Genetic code

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https://en.wikipedia.org/wiki/Genetic_code

 

A series of codons in part of a messenger RNA (mRNA) molecule. Each codon consists of three nucleotides, usually corresponding to a single amino acid. The nucleotides are abbreviated with the letters A, U, G and C. This is mRNA, which uses U (uracil). DNA uses T (thymine) instead. This mRNA molecule will instruct a ribosome to synthesize a protein according to this code.

The genetic code is the set of rules used by living cells to translate information encoded within genetic material (DNA or RNA sequences of nucleotide triplets, or codons) into proteins. Translation is accomplished by the ribosome, which links proteinogenic amino acids in an order specified by messenger RNA (mRNA), using transfer RNA (tRNA) molecules to carry amino acids and to read the mRNA three nucleotides at a time. The genetic code is highly similar among all organisms and can be expressed in a simple table with 64 entries.

The codons specify which amino acid will be added next during protein biosynthesis. With some exceptions, a three-nucleotide codon in a nucleic acid sequence specifies a single amino acid. The vast majority of genes are encoded with a single scheme (see the RNA codon table). That scheme is often referred to as the canonical or standard genetic code, or simply the genetic code, though variant codes (such as in mitochondria) exist.

History

The genetic code

 

Further information: Adaptor hypothesis

Efforts to understand how proteins are encoded began after DNA's structure was discovered in 1953. The key discoverers, English biophysicist Francis Crick and American biologist James Watson, working together at the Cavendish Laboratory of the University of Cambridge, hypothesied that information flows from DNA and that there is a link between DNA and proteins. Soviet-American physicist George Gamow was the first to give a workable scheme for protein synthesis from DNA. He postulated that sets of three bases (triplets) must be employed to encode the 20 standard amino acids used by living cells to build proteins, which would allow a maximum of 4³ = 64 amino acids. He named this DNA–protein interaction (the original genetic code) as the "diamond code."

In 1954, Gamow created an informal scientific organisation the RNA Tie Club, as suggested by Watson, for scientists of different persuasions who were interested in how proteins were synthesised from genes. However, the club could have only 20 permanent members to represent each of the 20 amino acids; and four additional honorary members to represent the four nucleotides of DNA.

The first scientific contribution of the club, later recorded as "one of the most important unpublished articles in the history of science" and "the most famous unpublished paper in the annals of molecular biology," was made by Crick. Crick presented a type-written paper titled "On Degenerate Templates and the Adaptor Hypothesis: A Note for the RNA Tie Club" to the members of the club in January 1955, which "totally change the way we thought about protein synthesis", as Watson recalled. The hypothesis states that the triplet code was not passed on to amino acids as Gamow thought, but carried by a different molecule, an adaptor, that interacts with amino acids. The adaptor was later identified as tRNA.

Codons

See also: DNA and RNA codon tables § Translation table 1

The Crick, Brenner, Barnett and Watts-Tobin experiment first demonstrated that codons consist of three DNA bases. Marshall Nirenberg and Heinrich J. Matthaei were the first to reveal the nature of a codon in 1961.

They used a cell-free system to translate a poly-uracil RNA sequence (i.e., UUUUU...) and discovered that the polypeptide that they had synthesized consisted of only the amino acid phenylalanine. They thereby deduced that the codon UUU specified the amino acid phenylalanine.

This was followed by experiments in Severo Ochoa's laboratory that demonstrated that the poly-adenine RNA sequence (AAAAA...) coded for the polypeptide poly-lysine and that the poly-cytosine RNA sequence (CCCCC...) coded for the polypeptide poly-proline. Therefore, the codon AAA specified the amino acid lysine, and the codon CCC specified the amino acid proline. Using various copolymers most of the remaining codons were then determined.

Subsequent work by Har Gobind Khorana identified the rest of the genetic code. Shortly thereafter, Robert W. Holley determined the structure of transfer RNA (tRNA), the adapter molecule that facilitates the process of translating RNA into protein. This work was based upon Ochoa's earlier studies, yielding the latter the Nobel Prize in Physiology or Medicine in 1959 for work on the enzymology of RNA synthesis.

Extending this work, Nirenberg and Philip Leder revealed the code's triplet nature and deciphered its codons. In these experiments, various combinations of mRNA were passed through a filter that contained ribosomes, the components of cells that translate RNA into protein. Unique triplets promoted the binding of specific tRNAs to the ribosome. Leder and Nirenberg were able to determine the sequences of 54 out of 64 codons in their experiments. Khorana, Holley and Nirenberg received the 1968 Nobel for their work.

The three stop codons were named by discoverers Richard Epstein and Charles Steinberg. "Amber" was named after their friend Harris Bernstein, whose last name means "amber" in German. The other two stop codons were named "ochre" and "opal" in order to keep the "color names" theme.

Expanded genetic codes (synthetic biology)

Main article: Expanded genetic code

See also: Nucleic acid analogues

In a broad academic audience, the concept of the evolution of the genetic code from the original and ambiguous genetic code to a well-defined ("frozen") code with the repertoire of 20 (+2) canonical amino acids is widely accepted. However, there are different opinions, concepts, approaches and ideas, which is the best way to change it experimentally. Even models are proposed that predict "entry points" for synthetic amino acid invasion of the genetic code.

Since 2001, 40 non-natural amino acids have been added into proteins by creating a unique codon (recoding) and a corresponding transfer-RNA:aminoacyl – tRNA-synthetase pair to encode it with diverse physicochemical and biological properties in order to be used as a tool to exploring protein structure and function or to create novel or enhanced proteins.

H. Murakami and M. Sisido extended some codons to have four and five bases. Steven A. Benner constructed a functional 65th (in vivo) codon.

In 2015 N. Budisa, D. Söll and co-workers reported the full substitution of all 20,899 tryptophan residues (UGG codons) with unnatural thienopyrrole-alanine in the genetic code of the bacterium Escherichia coli.

In 2016 the first stable semisynthetic organism was created. It was a (single cell) bacterium with two synthetic bases (called X and Y). The bases survived cell division.

In 2017, researchers in South Korea reported that they had engineered a mouse with an extended genetic code that can produce proteins with unnatural amino acids.

In May 2019, researchers reported the creation of a new "Syn61" strain of the bacterium Escherichia coli. This strain has a fully synthetic genome that is refactored (all overlaps expanded), recoded (removing the use of three out of 64 codons completely), and further modified to remove the now unnecessary tRNAs and release factors. It is fully viable and grows 1.6× slower than its wild-type counterpart "MDS42".

Features

Reading frames in the DNA sequence of a region of the human mitochondrial genome coding for the genes MT-ATP8 and MT-ATP6 (in black: positions 8,525 to 8,580 in the sequence accession NC_012920^[31]). There are three possible reading frames in the 5' → 3' forward direction, starting on the first (+1), second (+2) and third position (+3). For each codon (square brackets), the amino acid is given by the vertebrate mitochondrial code, either in the +1 frame for MT-ATP8 (in red) or in the +3 frame for MT-ATP6 (in blue). The MT-ATP8 genes terminates with the TAG stop codon (red dot) in the +1 frame. The MT-ATP6 gene starts with the ATG codon (blue circle for the M amino acid) in the +3 frame.

