One group exhibited gene expression similar to the B lymphocytes in germinal centers, while the other group exhibited gene expression similar to activated B lymphocytes Table 2 ; this also held true for many other genes examined. Nonetheless, the real question remained to be examined: Could these differences in gene expression between subgroups be used to predict clinical outcomes? Notably, when the authors examined the clinical data from the same patients, they found that patients in the GC-like subgroup exhibited much better survival rates than those in the activated B-like subgroup.
In fact, half of those patients with the activated B-like expression pattern died within two years of diagnosis, while over half of those with the GC-like pattern of gene expression were still alive 11 years after diagnosis. The researchers therefore concluded that "muddy diagnostic categories" can be clarified by means of DNA microarray analysis of gene expression. This study and others help provide the basis for the development of individualized medicine—the way of the future.
As previously mentioned, research efforts such as the DLBCL study suggested that DNA microarrays would someday serve as a valuable tool in the delivery of patient-specific, highly individualized medical care--and so far, they have been right. Today, testing for elevated expression of certain genes can assist in predicting cancer outcomes and in assigning appropriate treatment programs. For example, elevated expression of the estrogen receptor gene predicts a favorable response to breast cancer treatments that interfere with estrogen synthesis or that block estrogen receptors.
Similarly, Oncotype DX is a test that simultaneously examines 21 genes in patient biopsies. Combined with other information about a patient, details about the expression of these 21 genes can help doctors decide whether chemotherapy is the best course of action for the patient. Recently, DNA microarray technology has also been used to examine global gene-expression changes in disease, thereby shedding additional light on the complex nature of many seemingly straightforward conditions.
Since its development in the mids, DNA microarray technology has revealed a great deal about the genetic factors involved in a number of diseases, including multiple forms of cancer.
Early on, researchers used microarrays to identify differences in gene expression between normal cells and their cancerous counterparts. Shortly thereafter, scientists began to employ this technology to distinguish specific subtypes of certain cancers, as well as to determine which treatment methods would most likely be effective for particular patients.
Although individualized medicine such as this is still in its infancy, DNA microarrays will no doubt continue to play a leading role in the development of this field, as well as in continued research as to the complete genetic basis of all types of human disease.
Alizadeh, A. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genetics 14 , — doi Campbell, A. Make microarray data with known ratios. Schena, M. Quantitative monitoring of gene expression patterns with a complementary DNA microarray.
Science , — Tibshirani, R. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature , — doi Atavism: Embryology, Development and Evolution. Gene Interaction and Disease. If the expression of a particular gene is higher in the experimental sample than in the reference sample, then the corresponding spot on the microarray appears red.
In contrast, if the expression in the experimental sample is lower than in the reference sample, then the spot appears green. Finally, if there is equal expression in the two samples, then the spot appears yellow.
The data gathered through microarrays can be used to create gene expression profiles, which show simultaneous changes in the expression of many genes in response to a particular condition or treatment. Related Concepts 6. The next step is to cut the long strands of DNA into smaller, more manageable fragments and then to label each fragment by attaching a fluorescent dye there are other ways to do this, but this is one common method.
Both sets of labeled DNA are then inserted into the chip and allowed to hybridize - or bind - to the synthetic DNA on the chip. If the individual does not have a mutation for the gene, both the red and green samples will bind to the sequences on the chip that represent the sequence without the mutation the "normal" sequence.
If the individual does possess a mutation, the individual's DNA will not bind properly to the DNA sequences on the chip that represent the "normal" sequence but instead will bind to the sequence on the chip that represents the mutated DNA. What is a DNA microarray? What is a DNA microarray used for? The first step is to isolate and purify mRNA from samples of interest. Since we are interested in comparing gene expression, one sample usually serves as a control, and another sample would be the experiment e.
The next step is to reverse transcribe and label the mRNA. In order to detect the transcripts by hybridization, they need to be labeled, and because starting material may be limited, an amplification step is also used. Disease and healthy samples can be labeled with different dyes and co-hybridized onto the same microarray in the following step. The fluorescent tags on the bound cDNA are excited by a laser and the fluorescently labeled target sequences that bind to a probe generate a signal.
The total fluorescent intensity of the signal depends upon the amount of target sample binding to the probes present on that spot. Thus, the amount of target sequence bound to each probe correlates to the expression level of various genes expressed in the sample. The signals are detected, the signal intensity is quantified, and used to create a digital image of the array. If we are trying to calculate relative expression between two samples, each labeled with a different dye See figure 2, red for the experiment, green for the control , the resulting image is analyzed by calculating the ratio of the two dyes.
If a gene is over-expressed in the experimental sample, then more of that sample cDNA than control cDNA will hybridize to the spot representing that expressed gene. In turn, the spot will fluoresce red with greater intensity than it will fluoresce green. The red-to-green fluorescence ratio thus indicates which gene is up or downregulated in the appropriate sample. Microarray technology propelled functional genomics, a discipline that strives to identify the role of genes in cellular processes, into the spotlight because it allowed functional analysis of genome-wide differential RNA expression between different samples, states, and cell types to gain insights into molecular mechanisms that regulate cell fate, development, and disease progression.
Microarray data is used for gene expression profiling, which serves as a determinant of protein levels and therefore cellular function between biological samples. A single experiment can provide information on the expression of thousands of genes, virtually the entire human genome, to compare expression patterns between any two states.
Microarray experiments can indicate which genes are up- or down-regulated between samples from normal and diseased tissue, or two samples in the absence and presence of a certain stimulus.
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