It states that genes specify the sequence of mRNA molecules, which in turn specify the sequence of proteins. The translation of this information to a protein is more complex because three mRNA nucleotides correspond to one amino acid in the polypeptide sequence. Transcription is the first step in gene expression.
Some transcripts are used as structural or regulatory RNAs, and others encode one or more proteins. If the transcribed gene encodes a protein, the result of transcription is messenger RNA mRNA , which will then be used to create that protein in the process of translation. Translation is the process by which mRNA is decoded and translated to produce a polypeptide sequence, otherwise known as a protein.
The main function of tRNA is to transfer a free amino acid from the cytoplasm to a ribosome, where it is attached to the growing polypeptide chain.
The ribosome then releases the completed protein into the cell. Privacy Policy. Skip to main content. Genes and Proteins. Search for:. The Genetic Code. The Relationship Between Genes and Proteins Proteins, encoded by individual genes, orchestrate nearly every function of the cell. Mustafa, G.
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Yang, Y. Cancer Inst. Yeasmin, F. A number of these transcribed pseudogenes and ncRNA genes are, in fact, located within introns of protein-coding genes. One cannot simply ignore these components within introns because some of them may influence the expression of their host genes, either directly or indirectly. The roles of ncRNA genes are quite diverse, including gene regulation e. However, the example of the kb large XIST gene involved in dosage compensation shows that functional ncRNAs can expand significantly beyond constrained, computationally identifiable regions Chureau et al.
It is also possible that the RNA products themselves do not have a function, but rather reflect or are important for a particular cellular process. For example, transcription of a regulatory region might be important for chromatin accessibility for transcription factor binding or for DNA replication.
Such transcription has been found in the locus control region LCR of the beta-globin locus, and polymerase activity has been suggested to be important for DNA replication in E. Alternatively, transcription might reflect nonspecific activity of a particular region, for example, the recruitment of polymerase to regulatory sites.
In either of these scenarios, the transcripts themselves would lack a function and be unlikely to be conserved.
They are derived from functional genes through retrotransposition or duplication but have lost the original functions of their parental genes Balakirev and Ayala Sometimes swinging between dead and alive, pseudogenes can influence the structure and function of the human genome. Their prevalence as many as protein-coding genes and their close similarity to functional genes have already confounded gene annotation. Indeed, some of the novel TARs can be attributed to pseudogene transcription Bertone et al.
In a few surprising cases, a pseudogene RNA or at least a piece of it was found to be spliced with the transcript of its neighboring gene to form a gene—pseudogene chimeric transcript. These findings add one extra layer of complexity to establishing the precise structure of a gene locus.
Furthermore, functional pseudogene transcripts have also been discovered in eukaryotic cells, such as the neurons of the snail Lymnaea stagnalis Korneev et al.
Also, interestingly, the human XIST gene mentioned above actually arises from the dead body of a pseudogene Duret et al. Pseudogene transcription and the blurring boundary between genes and pseudogenes Zheng and Gerstein emphasizes once more that the functional nature of many novel TARs needs to be resolved by future biochemical or genetic experiments for review, see Gingeras The noncoding intergenic regions contain a large fraction of functional elements identified by examining evolutionary changes across multiple species and within the human population.
This suggests that protein-coding loci can be viewed as a cluster of small constrained elements dispersed in a sea of unconstrained sequences. The new ENCODE perspective does not, of course, fit with the metaphor of the gene as a simple callable routine in a huge operating system.
The execution of the genomic OS does not have as neat a quality as this idea of repetitive calls to a discrete subroutine in a normal computer OS.
However, the framework of describing the genome as executed code still has some merit. That is, one can still understand gene transcription in terms of parallel threads of execution, with the caveat that these threads do not follow canonical, modular subroutine structure. Given the provocative findings of the ENCODE project, one wonders to what degree the interpretation of the high-throughput experiments can be pushed.