Reading frame

A reading frame is defined by the initial triplet of nucleotides from which translation starts. It sets the frame for a run of successive, non-overlapping codons, which is known as an "open reading frame" (ORF). For example, the string 5'-AAATGAACG-3' (see figure), if read from the first position, contains the codons AAA, TGA, and ACG ; if read from the second position, it contains the codons AAT and GAA ; and if read from the third position, it contains the codons ATG and AAC. Every sequence can, thus, be read in its 5' → 3' direction in three reading frames, each producing a possibly distinct amino acid sequence: in the given example, Lys (K)-Trp (W)-Thr (T), Asn (N)-Glu (E), or Met (M)-Asn (N), respectively (when translating with the vertebrate mitochondrial code). When DNA is double-stranded, six possible reading frames are defined, three in the forward orientation on one strand and three reverse on the opposite strand. Protein-coding frames are defined by a start codon, usually the first AUG (ATG) codon in the RNA (DNA) sequence.

In eukaryotes, ORFs in exons are often interrupted by introns.

Start and stop codons

Translation starts with a chain-initiation codon or start codon. The start codon alone is not sufficient to begin the process. Nearby sequences such as the Shine-Dalgarno sequence in E. coli and initiation factors are also required to start translation. The most common start codon is AUG, which is read as methionine or as formylmethionine (in bacteria, mitochondria, and plastids). Alternative start codons depending on the organism include "GUG" or "UUG"; these codons normally represent valine and leucine, respectively, but as start codons they are translated as methionine or formylmethionine.

The three stop codons have names: UAG is amber, UGA is opal (sometimes also called umber), and UAA is ochre. Stop codons are also called "termination" or "nonsense" codons. They signal release of the nascent polypeptide from the ribosome because no cognate tRNA has anticodons complementary to these stop signals, allowing a release factor to bind to the ribosome instead.

Effect of mutations

Examples of notable mutations that can occur in humans.

During the process of DNA replication, errors occasionally occur in the polymerization of the second strand. These errors, mutations, can affect an organism's phenotype, especially if they occur within the protein coding sequence of a gene. Error rates are typically 1 error in every 10–100 million bases—due to the "proofreading" ability of DNA polymerases.

Missense mutations and nonsense mutations are examples of point mutations that can cause genetic diseases such as sickle-cell disease and thalassemia respectively. Clinically important missense mutations generally change the properties of the coded amino acid residue among basic, acidic, polar or non-polar states, whereas nonsense mutations result in a stop codon.

Mutations that disrupt the reading frame sequence by indels (insertions or deletions) of a non-multiple of 3 nucleotide bases are known as frameshift mutations. These mutations usually result in a completely different translation from the original, and likely cause a stop codon to be read, which truncates the protein. These mutations may impair the protein's function and are thus rare in in vivo protein-coding sequences. One reason inheritance of frameshift mutations is rare is that, if the protein being translated is essential for growth under the selective pressures the organism faces, absence of a functional protein may cause death before the organism becomes viable. Frameshift mutations may result in severe genetic diseases such as Tay–Sachs disease.

Although most mutations that change protein sequences are harmful or neutral, some mutations have benefits. These mutations may enable the mutant organism to withstand particular environmental stresses better than wild type organisms, or reproduce more quickly. In these cases a mutation will tend to become more common in a population through natural selection. Viruses that use RNA as their genetic material have rapid mutation rates, which can be an advantage, since these viruses thereby evolve rapidly, and thus evade the immune system defensive responses. In large populations of asexually reproducing organisms, for example, E. coli, multiple beneficial mutations may co-occur. This phenomenon is called clonal interference and causes competition among the mutations.

Degeneracy

Main article: Codon degeneracy

 

Grouping of codons by amino acid residue molar volume and hydropathicity. A more detailed version is available.

 

Axes 1, 2, 3 are the first, second, and third positions in the codon. The 20 amino acids and stop codons (X) are shown in single letter code.

Degeneracy is the redundancy of the genetic code. This term was given by Bernfield and Nirenberg. The genetic code has redundancy but no ambiguity (see the codon tables below for the full correlation). For example, although codons GAA and GAG both specify glutamic acid (redundancy), neither specifies another amino acid (no ambiguity). The codons encoding one amino acid may differ in any of their three positions. For example, the amino acid leucine is specified by YUR or CUN (UUA, UUG, CUU, CUC, CUA, or CUG) codons (difference in the first or third position indicated using IUPAC notation), while the amino acid serine is specified by UCN or AGY (UCA, UCG, UCC, UCU, AGU, or AGC) codons (difference in the first, second, or third position). A practical consequence of redundancy is that errors in the third position of the triplet codon cause only a silent mutation or an error that would not affect the protein because the hydrophilicity or hydrophobicity is maintained by equivalent substitution of amino acids; for example, a codon of NUN (where N = any nucleotide) tends to code for hydrophobic amino acids. NCN yields amino acid residues that are small in size and moderate in hydropathicity; NAN encodes average size hydrophilic residues. The genetic code is so well-structured for hydropathicity that a mathematical analysis (Singular Value Decomposition) of 12 variables (4 nucleotides x 3 positions) yields a remarkable correlation (C = 0.95) for predicting the hydropathicity of the encoded amino acid directly from the triplet nucleotide sequence, without translation. Note in the table, below, eight amino acids are not affected at all by mutations at the third position of the codon, whereas in the figure above, a mutation at the second position is likely to cause a radical change in the physicochemical properties of the encoded amino acid. Nevertheless, changes in the first position of the codons are more important than changes in the second position on a global scale. The reason may be that charge reversal (from a positive to a negative charge or vice versa) can only occur upon mutations in the first position of certain codons, but not upon changes in the second position of any codon. Such charge reversal may have dramatic consequences for the structure or function of a protein. This aspect may have been largely underestimated by previous studies.

Codon usage bias

The frequency of codons, also known as codon usage bias, can vary from species to species with functional implications for the control of translation. The codon varies by organism; for example, most common proline codon in E. coli is CCG, whereas in humans this is the least used proline codon.

Human genome codon frequency table

Alternative genetic codes

See also: DNA and RNA codon tables § Alternative codons

Non-standard amino acids

In some proteins, non-standard amino acids are substituted for standard stop codons, depending on associated signal sequences in the messenger RNA. For example, UGA can code for selenocysteine and UAG can code for pyrrolysine. Selenocysteine came to be seen as the 21st amino acid, and pyrrolysine as the 22nd. Unlike selenocysteine, pyrrolysine-encoded UAG is translated with the participation of a dedicated aminoacyl-tRNA synthetase. Both selenocysteine and pyrrolysine may be present in the same organism. Although the genetic code is normally fixed in an organism, the achaeal prokaryote Acetohalobium arabaticum can expand its genetic code from 20 to 21 amino acids (by including pyrrolysine) under different conditions of growth.

