revealed that they exhibited similar expression profiles of many target genes. Taken together, these data demonstrate that Genopal™ is an advantageous platform for focused microarrays with regard to its low cost, rapid results and reliable quality.     

Gene expression patterns associated with infertility in humans and rodent models

Modern genomic technologies such as DNA arrays provide the means to investigate molecular interactions at an unprecedented level, and arrays have been used to carry out gene expression profiling as a means of identifying candidate genes involved in molecular mechanisms underlying a variety of phenotypes. By comparing gene expression profiles from normal and abnormal human testes with those from comparable infertile mouse models, we endeavored to identify genes and gene networks critical for male fertility. We used commercially available filter-based DNA arrays to analyze testicular gene expression from eight human testis biopsies and three different infertile mouse models (atrichosis mutation, ataxia telangiectasia knockout and CREMtau knockout). Forty-seven mouse genes exhibited differential testicular gene expression (P <0.01) associated with male infertility. These included genes involved in DNA repair (Vim, Rad23A, Rad23B), glutathione metabolism (Gsr, Gstp 1, Mgst1), proteolysis (Ace, Casp1, Ctsd), spermatogenesis (Prlr, Tmsb4 and Zfp-37) and stress response (Hsp 1, Osp94). The expression of 19 human genes was different (P<0.05) between normal and abnormal samples, including those associated with apoptosis (GADD45), gonad development (SOX9), proteolysis (PSMC3, SPINK2, TIMP3, UBE213) and signal transduction (DLK1, NAP4, S100A10). Direct comparison of differentially expressed human and mouse genes identified glucose phosphate isomerase, and the highly similar human tissue inhibitor of metalloproteinase 3 (TIMP3) and mouse Timp2. Using DNA microarrays to profile gene expression in testes from infertile animal models and humans will be useful for understanding congenital infertility, and also infertility caused by environmental exposures where the same genes and molecular mechanisms are involved.     

Striptease on glass: validation of an improved stripping procedure for in situ microarrays

Microarrays have rapidly become an indispensable tool for gene analysis. Microarray experiments can be cost prohibitive, however, largely due to the price of the arrays themselves. Whilst different methods for stripping filter arrays on membranes have been established, only very few protocols are published for thermal and chemical stripping of microarrays on glass. Most of these protocols for stripping microarrays on glass were developed in combination with specific surface chemistry and different coatings for covalently immobilizing presynthesized DNA in a deposition process. We have developed a method for stripping commercial in situ microarrays using a multi-step procedure. We present a method that uses mild chemical degradation complemented by enzymatic treatment. We took advantage of the differences in biochemical properties of covalently linked DNA oligonucleotides on in situ synthesized microarrays and the antisense cRNA hybridization probes. The success of stripping protocols for microarrays on glass was critically dependent on the type of arrays, the nature of sample used for hybridization, as well as hybridization and washing conditions. The protocol employs alkali hydrolysis of the cRNA, several enzymatic degradation steps using RNAses and Proteinase K, combined with appropriate washing steps. Stripped arrays were rehybridized using the same protocols as for new microarrays. The stripping method was validated with microarrays from different suppliers and rehybridization of stripped in situ arrays yielded comparable results to hybridizations done on unused, new arrays with no significant loss in precision or accuracy. We show that stripping of commercial in situ arrays is feasible and that reuse of stripped arrays gave similar results compared to unused ones. This was true even for biological samples that show only slight differences in their expression profiles. Our analyses indicate that the stripping procedure does not significantly influence data quality derived from post-primary hybridizations. The method is robust, easy to perform, inexpensive, and results after reuse are of comparable accuracy to new arrays.     