This interpretation is, in fact, very contingent on using gene models. A large part of the transcription data was generated using high-density tiling microarrays Emanuelsson et al.
The advantage of such arrays is that they probe the transcription in an unbiased and detailed way, with no preconceptions as to where to look for activity. On the other hand, the output from a tiling array experiment can be noisy and needs careful interpretation in order to allow the collection of a reliable set of transcribed regions. The amount of detected transcription depends heavily on the thresholds used when calling transcribed regions and to some extent also on the segmentation algorithms used to delineate transcribed regions from nontranscribed regions.
Furthermore, since the ENCODE transcription mapping and other experiments were carried out on many different tissues and cell lines, direct comparison between experiments is not trivial, and the overlap between different transcription maps is sometimes quite low, partly due to the variable biological features of the samples used in the experiments.
The exact expected outcome of a transcription mapping experiment—the true transcription map—is, of course, unknown. Thus, a crucial part of interpreting transcription mapping tiling array data is to understand how the signal is different from various random expectations null models. The expected outcome also depends on the biological sample used: tissue or cell line, developmental stage, external stimuli, etc.
Integration of transcription maps from different biological sources tissues, cell lines provides greater confidence in the result. In the context of interpreting high-throughput experiments such as tiling arrays, the concept of a gene has an added practical importance—as a statistical model to help interpret and provide concise summarization to potentially noisy experimental data.
Therefore, the most appropriate gene models to be considered can be splicing graphs Heber et al. In order to build and adjust statistical models for experimental interpretation, other related biological knowledge e. For instance, the transcriptional array data can identify isolated transcribed regions, and experimental validation such as RACE can provide connectivity information.
Using these data together, the statistical models can be better trained and can then be used to analyze the rest of the high-throughput data that are not covered by the validation experiments.
Different statistical models Karplus et al. As shown in Figure 3 , these models can be trained using the tiling array data and other biological knowledge and then extrapolated to the whole genome sequence to best segment it into functional elements. As more and more biological knowledge is accumulated, especially via the experimental validation of predicted functional regions generated by the analysis procedure, we can expect that the models will be better trained, thus leading to refined analysis results of these experiments.
For each tiling array experiment, perhaps only a medium-sized set of predicted functional regions will be validated experimentally. Training statistical gene models based on high-density oligonucleotide tiling microarray data.
B Different strategies can be used to select genomic regions for validation; e. One question worth asking is whether an optimal way of selection exists to best help in training the statistical model.
As shown in Figure 3 , the regions for experimental validation can be picked using different strategies. It is obviously beneficial to pick these regions in an optimal way so that the model trained based on these validation results can most accurately analyze the remainder of the tiling array data.
In a specific case, when analyzing tiling array data using a hidden Markov model Du et al. For transcriptional tiling arrays, MaxEntropy will generally select regions containing both exons and introns.
As we have described above, our knowledge of genes has evolved greatly over the past century. While our understanding has grown, we have also uncovered an increasing number of problematic aspects with simple definitions of a gene Table 1. Splicing including alternative splicing and intergenic transcription are obviously some of the most problematic aspects. As shown in Figure 4 , the frequency of mention of these terms in the biological literature has been increasing considerably. Thus, the stage was set for the ENCODE project and the great complexity in transcriptional and regulatory apparatus that it highlighted.
At this point, it is not clear what to do: In the extreme, we could declare the concept of the gene dead and try to come up with something completely new that fits all the data. However, it would be hard to do this with consistency.
Here, we made a tentative attempt at a compromise, devising updates and patches for the existing definition of a gene. Keyword analysis and complexity of genes. First, we consider several criteria to be important while coming up with an updated definition for a gene: 1 A new definition must attempt to be backward compatible , in the sense that something that used to be called a gene should remain a gene.
For instance, it should be consistent with term regulome, which represents the complete set of regulatory interactions in an organism. There are three aspects to the definition that we will list below, before providing the succinct definition:.