Variations

See also: List of genetic codes

 

Genetic code logo of the Globobulimina pseudospinescens mitochondrial genome by FACIL. The logo shows the 64 codons from left to right, predicted alternatives in red (relative to the standard genetic code). Red line: stop codons. The height of each amino acid in the stack shows how often it is aligned to the codon in homologous protein domains. The stack height indicates the support for the prediction.

There was originally a simple and widely accepted argument that the genetic code should be universal: namely, that any variation in the genetic code would be lethal to the organism (although Crick had stated that viruses were an exception). This is known as the "frozen accident" argument for the universality of the genetic code. However, in his seminal paper on the origins of the genetic code in 1968, Francis Crick still stated that the universality of the genetic code in all organisms was an unproven assumption, and was probably not true in some instances. He predicted that "The code is universal (the same in all organisms) or nearly so". The first variation was discovered in 1979, by researchers studying human mitochondrial genes. Many slight variants were discovered thereafter, including various alternative mitochondrial codes. These minor variants for example involve translation of the codon UGA as tryptophan in Mycoplasma species, and translation of CUG as a serine rather than leucine in yeasts of the "CTG clade" (such as Candida albicans). Because viruses must use the same genetic code as their hosts, modifications to the standard genetic code could interfere with viral protein synthesis or functioning. However, viruses such as totiviruses have adapted to the host's genetic code modification. In bacteria and archaea, GUG and UUG are common start codons. In rare cases, certain proteins may use alternative start codons. Surprisingly, variations in the interpretation of the genetic code exist also in human nuclear-encoded genes: In 2016, researchers studying the translation of malate dehydrogenase found that in about 4% of the mRNAs encoding this enzyme the stop codon is naturally used to encode the amino acids tryptophan and arginine. This type of recoding is induced by a high-readthrough stop codon context and it is referred to as functional translational readthrough.

Despite these differences, all known naturally occurring codes are very similar. The coding mechanism is the same for all organisms: three-base codons, tRNA, ribosomes, single direction reading and translating single codons into single amino acids. The most extreme variations occur in certain ciliates where the meaning of stop codons depends on their position within mRNA. When close to the 3' end they act as terminators while in internal positions they either code for amino acids as in Condylostoma magnum or trigger ribosomal frameshifting as in Euplotes.

The origins and variation of the genetic code, including the mechanisms behind the evolvability of the genetic code, have been widely studied, and some studies have been done experimentally evolving the genetic code of some organisms.

Inferrence

Variant genetic codes used by an organism can be inferred by identifying highly conserved genes encoded in that genome, and comparing its codon usage to the amino acids in homologous proteins of other organisms. For example, the program FACIL infers a genetic code by searching which amino acids in homologous protein domains are most often aligned to every codon. The resulting amino acid (or stop codon) probabilities for each codon are displayed in a genetic code logo.

As of January 2022, the most complete survey of genetic codes is done by Shulgina and Eddy, who screened 250,000 prokaryotic genomes using their Codetta tool. This tool uses a similar approach to FACIL with a larger Pfam database. Despite the NCBI already providing 33 translation tables, the authors were able to find new 5 genetic code variations (corroborated by tRNA mutations) and correct several misattributions.

Origin

The genetic code is a key part of the history of life, according to one version of which self-replicating RNA molecules preceded life as we know it. This is the RNA world hypothesis. Under this hypothesis, any model for the emergence of the genetic code is intimately related to a model of the transfer from ribozymes (RNA enzymes) to proteins as the principal enzymes in cells. In line with the RNA world hypothesis, transfer RNA molecules appear to have evolved before modern aminoacyl-tRNA synthetases, so the latter cannot be part of the explanation of its patterns.

A hypothetical randomly evolved genetic code further motivates a biochemical or evolutionary model for its origin. If amino acids were randomly assigned to triplet codons, there would be 1.5 × 10⁸⁴ possible genetic codes. This number is found by calculating the number of ways that 21 items (20 amino acids plus one stop) can be placed in 64 bins, wherein each item is used at least once. However, the distribution of codon assignments in the genetic code is nonrandom. In particular, the genetic code clusters certain amino acid assignments.

Amino acids that share the same biosynthetic pathway tend to have the same first base in their codons. This could be an evolutionary relic of an early, simpler genetic code with fewer amino acids that later evolved to code a larger set of amino acids. It could also reflect steric and chemical properties that had another effect on the codon during its evolution. Amino acids with similar physical properties also tend to have similar codons, reducing the problems caused by point mutations and mistranslations.

Given the non-random genetic triplet coding scheme, a tenable hypothesis for the origin of genetic code could address multiple aspects of the codon table, such as absence of codons for D-amino acids, secondary codon patterns for some amino acids, confinement of synonymous positions to third position, the small set of only 20 amino acids (instead of a number approaching 64), and the relation of stop codon patterns to amino acid coding patterns.

Three main hypotheses address the origin of the genetic code. Many models belong to one of them or to a hybrid:

• Random freeze: the genetic code was randomly created. For example, early tRNA-like ribozymes may have had different affinities for amino acids, with codons emerging from another part of the ribozyme that exhibited random variability. Once enough peptides were coded for, any major random change in the genetic code would have been lethal; hence it became "frozen".
• Stereochemical affinity: the genetic code is a result of a high affinity between each amino acid and its codon or anti-codon; the latter option implies that pre-tRNA molecules matched their corresponding amino acids by this affinity. Later during evolution, this matching was gradually replaced with matching by aminoacyl-tRNA synthetases.
• Optimality: the genetic code continued to evolve after its initial creation, so that the current code maximizes some fitness function, usually some kind of error minimization.

Hypotheses have addressed a variety of scenarios:

• Chemical principles govern specific RNA interaction with amino acids. Experiments with aptamers showed that some amino acids have a selective chemical affinity for their codons. Experiments showed that of 8 amino acids tested, 6 show some RNA triplet-amino acid association.
• Biosynthetic expansion. The genetic code grew from a simpler earlier code through a process of "biosynthetic expansion". Primordial life "discovered" new amino acids (for example, as by-products of metabolism) and later incorporated some of these into the machinery of genetic coding. Although much circumstantial evidence has been found to suggest that fewer amino acid types were used in the past, precise and detailed hypotheses about which amino acids entered the code in what order are controversial. However, several studies have suggested that Gly, Ala, Asp, Val, Ser, Pro, Glu, Leu, Thr may belong to a group of early-addition amino acids, whereas Cys, Met, Tyr, Trp, His, Phe may belong to a group of later-addition amino acids.
• Natural selection has led to codon assignments of the genetic code that minimize the effects of mutations. A recent hypothesis suggests that the triplet code was derived from codes that used longer than triplet codons (such as quadruplet codons). Longer than triplet decoding would increase codon redundancy and would be more error resistant. This feature could allow accurate decoding absent complex translational machinery such as the ribosome, such as before cells began making ribosomes.
• Information channels: Information-theoretic approaches model the process of translating the genetic code into corresponding amino acids as an error-prone information channel. The inherent noise (that is, the error) in the channel poses the organism with a fundamental question: how can a genetic code be constructed to withstand noise while accurately and efficiently translating information? These "rate-distortion" models suggest that the genetic code originated as a result of the interplay of the three conflicting evolutionary forces: the needs for diverse amino acids, for error-tolerance and for minimal resource cost. The code emerges at a transition when the mapping of codons to amino acids becomes nonrandom. The code's emergence is governed by the topology defined by the probable errors and is related to the map coloring problem.
• Game theory: Models based on signaling games combine elements of game theory, natural selection and information channels. Such models have been used to suggest that the first polypeptides were likely short and had non-enzymatic function. Game theoretic models suggested that the organization of RNA strings into cells may have been necessary to prevent "deceptive" use of the genetic code, i.e. preventing the ancient equivalent of viruses from overwhelming the RNA world.
• Stop codons: Codons for translational stops are also an interesting aspect to the problem of the origin of the genetic code. As an example for addressing stop codon evolution, it has been suggested that the stop codons are such that they are most likely to terminate translation early in the case of a frame shift error. In contrast, some stereochemical molecular models explain the origin of stop codons as "unassignable".

