Genomic DNA as a cohybridization standard for mammalian microarray measurements

A persistent design problem for ratiometric microarray studies is selecting the ‘denominator’ RNA cohybridization standard. The ideal standard should be readily available, inexpensive, invariant over time and from laboratory to laboratory, and should represent all genes with a uniform signal. RNA references (both commercial ‘universal’ and experiment- specific types), fall short of these goals. We show here that mouse genomic DNA is a reliable microarray cohybridization standard which can meet these criteria. Genomic DNA was superior in universality of coverage (>98% of genes from a 16 000 feature mouse 70mer microarray) to the Stratagene Universal Mouse Reference RNA standard. Ratios for genes in very low abundance in the Stratagene standard were more unstable with the Stratagene standard than with genomic DNA. Genes with mid-range, and therefore presumably optimal RNA denominator values, showed comparable reproducibility with both standards. Inferred ratios made between two different experimental RNAs using a genomic DNA standard were found to correlate well with companion, directly measured ratios (Spearman correlation coefficient = 0.98). The advantage in array feature coverage of genomic DNA will likely increase as newer generation microarrays include genes which are expressed exclusively in minor tissue or developmental domains that are not represented in mixed tissue RNA standards.     

cDNA microarray technology and its applications

The cDNA microarray is the most powerful tool for studying gene expression in many different organisms. It has been successfully applied to the simultaneous expression of many thousands of genes and to large-scale gene discovery, as well as polymorphism screening and mapping of genomic DNA clones. It is a high throughput, highly parallel RNA expression assay technique that permits quantitative analysis of RNAs transcribed from both known and unknown genes. This technique provides diagnostic fingerprints by comparing gene expression patterns in normal and pathological cells, and because it can simultaneously track expression levels of many genes, it provides a source of operational context for inference and predication about complex cell control systems. This review describes this recently developed cDNA microarray technology and its application to gene discovery and expression, and to diagnostics for certain diseases.     

A novel medium throughput quantitative competitive PCR technology to simultaneously measure mRNA levels from multiple genes

There is a great demand for technologies to simultaneously measure mRNA levels from multiple genes. Here we report a new quantitative competitive PCR technology and demonstrate simultaneous quantification of mRNA from multiple genes. First, a sequential 2-fold dilution series containing equal amounts of gene-specific standard DNAs for 10–12 genes is prepared. Second, the serially diluted standard DNAs are individually added to equal amounts of tissue-derived cDNA and amplified with gene-specific primers for 19–26 PCR cycles. Each gene/standard DNA pair is amplified individually. All amplified DNA products (n = 80) are resolved by one microplate array diagonal gel electrophoresis using 5% polyacrylamide. Changes in mRNA levels of ~15% can be detected by this technology. The mRNA levels from 10–12 genes were simultaneously quantified. mRNA levels were compared in RNA samples from rat liver, kidney and skeletal muscle. This quick, specific, sensitive, reproducible and yet inexpensive technique is ideal for simultaneously studying co-ordinate changes in mRNA levels from multiple genes.     

Meiotic Transmission of an In Vitro–Assembled Autonomous Maize Minichromosome

Autonomous chromosomes are generated in yeast (yeast artificial chromosomes) and human fibrosarcoma cells (human artificial chromosomes) by introducing purified DNA fragments that nucleate a kinetochore, replicate, and segregate to daughter cells. These autonomous minichromosomes are convenient for manipulating and delivering DNA segments containing multiple genes. In contrast, commercial production of transgenic crops relies on methods that integrate one or a few genes into host chromosomes; extensive screening to identify insertions with the desired expression level, copy number, structure, and genomic location; and long breeding programs to produce varieties that carry multiple transgenes. As a step toward improving transgenic crop production, we report the development of autonomous maize minichromosomes (MMCs). We constructed circular MMCs by combining DsRed and nptII marker genes with 7–190 kb of genomic maize DNA fragments containing satellites, retroelements, and/or other repeats commonly found in centromeres and using particle bombardment to deliver these constructs into embryogenic maize tissue. We selected transformed cells, regenerated plants, and propagated their progeny for multiple generations in the absence of selection. Fluorescent in situ hybridization and segregation analysis demonstrated that autonomous MMCs can be mitotically and meiotically maintained. The MMC described here showed meiotic segregation ratios approaching Mendelian inheritance: 93% transmission as a disome (100% expected), 39% transmission as a monosome crossed to wild type (50% expected), and 59% transmission in self crosses (75% expected). The fluorescent DsRed reporter gene on the MMC was expressed through four generations, and Southern blot analysis indicated the encoded genes were intact. This novel approach for plant transformation can facilitate crop biotechnology by (i) combining several trait genes on a single DNA fragment, (ii) arranging genes in a defined sequence context for more consistent gene expression, and (iii) providing an independent linkage group that can be rapidly introgressed into various germplasms.     

