Expression Analyses Rnabased Studies

Gene expression is a complex and exquisitely regulated process that enables cells, tissues, systems, and even the entire organism to respond dynamically to both internal and external stimuli. While all nucleated cells each contain a copy of the entire genome, only a subset of its genes is expressed. Such cell- or even tissue-specific gene expression results in the production of a different set of proteins. This genetic expression renders the identity of these cells, as well as how they interact with their environment. In as much as one can isolate the cell- or tissue-type(s) of interest, the study of gene expression can provide unique and vital insights into biologic function and even the pathophysiology of disease. Expression analyses involve not only the presence or absence of gene expression, but the quantification of the level of expression. This approach provides more information than genetic analyses alone.

While the study of a single or a small set of genes expression has been possible for over three decades, recent technological advances have permitted the study of vast if not global patterns of gene expression (i.e. the sum total of a cell's gene expression termed the "transcriptome"). In addition, new technologies provide a level of both sensitivity and specificity hitherto unavailable using classical electrophoresis gel-based methods.

Perhaps the two most influential technological developments in molecular biology are the availability of quantitative real-time PCR (see Box 4.1) and the development of high-density arrays of oligonucleotides that are complementary to a large fraction of the RNA species

Box 4.1 Quantitative polymerase chain reaction

The most common method of DNA analysis is based on the amplification of segments of DNA from a small amount of DNA. The technology, polymerase chain reaction (PCR), is an enzymatic process that results in site-specific DNA replication by the inclusion of a specific set of small nucleotide sequences (termed primers) that flank a sequence of interest, an excess of nucleotide building blocks [A, T, G, C], and a heat-resistant form of DNA polymerase that can be manipulated thermally to rapidly replicate a sequence of interest. Because DNA replication results in a doubling of the target region with every cycle of the reaction, PCR results in the exponential amplification of a region of interest. This approach results in hundreds of millions of replicated copies within 35-40 cycles of PCR carried out over the period of a few hours. Resulting PCR products (termed amplicons) can be visualized using a host of solid-state (e.g. gel electrophoresis, oligonucleotide hybridization) or fluidic (e.g. florescence-resonance energy transfer) processes. Both DNA and RNA can be readily analyzed by this method.

Quantitative polymerase chain reaction (qPCR) is a modification of PCR that allows for the florescent quantitation of PCR amplicons at each cycle of the reaction. Where PCR is only semiquantitative in nature (since PCR-amplification is approximately exponential, the number of amplification cycles and the amount of amplicons measured after the last cycle can be used to derive the approximate initial concentration of starting material), qPCR measures the concentration of amplicons at each cycle and can more accurately estimate the starting concentrations of targets of interest (termed real-time PCR). RNA can be similarly estimated by first adding reverse transcriptase, which results in a complementary DNA molecule that can then be measured as described above (termed real-time (RT-PCR)).

transcribed in a cell. These arrays, termed microarrays, allow for the rapid, efficient, and simultaneous analysis of almost the entire transcriptome of an organism of interest. RNA analysis has always relied on the property of genetic material to recognize, or hybridize, its reverse complement (e.g. AGTTAC will recognize and hybridize to TCAATG). Microarray technology exploits this property on a solid support (e.g. a membrane or even a small glass microscope slide) and at an impressively high density to allow for the simultaneous analysis of hundreds to tens of thousands of RNA species in a single experiment.

Typically, experiments involving gene expression microarrays adopted one of two designs. In the first experimental design, the RNA from two different cell types (e.g. cells exposed to two different stimuli, cells from an individual with and an individual without a trait or disease, or even cells followed over time) serve as the template for the synthesis of complementary DNA

(cDNA) labeled with differentiating chromogenic reagents. The two cDNA pools are mixed and allowed to compete for the same target sequences on the array. The excess cDNA (i.e. the RNA surrogate) from one or the other comparison group results in an increase in the amount of that chromogen bound to the target sequence (Figure 4.6). In the second design, the cDNA from each tissue being compared is hybridized to different chips and the absolute levels of each RNA species bound to the target sequence are compared. Common uses of expression analyses include susceptibility gene discovery, drug development, drug response, and therapy development.