Transcription factor

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Transcription_factor 

 

Transcription factor glossary
gene expression – the process by which information from a gene is used in the synthesis of a functional gene product such as a protein transcription – the process of making messenger RNA (mRNA) from a DNA template by RNA polymerase transcription factor – a protein that binds to DNA and regulates gene expression by promoting or suppressing transcription transcriptional regulation – controlling the rate of gene transcription for example by helping or hindering RNA polymerase binding to DNA upregulation, activation, or promotion – increase the rate of gene transcription downregulation, repression, or suppression – decrease the rate of gene transcription coactivator – a protein (or a small molecule) that works with transcription factors to increase the rate of gene transcription corepressor – a protein (or a small molecule) that works with transcription factors to decrease the rate of gene transcription response element – a specific sequence of DNA that a transcription factor binds to

Illustration of an activator

In molecular biology, a transcription factor (TF) (or sequence-specific DNA-binding factor) is a protein that controls the rate of transcription of genetic information from DNA to messenger RNA, by binding to a specific DNA sequence. The function of TFs is to regulate—turn on and off—genes in order to make sure that they are expressed in the desired cells at the right time and in the right amount throughout the life of the cell and the organism. Groups of TFs function in a coordinated fashion to direct cell division, cell growth, and cell death throughout life; cell migration and organization (body plan) during embryonic development; and intermittently in response to signals from outside the cell, such as a hormone. There are 1500-1600 TFs in the human genome. Transcription factors are members of the proteome as well as regulome.

TFs work alone or with other proteins in a complex, by promoting (as an activator), or blocking (as a repressor) the recruitment of RNA polymerase (the enzyme that performs the transcription of genetic information from DNA to RNA) to specific genes.

A defining feature of TFs is that they contain at least one DNA-binding domain (DBD), which attaches to a specific sequence of DNA adjacent to the genes that they regulate. TFs are grouped into classes based on their DBDs. Other proteins such as coactivators, chromatin remodelers, histone acetyltransferases, histone deacetylases, kinases, and methylases are also essential to gene regulation, but lack DNA-binding domains, and therefore are not TFs.

TFs are of interest in medicine because TF mutations can cause specific diseases, and medications can be potentially targeted toward them.

Number

Main listing: List of human transcription factors

Transcription factors are essential for the regulation of gene expression and are, as a consequence, found in all living organisms. The number of transcription factors found within an organism increases with genome size, and larger genomes tend to have more transcription factors per gene.

There are approximately 2800 proteins in the human genome that contain DNA-binding domains, and 1600 of these are presumed to function as transcription factors, though other studies indicate it to be a smaller number. Therefore, approximately 10% of genes in the genome code for transcription factors, which makes this family the single largest family of human proteins. Furthermore, genes are often flanked by several binding sites for distinct transcription factors, and efficient expression of each of these genes requires the cooperative action of several different transcription factors (see, for example, hepatocyte nuclear factors). Hence, the combinatorial use of a subset of the approximately 2000 human transcription factors easily accounts for the unique regulation of each gene in the human genome during development.

Mechanism

Transcription factors bind to either enhancer or promoter regions of DNA adjacent to the genes that they regulate. Depending on the transcription factor, the transcription of the adjacent gene is either up- or down-regulated. Transcription factors use a variety of mechanisms for the regulation of gene expression. These mechanisms include:

• stabilize or block the binding of RNA polymerase to DNA
• catalyze the acetylation or deacetylation of histone proteins. The transcription factor can either do this directly or recruit other proteins with this catalytic activity. Many transcription factors use one or the other of two opposing mechanisms to regulate transcription:
  • histone acetyltransferase (HAT) activity – acetylates histone proteins, which weakens the association of DNA with histones, which make the DNA more accessible to transcription, thereby up-regulating transcription
  • histone deacetylase (HDAC) activity – deacetylates histone proteins, which strengthens the association of DNA with histones, which make the DNA less accessible to transcription, thereby down-regulating transcription
• recruit coactivator or corepressor proteins to the transcription factor DNA complex

Function

Transcription factors are one of the groups of proteins that read and interpret the genetic "blueprint" in the DNA. They bind to the DNA and help initiate a program of increased or decreased gene transcription. As such, they are vital for many important cellular processes. Below are some of the important functions and biological roles transcription factors are involved in:

Basal transcriptional regulation

In eukaryotes, an important class of transcription factors called general transcription factors (GTFs) are necessary for transcription to occur. Many of these GTFs do not actually bind DNA, but rather are part of the large transcription preinitiation complex that interacts with RNA polymerase directly. The most common GTFs are TFIIA, TFIIB, TFIID (see also TATA binding protein), TFIIE, TFIIF, and TFIIH. The preinitiation complex binds to promoter regions of DNA upstream to the gene that they regulate.

Differential enhancement of transcription

Other transcription factors differentially regulate the expression of various genes by binding to enhancer regions of DNA adjacent to regulated genes. These transcription factors are critical to making sure that genes are expressed in the right cell at the right time and in the right amount, depending on the changing requirements of the organism.

Development

Many transcription factors in multicellular organisms are involved in development. Responding to stimuli, these transcription factors turn on/off the transcription of the appropriate genes, which, in turn, allows for changes in cell morphology or activities needed for cell fate determination and cellular differentiation. The Hox transcription factor family, for example, is important for proper body pattern formation in organisms as diverse as fruit flies to humans. Another example is the transcription factor encoded by the sex-determining region Y (SRY) gene, which plays a major role in determining sex in humans.