In the case that there are several functional products sharing overlapping regions, one takes the union of all overlapping genomic sequences coding for them. This union must be coherent —i. The gene is a union of genomic sequences encoding a coherent set of potentially overlapping functional products. Figure 5 provides an example to illustrate the application of this definition. How the proposed definition of the gene can be applied to a sample case. A genomic region produces three primary transcripts.
After alternative splicing, products of two of these encode five protein products, while the third encodes for a noncoding RNA ncRNA product. In the case of the three-segment cluster A, B, C , each DNA sequence segment is shared by at least two of the products.
There is also one noncoding RNA product, and because its sequence is of RNA, not protein, the fact that it shares its genomic sequences X and Y with the protein-coding genomic segments A and E does not make it a co-product of these protein-coding genes. In summary, there are four genes in this region, and they are the sets of sequences shown inside the orange dashed lines: Gene 1 consists of the sequence segments A, B, and C; gene 2 consists of D; gene 3 of E; and gene 4 of X and Y.
In the diagram, for clarity, the exonic and protein sequences A and E have been lined up vertically, so the dashed lines for the spliced transcripts and functional products indicate connectivity between the proteins sequences ovals and RNA sequences boxes. Solid boxes on transcripts Untranslated sequences, open boxes translated sequences.
In simple cases where the gene is not discontinuous or there are no overlapping products, our definition collapses to the classical version of being a DNA sequence that codes for a protein or RNA product.
In our proposed definition of a gene, different functional products of the same class protein or RNA that overlap in their usage of the primary DNA sequence are combined into the same gene.
This overlap is done by projecting the sequence of the final product either amino acid or RNA sequence down onto the original genomic sequence from which it was derived. An obvious point that should still be stated is that, when looking at genomic products with common sequence segments, mere sequence identity is not enough; the products have to be encoded directly from the same genomic region.
Thus, paralogous proteins may share sequence blocks, but DNA sequences coding for them reside in separate locations in the genome, and so they would not constitute one gene. Thus, although the two mRNAs have coding sequences in common, the protein products may be completely different. This rather unusual case brings up the question of how exactly sequence identity is to be handled when taking the union of sequence segments that are shared among protein products.
If one considers the sequence of the protein products, there are two unrelated proteins, so there must be two genes with overlapping sequence sets. For this reason, generalizing from this special case, we favor the method of taking the union of the sequence segments, not of the products, but of the DNA sequences that code for the product sequences.
Although regulatory regions are important for gene expression, we suggest that they should not be considered in deciding whether multiple products belong to the same gene. This aspect of the definition results from our concept of the bacterial operon. The fact that genes in an operon share an operator and promoter region has traditionally not been considered to imply that their protein products are alternative products of a single gene.
Consequently, in higher eukaryotes, two transcripts that originate from the same transcription start site sharing the same promoter and regulatory elements but do not share any sequence elements in their final products e. A similar logic would apply to multiple transcripts sharing a common but distant enhancer or insulator.
Regulation is simply too complex to be folded into the definition of a gene, and there is obviously a many-to-many rather than one-to-one relationship between regulatory regions and genes.
As the updated definition emphasizes the final products of a gene, it disregards intermediate products originating from a genomic region that may happen to overlap. For example, an intronic transcript clearly shares sequences with an overlapping larger transcript, but this fact is irrelevant when we conclude that the two products share no sequence blocks.
This concept can be generalized to other types of discontinuous genes, such as rearranged genes e. This implies that the number of genes in the human genome is going to increase significantly when the survey of the human transcriptome is completed.
In light of the large amount of intertwined transcripts that were identified by the ENCODE consortium, if we tried to cluster entire transcripts together to form overlapping transcript clusters a potential alternate definition of a gene , then we would find that large segments of chromosomes would coalesce into these clusters. In relation to alternatively spliced gene products, there is the possibility that no one coding exon is shared among all protein products.