Transcription analysis using high-density micro-arrays of Aspergillus nidulans wild-type and creA mutant during growth on glucose or ethanol

Here, we describe how the recently published Aspergillus nidulans genome sequence [Galagan, J.E., Calvo, S.E., Cuomo, C., Li-Jun, M., Wortman, J.R., et al., 2005. Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae. Nature 438 (7071), 1105-1115] was used to design a high-density oligo array with probes for 3,278 selected genes using the Febit Geniom One array system. For this purpose, the program OligoWiz II was used to design 24,125 probes to cover the 3,278 selected genes. Subsequently, the Febit system was used to investigate carbon catabolite repression by comparing the gene expression of a creA deleted mutant strain with a reference strain grown either with glucose or ethanol as the sole carbon source. In order to identify co-regulated genes and genes influenced by either the carbon source or CreA, the most significantly regulated genes (p相似文献   

Application of microarray technology in primate behavioral neuroscience research

Gene expression profiling of brain tissue samples applied to DNA microarrays promises to provide novel insights into the neurobiological bases of primate behavior. The strength of the microarray technology lies in the ability to simultaneously measure the expression levels of all genes in defined brain regions that are known to mediate behavior. The application of microarrays presents, however, various limitations and challenges for primate neuroscience research. Low RNA abundance, modest changes in gene expression, heterogeneous distribution of mRNA among cell subpopulations, and individual differences in behavior all mandate great care in the collection, processing, and analysis of brain tissue. A unique problem for nonhuman primate research is the limited availability of species-specific arrays. Arrays designed for humans are often used, but expression level differences are inevitably confounded by gene sequence differences in all cross-species array applications. Tools to deal with this problem are currently being developed. Here we review these methodological issues, and provide examples from our experiences using human arrays to examine brain tissue samples from squirrel monkeys. Until species-specific microarrays become more widely available, great caution must be taken in the assessment and interpretation of microarray data from nonhuman primates. Nevertheless, the application of human microarrays in nonhuman primate neuroscience research recovers useful information from thousands of genes, and represents an important new strategy for understanding the molecular complexity of behavior and mental health.     

High-fidelity mRNA amplification for gene profiling

The completion of the Human Genome Project has made possible the comprehensive analysis of gene expression, and cDNA microarrays are now being employed for expression analysis in cancer cell lines or excised surgical specimens. However, broader application of cDNA microarrays is limited by the amount of RNA required: 50-200 microg of total RNA (T-RNA) and 2-5 microg poly(A) RNA. To broaden the use of cDNA microarrays, some methods aiming at intensifying fluorescence signal have resulted in modest improvement. Methods devoted to amplifying starting poly(A) RNA or cDNA show promise, in that detection can be increased by orders of magnitude. However, despite the common use of these amplification procedures, no systematic assessment of their limits and biases has been documented. We devised a procedure that optimizes amplification of low-abundance RNA samples by combining antisense RNA (aRNA) amplification with a template-switching effect (Clonetech, Palo Alto, CA). The fidelity of aRNA amplified from 1:10,000 to 1:100,000 of commonly used input RNA was comparable to expression profiles observed with conventional poly(A) RNA- or T-RNA-based arrays.     

Gene amplification as a developmental strategy: isolation of two developmental amplicons in Drosophila

Gene amplification is known to be critical for upregulating gene expression in a few cases, but the extent to which amplification is utilized in the development of diverse organisms remains unknown. By quantifying genomic DNA hybridization to microarrays to assay gene copy number, we identified two additional developmental amplicons in the follicle cells of the Drosophila ovary. Both amplicons contain genes which, following their amplification, are expressed in the follicle cells, and the expression of three of these genes becomes restricted to specialized follicle cells late in differentiation. Genetic analysis establishes that at least one of these genes, yellow-g, is critical for follicle cell function, because mutations in yellow-g disrupt eggshell integrity. Thus, during follicle cell differentiation the entire genome is overreplicated as the cells become polyploid, and subsequently specific genomic intervals are overreplicated to facilitate gene expression.     

Application of the S-score algorithm for analysis of oligonucleotide microarrays

In the past several years, oligonucleotide microarrays have emerged as a widely used tool for the simultaneous, non-biased measurement of expression levels for thousands of genes. Several challenges exist in successfully utilizing this biotechnology; principal among these is analysis of microarray data. An experiment to measure differential gene expression can consist of a dozen microarrays, each consisting of over a hundred thousand data points. Previously, we have described the use of a novel algorithm for analyzing oligonucleotide microarrays and assessing changes in gene expression. This algorithm describes changes in expression in terms of the statistical significance (S-score) of change, which combines signals detected by multiple probe pairs according to an error model characteristic of oligonucleotide arrays. Software is available that simplifies the use of the application of this algorithm so that it may be applied to improving the analysis of oligonucleotide microarray data. The application of this method to problems of the central nervous system is discussed.