Response to intercellular signals

Cells can communicate with each other by releasing molecules that produce signaling cascades within another receptive cell. If the signal requires upregulation or downregulation of genes in the recipient cell, often transcription factors will be downstream in the signaling cascade. Estrogen signaling is an example of a fairly short signaling cascade that involves the estrogen receptor transcription factor: Estrogen is secreted by tissues such as the ovaries and placenta, crosses the cell membrane of the recipient cell, and is bound by the estrogen receptor in the cell's cytoplasm. The estrogen receptor then goes to the cell's nucleus and binds to its DNA-binding sites, changing the transcriptional regulation of the associated genes.

Response to environment

Not only do transcription factors act downstream of signaling cascades related to biological stimuli but they can also be downstream of signaling cascades involved in environmental stimuli. Examples include heat shock factor (HSF), which upregulates genes necessary for survival at higher temperatures, hypoxia inducible factor (HIF), which upregulates genes necessary for cell survival in low-oxygen environments, and sterol regulatory element binding protein (SREBP), which helps maintain proper lipid levels in the cell.

Cell cycle control

Many transcription factors, especially some that are proto-oncogenes or tumor suppressors, help regulate the cell cycle and as such determine how large a cell will get and when it can divide into two daughter cells. One example is the Myc oncogene, which has important roles in cell growth and apoptosis.

Pathogenesis

Transcription factors can also be used to alter gene expression in a host cell to promote pathogenesis. A well studied example of this are the transcription-activator like effectors (TAL effectors) secreted by Xanthomonas bacteria. When injected into plants, these proteins can enter the nucleus of the plant cell, bind plant promoter sequences, and activate transcription of plant genes that aid in bacterial infection. TAL effectors contain a central repeat region in which there is a simple relationship between the identity of two critical residues in sequential repeats and sequential DNA bases in the TAL effector's target site. This property likely makes it easier for these proteins to evolve in order to better compete with the defense mechanisms of the host cell.

Regulation

It is common in biology for important processes to have multiple layers of regulation and control. This is also true with transcription factors: Not only do transcription factors control the rates of transcription to regulate the amounts of gene products (RNA and protein) available to the cell but transcription factors themselves are regulated (often by other transcription factors). Below is a brief synopsis of some of the ways that the activity of transcription factors can be regulated:

Synthesis

Transcription factors (like all proteins) are transcribed from a gene on a chromosome into RNA, and then the RNA is translated into protein. Any of these steps can be regulated to affect the production (and thus activity) of a transcription factor. An implication of this is that transcription factors can regulate themselves. For example, in a negative feedback loop, the transcription factor acts as its own repressor: If the transcription factor protein binds the DNA of its own gene, it down-regulates the production of more of itself. This is one mechanism to maintain low levels of a transcription factor in a cell.

Nuclear localization

In eukaryotes, transcription factors (like most proteins) are transcribed in the nucleus but are then translated in the cell's cytoplasm. Many proteins that are active in the nucleus contain nuclear localization signals that direct them to the nucleus. But, for many transcription factors, this is a key point in their regulation. Important classes of transcription factors such as some nuclear receptors must first bind a ligand while in the cytoplasm before they can relocate to the nucleus.

Activation

Transcription factors may be activated (or deactivated) through their signal-sensing domain by a number of mechanisms including:

• ligand binding – Not only is ligand binding able to influence where a transcription factor is located within a cell but ligand binding can also affect whether the transcription factor is in an active state and capable of binding DNA or other cofactors (see, for example, nuclear receptors).
• phosphorylation – Many transcription factors such as STAT proteins must be phosphorylated before they can bind DNA.
• interaction with other transcription factors (e.g., homo- or hetero-dimerization) or coregulatory proteins

Accessibility of DNA-binding site

In eukaryotes, DNA is organized with the help of histones into compact particles called nucleosomes, where sequences of about 147 DNA base pairs make ~1.65 turns around histone protein octamers. DNA within nucleosomes is inaccessible to many transcription factors. Some transcription factors, so-called pioneer factors are still able to bind their DNA binding sites on the nucleosomal DNA. For most other transcription factors, the nucleosome should be actively unwound by molecular motors such as chromatin remodelers. Alternatively, the nucleosome can be partially unwrapped by thermal fluctuations, allowing temporary access to the transcription factor binding site. In many cases, a transcription factor needs to compete for binding to its DNA binding site with other transcription factors and histones or non-histone chromatin proteins. Pairs of transcription factors and other proteins can play antagonistic roles (activator versus repressor) in the regulation of the same gene.

Availability of other cofactors/transcription factors

Most transcription factors do not work alone. Many large TF families form complex homotypic or heterotypic interactions through dimerization. For gene transcription to occur, a number of transcription factors must bind to DNA regulatory sequences. This collection of transcription factors, in turn, recruit intermediary proteins such as cofactors that allow efficient recruitment of the preinitiation complex and RNA polymerase. Thus, for a single transcription factor to initiate transcription, all of these other proteins must also be present, and the transcription factor must be in a state where it can bind to them if necessary. Cofactors are proteins that modulate the effects of transcription factors. Cofactors are interchangeable between specific gene promoters; the protein complex that occupies the promoter DNA and the amino acid sequence of the cofactor determine its spatial conformation. For example, certain steroid receptors can exchange cofactors with NF-κB, which is a switch between inflammation and cellular differentiation; thereby steroids can affect the inflammatory response and function of certain tissues.

Interaction with methylated cytosine

Transcription factors and methylated cytosines in DNA both have major roles in regulating gene expression. (Methylation of cytosine in DNA primarily occurs where cytosine is followed by guanine in the 5' to 3' DNA sequence, a CpG site.) Methylation of CpG sites in a promoter region of a gene usually represses gene transcription, while methylation of CpGs in the body of a gene increases expression. TET enzymes play a central role in demethylation of methylated cytosines. Demethylation of CpGs in a gene promoter by TET enzyme activity increases transcription of the gene.

The DNA binding sites of 519 transcription factors were evaluated. Of these, 169 transcription factors (33%) did not have CpG dinucleotides in their binding sites, and 33 transcription factors (6%) could bind to a CpG-containing motif but did not display a preference for a binding site with either a methylated or unmethylated CpG. There were 117 transcription factors (23%) that were inhibited from binding to their binding sequence if it contained a methylated CpG site, 175 transcription factors (34%) that had enhanced binding if their binding sequence had a methylated CpG site, and 25 transcription factors (5%) were either inhibited or had enhanced binding depending on where in the binding sequence the methylated CpG was located.

TET enzymes do not specifically bind to methylcytosine except when recruited (see DNA demethylation). Multiple transcription factors important in cell differentiation and lineage specification, including NANOG, SALL4A, WT1, EBF1, PU.1, and E2A, have been shown to recruit TET enzymes to specific genomic loci (primarily enhancers) to act on methylcytosine (mC) and convert it to hydroxymethylcytosine hmC (and in most cases marking them for subsequent complete demethylation to cytosine). TET-mediated conversion of mC to hmC appears to disrupt the binding of 5mC-binding proteins including MECP2 and MBD (Methyl-CpG-binding domain) proteins, facilitating nucleosome remodeling and the binding of transcription factors, thereby activating transcription of those genes. EGR1 is an important transcription factor in memory formation. It has an essential role in brain neuron epigenetic reprogramming. The transcription factor EGR1 recruits the TET1 protein that initiates a pathway of DNA demethylation. EGR1, together with TET1, is employed in programming the distribution of methylation sites on brain DNA during brain development and in learning (see Epigenetics in learning and memory).

