1. Gene-regulation function

2. The Boolean logic assumption

3. Identification and computational prediction

4. Classification

5. Mode of action

6. Cis-regulatory module in gene regulatory network

7. See also

8. References

9. External links

Gene-regulation function

The original definition presented cis-regulatory modules as enhancers of cis-acting DNA, which increased the rate of transcription from a linked promoter.^[4] 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.^[4]

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.^[1] 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.^[1] 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.^[4] 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,^[2] 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 "OR" logic gate – This design indicates that in an output will be given when either input is given [3]. "AND" logic gate – In this design two different regulatory factors are necessary to make sure that a positive output results.^[1] "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.^[1]

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.^[1]

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.^[1]

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.^[2] 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.^[2]

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.^[5] More advanced methods combine the search for significant motifs with correlation in gene expression datasets between transcription factors and target genes.^[6]

Both methods have been implemented, for example, in the ModuleMaster.

Other programs created for the identification and prediction of cis-regulatory modules include:

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.^[9]

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 ^[11]

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.^[9] Additionally, high costs limit the use of large whole genome tiling arrays.^[10]

Classification

Highly cooperative and coordinated cis-regulatory modules are classified as enhanceosomes.^[4] The architecture and the arrangement of the transcription factor binding sites are critical because disruption of the arrangement could cancel out the function.^[4]

Functional flexible cis-regulatory modules are called billboards. Their transcriptional output is the summation effect of the bound transcription factors.^[4]

Enhancers affect the probability of a gene being activated, but have little or no effect on rate.^[4]

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.^[4]

Rheostatic response model describes cis-regulatory modules as regulators of the initiation rate of transcription of its associated gene.^[4]

Mode of action

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.^[4] The facilitated tracking model combines parts of the two previous models.

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.^[1] The layout of cis-regulatory modules can provide enough information to generate spatial and temporal patterns of gene expression.^[1] 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 module(s).^[1] The design of regulatory modules help in producing feedback, feed forward, and cross-regulatory loops.^[12]

See also

• Cis-regulatory element
• Enhanceosome
• Enhancer
• Operator
• Operon
• Promoter
• Regulation of gene expression
• Trans-regulatory element

References

External links

• Gene Regulation Info – manually curated lists of resources, reviews, community discussions
• Cellular Darwinism
• {{MeshName|Regulation+of+Gene+Expression|3=Regulation of Gene Expression}}

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