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Gain insights into gene function and define cellular phenotypes through the analysis of transcriptome, the collection of all messenger RNAs (mRNAs) in a cell. Discover how gene expression profiling can reveal important information about the expression patterns of genes in specific cell types or developmental stages. Explore the use of clustering algorithms to identify co-regulated genes and the functional annotation of genes based on their expression profiles.
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PLANT BIOTECHNOLOGY & GENETIC ENGINEERING(3 CREDIT HOURS) LECTURE 13 ANALYSIS OF THE TRANSCRIPTOME
ANALYSIS OF THE TRANSCRIPTOME • Important insights into gene function can be gained by expression profiling, i.e., determining where and when particular genes are expressed. For example, some genes are switched on (induced) or switched off (repressed) by external chemical signals reaching the cell surface. • In multicellular organisms, many genes are expressed in particular cell types or at certain developmental stages. • Furthermore, mutating one gene can alter the expression of others. • All this information helps to link genes into functional networks, and genes can be used as markers to define particular cellular states.
ANALYSIS OF THE TRANSCRIPTOME • In the past, genes and their expression profiles have been studied on an individual basis. Therefore, defining functional networks in the cell has been rather like completing a large and complex jigsaw puzzle. • More recently, technological advances have made it possible to study the expression profiles of thousands of genes simultaneously, culminating in global expression profiling, where every single gene in the genome is monitored in one experiment. • This can be carried out at the RNA level (by direct sequence sampling or through the use of DNA arrays) or at the protein level. • Global expression profiling produces a holistic view of the cell’s activity.
ANALYSIS OF THE TRANSCRIPTOME • Complex aspects of biological change, including differentiation, response to stress, and the onset of disease, can thus be studied at the genomic level. • Instead of defining cell states using single markers, it is now possible to use clustering algorithms to group data obtained over many different experiments and identify groups of co-regulated genes. • This produces a new way to define cellular phenotypes, which can help to reveal novel drug targets and develop more effective pharmaceuticals. • Furthermore, anonymous genes can be functionally annotated on the basis of their expression profiles, since two or more genes that are co-expressed over a range of experimental conditions are likely to be involved in the same general function.
THE TRANSCRIPTOME IS THE COLLECTION OF ALL MESSENGER RNAs IN THE CELL • The full complement of mRNA molecules produced by the genome has been termed the transcriptome, and the methods for studying the transcriptome are grouped under the term transcriptomics. • Taking human beings as an example, it has been shown that only 3% of the genome is represented by genes, suggesting that the transcriptome is much simpler than the genome. • This is not the case, however, because the transcriptome is much more than just the transcribed portion of the genome. • The complexity of the transcriptome is increased by processes such as alternative splicing and RNA editing, so that each gene can potentially give rise to many transcripts, each of which may have a unique expression profile.
THE TRANSCRIPTOME IS THE COLLECTION OF ALL MESSENGER RNAs IN THE CELL • In extreme cases, where a gene has many introns and undergoes extensive differential processing, one gene may potentially produce thousands or even millions of distinct transcripts. • An example is the Drosophila gene Dscam (the homolog of the human Down Syndrome cell adhesion molecule), which can be alternatively spliced to generate nearly 40,000 different mature transcripts (twice the number of genes in the Drosophila genome). • Each of these transcripts potentially encodes a distinct receptor that may play a unique role in axon guidance.
THE TRANSCRIPTOME IS THE COLLECTION OF ALL MESSENGER RNAs IN THE CELL • Complex as the transcriptome is, it is never seen as a complete system in vivo. • This is because all genes are not expressed simultaneously, in the same tissues, at the same levels. • Cells transcribe a basic set of housekeeping genes whose activity is required at all times for elementary functions, but other luxury genes are expressed in a regulated manner, e.g., as part of the developmental program or in response to an external stimulus. • Similarly, post-transcriptional events such as splicing are also regulated processes. • Researchers use phrases such as “human brain transcriptome” or “yeast meiotic transcriptome” to emphasize this.
THE TRANSCRIPTOME IS THE COLLECTION OF ALL MESSENGER RNAs IN THE CELL • A typical human cell is thought to express, on average, about 15,000-20,000 different mRNAs, some of which have housekeeping functions and some of which are more specialized. • A proportion of these will be splice variants of the same primary transcript. • Some of the mRNAs will be very abundant, some moderately so, and others very rare. • For a truly global perspective of RNA expression in the cell, all of these transcripts must be quantified at the same time. • This requires a highly parallel assay format which is both sensitive and selective.
THE TRANSCRIPTOME IS THE COLLECTION OF ALL MESSENGER RNAs IN THE CELL • There are two major types of strategy currently used for global RNA expression analysis: • The direct sampling of sequences from source RNA populations or cDNA libraries, or from sequence databases derived therefrom. • Hybridization analysis with comprehensive, non-redundant collections of DNA sequences immobilized on a solid support. These are known as DNA arrays. • Although such analysis is often called transcriptional profiling it is important to emphasize that one is not really looking at the level of transcription, but at the steady-state mRNA level, which also takes into account the rate of RNA turnover. Furthermore, most of the transcriptional profiling techniques do not measure absolute RNA levels, but rather compare relative levels within and/or between samples.