Structure

Schematic diagram of the amino acid sequence (amino terminus to the left and carboxylic acid terminus to the right) of a prototypical transcription factor that contains (1) a DNA-binding domain (DBD), (2) signal-sensing domain (SSD), and Activation domain (AD). The order of placement and the number of domains may differ in various types of transcription factors. In addition, the transactivation and signal-sensing functions are frequently contained within the same domain.

 

Domain architecture example: Lactose Repressor (LacI). The N-terminal DNA binding domain (labeled) of the lac repressor binds its target DNA sequence (gold) in the major groove using a helix-turn-helix motif. Effector molecule binding (green) occurs in the regulatory domain (labeled). This triggers an allosteric response mediated by the linker region (labeled).

Transcription factors are modular in structure and contain the following domains:

• DNA-binding domain (DBD), which attaches to specific sequences of DNA (enhancer or promoter. Necessary component for all vectors. Used to drive transcription of the vector's transgene promoter sequences) adjacent to regulated genes. DNA sequences that bind transcription factors are often referred to as response elements.
• Activation domain (AD), which contains binding sites for other proteins such as transcription coregulators. These binding sites are frequently referred to as activation functions (AFs), Transactivation domain (TAD) or Trans-activating domain TAD but not mix with topologically associating domain TAD.
• An optional signal-sensing domain (SSD) (e.g., a ligand-binding domain), which senses external signals and, in response, transmits these signals to the rest of the transcription complex, resulting in up- or down-regulation of gene expression. Also, the DBD and signal-sensing domains may reside on separate proteins that associate within the transcription complex to regulate gene expression.

DNA-binding domain

DNA contacts of different types of DNA-binding domains of transcription factors

Main article: DNA-binding domain

The portion (domain) of the transcription factor that binds DNA is called its DNA-binding domain. Below is a partial list of some of the major families of DNA-binding domains/transcription factors:

Family	InterPro	Pfam	SCOP
basic helix-loop-helix	InterPro: IPR001092	Pfam PF00010	SCOP 47460
basic-leucine zipper (bZIP)	InterPro: IPR004827	Pfam PF00170	SCOP 57959
C-terminal effector domain of the bipartite response regulators	InterPro: IPR001789	Pfam PF00072	SCOP 46894
AP2/ERF/GCC box	InterPro: IPR001471	Pfam PF00847	SCOP 54176
helix-turn-helix
homeodomain proteins, which are encoded by homeobox genes, are transcription factors. Homeodomain proteins play critical roles in the regulation of development.	InterPro: IPR009057	Pfam PF00046	SCOP 46689
lambda repressor-like	InterPro: IPR010982		SCOP 47413
srf-like (serum response factor)	InterPro: IPR002100	Pfam PF00319	SCOP 55455
paired box
winged helix	InterPro: IPR013196	Pfam PF08279	SCOP 46785
zinc fingers
* multi-domain Cys2His2 zinc fingers	InterPro: IPR007087	Pfam PF00096	SCOP 57667
* Zn2/Cys6			SCOP 57701
* Zn2/Cys8 nuclear receptor zinc finger	InterPro: IPR001628	Pfam PF00105	SCOP 57716

Response elements

The DNA sequence that a transcription factor binds to is called a transcription factor-binding site or response element.

Transcription factors interact with their binding sites using a combination of electrostatic (of which hydrogen bonds are a special case) and Van der Waals forces. Due to the nature of these chemical interactions, most transcription factors bind DNA in a sequence specific manner. However, not all bases in the transcription factor-binding site may actually interact with the transcription factor. In addition, some of these interactions may be weaker than others. Thus, transcription factors do not bind just one sequence but are capable of binding a subset of closely related sequences, each with a different strength of interaction.

For example, although the consensus binding site for the TATA-binding protein (TBP) is TATAAAA, the TBP transcription factor can also bind similar sequences such as TATATAT or TATATAA.

Because transcription factors can bind a set of related sequences and these sequences tend to be short, potential transcription factor binding sites can occur by chance if the DNA sequence is long enough. It is unlikely, however, that a transcription factor will bind all compatible sequences in the genome of the cell. Other constraints, such as DNA accessibility in the cell or availability of cofactors may also help dictate where a transcription factor will actually bind. Thus, given the genome sequence, it is still difficult to predict where a transcription factor will actually bind in a living cell.

Additional recognition specificity, however, may be obtained through the use of more than one DNA-binding domain (for example tandem DBDs in the same transcription factor or through dimerization of two transcription factors) that bind to two or more adjacent sequences of DNA.

Clinical significance

Transcription factors are of clinical significance for at least two reasons: (1) mutations can be associated with specific diseases, and (2) they can be targets of medications.

Disorders

Due to their important roles in development, intercellular signaling, and cell cycle, some human diseases have been associated with mutations in transcription factors.

Many transcription factors are either tumor suppressors or oncogenes, and, thus, mutations or aberrant regulation of them is associated with cancer. Three groups of transcription factors are known to be important in human cancer: (1) the NF-kappaB and AP-1 families, (2) the STAT family and (3) the steroid receptors.

Below are a few of the better-studied examples:

Condition	Description	Locus
Rett syndrome	Mutations in the MECP2 transcription factor are associated with Rett syndrome, a neurodevelopmental disorder.	Xq28
Diabetes	A rare form of diabetes called MODY (Maturity onset diabetes of the young) can be caused by mutations in hepatocyte nuclear factors (HNFs) or insulin promoter factor-1 (IPF1/Pdx1).	multiple
Developmental verbal dyspraxia	Mutations in the FOXP2 transcription factor are associated with developmental verbal dyspraxia, a disease in which individuals are unable to produce the finely coordinated movements required for speech.	7q31
Autoimmune diseases	Mutations in the FOXP3 transcription factor cause a rare form of autoimmune disease called IPEX.	Xp11.23-q13.3
Li-Fraumeni syndrome	Caused by mutations in the tumor suppressor p53.	17p13.1
Breast cancer	The STAT family is relevant to breast cancer.	multiple
Multiple cancers	The HOX family are involved in a variety of cancers.	multiple
Osteoarthritis	Mutation or reduced activity of SOX9

Potential drug targets

See also: Therapeutic gene modulation

Approximately 10% of currently prescribed drugs directly target the nuclear receptor class of transcription factors. Examples include tamoxifen and bicalutamide for the treatment of breast and prostate cancer, respectively, and various types of anti-inflammatory and anabolic steroids. In addition, transcription factors are often indirectly modulated by drugs through signaling cascades. It might be possible to directly target other less-explored transcription factors such as NF-κB with drugs. Transcription factors outside the nuclear receptor family are thought to be more difficult to target with small molecule therapeutics since it is not clear that they are "drugable" but progress has been made on Pax2 and the notch pathway.