In this case, it is understood that the union of these sequence segments defines the gene, as long as each exon is shared among at least two members of this group of products. When using a strict definition of regions encoding the final product of a protein-coding gene, these regions would no longer be considered part of the gene, as is often the case in current usage.
Moreover, protein-coding transcripts that share DNA sequence only in their untranslated regions or introns would not be clustered together into a common gene. Moreover, it has been observed that most of the longer protein-coding transcripts identified by ENCODE differ only in their UTRs, and thus our definition is quite transparent to this degree of transcript complexity.
As described above, regulatory and untranslated regions that play an important part in gene expression would no longer be considered part of the gene. In this way, these regions still retain their important role in contributing to gene function. Moreover, their ability to contribute to the expression of several genes can be recognized. The remaining regions of the transcript, which include the protein-coding regions, are called exons , and they are spliced together to produce the mature mRNA.
Eukaryotic transcripts are also modified at their ends, which affects their stability and translation. Of course, there are many cases in which cells must respond quickly to changing environmental conditions.
In these situations, the regulatory control point may come well after transcription. For example, early development in most animals relies on translational control because very little transcription occurs during the first few cell divisions after fertilization. Eggs therefore contain many maternally originated mRNA transcripts as a ready reserve for translation after fertilization Figure 1. On the degradative side of the balance, cells can rapidly adjust their protein levels through the enzymatic breakdown of RNA transcripts and existing protein molecules.
Both of these actions result in decreased amounts of certain proteins. Often, this breakdown is linked to specific events in the cell. The eukaryotic cell cycle provides a good example of how protein breakdown is linked to cellular events.
This cycle is divided into several phases, each of which is characterized by distinct cyclin proteins that act as key regulators for that phase. Before a cell can progress from one phase of the cell cycle to the next, it must degrade the cyclin that characterizes that particular phase of the cycle.
Failure to degrade a cyclin stops the cycle from continuing. Some regions are removed introns during initial mRNA processing. The remaining exons are then spliced together, and the spliced mRNA molecule red is prepared for export out of the nucleus through addition of an endcap sphere and a polyA tail.
Once in the cytoplasm, the mRNA can be used to construct a protein. At the top of the diagram, within the nucleus, is a grey DNA double helix. A transparent, rectangular box is drawn on top of most of the double helix. The rectangular box is shaded with two alternating colors; the purple segments represent exons, and the light-green segments represent introns.
The pre-mRNA molecule is shown as a grey, single-stranded RNA molecule made up of a linear backbone with vertical rectangles arranged along its length. The tops of the rectangles are either pointed, rounded, cupped, or V-shaped to represent different nucleotides. A transparent, rectangular box is drawn over most of the pre-mRNA molecule, with red regions that align with the purple DNA exons and light-green regions that align with the light-green introns in the DNA template.
The mature mRNA also has a light peach-colored sphere attached to its left end to represent the 5-prime cap, and four adenosine molecules attached to its right end to represent the poly-A tail. In the background of the cytoplasm, thin black lines show silhouettes of cytoplasmic organelles, including the Golgi apparatus and endoplasmic reticulum. Translation of mRNA into protein occurs in the cytoplasm.
Only a fraction of the genes in a cell are expressed at any one time. The variety of gene expression profiles characteristic of different cell types arise because these cells have distinct sets of transcription regulators. Some of these regulators work to increase transcription, whereas others prevent or suppress it. This sequence is almost always located just upstream from the starting point for transcription the 5' end of the DNA , though it can be located downstream of the mRNA 3' end.
In recent years, researchers have discovered that other DNA sequences, known as enhancer sequences , also play an important part in transcription by providing binding sites for regulatory proteins that affect RNA polymerase activity.
Binding of regulatory proteins to an enhancer sequence causes a shift in chromatin structure that either promotes or inhibits RNA polymerase and transcription factor binding.
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