DNA microarrays refer to the class of high-throughput technologies that permit the screening of hundreds to hundreds of thousands of variant sequences (e.g. SNPs, copy number variations). DNA from an individual or organism under study is applied to the microarray. Complementary sequences hybridize to their target, which

Cancer cells

Normal cells

Cancer cells

Normal cells

mRNA

RNA Isolation

Reverse transcriptase labeling mRNA

"Red fluorescent" probes

"Green fluorescent" probes

Combine targets

Hybridize to

microarray

Figure 4.6 Two different tissues are isolated and rendered for their RNA. The RNA is reverse-transcribed into cDNA and each pool of RNA is labeled with a different chromogenic agent (i.e. red, green). The labeled specimens are mixed and allowed to compete for the same target sequences imbedded on the microarray. When a specific cDNA from a given pool is present at a greater concentration than the comparison group (e.g. green-labeled cDNA), more labeled cDNA species will hybridize to the target sequence and that "spot" on the microarray will fluoresce green not red. Thus, green spots indicate higher levels of a given cDNA from the green pool. Red spots indicate higher levels of a given cDNA from the pool labeled with the red chromogenic agent. Yellow spots indicate equal levels of each cDNA species. And black spots mean no cDNA from either pool was present in sufficient levels to bind a target sequence. Reprinted from http:// en.wikipedia.org/wiki/Image:Microarray-schema.jpg.

cDNA

cDNA

permits simultaneous assay of all of the sequences (i.e. polymorphisms) featured on the microarray. Common uses of DNA microarrays include susceptibility gene discovery and drug development. Recent success in the application of DNA microarray-based gene discovery for migraine suggest that population genetic studies of pain phenotypes are now tenable.31 In addition, the recent release of commercial DNA microarray-based pain candidate gene panels (http:// www.congenics.com) represents an intriguing research tool to explore inter-individual variation in pain, analgesia, and allodynia in human populations.

Both DNA and RNA expression arrays are visualized in a similar manner following hybridization. DNA micro-arrays are biotin-labeled and are recorded in black and white, with lack of hybridization recorded as black. With RNA expression arrays, the chromogenic agents are usually red and green. A red fluorescent signal indicates that there is excess of the RNA species labeled with the red chromogenic agent. A green fluorescent signal indicates that there is excess of the RNA species labeled with the green chromogenic agent. A yellow fluorescent signal indicates equal levels of each cell's RNA species binding to a given spot and black indicates lack of hybridization to a spot.

Microarrays are placed into a reader or scanner where a laser excites the chromomeric label and a microscope fitted with a high-resolution digital camera records the digital image of the array. The hybridization data are analyzed by computer software which processes the hybridization signals, as well as various quality control indicators imbedded on the array, to provide semiquantitative (e.g. DNA genotypes) or quantitative (e.g. RNA transcript level) information for each spot on the microarray. The algorithms used by microarray analysis software are evolving. Gene expression profiles are generated that can be used to examine differences in the level of single transcripts, differences in gene expression of an entire pathway, or even a series of pathways.

While microarrays allow for comprehensive surveys of gene expression, many challenges remain in how to interpret microarray data. The optimal method of statistical analysis is a subject of ongoing research and debate. Moreover, the expression profiles of a pathophysiologic process can only be studied if the tissue in question can be assessed directly. This requirement is a near impossible proposition for many human diseases and for many painful conditions where central nervous system tissue is involved in the pathophysiological process. Lastly, the function and catalog of genes and their expressed products is incomplete, which leaves many expression profiles difficult to interpret. Ultimately, as more information accumulates, microarray technology will allow for the pursuit of increasingly more complex questions. An active area of development is the combination of both DNA microarrays and gene expression microarrays in order to integrate the role of common gene variations with associated differences in gene expression.32

One could imagine that the identification of genes associated with a pain phenotype in a study population could be coupled with RNA expression profiles generated from tissue isolated from the same population. These data would be used to identify entire pathways of genes whose expression changes in individuals that carry specific gene variations associated with pain sensitivity. These observations could be used to identify specific genes whose expression may be rate-limiting for pain. Alternatively, some genes suggested by this approach may provide better targets for pharmacotherapy based on the population prevalence of a gene variation and its associated influence on gene expression. This combined comprehensive analysis is termed "systems biology.''

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