Role in evolution

Further information: Evolutionary developmental biology

Gene duplications have played a crucial role in the evolution of species. This applies particularly to transcription factors. Once they occur as duplicates, accumulated mutations encoding for one copy can take place without negatively affecting the regulation of downstream targets. However, changes of the DNA binding specificities of the single-copy Leafy transcription factor, which occurs in most land plants, have recently been elucidated. In that respect, a single-copy transcription factor can undergo a change of specificity through a promiscuous intermediate without losing function. Similar mechanisms have been proposed in the context of all alternative phylogenetic hypotheses, and the role of transcription factors in the evolution of all species.

Role in biocontrol activity

The transcription factors have a role in resistance activity which important for successful biocontrol activity. The resistant to oxidative stress and alkaline pH sensing were contributed from the transcription factor Yap1 and Rim101 of the Papiliotrema terrestris LS28 as molecular tools revealed an understanding of the genetic mechanisms underlying the biocontrol activity which will supports disease management programs based on biological and integrated control.

Analysis

There are different technologies available to analyze transcription factors. On the genomic level, DNA-sequencing and database research are commonly used. The protein version of the transcription factor is detectable by using specific antibodies. The sample is detected on a western blot. By using electrophoretic mobility shift assay (EMSA), the activation profile of transcription factors can be detected. A multiplex approach for activation profiling is a TF chip system where several different transcription factors can be detected in parallel.

The most commonly used method for identifying transcription factor binding sites is chromatin immunoprecipitation (ChIP). This technique relies on chemical fixation of chromatin with formaldehyde, followed by co-precipitation of DNA and the transcription factor of interest using an antibody that specifically targets that protein. The DNA sequences can then be identified by microarray or high-throughput sequencing (ChIP-seq) to determine transcription factor binding sites. If no antibody is available for the protein of interest, DamID may be a convenient alternative.

Cis-regulatory element

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Cis-regulatory_element

Cis-regulatory elements (CREs) or Cis-regulatory modules (CRMs) are regions of non-coding DNA which regulate the transcription of neighboring genes. CREs are vital components of genetic regulatory networks, which in turn control morphogenesis, the development of anatomy, and other aspects of embryonic development, studied in evolutionary developmental biology.

CREs are found in the vicinity of the genes that they regulate. CREs typically regulate gene transcription by binding to transcription factors. A single transcription factor may bind to many CREs, and hence control the expression of many genes (pleiotropy). The Latin prefix cis means "on this side", i.e. on the same molecule of DNA as the gene(s) to be transcribed.

CRMs are stretches of DNA, usually 100–1000 DNA base pairs in length, where a number of transcription factors can bind and regulate expression of nearby genes and regulate their transcription rates. They are labeled as cis because they are typically located on the same DNA strand as the genes they control as opposed to trans, which refers to effects on genes not located on the same strand or farther away, such as transcription factors. One cis-regulatory element can regulate several genes, and conversely, one gene can have several cis-regulatory modules. Cis-regulatory modules carry out their function by integrating the active transcription factors and the associated co-factors at a specific time and place in the cell where this information is read and an output is given.

CREs are often but not always upstream of the transcription site. CREs contrast with trans-regulatory elements (TREs). TREs code for transcription factors.

Overview

Diagram showing at which stages in the DNA-mRNA-protein pathway expression can be controlled

The genome of an organism contains anywhere from a few hundred to thousands of different genes, all encoding a singular product or more. For numerous reasons, including organizational maintenance, energy conservation, and generating phenotypic variance, it is important that genes are only expressed when they are needed. The most efficient way for an organism to regulate gene expression is at the transcriptional level. CREs function to control transcription by acting nearby or within a gene. The most well characterized types of CREs are enhancers and promoters. Both of these sequence elements are structural regions of DNA that serve as transcriptional regulators.

Cis-regulatory modules are one of several types of functional regulatory elements. Regulatory elements are binding sites for transcription factors, which are involved in gene regulation. Cis-regulatory modules perform a large amount of developmental information processing. Cis-regulatory modules are non-random clusters at their specified target site that contain transcription factor binding sites.

The original definition presented cis-regulatory modules as enhancers of cis-acting DNA, which increased the rate of transcription from a linked promoter. However, this definition has changed to define cis-regulatory modules as a DNA sequence with transcription factor binding sites which are clustered into modular structures, including -but not limited to- locus control regions, promoters, enhancers, silencers, boundary control elements and other modulators.

Cis-regulatory modules can be divided into three classes; enhancers, which regulate gene expression positively; insulators, which work indirectly by interacting with other nearby cis-regulatory modules; and silencers that turn off expression of genes.

The design of cis-regulatory modules is such that transcription factors and epigenetic modifications serve as inputs, and the output of the module is the command given to the transcription machinery, which in turn determines the rate of gene transcription or whether it is turned on or off. There are two types of transcription factor inputs: those that determine when the target gene is to be expressed and those that serve as functional drivers, which come into play only during specific situations during development. These inputs can come from different time points, can represent different signal ligands, or can come from different domains or lineages of cells. However, a lot still remains unknown.

Additionally, the regulation of chromatin structure and nuclear organization also play a role in determining and controlling the function of cis-regulatory modules. Thus gene-regulation functions (GRF) provide a unique characteristic of a cis-regulatory module (CRM), relating the concentrations of transcription factors (input) to the promoter activities (output). The challenge is to predict GRFs. This challenge still remains unsolved. In general, gene-regulation functions do not use Boolean logic, although in some cases the approximation of the Boolean logic is still very useful.

The Boolean logic assumption

Within the assumption of the Boolean logic, principles guiding the operation of these modules includes the design of the module which determines the regulatory function. In relation to development, these modules can generate both positive and negative outputs. The output of each module is a product of the various operations performed on it. Common operations include the OR gate – this design indicates that in an output will be given when either input is given, and the AND gate – in this design two different regulatory factors are necessary to make sure that a positive output results. "Toggle Switches" – This design occurs when the signal ligand is absent while the transcription factor is present; this transcription factor ends up acting as a dominant repressor. However, once the signal ligand is present the transcription factor's role as repressor is eliminated and transcription can occur.

Other Boolean logic operations can occur as well, such as sequence specific transcriptional repressors, which when they bind to the cis-regulatory module lead to an output of zero. Additionally, besides influence from the different logic operations, the output of a "cis"-regulatory module will also be influenced by prior events. 4) Cis-regulatory modules must interact with other regulatory elements. For the most part, even with the presence of functional overlap between cis-regulatory modules of a gene, the modules' inputs and outputs tend to not be the same.

While the assumption of Boolean logic is important for systems biology, detailed studies show that in general the logic of gene regulation is not Boolean. This means, for example, that in the case of a cis-regulatory module regulated by two transcription factors, experimentally determined gene-regulation functions can not be described by the 16 possible Boolean functions of two variables. Non-Boolean extensions of the gene-regulatory logic have been proposed to correct for this issue.