A computational approach to identify genes for functional RNAs in genomic sequences

Currently there is no successful computational approach for identification of genes encoding novel functional RNAs (fRNAs) in genomic sequences. We have developed a machine learning approach using neural networks and support vector machines to extract common features among known RNAs for prediction of new RNA genes in the unannotated regions of prokaryotic and archaeal genomes. The Escherichia coli genome was used for development, but we have applied this method to several other bacterial and archaeal genomes. Networks based on nucleotide composition were 80–90% accurate in jackknife testing experiments for bacteria and 90–99% for hyperthermophilic archaea. We also achieved a significant improvement in accuracy by combining these predictions with those obtained using a second set of parameters consisting of known RNA sequence motifs and the calculated free energy of folding. Several known fRNAs not included in the training datasets were identified as well as several hundred predicted novel RNAs. These studies indicate that there are many unidentified RNAs in simple genomes that can be predicted computationally as a precursor to experimental study. Public access to our RNA gene predictions and an interface for user predictions is available via the web.     

Gene structure conservation aids similarity based gene prediction

One of the primary tasks in deciphering the functional contents of a newly sequenced genome is the identification of its protein coding genes. Existing computational methods for gene prediction include ab initio methods which use the DNA sequence itself as the only source of information, comparative methods using multiple genomic sequences, and similarity based methods which employ the cDNA or protein sequences of related genes to aid the gene prediction. We present here an algorithm implemented in a computer program called Projector which combines comparative and similarity approaches. Projector employs similarity information at the genomic DNA level by directly using known genes annotated on one DNA sequence to predict the corresponding related genes on another DNA sequence. It therefore makes explicit use of the conservation of the exon–intron structure between two related genes in addition to the similarity of their encoded amino acid sequences. We evaluate the performance of Projector by comparing it with the program Genewise on a test set of 491 pairs of independently confirmed mouse and human genes. It is more accurate than Genewise for genes whose proteins are <80% identical, and is suitable for use in a combined gene prediction system where other methods identify well conserved and non-conserved genes, and pseudogenes.     

Variation of Expression Levels of Seven Housekeeping Genes at Different Life-History Stages in Porphyra yezoensis

In order to identify the optimal internal control for relative real-time PCR when studying target gene expression in the red alga Porphyra yezoensis, we quantified the expression of seven housekeeping genes (18S ribosomal RNA, 30S ribosomal protein S8, Polyubiquitin-2, Glyceraldehyde-3-phosphate dehydrogenase, Elongation factor 1-alpha, Beta-tubulin and Actin 3) at different life-history stages. Absolute quantification was done by normalization to total RNA quantity and by normalization to genomic DNA quantity. We used these two normalization approaches, comparing the differences of expression levels of all candidate housekeeping genes between any two generations and across three life-history stages (filamentous sporophytes, leafy gametophytes and conchospores). We found GAPDH had the best stability in all cases and we recommend that GAPDH be considered as a potential internal control for gene expression studies at different life-history stages in P. yezoensis.     

The Hydrogenase Chip: a tiling oligonucleotide DNA microarray technique for characterizing hydrogen-producing and -consuming microbes in microbial communities

We developed a broad-ranging method for identifying key hydrogen-producing and consuming microorganisms through analysis of hydrogenase gene content and expression in complex anaerobic microbial communities. The method is based on a tiling hydrogenase gene oligonucleotide DNA microarray (Hydrogenase Chip), which implements a high number of probes per gene by tiling probe sequences across genes of interest at 1.67 × –2 × coverage. This design favors the avoidance of false positive gene identification in samples of DNA or RNA extracted from complex microbial communities. We applied this technique to interrogate interspecies hydrogen transfer in complex communities in (i) lab-scale reductive dehalogenating microcosms enabling us to delineate key H₂-consuming microorganisms, and (ii) hydrogen-generating microbial mats where we found evidence for significant H₂ production by cyanobacteria. Independent quantitative PCR analysis on selected hydrogenase genes showed that this Hydrogenase Chip technique is semiquantitative. We also determined that as microbial community complexity increases, specificity must be traded for sensitivity in analyzing data from tiling DNA microarrays.     

Discovery of Possible Gene Relationships through the Application of Self-Organizing Maps to DNA Microarray Databases

DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques –an unsupervised artificial neural network called a Self-Organizing Map (SOM)–which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.     