Classification

Cis-regulatory modules can be characterized by the information processing that they encode and the organization of their transcription factor binding sites. Additionally, cis-regulatory modules are also characterized by the way they affect the probability, proportion, and rate of transcription. Highly cooperative and coordinated cis-regulatory modules are classified as enhanceosomes. The architecture and the arrangement of the transcription factor binding sites are critical because disruption of the arrangement could cancel out the function. Functional flexible cis-regulatory modules are called billboards. Their transcriptional output is the summation effect of the bound transcription factors. Enhancers affect the probability of a gene being activated, but have little or no effect on rate. The Binary response model acts like an on/off switch for transcription. This model will increase or decrease the amount of cells that transcribe a gene, but it does not affect the rate of transcription. Rheostatic response model describes cis-regulatory modules as regulators of the initiation rate of transcription of its associated gene.

Promoter

Main article: Promoter (genetics)

Promoters are CREs consisting of relatively short sequences of DNA which include the site where transcription is initiated and the region approximately 35 bp upstream or downstream from the initiation site (bp). In eukaryotes, promoters usually have the following four components: the TATA box, a TFIIB recognition site, an initiator, and the downstream core promoter element. It has been found that a single gene can contain multiple promoter sites. In order to initiate transcription of the downstream gene, a host of DNA-binding proteins called transcription factors (TFs) must bind sequentially to this region. Only once this region has been bound with the appropriate set of TFs, and in the proper order, can RNA polymerase bind and begin transcribing the gene.

Enhancers

Main article: Enhancer (genetics)

Enhancers are CREs that influence (enhance) the transcription of genes on the same molecule of DNA and can be found upstream, downstream, within the introns, or even relatively far away from the gene they regulate. Multiple enhancers can act in a coordinated fashion to regulate transcription of one gene. A number of genome-wide sequencing projects have revealed that enhancers are often transcribed to long non-coding RNA (lncRNA) or enhancer RNA (eRNA), whose changes in levels frequently correlate with those of the target gene mRNA.

Silencers

Main article: Silencer (genetics)

Silencers are CREs that can bind transcription regulation factors (proteins) called repressors, thereby preventing transcription of a gene. The term "silencer" can also refer to a region in the 3' untranslated region of messenger RNA, that binds proteins which suppress translation of that mRNA molecule, but this usage is distinct from its use in describing a CRE.

Operators

Operators are CREs in prokaryotes and some eukaryotes that exist within operons, where they can bind proteins called repressors to affect transcription.

Evolutionary role

CREs have an important evolutionary role. The coding regions of genes are often well conserved among organisms; yet different organisms display marked phenotypic diversity. It has been found that polymorphisms occurring within non-coding sequences have a profound effect on phenotype by altering gene expression. Mutations arising within a CRE can generate expression variance by changing the way TFs bind. Tighter or looser binding of regulatory proteins will lead to up- or down-regulated transcription.

Cis-regulatory module in gene regulatory network

The function of a gene regulatory network depends on the architecture of the nodes, whose function is dependent on the multiple cis-regulatory modules. The layout of cis-regulatory modules can provide enough information to generate spatial and temporal patterns of gene expression. During development each domain, where each domain represents a different spatial regions of the embryo, of gene expression will be under the control of different cis-regulatory modules. The design of regulatory modules help in producing feedback, feed forward, and cross-regulatory loops.

Mode of action

Cis-regulatory modules can regulate their target genes over large distances. Several models have been proposed to describe the way that these modules may communicate with their target gene promoter. These include the DNA scanning model, the DNA sequence looping model and the facilitated tracking model. In the DNA scanning model, the transcription factor and cofactor complex form at the cis-regulatory module and then continues to move along the DNA sequence until it finds the target gene promoter. In the looping model, the transcription factor binds to the cis-regulatory module, which then causes the looping of the DNA sequence and allows for the interaction with the target gene promoter. The transcription factor-cis-regulatory module complex causes the looping of the DNA sequence slowly towards the target promoter and forms a stable looped configuration. The facilitated tracking model combines parts of the two previous models.

Identification and computational prediction

Besides experimentally determining CRMs, there are various bioinformatics algorithms for predicting them. Most algorithms try to search for significant combinations of transcription factor binding sites (DNA binding sites) in promoter sequences of co-expressed genes. More advanced methods combine the search for significant motifs with correlation in gene expression datasets between transcription factors and target genes. Both methods have been implemented, for example, in the ModuleMaster. Other programs created for the identification and prediction of cis-regulatory modules include:

INSECT 2.0 is a web server that allows to search Cis-regulatory modules in a genome-wide manner. The program relies on the definition of strict restrictions among the Transcription Factor Binding Sites (TFBSs) that compose the module in order to decrease the false positives rate. INSECT is designed to be user-friendly since it allows automatic retrieval of sequences and several visualizations and links to third-party tools in order to help users to find those instances that are more likely to be true regulatory sites. INSECT 2.0 algorithm was previously published and the algorithm and theory behind it explained in

Stubb uses hidden Markov models to identify statistically significant clusters of transcription factor combinations. It also uses a second related genome to improve the prediction accuracy of the model.

Bayesian Networks use an algorithm that combines site predictions and tissue-specific expression data for transcription factors and target genes of interest. This model also uses regression trees to depict the relationship between the identified cis-regulatory module and the possible binding set of transcription factors.

CRÈME examine clusters of target sites for transcription factors of interest. This program uses a database of confirmed transcription factor binding sites that were annotated across the human genome. A search algorithm is applied to the data set to identify possible combinations of transcription factors, which have binding sites that are close to the promoter of the gene set of interest. The possible cis-regulatory modules are then statistically analyzed and the significant combinations are graphically represented

Active cis-regulatory modules in a genomic sequence have been difficult to identify. Problems in identification arise because often scientists find themselves with a small set of known transcription factors, so it makes it harder to identify statistically significant clusters of transcription factor binding sites. Additionally, high costs limit the use of large whole genome tiling arrays.

Binding sites of gene regulatory factors. Transcription factors binding DNA, RNA-binding proteins and microRNAs binding RNA

Examples

An example of a cis-acting regulatory sequence is the operator in the lac operon. This DNA sequence is bound by the lac repressor, which, in turn, prevents transcription of the adjacent genes on the same DNA molecule. The lac operator is, thus, considered to "act in cis" on the regulation of the nearby genes. The operator itself does not code for any protein or RNA.

In contrast, trans-regulatory elements are diffusible factors, usually proteins, that may modify the expression of genes distant from the gene that was originally transcribed to create them. For example, a transcription factor that regulates a gene on chromosome 6 might itself have been transcribed from a gene on chromosome 11. The term trans-regulatory is constructed from the Latin root trans, which means "across from".

There are cis-regulatory and trans-regulatory elements. Cis-regulatory elements are often binding sites for one or more trans-acting factors.

To summarize, cis-regulatory elements are present on the same molecule of DNA as the gene they regulate whereas trans-regulatory elements can regulate genes distant from the gene from which they were transcribed.