Digital signal processing reveals circadian baseline oscillation in majority of mammalian genes

In mammals, circadian periodicity has been described for gene expression in the hypothalamus and multiple peripheral tissues. It is accepted that 10%–15% of all genes oscillate in a daily rhythm, regulated by an intrinsic molecular clock. Statistical analyses of periodicity are limited by the small size of datasets and high levels of stochastic noise. Here, we propose a new approach applying digital signal processing algorithms separately to each group of genes oscillating in the same phase. Combined with the statistical tests for periodicity, this method identifies circadian baseline oscillation in almost 100% of all expressed genes. Consequently, circadian oscillation in gene expression should be evaluated in any study related to biological pathways. Changes in gene expression caused by mutations or regulation of environmental factors (such as photic stimuli or feeding) should be considered in the context of changes in the amplitude and phase of genetic oscillations.     

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers.     

Increased Expression and Protein Divergence in Duplicate Genes Is Associated with Morphological Diversification

The differentiation of both gene expression and protein function is thought to be important as a mechanism of the functionalization of duplicate genes. However, it has not been addressed whether expression or protein divergence of duplicate genes is greater in those genes that have undergone functionalization compared with those that have not. We examined a total of 492 paralogous gene pairs associated with morphological diversification in a plant model organism (Arabidopsis thaliana). Classifying these paralogous gene pairs into high, low, and no morphological diversification groups, based on knock-out data, we found that the divergence rate of both gene expression and protein sequences were significantly higher in either high or low morphological diversification groups compared with those in the no morphological diversification group. These results strongly suggest that the divergence of both expression and protein sequence are important sources for morphological diversification of duplicate genes. Although both mechanisms are not mutually exclusive, our analysis suggested that changes of expression pattern play the minor role (33%–41%) and that changes of protein sequence play the major role (59%–67%) in morphological diversification. Finally, we examined to what extent duplicate genes are associated with expression or protein divergence exerting morphological diversification at the whole-genome level. Interestingly, duplicate genes randomly chosen from A. thaliana had not experienced expression or protein divergence that resulted in morphological diversification. These results indicate that most duplicate genes have experienced minor functionalization.     

Mutation analysis of the entire mitochondrial genome using denaturing high performance liquid chromatography

In patients with mitochondrial disease a continuously increasing number of mitochondrial DNA (mtDNA) mutations and polymorphisms have been identified. Most pathogenic mtDNA mutations are heteroplasmic, resulting in heteroduplexes after PCR amplification of mtDNA. To detect these heteroduplexes, we used the technique of denaturing high performance liquid chromatography (DHPLC). The complete mitochondrial genome was amplified in 13 fragments of 1–2 kb, digested in fragments of 90–600 bp and resolved at their optimal melting temperature. The sensitivity of the DHPLC system was high with a lowest detection of 0.5% for the A8344G mutation. The muscle mtDNA from six patients with mitochondrial disease was screened and three mutations were identified. The first patient with a limb-girdle-type myopathy carried an A3302G substitution in the tRNA^Leu(UUR) gene (70% heteroplasmy), the second patient with mitochondrial myopathy and cardiomyopathy carried a T3271C mutation in the tRNA^Leu(UUR) gene (80% heteroplasmy) and the third patient with Leigh syndrome carried a T9176C mutation in the ATPase6 gene (93% heteroplasmy). We conclude that DHPLC analysis is a sensitive and specific method to detect heteroplasmic mtDNA mutations. The entire automatic procedure can be completed within 2 days and can also be applied to exclude mtDNA involvement, providing a basis for subsequent investigation of nuclear genes.     

Use of RNA and genomic DNA references for inferred comparisons in DNA microarray analyses

In most microarray assays, labeled cDNA molecules derived from reference and query RNA samples are co-hybridized to probes arrayed on a glass surface. Gene expression profiles are then calculated for each gene based on the relative hybridization intensities measured between the two samples. The most commonly used reference samples are typically isolates from a single representative RNA source (RNA-0) or pooled mixtures of RNA derived from a plurality of sources (RNA-p). Genomic DNA offers an alternative reference nucleic acid with a number of potential advantages, including stability, reproducibility, and a potentially uniform representation of all genes, as each unique gene should have equal representation in a haploid genome. Using hydrogen peroxide-treated Arabidopsis thaliana plants as a model, we evaluated genomic DNA and RNA-p as reference samples and compared expression levels inferred through the reference relative to unexposed plants with expression levels measured directly using an RNA-0 reference. Our analysis demonstrates that while genomic DNA can serve as a reasonable reference source for microarray assays, a much greater correlation with direct measurements can be achieved using an RNA-based reference sample.